Rewiring of the cellular and inter-cellular landscape of the human colon during ulcerative colitis

Cs, Smillie; Biton, Moshe; Ordovás-Montañés, José; Sullivan, KM; Burgin, Grace; Db, Graham; Rh, Herbst; Rogel, Noga; Slyper, Michal; Waldman, Julia; M, Sud; Andrews, Elizabeth; Al, Haber; Vicković, Sanja; Lt, Nguyen; Ac, Villani; Hofree, Matan; Ea, Creasey; Huang, Hailiang; Rozenblatt-Rosen, Orit; Jj, Garber; Khalili, Hamed; An, Desch; Mj, Daly; An, Ananthakrishnan; Ak, Shalek; Rj, Xavier; Regev, Aviv

doi:10.1101/455451

Cited by 34 publications

(48 citation statements)

References 103 publications

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“…Pooled analysis and visualisation with Uniform Manifold Approximation and Projection (UMAP) of all four tissues from all donors revealed distinct clusters in the lymphoid and colonic tissues (Figure 1d & Supp Figure 1e). We identified 25 cell types and states in the intestinal lamina propria and mLN (Figure 1d & Supp Table 3 & Supp Table 4), consistent with the immune populations described in recent reports 18 . Among these were follicular and memory B cells, IgA + and IgG + plasma cells, effector and memory CD4 + T cells, T regulatory (Treg) cells, CD8 + T cells, γδ T cells, innate lymphoid cells (ILCs), natural killer (NK) cells, mast cells and myeloid cells (Figure 1d).…”

Section: Resultssupporting

confidence: 88%

Distinct microbial and immune niches of the human colon

James

Gomes

Elmentaite

et al. 2019

Preprint

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42Gastrointestinal microbiota and immune cells interact closely and display regional 43 specificity, but little is known about how these communities differ with location. Here, 44we simultaneously assess microbiota and single immune cells across the healthy, 45 adult human colon, with paired characterisation of immune cells in the mesenteric 46 lymph nodes, to delineate colonic immune niches at steady-state. We describe 47 distinct T helper cell activation and migration profiles along the colon and 48 characterise the transcriptional adaptation trajectory of T regulatory cells between 49 lymphoid tissue and colon. Finally, we show increasing B cell accumulation, clonal 50 expansion and mutational frequency from caecum to sigmoid colon, and link this to 51 the increasing number of reactive bacterial species. 52 53

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Section: Resultssupporting

confidence: 88%

Distinct microbial and immune niches of the human colon

James

Gomes

Elmentaite

et al. 2019

Preprint

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“…10X cannot guarantee the yield of cells, and cell number may fluctuate wildly among experiments. For example, 60-4,930 cells among 68 samples [45], and 1,052-7,247 cells among 25 samples [46] were obtained in two reports, respectively. The huge variability may come from tissue/cell types, inaccurate estimation of input cell number, or poor conditions and death of cells during experiments.…”

Section: Discussionmentioning

confidence: 99%

Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

Wang

Zhang

et al. 2019

Preprint

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Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45-cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

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“…To be able to do so, the model needs to capture features that distinguish weakly from strongly responding genes and cells. Building biological interpretations of these features, for instance, along the lines of Gharamani et al [18] or Way and Greene [52], could help in understanding the differences between cells that respond to certain drugs and cells that do not respond, which is often crucial for understanding patient response to drugs [53].…”

Section: Discussionmentioning

confidence: 99%

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Lotfollahi

Wolf

Theis

2018

Preprint

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Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (‘out-of-sample’) has yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

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Rewiring of the cellular and inter-cellular landscape of the human colon during ulcerative colitis

Abstract: The paper has been withdrawn owing to erroneous inclusion of confidential information relating to a third party.

Cited by 34 publications

References 103 publications

Distinct microbial and immune niches of the human colon

Distinct microbial and immune niches of the human colon

Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

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