Grace is an LC-MS/MS Applications Development Specialist at St Paul’s Hospital in Vancouver BC. She is passionate about developing the most user friendly and streamlined LC-MS/MS assays as possible for routine use in the Special Chemistry Mass Spec Lab. She loves troubleshooting, especially when the cause of problem has been discovered and the issue solved!

Deborah French Ph.D., DABCC (CC, TC) is currently Assistant Director of Chemistry and Director of Mass Spectrometry at the University of California San Francisco Clinical Laboratories. Her work currently focuses on the development and validation of LC-MS/MS assays for small molecules, specifically therapeutic drug monitoring, steroid hormones and toxicology. Deborah received her Ph.D. in biochemistry from the University of Strathclyde in Glasgow, Scotland and then completed a postdoctoral fellowship at St. Jude Children’s Research Hospital in Memphis, TN. She subsequently completed a ComACC Clinical Chemistry postdoctoral fellowship under the direction of Dr Alan Wu at the University of California San Francisco and is now board certified in Clinical Chemistry and Toxicological Chemistry by the American Board of Clinical Chemistry.

Lorin Bachmann joined the VCU Department of Pathology in 2007. She currently serves as Co-Director of Clinical Chemistry, Co-Director of Point-of-Care Testing, Director of the New Kent Emergency Department Laboratory, Technical Advisor for the Operating Room Laboratory, Pathology Outreach and Clinical Trials, and Laboratory Director for multiple VCUHS outreach laboratories. Dr. Bachmann received her PhD in Molecular Medicine from the University of Virginia, followed by a fellowship in clinical chemistry and proteomics research at the University of Virginia. Dr. Bachmann is certified by the American Board of Clinical Chemistry.

Dr. Bachmann is active within the American Association for Clinical Chemistry (AACC), where she serves on the Board of Directors. She also serves as the Chair of the Chemistry and Toxicology Expert Panel for the Clinical Laboratory and Standards Institute (CLSI).

Dr. Bachmann’s research interests include evaluation and validation of new clinical laboratory assays, clinical laboratory analyzer design, development of mass spectrometry-based assays for the clinical laboratory and standardization of laboratory testing. She serves as the Chair of the National Kidney Disease Education Program (NKDEP)/International Federation of Clinical Chemistry Laboratory (IFCC) Joint Lab Working Group, whose goal is to accomplish standardization of urine albumin methods to enable utility of clinical decision thresholds.

Dr. Bachmann has received numerous awards for her contributions to professional societies, education and research. She serves as principal investigator for multiple industry-sponsored studies.

This is a VIRTUAL 4-Day Course with 16 total contact hours. CE to be provided (16 hrs).

There will be no e-copy of the slides provided.

You can PURCHASE a copy of the slide deck off of AMAZON by searching for 9781950526062 (ISBN).

There WILL be provided online-only (not downloadable, not printable) versions of the slides via google classroom.

Online Format

This is a virtual version of the short course that has been presented at MSACL. This online course includes 2.5-5 hours of live lecture each day for four days. Using Zoom (live online lectures, breakout rooms for interactive sessions) and Google Classroom (forms and job aids for download, on demand short lecture videos) provides additional opportunities for interactive and self-paced learning, both during the four-day course and beyond.

The resources posted in Google Classroom will be available to registrants for at least 3 months. We can’t give you hands-on experience with cutting PEEK tubing or changing check valves in an online course. But the potential with a virtual platform for networking and updates with actionable information to help you with your daily LC-MS/MS clinical practice seems almost limitless.

Course Content Description

Is your laboratory under pressure to purchase an LC-tandem MS or is the ROI you wrote last year haunting you now? This short course is designed for attendees implementing quantitative LC-tandem MS for patient testing who have laboratory medicine experience but no mass spectrometry training - CLS bench analysts, supervisors, R&D scientists, and laboratory directors. Theoretical concepts necessary for a robust implementation of clinical mass spectrometry will be presented – but the emphasis is on practical recommendations for:

1. LC-MS/MS system purchasing
2. site preparation and installation
3. defining preliminary MSMS and LC parameters for your first method
4. selecting and optimizing sample preparation for your first method
5. choosing internal standards, solvents, and water, making reagents and calibrators
6. adjusting sample preparation, LC and MSMS parameters to achieve the desired assay performance
7. establishing data analysis & batch review criteria
8. pre-validation stress testing and method validation
9. preventative maintenance and troubleshooting
10. maintaining quality in production.

Our goal is to present just enough theory so you can report high quality results, while opening a window to the depth and complexity of clinical mass spectrometry such that your appetite is whetted to learn more. The IFCC Committee for Distance Learning published a curriculum guideline for post-graduate trainees in laboratory medicine in 2017. This course includes all of the IFCC mass spectrometry curriculum listed in the “Principles of Analysis X-MS” section, except that we focus only on triple quadrupole mass spectrometry; metabolomics, proteomics and inborn errors of metabolism are not discussed. See also this reference by one of the team of short course instructors “Liquid chromatography-mass spectrometry education for clinical laboratory scientists”.

Previous exposure to the principles of clinical method validation, either didactic or practical, is assumed.

Dr. Hamid's PhD studies in Prof. M. Samy El-Shall’s research laboratory in Virginia Commonwealth University were focused on studying ion-molecule kinetics, thermochemistry, and structures using drift tube ion mobility spectrometer measurements associated with quantum mechanics calculations. Then, Ahmed joined Purdue University to work under the supervision of Prof. R. Graham Cooks where he focused on the applications of mass spectrometry to various biological systems, in particular on differentiation of microorganisms using various ambient ionization techniques coupled to mass spectrometry which resulted in novel approaches in the ionization and the identification of bacteria and fungi with high prediction rates without sample preparation. To learn new skills, Dr. Hamid joined the team of Dr. Richard D. Smith in Pacific Northwest National Laboratory to work on the development of novel Structures for Lossless Ion Manipulations (SLIM) which allows previously unattainable ion mobility resolution and remarkable ion manipulations. Moreover, as a senior scientist in MOBILion Systems, Ahmed transferred and further developed the SLIM technology. In summary, Dr. Hamid has more than 14 years of experience researching in the fields of technology development and the applications of mass spectrometry and ion mobility spectrometry which resulted in more than 29 peer-reviewed publications in high impact journals and 7 patents.

CDC estimates that each year 48 million people get sick from a foodborne illness where 128,000 are hospitalized leading to 3,000 deaths. They are caused by pathogenic bacteria, parasites, or viruses that enter the body through the consumption of contaminated food or beverages followed by growth inside the host body. Therefore, rapid methods are crucial for the effective detection of foodborne pathogens in food products to provide safe food supply and to prevent outbreaks of foodborne diseases and the spread of foodborne pathogens. In addition, developing fast robust diagnosis techniques will increase the recovery rates of patients suffering from foodborne infections and will lead to reduced antibiotic resistance.

The membrane of these bacteria contains varying lipid composition that can be utilized as diagnostic biomarker for foodborne disease diagnosis. However, their full characterization remains analytically challenging due to their enormous structural diversity and complexity (e.g., varying acyl chain positions and/or double bond geometries). This presentation will demonstrate the advantages of interfacing liquid chromatography and structurally-based ion mobility with tandem mass spectrometry in the resolution of isomeric lipids. Moreover, this presentation will demonstrate the developments of ambient ionization techniques when coupled to high-resolution ion mobility spectrometry. We expect these developments to enable portable diagnosis devices that can be used for sensitive detection and superior separation power in the analysis of contaminated food samples and foodborne clinical samples without any prior sample preparation. This will enable high throughput analyses that is affordable and can be performed by less-skilled users, such as nurses and farmers.

A native of Florida, Kelly completed her undergraduate studies in Chemistry at the University of Florida. After graduating with honors in 2009, Kelly joined the Department of Chemistry at Vanderbilt University as a graduate student. Her research in the lab of John A. McLean focused on the development of ion mobility-mass spectrometry (IM-MS) methods for the identification of metabolite, lipid, and peptide biomolecular signatures of disease from complex biological samples. After receiving her Ph.D. in 2014, Dr. Hines completed a one-year postdoctoral fellowship in the Mayo Clinic Regional Metabolomics Core where she established quantitative MS methods for lipidomics and protein metabolism using isotope labeling. In 2015, Dr. Hines joined the lab of Libin Xu at the University of Washington. Her work in the Xu Lab focused on the development of IM-MS methods for lipidomics, high-throughput IM-MS measurements of drugs and small molecules, and the significance of lipids in human diseases, environmental exposure and antibiotic resistance. Kelly has authored research publications in top-tier analytical chemistry and molecular sciences journals such as Analytical Chemistry, Journal of Lipid Research, and Molecular Cell. Kelly has been the recipient of several noteworthy awards, including a U.S. Pharmacopoeia Fellowship, the ACS Dan Su Travel Award, and several Young Investigator travel awards. She is most proud of being selected as a Finalist for the University of Washington Graduate School Postdoc Mentoring Award during her time at UW. Outside the lab, Kelly is an avid reader of fiction, a college football enthusiast and enjoys being outdoors.

Our goal is to better understand the ways in which lipids and small molecules contribute to diseases affecting human health and use this knowledge to develop diagnostic and prognostic tools based on molecular signatures of the disease. To achieve this, the Hines Lab develops and applies bioanalytical methods using ion mobility-mass spectrometry (IM-MS) to enhance the throughput and dimensionality of lipidomics and metabolomics experiments. One focus of the group is antibiotic resistance, where we are characterizing the metabolic alterations in antibiotic resistant pathogens and developing IM-MS methods for antibiotic susceptibility testing and small molecule screening.

Daniel Globisch studied Chemistry at the Technical University of Kaiserslautern (Germany) and the University of Southern Denmark, Odense (Denmark). He received his Ph.D. from the Ludwig-Maximilians-University Munich (Germany) with Professor Thomas Carell in 2011 and joined the laboratory of Professor Kim D. Janda at The Scripps Research Institute (CA, USA) for his postdoctoral studies. He started his independent career in 2015 at Uppsala University (Sweden) as a Science For Life Laboratory Fellow and was appointed as Associate Professor in 2017. He received tenure in December 2020. Daniel has been elected as a board member of the Nordic Metabolomics Society and is an Editorial Board Member for the metabolomics society journal Metabolites. The interdisciplinary nature of the research projects is focused on the elucidation of the metabolic interaction between the gut microbiota and their human host. His laboratory develops new Chemical Biology tools to extend the scope of metabolomics research for discovery of unknown biomarkers and bioactive metabolites.

One of the most exciting scientific developments in the past decade has been the understanding that gut microbiota profoundly impact human physiology. The complex consortium of trillions of microbes possesses a wide range of metabolic activity. This metabolic interspecies communication represents a tremendous opportunity for the discovery of unknown bioactive molecules as only limited information on this co-metabolism has been elucidated on a molecular level. Mass spectrometry-based metabolomics analysis is the method of choice for the analysis of known and discovery of unknown metabolites. Chemical Biology tools are still limited in metabolomics compared to other ‘omics research fields. Especially, the detailed and selective analysis of microbial metabolism remains a major challenge that requires specific techniques.

We have developed new state-of-the-art Chemical Biology methodologies for that exceed the scope of global metabolomics analysis. These unique metabolite analysis tools overcome limitations in metabolomics and were applied for the discovery of unknown metabolites in human samples to evaluate their potential as biomarkers. We have developed arylsulfatase-based metabolite analysis methods for specific identification of sulfated metabolites as this compound class represents a signature for microbiome-host co-metabolism. This analysis led to identification of more than 230 sulfated metabolites, exceeding the number of this metabolite class in common metabolomics databases. An optimized enzymatic method was applied for a dietary intervention study to investigate the dietary sulfatome. Several previously unknown and undetected metabolites are derived from specific microbiota metabolism. Based on our interested in phase II modifications, we identified a series of unexpected substrates for the common human N-acetyltransferase NAT2. Furthermore, we have also designed unique chemoselective probes immobilized to magnetic beads that allow for facile extraction of metabolites with increased mass spectrometric sensitivity by six orders of magnitude.

Already at the end of the last century, MALDI-TOF mass spectrometry protein profiles were proposed as specific fingerprints for microorganisms which can be used for their unequivocal identification by comparison of dedicated reference databases. Nevertheless, it took a further decade until the technology entered routine diagnostic laboratories, suddenly considered as a revolution in microbiology diagnostics. After introduction into the first routine laboratories, it took only a few. years until it became the new microbial identification standard.

A prerequisite of success was the offering of a comprehensive system, a package consisting of the mass spectrometer including user friendly software for data acquisition, interpretation and report, reference databases, standard procedures, and consumables. Reliable service for the instrument and software, but also application support had to be offered to gain confidence of microbiologists and lab technicians in the novel technology. Regulatory approvals are prerequisite for diagnostic in most countries. This could only be achieved by companies who took over the central responsibility for the new diagnostic system.

Three main arguments made MALDI-TOF MS successful, being faster, better and more cost-effective than the previously applied techniques, i.e. biochemical and other phenotypical tests. Superior quality of MALDI-TOF based identification was demonstrated already with early versions of reference databases. Over the years, the libraries for different organism groups have been significantly expanded, today covering thousands of species from different microorganism groups. Species previously not considered to be pathogenic for humans have been detected as causative for infections, and even new microbial species have been discovered. Time to identification has been shortened by at least one day, and even more for slow-growing organisms and for microbes isolated directly from positive blood cultures. Although the initial investment for a MALDI-TOF instrument is considerably higher than for former standard identification systems, the very low running costs make it more cost-effective at least for medium and high throughput laboratories.

While MALDI-TOF is undisputed as the standard routine identification tool for microbiology laboratories, further applications have been proposed which might increase the value of MALDI-TOF MS for clinical microbiology. The considerable diversity in mass spectra that can be observed for many species was proposed for subtyping and epidemiology purposes. Several different approaches have been published for antibiotic resistance or even susceptibility testing. A first IVD-CE marked test for detection of a resistance mechanism has been introduced. Simple detection of specific marker peaks in the mass spectra enables detection of particular strains exhibiting antibiotic resistance. Other procedures comprise the observation of incorporation of isotope-labelled nutrients or the semi-quantitative determination of microbial biomass for susceptibility testing. Recently, besides proteins also lipids get into the focus of MALDI-TOF MS analysis as specific markers, for resistance detection as well as for identification or typing purposes.

My talk will describe the evolution of MALDI-TOF mass spectrometry in the microbiology lab, the conditions for its success and status of today, and I will give an outlook for its future potential.

Katarina Madunic is a PhD candidate at the Center for Proteomics and Metabolomics under the supervision of Prof. dr. Manfred Wuhrer in the field of colorectal cancer glycomics. She is using higly sensitive isomer specific porous graphitized carbon liquid chromatography system coupled with nano ESI ion trap mass spectrometer allowing in depth structural analysis of glycans present on cells and tissues.

