Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.

BACKGROUND: Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. METHODS: This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. RESULTS: Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. CONCLUSIONS: We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing.

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time.

Proteomics of novel induced pluripotent stem cell-derived vascular endothelial cells reveal extensive similarity with an immortalized human endothelial cell line.

The vascular endothelium constitutes the inner lining of the blood vessel, and malfunction and injuries of the endothelium can cause cardiovascular diseases as well as other diseases including stroke, tumor growth, and chronic kidney failure. Generation of effective sources to replace injured endothelial cells (ECs) could have significant clinical impact, and somatic cell sources like peripheral or cord blood cannot credibly supply enough endothelial cell progenitors for multitude of treatments. Pluripotent stem cells are a promising source for a reliable EC supply, which have the potential to restore tissue function and treat vascular diseases. We have developed methods to differentiate induced pluripotent stem cells (iPSCs) efficiently and robustly across multiple iPSC lines into nontissue-specific pan vascular ECs (iECs) with high purity. These iECs present with canonical endothelial cell markers and exhibit measures of endothelial cell functionality with the uptake of Dil fluorescent dye-labeled acetylated low-density lipoprotein (Dil-Ac-LDL) and tube formation. Using proteomic analysis, we revealed that the iECs are more proteomically similar to established human umbilical vein ECs (HUVECs) than to iPSCs. Posttranslational modifications (PTMs) were most shared between HUVECs and iECs, and potential targets for increasing the proteomic similarity of iECs to HUVECs were identified. Here we demonstrate an efficient robust method to differentiate iPSCs into functional ECs, and for the first time provide a comprehensive protein expression profile of iECs, which indicates their similarities with a widely used immortalized HUVECs, allowing for further mechanistic studies of EC development, signaling, and metabolism for future regenerative applications.NEW & NOTEWORTHY We have developed methods to differentiate induced pluripotent stem cells (iPSCs) across multiple iPSC lines into nontissue-specific pan vascular ECs (iECs) and demonstrated the proteomic similarity of these cells to a widely used endothelial cell line (HUVECs). We also identified posttranslational modifications and targets for increasing the proteomic similarity of iECs to HUVECs. In the future, iECs can be used to study EC development, signaling, and metabolism for future regenerative applications.

High-Throughput Single-Cell Proteomic Analysis of Organ-Derived Heterogeneous Cell Populations by Nanoflow Dual-Trap Single-Column Liquid Chromatography.

Identification and proteomic characterization of rare cell types within complex organ-derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 min of total run time per cell with peptides quantified over 11.5 min using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single cells per day. At this throughput, nanoDTSC quantified over 1000 proteins in individual cardiomyocytes and heterogeneous populations of single cells from the aorta.

Quantitative Proteomics Reveals that the OGT Interactome Is Remodeled in Response to Oxidative Stress.

The dynamic modification of specific serine and threonine residues of intracellular proteins by O-linked N-acetyl-ß-D-glucosamine (O-GlcNAc) mitigates injury and promotes cytoprotection in a variety of stress models. The O-GlcNAc transferase (OGT) and the O-GlcNAcase are the sole enzymes that add and remove O-GlcNAc, respectively, from thousands of substrates. It remains unclear how just two enzymes can be specifically controlled to affect glycosylation of target proteins and signaling pathways both basally and in response to stress. Several lines of evidence suggest that protein interactors regulate these responses by affecting OGT and O-GlcNAcase activity, localization, and substrate specificity. To provide insight into the mechanisms by which OGT function is controlled, we have used quantitative proteomics to define OGT's basal and stress-induced interactomes. OGT and its interaction partners were immunoprecipitated from OGT WT, null, and hydrogen peroxide-treated cell lysates that had been isotopically labeled with light, medium, and heavy lysine and arginine (stable isotopic labeling of amino acids in cell culture). In total, more than 130 proteins were found to interact with OGT, many of which change their association upon hydrogen peroxide stress. These proteins include the major OGT cleavage and glycosylation substrate, host cell factor 1, which demonstrated a time-dependent dissociation after stress. To validate less well-characterized interactors, such as glyceraldehyde 3-phosphate dehydrogenase and histone deacetylase 1, we turned to parallel reaction monitoring, which recapitulated our discovery-based stable isotopic labeling of amino acids in cell culture approach. Although the majority of proteins identified are novel OGT interactors, 64% of them are previously characterized glycosylation targets that contain varied domain architecture and function. Together these data demonstrate that OGT interacts with unique and specific interactors in a stress-responsive manner.

