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      Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

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          Abstract

          Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

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          Most cited references314

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                230198401@aa.seu.edu.cn
                shha@sdfmu.edu.cn
                xiaokunli@wmu.edu.cn
                wangzhouguang@wmu.edu.cn
                Journal
                Mil Med Res
                Mil Med Res
                Military Medical Research
                BioMed Central (London )
                2095-7467
                2054-9369
                27 May 2024
                27 May 2024
                2024
                : 11
                : 31
                Affiliations
                [1 ]Affiliated Cixi Hospital, Wenzhou Medical University, ( https://ror.org/00rd5t069) Ningbo, 315300 Zhejiang China
                [2 ]Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, ( https://ror.org/00rd5t069) Wenzhou, 325035 Zhejiang China
                [3 ]National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, ( https://ror.org/00rd5t069) Wenzhou, 325035 Zhejiang China
                [4 ]GRID grid.263826.b, ISNI 0000 0004 1761 0489, State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, , Southeast University, ; Nanjing, 210096 China
                [5 ]School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, ( https://ror.org/01vjw4z39) Guangzhou, 510515 China
                [6 ]Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, ( https://ror.org/05jb9pq57) Taian, 271000 Shandong China
                [7 ]Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, ( https://ror.org/0011qv509) Memphis, TN 38163 United States
                [8 ]Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, ( https://ror.org/00rd5t069) Wenzhou, 325035 Zhejiang China
                [9 ]Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, ( https://ror.org/00rd5t069) Lishui, 323000 Zhejiang China
                [10 ]School and Hospital of Stomatology, Wenzhou Medical University, ( https://ror.org/00rd5t069) Wenzhou, 324025 Zhejiang China
                [11 ]Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, ( https://ror.org/01tgyzw49) Singapore, 119074 Singapore
                [12 ]Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, ( https://ror.org/01tgyzw49) Singapore, 117599 Singapore
                [13 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, , National University of Singapore, ; Singapore, 117597 Singapore
                [14 ]Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), ( https://ror.org/04xpsrn94) Singapore, 138673 Singapore
                Author information
                http://orcid.org/0000-0001-9903-0362
                Article
                537
                10.1186/s40779-024-00537-4
                11129507
                38797843
                b044f5c6-8178-44cf-a427-4aed3a356aa2
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 December 2023
                : 7 May 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82271629
                Award ID: 82301790
                Award Recipient :
                Funded by: Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang
                Award ID: 2023R01002
                Award Recipient :
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                © People´s Military Medical Press 2024

                spatiotemporal multi-omics,aging and regeneration,cellular interactions,innovative therapeutic strategies

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