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Effective detection of variation in single-cell transcriptomes using MATQ-seq

Nature Methods volume 14, pages 267–270 (2017)Cite this article

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Abstract

The quantification of transcriptional variation in single cells, particularly within the same cell population, is currently limited by the low sensitivity and high technical noise of single-cell RNA-seq assays. We report multiple annealing and dC-tailing-based quantitative single-cell RNA-seq (MATQ-seq), a highly sensitive and quantitative method for single-cell sequencing of total RNA. By systematically determining technical noise, we show that MATQ-seq captures genuine biological variation between whole transcriptomes of single cells.

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Figure 1: Coverage, sensitivity and accuracy of MATQ-seq compared with SMART-seq2.
Figure 2: Principal component analysis (PCA) indicates successful capture of biological variation between single cells.
Figure 3: Comparison between single cells and single-cell averages for identifying genuine biological variation.

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Acknowledgements

This work is supported by the McNair Scholarship and McNair Single Cell Initiative. We are grateful to the McNair family and C. Neblett for their kind support. We would like to thank Z. Hu, Y. Zhao and J. Yuan for their help. The MCF10A cell line was generously provided by S. Zhang (Baylor College of Medicine). We also would like to thank S. Rosenberg, C. Herman and H. Dierick for their helpful comments on the manuscript.

Author information

Authors and Affiliations

  1. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA

    Kuanwei Sheng, Wenjian Cao, Yichi Niu, Qing Deng & Chenghang Zong

  2. Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA

    Kuanwei Sheng & Chenghang Zong

  3. Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

    Chenghang Zong

  4. McNair Medical Institute, Baylor College of Medicine, Houston, Texas, USA

    Chenghang Zong

Authors
  1. Kuanwei Sheng
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  2. Wenjian Cao
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  3. Yichi Niu
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  4. Qing Deng
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  5. Chenghang Zong
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Contributions

K.S. and C.Z. developed the MATQ-seq assay, performed the data analysis and wrote the manuscript. K.S., W.C., Y.N. and Q.D. performed the single-cell RNA sequencing.

Corresponding author

Correspondence to Chenghang Zong.

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Competing interests

Baylor College of Medicine has submitted a patent application on MATQ-seq.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–24 and Supplementary Tables 1–4. (PDF 4066 kb)

Supplementary Data 1

ERCC mapping data. (XLSX 12 kb)

Supplementary Data 2

Exon-based gene list. (XLSX 20 kb)

Supplementary Data 3

Intron-based gene list. (XLSX 22 kb)

Supplementary Data 4

Exon-based transcriptional factors. (XLSX 15 kb)

Supplementary Data 5

Intron-based transcriptional factors. (XLSX 17 kb)

Supplementary Software

MATLAB Scripts (ZIP 28495 kb)

Supplementary Protocol

MATQ-seq protocol (PDF 168 kb)

Source data

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Sheng, K., Cao, W., Niu, Y. et al. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat Methods 14, 267–270 (2017). https://doi.org/10.1038/nmeth.4145

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