We are planning to provide full registration refunds.
If you have a ROOM RESERVATION at the Riviera Hotel under the MSACL Group, it has been AUTOMATICALLY CANCELLED with no penalty. You can rebook at the group rate if desired by contacting the hotel directly.
My Path to Enlightenment, or How I Became a Clinical Mass Spectrometrist and Clinical Chemist Alan Rockwood Professor Emeritus, University of Utah
Alan Rockwood, PhD, DABCC is Professor (Clinical) Emeritus of Pathology at the University of Utah School of Medicine in Salt Lake City, Utah, USA. Originally trained in Physical Chemistry, he performed research on the fundamentals of mass spectrometry and instrumentation development before focusing his career on Clinical Chemistry. He is certified by the American Board of Clinical Chemistry and has held a Certificate of Qualification in Clinical Chemistry from the New York State Board of Health. Currently, his primary area of research is the development of mass spectrometry-based quantitative assays for targeted analytes of clinical interest, including small molecules and more recently proteins and peptides. Additionally, he maintains a smaller research effort on fundamentals of mass spectrometry, particularly novel approaches for isotopic profile calculations. He has published >150 papers in peer reviewed journals.
>> Tuesday 14:15 in Rm 4-6
Comprehensive Mapping of Cell Lineage and Function in Human Tissue Using MIBI-TOF Michael Angelo Stanford Bio-X
Michael Angelo is an assistant professor in the Department of Pathology at Stanford University. He is board certified in clinical pathology and a recipient of the NIH Director’s Early Independence Award. Dr. Angelo received a BS in Physics from the University of Mississippi in 2002 and subsequently enrolled at Duke University, where he received an MD and PhD in Electrical and Computer Engineering in 2010. He trained in clinical pathology at UCSF and completed a postdoctoral research fellowship in the lab of Garry Nolan. His main research focus is creating and applying next generation instrumentation and methods for nanometer scale, multiplexed, quantitative imaging of genes and proteins in clinical tissue biopsies. With this in mind, his lab has developed a purpose-built instrument that utilizes high brightness primary ion sources and orthogonal time-of-flight mass spectrometry to rapidly image antibodies tagged with elemental metal reporters in intact tissue sections at sub-cellular resolution. Multiplexed ion beam imaging by time of flight (MIBI-TOF) permits simultaneous, rapid, and quantitative imaging of up to 42 metal-labeled antibodies at resolutions down to 250nm. This technology is being utilized in Dr. Angelo’s lab to characterize the phenotype and spatial organization of infiltrating immune cells in breast carcinoma, lung carcinoma, and melanoma. In addition to immune oncology, MIBI-TOF is being utilized to study immune tolerance in granulomatous inflammation, at the maternal fetal interface, and in solid organ transplantation.
>> Thursday 17:00 in Rm 4-6
Brain Organoids as a Model System for Human Neurodevelopment and Evolution Alysson Muotri University of California, San Diego
Dr. Muotri earned a BSc in Biological Sciences from the State University of Campinas in 1995 and a Ph.D. in Genetics in 2001 from University of Sao Paulo, in Brazil. He moved to the Salk Institute as Pew Latin America Fellow in 2002 for a postdoctoral training in the fields of neuroscience and stem cell biology. He has been a Professor at the School of Medicine, University of California in San Diego since late 2008. His research focuses on modeling neurological diseases, such as Autism Spectrum Disorders, using human induced pluripotent stem cells and brain organoids. He has received several awards, including the prestigious NIH Director’s New Innovator Award, NARSAD, Rock Star of Innovation from CONNECT, NIH EUREKA Award among others.
