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Nature Reviews. Genetics Aug 2023Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data... (Review)
Review
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
Topics: Proteomics; Gene Expression Profiling; Single-Cell Analysis
PubMed: 37002403
DOI: 10.1038/s41576-023-00586-w -
Molecular & Cellular Proteomics : MCP Jun 2023The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease.... (Review)
Review
The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.
Topics: Humans; Genomics; Proteomics; Multiomics; Metabolomics; Neoplasms
PubMed: 37119971
DOI: 10.1016/j.mcpro.2023.100561 -
Cancer Cell Aug 2023The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics... (Review)
Review
The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.
Topics: Humans; Proteogenomics; Proteomics; Genomics; Neoplasms; Gene Expression Profiling
PubMed: 37582339
DOI: 10.1016/j.ccell.2023.06.009 -
Science Translational Medicine Jul 2023Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics...
Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.
Topics: Humans; Proteogenomics; Proteomics; Precision Medicine; Liver Neoplasms; Organoids
PubMed: 37494474
DOI: 10.1126/scitranslmed.adg3358 -
Cell Feb 2024Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune...
Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.
Topics: Humans; Combined Modality Therapy; Genomics; Neoplasms; Proteogenomics; Proteomics; Tumor Escape
PubMed: 38359819
DOI: 10.1016/j.cell.2024.01.027 -
Experimental & Molecular Medicine Aug 2023Ferroptosis is a form of regulated cell death characterized by iron-dependent lipid peroxidation. This process contributes to cellular and tissue damage in various human... (Review)
Review
Ferroptosis is a form of regulated cell death characterized by iron-dependent lipid peroxidation. This process contributes to cellular and tissue damage in various human diseases, such as cardiovascular diseases, neurodegeneration, liver disease, and cancer. Although polyunsaturated fatty acids (PUFAs) in membrane phospholipids are preferentially oxidized, saturated/monounsaturated fatty acids (SFAs/MUFAs) also influence lipid peroxidation and ferroptosis. In this review, we first explain how cells differentially synthesize SFA/MUFAs and PUFAs and how they control fatty acid pools via fatty acid uptake and β-oxidation, impacting ferroptosis. Furthermore, we discuss how fatty acids are stored in different lipids, such as diacyl or ether phospholipids with different head groups; triglycerides; and cholesterols. Moreover, we explain how these fatty acids are released from these molecules. In summary, we provide an integrated view of the diverse and dynamic metabolic processes in the context of ferroptosis by revisiting lipidomic studies. Thus, this review contributes to the development of therapeutic strategies for ferroptosis-related diseases.
Topics: Humans; Lipid Metabolism; Ferroptosis; Lipidomics; Fatty Acids; Biological Transport
PubMed: 37612411
DOI: 10.1038/s12276-023-01077-y -
Mass Spectrometry Reviews 2024Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it... (Review)
Review
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
Topics: Humans; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Artificial Intelligence; Metabolomics; Neoplasms; Lipidomics
PubMed: 36065601
DOI: 10.1002/mas.21804 -
Nature Methods Jun 2023The incorporation of light-responsive domains into engineered proteins has enabled control of protein localization, interactions and function with light. We integrated...
The incorporation of light-responsive domains into engineered proteins has enabled control of protein localization, interactions and function with light. We integrated optogenetic control into proximity labeling, a cornerstone technique for high-resolution proteomic mapping of organelles and interactomes in living cells. Through structure-guided screening and directed evolution, we installed the light-sensitive LOV domain into the proximity labeling enzyme TurboID to rapidly and reversibly control its labeling activity with low-power blue light. 'LOV-Turbo' works in multiple contexts and dramatically reduces background in biotin-rich environments such as neurons. We used LOV-Turbo for pulse-chase labeling to discover proteins that traffic between endoplasmic reticulum, nuclear and mitochondrial compartments under cellular stress. We also showed that instead of external light, LOV-Turbo can be activated by bioluminescence resonance energy transfer from luciferase, enabling interaction-dependent proximity labeling. Overall, LOV-Turbo increases the spatial and temporal precision of proximity labeling, expanding the scope of experimental questions that can be addressed with proximity labeling.
Topics: Proteomics; Mitochondria; Endoplasmic Reticulum; Biotin
PubMed: 37188954
DOI: 10.1038/s41592-023-01880-5 -
Cell Mar 2024The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge...
The repertoire of modifications to bile acids and related steroidal lipids by host and microbial metabolism remains incompletely characterized. To address this knowledge gap, we created a reusable resource of tandem mass spectrometry (MS/MS) spectra by filtering 1.2 billion publicly available MS/MS spectra for bile-acid-selective ion patterns. Thousands of modifications are distributed throughout animal and human bodies as well as microbial cultures. We employed this MS/MS library to identify polyamine bile amidates, prevalent in carnivores. They are present in humans, and their levels alter with a diet change from a Mediterranean to a typical American diet. This work highlights the existence of many more bile acid modifications than previously recognized and the value of leveraging public large-scale untargeted metabolomics data to discover metabolites. The availability of a modification-centric bile acid MS/MS library will inform future studies investigating bile acid roles in health and disease.
Topics: Animals; Humans; Bile Acids and Salts; Metabolomics; Polyamines; Tandem Mass Spectrometry; Gastrointestinal Microbiome; Databases, Chemical
PubMed: 38471500
DOI: 10.1016/j.cell.2024.02.019 -
Cell Reports. Medicine Sep 2023We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with...
We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.
Topics: Humans; Proteogenomics; Deep Learning; Neoplasms; Proteomics; Machine Learning
PubMed: 37582371
DOI: 10.1016/j.xcrm.2023.101173