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Molecular & Cellular Proteomics : MCP Jan 2022Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these...
Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.
Topics: Peptides; Proteins; Proteomics; Single-Cell Analysis
PubMed: 34808355
DOI: 10.1016/j.mcpro.2021.100179 -
IScience Jun 2023Advanced gastric adenocarcinoma (GAC) often leads to peritoneal carcinomatosis (PC) and is associated with very poor outcome. Here we report the comprehensive...
Advanced gastric adenocarcinoma (GAC) often leads to peritoneal carcinomatosis (PC) and is associated with very poor outcome. Here we report the comprehensive proteogenomic study of ascites derived cells from a prospective GAC cohort (n = 26 patients with peritoneal carcinomatosis, PC). A total of 16,449 proteins were detected from whole cell extracts (TCEs). Unsupervised hierarchical clustering resulted in three distinct groups that reflected extent of enrichment in tumor cells. Integrated analysis revealed enriched biological pathways and notably, some druggable targets (cancer-testis antigens, kinases, and receptors) that could be exploited to develop effective therapies and/or tumor stratifications. Systematic comparison of expression levels of proteins and mRNAs revealed special expression patterns of key therapeutics target notably high mRNA and low protein expression of HAVCR2 (TIM-3), and low mRNA but high protein expression of cancer-testis antigens CTAGE1 and CTNNA2. These results inform strategies to target GAC vulnerabilities.
PubMed: 37305699
DOI: 10.1016/j.isci.2023.106913 -
Nature Communications Dec 2022Microphthalmia transcription factor (MiT) family translocation renal cell carcinoma (tRCC) is a rare type of kidney cancer, which is not well characterized. Here we show...
Microphthalmia transcription factor (MiT) family translocation renal cell carcinoma (tRCC) is a rare type of kidney cancer, which is not well characterized. Here we show the comprehensive proteogenomic analysis of tRCC tumors and normal adjacent tissues to elucidate the molecular landscape of this disease. Our study reveals that defective DNA repair plays an important role in tRCC carcinogenesis and progression. Metabolic processes are markedly dysregulated at both the mRNA and protein levels. Proteomic and phosphoproteome data identify mTOR signaling pathway as a potential therapeutic target. Moreover, molecular subtyping and immune infiltration analysis characterize the inter-tumoral heterogeneity of tRCC. Multi-omic integration reveals the dysregulation of cellular processes affected by genomic alterations, including oxidative phosphorylation, autophagy, transcription factor activity, and proteasome function. This study represents a comprehensive proteogenomic analysis of tRCC, providing valuable insights into its biological mechanisms, disease diagnosis, and prognostication.
Topics: Humans; Carcinoma, Renal Cell; Proteogenomics; Transcription Factors; Microphthalmos; Proteomics; Kidney Neoplasms; Basic Helix-Loop-Helix Leucine Zipper Transcription Factors; Translocation, Genetic
PubMed: 36470859
DOI: 10.1038/s41467-022-34460-w -
Plant Physiology Mar 2020Rice () molecular breeding has gained considerable attention in recent years, but inaccurate genome annotation hampers its progress and functional studies of the rice...
Rice () molecular breeding has gained considerable attention in recent years, but inaccurate genome annotation hampers its progress and functional studies of the rice genome. In this study, we applied single-molecule long-read RNA sequencing (lrRNA_seq)-based proteogenomics to reveal the complexity of the rice transcriptome and its coding abilities. Surprisingly, approximately 60% of loci identified by lrRNA_seq are associated with natural antisense transcripts (NATs). The high-density genomic arrangement of NAT genes suggests their potential roles in the multifaceted control of gene expression. In addition, a large number of fusion and intergenic transcripts have been observed. Furthermore, 906,456 transcript isoforms were identified, and 72.9% of the genes can generate splicing isoforms. A total of 706,075 posttranscriptional events were subsequently categorized into 10 subtypes, demonstrating the interdependence of posttranscriptional mechanisms that contribute to transcriptome diversity. Parallel short-read RNA sequencing indicated that lrRNA_seq has a superior capacity for the identification of longer transcripts. In addition, over 190,000 unique peptides belonging to 9,706 proteoforms/protein groups were identified, expanding the diversity of the rice proteome. Our findings indicate that the genome organization, transcriptome diversity, and coding potential of the rice transcriptome are far more complex than previously anticipated.
