Construction of a human cell landscape at single-cell level

Han, Xiaoping; Zhou, Ziyu; Fei, Lijiang; Sun, Huiyu; Wang, Renying; Chen, Yao; Chen, Haide; Wang, Jingjing; Tang, Huanna; Ge, Weifeng; Zhou, Yincong; Ye, Fang; Jiang, Mengmeng; Wu, Jinliang; Xiao, Yanyu; Jia, Xiaoning; Zhang, Tingyue; Ma, Xiaojie; Zhang, Qi; Bai, Xueli; Lai, Shih‐Wei; Yu, Chengxuan; Zhu, Lijun; Lin, Rui; Gao, Yuchi; Wang, Min; Wu, Yiqing; Zhang, Jianming; Zhan, Renya; Zhu, Saiyong; Hu, Hailan; Wang, Changchun; Chen, Ming; Huang, He; Liang, Tingbo; Chen, Jianghua; Wang, Weilin; Zhang, Dan; Guo, Guoji

doi:10.1038/s41586-020-2157-4

Cited by 849 publications

(965 citation statements)

References 65 publications

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“…The rise-then-fall in cell uncertainty agrees well with other reports from different cell differentiation systems [8][9][10][11][12]54,55 , suggesting that stem cells go through a transition state of high gene expression uncertainty before committing to a particular cell fate. The existence of a hill or barrier during the intermediate stage of cell differentiation has also been proposed in previous studies 14,31,56 .…”

Section: Discussionsupporting

confidence: 89%

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Gao

Gandrillon

Páldi

et al. 2020

Preprint

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We employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddington's epigenetic landscape, that is generalizable to every cell differentiation system. MAINCell differentiation is the process through which unspecialized stem cells become more specialized. Because of its importance in development, cellular repair, and organismal homeostasis, the molecular mechanisms of cell differentiation has been the subject of intense scrutiny. Since roughly 50 years ago -along with the promulgation of the central dogma of molecular biology by Francis Crick and the characterization of the lactose operon by François Jacob and Jacques Monod -the existence of a genetic program has become a prevailing explanation for the cell differentiation process. Although the details were originally not defined, at least not formally, such a genetic program purports a constellation of master genes (i.e., transcription factors) that orchestrate the transcription of downstream target genes in a precise spatiotemporal fashion, resulting in long-lasting alterations in the gene expression patterns 1-3 . A notable experimental evidence substantiating this view is the overexpression of myoD inducing a myogenic phenotype in seemingly naive cells 4 . Over the past few decades, the repertoire of such master genes across numerous stem cell systems, such as Nanog, Oct4, Sox2, BATF and MyoD, begin to coalesce 5-7 .Recent advances in single-cell technologies has revealed new aspects of the cell differentiation that are incompatible with the idea of ordered and programmed (i.e., deterministic) gene expression. More specifically, single-cell data paint a stochastic differentiation process that increases cell-to-cell variability of gene expression.

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Section: Discussionsupporting

confidence: 89%

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Gao

Gandrillon

Páldi

et al. 2020

Preprint

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“…To begin addressing these gaps, we curated a list of 28 human genes referred to as SCARFs for SARS-CoV-2 and Coronavirus-Associated Receptors and Factors ( Figure 1A and Table S2) and surveyed their basal RNA expression levels across a wide range of healthy tissues. Specifically, we mined publicly available scRNA-seq datasets using consistent normalization procedures to integrate and compare the dynamics of SCARF expression in human pre-implantation embryos (Yan et al, 2013), at the maternal-fetal interface (Vento-Tormo et al, 2018), in male and female gonads (Sohni et al, 2019;Wagner et al, 2020) and 14 other adult tissues (Han et al, 2020), as well as nasal brushing from young and old healthy donors (Deprez et al, 2019;Garcıá et al, 2019;Vieira Braga et al, 2019). Additionally, we use bulk transcriptomics for four organs of interest (lung, kidney, liver, heart) from human, chimpanzee, and macaque (Blake et al, 2020) to examine the conservation of SCARF expression across primates.…”

