Search

Descending thoracic aortic aneurysms may go undetected until severe and catastrophic complications such as rupture or dissection occur. Few clinical indicators exist to screen for aneurysms or predict risk of dissection or rupture. This study generated a plasma proteomic dataset from 150 patients with descending thoracic aortic disease and 52 controls to identify proteomic signatures capable of differentiating descending thoracic aortic disease from controls, as well as aneurysm from descending aortic dissections. Of the 1,468 peptides and 195 proteins quantified, 853 peptides and 99 proteins were significantly different between disease and controls. Using machine learning to classify disease from control, the highest precision-recall area under the curve (PR AUC) was achieved using significantly different proteins (PR AUC 0.99), followed by significant peptides (PR AUC 0.96). Despite no statistically significant protein signatures between aneurysm and dissection, use of all proteins was able to modestly classify between the disease states (PR AUC 0.77). To address correlation between proteins, a disease versus control classifier was optimized using only seven unique protein clusters, which achieved comparable performance to models trained on all/significant proteins (PR AUC 0.90). Model interpretation with permutation importance revealed that proteins differentiating disease and control function in coagulation, protein-lipid complex remodeling, and acute inflammatory response.

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.

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.

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes pre-fractionation reduces the cellular protein dynamic range and increases the relative abundance of non-sarcomeric proteins. We previously described “IN-Sequence” (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage 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. FAIMS approach substantially reduces manual sample handling, significantly shortens MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in for less time requirement.

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 profile 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 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 analysis of the previous sample. We achieved 90% IU in low micro-flow (9.5 µL/min) analysis of blood while reproducibly quantifying 300-400 proteins and over 6,000 precursor ions. The same IU was achieved for cell lysates, in which over 4,000 proteins (3,000 at CV below 20%) and 40,000 precursor ions were quantified at a rate of 15 minutes/sample. Thus, deployment of this dual-trap single column configuration enables high throughput epidemiological blood-based biomarker cohort studies and cell-based perturbation screening.

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.

Dual trap methods for TIMSTOF

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.

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.

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.

The dynamic modification of specific serine and threonine residues of intracellular proteins by O-linked N-acetyl-β-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., Graphical Abstract, Highlights • O-GlcNAc levels change dynamically in response to injury. • Injury does not induce changes in activity of the enzymes that cycle O-GlcNAc. • Quantitative proteomics identified and validated interactors of OGT. • The interactome of OGT changes significantly in response to oxidative stress., In Brief The goal of these studies was to provide insight into the regulation of the O-GlcNAc transferase (OGT) basally and in response to oxidative stress, as well as the role that O-GlcNAc plays in promoting cytoprotection. Using quantitative proteomics, the basal and injury-induced interactome of OGT has been defined and validated. Protein interactors are anticipated to regulate either the activity or substrate targeting of OGT, or to be substrates of OGT, thus affecting cytoprotection.

Introduction: Patients with heart failure and a preserved ejection fraction (HFpEF) present heart function abnormalities that remain poorly understood. Defining proteomic signature of HF that is independent of left ventricular hypertrophy (LVH) should allow for stratification of its subtypes and potential mechanism that contributes to the disease. Hypothesis: We hypothesized that HFpEF proteomic signature would be comprised of the hypertrophy and contractile protein phenotype. Methods: Intraoperative left ventricular (LV) myocardial biopsies were obtained from patients (n=21) recruited to undergo coronary artery bypass grafting (CABG). Patients were categorized to: control non-hypertensive (n=9), LVH (n=5), and HFpEF (n=7). Myocardial tissue was subfractionated: cytoplasmic- (neutral pH), myofilament- (acidic pH), and membrane-enriched extract (SDS-soluble). All fractions were assessed for protein quantity and Lys/Arg modifications using liquid chromatography mass spectrometry (LC-MS). Results: In HFpEF, 13% of the cardiac LV proteome changed compared to control heart, with a substantial proportion (77%) decreasing in quantity across all three cardiac fractions, while with LVH, 61% of the proteomic LV changes were increased. Although glycolysis and gluconeogenesis increased in both cardiopathies with respect to control, in HFpEF more subtly than in LVH. Modified proteome of the HFpEF was dominated by decreases in protein succinylation (e.g. ATP5L, THIM, IDHP, APOB, GSH1, KNTC1) and to a lesser degree in methylation (ROA3, HSP7C) or acetylation compared to control. This general trend of down-regulation of succinylation can be attributed to depletion in the levels of succinyl-CoA, the cofactor of enzymatic Lys succinylation. Importantly, there was a striking discordant activation/inhibition of cell death and proliferation pathways between the HFpEF and LVH. Two major upstream regulator clusters linked the proteome changes in cell growth and proliferation to RICTOR and Myc that showed completely opposite trends in LVH and HFpEF groups. Conclusions: HFpEF has a unique proteome signature compared to LV hypertrophy profile which does not arise from sub-proteome involved in contraction but rather is involved in overall cell death.

