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

Mori, Takahide; Yamane, Junko; Kobayashi, Kensuke; Taniyama, Nobuko; Tano, Takanori; Fujibuchi, Wataru

doi:10.18547/gcb.2017.vol3.iss1.e53

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In both cases, publicly available in situ gene expression databases could be used for spatial inference of a profiled cell's spatial location, with mapping resolution determined by the number of known in situ patterns, data quality, and tissue structural organization. Similar approaches have since been successfully applied in the mouse blastocyst, mouse brain, Drosophila embryo, and mammalian liver (Habib et al, 2016;Halpern et al, 2017;Karaiskos et al, 2017;Mori et al, 2017;Stuart et al, 2019). Remarkably, these methods can even be extended to operate fully unsupervised in a de novo setting (that is, without any in situ reference data) (Nitzan et al, 2018) assuming spatial ''smoothness'' in gene expression space.…”

Section: Figure 2 Hierarchical Organization Of Coordinate Systemsmentioning

confidence: 99%

Toward a Common Coordinate Framework for the Human Body

Rood

Stuart

Ghazanfar

et al. 2019

Cell

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Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.

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Section: Figure 2 Hierarchical Organization Of Coordinate Systemsmentioning

confidence: 99%

Toward a Common Coordinate Framework for the Human Body

Rood

Stuart

Ghazanfar

et al. 2019

Cell

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“…scRNA-seq-based maps have revealed cell-type-specific functions in the liver 26 , blastocyst 27 , and growth plate 28 . The expression of cell adhesion genes and specific gene functions defined by Gene Ontology (GO) terms is being used to develop new tools for single-cell 3D transcriptome analysis that enhance spatial prediction 29 , 30 . Although the identification of cell types in a spatial context is expected to yield more information relevant to the in vivo environment, these cutting-edge approaches are still at the elementary stage and need further improvement before they can be widely used.…”

Section: Challenge Of Human Cell-type Classifications In the Hcamentioning

confidence: 99%

Human Cell Atlas and cell-type authentication for regenerative medicine

et al. 2020

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In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.

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“…Thus, although current principal component analysis (PCA)-based methods are used for 3D visualization, an ab initio approach that does not depend on the spatial information of marker genes obtained by in situ hybridization is promising for 3D reconstruction. Previously, we reported a 3D reconstruction method for mouse blastocyst consisting of two cell types that successfully enhances spatial prediction by combining PCA and cell type-specific marker genes coding for cell adhesion molecules 23,24 .…”

Section: Introductionmentioning

confidence: 99%

Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering

Mori

Takaoka

Yamane

et al. 2019

Sci Rep

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Deciphering the key mechanisms of morphogenesis during embryonic development is crucial to understanding the guiding principles of the body plan and promote applications in biomedical research fields. Although several computational tissue reconstruction methods using cellular gene expression data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we report SPRESSO, a new in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, to estimate the spatial domains of cells in tissues or organs from only their gene expression profiles. With only five gene sets defined by Gene Ontology (GO), we successfully demonstrated the reconstruction of a four-domain structure of mid-gastrula mouse embryo (E7.0) with high reproducibility (success rate = 99%). Interestingly, the five GOs contain 20 genes, most of which are related to differentiation and morphogenesis, such as activin A receptor and Wnt family member genes. Further analysis indicated that Id2 is the most influential gene contributing to the reconstruction. SPRESSO may provide novel and better insights on the mechanisms of 3D structure formation of living tissues via informative genes playing a role as spatial discriminators.

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

Cited by 3 publications

References 12 publications

Toward a Common Coordinate Framework for the Human Body

Toward a Common Coordinate Framework for the Human Body

Human Cell Atlas and cell-type authentication for regenerative medicine

Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering

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