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      Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

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          Abstract

          Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics techniques have been combined to analyze plant development. These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level, enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages. In this review, we present an overview of significant breakthroughs in spatial multi-omics in plants, and we discuss how these approaches may soon play essential roles in plant research.

          Abstract

          This review provides an overview of recent major advances in plant single-cell transcriptomics, single-cell epigenomics, and spatial multi-omics and discusses applications of these techniques in plant research and potential future developments in plant spatial multi-omics.

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          Most cited references133

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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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            Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

            Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.
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              ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide.

              This unit describes Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq), a method for mapping chromatin accessibility genome-wide. This method probes DNA accessibility with hyperactive Tn5 transposase, which inserts sequencing adapters into accessible regions of chromatin. Sequencing reads can then be used to infer regions of increased accessibility, as well as to map regions of transcription-factor binding and nucleosome position. The method is a fast and sensitive alternative to DNase-seq for assaying chromatin accessibility genome-wide, or to MNase-seq for assaying nucleosome positions in accessible regions of the genome.
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                Author and article information

                Contributors
                Journal
                Plant Commun
                Plant Commun
                Plant Communications
                Elsevier
                2590-3462
                20 December 2022
                08 May 2023
                20 December 2022
                : 4
                : 3
                : 100508
                Affiliations
                [1 ]State Key Laboratory of Cotton Biology, State Key Laboratory of Crop Stress Adaptation and Improvement, Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, 85 Minglun Street, Kaifeng 475001, P.R. China
                Author notes
                []Corresponding author sunxuwussd@ 123456sina.com
                [2]

                These authors contributed equally to this article.

                Article
                S2590-3462(22)00355-8 100508
                10.1016/j.xplc.2022.100508
                10203395
                36540021
                2fe305bd-f97c-483d-8ac5-9d2fed869f92
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 10 July 2022
                : 9 November 2022
                : 16 December 2022
                Categories
                Review Article

                mass spectrometry imaging,plants,single-cell transcriptomics,spatial transcriptomics,spatial metabolomics

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