SnapShot: Clinical proteomics.

In-depth quantitative proteomic analysis of clinical specimens offers unique and invaluable insights into their protein composition and biochemical state in physiological and pathological conditions. A plethora of methodologies have been developed and customized. Here, we summarize the most common and emerging mass spectrometry (MS)-based workflows. Due to space limitations the overview cannot be complete. To view this SnapShot, open or download the PDF.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection.

Although host responses to the ancestral SARS-CoV-2 strain are well described, those to the new Omicron variants are less resolved. We profiled the clinical phenomes, transcriptomes, proteomes, metabolomes, and immune repertoires of >1,000 blood cell or plasma specimens from SARS-CoV-2 Omicron patients. Using in-depth integrated multi-omics, we dissected the host response dynamics during multiple disease phases to reveal the molecular and cellular landscapes in the blood. Specifically, we detected enhanced interferon-mediated antiviral signatures of platelets in Omicron-infected patients, and platelets preferentially formed widespread aggregates with leukocytes to modulate immune cell functions. In addition, patients who were re-tested positive for viral RNA showed marked reductions in B cell receptor clones, antibody generation, and neutralizing capacity against Omicron. Finally, we developed a machine learning model that accurately predicted the probability of re-positivity in Omicron patients. Our study may inspire a paradigm shift in studying systemic diseases and emerging public health concerns.

Immunometabolism the CyTOF way.

In this issue of Immunity, Levine et al. report a CyTOF-based approach for the analyses of CD8+ T cells metabolic changes at the single-cell level. This approach identified a transition state early in T cell activation that is characterized by high glycolytic and oxidative activity, providing new insight into the metabolic changes that underlie the transition to effector and memory T cell fates.

Targeted protein degradation reveals RNA Pol II heterogeneity and functional diversity.

RNA polymerase II (RNA Pol II) subunits are thought to be involved in various transcription-associated processes, but it is unclear whether they play different regulatory roles in modulating gene expression. Here, we performed nascent and mature transcript sequencing after the acute degradation of 12 mammalian RNA Pol II subunits and profiled their genomic binding sites and protein interactomes to dissect their molecular functions. We found that RNA Pol II subunits contribute differently to RNA Pol II cellular localization and transcription processes and preferentially regulate RNA processing (such as RNA splicing and 3' end maturation). Genes sensitive to the depletion of different RNA Pol II subunits tend to be involved in diverse biological functions and show different RNA half-lives. Sequences, associated protein factors, and RNA structures are correlated with RNA Pol II subunit-mediated differential gene expression. These findings collectively suggest that the heterogeneity of RNA Pol II and different genes appear to depend on some of the subunits.

The methyltransferase Ezh2 controls cell adhesion and migration through direct methylation of the extranuclear regulatory protein talin.

A cytosolic role for the histone methyltransferase Ezh2 in regulating lymphocyte activation has been suggested, but the molecular mechanisms underpinning this extranuclear function have remained unclear. Here we found that Ezh2 regulated the integrin signaling and adhesion dynamics of neutrophils and dendritic cells (DCs). Ezh2 deficiency impaired the integrin-dependent transendothelial migration of innate leukocytes and restricted disease progression in an animal model of multiple sclerosis. Direct methylation of talin, a key regulatory molecule in cell migration, by Ezh2 disrupted the binding of talin to F-actin and thereby promoted the turnover of adhesion structures. This regulatory effect was abolished by targeted disruption of the interactions of Ezh2 with the cytoskeletal-reorganization effector Vav1. Our studies reveal an unforeseen extranuclear function for Ezh2 in regulating adhesion dynamics, with implications for leukocyte migration, immune responses and potentially pathogenic processes.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer.

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging-based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Proteomic and Cellular Characterization of Omicron Breakthrough Infections and a Third Homologous or Heterologous Boosting Vaccination in a Longitudinal Cohort.

The understanding of dynamic plasma proteome features in hybrid immunity and breakthrough infection is limited. A deeper understanding of the immune differences between heterologous and homologous immunization could assist in the future establishment of vaccination strategies. In this study, 40 participants who received a third dose of either a homologous BBIBP-CorV or a heterologous ZF2001 protein subunit vaccine following two doses of inactivated coronavirus disease 2019 vaccines and 12 patients with BA2.2 breakthrough infections were enrolled. Serum samples were collected at days 0, 28, and 180 following the boosting vaccination and breakthrough and then analyzed using neutralizing antibody tests and mass spectrometer-based proteomics. Mass cytometry of peripheral blood mononuclear cell samples was also performed in this cohort. The chemokine signaling pathway and humoral response markers (IgG2 and IgG3) associated with infection were found to be upregulated in breakthrough infections compared to vaccination-induced immunity. Elevated expression of IGKV, IGHV, IL-17 signaling, and the phagocytosis pathway, along with lower expression of FGL2, were correlated with higher antibody levels in the boosting vaccination groups. The MAPK signaling pathway and Fc gamma R-mediated phagocytosis were more enriched in the heterologous immunization groups than in the homologous immunization groups. Breakthrough infections can trigger more intensive inflammatory chemokine responses than vaccination. T-cell and innate immune activation have been shown to be closely related to enhanced antibody levels after vaccination and therefore might be potential targets for vaccine adjuvant design.

