Multi-Omics Profiling for Health

Babu, M. Madan; Snyder, M

doi:10.1016/j.mcpro.2023.100561

Cited by 103 publications

(25 citation statements)

References 154 publications

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“…Pathology is the core medical discipline to bring proteomics to patients via a new class of “postgenomic” protein-directed assays for early disease detection, risk prediction, choice of therapy and combination therapies, and surveillance. Combined with improvements in instrument design, orthogonal , and multiomic approaches, − rapid progress in advanced computational capabilities including AI and machine learning ,− can now facilitate the development of personalized/precision medicine for the improvement of human health. ,, …”

Section: Pathology Pillarmentioning

confidence: 99%

“…Combined with improvements in instrument design, 94 orthogonal 95,96 and multiomic approaches, 97−100 rapid progress in advanced computational capabilities including AI and machine learning 76,101−105 can now facilitate the development of personalized/precision medicine 106 for the improvement of human health. 101,106,107 The U.S. National Cancer Institute's Clinical Proteomics Tumor Analysis Consortium (CPTAC) and International Cancer Proteomics Consortium (ICPC) have set standards, established procedures, provided reagents, and populated massive data sets on more than a dozen major types of cancers (see Cancer B-D/HPP report). Proteomics has also advanced insights into COVID-19, 85,93,108 cardiovascular, 76,77 and neurological disorders, and diseases of the eye.…”

Section: ■ Pathology Pillarmentioning

confidence: 99%

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

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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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.

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Section: Pathology Pillarmentioning

confidence: 99%

Section: ■ Pathology Pillarmentioning

confidence: 99%

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

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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“…Today, the study of complex diseases and biological processes at the systems level increasingly relies on the use of multiplex and high-throughput experiments. One particular experimental design, known as "profiling," has emerged as a powerful approach to characterize biological functions, classify patient subpopulations, and identify promising therapeutic targets [1][2][3] . A typical profiling experiment measures hundreds to tens of thousands of features of a biological system simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A versatile information retrieval framework for evaluating profile strength and similarity

Kalinin,

Arevalo,

Serrano

et al. 2024

Preprint

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In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.

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“…7,8 In recent years, spatiotemporal multi-omics technology has expanded our understanding of the interactions between genes, proteins, and metabolites at the single-cell level. 9,10 Additionally, lipids, which make up the lipidome in biological samples, 11 serve as substrates for post-translational modification of proteins, contribute to the structure of cell membranes, and play a role in energy metabolism. 12,13 The intricate interplay between lipids and signaling pathways highlights their potential as biomarkers for screening and development of novel therapeutics.…”

Section: Introductionmentioning

confidence: 99%