Deep Proteome Research Articles

Architecture of the outbred brown fat proteome defines regulators of metabolic physiology

Brown adipose tissue (BAT) regulates metabolic physiology. However, nearly all mechanistic studies of BAT protein function occur in a single inbred mouse strain, which has limited the understanding of generalizable mechanisms of BAT regulation over physiology. Here, we perform deep quantitative proteomics of BAT across a cohort of 163 genetically defined diversity outbred mice, a model that parallels the genetic and phenotypic variation found in humans. We leverage this diversity to define the functional architecture of the outbred BAT proteome, comprising 10,479 proteins. We assign co-operative functions to 2,578 proteins, enabling systematic discovery of regulators of BAT. We also identify 638 proteins that correlate with protection from, or sensitivity to, at least one parameter of metabolic disease. We use these findings to uncover SFXN5, LETMD1, and ATP1A2 as modulators of BAT thermogenesis or adiposity, and provide OPABAT as a resource for understanding the conserved mechanisms of BAT regulation over metabolic physiology.

Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.

Comparative Evaluation of Proteome Discoverer and FragPipe for the TMT-Based Proteome Quantification.

Isobaric labeling-based proteomics is widely applied in deep proteome quantification. Among the platforms for isobaric labeled proteomic data analysis, the commercial software Proteome Discoverer (PD) is widely used, incorporating the search engine CHIMERYS, while FragPipe (FP) is relatively new, free for noncommercial purposes, and integrates the engine MSFragger. Here, we compared PD and FP over three public proteomic data sets labeled using 6plex, 10plex, and 16plex tandem mass tags. Our results showed the protein abundances generated by the two software are highly correlated. PD quantified more proteins (10.02%, 15.44%, 8.19%) than FP with comparable NA ratios (0.00% vs. 0.00%, 0.85% vs. 0.38%, and 11.74% vs. 10.52%) in the three data sets. Using the 16plex data set, PD and FP outputs showed high consistency in quantifying technical replicates, batch effects, and functional enrichment in differentially expressed proteins. However, FP saved 93.93%, 96.65%, and 96.41% of processing time compared to PD for analyzing the three data sets, respectively. In conclusion, while PD is a well-maintained commercial software integrating various additional functions and can quantify more proteins, FP is freely available and achieves similar output with a shorter computational time. Our results will guide users in choosing the most suitable quantification software for their needs.

Quantitative proteomics reveal the potential biological functions of the deubiquitinating enzyme Ubp14 in Saccharomyces cerevisiae

Ubiquitination is one of the reversible protein post-translational modifications, in which ubiquitin molecules bind to the target protein in a cascade reaction of ubiquitin activating enzymes, ubiquitin conjugating enzymes, and ubiquitin ligases. The deubiquitinating enzymes (DUBs) remove ubiquitin residues from the substrates, which play key roles in the formation of mature ubiquitin, the removal and trimming of ubiquitin chains, as well as the recycling of free ubiquitin chains. Ubp14, a member of the ubiquitin specific proteases family in Saccharomyces cerevisiae, is mainly responsible for the recycling of intracellular free ubiquitin chains. To investigate its global biological function, a ubp14∆ mutant was constructed by homologous recombination technique. The growth rate of ubp14∆ mutant was lower than that of the wild-type (WT) strain. Using stable isotope labeling by amino acids in cell culture (SILAC) combined with deep coverage proteomics analysis, the differentially expressed proteins of ubp14∆ mutant relative to the wild-type strain were systematically analyzed. A total of 3 685 proteins were identified in this study, and 109 differentially expressed proteins were filtered out by statistical analysis. Gene ontology analysis found that differentially expressed proteins caused by Ubp14 loss were mainly involved in amino acid metabolism, REDOX, heat shock stress and etc, which shed light on the broad biological function of this DUB. This study provides highly reliable proteomic data for further exploring the biological functions of the deubiquitination enzyme Ubp14, and further understanding the relationship between the free ubiquitin homeostasis and biological process regulation.

Proteome Integral Solubility Alteration (PISA) for High-Throughput Ligand Target Deconvolution with Increased Statistical Significance and Reduced Sample Amount.

