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      From whole-mount to single-cell spatial assessment of gene expression in 3D

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          Abstract

          Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.

          Abstract

          In this Review, Lisa Waylen and colleagues provide an overview of techniques used for spatial resolution of gene expression in a tissue or organ. They discuss the advantages, disadvantages and future directions of current methods and illustrate how spatial transcriptomics has impacted our understanding of biology.

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          Comprehensive Integration of Single-Cell Data

          Tim Stuart, Andrew Butler, Paul Hoffman … (2019)
          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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            Fast, sensitive, and accurate integration of single cell data with Harmony

            Ilya Korsunsky, Nghia Millard, Jean Fan … (2019)
            The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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              Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

              Evan Z. Macosko, Anindita Basu, Rahul Satija … (2015)
              Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
              Article 0 comments Cited 1642 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Mirana Ramialison:
                ORCID: http://orcid.org/0000-0002-9037-0886
                mirana.ramialison@monash.edu
                Journal
                Journal ID (nlm-ta): Commun Biol
                Journal ID (iso-abbrev): Commun Biol
                Title: Communications Biology
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2399-3642
                Publication date (Electronic): 23 October 2020
                Publication date PMC-release: 23 October 2020
                Publication date Collection: 2020
                Volume: 3
                Electronic Location Identifier: 602
                Affiliations
                [1 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Australian Regenerative Medicine Institute and Systems Biology Institute, , Monash University, ; Clayton, VIC Australia
                [2 ]GRID grid.1058.c, ISNI 0000 0000 9442 535X, Transcriptomics and Bioinformatics Group, , Murdoch Children’s Research Institute, ; Parkville, VIC Australia
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Single Cell Core Laboratory, , Harvard Medical School, Department of System Biology, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0002-9037-0886
                Article
                Publisher ID: 1341
                DOI: 10.1038/s42003-020-01341-1
                PMC ID: 7584572
                PubMed ID: 33097816
                SO-VID: 4d51dd70-f7c1-4e0a-a802-cac218a4fcaa
                Copyright © © The Author(s) 2020
                License:

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 23 March 2020
                Date accepted : 10 September 2020
                Categories
                Subject: Review Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                Keywords: transcriptomics,data acquisition
                Data availability:
                Keywords: transcriptomics, data acquisition

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