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Mass Spectrometry-Based Proteomics of Epithelial Ovarian Cancers: A Clinical Perspective.

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery of proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990 and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Identifying Patients With Rapid Progression From Hormone-Sensitive to Castration-Resistant Prostate Cancer: A Retrospective Study.

Prostate cancer (PCa) is the second most prevalent malignancy and the fifth cause of cancer-related deaths in men. A crucial challenge is identifying the population at risk of rapid progression from hormone-sensitive prostate cancer (HSPC) to lethal castration-resistant prostate cancer (CRPC). We collected 78 HSPC biopsies and measured their proteomes using pressure cycling technology and a pulsed data-independent acquisition pipeline. We quantified 7355 proteins using these HSPC biopsies. A total of 251 proteins showed differential expression between patients with a long- or short-term progression to CRPC. Using a random forest model, we identified seven proteins that significantly discriminated long- from short-term progression patients, which were used to classify PCa patients with an area under the curve of 0.873. Next, one clinical feature (Gleason sum) and two proteins (BGN and MAPK11) were found to be significantly associated with rapid disease progression. A nomogram model using these three features was generated for stratifying patients into groups with significant progression differences (p-value = 1.3×10-4). To conclude, we identified proteins associated with a fast progression to CRPC and an unfavorable prognosis. Based on these proteins, our machine learning and nomogram models stratified HSPC into high- and low-risk groups and predicted their prognoses. These models may aid clinicians in predicting the progression of patients, guiding individualized clinical management and decisions.

Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma.

Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry.

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Multitissue Circadian Proteome Atlas of WT and Per1-/-/Per2-/- Mice.

The molecular basis of circadian rhythm, driven by core clock genes such as Per1/2, has been investigated on the transcriptome level, but not comprehensively on the proteome level. Here we quantified over 11,000 proteins expressed in eight types of tissues over 46 h with an interval of 2 h, using WT and Per1/Per2 double knockout mouse models. The multitissue circadian proteome landscape of WT mice shows tissue-specific patterns and reflects circadian anticipatory phenomena, which are less obvious on the transcript level. In most peripheral tissues of double knockout mice, reduced protein cyclers are identified when compared with those in WT mice. In addition, PER1/2 contributes to controlling the anticipation of the circadian rhythm, modulating tissue-specific cyclers as well as key pathways including nucleotide excision repair. Severe intertissue temporal dissonance of circadian proteome has been observed in the absence of Per1 and Per2. The γ-aminobutyric acid might modulate some of these temporally correlated cyclers in WT mice. Our study deepens our understanding of rhythmic proteins across multiple tissues and provides valuable insights into chronochemotherapy. The data are accessible at https://prot-rhythm.prottalks.com/.

Proteomics Investigation of Diverse Serological Patterns in COVID-19.

Serum antibodies IgM and IgG are elevated during Coronavirus Disease 2019 (COVID-19) to defend against viral attacks. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative, whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their 2-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense and that high titers of IgM might not be favorable to COVID-19 recovery.

Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets.

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy.

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

The 2023 Report on the Proteome from the HUPO Human Proteome Project.

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

DIA-Based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia.

Drug resistance is a critical obstacle to effective treatment in patients with chronic myeloid leukemia. To understand the underlying resistance mechanisms in response to imatinib mesylate (IMA) and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for 2 months to generate derivative cells with mild, intermediate, and severe resistance to the drugs as defined by their increasing resistance index. PulseDIA-based (DIA [data-independent acquisition]) quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7082 proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin signaling pathway was found to be significantly enriched in both ADR-resistant and IMA-resistant K562 cells. In particular, isocitrate dehydrogenase (NADP(+)) 2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated isocitrate dehydrogenase (NADP(+)) 2 as a potential target that can be therapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMA and ADR.

Tissue-Characteristic Expression of Mouse Proteome.

The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition-based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.