Proteome Integral Solubility Alteration (PISA) is a recently developed mass spectrometry-based, deep proteomics method for unbiased, proteome-wide target deconvolution of ligands, requiring no chemical ligand modification. PISA can be applied to living cells for studying target engagement in vivo or alternatively to protein extracts to identify in vitro ligand-interacting proteins. Here we describe the PISA workflow optimized in our lab. PISA improves the target discovery throughput 10-100 folds compared to the previously used proteomics methods and provides higher statistical significance for target candidates by enabling several biological replicates. Sample multiplexing makes all-in-one analysis of multiple ligands simultaneously possible. PISA dramatically reduces analysis costs, allowing many research questions in need of target deconvolution to be addressed, and unlocks the potential of miniaturizing biological models, including primary cells.

BTK kinase activity is dispensable for the survival of diffuse large B-cell lymphoma

Inhibitors targeting Bruton's tyrosine kinase (BTK) have revolutionized the treatment for various B-cell malignancies but are limited by acquired resistance after prolonged treatment as a result of mutations in BTK. Here, by a combination of structural modeling, invitro assays, and deep phospho-tyrosine proteomics, we demonstrated that four clinically observed BTK mutations-C481F, C481Y, C481R, and L528W-inactivated BTK kinase activity both invitro and in diffused large B-cell lymphoma (DLBCL) cells. Paradoxically, we found that DLBCL cells harboring kinase-inactive BTK exhibited intact B cell receptor (BCR) signaling, unperturbed transcription, and optimal cellular growth. Moreover, we determined that DLBCL cells with kinase-inactive BTK remained addicted to BCR signaling and were thus sensitive to targeted BTK degradation by the proteolysis-targeting chimera. By performing parallel genome-wide CRISPR-Cas9 screening in DLBCL cells with WT or kinase-inactive BTK, we discovered that DLBCL cells with kinase-inactive BTK displayed increased dependence on Toll-like receptor 9 (TLR9) for their growth and/or survival. Our study demonstrates that the kinase activity of BTK is not essential for oncogenic BCR signaling and suggests that BTK's noncatalytic function is sufficient to sustain the survival of DLBCL.

Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes.

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enablesscalable systems that leverage protein-nano interactions toovercome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

JUMPptm: Integrated software for sensitive identification of post-translational modifications and its application in Alzheimer's disease study.

Mass spectrometry (MS)-based proteomic analysis of posttranslational modifications (PTMs) usually requires the pre-enrichment of modified proteins or peptides. However, recent ultra-deep whole proteome profiling generates millions of spectra in a single experiment, leaving many unassigned spectra, some of which may be derived from PTM peptides. Here we present JUMPptm, an integrative computational pipeline, to extract PTMs from unenriched whole proteome. JUMPptm combines the advantages of JUMP, MSFragger and Comet search engines, and includes de novo tags, customized database search and peptide filtering, which iteratively analyzes each PTM by a multi-stage strategy to improve sensitivity and specificity. We applied JUMPptm to the deep brain proteome of Alzheimer's disease (AD), and identified 34,954 unique peptides with phosphorylation, methylation, acetylation, ubiquitination, and others. The phosphorylated peptides were validated by enriched phosphoproteome from the same sample. TMT-based quantification revealed 482 PTM peptides dysregulated at different stages during AD progression. For example, the acetylation of numerous mitochondrial proteins is significantly decreased in AD. A total of 60 PTM sites are found in the pan-PTM map of the Tau protein. The JUMPptm program is an effective tool for pan-PTM analysis and the resulting AD pan-PTM profile serves as a valuable resource for AD research.

Rapid and deep plasma proteomics workflows for robust identification and quantification of biomarkers of sickle cell anaemia

Rapid and deep plasma proteomics workflows for robust identification and quantification of biomarkers of sickle cell anaemia

Deep Single-Shot NanoLC-MS Proteome Profiling with a 1500 Bar UHPLC System, Long Fully Porous Columns, and HRAM MS.

This study demonstrates how the latest ultrahigh-performance liquid chromatography (UHPLC) technology can be combined with high-resolution accurate-mass (HRAM) mass spectrometry (MS) and long columns packed with fully porous particles to improve bottom-up proteomics analysis with nanoflow liquid chromatography–mass spectrometry (nanoLC-MS) methods. The increased back pressures from the UHPLC system enabled the use of 75 μm I.D. × 75 cm columns packed with 2 μm particles at a typical 300 nL/min flow rate as well as elevated and reduced flow rates. The constant pressure pump operation at 1500 bar reduced sample loading and column washing/equilibration stages and overall overhead time, which maximizes MS utilization time. The versatility of flow rate optimization to balance the sensitivity, throughput with sample loading amount, and capability of using longer gradients contributes to a greater number of peptide and protein identifications for single-shot bottom-up proteomics experiments. The routine proteome profiling and precise quantification of >7000 proteins with single-shot nanoLC-MS analysis open possibilities for large-scale discovery studies with a deep dive into the protein level alterations. Data are available via ProteomeXchange with identifier PXD035665.

