Spatial reconstruction of single-cell gene expression data

Satija, Rahul; Farrell, Jeffrey A.; Gennert, David; Schier, Alexander F.; Regev, Aviv

doi:10.1038/nbt.3192

Cited by 4,494 publications

(4,322 citation statements)

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“…Clustering of cells was done using Seurat (v1.4.0.8) 66 , in a step-wise manner. We initially was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”

Section: Cell Clusteringmentioning

confidence: 99%

The immune cell landscape in kidneys of lupus nephritis patients

Arazi

Rao

Berthier

et al. 2018

Preprint

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Lupus nephritis is a potentially fatal autoimmune disease, whose current treatment is ineffective and often toxic. To gain insights into disease mechanisms, we analyzed kidney samples from lupus nephritis patients and healthy controls using single-cell RNA-seq. Our analysis revealed 21 subsets of leukocytes active in disease, including multiple populations of myeloid, T, NK and B cells, demonstrating both pro-inflammatory and resolving responses. We found evidence of local activation of B cells correlated with an age-associated B cell signature, and of progressive stages of monocyte differentiation within the kidney. A clear interferon response was observed in most cells. Two chemokine receptors, CXCR4 and CX3CR1, were broadly expressed, pointing to potential therapeutic targets. Gene expression of immune cells in urine and kidney was highly correlated, suggesting urine may be a surrogate for kidney biopsies. Our results provide a first comprehensive view of the complex network of leukocytes active in lupus nephritis kidneys.

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Section: Cell Clusteringmentioning

confidence: 99%

The immune cell landscape in kidneys of lupus nephritis patients

Arazi

Rao

Berthier

et al. 2018

Preprint

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“…However, each cell was mapped to only the surface of a sphere and it was still insufficient to map cells on the original tissue structure. 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].…”

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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Spatial reconstruction of single-cell gene expression data

Cited by 4,494 publications

References 50 publications

The immune cell landscape in kidneys of lupus nephritis patients

The immune cell landscape in kidneys of lupus nephritis patients

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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