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confidence: 99%

A single-cell RNA expression map of human coronavirus entry factors

Singh

Bansal

Feschotte

2020

Preprint

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To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell RNAsequencing data from a wide range of healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Among adult organs, enterocytes and goblet cells of the small intestine and colon, kidney proximal tubule cells, and gallbladder basal cells appear most permissive to SARS-CoV-2, consistent with clinical data. Our analysis also suggests alternate entry paths for SARS-CoV-2 infection of the lung, central nervous system, and heart. We predict spermatogonial cells and prostate endocrine cells, but not ovarian cells, to be highly permissive to SARS-CoV-2, suggesting male-specific vulnerabilities. Early stages of embryonic and placental development show a moderate risk of infection. The nasal epithelium looks like another battleground, characterized by high expression of both promoting and restricting factors and a potential age-dependent shift in SCARF expression. Lastly, SCARF expression appears broadly conserved across human, chimpanzee and macaque organs examined. Our study establishes an important resource for investigations of coronavirus biology and pathology.

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“…The advent of single-cell RNA sequencing (scRNA-seq) has unlocked the cell transcriptome at cellular-level resolution, providing theoretically optimal resolution potential. Extensive single cell surveys of tissues in mice [41] or humans [42] provide a portrait of variation in gene expression of cell-types in different tissue contexts. Compared to bulk RNA-seq, however, scRNA-seq is a much newer technology with more unknown technical biases, is much more expensive, is more challenging to perform, and is less sensitive to detect lowly expressed genes and prone to sequencing error [43,44].…”

Section: Discussionmentioning

confidence: 99%

“…As future works, we will further explore our approach in its ability to model massive scale singlecell sequencing data including cross-species, multi-omics (e.g., simultaneously modeling singlecell RNA-seq, single-cell ATAC-seq, and single-cell methylation while accounting for platformdependent batch effects), multi-tissues, and multi-subjects. We will harness recently available human/mouse atlas data [41,42] and disease-focused data such as scRNA-seq in patients with Alzheimer's disease [6] and cancer patients tumors [45]. From methodological stand-point, like all of the neural network-based method, scGAN requires specification of the network architecture before training.…”

Section: Discussionmentioning

confidence: 99%

Deep feature extraction of single-cell transcriptomes by generative adversarial network

Bahrami

Maitra

Nagy

et al. 2020

Preprint

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Motivation: Single-cell RNA-sequencing (scRNA-seq) has opened the opportunities to dissect the heterogeneous cellular composition and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition design. Results: We present single-cell Generative Adversarial Network (scGAN). Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network, which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects. We demonstrate scGAN on three public scRNAseq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder. Availability: The code is available at https://github.com/li-lab-mcgill/singlecell-deepfeature Contact: yueli@cs.mcgill.ca IntroductionSingle-cell RNA sequencing (scRNA-seq) technologies profile the transcriptomes of individual cells rather than bulk samples [1,2]. The wide adoption of scRNA-seq technologies enables the investigations of the molecular footprints at the unprecedentedly high-resolution for a wide spectrum of human diseases including cancer [3], autoimmune diseases [4, 5], Alzheimer's disease [6], and major depressive disorder (MDD) [7]. However, single-cell data analysis still remains challenging due to confounding and nuisance factors, that manifest as individual variations or experimental biases such as different scRNA-seq technologies rather than biological variation. These confounding factors are often known as batch effects. Batch effects are the subsets of measurements that have different distributions because of being affected by laboratory conditions, reagent lots and personnel differences. [8]. The massive parallel sequencing [1] enable measurements with more than tens of thousands single-cell samples cross tens of human subjects in a single study (e.g., [3,6]) further underscores the importance of addressing subject-level demographic confounders such as age and sex. Currently, there is a lack of highly scalable and robust model that enables systematic analysis of large-scale datasets while accounting for various confounding batch effects. A number of methods have been developed for normalization, batch-effect correction, embedding, visualization and clustering of scRNA-seq gene expression profiles.[9] used mutual nearest neighbors (MNN) matching to account for batch effects. MNN operates on either the original space of the raw gene expression counts or the projected linear embedding space from the principal components analysis (PCA). However, MNN may be inadequate to model the non-linear effects known to exist in the scRNA-seq data [8]. Seurat [10] is another useful approach,...

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Construction of a human cell landscape at single-cell level

Cited by 849 publications

References 65 publications

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

A single-cell RNA expression map of human coronavirus entry factors

Deep feature extraction of single-cell transcriptomes by generative adversarial network

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