Structural and transcriptional changes during early brain maturation follow fixed developmental programs defined by genetics. However, whether this is true for functional network activity remains unknown, primarily due to experimental inaccessibility of the initial stages of the living human brain. We developed cortical organoids that spontaneously display periodic and regular oscillatory network events that are dependent on glutamatergic and GABAergic signaling. These nested oscillations exhibit cross-frequency coupling, proposed to coordinate neuronal computation and communication. As evidence of potential network maturation, oscillatory activity subsequently transitioned to more spatiotemporally irregular patterns, capturing features observed in preterm human electroencephalography (EEG). These results show that the development of structured network activity in the human neocortex may follow stable genetic programming, even in the absence of external or subcortical inputs. Our approach provides novel opportunities for investigating and manipulating the role of network activity in the developing human cortex. Applications for neurodevelopmental disorders and brain evolution will be discussed.
Components of Reproducible Quantitative Mass Spectrometry-based Proteomics: A Statistician’s Perspective Olga Vitek Northeastern University
Quantitative mass spectrometry-based proteomics aims to distinguish systematic variation in protein abundance (due, e.g., to a treatment or a disease) from nuisance biological and technological variation. Statistical mindset is key for doing so in both repeatable and reproducible manner. Frequently, statistical tasks are viewed as limited to detecting differentially abundant proteins. In reality, statistical components of reproducibility are substantially broader. They include all aspects of data processing (Which features should we use to quantify a protein? How should we combine the features into a protein-level conclusion?). They also include aspects of experimental design, from both biological perspective (Which proteins and samples, and how many, do we need to quantify?) and technological perspective (Are the assays appropriate for the task? Do the experimental steps run properly?). Answering these questions requires the availability of statistical methods, and but also of publicly available data that help understand the advantages and the limitations of the methodological choices. This talk will highlight the contributions of our lab to these components of reproducible research.
Mapping the Chemical Space of Biological Systems via MALDI Mass Spectrometric Imaging and <i>in situ</i> Molecular Analysis Lingjun Li University of Wisconsin-Madison
Mass spectrometric imaging (MSI) provides an attractive opportunity to detect and probe the molecular content of tissues in an anatomical context. This technique creates distribution maps of select compounds without the need for priori knowledge of target analytes. In this presentation, I will describe our efforts and recent progress in mapping and imaging of a wide variety of signaling molecules in several biological systems, highlighting the unique challenges and important roles of MSI in the areas of proteomics, peptidomics, and metabolomics.
Although high resolution accurate mass (HRAM) MSI platform offers unique advantages for mapping small molecule metabolites due to its high resolution and accuracy measurement, typical MALDI-LTQ-Orbitrap platform suffers from limited utility for large peptide and protein analysis due to its maximum m/z 4000. To overcome this challenge, we employed volatile matrices to produce multiply charged ions in MALDI source via laserspray ionization (LSI) and matrix assisted ionization in vacuum (MAIV) techniques on the MALDI Orbitrap platform. These new ionization techniques enabled substantial expansion of the mass range of the instrument and generated improved fragmentation efficiency compared to traditional MALDI-MS. To further enhance the chemical information extracted from in situ MALDI MSI experiments, we report on a multiplex-MSI method, which combines HRAM MSI technology with data dependent acquisition (DDA) tandem MS analysis in a single experiment. To improve the dynamic range and efficiency of in situ DDA, we introduce a novel gas-phase fractionation strategy prior to MS/MS scans, to decrease molecular complexity of tissue samples for enhanced peptidome coverage. In addition, the application of HRAM MALDI MSI to lipid analysis in a restenosis rat model and the utility of a novel subatmospheric pressure (SubAP)/MALDI source coupled with a Q Exactive HF hybrid quadrupole-orbitrap mass spectrometer for in situ imaging of glycans from formalin-fixed paraffin-embedded (FFPE) tissue sections and its translation to clinical cancer tissue microarray analysis will be highlighted. Finally, to further improve the sensitivity of MALDI MSI, a photoactive compound, 2-nitrobenzaldehyde is used to initiate a nanosecond photochemical reaction (nsPCR). This nsPCR strategy enables enhanced neuropeptide identification and visualization from complex tissue samples through on-demand removal of surrounding matrices within nanoseconds. The utility of this new approach for in situ analysis of endogenous biomolecules is evaluated and demonstrated.