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, there is no single imputation method that is best suited for a diverse range of data sets and no clear strategy exists for evaluating imputation methods for large-scale 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 workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used two distinct DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set and an independent data set with actual experimental samples. 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 our independent data set. We also found that imputation at the most basic level of protein quantification – fragment level-improved accuracy and number of proteins quantified. Overall, this study indicates that the most suitable imputation method depends on the overall structure and correlations of proteins within the data set and can be identified with the workflow presented here.

Proteins of the serum amyloid A (SAA) family have been remarkably conserved in evolution. Their biologic function(s) are not fully defined but they are likely to be a part of primordial host defense. We have detected a ∼ 12-kDa protein reacting with antibodies against serum amyloid A (SAA) in human colostrum by western blotting. Mass spectrometry identified the reactive species as SAA1, previously identified as a prominent member of the acute-phase response in serum. Our finding SAA1 in human colostrum contrasts with bovine, caprine and ovine colostrum where a species corresponding to putative SAA3 is uniformly present. SAA1 protein in human colostrum presumably contributes to neonatal protection.

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.

Skin barrier dysfunction has been reported in both atopic dermatitis (AD) and food allergy (FA). However, only one-third of patients with AD have FA. The purpose of this study was to use a minimally invasive skin tape strip sampling method and a multiomics approach to determine whether children with AD and FA (AD FA+) have stratum corneum (SC) abnormalities that distinguish them from AD without FA (AD FA−) and nonatopic (NA) controls. Transepidermal water loss was found to be increased in AD FA+. Filaggrin and the proportion of ω-hydroxy fatty acid sphingosine ceramide content in nonlesional skin of children with AD FA+ were substantially lower than in AD FA− and NA skin. These abnormalities correlated with morphologic changes in epidermal lamellar bilayer architecture responsible for barrier homeostasis. Shotgun metagenomic studies revealed that the nonlesional skin of AD FA+ had increased abundance of Staphylococcus aureus compared to NA. Increased expression of keratins 5, 14, and 16 indicative of hyperproliferative keratinocytes was observed in the SC of AD FA+. The skin transcriptome of AD FA+ had increased gene expression for dendritic cells and type 2 immune pathways. A network analysis revealed keratins 5, 14, and 16 were positively correlated with AD FA+, whereas filaggrin breakdown products were negatively correlated with AD FA+. These data suggest that the most superficial compartment of nonlesional skin in AD FA+ has unique properties associated with an immature skin barrier and type 2 immune activation.

Brain derived peptides function as signaling molecules in the brain and regulate various physiological and behavioral processes. The low abundance and atypical fragmentation of these brain derived peptides makes detection using traditional proteomic methods challenging. In this study, we introduce and validate a new methodology for the discovery of novel peptides derived from mammalian brain. This methodology combines ribosome profiling and mass spectrometry-based peptidomics. Using this framework, we have identified a novel peptide in mouse whole brain whose expression is highest in the basal ganglia, hypothalamus and amygdala. Although its functional role is unknown, it has been previously detected in peripheral tissue as a component of the mRNA decapping complex. Continued discovery and studies of novel regulating peptides in mammalian brain may also provide insight into brain disorders.

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.

Since the neoplastic phenotype of a cell is largely driven by aberrant gene expression patterns, increasing attention has been focused on transcription factors that regulate critical mediators of tumorigenesis such as signal transducer and activator of transcription 3 (STAT3). As proteins that interact with STAT3 may be key in addressing how STAT3 contributes to cancer pathogenesis, we took a proteomics approach to identify novel STAT3-interacting proteins. We performed mass spectrometry-based profiling of STAT3-containing complexes from breast cancer cells that have constitutively active STAT3 and are dependent on STAT3 function for survival. We identified granulin (GRN) as a novel STAT3-interacting protein that was necessary for both constitutive and maximal leukemia inhibitory factor (LIF)induced STAT3 transcriptional activity. GRN enhanced STAT3 DNA binding and also increased the time-integrated amount of LIF-induced STAT3 activation in breast cancer cells. Furthermore, silencing GRN neutralized STAT3-mediated tumorigenic phenotypes including viability, clonogenesis, and migratory capacity. In primary breast cancer samples, GRN mRNA levels were positively correlated with STAT3 gene expression signatures and with reduced patient survival. These studies identify GRN as a functionally important STAT3-interacting protein that may serve as an important prognostic biomarker and potential therapeutic target in breast cancer.