INTRODUCTION
Colorectal cancer (CRC) is one of the leading malignancies worldwide with over 900,000 deaths in 20201. To provide a better treatment for a larger group of patients with CRC, new specific molecular targets are needed that can be utilized for the development for new targeted therapies. A potential target might be the changes in protein glycosylation as these are found to be a hallmark of many diseases. For example, heavily glycosylated proteins mucins play a very important role in the gut homeostasis, however, little attention was given to their O-glycosylation. The characteristic glycan alterations in cancer include specific aberrant expression of incomplete carbohydrate structures or de novo expression of carbohydrate antigens also known as tumor-associated carbohydrate antigens (TACAs) which show a great potential for the development of new immunotherapeutic strategies for CRC.

METHODS
Here we present an in depth study of CRC glycosylation, with a focus on decoding the tumor specific O-glycan signatures of CRC that are derived from epithelial regions of both primary tumors and metastatic sites compared to healthy colon mucosa from the same patients. We established a high throughput workflow that sequentially releases the N-and O-glycans from laser capture microdissected regions of FFPE tissues followed by analysis with porous graphitized carbon (PGC)-LC-MS/MS in negative ion mode, enabling powerful separation of isomeric species, as well as in-depth structural characterization of TACAs.

RESULTS
Overall, a total of 125 O-glycans were detected in the cancer tissues. From those, 87 were solely observed in the cancers (TACAs). Five glycans were not detected in normal mucosa and were exclusively found in more than 33% of the cancers. These glycans share structural similarities indicating a cancer specific glycosylation pathway upregulation. In particular, at least one of two specific TACAs was detected in 94% of the cancers in our study demonstrating great specificity. Our data provides strong evidence that specific O-glycans as well as O-glycomic traits such as epitope expression and different core structures are significantly changed between CRC and normal colon mucosa tissue.

CONCLUSION
In this study we present a novel panel of highly specific TACA’s, based upon changes in the O-glycomic profile between CRC and healthy colon mucosa. These TACA’s are promising new targets for development of innovative cancer immunotherapeutics and lay the foundation for the treatment of invasive CRC.

Introduction
Congenital disorders of glycosylation (CDG) form a group of inherited metabolic disorders that affect the synthesis and processing of glycoproteins. Over the last years there is an increase in newly reported CDG where for some the classical diagnostic screening is not sufficient. One of these CDG, MOGS-CDG, is the result of a defective mannosyl-oligosaccharide glucosidase I, the first enzyme in the processing pathway of protein-linked N-glycans where it where removes the distal α1,2-linked glucose residue from the N-linked Glc3Man9GlcNAc2 precursor to produce Glc2Man9GlcNAc2. As primary aim, we developed a novel diagnostic assay for detection of the urinary Glc3Man tetrasaccharide in MOGS-CDG as well as performed biochemical characterization of plasma N-glycosylation using glycoproteomics and glycomics.

Methods
Pathogenic variants in MOGS were found via whole exome sequencing (WES). A novel LC-MS/MS method using multiple reaction monitoring (MRM) was established for quantitative urinary oligosaccharide analysis by 12C- and 13C-labeling of reducing oligosaccharides with 2-aminobenzamide (2AA). For glycomics, plasma samples were treated with PNGase F and glycans enriched using hypercarb tips and diluted 1:10 before injection on a Waters Acquity PGC column and analysis on a timsTOF pro mass spectrometer (MS). For glycoproteomics, plasma samples were trypsin digested, enriched using sepharose beads and diluted 1:10 before injection. Samples were measured on the timsTOF pro MS.

Results
Here, we present urinary and plasma glycan data of 6 novel MOGS-CDG patients. Our novel diagnostic assay reliably quantified the accumulation of Glc3Man in all MOGS-CDG cases which is known to be a biomarker from literature. Furthermore we can differentiate MOGS-CDG from Pompe disease which also shows a tetresaccharide accumulation. Additionally, glycomics anlaysis revealed the Hex10HexNAc2 glycan in all MOGS-CDG patients .Glycoproteomics data confirmed the presence of this glycan on other proteins inclusing IgG while transferrin showed normal glycosylation.

Conclusion
We described 6 novel MOGS-CDG cases with similar glycosylation characteristics with clear urinary excretion of the Glc3Man tetrasaccharide as established via a novel clinical MS assay. Although transferrin glycosylation was normal, specific glycan and glycopeptide changes were found for the MOGS-CDG patients that were consistent with the enzymatic defect.

Introduction
Changes in plasma protein glycosylation are known to functionally affect proteins and to associate with liver diseases, including liver cirrhosis and hepatocellular carcinoma. Autoimmune hepatitis (AIH) is a liver disease characterized by liver inflammation and raised immunoglobulin G (IgG), and can be difficult to distinguish from other liver diseases.

Aims
The aim of this study was to examine total plasma and IgG-specific N-glycosylation in AIH and to compare this to healthy controls and other liver diseases.

Methods
In this retrospective-longitudinal, single center cohort study, total plasma N-glycosylation (TPNG) and IgG fragment crystallizable (Fc) glycosylation analysis was performed by mass spectrometry for a total of 66 AIH patients, 60 age- and sex-matched healthy controls, 31 primary biliary cholangitis patients, 10 primary sclerosing cholangitis patients, 30 non-alcoholic fatty liver disease patients, 74 patients with viral or alcoholic hepatitis. Associations between glycosylation traits and AIH were investigated as compared to healthy controls and other liver diseases, considering known confounders of glycosylation including age, sex, cirrhosis and inflammation.

Results
81 TPNG glycoforms and 40 IgG glycoforms were quantified per individual. Glycan traits bisection (CB) (odds ratio (OR): 3.78 95% confidence interval: [1.88-9.35], p-value: 5.88 x 10-3), tetraantennary sialylation per galactose (A4GS) (OR: 2.88 [1.75-5.16], p-value: 1.63 x 10-3), IgG1 galactosylation (OR: 0.35 [0.2-0.58], p-value: 3.47 x 10-5) and hybrid type glycans (THy) (OR: 2.73 [1.67-4.89], p-value: 2.31 x 10-3) were found as discriminators between AIH patients and healthy controls. Raised A4GS differentiated AIH from other liver diseases. IgG1 galactosylation associated with disease activity.

Conclusions
Compared to other liver diseases, AIH featured uniquely raised A4GS levels, which suggests the high potential of this glycosylation trait as a diagnostic marker in the future. IgG galactosylation associated with disease activity and might be used for monitoring disease activity.

I am a senior research scientist at the MS Proteomics Research Group of the Research Centre for Natural Sciences in Budapest, Hungary. After obtaining my PhD degree I spent 2.5 years at Boston University in Prof. Joseph Zaia's lab as a postdoctoral researcher developing a serial enzymatic digestion method to analyze various compounds from tissue surfaces (prtoeins, N-glycans and GAGs). My recent research focuses on the proteomics, glycoproteomics, phosphoprotoemics and glycosaminoglycan analysis of small tissue sections to identify molecular alterations occuring in prostate and lung cancer. Currently I am the supervisor of three PhD and three undergraduate students.

Introduction
Analysis of tissue specimen using mass spectrometry offers valuable information of biological processes taking place at the origin of a disease. Identifying differences in protein expression levels and site-specific N-glycosylation of glycoproteins among cancerous and healthy tissues can be an attractive approach in biomarker research. Our aim was to analyze prostate cancer (PCa) tissue microarray samples (n=95) and compare protein expression levels and changes in N-glycosylation features among various pathological grades of PCa and healthy tissues.

Methods
Following on surface proteolytic digestion [1] glycopeptides were enriched using acetone precipitation. Peptide and glycopeptide fractions were analyzed separately using reversed phase nanoHPLC-MS(MS) and nanoHPLC-MS. Label free protein quantitation was performed using MaxQuant, while glycopeptides were identified and quantified using Byonic and Glycopattern software, respectively. Statistical analysis was carried out using Perseus.

Results
Several statistically significant proteins were identified that were altered between different cancer grades and healthy tissue. STRING analysis revealed protein pathways involved in disease progression. Regarding N-glycosylation analysis the changes identified at specific glycosylation sites were of interest. Alterations in sialylation, fucosylation and galactosylation of individual glycosites were detected that could potentially be important targets of future studies. We found that in case of Collagen 6 subunit both protein expression levels and fucosylation at N785 changed significantly between the different pathological grades and healthy tissue.

Conclusions
Our integrated proteomics and glycoproteomics workflow was used to identify molecular alterations occurring during PCa progression from limited size tissue samples and can also be used to analyze other types of cancerous tissue biopsies in the future.

References & Acknowledgments

1. Turiák, L., et al., Workflow for Combined Proteomics and Glycomics Profiling from Histological Tissues. Analytical Chemistry, 2014. 86(19): p. 9670-9678.

LT is grateful for funding from the National Research Development and Innovation Office (NKFIH PD-121187, NKFIH FK-131603) and for the support from the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

Dirk Lefeber, Ph.D., is Professor at the Radboud University Medical Center, Translational Metabolic Laboratory, Department of Neurology. After a PhD in chemical and analytical glycosciences at Utrecht University, followed by a post-doctoral training in polysaccharide infectiology at Utrecht UMC, he took a position as staff member Glycosylation Disorders at the Radboud UMC to establish an independent research group. He completed a training to Laboratory specialist clinical genetics in 2008, founded the Radboudumc Expertise Center for Disorders of Glycosylation, formally recognized by the Dutch government, and received several awards, including the SSIEM award (2010&2016) and prestigious personal VIDI and VICI grants from the Netherlands Scientific Organisation. He has established the world-wide quality control scheme for CDG diagnostics within ERNDIM.

Congenital Disorders of Glycosylation (CDG) form a fast growing group of genetic defects, that are characterized by abnormal glycosylation of proteins and lipids. With already more than 140 different genetic diseases discovered in all glycosylation pathways, the need for better biomarkers is increasing. This includes diagnostic biomarkers with increased specificity and sensitivity, as well as biomarkers that can predict disease progression and therapy response.

This presentation will mainly focus on CDGs due to defects in protein N-glycosylation. Screening for defects in protein N-glycosylation is occurring broadly via analysis of plasma transferrin by isofocusing, HPLC or capillary electrophoresis. Increased specificity and sensitivity is obtained by high-resolution QTOF mass spectrometry of intact plasma transferrin, which allows for screening and CDG subtyping in a single diagnostic test. For example, diagnostic glycan profiles are obtained for intellectual disability due to MAN1B1-CDG and exercise intolerance due to PGM1-CDG.

More sophisticated glycomics and glycoproteomics techniques are being applied and developed for CDG gene discovery, by stratification of patient groups. Moreover, these technologies are applied to diagnose CDG defects that don’t affect transferrin glycosylation, such as SLC35C1-CDG. Defects in this GDP-fucose transporter can be diagnosed by glycosylation analysis of IgG. Although biomarkers for prognosis and therapy monitoring are not yet broadly applied in CDG, both of these -omics technologies are very promising to obtain individualized insights in treatment response in CDG, as will be discussed for PGM1-CDG and SLC35C1-CDG.

INTRODUCTION. Inflammatory bowel diseases (IBD) such as ulcerative colitis (UC) and Crohn’s disease (CD) are chronic and relapsing inflammatory conditions of unknown etiology. Current treatments are not universally effective and can have severe adverse effects. Therefore, early detection and precise identification of the type of disease is important in order to apply the correct treatment. Colon tissue is complex and contains different types of cells, and therefore a cell-specific analytical tool is required to improve diagnosis and prognosis. Imaging mass spectrometry (IMS) of lipids may help in such tasks, as it enables recording of images that reflect the metabolic stage of the tissues at near-cellular resolution. The main requirement is to have a precise knowledge of the lipid signature of the disease. Here we present images of lipid distributions in sections of colon biopsies from patients with IBD, recorded using MALDI imaging mass spectrometry, at 10 micron (µm) pixel size. As lipid composition varies between cell types, the lipid fingerprint may serve as a signature of each type of lesion.

OBJECTIVES. The main objective of this work is to identify the lipid signature of healthy colon and to investigate its changes in the context of CD and UC, to set the basis for future development of diagnostic tools.

METHODS. The sample collection for this study was specifically approved by the Ethics Research Committee of the Balearic Islands (IB 2118/13 PI). 67 samples from healthy, UC and CD were studied. Excised human colon tissue was snap frozen in liquid nitrogen and cryo-sectioned at 10 µm thickness. Sections were covered with a suitable matrix for negative-ion detection (1,5-diaminonaphthalene, DAN) using a stainless-steel sublimator, and imaged using a MALDI-7090 TOF/TOF mass spectrometer (Shimadzu, laser spot diameter 10 µm, pixel size 10 µm, scanning range 200-1200 Da).

RESULTS. The MALDI-MSI data unveiled an unexpected heterogeneity in the samples explored. Previous publications already reported a variety of lipid fingerprints in human colon sections. However, the complexity found in pathological samples is substantially bigger. The high spatial resolution enabled us not only to distinguish between the lipid profiles of the lamina propria, colonocites and muscularis mucosae tissues, but also to clearly identify lipid clusters that could be associated with the presence of two different classes of immune cells i.e. lymphocytes and macrophages. Interestingly, these cells appeared intermixed with the cell populations characteristic of the lamina propria, a clear symptom of chronic tissue inflammation. Data analysis was challenging and demonstrated that a simple comparison between the lipid fingerprint of healthy and pathological samples might not be the best approach. Conversely, a more specific cell-to-cell analysis is required to understand the metabolic changes concomitant to the onset of IBD.
Data segmentation using random walkers enabled the extraction of cell-type specific lipid signatures. Comparison between similar tissue areas in the three states: healthy, UC and CD show marked differences in lipid expression. Interestingly, statistically significant differences were also found between the healthy samples and the supposedly healthy tissue of UC and CD samples.

CONCLUSIONS. Lipid expression highlighted by IMS is a powerful tool that enables fast and accurate visualization of the changes in the metabolism of a tissue in the context of a disease. In the case of IBD, the largest differences were found in the relative abundance of PI, PE and SM lipid classes. Such differences existed even between healthy tissue and the samples from tissue supposedly not affected by the disease, demonstrating that the technique is able to identify the disease even at early stage and that may become a helpful clinical complementary tool to aid in the diagnosis.

Introduction:
The diagnosis of thyroid cancer begins with the detection of thyroid nodules, that is, lesions that are radiologically distinct from the normal types of tissue of the thyroid gland. Thyroid nodules are very common and are often incidentally detected during imaging procedures for other indications. Approximately, 7-15% of patients with thyroid nodules are affected by thyroid carcinoma, so that an early identification of malignant nodules is crucial. Fine Needle Aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After biopsy, the 20-30% of cases have an indeterminate for malignancy final report and undergo surgery, but after the thyroidectomy 80% of these nodules result benign. This overtreatment has of course important consequences in the quality of life of the patients and high healthcare costs.

