Spatial reconstruction of single-cell gene expression

By Rahul Satija, J. A. Farrell, D. Gennert, A. Schier, Aviv Regev. 2015

5,883 citations recorded by the source corpus.

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

Subfields: Molecular Biology

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Citations in the corpus

Prioritizing network communities (2018, methodology): “However, when domain-speci ﬁ c supervised information is available, CR ANK can integrate this extra information to boost performance (Supplementary Notes 9 and 10).”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “html and one of SEURAT’s tutorials [2]: https://github.”
Clustering trees: a visualization for evaluating clusterings at multiple resolutions (2018, methodology): “We analyzed this dataset using the Seu-rat package [21], a commonly used toolkit for scRNA-seq analysis, following the instructions in their tutorial with the excep-tionofvaryingtheclusteringresolutionparameterfromzeroto5 (see Methods).”
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, background): “0, Satija Lab [29]) which enables the integrated processing of multi-modal (RNA, ADT, HTO) single cell datasets [31, 32].”
A Single-Cell Atlas of Cell Types, States, and Other Transcriptional Patterns from Nine Regions of the Adult Mouse Brain (2018, background): “…among cells of the same class presents formidable analytical challenges – particularly when these distinctions are to be made in systematic, inductive (unsupervised), data-driven ways (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic et al., 2016b).”
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain (2018, background): “Inference of Cell Types and States Using Independent Components Analysis The recognition of subtle variation among cells of the same class presents a formidable analytical challenge for unsupervised analysis (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic”
DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, methodology): “DoubletFinder begins by simulating artificial doublets and incorporating these ‘‘cells’’ into existing scRNAseq data that has been processed using the popular ‘‘Seurat’’ analysis pipeline (Box 1; Satija et al., 2015; Butler et al., 2018).”
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions (2019, methodology): “To this end, we computed the cell-to-cell similarity matrices inferred by Seurat ’ s [12] PCA-based approach and by a diffusion strategy as implemented in MAGIC [25].”
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (2021, positive): “As a summary of single-cell cell cycle states, we performed a Uniform Manifold Approximation and Projection (UMAP) dimension reduction based on the expression n=199 known cell cycle genes [obtained from Seurat (Satija et al., 2015) and (Adamson et al.”