Sandra Spencer (Presenter)
University of Washington
Bio: Sandi earned her Ph.D. from the University of North Carolina at Chapel Hill working in the Glish lab on real-time analysis of compounds in organic aerosol particles. She is currently a postdoctoral researcher in the MacCoss lab at the University of Washington working clinical proteomic assay development and quantitative method improvement. Her current projects include development of a kinase activity assay for prediction and monitoring of targeted therapeutic efficacy in cancer, developing an assay to monitor immune checkpoint proteins in formalin fixed paraffin embedded tissues to type solid tumors, and using data independent acquisition to empirically derive targeted assays and molecular signatures for neurodegenerative disease.
Authorship: Sandra E. Spencer,(1) Deanna L. Plubell,(1) Ian R. Smith,(1) Jarrett D. Egertson,(1) Tom Montine,(2) Andrew N. Hoofnagle,(3) Michael J. MacCoss(1)
(1) University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA (2) Stanford University, Department of Pathology, Stanford, CA, 94305, USA (3) University of Washington, Department of Laboratory Medicine, Seattle, WA, 98105, USA
Alzheimer’s disease (AD) represents an increasing and unchecked burden on the population and the economy, projected to affect 16 million people with a healthcare cost of $1.1 trillion by 2050.1 Definitive diagnosis of AD uses outdated technology and can only be performed post-mortem; far too late to inform treatment and prognosis. We use data independent acquisition (DIA) of MS data to establish a well characterized proteomic profile of cerebrospinal fluid (CSF) from patients with AD). A molecular signatures of AD in CSF and plasma is developed and a targeted assay for each of the reliably detected peptides will be developed and validated- in essence an expanded version of an assay offered by Caprion for CSF2 but designed based on deep-dive empirical data. This work is expected to improve early detection of AD using a biofluid that can be safely collected during a routine clinical visit.
In 2016 it was estimated that over 5.4 million Americans had Alzheimer’s disease (AD),3 a neurodegenerative disease that constitutes the most common cause of dementia4 and the 6th most common cause of death in the United States1. In fact, 1 in 9 people over the age of 65 has AD and the risk of AD increases to 43% for individuals between 75 and 84 years of age.3 Thus, with the first of the baby boomer generation turning 70 in 2016,3 AD poses an increasing and currently unchecked threat to the population and the economy, projected to affect 16 million people with an estimated healthcare cost of $1.1 trillion by 2050.1 Recently, Eli Lilly reported the failure of its promising AD drug.5 This failure has resulted in many AD researchers voicing the same concern-- that once a patient shows symptoms of AD it may be too late to halt or reverse the cognitive impact.5,6 Thus, the success of future AD treatment and prevention depends upon early detection of the disease. Currently, the only definitive diagnosis for AD is post-mortem examination of brain tissue—this analysis itself is subjective and relies on outdated technology—and clinical diagnosis pre-mortem relies upon the presence of physical and/or cognitive symptoms.7 To facilitate early detection of AD it is of the utmost importance to develop methods to identify and classify the disease in a sample that can be safely collected pre-mortem.
Emerging technologies now permit a depth of molecular phenotyping that until recently was difficult even to imagine. To inform advances in early diagnosis and treatment of AD, we use data independent acquisition (DIA) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to generate molecular profiles of AD, PD, and cognitive decline/dementia from cerebrospinal fluid (CSF) or plasma collected post-mortem. The biofluids were obtained from existing well-characterized, longitudinal, community-based cohort studies, allowing us to leverage detailed patient information including age, sex, diagnosis, and exome sequence. Not only will the detailed sample information provide more depth and context for the molecular profiles developed in this work but the data will be made publicly available through a cloud based solution called the Chorus Project, engineered to enable big data re-analysis by the scientific community. Thus, the molecular profiles generated will supplement the longitudinal data already associated with this high-quality AD cohort.