Objectives:
A clinical study was conducted with the aim to build a diagnostic model for the characterization of malignancy thyroid nodules based on proteomic data that could be potentially used to assist pathologists in diagnosis of indeterminate cases. Mass spectrometry is one of the most important analytical tools able to obtain information regarding the molecular composition of a sample. In this work, we propose a framework for the automatic analysis of MALDI-MSI (Matrix Assisted Laser Desorption Ionization - mass spectrometry-imaging) data of FNA thyroid biopsy. Since malignant thyroid samples can have different spectral footprints, we investigate the feasibility of supervised and unsupervised method to detect different morphological regions, using penalized regression model (LASSO) and Self-Organizing Maps neural networks (SOM).

Methods:
Thyroid FNAs were collected from patients (San Gerardo Hospital, Monza, Italy) and MALDI-MSI proteomics analysis was performed within the m/z 3000–20,000 range. Regions of interest (ROIs) containing pathological areas (thyrocytes clusters) were comprehensively annotated by pathologist.
We applied the LASSO and SOMs to multiple examples of MSI data measured on thyroid nodules to investigate their impact on the discrimination efficacy of different morphological areas, as epithelial cells, malignant thyrocytes and inflammatory background.

Results:
The results of our pilot study show that either LASSO and SOM can be effective in separating the areas of a sample containing benign cells from those containing malignant cells. The performance can be hampered in the case of strongly unbalanced datasets and in the presence of empty and/or noisy spectra, two circumstances that mandate further investigations.

Conclusion:
The present study introduces an original methodological approach to build a proteomic diagnostic tool in thyroid cytopathology by taking advantage of MALDI-MSI technology. The direct consequences of successful results could be the use of MALDI-MSI proteomics and a correct statistical framework, as a complementary approach for the pathologist diagnosis of indeterminate for malignancy thyroid nodules. This could reduce the number of unnecessary thyroidectomies, healthcare costs and potential need for a lifelong hormone replacement therapy for patients.

Introduction: While mass spectrometry imaging (MSI) is expanding its fields of possibilities, the analysis of samples with large topographical height differences remains a challenge. Ambient sampling and ionization MS techniques allow the analysis of these uneven surfaces. Nevertheless, application to the visualization of the molecular distributions in 3D is limited, while e.g. this can improve defining of in vivo sampling points. Recent studies have applied different ionization techniques for the 3D molecular visualization of uneven objects. Nevertheless, these cannot be applied to hard tissues, like bone and cartilage. Here, we developed an automated device for the sampling of 3D surfaces (e.g. femoral heads) with laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS).

Methods: The developed automated 3D MS scanner has four degrees of freedom (three translational axes and one rotational axis) that allow the sample to be placed perpendicular under the laser probe with a constant distance between the probe and the sample surface. The topography of the surface is scanned with a laser distance sensor before MS acquisition, using a custom-built automated scanning protocol. For the experiments, a CO2 laser was coupled to a qTOF equipped with a REIMS source. Acquisitions were performed on an apple, bone marrow pipe, and human femoral heads.

Results: The 3D MS scanner allows for the fast acquisition of large areas on uneven samples (maximum height difference 40 mm) with equal distances (i.e. referred to as spatial resolution) between measurement points based on the topographical scan. Within one hour, the topographical scan and mass spectrometry acquisition can be completed for 300 measurement points with a spatial resolution of 2 mm, corresponding to 22.04 cm2. Comparison between the automated and manual acquisitions showed that the automation resulted in increased reproducibility in mass spectra intensities between the measurement points. The coefficients of variance (COVs) for the TIC peak area were lower for all samples for the automated set-up than for the manual set-up. The COVs for the automated set-up were in the range of 7.49-19.74%, while for the manual set-up the range was 15.93-42.70%, depending on the sample. 3D visualization of molecular distributions related to structural differences were shown for an apple, a bone marrow pipe, and a human femoral head. For the human femoral head, differences in the laser burning spots were seen, which were related to different molecular distributions for sclerosis, bone, and cartilage.

Conclusion: The developed 3D MS Scanner allows for the automated sampling of a 3D surface with LA-REIMS, which can be used for the visualization of molecular distributions of these surfaces. Possible applications include improved molecular understanding of sample surfaces, including joint surfaces, and determining optimal in vivo sampling points to reduce tissue damage.

Dr Richard Goodwin started research with MS imaging as postdoc in 2006 before moving to Uppsala University to work as an AstraZeneca post doc for Prof. Per Andrén. Richard now leads a global team of passionate imaging scientists that deliver expertise across AstraZeneca cutting-edge multidimensional imaging modalities. Deriving project insight and impact by connecting safety and efficacy endpoints using advanced tissue histology, molecular imaging, in vivo PET, MRI and much more. All connected by an integrated image analysis, digitization and AI. Passionate about collaboration and embedding new technologies and data analysis into the drug discovery process. Published over 75 publications while at AstraZeneca on the development and deployment of new technology in support of the portfolio. Honorary Professor at University of Glasgow.

Drug discovery and development constantly asks scientists to unlock more from every patient biopsy or preclinical study. Questions around drug biodistribution and metabolism merge with studies examining efficacy and safety. Therefore, researchers as AstraZeneca are collaborating extensively with researchers do develop, deploy and integrate new imaging technologies that provide a holistic view of the hidden molecular landscape in tissues. This provides data volumes and complexity ripe for AI exploration. The presentation will cover the opportunities and challenges in utilizing molecular imaging endpoints to better understand complex disease and investigate therapeutic efficacy across the portfolio of a large pharmaceutical organization.

INTRODUCTION
Routine urine drug screening is generally performed in a targeted manner, where analysis is limited to the detection of predefined drugs and metabolites. Untargeted methods for the detection and identification of exogenous molecules, in which analysis is performed in an unbiased manner, have the potential to detect additional drugs and unexpected additives. Here, we explore the use of an untargeted data-dependent LC-MS/MS method combined with external database searching to preliminarily identify exogenous compounds in urine.

METHODS
Each urine sample (100 uL) was mixed with 300 uL of acetonitrile, centrifuged at 1500xg for 3 min, then the supernatant was removed to a mass spec vial containing 400 uL of Mobile Phase A, and 10 uL of sample was injected for analysis. Mobile phases were 0.1% formic acid in water (A) and in 80:20 methanol:acetonitrile (B), chromatography was initiated at 25% mobile phase B, increased up to 100% B, and concluded with re-equilibration at 25% B. Samples were analyzed using a VanquishTM UPLC system with a Hypersil Gold PFP 250 mm column and Thermo Q ExactiveTM mass spectrometer with data-dependent acquisition using positive mode. Compound DiscovererTM 3.1 analysis was performed using a custom workflow to identify compounds from raw spectra, and compound identification was accomplished by searching the mzCloudTM library and returning identifications for scores greater than 80.

RESULTS
Out of an initial test set of 40 drugs and metabolites, 38 were identified from spiked commercial urine samples, with the lower limit of detection ranging from 15-125 ng/mL. Within this compound set, four isobaric pairs were fully separated and correctly identified. Identifications were confirmed either by previously validated targeted LC-MS/MS SRM methods or by comparison to a commercial pure standard within the same method. From the analysis of one hundred de-identified urine samples, 106 additional exogenous drugs or metabolites were identified in one or more samples. Commercial standards were obtained for 25 of these compounds, analyzed on the same method, and compiled into a local spectral database for comparison.

DISCUSSION
Preliminary identification of exogenous compounds from human urine can be accomplished using data-dependent acquisition and spectral data matching to an external library. Confirmation of drug identity can additionally be accomplished by comparison to local analysis of a commercial standard. Advantages of this approach include the use of a large central spectral library, as opposed to a local library requiring purchased standards for all analytes, and the ability to re-analyze previously acquired data upon library updates. Additionally, this approach allows for further confirmation of compound identity through the use of a local spectral library that incorporates method-specific retention times.

Objectives:
In many countries, driving under the influence of drugs (DUID) legislations apply a zero tolerance law for illicit drugs in oral fluid (OF) or whole blood (WB). Accordingly, the labs have to detect a list of molecules at a minimum concentration using a gold standard method (usually, LC-MS). In France, for cocaine derivatives (cocaine, benzoylecgonine; BZE), opiates (morphine, 6‐acetylmorphine; 6‐AM), and amphetamines (amphetamine, methamphetamine, 3,4‐methylenedioxy‐N‐ethylamphetamine; MDEA, 3,4‐methylenedioxyamphetamine; MDA, and 3,4‐methylenedioxy‐Nmethylamphetamine; MDMA) this “cut-off” is 10 ng/mL. For tetrahydrocannabinol (THC), this value is 0.5 ng/ml in WB and 1 ng/ml in OF.
Here, we compared the performances of 2 LC-MS systems, one with low-resolution (LR) and the other with high-resolution (HR), for detection and quantitation of illicit drugs in DUID context.

Material and methods:
For THC, the sample preparation was a LLE (hexane/ ethyle acetate in acidic conditions). For other molecules, a sample preparation with QuEChERS’s salts was applied. The chromatographic system consisted of two LC-30 AD pumps (Nexera X2, Shimadzu) and separation was performed using a Raptor biphenyl, 2.7 µm (100 × 2.1 mm I.D.) column (Restek). The MS acquisitions were operated either with MRM-HR mode using a Q-TOF mass spectrometer (LCMS-9030, Shimadzu) or with MRM mode using a triple quadrupole (LCMS-8060, Shimadzu). The lower limit of quantitation (LLOQ) was the lowest concentration measured with imprecision (CV%) and inaccuracy (bias) < 20%. The lower limit of detection (LLOD) was the value of the lowest non-zero calibrator. Finally, 50 real cases of DUID were analyzed to compare the performances of the HR and LR procedures.

Results:
Both the LR and the HR systems were able to detect THC at 0.5 ng/ml in OF and WB. For other illicit drugs (cocaine derivatives, opiates and amphetamines), the LLOD were at most 5 ng/ml. For quantitation of THC, LR and HR approaches presented a LLOQ of 0.5 ng/ml. For other illicit drugs, the LLOQ were 0.5 ng/ml using the LR approach, while they were from 0.5 to 5 ng/ml with the HR approach. Consequently, depending on the molecule and the detection mode, the dynamic ranges were from 0.5 to 100 or from 5 to 100 ng/ml. For each real case, when a compound was detected with the LR method, it was systematically detected with the HR method.

Conclusion:
The LC LRMS and the LC HRMS methods presented LLOD far less than those expected by law. This also suggests that a HRMS system can be suitable for the quantitation of illicit drugs in OF and WB for DUID cases.

Background and Rationale - Tuberculosis especially Multidrug resistant Tuberculosis (MDR-TB) is a leading cause of death in India. The recent WHO and National guidelines have recently approved Bedaquiline (BDQ), a diarylquinoline for treatment of MDR-TB. It kills both dormant and actively replicating mycobacteria by inhibiting mycobacterial adenosine triphosphate (ATP) synthase interfering with energy production and disrupting intracellular metabolism. It has a large volume of distribution however a very slow terminal elimination with a terminal half life of 5.5 months. There is limited literature on pharmacokinetics of BDQ in MDR TB patients globally. The present study aims to develop a validated method to enable performing therapeutic drug monitoring of BDQ in patients suffering with MDR-TB.

Methods- BDQ quantification was standardized & validated using a deuterated internal standard using the liquid chromatography tandem mass spectrometry approach. The detection was obtained at mass transitions m/z 555.1 to 58.1. Therapeutic range of BDQ is not well established however a range between 0.6 – 2mg/L is considered for effective treatment management. Plasma samples were collected and quantitated from treatment-naive MDR-TB patients at pre-dose, 2, 5 & 24 hours post dose timepoints.

Results - The method was optimized for a linearity from 0.0313 - 4 mg/L with tri-level controls. The method has been validated for all parameters with the recovery of BDQ was close to 100% and ranged from 80% to 120% as per guidelines. Inter-day and intra-day accuracy and precision passed the % coefficient of variance (CV) of < 20%. Drug levels of 42 patients on ongoing BDQ therapy are estimated and the remaining samples will be processed and correlated with factors affecting serum drug levels. Intensive pharmacokinetic assessments reveal the tmax of BDQ is attained at 5 hour post dose. Most patients achieve therapeutic levels within the first month of treatment i.e >0.6 mg/l. A significant difference was observed in pre-dose and 2 hour post dose drug levels (p=0.016).

Conclusion and Recommendations - The method is validated as per the recent FDA 2018 Bioanalytical Method Validation Guidelines. The developed method is a simple & robust method to estimate BDQ levels in plasma samples.

Key words - Bedaquiline, MDR-TB, LC-MS/MS, Therapeutic drug monitoring, India.

BA Biochemistry at Columbia University
MD PhD University of California San Diego
5 years management consulting experience
Residency in clinical pathology at UCSD
Fellowship in molecular genetic pathology at UCSD

Background: Individual genetic differences in the cytochrome p450 oxidase family of enzymes are responsible for most aberrantly fast or slow drug metabolisms leading to therapeutic failures or unexpected toxicity. Metabolic ratios of drug to metabolite measured in urine can be used to identify aberrant metabolism.

Methods: We utilized a dataset of 174,145 observations from 16,400 patients to 1) develop oxycodone/ metabolite ratio reference range enabling identification of CYP2D6 and CYP3A4/5 abnormally fast or slow metabolizers and 2) validate predicted metabolism using co-incident observations of other drug/ metabolite pairs. Drugs and metabolites were measured using a multiplexed, high-sensitivity, clinically validated LC-MS/MS method.

Results: Prevalence of predicted CYP2D6 fast and slow metabolizers and CYP3A4/5 fast and slow metabolizers were 10% and 14%, respectively. These prevalence rates are consistent with genotypic studies. Metabolic ratios of co-incidentally observed dextromethorphan/ dextrorphan, a drug metabolite pair classically utilized to assess CYP2D6 metabolism, were lowest in predicted CYP2D6 slow metabolizers and lowest in predicted CYP2D6 fast metabolizers. Predicted CYP3A4/5 metabolism did not correlate with dextromethorphan/ dextrorphan. Correlation between metabolic ratios of 12 other drug/ metabolite pairs and predicted CYP2D6 and CYP3A4/5 kinetics are also reported. Individual patients with metabolic ratios consistently outside the reference range were identified. These patients could either be aberrant metabolizers or the specimens may have been contaminated with parent drug.

Conclusions: Development and utilization of a reference range for metabolic ratios can enable detection of aberrant drug metabolism.

Young graduate in forensic chemistry with a curricular internship consisting of coaching a professional medical examiner. The internship consisted of observation in the activity of autopsy and thanatological investigation, learning of techniques and working methods in the medical-legal, criminalistic field and in Forensic Toxicology laboratories.
Deepening of the notions in the anatomical field and in particular in-depth observation of the nature and post-mortem evolution of biological tissues.
Recently concluded a scholarship for research activities within the project "Analysis of New Psychoactive Substances in unconventional biological matrices"
The research activity consisted in the development of innovative analytical approaches useful for the identification of New Psychoactive Substances (NSP) in unconventional biological samples. To this end, instrumentation based on high resolution mass spectrometry was used and the most suitable biological matrices were selected. Particular attention is paid to the keratin matrix.
Currently on a scholarship to an anti-doping center.

