Cluster similarity spectrum integration of single-cell genomics data

He, Zhisong; Brazovskaja, Agnieska; Ebert, Sebastian; Camp, J. Gray; Treutlein, Barbara

doi:10.1101/2020.02.27.968560

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…We note that for some of the time points, we have sequenced multiple organs from the same human specimens ( Figure S3C). We integrated all of the developing human scRNA-seq data using Cluster Similarity Spectrum (CSS) [39] to remove confounding effects of random or technical differences between samples. High-level clustering resolved multiple major cell classes, including epithelial, mesenchymal, immune, endothelial, neuronal, and erythroid populations ( Figures 2B, 2C and S3A-S3D).…”

Section: R a F Tmentioning

confidence: 99%

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An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine

Kilik

Holloway

et al. 2020

Preprint

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Human intestinal organoids (HIOs) generated from pluripotent stem cells provide extraordinary opportunities to explore development and disease. Here, we generate a single-cell transcriptome reference atlas from HIOs and from multiple developing human organs to quantify the specificity of HIO cell fate acquisition, and to explore alternative fates. We identify epithelium-mesenchyme interactions, transcriptional regulators involved in cell fate specification, and stem cell maturation features in the primary tissue that are recapitulated in HIOs. We use an HIO time course to reconstruct the molecular dynamics of intestinal stem cell emergence, as well as the specification of multiple mesenchyme subtypes. We find that the intestinal master regulator CDX2 correlates with distinct phases of epithelial and mesenchymal development, and CDX2 deletion perturbs the differentiation of both intestinal epithelium and mesenchyme. Collectively our data provides a comprehensive and quantitative assessment of HIO development, and illuminates the molecular machinery underlying endodermal and mesodermal cell fate specification.

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Section: R a F Tmentioning

confidence: 99%

“…To integrate data of different samples, Cluster Similarity Spectrum (CSS) [39] was calculated as described. In brief, cells from each organoid were subsetted, and Louvain clustering (with resolution 0.6), implemented in Seurat, was applied based on the pre-calculated top 20 PCs.…”

Section: R a F Tmentioning

confidence: 99%

An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine

Kilik

Holloway

et al. 2020

Preprint

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“…The single-cell RNA-seq data using inDrop and the single-cell RNA-seq data of the fixation experiment were deposited on ArrayExpress with the accession number E-MTAB-9473 [32], and Mendeley Data with DOI: https://doi.org/10.17632/ 3kthhpw2pd [33]. The source codes were deposited on GitHub (https://github.com/quadbiolab/simspec) [15] and Mendeley Data with DOI: https://doi.org/10.17632/3kthhpw2pd [33] under license CCBY 4.0.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…We show that CSS also allows projection of new data, either scRNA-seq or scATAC-seq, to the CSS-represented scRNA-seq reference atlas for visualization and cell type identity prediction. The CSS codes are available at https://github.com/quadbiolab/simspec [15].…”

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CSS: cluster similarity spectrum integration of single-cell genomics data

Brazovskaja

Ebert

et al. 2020

Genome Biol

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It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, cluster similarity spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other singlecell transcriptomic data, and to integrate data across experimental conditions and human individuals. Background Recent advances in molecular, engineering, and sequencing technologies have enabled the high-throughput measurement of transcriptomes and other genomic features in thousands of single cells in a single experiment [1-4]. Single-cell RNA sequencing (scRNA-seq) greatly enhances our capacity to resolve the heterogeneity of cell types and cell states in biological samples, as well as to understand how systems change during dynamic processes such as development. However, current scRNA-seq technologies only provide molecular snapshots of a limited number of measured samples at a time. Joint analysis on many samples across multiple experiments and conditions is often required. In such a scenario, the biological variation of interest is usually confounded by other factors, including sample sources and experimental batches. This is particularly challenging for developing systems, where cell states coexist at different points along various differentiation trajectories such as mature cell types as well as intermediate states. Several computational integration methods, including but not limited to MNN [5], Seurat [6, 7], Harmony [8], LIGER [9], Scanorama [10], and Reference Similarity Spectrum (RSS) [11, 12], have been developed to address some of these issues. Among them, MNN identifies mutual nearest neighbors between two data sets and derives cell-specific batch-correction vectors for integration. Seurat corrects for batch effects by introducing an anchoring strategy, with anchors between samples

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Lineage recording reveals dynamics of cerebral organoid regionalization

Gerber

Maynard

et al. 2020

Preprint

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Diverse regions develop within cerebral organoids generated from human induced pluripotent stem cells (iPSCs), however it has been a challenge to understand the lineage dynamics associated with brain regionalization. Here we establish an inducible lineage recording system that couples reporter barcodes, inducible CRISPR/Cas9 scarring, and single-cell transcriptomics to analyze lineage relationships during cerebral organoid development. We infer fate-mapped whole organoid phylogenies over a scarring time course, and reconstruct progenitor-neuron lineage trees within microdissected cerebral organoid regions. We observe increased fate restriction over time, and find that iPSC clones used to initiate organoids tend to accumulate in distinct brain regions. We use lineage-coupled spatial transcriptomics to resolve lineage locations as well as confirm clonal enrichment in distinctly patterned brain regions. Using long term 4-D light sheet microscopy to temporally track nuclei in developing cerebral organoids, we link brain region clone enrichment to positions in the neuroectoderm, followed by local proliferation with limited migration during neuroepithelial formation. Our data sheds light on how lineages are established during brain organoid regionalization, and our techniques can be adapted in any iPSC-derived cell culture system to dissect lineage alterations during perturbation or in patient-specific models of disease.

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Cluster similarity spectrum integration of single-cell genomics data

Cited by 3 publications

References 27 publications

An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine

An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine

CSS: cluster similarity spectrum integration of single-cell genomics data

Lineage recording reveals dynamics of cerebral organoid regionalization

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