The State of the DART: Does Direct Analysis in Real-Time Mass Spectrometry have a Future in Clinical Chemistry? Chip Cody JEOL
It has now been 17 years since a patent was filed describing the Direct Analysis in Real Time (DART) ion source, yet no clinical applications of DART MS are currently in use. This is not to say that DART has no potential for clinical applications! As an ambient ionization method, DART has several attractive characteristics for clinical chemistry. DART analysis is rapid and robust, and can be applied to a wide range of analytes. In combination with a high-resolution and/or tandem mass spectrometer, DART can be quite sensitive and selective. Point-of-care applications are possible if DART is combined with a compact mass spectrometer.
Several promising DART applications have been reported. Because it produces a broad profile of small-molecule biomarkers, DART is well matched with chemometric analysis for speciation and classification. Two published feasibility studies have shown the potential for microbial identification using DART MS. The first (from CDC and GA Tech) used in-situ methylation and DART to identify bacterial fatty acid profiles. The second study found that free fatty acids from a simple extraction method could identify ten different pathogens. Another study from the Fernandez lab at GA Tech showed a DART method for ovarian cancer screening with statistics that showed 100% accuracy!
Clinical toxicology is another area of potential application. DART is well established for forensic drug screening. That same capability could be used to screen for drugs and toxins to guide treatment in victims of poisoning or overdose. With relatively simple sample handling methods, detection limits for drugs in body fluids are suitable for rapid screening. DART has demonstrated the potential for monitoring drug excretion kinetics and in at least one case, detection of biomarkers for disease conditions. In a recent study, we have found that DART can be combined with another ambient ionization method (Coated Blade Spray) to provide complementary data from minimal sample volumes.
So, why has DART not yet found a place in clinical chemistry? Commercially available laboratory systems have been on the market for 15 years, and portable systems are also now commercially available. Perhaps the answer is just a need for early adopters who are willing to carry out clinical validation studies, much as the VA DFS did for forensic drug screening.
MCR and VCA – Two R Packages to Facilitate Your Method Comparison and Precision Studies Andrea Geistanger Roche
Andrea Geistanger is Head of Systems Data Analytics, at Roche Diagnostics in Germany. Her department of biostatisticians supports system and assay development through the whole life cycle of Roche’s cobas products. Her team is involved in the early development phases, including biomarker search projects with machine learning and multivariate statistics analysis. During product development phases, Andrea’s data analysts support scientists in experimental planning with Design of experiments, as well as in the experiment of validation studies according to regulatory requirements. Furthermore, they develop standardization schemes and calibration concepts for cobas analyzers. Throughout the development phase, software tools are designed and developed as needed. These programs are also made available to a broader community through open software projects.
Andrea Geistanger studied mathematics and economics, however during her PhD thesis in statistics, back in 2006, she immersed in standardization and traceability topics of diagnostic assays, by developing the data analysis scheme of the IFCC HbA1c standardization network. Since then the diversity of data science topics for diagnostic assays kept her busy and excited.
Trueness and precision are the key quality attributes of a diagnostic assay and have to be proven in validation experiments throughout each assay development. CLSI does also acknowledge the importance of these criteria, having two guidelines in place, EP9 for method comparison, and EP5 for precision studies describing the design and the analysis of the corresponding experiments. The statistical methodology for both experiments is quite advanced and cannot be operated in a bread and butter software such as Excel. For method comparison studies a Deming regression is required and in some cases also a robust Passing-Bablok regression is state-of-the-art. Classical linear regression methods are not appropriate here, as measurement errors occur for both measurement methods. For precision studies, an appropriate variance-components design should be used and statistically analyzed accordingly.
The mcr R-package is a free available open source R package, which incorporates all analysis methods for method comparison studies, with special focus on the regression methods as Deming or Passing-Bablok regression.
The VCA package is the pendant for precision experiments, where different measurement designs can be analyzed. It is also freely available as open source R-package. Both R packages have been developed and are maintained by the Roche Diagnostics R&D biostatistics department.