Background: A drop of whole blood dried on filter paper (Dried Blood Spots, DBS) represents an innovative and promising technique for minimally invasive sample collection in a multitude of analytical disciplines. Several applications can take advantage of the DBS technique, such as therapeutic drug monitoring, preclinical drug development, diagnostic analysis of metabolic disorders in newborns and Workplace Drug Testing. DBS sampling is characterized by cost-effectiveness, straightforwardness, robustness, facilitated storage and shipment conditions. Also, the technique can be effectively exploited to identify and quantify a wide range of compounds only using a small volume of blood and thus provide information on the current consumption of substances. This study aimed to develop and validate a procedure for the detection of a large and diverse range of substances originally designed as legal alternatives to more conventional illicit drugs: New Psychoactive Substances (NPS).

Methods: A drop of whole blood sample was collected on Whatman card 903 and dried for 3 hours. A total of 136 analytes (identified among the most common synthetic cannabinoids, synthetic opioids, synthetic cathinones and hallucinogens monitored nationally and internationally) plus 13 deuterated internal standards were separated and identified after liquid-liquid extraction using modern UHPLC-QTOF-HRMS instrumentation. The method was fully validated.
Results: The developed method showed an extraction efficiency higher than 40-55 %. Accuracy and repeatability inter and intra-day fulfilled acceptance criteria for most synthetic opioids, cathinones and hallucinogens (bias and CV% below ±20 %). Quantitation limits ranged from 4 ng/mL for 1-(5-fluoropentyl)-N-(2-phenylpropan-2-yl)-1H-indazole-3-carboxamide (5F-CUMYL-PINACA) and 3,4-dicloro-N-[(1-(dimetilammino)cicloesil)metil] benzamide (AH-7921) up to 10 ng/mL for 2-(2,5-Dimethoxy-4-propylphenyl)ethan-1-amine (2C-P). A moderate ion suppression was observed, with final values ranging between 6 and 35% after compensation with appropriate internal standards.

The stability observed at 100 ng/g of NPS in the Whatman 903 card was shown to be good for synthetic opioids, cathinones and hallucinogens (<< 15% variation in concentration calculation) within 7 days of storage at 4 ° C, 35 ° C and -20 ° C. For synthetic cannabinoids the temperature and time factors would be critical variables, in fact, from the third day of storage, a non-negligible change in signals is detected. The stability observed at 10 ng/g respects the trend obtained for the high concentrations for synthetic cannabinoids, cathinones and hallucinogens, while a decrease in the signal with increasing days and temperature is observed for synthetic opioids.

Conclusion: In this study, we present a comprehensive workflow that combines the use of UHPLC-QTOF-HRMS instrumentation with a simple DBS sample extraction procedure for the detection of a huge variety of small molecules, including synthetic substances and their metabolites. A targeted analysis was applied and MS and MS/MS data were collected for each sample. Data acquisition included a TOF-MS high-resolution scan combined with TOF-MS/MS acquisition demonstrating a considerable capability to detect and identify 136 target substances at low concentrations. Furthermore, this study proved capable to extract and identify a high number of NPS starting from 30 μL of whole blood using a simple, inexpensive, and minimally invasive technique.

The results obtained from the validation allow us to affirm the validity of the procedure for the analysis of structurally different compounds. In addition, the results obtained confirm the extraordinary validity in the use of DBS, opening the way to different possible applications including monitoring of drugs or biomarkers in clinical and forensic contexts.

Introduction
Tyrosine kinase inhibitors (TKIs) are small molecule drugs used for the treatment of several cancers. Therapeutic drug monitoring (TDM) of TKIs shows significant potential in guiding personalized anticancer treatment to improve treatment efficacy while minimizing toxicity. Dried blood microsampling could be a valuable alternative for traditional plasma sampling to provide TDM results faster and to reach a wider audience. Sample collection is easy and patient friendly as only a small volume of blood is collected via a fingerprick. This enables the possibility of home sampling by the patients themselves. Therefore, a LC-MS/MS method was developed and validated, for the quantification of bosutinib, dasatinib, gilteritinib, ibrutinib, imatinib, midostaurin, nilotinib and ponatinib in samples collected by volumetric absorptive microsampling (VAMS). A VAMS device collects a fixed volume of blood (approximately 10 µL), irrespective of the sample’s hematocrit (Hct). When setting up a VAMS-based quantitative method, additional parameters need to be evaluated compared to traditional plasma methods.

Material & methods
The extraction procedure involved pre-wetting of the VAMS samples with 100 µL water, followed by vortexing and 5’ shaking at 1400 rpm. Following the addition of stably labeled internal standards for all analytes (in 200 µL methanol), vortexing and 10’ sonication, a liquid-liquid extraction was performed using 800 µL methyl-t-butyl ether. Following drying, the extracts were redissolved in 70 µL (start conditions of the gradient run), of which 25 µL was injected onto an Acquity UPLC® BEH C18 column, followed by detection with tandem mass spectrometry. The total run time was 4 min. During method validation, special attention was paid to the possible impact of Hct (range 0.18-0.55) on matrix effect (ME), extraction recovery and accuracy of the method.

Results
The method was successfully validated based on international guidelines in terms of calibration curves, precision (within-run CV 2.20-14.8%; between-run CV 2.40-12.3%), accuracy (within-run bias 0.34-12.5%; between-run bias -0.15-16.2%), carry-over and selectivity. IS-compensated ME and recovery were Hct independent and no significant impact of Hct on the accuracy of the TKI quantifications was observed. All TKIs were stable in VAMS samples stored at -20°C, 4°C and room temperature (RT) for at least 2 weeks and for 2 days at 60°C (except ibrutinib). Lastly, the developed method was successfully applied on VAMS samples obtained from patients on TKI treatment.

Conclusion
The presented method can be applied in clinical practice for performing TDM of TKIs in VAMS samples with varying Hct levels.

The primary focus of my research is working with physicians and scientists to solve complex problems in pediatric drug development. Our laboratory is equipped with UPLC-MS/MS and UHPLC-MS/MS systems and Watson LIMS. We specialize in the development and validation of analytical methods and their implementation for pediatric and adult clinical sample analysis. Our clinical research is focused on the pharmacokinetic analysis of dexmedetomidine, midazolam, morphine, ketamine, cefepime, cefazolin, voriconazole, efavirenz, tenofovir, vancomycin, and cannabinoids. We also have expertise in the fast assay development for preclinical studies in various biological matrices. Our preclinical research involves the pharmacokinetic analysis of treprostinil, oxycodone, SN-38, irinotecan, vemurafenib, and tamoxifen. In addition, I contribute to building a biorepository for samples from critically ill children. We continue to evaluate and utilize new tools and microsampling technologies to facilitate preclinical, translational, and clinical research.

INTRODUCTION: Cefepime is a fourth-generation cephalosporin antibiotic with an extended spectrum of activity against many Gram-positive and Gram-negative bacteria. Vancomycin is the antibiotic of choice for the treatment of serious Gram-positive infections, such as methicillin-resistant Staphylococcus aureus. However, dosing and drug concentrations need to be monitored closely to confirm optimal treatment for the severity of the infection present.

OBJECTIVES: The primary objective is to develop sensitive, small volume assays, along with less invasive sample collection to facilitate pediatric pharmacokinetic clinical studies and therapeutic drug monitoring.

METHODS: We developed liquid chromatographic - tandem mass spectrometry assays for quantification of cefepime, and vancomycin. Sample extraction of Mitra tips, followed by reversed-phase chromatographic separation and selective detection using tandem mass spectrometry was employed for the quantitation of anti-infective drugs. Volumetric absorptive microsampling (VAMS) approach that enables accurate and precise collection of a fixed volume (10 -20 µL of blood), reducing or eliminating the volumetric blood hematocrit assay-bias associated with the dried blood spotting technique.

RESULTS: Standard curves were linear between 0.1 – 100 µg/mL for cefepime. Intra- and inter-day accuracies were within 95.4 – 113% and precision (CV) was < 11.3% based on a 3-day validation study. Recoveries were ≥ 45.5 % and the matrix effect was within 89.5 – 96.7 % for cefepime. Cefepime was stable in human whole blood under assay conditions. Cefepime was also stable for at least 1 week (7 days) at 4 °C, 1 month (39 days) at -20 °C, and 3 months (91 days) at -78°C as dried microsamples. Standard curves were linear between 1.0 – 100 µg/mL for vancomycin. Intra- and inter-day accuracies were within 90.3 – 107% and precision (CV) was < 12% based on a 3-day validation study. Recoveries were ≥ 16.6 % for vancomycin and matrix effect was 82.8 – 91.3 %. Vancomycin was stable in human whole blood under assay conditions. Stability for vancomycin was established for at least 52 days as dried microsamples at -78 °C.

CONCLUSION: Cefepime and vancomycin VAMS assays were successfully implemented for the analysis of whole blood microsamples in pediatric clinical studies. The results from the analysis of the representative clinical samples and the future directions will be discussed.

Introduction
The role of phosphatidylethanol (PEth) as a direct biomarker for alcohol intake is more and more valued. PEth increases the sensitivity of uncovering alcohol consumption and monitoring abstinence. Upon chronic and excessive alcohol use PEth values are easily over 300 ng/mL. With a half-life of 7 – 8 days, it can take weeks for PEth values to drop below the decision limit for monitoring abstinence (20 ng/mL). The question arises whether abstinence can be confirmed based on two consecutive PEth positive results.

Methods
A large scale PEth monitoring study was set up in which over 500 participants agreed to stay sober for one month. During this period, they took 3 finger prick samples via self-sampling at home using volumetric absorptive micro sampling devices (VAMS, Mitra®, Neoteryx). PEth 16:0/18:1 was quantified using a validated liquid chromatography – tandem mass spectrometry method. A population-based algorithm capable of predicting abstinence with 95% probability was set up by fitting a linear mixed effect model to discern patterns in PEth elimination over time while accounting for intra- and interindividual variability in PEth scores. The latter was further incorporated in a 2-step decision tree, looking (i) whether or not PEth-values fall outside the prediction interval, and (ii) whether the slope between two PEth values is compatible with abstinence. Validation of this decision three was based on data of 74 people reporting to drink alcoholic beverages, alcohol free beverages or having eaten liquor-containing sweets.

Results
Allowing a cut-off of “4 units spread over 14 days”, the sensitivity and specificity of the decision tree was 89%. This is a realistic cut-off compared to other reports on detectability of a single dose intake of ethanol.

Conclusion
Monitoring the decrease in PEth, while the latter is still positive, can underpin claims of abstinence upon short term monitoring.

I am a chemist (University of Murcia, Spain) and hold a PhD in Mechanical Engineering (University Carlos III, Spain). I performed my doctoral research in Madrid and at Yale University in the former lab of Prof. John B. Fenn (Nobel Prize winner in 2002) under the supervision of Prof. Juan Fernandez de la Mora. During this period, I became acquainted with electrospray ionization, ion mobility spectrometry and mass spectrometry. During my post-doctorate, I further developed mass-spectrometric methods for the real-time analysis of trace gases. Between 2011 and 2017, I was appointed senior scientist at ETH Zurich (Renato Zenobi’s group), where I deployed these novel analytical methods in a translational research program in collaboration with the University Hospital Zurich. I eventually earned my Habilitation in analytical chemistry at the ETH. I remain affiliated with the Department of Chemistry and Applied Biosciences at ETH as Privatdozent ever since. Since 2017, I hold a Tenure-Track Assistant Professor position (Botnar Research Professorship) at the University of Basel (Department of Biomedical Engineering). I am also principal investigator of the Research Network Zurich Exhalomics, which is an initiative by scientists from the Zurich area with the goal to provide technical solutions for the rapid and sensitive on-line analysis of breath. I have co-authored > 50 peer-reviewed papers covering fields ranging from engineering to medicine. In 2018, I was recipient of the Swiss National Science Foundation Eccellenza grant. In 2020 I received the biannual Swiss Group for Mass Spectrometry award for outstanding independent research in the field of mass spectrometry.

Exhaled breath contains valuable biochemical information at the metabolic level. Hence, it holds great potential as a non-invasive method to assist in clinical diagnosis and therapeutic. While there has been progress in making use of breath tests to guide clinical decision making, the full potential of exhaled breath analysis still remains to be exploited.

I will present a summary of the technical developments that allowed secondary electrospray ionization-high resolution mass spectrometry to become a reference analytical platform for real-time trace gas analysis. Subsequently, I will illustrate how are we exploiting this technology in a clinical setting to address unmet clinical needs. For example, I will showcase how we use breath analysis to predict drug blood concentrations in epileptic patients, as well as risk estimates for drug therapy effects as well as side effects. Ultimately, this work aims to improve diagnosis, to better phenotype complex pathophysiological processes, as well as to personalize therapy.

My current research interest lays in an exciting area of breath analysis, which carries a promise to change the scope of human diagnosis and point of care toxicological screening. I have spent more than last four years of my carrier as a postdoctoral university researcher in discovery breath biomarkers, creating and validating analytical methods for their detection, coordinating clinical studies, analysing and modelling complex metabolomic data, building cooperation and networks across international breath research communities around the world as well as incorporating data science to enhance data management and analysis efficiency. My current research centres around using non-invasive breath tests using field-adaptable systems based on atmospheric pressure ionisation techniques for detection of biomarkers in:
1) Detection of biomarkers of SARS-COV-2 infection
2) detection of biomarkers to characterise respiratory disease signatures (EMBER)
3) detection of biomarkers from exposure to CBRN agents (Toxi-Triage)

Introduction:
There is an urgent need for point-of-care and community tests not requiring laboratory- support to be developed to distinguish Covid-19 from other respiratory conditions and or other viral infections. We studied using breath-analysis to distinguish these conditions with gas chromatography-ion mobility spectrometry (GC-IMS) at four different centres located in Europe, Asia and Canada.

Aim: The primary aim of the study is to develop rapid test for diagnosis of Covid-19.

Methods: Observational prevalence studies at Edinburgh (EDR), UK, and Dortmund (KD), Germany, Peradeniya (PH), Sri Lanka recruited adult patients with possible COVID-19 at hospital presentation. Another study from Vancouver Canada (BCCRC) recruited mildly symptomatic participants in community testing centres and analysed for VOC’s. Participants gave a single breath sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review for the hospitalised patients. Potential COVID-19 breath-biomarkers were first identified in pilot study, including EDR and KD centres by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data. The extended study with further two centres, allowed for the validation of the proposed biomarkers and is ongoing.

Results: Ninety-eight patients were recruited in the pilot study, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes, ketones, methanol and an unidentified feature discriminated COVID-19 from other conditions in both centres. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1).
Further studies in Peradeniya and Vancouver recruited 160 and 200 participants with multiple variants of the virus. The results showed similar discriminatory power. However, some differences in the observed bio-markers were observed.