Diverse mitochondrial abnormalities in a new cellular model of TAFFAZZIN deficiency are remediated by cardiolipin-interacting small molecules.

Barth syndrome (BTHS) is an X-linked disorder of mitochondrial phospholipid metabolism caused by pathogenic variants in TAFFAZIN, which results in abnormal cardiolipin (CL) content in the inner mitochondrial membrane. To identify unappreciated pathways of mitochondrial dysfunction in BTHS, we utilized an unbiased proteomics strategy and identified that complex I (CI) of the mitochondrial respiratory chain and the mitochondrial quality control protease presenilin-associated rhomboid-like protein (PARL) are altered in a new HEK293-based tafazzin-deficiency model. Follow-up studies confirmed decreased steady state levels of specific CI subunits and an assembly factor in the absence of tafazzin; this decrease is in part based on decreased transcription and results in reduced CI assembly and function. PARL, a rhomboid protease associated with the inner mitochondrial membrane with a role in the mitochondrial response to stress, such as mitochondrial membrane depolarization, is increased in tafazzin-deficient cells. The increased abundance of PARL correlates with augmented processing of a downstream target, phosphoglycerate mutase 5, at baseline and in response to mitochondrial depolarization. To clarify the relationship between abnormal CL content, CI levels, and increased PARL expression that occurs when tafazzin is missing, we used blue-native PAGE and gene expression analysis to determine that these defects are remediated by SS-31 and bromoenol lactone, pharmacologic agents that bind CL or inhibit CL deacylation, respectively. These findings have the potential to enhance our understanding of the cardiac pathology of BTHS, where defective mitochondrial quality control and CI dysfunction have well-recognized roles in the pathology of diverse forms of cardiac dysfunction.

US Severe Acute Respiratory Syndrome Coronavirus 2 Epsilon Variant: Highly Transmissible but With an Adjusted Muted Host T-Cell Response.

BACKGROUND: The multiple mutations comprising the epsilon variant demonstrate the independent convergent evolution of severe acute respiratory syndrome coronavirus (SARS-CoV-2), with its spike protein mutation L452R present in the delta (L452R), kappa (L452R), and lambda (L452Q) variants. METHODS: Coronavirus disease 2019 (COVID-19) variants were detected in 1017 patients using whole-genome sequencing and were assessed for outcome and severity. The mechanistic effects of the epsilon versus non-epsilon variants were investigated using a multiomic approach including cellular response assays and paired cell and host transcriptomic and proteomic profiling. RESULTS: We found that patients carrying the epsilon variant had increased mortality risk but not increased hospitalizations (P < .02). Cells infected with live epsilon compared with non-epsilon virus displayed increased sensitivity to neutralization antibodies in all patients but a slightly protective response in vaccinated individuals (P < .001). That the epsilon SARS-CoV-2 variant is more infectious but less virulent is supported mechanistically in the down-regulation of viral processing pathways seen by multiomic analyses. Importantly, this paired transcriptomics and proteomic profiling of host cellular response to live virus revealed an altered leukocyte response and metabolic messenger RNA processing with the epsilon variant. To ascertain host response to SARS-CoV-2 infection, primary COVID-19-positive nasopharyngeal samples were transcriptomically profiled and revealed a differential innate immune response (P < .001) and an adjusted T-cell response in patients carrying the epsilon variant (P < .002). In fact, patients infected with SARS-CoV-2 and those vaccinated with the BNT162b2 vaccine have comparable CD4+/CD8+ T-cell immune responses to the epsilon variant (P < .05). CONCLUSIONS: While the epsilon variant is more infectious, by altering viral processing, we showed that patients with COVID-19 have adapted their innate immune response to this fitter variant. A protective T-cell response molecular signature is generated by this more transmissible variant in both vaccinated and unvaccinated patients.

Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis.

Proteomic analysis on the scale that captures population and biological heterogeneity over hundreds to thousands of samples requires rapid mass spectrometry methods, which maximize instrument utilization (IU) and proteome coverage while maintaining precise and reproducible quantification. To achieve this, a short liquid chromatography gradient paired to rapid mass spectrometry data acquisition can be used to reproducibly quantify a moderate set of analytes. High-throughput profiling at a limited depth is becoming an increasingly utilized strategy for tackling large sample sets but the time spent on loading the sample, flushing the column(s), and re-equilibrating the system reduces the ratio of meaningful data acquired to total operation time and IU. The dual-trap single-column configuration (DTSC) presented here maximizes IU in rapid analysis (15 min per sample) of blood and cell lysates by parallelizing trap column cleaning and sample loading and desalting with the analysis of the previous sample. We achieved 90% IU in low microflow (9.5 µL/min) analysis of blood while reproducibly quantifying 300-400 proteins and over 6000 precursor ions. The same IU was achieved for cell lysates and over 4000 proteins (3000 at CV below 20%) and 40,000 precursor ions were quantified at a rate of 15 min/sample. Thus, DTSC enables high-throughput epidemiological blood-based biomarker cohort studies and cell-based perturbation screening.

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

Skin tape proteomics identifies pathways associated with transepidermal water loss and allergen polysensitization in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) and food allergy (FA) are associated with skin barrier dysfunction. OBJECTIVE: Skin biomarkers are needed for skin barrier interventions studies. METHODS: In this study, skin tape strip (STS) samples were collected from nonlesional skin of 62 children in AD FA+, AD FA-, and nonatopic groups for mass spectrometry proteomic analysis. transepidermal water loss and allergic sensitization were assessed. STS proteomic analysis results were validated in an independent cohort of 41 adults with AD with and without FA versus nonatopic controls. RESULTS: A group of 45 proteins was identified as a principal component 1 (PC1) with the highest expression in AD FA+ STSs. This novel set of STS proteins was highly correlative to skin transepidermal water loss and allergic sensitization. PC1 proteins included keratin intermediate filaments; proteins associated with inflammatory responses (S100 proteins, alarmins, protease inhibitors); and glycolysis and antioxidant defense enzymes. Analysis of PC1 proteins expression in an independent adult AD cohort validated differential expression of STS PC1 proteins in the skin of adult patients with AD with the history of clinical reactions to peanut. CONCLUSIONS: STS analysis of nonlesional skin of AD children identified a cluster of proteins with the highest expression in AD FA+ children. The differential expression of STS PC1 proteins was confirmed in a replicate cohort of adult AD patients with FA to peanut, suggesting a unique STS proteomic endotype for AD FA+ that persists into adulthood. Collectively, PC1 proteins are associated with abnormalities in skin barrier integrity and may increase the risk of epicutaneous sensitization to food allergens.

Binding Site Characterization of AM1336, a Novel Covalent Inverse Agonist at Human Cannabinoid 2 Receptor, Using Mass Spectrometric Analysis.