Topics: Oryza; Proteogenomics; Proteome; RNA, Antisense; Sequence Analysis, RNA; Transcriptome
PubMed: 31857423
DOI: 10.1104/pp.19.00430 -
Nature Reviews. Clinical Oncology Apr 2019Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology,... (Review)
Review
Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.
Topics: Biomarkers, Tumor; Humans; Mass Spectrometry; Mutation; Neoplasms; Precision Medicine; Prognosis; Proteogenomics
PubMed: 30487530
DOI: 10.1038/s41571-018-0135-7 -
American Journal of Cancer Research 2021Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and... (Review)
Review
Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and personalized oncomedicine. Besides traditional single-omics fields (such as genomics, epigenomics, transcriptomics and metabolomics) and multi-omics contributions (proteogenomics, proteotranscriptomics or reproductomics), several new "-omics" approaches and exciting proteomics subfields are contributing to basic and advanced understanding of these "": phenomics/cellomics, connectomics and interactomics, secretomics, matrisomics, exosomics, angiomics, chaperomics and epichaperomics, phosphoproteomics, ubiquitinomics, metalloproteomics, terminomics, degradomics and metadegradomics, adhesomics, stressomics, microbiomics, immunomics, salivaomics, materiomics and other biomics. Throughout the extremely complex neoplastic process, a Breast Cancer Cell Continuum Concept (BCCCC) has been modeled in this review as a spatio-temporal and holistic approach, as long as the breast cancer represents a complex cascade comprising successively integrated populations of heterogeneous tumor and cancer-associated cells, that reflect the carcinoma's progression from a "driving mutation" and formation of the breast primary tumor, toward the distant secondary tumors in different tissues and organs, via circulating tumor cell populations. This BCCCC is widely sustained by a Breast Cancer Proteomic Continuum Concept (BCPCC), where each phenotype of neoplastic and tumor-associated cells is characterized by a changing and adaptive proteomic profile detected in solid and liquid minimal invasive biopsies by complex proteomics approaches. Such a profile is created, beginning with the proteomic landscape of different neoplastic cell populations and cancer-associated cells, followed by subsequent analysis of protein biomarkers involved in epithelial-mesenchymal transition and intravasation, circulating tumor cell proteomics, and, finally, by protein biomarkers that highlight the extravasation and distant metastatic invasion. Proteomics technologies are producing important data in breast cancer diagnostic, prognostic, and predictive biomarkers discovery and validation, are detecting genetic aberrations at the proteome level, describing functional and regulatory pathways and emphasizing specific protein and peptide profiles in human tissues, biological fluids, cell lines and animal models. Also, proteomics can identify different breast cancer subtypes and specific protein and proteoform expression, can assess the efficacy of cancer therapies at cellular and tissular level and can even identify new therapeutic target proteins in clinical studies.
PubMed: 34659875
DOI: No ID Found -
Expert Review of Proteomics 2022
Topics: Humans; Mutation; Drug Resistance, Neoplasm; Genomics; Leukemia, Myeloid, Acute; Protein Kinase Inhibitors
PubMed: 36734985
DOI: 10.1080/14789450.2023.2176757 -
Nature Communications Oct 2022Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based...
Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.
Topics: Humans; Leukemia, Lymphocytic, Chronic, B-Cell; Proteomics; Proteogenomics; Proteome; Mutation; Receptors, Antigen, B-Cell
PubMed: 36266272
DOI: 10.1038/s41467-022-33385-8 -
Journal For Immunotherapy of Cancer Oct 2023Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that... (Review)
Review
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
Topics: Humans; Histocompatibility Antigens Class I; Mass Spectrometry; Antigens, Neoplasm; Peptides; HLA Antigens; Neoplasms; Histocompatibility Antigens Class II; RNA; DNA
PubMed: 37899131
DOI: 10.1136/jitc-2023-007073 -
Frontiers in Aging 2023Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants,... (Review)
Review
Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.
PubMed: 37168844
DOI: 10.3389/fragi.2023.1191993