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.

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.

Hyperphosphorylation of hepatic proteome characterizes nonalcoholic fatty liver disease in S-adenosylmethionine deficiency.

Methionine adenosyltransferase 1a (MAT1A) is responsible for hepatic S-adenosyl-L-methionine (SAMe) biosynthesis. Mat1a -/- mice have hepatic SAMe depletion, develop nonalcoholic steatohepatitis (NASH) which is reversed with SAMe administration. We examined temporal alterations in the proteome/phosphoproteome in pre-disease and NASH Mat1a -/- mice, effects of SAMe administration, and compared to human nonalcoholic fatty liver disease (NAFLD). Mitochondrial and peroxisomal lipid metabolism proteins were altered in pre-disease mice and persisted in NASH Mat1a -/- mice, which exhibited more progressive alterations in cytoplasmic ribosomes, ER, and nuclear proteins. A common mechanism found in both pre-disease and NASH livers was a hyperphosphorylation signature consistent with casein kinase 2α (CK2α) and AKT1 activation, which was normalized by SAMe administration. This was mimicked in human NAFLD with a metabolomic signature (M-subtype) resembling Mat1a -/- mice. In conclusion, we have identified a common proteome/phosphoproteome signature between Mat1a -/- mice and human NAFLD M-subtype that may have pathophysiological and therapeutic implications.

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.

A methodology for discovering novel brain-relevant peptides: Combination of ribosome profiling and peptidomics.

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.

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.

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.

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.

Granulin, a novel STAT3-interacting protein, enhances STAT3 transcriptional function and correlates with poorer prognosis in breast cancer.

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.

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.