Sample Description. Plasma and cerebrospinal fluid were collected pre-mortem from patients in three groups: healthy control, Parkinson’s Disease, and Alzheimer’s Disease. The PD group was subsetted based on cognitive function: no cognitive impairment, mild cognitive impairment, and dementia. Two longitudinal NIA-funded cohorts were leveraged for sample acquisition: the University of Washington (UW) Alzheimer’s Disease Research Center (ADRC) and the Adult Changes in Thought (ACT) study. The UW ADRC emphasizes the genetic causes of AD and comorbidity of AD and Lewy body disease (LBD) in their sample set. The ACT consists of a population-based cohort of brain aging and incident dementia in the Seattle area. As such, the ACT cohort has a higher relative quantity of cognitively normal cases and comorbidity of AD and vascular brain injury (VBI), resulting in a high degree of complementarity between cohorts. All brain autopsies from these longitudinal cohorts have been evaluated by the UW ADRC neuropathology core with identical protocols6,8 by the same personnel. An added benefit of utilizing these long standing, high-quality resources is the ability to correlate the data from the experiments described in this proposal with the publicly available data that has been collected previously, is currently being collected, and will be collected in the future. The presently available longitudinal data includes information such as clinical diagnosis, cognitive function, genotype, AD/PD neuropathy, comorbidity, age, gender, exome sequence, and the proteomic profile of four brain regions (inferior parietal lobule (IPL, Brodmann area 9), superior and middle temporal gyrus (Brodmann areas 21 and 22), hippocampus at the level of the lateral geniculate nucleus, and caudate nucleus at the level of the anterior commissure. This data will be used to inform and validate the results generated from the proposed experiments, ensuring high-quality results of great significance.
Sample Preparation. Protein concentration was quantified according to manufacturer instructions using a PierceTM BCA Protein Assay Kit. A 50 µL aliquot of neat CSF or 100x diluted plasma was combined with an equal volume of 0.2% PPS Silent Surfactant in 50 mM ammonium bicarbonate with 2.088 pg/µL 15N-labeled apolipoproteinA1 (ApoA1). Samples were heated at 95 ℃ to facilitate protein denaturation. A 1 µL aliquot of 500 mM DTT in 18 MΩ water was added to the sample and the solution was incubated at 60 ℃ for 30 minutes to reduce side-chain thiols. A 3 µL aliquot of a 500 mM solution of IAA in 18 Ω water was added to the solution prior to incubation for 30 minutes in the dark at room temperature to alkylate thiols. Alkylation reactions were quenched using an additional 2 µL aliquot of 500 mM DTT. Proteolytic digestion was initiated via the addition of adequate 0.5 mg/mL trypsin in 50 mM ammonium bicarbonate to reach a 10:1 protein:protease ratio. Digestion reactions were incubated with shaking at 800 RPM at 37 ℃ for 18 hours. Proteolysis was quenched by the addition of a 4 µL aliquot of 6N hydrochloric acid (HCl) and samples were incubated for one hour at room temperature to cleave surfactant. A pooled library was prepared by combining equal volumes of each sample. Samples were stored at -80 ℃ prior to analysis.
Liquid Chromatography. Nano-HPLC-MS/MS was performed using a NanoAcquity LC system (Waters, Milford, MA) coupled to a Thermo Scientific Q Exactive HF Orbitrap mass spectrometer. Solvent A was 0.1% formic acid in water and solvent B was 0.1% formic acid in acetonitrile. Silica trapping columns were self-packed with 4 µm Jupiter Proteo 90 Å C12 particles (150 µm i.d., Phenomenex, Torrence, CA) and cut to 5.0 ± 0.2 cm. The pulled tip silica capillary analytical column (75 µm i.d., 18 cm) was self-packed with Dr. Maisch GmbH ReproSil-Pur 120 C18-AQ 3 µm C18 particles (Ammerbuch, Germany) and maintained at 50.0 ± 0.1 ℃ throughout the analysis. A 3 µL aliquot of digested biosample (1 µg protein) was loaded onto the trapping column and washed for 5 minutes at 2 µL/min with 98% A. The analytical gradient was performed as follows (0.250 µL/min): increased from 5% to 30% B over 90 min, increase to 60% B from 90-100 min, increase to 95% B from 100-101 min, hold at 95% B for 5 min, decrease to 2% B from 106-107 min, equilibrate column for 23 minutes (140 minutes total).
Library Generation. Preliminary DIA data was collected on a subset of samples consisting of CSF from 3 healthy control subjects and 5 subjects with AD. Library data was collected for the pooled CSF samples using 6 m/z windows for 6 gas-phase fractions of 1000 m/z to cover the range of 400-1000 m/z. Peptides detected in this narrow-window data were detected using PECAN and the false discovery rate (FDR) was estimated using Percolator. In the combined library 18,672 unique peptides were identified at a FDR of 1%. From the peptides identified, 3,155 proteins with at least one uniquely mapping peptide were inferred and 2,100 proteins were detected with at least 2 uniquely mapping peptides.