Conclusions: The studies showed that COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact as well as within mildly symptomatic community. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. The validation of this approach is ongoing and may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons.

Pierre-Hugues Stefanuto is lead scientist and lecturer at Liège University in Belgium. His main research interest is the development of analytical solutions based on chromatography and mass spectrometry technology. He is interested in the development of statistical models for method optimization and data handling. He is working on the development on multimodal solutions of untargeted screening of small molecules.
Driving Research Goal: Development of multi-omics screening to tackle biomedical challenges at the molecular level

Background: The ballistic rise of analytical technologies has opened a large playground for all type of untargeted “omics” screening. In that trend, there is a rising interest for the characterization of the human volatilome. Indeed, the characterization and the understanding of the volatile organic compounds (VOCs) production in different ex vivo matrices could open the route for improved diagnosis approaches and more individualized treatments. For large-scale screening, direct introduction instruments, such as selected ion flow tube mass spectrometry (SIFT-MS) offer the capacity to perform both targeted and untargeted analyses within a few minutes. SIFT-MS can generate compositional patterns from direct sample introduction at the same time than other routine medical actions. However, the use of SIFT-MS for untargeted screening requires the acquisition of full-scan mass spectra for every precursor of interest. To investigate this type of data, multiple factors such as the different chemistries of each precursor and structure of the data set, have to be considered to extract useful information.

Objectives: We compared full scan SIFT-MS with another exhaled breath analysis method, namely comprehensive gas chromatography coupled to high resolution mass spectrometry (GC×GC-HRTOFMS) and fractional exhaled nitric oxide (FeNO), for asthma phenotyping using exhaled breath. The comparison was addressing the classification accuracy and the easiness of implementation into the clinic.

Methods: We analyzed the exhaled breath of 50 well characterized patients suffering from different type of asthma. Breath samples were collected in duplicate using 5 L Tedlar bags; one bag for the SIFT-MS and one for the GC×GC-HRTOFMS. For the GC×GC-HRTOFMS, a previously validated protocol was employed. For the SIFT-MS, the VOC profile was analyzed using three chemical precursors (i.e., H3O+, NO+, and O2+), using the quadrupole analyzer in full scan mode. From there, different data processing approaches were compared to identify specific asthma inflammatory phenotypes in a mixed asthma population. Targeted and untargeted approaches have been applied and compared to evaluate their potential to translate into the clinic.

Results: We had to develop a dedicated data processing workflow for the full scan SIFT-MS data. We evaluate the impact of normalization and scaling for the pre-processing. Then, we used machine learning algorithms (e.g., random forest and PLS-DA) to build a classification model. A similar workflow was used for GC×GC-HRTOFMS data. SIFT-MS and GC×GC-HRTOFMS techniques provided good classification accuracy (around 75%), similar to the efficiency of other clinical tools routinely used for asthma phenotyping. Different data fusion methods were also applied to the full scan SIFT-MS data to combine the information from the different precursors. This approach allows identifying the most informative ion channels in the data matrix and improved the classification accuracy to 80%.

Conclusion: SIFT-MS used in targeted or untargeted mode clearly meet the criteria to make routine clinical breath analysis a reality.

Introduction:
Both circadian misalignment and sleep deficiency disrupt normal metabolism, increasing risk of metabolic disorders. Therefore, not only in fundamental research but also from a medical perspective, there is a great interest in obtaining a better understanding of the underlying molecular mechanisms. It has been well established that a circadian clock directly regulates many metabolic pathways, and the biological pathways underlying this control have been studied extensively. Effects of sleep restriction and sleep deprivation on human metabolism have also been studied recently. In contrast, metabolism during sleep and the impact of different stages of sleep on metabolism are poorly understood. The main limitation is a lack of sufficient sampling rates for biofluids such as cerebrospinal fluid, urine, saliva or blood during sleep. This limitation can be overcome by analyzing exhaled breath.

Methods:
Using exhaled breath analysis by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), we measured for the first time the human exhaled metabolome at ten seconds resolution across a night of sleep in combination with conventional polysomnography. Using the same measurement technique, we investigated breath metabolites in 18 individuals suffering from obstructive sleep apnea (OSA), as well as 26 healthy age- and sex-matched controls.

Results:
Our subsequent analysis of almost two thousand metabolite features demonstrates rapid, reversible control of major metabolic pathways by the individual vigilance states of slow-wave sleep (SWS), rapid eye movement (REM) sleep, and wake. Within this framework, whereas a switch to wake increases fatty acid oxidation, a switch to SWS reduces it, and the subsequent transition to REM sleep results in elevation of TCA cycle intermediates preparing for mitochondrial oxidation. We further found that patients suffering from OSA show dysregulation of the same sleep-regulated metabolic pathways.

Conclusion:
Thus, in addition to daily regulation of metabolism, there exists a surprising and complex underlying orchestration across sleep and wake that is misregulated in OSA. Control of metabolism by vigilance state therefore likely plays an important role in optimizing metabolic circuits for human performance and health.

Introduction: MALDI-TOF MS is a reference method for microbial identification at clinical microbiology laboratories that has made it possible to anticipate infectious diagnosis within 24 hours. However, the prediction of antibiotic resistance is still pending and its development would allow optimizing the antimicrobial therapy.
Objectives: Design a mathematical model that integrates both phenotypic antibiotic susceptibility data and MALDI-TOF protein spectra to predict the production of beta-lactamases (ESBL) or carbapenamases (CARB) in clinical isolates of Klebsiella pneumoniae.

Methods: Overall, 282 clinical K. pneumoniae isolates were collected between 2014 and 2018, and divided into three categories: susceptible (n=91, 32.0%), ESBL (n=9, 3.0%) and ESBL+CARB (n=182, 65.0%) by their antibiotic phenotypic profile determined first by Wider semi-automated method and further confirmed by PCR amplification and sequencing. MALDI-TOF spectra represent the median of normalized data from three independent replicates per session and at least two sessions on separate days; 6 spectra in total for each strain. To classify each MALDI-TOF spectrum we proposed to use SSHIBA (Sevilla-Salcedo et al. 2021) and its kernel extension, KSSHIBA (Sevilla-Salcedo et al. 2020), a Bayesian multiview model capable of working with heterogeneous multivariate data such as kernelized continuous data (MALDI-TOF spectra) or multi-label (phenotypic susceptibility data). To evaluate the performance of the model, we compared its test performance (with a 10-fold cross-validation process) against several supervised classifiers such as Random Forest, K Nearest Neighbours, Gaussian Process or Support Vector Machine (SVM) and the previously published model (Weis et al. 2020) based on peak detection and ad-hoc kernel evaluating the nonlinear relationship was also performed.

Results: The region in the MALDI-TOF MS protein spectra between 2,000 and 12,000 m/z was selected for its relevance in both K. pneumoniae identification and discrimination of the three categories. Our model achieved AUC scores of 0.822 for predicting the simultaneous production of ESBL+CARB, 0.641 for ESBL and 0.665 for susceptible isolates, while the best reference classifier (SVM) obtained AUC values of 0.820, 0.530 and 0.591, respectively. In addition, KSSHIBA provides explainable results by pointing out MALDI-TOF peaks specific for ESBL (2430-2450, 7400-7410, and 7220-7240 m/z), and CARB production (9940-9950 m/z).

Conclusions: The developed model has shown a strong ability to predict the presence of ESBL and CARB in clinical strains of K. pneumoniae. Although external validation of the model is still needed, the developed methodology has great potential to predict antibiotic susceptibility profiling using MALDI-TOF in a rapid and reliable manner.

References
Sevilla-Salcedo C., Gómez-Verdejo V., Martinez-Olmos P. Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis. Pattern Recognition, 2021. doi.org/10.1016/j.patcog.2021.108141
Sevilla-Salcedo C., Guerrero-López A., Martinez-Olmos P., Gómez-Verdejo V. Bayesian Sparse Factor Analysis with Kernelized Observations. 2020. arXiv:2006.00968
Weis CV, Jutzeler CR, Borgwardt K. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clin Microbiol Infect. 2020 Oct;26(10):1310-1317. doi: 10.1016/j.cmi.2020.03.014.

Introduction – Clinical diagnosis of bacteria by mass spectrometry (MS) is typically based upon MALDI MS profiles of ribosomal proteins which are matched against a reference database. Closely related species can possess a high degree of similarity in their proteomes, therefore some proteins may be of the same, or very similar, m/z value leading to potential misidentification. Liquid AP-MALDI MS is an emerging technique which has recently been demonstrated for bacterial identification based upon lipid profiles. Here we build on this identification strategy by adding subsequent MS/MS sequencing of multiply charged proteins on high-performance Q-TOF/ion mobility mass spectrometer, which, in combination with lipid profiles, has the potential to provide a higher identification confidence than conventional axial MALDI MS as found in clinical biotyping.

Objectives – The objective of this study is to identify different bacterial species based upon MS profiling and MS/MS sequencing of biomarkers using liquid AP-MALDI.
Methods – Bacteria were grown according to growth guidelines from the NCTC. Following the recommended growth period, half a loopful of biological material was harvested and resuspended in 1 mL of 1X PBS. Suspensions were prepared using simple TCA precipitation, with a preparation time of approximately 40 min. C18 ZipTips were used for sample clean-up. An α-cyano-4-hydroxycinnamic acid-based liquid support matrix (LSM) was used for all analyses. A volume of 0.5 µL of LSM was spotted on the target plate, followed by 0.5 µL of bacterial analyte solution. An in-house built AP-MALDI source coupled to a Synapt G2-Si (Waters) was used at standard operating settings of 3.0 kV extraction potential with 180 L/h counterflow N2 gas for all analyses. A pulsed N2 laser was operated at 21 µJ/pulse at a repetition rate of 30 Hz. MassLynx 4.2 software was used for data acquisition and processing. AMX software (Waters) was used for statistical analysis.

Results – Liquid AP-MALDI mass spectra were acquired in the m/z range of 100-2000, with each bacterium possessing a unique spectral profile formed of lipids and/or proteins. Not only does the MS profile contain a higher variety of biomarkers than traditional (vacuum) MALDI biotyping mass spectra, the use of an AP source allows coupling to high performing hybrid Q-TOF instruments with MS/MS capabilities. This and the use of liquid MALDI means that multiply charged protein ions can be easily fragmented using CID, subsequently allowing for superior MS/MS sequencing of biomolecules. Various proteins were identified from each bacterium, up to approximately 20 kDa in molecular weight, including DNA binding proteins, unique to each species. Acquisition of an MS profile allows a rapid identification in 1 minute of analysis time. In cases where this is not sufficient, such as closely related species, MS/MS sequencing can be done to provide a higher identification confidence within approximately 2 minutes.

Conclusion – Liquid AP-MALDI can be used for bacterial identification using lipidomic and proteomic profiling of bacteria. Additional MS/MS analysis can be performed to increase the confidence of identification.

Lucia Grenga is a researcher in the laboratory of Innovative Technologies for Detection and Diagnostics at the French Alternative Energies and Atomic Energy Commission (CEA). She is interested in the development of tandem mass spectrometry‐based methodologies and their integration with other omics approaches for the characterization of clinically relevant microbiomes.

Proteomics offers a wide collection of methodologies to study biological systems at the finest granularity. Faced with COVID-19, the most worrying pandemic in a century, a collective mass spectrometry effort was launched to study COVID-19 and propose alternative solutions for the detection of SARS-CoV-2. Since then, proteomics researchers have made significant progress in understanding how its causative virus SARS-CoV-2 hijacks the host's cellular machinery and multiplies exponentially, how the disease can be diagnosed, and how it develops, as well as its severity predicted. Numerous cellular targets of potential interest for the development of new antiviral drugs have been documented. The main findings gathered by our group and other proteomists will be discussed while pointing out how they could represent game-changers in the COVID-19 battlefield and in deciphering the long-term consequences of the infection.

INTRODUCTION: Lipids and lipid mediators are of great interest as potential biomarkers for several diseases. Yet, the correct preanalytical procedure is often neglected or essential information about sample handling is missing.

OBJECTIVES: The primary objective of the study was assessing the preanalytical stability of lipids with a focus on lipid mediators in different biological matrices like human whole blood, plasma and serum as well as murine tissue samples under different sampling and storage conditions.

METHODS: Preanalytical factors like storage time after sampling, storage temperature and centrifugation speed were tested for human whole blood and plasma with K3EDTA or sodium fluoride/citrate as anticoagulant as well as serum. Furthermore, the effects of the sample storage period on lipid stability in murine tissue samples like liver, kidney, heart and spleen were analyzed post-mortem in whole and in homogenized tissue. The quantification of low-concentrated lipid mediators (e.g. eicosanoids, endocannabinoids, sphingolipids and lysophosphatidic acids) was performed via targeted LC-MS/MS methods. The non-targeted lipidomics screening was achieved using LC-HRMS.

RESULTS: Preanalytical sample handling conditions can have varying impact on concentrations of lipids and lipid mediators. For example, the concentration of the endocannabinoid 2-arachidonoyl glycerol increases by more than 90% when K3EDTA whole blood is stored for 20 min in ice water after the blood collection. The concentration changes of this group of lipid mediators can be lowered by using sodium fluoride/citrate blood sampling tubes. Still, sample-processing time should not exceed one hour for reliable detection of the endocannabinoids. However, most lipids were stable in K3EDTA whole blood and plasma for 4h in ice water and several of them are not changing even if the sample is handled at room temperature. This is also true for tissue samples. Storage at room temperature leads to lipolysis influencing several lipid classes whereas most analytes were stable in case the samples were stored in ice water for 35 or even 90 min.

CONCLUSION: The results of the analysis indicate clearly that preanalytical sample handling is a critical part of performing any investigation on lipids and especially lipid mediators. Not all lipids need the strictest sample handling conditions like immediate centrifugation after blood sampling and cooled storage while handling blood based samples. The sampling protocol can be adjusted based on the spectrum of lipids targeted in the respective study. However, it is of high relevance to standardize the preanalytical protocol to obtain reliable and reproducible results. Recording the actual practice for every sample taken is necessary to verify the protocol adherence.

INTRODUCTION: Lipid metabolism plays a central role in maintaining homeostasis and the normal functioning of the human body. Aberrations of lipid level are the key hallmarks of multiple human diseases, including coronary artery disease, diabetes, cancer, and immunodeficiency. Accordingly, precise identification of disease-associated changes in lipid-regulating pathways is needed to inform individualized diagnostic and therapeutic strategies.

OBJECTIVE: Here, we aim to determine the lipidomic and proteomic signatures of different types of dyslipidemic conditions, including hyperlipidemia, hypercholesterolemia, hypertriglyceridemia, and hyperglycemia, and to establish new functional connectivity between lipid species and lipoproteins in the context of known metabolic pathways.

METHODS: Quantitative targeted lipidomics and proteomics approaches were used to characterize serum samples from 146 individuals with a clinical diagnosis of lipid metabolic disorder. Lipidomics analysis was performed using a direct infusion system with differential mobility spectrometry coupled MS/MS. Proteomics data were acquired with the on-column trypsin digestion coupled with LC-MS/MS.