The presented procedure allows rapid isolation of extracellular vesicles (EVs) from plasma using size-exclusion chromatography (SEC). Additionally, an approach for reducing the lipid and salt content of the EV isolate in preparation for mass spectrometry (MS)-based proteomic analysis is presented. An example setup for proteomic profiling of the processed samples by nanoflow liquid chromatography coupled to tandem mass spectrometry (nLC-MS/MS) is also presented. Approximately 1000 protein groups in blood plasma-derived EVs can be identified and quantitated following this procedure and using the described instrumentation.

Since the neoplastic phenotype of a cell is largely driven by its gene expression patterns, increasing attention is focused on transcription factors that regulate critical mediators of tumor formation and metastatic progression like the oncogenic transcription factor, signal transducer and activator of transcription 3 (STAT3). Whereas normal cells have transient activation of STAT3 due to tight control by negative regulators, cancer cells frequently have inappropriate constitutive activation of STAT3 which drives increased expression of genes involved in tumorigenesis. However, little is known about proteins that interact with STAT3 to modulate its function. To identify novel STAT3-interacting proteins, we performed liquid chromatography tandem mass spectrometry-based profiling of STAT3-containing complexes immunoprecipitated from the triple-negative breast cancer cell lines MDA-MB-468 and SUM159PT, which have constitutively active STAT3. We identified granulin (GRN) as a novel STAT3-interacting protein and validated the STAT3-GRN interaction in breast cancer cells by co-immunoprecipitation. To investigate the functional effect of GRN on STAT3 activity, we silenced GRN using small interfering RNA. We found that GRN was necessary for constitutive and maximal cytokine-induced STAT3 transcriptional activity in breast cancer cells. GRN modulated cytokine-induced STAT3 function by enhancing STAT3 DNA binding and increasing the time-integrated amount of STAT3 activation and nuclear translocation. Silencing GRN mirrored the effect of silencing STAT3 on reducing the viability, clonogenesis, and migratory capacity of triple-negative breast cancer cells. Furthermore, GRN mRNA levels were significantly and positively correlated with STAT3 gene expression signatures indicative of STAT3 activation as well as with reduced overall survival in breast cancer patients. These studies used a proteomics approach to identify GRN as a novel STAT3 interacting protein that may serve as an important prognostic biomarker and potential therapeutic target in breast cancer. Citation Format: Jennifer E Yeh, Simion Kreimer, Sarah R Walker, Andrea Richardson, Alexander R Ivanov, David A Frank. Granulin, a novel STAT3-interacting protein, promotes breast cancer tumorigenicity [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-07-01.

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.

The identification of extracellular vesicles (EVs) as intercellular conveyors of biological information has recently emerged as a novel paradigm in signaling, leading to the exploitation of EVs and their contents as biomarkers of various diseases. However, whether there are diurnal variations in the size, number, and tissue of origin of blood EVs is currently not known, and could have significant implications when using EVs as biomarkers for disease progression. Currently available technologies for the measurement of EV size and number are either time consuming, require specialized equipment, or lack sufficient accuracy across a range of EV sizes. Flow cytometry represents an attractive alternative to these methods; however, traditional flow cytometers are only capable of measuring particles down to 500 nm, which is significantly larger than the average and median sizes of plasma EVs. Utilizing a Beckman Coulter MoFlo XDP flow cytometer with NanoView module, we employed nanoscale flow cytometry (termed nanoFCM) to examine the relative number and scatter distribution of plasma EVs at three different time points during the day in 6 healthy adults. Analysis of liposomes and plasma EVs proved that nanoFCM is capable of detecting biologically-relevant vesicles down to 100 nm in size. With this high resolution configuration, we observed variations in the relative size (FSC/SSC distributions) and concentration (proportions) of EVs in healthy adult plasma across the course of a day, suggesting that there are diurnal variations in the number and size distribution of circulating EV populations. The use of nanoFCM provides a valuable tool for the study of EVs in both health and disease; however, additional refinement of nanoscale flow cytometric methods is needed for use of these instruments for quantitative particle counting and sizing. Furthermore, larger scale studies are necessary to more clearly define the diurnal variations in circulating EVs, and thus further inform their use as biomarkers for disease.