Assessing Statistical Power. The CSF samples from AD and control subjects were analyzed individually using overlapping 20 m/z windows. The library generated was used to identify peptides in this wide-window sample data. Though the sample size for this dataset was too small to infer differences in the proteomic profile between AD and control CSF samples, this pilot data was used to evaluate the required sample size for future experiments. A power analysis was performed on the data to determine the minimum sample size required to reliably detect an effect, measured as the fold change in the peptide abundance. We determined that to detect a 2-fold change in peptide abundance with a power of 0.8, 20 control/disease pairs were required. With 60 control/disease pairs we could reasonably detect a 1.5-fold change in peptide abundance with a power of 0.8.
Future Directions. This library information is being used to develop a quantitative method for peptides associated with AD and PD in CSF. However, DIA is a relatively difficult technique to implement as a routine clinical screen-- not only is the instrument a large capital expense compared to a triple quadruple instrument capable of selected reaction monitoring (SRM), but the data analysis is more complex and time-intensive. Thus, we will use the discovery data described above to develop a targeted method for analysis of each of the peptides identified in the library. In essence, this assay will be an expanded version of the MS-based CSF assay currently offered by Caprion, the peptides selected based on deep-dive empirical data. Based on the power analysis described above for LC-DIA-MS/MS of AD and control CSF described above, library data is being collected for plasma and CSF samples from AD (n = 11), PD (n = 30), and control (n = 30) subjects and the stability of the identified peptides is evaluated during at 7 ℃ for 72 hours. Indexed retention times (iRT) are being used to develop a single scheduled method for all peptides identified in the libraries. The limit of detection and lower limit of quantification will be assessed using a calibration curve of human CSF in chicken plasma diluted 1:200 in buffer as a background matrix.8 The inter- and intra-assay variability will be assessed by performing a 5x5 analysis as previously described.9 Wide-window DIA data will be used to generate a molecular signature for AD, PD, and cognitive decline which will be validated using the SRM assay developed.
Conclusions & Discussion
We are using nanoLC-DIA-MS/MS to develop a molecular fingerprint of AD, PD and cognitive decline in CSF and plasma. This deep-dive data is also being used to develop a detailed targeted SRM assay for neurodegenerative disease. We will evaluate the reproducibility and linearity for each peptide in the assay as well as assess the limit of detection and lower limit of quantification. The SRM assay developed will then be implemented to validate the molecular signatures originally constructed by DIA.
References & Acknowledgements:
1. Milken Institute Philanthropy Advisory Service Alzheimer's Disease: A Giving Smarter Guide. 2015, 1-37.
2. Spellman, D. S.; Wildsmith, K. R.; Honigberg, L. A.; Tuefferd, M.; Baker, D.; Raghavan, N.; Nairn, A. C.; Croteau, P.; Schirm, M.; Allard, R.; Lamontagne, J.; Chelsky, D.; Hoffmann, S.; Potter, W. Z. Development and evaluation of a multiplexed mass spectrometry based assay for measuring candidate peptide biomarkers in Alzheimer's Disease Neuroimaging Initiative (ADNI) CSF. PROTEOMICS – Clinical Applications 2015, 9, 715-731.
3. Alzheimer's Association 2016 Alzheimer's Disease Facts and Figures. Alzheimer's & Dementia 2016, 12.
4. National Institute on Aging About Alzheimer's Disease: Diagnosis. (accessed November, 2016).
5. PAM BELLUCK Eli Lilly’s Experimental Alzheimer’s Drug Fails in Large Trial. http://www.nytimes.com/2016/11/23/health/eli-lillys-experimental-alzheimers-drug-failed-in-large-trial.html?_r=0.
6. Abbot, A.; Dolgin, E. Failed Alzheimer’s trial does not kill leading theory of disease. Nature News , 540, 15-16.
7. Hyman, B. T.; Phelps, C. H.; Beach, T. G.; Bigio, E. H.; Cairns, N. J.; Carrillo, M. C.; Dickson, D. W.; Duyckaerts, C.; Frosch, M. P.; Masliah, E.; Mirra, S. S.; Nelson, P. T.; Schneider, J. A.; Thal, D. R.; Thies, B.; Trojanowski, J. Q.; Vinters, H. V.; Montine, T. J. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association 2012, 8, 1-13.
8. Hooshfar, S.; Basiri, B.; Bartlett, M. G. Development of a surrogate matrix for cerebral spinal fluid for liquid chromatography/mass spectrometry based analytical methods. Rapid Communications in Mass Spectrometry 2016, 30, 854-858.
9. Grant, R. P.; Hoofnagle, A. N. From Lost in Translation to Paradise Found: Enabling Protein Biomarker Method Transfer by Use of Mass Spectrometry. Clinical Chemistry 2014.
IP Royalty: no
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