RESULTS: We have identified 22 proteins and 575 individual lipid species across 10 lipid subclasses, including cholesteryl esters (CE), diacylglycerols (DAG), phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), free fatty acids (FFA), sphingomyelin (SM), ceramides (CER), and triglycerides (TAG). Differential analysis of lipid and protein concentrations revealed the sets of lipids and lipoproteins that were significantly dysregulated in dyslipidemia patients as compared to the normal patient. We established network association among lipid species and individual lipoproteins based on partial correlation analysis for dyslipidemia groups. We found that in the network established for hypertriglyceridemia, TAG and CE lipid species were centered around APOC3 and APOB due to significant positive correlations. In the case of hypercholesterolemia, the network of TAG species was dominated by positive correlations with APOC2 and negative correlations with APOC1. In contrast, APOC1 showed significant positive correlations with TAGs in the lipid network obtained for hyperglycemia. Moreover, APOE, APOA1, APOA2, and APOM appeared to correlate with SM, CE, LPC, and DAG species levels. In hyperlipidemia samples, TAGs showed strong positive correlations with APOE and APOA4 proteins, while CEs had negative correlations with APOA4.

CONCLUSIONS: The comprehensive lipidome-proteome network analysis can allow improved categorization of patients and lead to more effective and individualized therapeutic interventions.

The findings and conclusions are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

INTRODUCTION: Among the numerous protocols tested for the diagnosis and prognosis of melanoma, the use of the lipid profile of cells has no precedent, despite that, lipids are probably the most important family of metabolites and are involved in all the fundamental processes of the cell. Any change in the metabolism of a cell has an impact on its lipidome. They are, therefore, excellent candidates for biomarkers for the early detection of melanoma.

OBJECTIVES: In this work we explore the use of the lipid profile of cells in sections of melanoma biopsies sections to create a robust classification model that identifies the different stages of the disease using matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS).

METHODS: 54 samples (15 nevus, 25 primary melanomas and 14 metastatic melanomas) were used in this analysis. Samples were immediately frozen in liquid nitrogen and stored at -80ºC until they were used. 15 μm sections were prepared on a cryomicrotome and placed on a glass slide. 1,5-diaminonaphtalene was used as matrix to cover the samples with the aid of a sublimator. Then, they were scanned with a MALDI-LTQ-Orbitrap XL (ThermoFisher) equipped with a N2 laser, with a mass resolution of 60,000 at m/z=400 Da. The recording was made with 2 microscans of 10 laser shots by each pixel, with an energy of 15 μJ. The selected pixel size was 25 μm. In -house designed software built in MATLAB (Mathworks, MA, EE.UU) was used for data analysis.

RESULTS: Lipid distribution images show tissue histology, revealing intratumoral heterogeneity and details of the tumor microenvironment that are not visible with other techniques. Clear differences were observed between the lipid profiles of nevi, primary melanoma, and metastasis. Classification models based on artificial intelligence gave mean specificities and precisions of 97% and 97% respectively in the training data set, and 93% and 95% in the validation data set for classification of the three types of histological sections.

CONCLUSIONS: Lipid expression changes with the appearance of the tumor. There is a concerted and statistically significant change in many lipid species. The largest change occurs between melanocytes from nevi and melanoma. Differences in lipid expression were also found between primary melanoma and metastasis, but they are less evident than at the first instants of tumor transformation. The results show that the use of MALDI-IMS of lipids can be very useful for tumor histopathological study and can become a useful tool in the diagnosis, prognosis and stratification of patients with melanoma.

Introduction: Diabetic Nephropathy (DN) is a complication that develops in late stage type 2 diabetics and includes structural and functional kidney abnormalities. Heavy lipid deposition in kidneys is common in humans with DN, but the lipid species impacted and their effect on glomerular function is poorly understood. Matrix assisted desorption/ionization mass spectrometry (MALDI IMS) is an untargeted, highly multiplexed molecular imaging tool that has been used to spatially investigate lipids in tissue.

Objective: Primary objective is to utilize high spatial resolution multimodal imaging to identify differences in lipidomic profiles between normal and DN kidney.

Methods: Human kidney donor tissue collected through the Cooperative Human Tissue Network at Vanderbilt University was cryosectioned (10µm thickness) and thaw mounted onto ITO-coated glass slides. 2,5-Dihydroxyacetophenone (DHA) and acetonitrile were applied to the tissues by a robotic sprayer (HTX M5-Sprayer). MALDI imaging data were collected from measurement regions of approximately the same size (171,905 pixels for normal and 189,140 pixels for DN) from both DN and normal kidneys using a MALDI timsTOF flex mass spectrometer at 10 µm spatial resolution. Fragmentation data were collected from a MALDI timsTOF (Bruker Daltonics). Complimentary high mass resolution MALDI imaging data was acquired on a Solarix 15T FT-ICR (Bruker Daltonik, Billercia, MA) to assist with identification. Analysis was performed using SCiLS and in-house software.

Results: Autofluorescence (AF) microscopy with DAPI, GFP, and DsRed filter sets was used to visualize functional tissue units within the kidney, including healthy and diseased glomeruli. MALDI IMS revealed several differences between normal and DN human kidneys, including differentiation of lipidomic profiles for glomeruli in diseased tissue. Preliminary assessment of these data show a dramatic increase in relative abundance of lysophosphatidylinositol (LPI) 18:0 (m/z 599.3215) in DN tissue compared to normal tissue. The identity of this species was confirmed using both mass accuracy and MS/MS. Phosphatidylinositol 38:4 (PI 38:4) (m/z 885.5547) was found at higher intensity in normal kidney tissues. Ions were tentatively identified as Ceramide-1-Phosphate (CerP (36:2)) (m/z 642.4879) and phosphoethanolamine cermide (Pe-Cer (36:1)) (m/z687.542). Lipids tentatively identified as Sulfodihexosylcermide (SulfoHex2Cer (42:1)) (m/z 1052.6916), and Sulfohexosylcermide (SulfoHexCer (34:1) (m/z 778.5159) also have higher relative abundance in DN kidney tissue compared to normal kidney tissue.

Conclusions: Current studies are underway to confirm and determine the identity of these ions by ultra-high mass resolution FT-ICR MS and MS/MS experiments. We are also optimizing instrumental methods to maximize imaging resolution and analysis pipelines are being developed to determine lipidomic profiles associated with specific functional tissue units in DN and normal human kidneys. Multimodal imaging mass spectrometry can identify lipidomic differences for specific functional tissue units between normal and DN kidney tissue.

Dr. Uta Ceglarek, PhD, EuSpLM is senior scientist at the University Hospital Leipzig, Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (ILM). She is board certified in Clinical Chemistry and Laboratory medicine (EuSpLM), in Toxicology and passed her postdoctoral lecture qualification in Clinical Chemistry and Laboratory Medicine.
Her research efforts are focused on the development of new mass spectrometric concepts for metabolome and proteome analysis in human body fluids and the investigations of metabolic alterations and lipid-modification in dyslipidemia, metabolic syndrome and cardiovascular diseases.

Introduction.
Breast cancer is a very serious problem worldwide. Breast cancer is accompanied by the metastatic lesion of the lymph nodes in 40-50% of cases. Currently, histology analysis is used during the surgery in order to verify the occurrence of metastasis in lymph nodes. Faster and more specific methods are urgently required for the identification of metastatic lesion in lymph nodes directly during the surgery.

Methods.
We present the new method based on the internal extractive electrospray ionization (iEESI) combined with high-resolution mass spectrometry analysis. It allows us to perform rapid (less than 5 minutes) molecular profiling of breast tissue and signal lymph nodes tissue with regard to presence of metastatic lesion. The distinctive feature of proposed ionization method allows the direct extraction of whole-volume biopsy tissue samples rather than just their surface. It makes it possible to substantially increase the sensitivity, accuracy and reproducibility of analysis compared with existing analogues. New analytical advancements were implemented which allow the sequential ionization of lipids, metabolites and proteins from the same single biopsy tissue sample.

Results.
Rapid diagnosis of the metastatic lesion of the signal lymph nodes upon invasive breast cancer on the biopsy tissue samples was performed in 50 patients. The signals of differential metabolites associated with the occurrence of lymphatic lesion were identified using high-resolution tandem MS analysis and reference LC-MS analysis. Bioinformatics approaches were developed in order to study the complex relationships between the identified metabolites. This led us to create of a molecular model of metastasis process.

Conclusions.
A minimally invasive method has been developed for the diagnosis of metastatic lesions of signal lymph nodes in invasive breast cancer based on direct mass spectrometric analysis. A mechanism for metastasis to the signal lymph nodes in breast cancer was proposed.
Novel Aspect.
A new method for mass spectrometric analysis of biopsy samples based on internal extractive electrospray ionization has been developed.

Acknowledgments.
The work was supported by Russian Foundation for Basic Research (Agreement ? 19-515-5502119).

Background: Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine catabolism that presents with refractory epilepsy in newborns. Bi-allelic ALDH7A1 variants lead to deficiency of α-aminoadipic semialdehyde dehydrogenase/antiquitin, resulting in accumulation of piperideine-6-carboxylate (P6C), and secondary deficiency of the important co-factor pyridoxal-5’-phosphate (PLP, active vitamin B6) through its complexation with P6C. Vitamin B6 supplementation resolves epilepsy in patients, but still intellectual disability may develop. Early diagnosis and treatment, preferably based on newborn screening, could optimize long-term clinical outcome. However, no suitable PDE-ALDH7A1 newborn screening biomarkers are currently available.

Methods: We combined the innovative analytical methods untargeted metabolomics (UHPLC-QTOF-MS) and infrared ion spectroscopy (IRIS-MS) to discover and identify biomarkers in plasma that would allow for PDE-ALDH7A1 diagnosis in newborn screening.

Results: We identified 2S,6S-/2S,6R-oxopropylpiperidine-2-carboxylic acid (2-OPP) as PDE-ALDH7A1 biomarker, and confirmed 6-oxopiperidine-2-carboxylic acid (6-oxoPIP) as biomarker. We have subsequently implemented targeted LC-MS/MS assays for 2-OPP and 6-oxoPIP in plasma, urine and CSF under ISO:15189 accredition for clinical diagnostics. The suitability of 2-OPP as a potential PDE-ALDH7A1 newborn screening biomarker in dried bloodspots was also shown. Additionally, we found that 2-OPP accumulates in brain tissue of patients and Aldh7a1 knock-out mice, and induced epilepsy-like behavior in a zebrafish model system.

Conclusion: This study has opened the way to newborn screening for PDE-ALDH7A1 based on 2-OPP as a biomarker. We speculate that 2-OPP may contribute to ongoing neurotoxicity, also in treated PDE-ALDH7A1 patients. As 2-OPP formation appears to increase upon ketosis, we emphasize the importance of avoiding catabolism in PDE-ALDH7A1 patients.

Wojciech Michno has a highly international background. He is a United World College (Norway) scholar and a Macalester College (USA) alumnus. At the university he completed two degrees in parallel, one in Chemistry and one in Neuroscience, with a focus on molecular neurobiology. Wojciech did his master’s thesis in pharmacology, and PhD in analytical neurochemistry at Sahlgrenska Academy, University of Gothenburg, Sweden. He just started his appointment at Stanford University. He is also a honorary research fellow at University College London (UCL). The focus of his work is Alzheimer’s disease.

Introduction
The advantage of higher cognitive abilities that humans possess depends on the enlarged and highly interconnected human cerebral cortex. Its complexity, and prolonged timeline for maturation, predispose humans to developmental injuries. During the period of cortical expansion, specialized progenitor cells multiply and, in a well-controlled fashion, differentiate to generate first neurons and later astrocytes. Intracellular metabolism has been suggested to regulate the proliferation, differentiation and maturation of neural cells. However, our understanding of how metabolism regulates these processes remains extremely limited.

Objective
This project bypasses the traditional limitations associated with the study of human cortical development by integrated advanced stem cell technology and comprehensive metabolic analysis.

Methods
Here we explore the use of extremely rare human fetal brain tissue and human brain organoids derived from induced pluripotent stem cells as platforms to study metabolic alterations on expanding human cerebral cortex. We employ broad metabolic phenotyping using Seahorse technology and couple it with specifically developed metabolic labeling in a dish (13C labeled substrates) of both ex-vivo tissue, and brain-region specific organoids, to identify any changes in metabolite flows in conditions of environmental insults, such as hypoxia.

Results
We demonstrate that age dependent cellular maturation from radial glia cells and progenitors towards mature cell types is associated with a metabolic reprograming that increases cellular reliance on mitochondria for bioenergetics processes. Interestingly, while less dependent on mitochondrial respiration, immature cells exhibit higher spare respiratory capacity as compared mature cells. We further show that the brain-region-specific organoids and ex-vivo tissue can be used for studies of metabolic reprograming upon both environmental insults, and drug interventions. These approaches allowed us to identify potential role of both hypoxia and glutamine/glutamate balance in in bioenergetics processes of radial glia and neuronal progenitor cells.

Conclusions
These results expand the fundamental knowledge of metabolic regulation of human cerebral cortex growth, and establish a platform for the investigation of many neurometabolic diseases that remain poorly understood.

Associate Professor Emma Schymanski is head of the Environmental Cheminformatics (ECI) group at the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg. In 2018 she received a Luxembourg National Research Fund (FNR) ATTRACT Fellowship to establish her group in Luxembourg, following a 6 year postdoc at Eawag, the Swiss Federal Institute of Aquatic Science and Technology and a PhD at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. Before undertaking her PhD, she worked as a consulting environmental engineer in Perth, Australia. She has over 80 publications and a book, and is involved in many collaborative software efforts. Her research combines cheminformatics and computational (high resolution) mass spectrometry approaches to elucidate the unknowns in complex samples, primarily with non-target screening, and relating these to environmental causes of disease. An advocate for open science, she is involved in and organizes several European and worldwide activities to improve the exchange of data, information and ideas between scientists to push progress in this field, including NORMAN Network activities (e.g. NORMAN-SLE https://www.norman-network.com/nds/SLE/), MassBank (https://massbank.eu/MassBank/), MetFrag (https://msbi.ipb-halle.de/MetFrag/) and PubChemLite for Exposomics (https://doi.org/10.5281/zenodo.4432123).