The talk will cover the major aspects of the analysis requirements for method comparison and variance-components studies. In addition, we show the features of both R packages, their calculation capabilities as well as the graphical representation possibilities.
The Changing Landscape of Immune-oncology and How Mass Spectrometry Might Help Guide Patient Care Michael Lassman Merck
Immune-oncology therapeutics take advantage of the body’s immune system to fight cancers. Unlike chemotherapy, which acts directly on the tumor. Immune-oncology therapies, specifically anti-PD-1 therapies have been demonstrated to be broadly successful across many indications and tumor types. Patients’ and family members’ lives have been changed as a result of these therapies; but not all patients benefit similarly or achieve complete responses. Analytical platforms such as IHC and Genomic analyses have been widely used to predict the likelihood of a patient to respond to anti-PD-1 therapy. Why not Mass Spectrometry?
Single-plex IHC is readily available but is limited in terms of multiplexing and is not quantitative. Genomic analyses can measure thousands of genes from a limited amount of tumor material and is an attractive tool relative to traditional IHC analyses despite that it cannot directly measure whether gene expression represents protein expression. Mass Spectrometry is an attractive quantitative platform as it is readily multiplexed and has been demonstrated to be capable of the simultaneous measurement of multiple proteins, including post-translational modifications, from a single sample. Furthermore, as mass spectrometry is quantitative, the platform could readily measure changes in tumors, indicating the effect of an immune-oncology therapy and potentially driving patient care.
Here, we will describe advances in immune-oncology that have demonstrated this to be such a promising field. We describe biomarker assays that have been critical to identifying patient populations most likely to receive benefit or that drive the use of combination therapies. We highlight some limitations to current methodologies as well as current demonstrations of the use of mass spectrometry to “fill in the gaps”.
Evolving Role of Nominal and High Resolution Mass Spectroscopy in Routine Toxicology Casework Tom Rosano Albany Medical College
Advancing analytical technology serves as the foundation of our toxicology practice and the explosion in pharmaceutical and illicit drug use now mandates the application of definitive testing technology in both our screening and confirmatory test protocols. While nominal mass GC-MS traditionally served as the analytical technology for confirmatory drug testing, the transition to liquid chromatography coupled with tandem mass spectroscopy has largely occurred and has brought with it an emerging application of high resolution mass spectroscopy. As definitive methods further the molecular identification and certainty of drug and metabolite confirmation work, our screening protocols in many areas of clinical toxicology still rely on presumptive methods with their high false negative rates and lack of selectivity. Conversion to definitive methods of screening with expanded drug panels is clearly needed but the challenges of high-volume screening with mass spectrometry has slowed the conversion to definitive screening across many areas of clinical toxicology. The hurdles on the way to definitive screening include automated sample preparation, rapid chromatography separation, analyte-specific matrix normalization, data management, alternative confirmatory methodology and interpretive reporting of findings. The presentation will focus on one laboratory’s journey and experience with definitive screening and confirmation protocols using a novel calibration technique for matrix normalization and application of high resolution mass spectroscopy for confirmation testing. Findings in addiction medicine and pain management casework will be presented and compared with the authors experience in court-ordered and postmortem casework.
Spatial Metabolomics: From Big Data to Single Cells Theodore Alexandrov European Molecular Biology Laboratory - Heidelberg
Theodore Alexandrov is a group leader at the European Molecular Biology Laboratory (EMBL) in Heidelberg, the head of the EMBL Metabolomics Core Facility and an Assistant Adjunct Professor at the Skaggs School of Pharmacy, University of California San Diego. The Alexandrov team at EMBL aims to reveal secrets of metabolism in time and space in tissues and single cells by developing experimental and computational methods. The team unites interdisciplinary scientists from biology, chemistry, and computer science as well as software and machine learning engineers. Theodore Alexandrov is a grantee of an ERC Consolidator project focused on studying metabolism in single cells, as well as of various other European, national, NIH, and industrially-funded projects. He has co-founded and scientifically directed the company SCiLS and has over 70 journal publications and patents in the field of spatial -omics.