HypDB: A functionally annotated web-based database of the proline hydroxylation proteome.

Proline hydroxylation (Hyp) regulates protein structure, stability, and protein–protein interaction. It is widely involved in diverse metabolic and physiological pathways in cells and diseases. To reveal functional features of the Hyp proteome, we integrated various data sources for deep proteome profiling of the Hyp proteome in humans and developed HypDB (https://www.HypDB.site), an annotated database and web server for Hyp proteome. HypDB provides site-specific evidence of modification based on extensive LC-MS analysis and literature mining with 14,413 nonredundant Hyp sites on 5,165 human proteins including 3,383 Class I and 4,335 Class II sites. Annotation analysis revealed significant enrichment of Hyp on key functional domains and tissue-specific distribution of Hyp abundance across 26 types of human organs and fluids and 6 cell lines. The network connectivity analysis further revealed a critical role of Hyp in mediating protein–protein interactions. Moreover, the spectral library generated by HypDB enabled data-independent analysis (DIA) of clinical tissues and the identification of novel Hyp biomarkers in lung cancer and kidney cancer. Taken together, our integrated analysis of human proteome with publicly accessible HypDB revealed functional diversity of Hyp substrates and provides a quantitative data source to characterize Hyp in pathways and diseases.

Subgroup-Enriched Pathways and Kinase Signatures in Medulloblastoma Patient-Derived Xenografts.

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. MB is classified into four primary molecular subgroups: wingless (WNT), sonic hedgehog (SHH), Group 3 (G3), and Group 4 (G4), and further genomic and proteomic subtypes have been reported. Subgroup heterogeneity and few actionable mutations have hindered the development of targeted therapies, especially for G3 MB, which has a particularly poor prognosis. To identify novel therapeutic targets for MB, we performed mass spectrometry-based deep expression proteomics and phosphoproteomics in 20 orthotopic patient-derived xenograft (PDX) models of MB comprising SHH, G3, and G4 subgroups. We found that the proteomic profiles of MB PDX tumors are closely aligned with those of primary human MB tumors illustrating the utility of PDX models. SHH PDXs were enriched for NFκB and p38 MAPK signaling, while G3 PDXs were characterized by MYC activity. Additionally, we found a significant association between actinomycin D sensitivity and increased abundance of MYC and MYC target genes. Our results highlight several candidate pathways that may serve as targets for new MB therapies. Mass spectrometry data are available via ProteomeXchange with identifier PXD035070.

Evaluation of a Pooling Chemoproteomics Strategy with an FDA-Approved Drug Library.

Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (> 12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.

Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry–based proteomics because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation–serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility (IM) separated quadrupole time-of-flight mass spectrometer. This requires alignment of the IM separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared with DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and IM plane. This is implemented in the freely available py_diAID (Python package for DIA with an automated isolation design) package. In combination with in-depth project-specific proteomics libraries and the Evosep liquid chromatography system, we reproducibly identified over 7700 proteins in a human cancer cell line in 44 min with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 min liquid chromatography gradients), we consistently quantified more than 6000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor in triplicate 21 min runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.

Multi-nanoparticle Workflow Enables Deep Plasma Proteomics at Scale, with Enhanced Precision, and Depths of Coverage

Multi-nanoparticle Workflow Enables Deep Plasma Proteomics at Scale, with Enhanced Precision, and Depths of Coverage

Pan-cancer proteomic map of 949 human cell lines.

SummaryThe proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.

Proteomic Analysis of Methanococcus voltae Grown in the Presence of Mineral and Nonmineral Sources of Iron and Sulfur.