The multitude of chemicals to which we are exposed is ever increasing, with over 110 million chemicals in the largest open chemical databases, over 350,000 in global use inventories, and over 70,000 estimated to be in household use alone. Detectable molecules in exposomics can be captured using non-target high resolution mass spectrometry (HRMS), which provides a “snapshot” of all chemicals present in a sample and allows for retrospective data analysis through digital archiving. However, there is no “one size fits all” data or analytical method, and scientists cannot yet identify most of the tens of thousands of features in each sample, let alone associate them with health or disease, leading to critical bottlenecks in identification and data interpretation and thus clinical outcomes. Despite the size of the open chemicals databases, critical knowledge gaps still exist in them; environmental transformation products and metabolites forming a key part of these missing puzzle pieces. Defining the chemical space to search, the analytical methods to use, prioritizing efforts to find significant environmental chemicals, metabolites or biomarkers as well as filling the knowledge gaps are the key to transition non-target HRMS into routine applications such as the clinic. This involves reconciling complex samples with expert knowledge and careful validation in an automatable and reliable manner. This talk will cover European and worldwide community initiatives and resources to help connect environmental expert knowledge and observations towards generating clinical outcomes. Various open cheminformatics and computational mass spectrometry approaches including patRoon, Shinyscreen, MassBank, MetFrag and PubChemLite for Exposomics will be covered. Additionally, possibilities for disease-specific metadata retrieval to connect chemical observations with potential disease associations will be demonstrated, alongside how FAIRifying Transformation information in the NORMAN Suspect List Exchange and PubChem helps close database gaps and create new possibilities for dynamic suspect screening efforts. This talk will show how interdisciplinary efforts and data sharing can facilitate research in environmental sciences, metabolomics, exposomics and beyond, towards a not-to-distant furture where non-target HRMS could be used in the clinic. The very many contributors to these collaborative efforts will be acknowledged throughout the talk.

Serge Rudaz is Associate Professor at the University of Geneva where he leads the biomedical and metabolomics analysis (BMA) group. He's President of the Swiss Metabolomics Society (SMS), vice-president of the Competence Center in Chemical and Toxicological Analysis (ccCTA) and member of the management Board of the Swiss Centre for Applied Human Toxicology (SCAHT) Foundation. Serge Rudaz contributed to the field of analytical sciences with diverse activities, including invited lectures and invited/visiting professorships at various Universities; Lyon (France), Pavia (Italy), Guanzhou (China).

He is interested in steroids analysis, metabolomics, (UHP)LC and CE coupled to MS, advances in sample preparation, analysis of pharmaceuticals and counterfeits medicines, biological matrices, clinical and preclinical studies, including metabolism and toxicological analysis. He is a (co)author of over 10 book chapters and more than 320 peer-reviewed papers, with an H-index (Scopus) of 57. He was chair/co-chair of several national or international congress, such as Chimiométrie 2015, SEP 2017 and MSB 2020.

Web: https://epgl.unige.ch/labs/fanal/analyses-biomedicales

Historical compounds of interest are pharmaceutical substances, chiral products, and exogenous analytes. For several years, a major focus has been placed on the implementation of various strategy allowing the investigation of endogenous metabolites, including an extended steroid profiles (i.e. “steroidomics”). For that purpose, MS detection is generally used to offer improved the steroidome coverage. This has led to the development of various methodologies. Among them, an automated steroids annotation using a dedicated dynamic database involving retention time prediction from state-of-the-art in silico models was proposed. Numerous results were obtained using this steroidomics workflow which have provided valuable information for the early detection and diagnosis of various diseases and chemical exposures, whether it is to analyze data collected from biological samples such as plasma, seminal fluids, in vitro cell models, toxicological analysis or clinical studies. Following the exploratory step that can be achieved with the extended profile, the measurement of a selected sets of steroids using LC–MS in plasma could be now relatively rapidly achieved. Hence, the quantification of a limited number of steroid compound that are relevant for clinical, toxicological or anti-doping purposes could be done thanks to the internal calibration methodology. This targeted approach allows an absolute steroid quantification, which is crucial for a better understanding, diagnosis, prognosis and treatment of many endocrine alterations.

Dr Catarina Horro Pita possesses a BSc in Chemistry and Environmental Chemistry and a PhD in Synthetic Organic Chemistry. After concluding her PhD, she initiated her professional career as a Synthetic Chemist, prior to moving into Analytical Chemistry and Bioanalysis. She has been working in the field of Bioanalysis for 15 years, holding both scientific and managerial positions. Catarina currently works as a PK Manager at A4P Consulting. During her career, she has gained significant experience in the development, validation and management of LC-MS/MS assays for the analysis of both xenobiotic compounds and small molecule biomarkers. In addition, due to her expertise in Organic Chemistry, she also has an extensive knowledge of derivatization techniques for the improvement of LC-MS/MS methods.

Derivatization is a simple chemical reaction, often used in Bioanalysis to transform a “not so ideal” candidate for LC-MS/MS analysis into a molecule with enhanced phys-chem properties, which leads to improved detectability of the compound.

The objective of this presentation is to describe some of the most commonly used derivatization procedures and explain the benefits and drawbacks of these techniques.

When performing analysis by LC-MS/MS, the ideal analyte should be chemically stable and have moderate retention on the LC column. The molecule should also be ionisable under ESI or APCI conditions and have an efficient fragmentation upon collision. In addition, the target compound should be selectively separated from other xenobiotics or endogenous products present in the extracted matrix. When the analyte does not meet all of the above criteria, derivatisation can be used to modify its chemical structure and address these issues.

A range of derivatization processes will be reviewed, indicating their bioanalytical applications and type of chemical reactions involved. In addition, examples of derivatization reagents and reaction conditions will be described.

Although the application of derivatization procedures can lead to an improved bioanalytical performance, these techniques also present a range of disadvantages, such as extended method development times, lengthy extraction procedures and increased method variability.

Despite all the recent technological improvements in the field of Bioanalysis, derivatization remains a very powerful tool to overcome problems associated with analyte instability, poor chromatographic performance, inadequate analyte ionization or unacceptable method selectivity. However, the application of derivatization procedures can often lead to an increase in assay complexity and may reduce method robustness. Therefore, both the advantages and disadvantages of these procedures should always be considered before their application to a bioanalytical method.

Ivayla Roberts is a doctoral researcher at the University of Liverpool in Prof. Kell system biology group. Originally coming from computer science background (MSc, Montpellier France) Iva transitioned to molecular biology research via a Translational Molecular Medicine MRes (Manchester, UK).
Iva’s research interests are metabolomics, mass-spectrometry, and the application of statistical and machine learning computational approaches to metabolomics.
Iva’s PhD is focused on metabolomics in COVID-19.

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites (‘targeted metabolomics’), compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.

We here provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict patient’s infection severity (i.e. mild or severe) and potential outcome (i.e. discharged or deceased).

High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model. The predictors were selected for their relevant biological function and include cytosine and ureidopropionic acid (reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others.

Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on additional 90 patients predicted outcome and severity at ROC_AUC of 0.83 (CI 0.74 – 0.91) and 0.76 (CI 0.67 – 0.86). Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Josef Dvořák is studying doctoral studies at the Department of Biochemistry by the Faculty of Science at Charles University in Prague. As a part of his studies, he is employed by the Institute of Microbiology of the CAS. v. v. i. His work is mainly focused on the use of affinity carriers in combination with mass spectrometry in clinical diagnostics. He is part of three projects at this moment. The first project is dealing with the detection and characterization of procalcitonin in septic patient's serum. The second project focuses on detecting C. Difficile's ToxinB presence by using its enzymatic ability. The last project is dealing with 3D printed affinity carriers for clinical MALDI mass spectrometry.

Introduction:
Sepsis is a worldwide health condition caused by a disproportionately large immunity system response for pathogen presence. Accurate and fast diagnosis can make difference between the life and death of the patient. For purpose of clinical diagnosis of sepsis, a serum protein called procalcitonin (PCT) is used. Procalcitonin concentration in the bloodstream correlates with sepsis severity and increases up to a thousand times in a short period.

Objectives:
This study was aimed at the matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) compatible method development for in-situ enrichment of PCT from patient serum. The objective of this study evolved to the characterization of the truncated form of PCT after we successfully applied the method for in-situ enrichment of PCT from the serum of patients suffering from severe sepsis.

Methods:
The affinity carriers prepared by ambient ion soft landing of anti-PCT antibody on conductive indium-tin-oxide coated glass slides were used for in-situ PCT enrichment. To reach desired detection limit, a pre-concentration of PCT by acetonitrile precipitation protocol is needed before the detection by MALDI-TOF MS. To confirm a PCT presence in the samples, enrichment by magnetic beads with covalently bound anti-PCT antibody followed by peptide mapping was performed.

Results:
The affinity carriers were successfully used to reach a MALDI-TOF MS detection limit of PCT serum concentration of 10 ng/ml; a value corresponding with desired value for clinical application of this method. After applying this method to samples of patients suffering from severe sepsis, a signal that may represent the truncated PCT form was observed in all obtained mass spectra.

Conclusion:
Signals representing fragments of PCT were observed in all obtained mass spectra that can be explained by the surplus of proteases in the serum of septic patients. The presence of PCT in samples was confirmed by peptide mapping. Incubation of recombinant PCT in healthy patient serum suggested PCT does not undergo the same changes as the natural PCT in septic serum. Future studies are needed to confirm our observations.

Introduction
Hereditary deficiency of antithrombin, a natural anticoagulant, causes thrombophilia with high risk for venous thromboembolism. Guidance for laboratory testing to diagnose antithrombin deficiency recommends a first line activity test. However, this test may lead to diagnostic uncertainty around the decision limit, depending on the brand or principle of the test. Antithrombin exists in various molecular forms, caused by genetic mutations and post-translational modifications (e.g. α- and β-antithrombin), which vary in heparin affinity and activity levels (1). Therefore, molecular testing has the potential to refine the diagnosis of antithrombin deficiency. Here we report the establishment of an LC-MRM-MS test that quantifies antithrombin mass and detects specific proteoforms caused by genetic variation and glycosylation. The method comprises of immunocapture followed by LC-MRM-MS analysis (2), and has been implemented on a liquid handler to enhance throughput.

Objective
To enable precision diagnostics for antithrombin deficiency, an LC-MRM-MS test for molecular characterization of antithrombin proteoforms, including the identification of α- and β-antithrombin, was developed. With this MS-based all-in-one-test, information on (dys)functional antithrombin proteoforms is obtained, which should ideally enable clinicians to make better informed decisions.

Method
Antithrombin was captured from fifty microliters of 100x diluted citrate plasma using a biotinylated llama VHH-antibody coupled to streptavidin coated 96-well plates. After denaturation and alkylation, antithrombin was digested using trypsin (sequence-grade, Promega). Proteotypic peptides were quantified using LC-MRM-MS relative to stable isotope labelled internal standard peptides. External calibration was based on native citrate plasmas, which were value assigned using an antithrombin standard (Hyphen Biomed). The sample preparation was implemented on a BRAVO liquid handling platform, and can be executed in less than 4 hours. The method was developed in agreement with CLSI guideline C62 and analytical validation included precision, linearity, measuring range intervals and carry-over.

Results
A total of 23 peptides, including 4 glycopeptides, are monitored to enable identification of the most common genetic variations. Detection of the glycopeptide KANK enables discrimination between α- and β-antithrombin. Total imprecision of five different samples for the three quantifying peptides (LVSANR, EVPLNTIIFMGR and HGSPVDICTAKPR) varied between 4.7-8.2%, 4.9-8.0% and 6.3-8.8% respectively. The test showed good linearity in the clinical range with reference values for healthy individuals between 1.33 and 1.91 μmol/L for peptide LVSANR. Clinical application of the test has already resulted in identification of an antithrombin deficient patient, who could not be diagnosed using the regular AT-activity test.

Conclusion
The here presented semi-automated immunocapture LC-MRM-MS test enables molecular characterization of antithrombin at the protein level. The plate-based strategy allows for high-throughput needed for clinical trials and provides good precision within predefined analytical performance specifications. This performance allows identification of mutations in a clinical setting highlighting that the test is ready for translation. We envision clinical validations in specific target groups to unravel the value of antithrombin proteoforms in the context of antithrombin deficiency and thrombophilia.

(1) Peterson et al. J Biol Chem 1985 260:610-615
(2) Ruhaak et al. Clin Chem Lab Med 2018 56:1704-1714.

Ph.D. candidate in the Alan G. Marshall research group at Florida State University focusing on top-down mass spectrometry using 21 T Fourier transform ion cyclotron resonance mass spectrometry at the National High Magnetic Field Laboratory. The research includes analysis of hemoglobinopathies and other intact proteins by online liquid chromatography-mass spectrometry experiments.

Introduction: Hemoglobin (Hb), the most abundant protein in human red blood cells, is composed of four globin subunits with one heme group in each. Normal adult hemoglobin (HbA) has two α and two β subunits. Structurally abnormal Hb variants come from gene mutations and most have one altered amino acid. Although many Hb variants are clinically silent, accurate identification by routine clinical tests has become more complicated as the number of known Hb variants has increased. Top-down tandem mass spectrometry has been applied to analyze mutated Hb subunits. Traditionally, either electron transfer dissociation (ETD) or collision-induced dissociation (CID) is applied to fragment Hb subunits. Here, we employ “chimeric ion loading”[1] in which CID (b/y ions) and ETD (c/z• ions) product ions from the same precursor ions are accumulated in a multipole storage device external to the ICR cell. Then, these four types of product ions are simultaneously detected in a single time-domain transient which reduces data acquisition time, while the combination of b/y ions and c/z• ions can provide complementary information to achieve better sequence coverage.
Objectives: Use of top-down tandem mass spectrometry to identify Hb variants from accurate intact masses and diagnostic product ions. R scripts are created to achieve automatic identification.
Methods: Chimeric ion loading of Hb variant fragment ions was performed in combination with 21 tesla FT-ICR MS detection. Diagnostic product ions that uniquely identify and site-localize mutated amino acid residues were manually identified for each of six Hb beta heterozygous variants and compiled in a database. A “Variants Identifier” R script was created to compare the experimental data to the database to automatically identify Hb variants. Another R script, “PredictDiag”, can calculate product ions for untested Hb variants, determine which fragments may be of diagnostic value based on empirically observed fragments from HbA β, and update the “Variants Identifier” database to include those fragments.
Results: With chimeric ion loading, a maximum of 83% sequence coverage is achieved for HbA β subunit. Chimeric ion loading, which rapidly provides a greater variety of diagnostic product ions relative to CID or ETD alone, in combination with “Variants Identifier” and “PredictDiag” successfully identifies several Hb heterozygous variants, such as HbAC (Δm = -0.94763 Da) as well as the gene fusion variant, Hb Lepore-Washington-Boston (δ through 87; β from 116).
Conclusion: Utilization of high-resolution mass spectrometry and R scripts can achieve unambiguous identification of Hb variants. Chimeric ion loading provides extensive diagnostic product ions that can be used to site-localize mutated amino acids and identify the corresponding Hb variant. “Variants Identifier” significantly reduces the manual validation time, and “PredictDiag” enables the prediction of diagnostic fragment ions for all known Hb variants in the HbVar database.

ACKNOWLEDGMENT:
[1] C. R. Weisbrod, L. C. Anderson, J. B. Greer, C. J. DeHart, C. L. Hendrickson, Anal. Chem. 2020, 92, 12193–12200.
[2] A portion of this work was performed at the National High Magnetic Field Laboratory ICR User Facility, supported by the National Science Foundation Division of Chemistry through Cooperative Agreement No. DMR-1644779 and the State of Florida.

