A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

M, Baron; Veres, Adrian; Wolock, Samuel L.; Faust, Aubrey L.; Gaujoux, Renaud; Vetere, Amedeo; Ryu, Jennifer Hyoje; Wagner, Bridget K.; Shen-Orr, Shai S.; Klein, Allon M.; Melton, Douglas A.; Yanai, Itai

doi:10.1016/j.cels.2016.08.011

Cited by 1,280 publications

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“…Application of k-nearest neighbor smoothing to scRNA-Seq data from human pancreatic islet tissue. All panels show data from the PANCREAS dataset, from a study by Baron et al (2016). Smoothing was performed using k = 15. a Principal component analysis (PCA) with (top) and without (bottom) smoothing.…”

Section: /33mentioning

confidence: 99%

“…Axis labels indicate the fraction of variance explained. Cell types were identified based on the smoothed data, using ad-hoc expression thresholds for the marker genes listed in Baron et al (2016). Beta cells were defined as having expression of INS ≥ 40,000 TPM (UMI-filtered transcripts per million); alpha cells, GCG ≥ 5,000 TPM; delta cells, SST ≥ 20,000 TPM; acinar cells, CPA1 ≥ 1,000 TPM.…”

Section: /33mentioning

confidence: 99%

“…c Heatmap providing a zoomed-in view of the area marked in blue in (b), with (left) and without (right) smoothing. d Expression of cell type-specific marker genes (Baron et al 2016) with (top) and without (bottom) smoothing. Cells are ordered as in (b).…”

Section: /33mentioning

confidence: 99%

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K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

Wagner¹,

Yao²,

Yanai³

2017

Preprint

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120

129

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High-throughput single-cell RNA-Seq (scRNA-Seq) methods can efficiently generate expression profiles for thousands of cells, and promise to enable the comprehensive molecular characterization of all cell types and states present in heterogeneous tissues. However, compared to bulk RNA-Seq, single-cell expression profiles are extremely noisy and only capture a fraction of transcripts present in the cell. Here, we propose an algorithm to smooth scRNA-Seq data, with the goal of significantly improving the signal-tonoise ratio of each profile, while largely preserving biological expression heterogeneity. The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on variance-stabilized and partially smoothed expression profiles, and then aggregating their transcript counts. On data from human pancreatic islet tissue and peripheral blood mononuclear cells, we show that smoothing greatly facilitates the identification of clusters of cells and co-expressed genes. Using simulated datasets that closely mimic real expression data, we show that our algorithm drastically improves upon the accuracy of other smoothing methods. Our work implies that there exists a quantitative relationship between the number of cells profiled and the potential accuracy with which individual cell types or states can be characterized, and helps unlock the full potential of scRNA-Seq to elucidate molecular processes in healthy and disease tissues. Reference implementations of our algorithm can be found at https://github.com/yanailab/knn-smoothing.

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Section: /33mentioning

confidence: 99%

Section: /33mentioning

confidence: 99%

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K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

Wagner¹,

Yao²,

Yanai³

2017

Preprint

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120

129

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“…However, other more complex organs such as the brain do not exhibit a corresponding number of 15 expressed genes despite the fact that they consist of a substantially greater number of distinct cell types 3,10, [14][15][16] . Moreover, recent animal studies have shown that many testis-enriched and evolutionarily-conserved genes are not required for male fertility in mice 17 .…”

Section: Main Textmentioning

confidence: 99%

Widespread transcriptional scanning in the testis modulates gene evolution rates

Xia

Yan

et al. 2018

Preprint

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Abstract:A long-standing question in molecular biology relates to why the testes express the largest number of genes relative to all other organs. Here, we report a detailed gene expression map of human spermatogenesis using single-cell RNA-Seq. Surprisingly, we found that 20 spermatogenesis-expressed genes contain significantly fewer germline mutations than unexpressed genes, with the lowest mutation rates on the transcribed DNA strands. These results suggest a model of 'transcriptional scanning' to reduce germline mutations by correcting DNA damage. This model also explains the rapid evolution in sensory-and immune-defense related genes, as well as in male reproduction genes. Collectively, our results indicate that widespread 25 expression in the testes achieves a dual mechanism for maintaining the DNA integrity of most genes, while selectively promoting variation of other genes.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/282129 doi: bioRxiv preprint first posted online Mar. 14, 2018; 2 Main Text:Human tissues and organs are distinguished by the genes that they express and those that they do not 1,2 . Tissues have transcriptomes of different complexities in terms of uniquely-expressed genes, as well as those genes expressed at differential levels [3][4][5][6] . One overarching goal in the life sciences is to characterize the specific transcriptomic signatures of all human tissues, and 5 ultimately each different cell type at the single-cell level 7 .In males, the testis is unique in comparison with somatic tissues in that it contains germ cells which pass the genetic information on to the next generation 8 . Interestingly, it has been known for many years that the testis stands out as having the most complex transcriptome with the highest number of expressed genes [9][10][11][12] . Widespread transcription in the testes has been 10 reported to account for an amazing expression of over 80% of all our protein-coding genes 10,11,13 , as well as across many other mammals 3,10 .Several hypotheses have been proposed to explain this observation. Widespread expression may represent a functional requirement for the gene-products in question 12 .However, other more complex organs such as the brain do not exhibit a corresponding number of 15 expressed genes despite the fact that they consist of a substantially greater number of distinct cell types 3,10,14-16 . Moreover, recent animal studies have shown that many testis-enriched and evolutionarily-conserved genes are not required for male fertility in mice 17 . A second hypothesis implicates leaky transcription during the massive chromatin remodeling that occurs throughout spermatogenesis 12,18,19 . However, this model predicts more expression during later stages of 20 spermatogenesis -when the genome is undergoing the most chromatin changes -contradicting ...

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“…For example, Baron et al has successfully implemented InDrops to compare the single cell transcriptome between mouse and human pancreas. 32 Briggs et al applied inDrops to compare motor neurons generated by two differentiation protocols and revealed that mature cell state can be reached via multiple differentiation paths. 33 …”

Section: Microfluidic Based Scrna-seqmentioning

confidence: 99%

The promise of single-cell RNA sequencing for kidney disease investigation

Humphreys

2017

Kidney International

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New techniques for single cell RNA sequencing (scRNA-seq) at high throughput are leading to profound new discoveries in biology. The ability to generate vast amounts of transcriptomic data at cellular resolution represents a transformative advance allowing the identification of novel cell types, states and dynamics. In this review, we summarize the development of scRNA-seq methodologies and highlight their advantages and drawbacks. We discuss available software tools for analyzing scRNA-Seq data and summarize current computational challenges. Finally, we outline ways in which this powerful technology might be applied to discovery research in kidney development and disease.

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A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

Cited by 1,280 publications

References 64 publications

K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

Widespread transcriptional scanning in the testis modulates gene evolution rates

The promise of single-cell RNA sequencing for kidney disease investigation

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