High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Achim, Kaia; Pettit, Jean-Baptiste; Saraiva, Luís R.; Gavriouchkina, Daria; Larsson, Tomas; Arendt, Detlev; Marioni, John C.

doi:10.1038/nbt.3209

Cited by 386 publications

(377 citation statements)

References 48 publications

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“…This capability has allowed researchers to address exciting questions ranging from the response of single immune cells to antigen (4)(5)(6) to the number of transcriptionally distinct cell types and the cellular heterogeneity within complex tissues (7)(8)(9)(10)(11)(12)(13). Recent advances in the automated handling of individual cells and the sequencing library preparation for these cells have substantially increased the number of cells that can be routinely characterized with these approaches; notably, state-of-the-art droplet-based RNA-seq approaches provide the ability to quantify the transcriptome of tens of thousands or more cells (14,15).…”

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confidence: 99%

High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization

Moffitt

Hao

Wang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

443

483

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Image-based approaches to single-cell transcriptomics, in which RNA species are identified and counted in situ via imaging, have emerged as a powerful complement to single-cell methods based on RNA sequencing of dissociated cells. These image-based approaches naturally preserve the native spatial context of RNAs within a cell and the organization of cells within tissue, which are important for addressing many biological questions. However, the throughput of these imagebased approaches is relatively low. Here we report advances that lead to a drastic increase in the measurement throughput of multiplexed error-robust fluorescence in situ hybridization (MERFISH), an imagebased approach to single-cell transcriptomics. In MERFISH, RNAs are identified via a combinatorial labeling approach that encodes RNA species with error-robust barcodes followed by sequential rounds of single-molecule fluorescence in situ hybridization (smFISH) to read out these barcodes. Here we increase the throughput of MERFISH by two orders of magnitude through a combination of improvements, including using chemical cleavage instead of photobleaching to remove fluorescent signals between consecutive rounds of smFISH imaging, increasing the imaging field of view, and using multicolor imaging. With these improvements, we performed RNA profiling in more than 100,000 human cells, with as many as 40,000 cells measured in a single 18-h measurement. This throughput should substantially extend the range of biological questions that can be addressed by MERFISH.single-cell analysis | fluorescence | in situ hybridization | transcriptomics | multiplexed imaging

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mentioning

confidence: 99%

High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization

Moffitt

Hao

Wang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

443

483

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“…On the other hand, Satija et al used 851 single-cells dissociated from zebrafish embryos and inferred their spatial positions by combining gene expression patterns and in situ hybridization patterns obtained from a database [6]. Similarly, Achim et al also proposed another in situ hybridization based reconstruction method and applied it to 213 single-cell RNA-seq data of a developing brain of a marine annelid (P. dumerilii) [7]. Nevertheless, both approaches require in situ hybridization data as a reference map for accurate mapping of cells, which limits applicability of the approaches.…”

Section: Introductionmentioning

confidence: 99%

Development of 3D Tissue Reconstruction Method from Single-cell RNA-seq Data

Mori

Yamane

Kobayashi

et al. 2017

Genomics Comput Biol

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In silico three-dimensional (3D) reconstruction of tissues/organs based on single-cell profiles is required to comprehensively understand how individual cells are organized in actual tissues/organs. Although several tissue reconstruction methods have been developed, they are still insufficient to map cells on the original tissues in terms of both scale and quality. In this study, we aim to develop a novel informatics approach which can reconstruct whole and various tissues/organs in silico. As the first step of this project, we conducted single-cell transcriptome analysis of 38 individual cells obtained from two mouse blastocysts (E3.5d) and tried to reconstruct blastocyst structures in 3D. In reconstruction step, each cell position is estimated by 3D principal component analysis and expression profiles of cell adhesion genes as well as other marker genes. In addition, we also proposed a reconstruction method without using marker gene information. The resulting reconstructed blastocyst structures implied an indirect relationship between the genes of Myh9 and Oct4.

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“…Moreover, at the organ level, it enables for the cellular deconstruction of morphological character development, which will help to resolve confounding organ cell heterogeneity that might differ from species to species [54]. Spatial transcriptome information lost during organ dissociation can then be recovered in silico, down to cellular resolution, by remapping single-cell RNA-seq data onto grids of known marker gene in situ hybridization patterns [89,90]. Such high-throughput in vivo approaches will benefit from complementary cell culture experiments, where the controlled parameters of an in vitro environment can be exploited.…”

Section: Homology Assessment: Gene Expression and Regulatory Strategiesmentioning

confidence: 99%

Deep homology in the age of next-generation sequencing

Tschopp

Tabin

2017

Phil. Trans. R. Soc. B

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One contribution of 17 to a theme issue 'Evodevo in the genomics era, and the origins of morphological diversity'. The principle of homology is central to conceptualizing the comparative aspects of morphological evolution. The distinctions between homologous or non-homologous structures have become blurred, however, as modern evolutionary developmental biology (evo-devo) has shown that novel features often result from modification of pre-existing developmental modules, rather than arising completely de novo. With this realization in mind, the term 'deep homology' was coined, in recognition of the remarkably conserved gene expression during the development of certain animal structures that would not be considered homologous by previous strict definitions. At its core, it can help to formulate an understanding of deeper layers of ontogenetic conservation for anatomical features that lack any clear phylogenetic continuity. Here, we review deep homology and related concepts in the context of a gene expression-based homology discussion. We then focus on how these conceptual frameworks have profited from the recent rise of high-throughput nextgeneration sequencing. These techniques have greatly expanded the range of organisms amenable to such studies. Moreover, they helped to elevate the traditional gene-by-gene comparison to a transcriptome-wide level. We will end with an outlook on the next challenges in the field and how technological advances might provide exciting new strategies to tackle these questions.This article is part of the themed issue 'Evo-devo in the genomics era, and the origins of morphological diversity'.

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High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Cited by 386 publications

References 48 publications

High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization

High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization

Development of 3D Tissue Reconstruction Method from Single-cell RNA-seq Data

Deep homology in the age of next-generation sequencing

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