Introduction: Due to improved treatment, more patients with multiple myeloma (MM) reach a state of minimal residual disease (MRD), where disease load can no longer be monitored by routine, electrophoretic based methods. Different strategies for MM MRD-monitoring include flow cytometry, allele-specific oligonucleotide qPCR, next-generation sequencing and mass spectrometry (MS). The latter three methods rely on the presence and the stability of a unique immunoglobulin fingerprint derived from the clonal plasma cell population. Additionally, For MS-MRD monitoring it is imperative that MS-compatible clonotypic M-protein peptides are identified. To support implementation of molecular MRD techniques, we studied the presence and stability of these clonotypic features in the CoMMpass database, the largest genomic database of MM patients.

Methods: An analysis pipeline based on MiXCR and HIGH-VQUEST was constructed to identify clonal molecular fingerprints and their clonotypic peptides based on transcriptomic datasets. We validated this pipeline in cell line models. We assessed the amount of clonotypic peptides identified in each patient and their suitability for MS targeting. To determine the stability of the clonal fingerprints, we compared the clonal fingerprints during disease progression for each patient.

Results: The analysis pipeline to establish the clonal fingerprint and MS-suitable clonotypic peptides was successfully validated in MM cell lines. In a cohort of 609 patients with MM, we demonstrated that the most abundant clone harbored a unique clonal molecular fingerprint and that multiple unique clonotypic peptides compatible with MS measurements could be identified for all patients. Furthermore, the clonal immunoglobulin gene fingerprints of both the light and heavy chain remained stable during MM disease progression. To implement MS-MRD in clinical practice, MS-MRD evaluation should be performed on large cohorts to study its clinical value as prognostic marker and its capacity to detect early relapsed.

Conclusion: Our data support the use of the clonal immunoglobulin gene fingerprints in patients with MM as a suitable MRD target for MS-MRD analyses in blood.

Charlotte Teunissen’s drive is to improve care of patients with neurological diseases by developing body fluid biomarkers for diagnosis, stratification, prognosis and monitoring treatment responses. Studies of her research group span the entire spectrum of biomarker development, starting with biomarker identification, often by –omics methods, followed by biomarker assay development and analytical validation, and lastly, extensive clinical validation and implementation of novel biomarkers in clinical practice.
She has extensive expertise with assay development on state of the art technologies, such as mass spectrometry and antibody-based arrays for biomarker discovery, ultrasensitive immunoassays, and in in implementation of vitro diagnostic technologies for clinical routine lab analysis. She is responsible for the large well-characterised biobank of the Amsterdam Dementia cohort, containing >5200 paired CSF and serum samples of individuals visiting the memory clinical of the Alzheimer Center Amsterdam (a.o. controls, patients with Alzheimer, Frontotemporal, Lewy Bodies).To ensure the quality of the biosamples, the group studies pre-analytical effects, which are key to implementation. Charlotte is leading several collaborative international biomarker networks, such as the Society for Neurochemistry and routine CSF analysis and the Alzheimer Association-Global Biomarker Standardization and Blood Based Biomarkers consortia. She is the coordinator of the Marie Curie MIRIADE project, aiming to train 15 novel researchers into innovative strategies to develop dementia biomarkers (10 academic centers + 10 non-academic centers), and the JPND bPRIDE project, that aims to develop targeted blood based biomarker panels for early differential diagnoses of specific dementias and is a collaborative project between 7 European and 1 Australian centers.

The development of disease-modifying therapies against neurological diseases such as Alzheimer’s disease (AD) requires ?biomarkers reflecting the diverse pathological pathways. Cerebrospinal fluid (CSF) is a widely used matrix to identify and develop novel biomarkers for dementias. It reflects the ante-mortem biochemical alterations occurring in the brain, whereby it can provide the diverse pathobiological fingerprint of different dementias.
Our strategy is to employ array based proteomics to identify novel biomarkers, considering the high- throughput of the discovery process and the versatility to develop smaller panels using immunoassay technologies. Here we measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(A?+): n=50), AD-dementia (n=230), non- AD dementias (n=322) and cognitively unimpaired controls (n=195) using proximity ligation-based immunoassays. Nested linear models identified >100 CSF proteins dysregulated in MCI(A?+) or AD compared to controls, as well as between AD and non-AD dementias. Distinct CSF profiles associated to different biological processes were identified along the symptomatic AD continuum. Proteins dysregulated in MCI(A?+) were primarily related to oxidative stress and energy metabolism, while those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled different and translatable biomarker panels that discriminate specific clinical groups with high accuracies (AUCs>0.85). The proteins and panels identified in this study may provide novel pathophysiological targets and biomarker tools covering the heterogeneous nature of AD.

Steve graduated from Imperial College of Science and Technology (Imperial College London) with a joint honours degree in Chemistry and Biochemistry before completing a PhD in Biochemistry at the University of Cambridge. Subsequently, he was an Elmore Medical Research Fellow in the Department of Biochemistry in Cambridge University.
His research team in University College Dublin (UCD; www.ucd.ie) is currently developing multiplexed protein biomarker measurements using multiple reaction monitoring mass spectrometry to support the translation of novel multiplexed blood protein biomarkers to clinical diagnostic tests. He founded the UCD spin out company, Atturos (www.Atturos.com) in late 2016 and was awarded UCD's Innovator of the Year in 2018. Atturos aims to develop and deliver advanced diagnostic tests to support better decision making for better patient outcomes.

In 2017 he was the lead organiser of the 16th Human Proteome Organisation (HUPO) World Congress, which was held in Dublin and included a Gala Dinner at which former US Vice-President Joe Biden was guest speaker.

Steve is currently Professor of Proteomics and Senior Fellow, UCD Conway Institute and past President of HUPO (www.hupo.org)

The challenges of developing new protein biomarker tests and delivering them to use for patient benefit will be presented. In the context of a recently initiated EU IMI consortium, particular emphasis will be placed on analytical validation of new multiplexed protein biomarker assays and their use in multi-centre evaluation and validation studies to support the development of new tests in psoriatic disease. Patient engagement and use of patient centric sampling devices for effective test delivery will be introduced.

Boone M. Prentice received his B.S. degree in Chemistry with Honors and Distinction from Longwood University (Farmville, VA) in 2008 with minors in Biology and Mathematics. At Longwood, he conducted electroanalytical research focused on developing biosensors under the supervision of Professor Melissa C. Rhoten. He attended graduate school at Purdue University (West Lafayette, IN) under the mentorship of Professor Scott A. McLuckey and received a Ph.D. in Chemistry in 2013. His Ph.D. research focused on ion trap mass spectrometry (MS) instrumentation development as well as gas-phase ion/ion and ion/molecule reactions involving biopolymers. Boone then worked as a postdoctoral research fellow in Professor Richard Caprioli’s laboratory at Vanderbilt University (Nashville, TN) where he studied matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) instrumentation and applications. Specifically, he worked on Fourier transform ion cyclotron resonance (FT-ICR) and time-of-flight (TOF) MS systems and applied IMS to the study of diabetes, cancer, drug delivery, and infectious disease. Boone joined the Department of Chemistry at the University of Florida as an Assistant Professor in the fall of 2018. His research focuses on developing next-generation bioanalytical mass spectrometry to better understand the molecular basis of health and disease.

Imaging mass spectrometry is a powerful technology that enables the visualization of biochemical processes directly in tissues by combining the molecular specificity of mass spectrometry with the spatial fidelity of microscopic imaging. Especially when studying lipids, there are many isobaric and isomeric molecules that complicate spectral analysis, with each isoform having a potentially unique cellular function. While traditional tandem mass spectrometry (MS/MS) approaches can distinguish amongst these compounds in select instances, this is often not the case. Our group is developing gas-phase reactions that afford the ability to provide improved molecular specificity without manipulating the sample. These gas-phase transformations are fast, efficient, and specific, making them ideally suited for implementation into imaging mass spectrometry workflows to enable novel structural identification and separation based on chemical reactivity. While traditional analytical analyses oftentimes simply use the mass spectrometer as a detector of molecular mass, we instead use the mass spectrometer as a reaction vessel to perform unique gas-phase transformations to provide unparalleled levels of chemical resolution.

Chris received his PhD in Analytical Chemistry from the University of Wisconsin Madison where he developed novel quantitative proteomics approaches in the lab of Joshua Coon. Chris continued his post-doctoral studies in the lab of Steve Gygi at Harvard Medical School where he co-developed TOMAHAQ - a targeted mass spectrometry method that combines sample and peptide multiplexing - and developed the proof-of-principle implementation of real-time search for isobaric label based quantitation. Following his post-doc, Chris joined the Microchemistry, Proteomics & Lipidomics department at Genentech where his group develops and applies leading edge quantitative proteomics methods to explore biological pathways related to potential therapeutic targets. Chris' group also focuses on analysis of low-level proteomes with a special focus on understanding technical challenges of current approaches to single cell proteomics and proposing new methods to analyze such samples.

Dr. Stefani Thomas earned her BA in Biological Sciences from Dartmouth College and her PhD in Pharmaceutical Sciences from the University of Southern California. She pursued postdoctoral training in Dr. Robert Cotter’s Middle Atlantic Mass Spectrometry laboratory, conducted clinical proteomics research in the Center for Biomarker Discovery and Translation, and completed a Clinical Chemistry postdoctoral fellowship at Johns Hopkins University. She is currently an Assistant Professor, a faculty member of the Advanced Research and Diagnostics Laboratory (ARDL) and a member of the Masonic Cancer Center at the University of Minnesota. She is also the Associate Medical Director of the M Health Fairview West Bank Laboratory. Stefani's research laboratory applies discovery and targeted mass spectrometry-based proteomics methods to elucidate the biology of ovarian cancer and to identify proteome-level mechanisms of improved ovarian cancer treatment response.

Lab Showcase meetings are opportunities for lab PIs and Directors to share their scientific goals and ambitions, and introduce the labs' current projects and scientists to our community. This provides opportunities for
(1) identifying potential points of collaboration, and
(2) engaging with early career and underrepresented scientists.

I grew up in San Diego, CA and Arlington, VA. I attended Santa Clara University where I received a B.S. in chemistry. I joined the MD PhD program at UCSD in 2007 and completed a PhD in Biomedical Sciences with a specialization in Anthropogeny. I continued at UCSD for residency training in internal medicine and completed a year as chief resident in 2019-2020. In June of 2020, I started my Endocrinology Fellowship. I plan to pursue a career in academic medicine that combines clinical endocrinology with outcomes research in Vitamin D metabolism.

Using the MrOS database and mass spectrometry, we demonstrate that 1,25(OH)2 Vitamin D, 24,25(OH)2 Vitamin D, and their associated metabolite ratios are associated with gut microbial diversity in older community dwelling men. Notably, 25(OH) Vitamin D levels did not show this association. We also demonstrate that firmicutes which frequently produce butyrate are associated with higher levels of activated Vitamin D. Overall, the project demonstrates that quantifying Vitamin D metabolites may provide more clinically relevant measures of Vitamin D metabolism.

Vitamin D metabolites and the gut microbiome in older men

Chris Shuford, Ph.D., is Associate Vice President and Technical Director for research and development at Laboratory Corporation of America in Burlington, North Carolina. Chris received his B.S. in Chemistry & Physics at Longwood University and obtained his Ph.D. in Bioanalytical Chemistry from North Carolina State University under the tutelage of Professor David Muddiman, where his research focused on applications of nano-flow chromatography for multiplexed peptide quantification using protein cleavage coupled with isotope dilution mass spectrometry (PC-IDMS). In 2012, Chris joined LabCorp’s research and development team where his efforts have focused on development of high-flow chromatographic methods (>1 mL/min) for multiplexed and single protein assays for clinical diagnostics.

The Michael S. Bereman Award for Innovative Clinical Proteomics recognizes recent innovations in clinical proteomics from early- and mid-career scientists. The awardee is honored at the annual meeting of MSACL, where they will be invited to give a lecture. Nominees will be reviewed annually by a rolling award committee.

The 2021 Awardee of the Michael S. Bereman Award for Innovative Clinical Proteomics is Christopher Shuford, Ph.D. for groundbreaking work on calibration and sensitivity.

Dr. Shuford will accept this award with a presentation : Breakthrough or Bust? Thyroglobulin Measurement by LC-MS/MS.

Proteolysis-aided workflows can effectively eliminate autoantibody complexes and allow for interference-free protein quantification by mass spectrometry with surrogate peptides. Owing to the prevalence of autoantibodies in thyroid cancer patients and associated interference among immunoassay platforms, the purported poster child for mass spectrometry-based protein measurements has been Thyroglobulin (Tg). However, many other factors contribute to the utility of measurement procedures for tumor makers such as Tg, including throughput, ruggedness, trueness, and sensitivity. This presentation will focus on these limitations – real or perceived – as it pertains to application of mass spectrometry measurements for Tg.

This year's Michael S. Bereman Award for Innovative Clinical Proteomics is proudly sponsored by Agilent with an MSACL Open Educational Grant Fund donation of $10,000. The MSACL Open Educational Grant program provides awards to early and mid-career community members to fund conference and short course attendance.

Amanda Hummon earned her A.B. in chemistry at Cornell University in 1999 with honors. She completed her graduate studies in analytical chemistry at the University of Illinois, Urbana-Champaign, in the laboratory of Prof. Jonathan V. Sweedler. Her thesis work focused on the development of mass spectrometric and bioinformatic strategies to predict and identify neuropeptides. She received her Ph.D. in 2004. Amanda participated in the annotation of the newly sequenced honey bee genome as a post-doctoral fellow in the laboratories of Prof. Gene E. Robinson and Prof. Sandra L. Rodriguez-Zas at the University of Illinois from 2004-2005. The focus of her research was constructing a methodology to utilize detected gene products to decipher an unannotated genome.

From 2005-2009, Amanda was a Sallie Rosen Kaplen Post-Doctoral Fellow at the National Cancer Institute in the laboratory of Dr. Thomas Ried. During her time in the Ried lab, she utilized RNA interference screening techniques followed by microarray analysis to elucidate genes that regulate the viability of colorectal cancer cells.

In 2009, she began her independent career as the Walther Cancer Assistant Professor in the Department of Chemistry and Biochemistry at the University of Notre Dame and was promoted to the Charles L. Huisking Associate Professor in 2015. She has been recognized with a NSF CAREER award (2014), a Society for Analytical Chemists of Pittsburgh Starter Grant Award (2011), and a Rising Star Award from the American Chemical Society (2016). Amanda moved her research program to the Department of Chemistry and Biochemistry at The Ohio State University in January 2018 and was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2019. In 2020, she was awarded a Fulbright Scholar Award and will spend a semester as a Visiting Professor at the Maastricht MultiModal Molecular Imaging Institute at Maastricht University in the Netherlands.