Recent discoveries put metabolism into the spotlight. Metabolism not only fuels cells but also plays key roles in health and disease in particular in cancer, inflammation, and immunity. In parallel, emerging single-cell technologies opened a new world of heterogeneous cell types and states previously hidden beneath population averages. Yet, methods for discovering links between metabolism, cell states, metabolic plasticity and reprogramming on the single-cell level and in situ are crucially lacking. Our research aims to bridge this gap. First, I will explain how the emerging technology of imaging mass spectrometry can be used for the spatial profiling of metabolites, lipids, and drugs in tissues. I will present our cloud and Artificial Intelligence-powered platform METASPACE which is increasingly used across the world. In the second part of my talk I will focus on our method SpaceM for spatial single-cell metabolomics in situ. We applied SpaceM to investigate hepatocytes stimulated with fatty acids and cytokines, a model mimicking the inflammation-associated transition from the fatty liver disease NAFLD to steatohepatitis NASH. We characterized the metabolic state of steatotic hepatocytes and metabolic plasticity associated with the inflammation. We discovered that steatosis and proliferation take place in distinct cell subpopulations, each with a characteristic spatial organization and metabolic signatures. Overall, such methods open novel avenues for understanding metabolism in tissues and cell cultures on the single-cell level.
Emerging Opportunities for Incorporation of Proteomics and Glycoproteomics into Clinical Analyses Catherine Costello Boston University School of Medicine
Determination of the relationship between changes in tissue and circulating levels of proteins and their derivative peptides and the development of disease- or age-related physiological changes poses major challenges because of the broad dynamic range of the biologically active species, the structural complexity and diversity of co- and post-translational modifications, and the presence of numerous isomeric structures in biological samples. To minimize losses and avoid introduction of artifacts that are due to sample handling, to discriminate among similar components, and to make efficient use of instrument time, we are developing ion mobility and mass spectrometry-based methods that are compatible with on-line separations and increase the yield of detailed structural information. We are also exploring the suite of electron-based dissociation methods that preserve labile protein modifications while producing highly informative fragmentation. Illustrations will include results from recent and ongoing studies in which a variety of MS techniques are being applied to enable comprehensive proteomic and glycoproteomics analyses of clinical samples and model cell systems that are relevant to development of vaccines, cancer therapeutics, and tools for combating infectious diseases, and to increased understanding of the pathways underlying protein misfolding disorders.
The Chemical Characterization of the Cells in the Brain Using Mass Spectrometry Jonathan Sweedler University of Illinois at Urbana-Champaign
In the postgenomic era, one expects the suite of chemical players in a brain region to be known and their functions uncovered. Perhaps surprisingly, many neurochemicals remain poorly characterized and for those that are known, their localization, dynamics and function are oftentimes unknown. Mass spectrometry imaging (MSI) and single cell measurements using spatially targeted MS are highlighted. Using these approaches, we can measure lipids, fatty acids, neurotransmitters and neuropeptides, among others. For single cell measurements, the cells of interest are scattered across a microscope slide, the exact cell positions determined via optical microscopy, and mass spectra are acquired only at the cell positions. The single cell assays allow differences in the metabolome and peptidome from supposedly homogeneous populations of cells to be explored. By obtaining information from tens of thousands of individual cells, rare cells are found and unusual neurochemicals are discovered. Machine learning based approaches are highlighted to extract details on differences between targeted cellular populations.
While MS is one of the most information rich chemical characterization approaches, additional complementary information ranging including immunohistochemistry and vibrational spectroscopy aids in identifying cell types and in determining optimum follow-up studies. For select cells, follow-up capillary electrophoresis-mass spectrometry also is performed. Several applications of MSI and single cell mass spectrometry are highlighted from the discovery of unusual metabolites to characterizing the both known and previously unknown neuropeptides and hormones. Our overarching goal is to uncover the complex chemical mosaic of the brain and pinpoint key cellular players involved in a range of physiological and pathological processes.