ABSTRACTIron sulfur (Fe-S) proteins are essential and ubiquitous across all domains of life, yet the mechanisms underpinning assimilation of iron (Fe) and sulfur (S) and biogenesis of Fe-S clusters are poorly understood. This is particularly true for anaerobic methanogenic archaea, which are known to employ more Fe-S proteins than other prokaryotes. Here, we utilized a deep proteomics analysis of Methanococcus voltae A3 cultured in the presence of either synthetic pyrite (FeS2) or aqueous forms of ferrous iron and sulfide to elucidate physiological responses to growth on mineral or nonmineral sources of Fe and S. The liquid chromatography-mass spectrometry (LCMS) shotgun proteomics analysis included 77% of the predicted proteome. Through a comparative analysis of intra- and extracellular proteomes, candidate proteins associated with FeS2 reductive dissolution, Fe and S acquisition, and the subsequent transport, trafficking, and storage of Fe and S were identified. The proteomic response shows a large and balanced change, suggesting that M. voltae makes physiological adjustments involving a range of biochemical processes based on the available nutrient source. Among the proteins differentially regulated were members of core methanogenesis, oxidoreductases, membrane proteins putatively involved in transport, Fe-S binding ferredoxin and radical S-adenosylmethionine proteins, ribosomal proteins, and intracellular proteins involved in Fe-S cluster assembly and storage. This work improves our understanding of ancient biogeochemical processes and can support efforts in biomining of minerals.IMPORTANCE Clusters of iron and sulfur are key components of the active sites of enzymes that facilitate microbial conversion of light or electrical energy into chemical bonds. The proteins responsible for transporting iron and sulfur into cells and assembling these elements into metal clusters are not well understood. Using a microorganism that has an unusually high demand for iron and sulfur, we conducted a global investigation of cellular proteins and how they change based on the mineral forms of iron and sulfur. Understanding this process will answer questions about life on early earth and has application in biomining and sustainable sources of energy.

Solvent Precipitation SP3 (SP4) Enhances Recovery for Proteomics Sample Preparation without Magnetic Beads.

Complete, reproducible extraction of protein material is essential for comprehensive and unbiased proteome analyses. A current gold standard is single-pot, solid-phase-enhanced sample preparation (SP3), in which organic solvent and magnetic beads are used to denature and capture protein aggregates, with subsequent washes removing contaminants. However, SP3 is dependent on effective protein immobilization onto beads, risks losses during wash steps, and exhibits losses and greater costs at higher protein inputs. Here, we propose solvent precipitation SP3 (SP4) as an alternative to SP3 protein cleanup, capturing acetonitrile-induced protein aggregates by brief centrifugation rather than magnetism—with optional low-cost inert glass beads to simplify handling. SP4 recovered equivalent or greater protein yields for 1–5000 μg preparations and improved reproducibility (median protein R2 0.99 (SP4) vs 0.97 (SP3)). Deep proteome profiling revealed that SP4 yielded a greater recovery of low-solubility and transmembrane proteins than SP3, benefits to aggregating protein using 80 vs 50% organic solvent, and equivalent recovery by SP4 and S-Trap. SP4 was verified in three other labs across eight sample types and five lysis buffers—all confirming equivalent or improved proteome characterization vs SP3. With near-identical recovery, this work further illustrates protein precipitation as the primary mechanism of SP3 protein cleanup and identifies that magnetic capture risks losses, especially at higher protein concentrations and among more hydrophobic proteins. SP4 offers a minimalistic approach to protein cleanup that provides cost-effective input scalability, the option to omit beads entirely, and suggests important considerations for SP3 applications—all while retaining the speed and compatibility of SP3.

Mapping the Serum Proteome of COVID-19 Patients; Guidance for Severity Assessment.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), whose outbreak in 2019 led to an ongoing pandemic with devastating consequences for the global economy and human health. According to the World Health Organization, COVID-19 has affected more than 481 million people worldwide, with 6 million confirmed deaths. The joint efforts of the scientific community have undoubtedly increased the pace of production of COVID-19 vaccines, but there is still so much uncharted ground to cover regarding the mechanisms of SARS-CoV-2 infection, replication and host response. These issues can be approached by proteomics with unprecedented capacity paving the way for the development of more efficient strategies for patient care. In this study, we present a deep proteome analysis that has been performed on a cohort of 72 COVID-19 patients aiming to identify serum proteins assessing the dynamics of the disease at different age ranges. A panel of 53 proteins that participate in several functions such as acute-phase response and inflammation, blood coagulation, cell adhesion, complement cascade, endocytosis, immune response, oxidative stress and tissue injury, have been correlated with patient severity, suggesting a molecular basis for their clinical stratification. Eighteen protein candidates were further validated by targeted proteomics in an independent cohort of 84 patients including a group of individuals that had satisfactorily resolved SARS-CoV-2 infection. Remarkably, all protein alterations were normalized 100 days after leaving the hospital, which further supports the reliability of the selected proteins as hallmarks of COVID-19 progression and grading. The optimized protein panel may prove its value for optimal severity assessment as well as in the follow up of COVID-19 patients.

Dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.