Cannabinoid 2 receptor (CB2R), a Class-A G-protein coupled receptor (GPCR), is a promising drug target under a wide array of pathological conditions. Rational drug design has been hindered due to our poor understanding of the structural features involved in ligand binding. Binding of a high-affinity biarylpyrazole inverse agonist AM1336 to a library of the human CB2 receptor (hCB2R) cysteine-substituted mutants provided indirect evidence that two cysteines in transmembrane helix-7 (H7) were critical for the covalent attachment. We used proteomics analysis of the hCB2R with bound AM1336 to directly identify peptides with covalently attached ligand and applied in silico modeling for visualization of the ligand-receptor interactions. The hCB2R, with affinity tags (FlaghCB2His6), was produced in a baculovirus-insect cell expression system and purified as a functional receptor using immunoaffinity chromatography. Using mass spectrometry-based bottom-up proteomic analysis of the hCB2R-AM1336, we identified a peptide with AM1336 attached to the cysteine C284(7.38) in H7. The hCB2R homology model in lipid bilayer accommodated covalent attachment of AM1336 to C284(7.38), supporting both biochemical and mass spectrometric data. This work consolidates proteomics data and in silico modeling and integrates with our ligand-assisted protein structure (LAPS) experimental paradigm to assist in structure-based design of cannabinoid antagonist/inverse agonists.

Host Cell Protein Profiling by Targeted and Untargeted Analysis of Data Independent Acquisition Mass Spectrometry Data with Parallel Reaction Monitoring Verification.

Host cell proteins (HCPs) are process-related impurities of biopharmaceuticals that remain at trace levels despite multiple stages of downstream purification. Currently, there is interest in implementing LC-MS in biopharmaceutical HCP profiling alongside conventional ELISA, because individual species can be identified and quantitated. Conventional data dependent LC-MS is hampered by the low concentration of HCP-derived peptides, which are 5-6 orders of magnitude less abundant than the biopharmaceutical-derived peptides. In this paper, we present a novel data independent acquisition (DIA)-MS workflow to identify HCP peptides using automatically combined targeted and untargeted data processing, followed by verification and quantitation using parallel reaction monitoring (PRM). Untargeted data processing with DIA-Umpire provided a means of identifying HCPs not represented in the assay library used for targeted, peptide-centric, data analysis. An IgG1 monoclonal antibody (mAb) purified by Protein A column elution, cation exchange chromatography, and ultrafiltration was analyzed using the workflow with 1D-LC. Five protein standards added at 0.5 to 100 ppm concentrations were detected in the background of the purified mAb, demonstrating sensitivity to low ppm levels. A calibration curve was constructed on the basis of the summed peak areas of the three highest intensity fragment ions from the highest intensity peptide of each protein standard. Sixteen HCPs were identified and quantitated on the basis of the calibration curve over the range of low ppm to over 100 ppm in the purified mAb sample. The developed approach achieves rapid HCP profiling using 1D-LC and specific identification exploiting the high mass accuracy and resolution of the mass spectrometer.

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling.

Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC-MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.

Mass-spectrometry-based molecular characterization of extracellular vesicles: lipidomics and proteomics.

This review discusses extracellular vesicles (EVs), which are submicron-scale, anuclear, phospholipid bilayer membrane enclosed vesicles that contain lipids, metabolites, proteins, and RNA (micro and messenger). They are shed from many, if not all, cell types and are present in biological fluids and conditioned cell culture media. The term EV, as coined by the International Society of Extracellular Vesicles (ISEV), encompasses exosomes (30-100 nm in diameter), microparticles (100-1000 nm), apoptotic blebs, and other EV subsets. EVs have been implicated in cell-cell communication, coagulation, inflammation, immune response modulation, and disease progression. Multiple studies report that EV secretion from disease-affected cells contributes to disease progression, e.g., tumor niche formation and cancer metastasis. EVs are attractive sources of biomarkers due to their biological relevance and relatively noninvasive accessibility from a range of physiological fluids. This review is focused on the molecular profiling of the protein and lipid constituents of EVs, with emphasis on mass-spectrometry-based "omic" analytical techniques. The challenges in the purification and molecular characterization of EVs, including contamination of isolates and limitations in sample quantities, are discussed along with possible solutions. Finally, the review discusses the limited but growing investigation of post-translational modifications of EV proteins and potential strategies for future in-depth molecular characterization of EVs.

Blood to Biomarker Quantitation in Under One Hour with Rapid Proteomics using a Hyperthermoacidic Protease.

Multi-step multi-hour tryptic proteolysis has limited the utility of bottom-up proteomics for cases that require immediate quantitative information. The recently available hyperthermoacidic (HTA) protease "Krakatoa" digests samples in a single 5 to 30-minute step at pH 3 and >80 °C; conditions that disrupt most cells and tissues, denature proteins, and block disulfide reformation. The combination of quick single-step sample preparation with high throughput dual trapping column single analytical column (DTSC) liquid chromatography-mass spectrometry (LC-MS) achieves "Rapid Proteomics" in which the time from sample collection to actionable data is less than 1 hour. The presented development and systematic evaluation of this methodology found reproducible quantitation of over 160 proteins from just 1 microliter of whole blood. Furthermore, the preference of the HTA-protease for intact proteins over peptides allows for sensitive targeted quantitation of the Angiotensin I and II bioactive peptides in under half an hour. With these methods we analyzed serum and plasma from 53 individuals and quantified Angiotensin and proteins that were not detected with trypsin. This assessment of Rapid Proteomics suggests that concentration of circulating protein and peptide biomarkers could be measured in almost real-time by LC-MS. TOC Figure: Rapid proteomics enables near real-time monitoring of circulating blood biomarkers. One microliter of blood is collected every 8 minutes, digested for 20 minutes, and then analyzed by targeted mass spectrometry for 8 minutes. This results in a 30-minute delay with datapoints every 8 minutes.

High Throughput Single Cell Proteomic Analysis of Organ Derived Heterogeneous Cell Populations by Nanoflow Dual Trap Single Column Liquid Chromatography.

Identification and proteomic characterization of rare cell types within complex organ derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 minutes of total run time per cell with peptides quantified over 11.5 minutes using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single-cells per day. At this throughput, nanoDTSC quantified over 1,000 proteins in individual cardiomyocytes and heterogenous populations of single cells from aorta.

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Decoding Angiotensin Receptors: TOMAHAQ-Based Detection and Quantification of Angiotensin Type-1 and Type-2 Receptors.

Background The renin-angiotensin system plays a crucial role in human physiology, and its main hormone, angiotensin, activates 2 G-protein-coupled receptors, the angiotensin type-1 and type-2 receptors, in almost every organ. However, controversy exists about the location, distribution, and expression levels of these receptors. Concerns have been raised over the low sensitivity, low specificity, and large variability between lots of commercially available antibodies for angiotensin type-1 and type-2 receptors, which makes it difficult to reconciliate results of different studies. Here, we describe the first non-antibody-based sensitive and specific targeted quantitative mass spectrometry assay for angiotensin receptors. Methods and Results Using a technique that allows targeted analysis of multiple peptides across multiple samples in a single mass spectrometry analysis, known as TOMAHAQ (triggered by offset, multiplexed, accurate mass, high resolution, and absolute quantification), we have identified and validated specific human tryptic peptides that permit identification and quantification of angiotensin type-1 and type-2 receptors in biological samples. Several peptide sequences are conserved in rodents, making these mass spectrometry assays amenable to both preclinical and clinical studies. We have used this method to quantify angiotensin type-1 and type-2 receptors in postmortem frontal cortex samples of older adults (n=28) with Alzheimer dementia. We correlated levels of angiotensin receptors to biomarkers classically linked to renin-angiotensin system activation, including oxidative stress, inflammation, amyloid-ß load, and paired helical filament-tau tangle burden. Conclusions These robust high-throughput assays will not only catalyze novel mechanistic studies in the angiotensin research field but may also help to identify patients with an unbalanced angiotensin receptor distribution who would benefit from angiotensin receptor blocker treatment.

Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures.

Background: Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. Methods: This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. Results: Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. Conclusions: We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.