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Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells

Nature Biotechnology volume 36, pages 1197–1202 (2018)Cite this article

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Abstract

Full-length RNA sequencing (RNA-Seq) has been applied to bulk tissue, cell lines and sorted cells to characterize transcriptomes1,2,3,4,5,6,7,8,9,10,11, but applying this technology to single cells has proven to be difficult, with less than ten single-cell transcriptomes having been analyzed thus far12,13. Although single splicing events have been described for ≤200 single cells with statistical confidence14,15, full-length mRNA analyses for hundreds of cells have not been reported. Single-cell short-read 3′ sequencing enables the identification of cellular subtypes16,17,18,19,20,21, but full-length mRNA isoforms for these cell types cannot be profiled. We developed a method that starts with bulk tissue and identifies single-cell types and their full-length RNA isoforms without fluorescence-activated cell sorting. Using single-cell isoform RNA-Seq (ScISOr-Seq), we identified RNA isoforms in neurons, astrocytes, microglia, and cell subtypes such as Purkinje and Granule cells, and cell-type-specific combination patterns of distant splice sites6,7,8,9,22,23. We used ScISOr-Seq to improve genome annotation in mouse Gencode version 10 by determining the cell-type-specific expression of 18,173 known and 16,872 novel isoforms.

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Figure 1: Outline of approach, cell-type and barcode identification.
Figure 2: Improved cell-type-specific annotation.
Figure 3: Quantitative isoform analysis.

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Acknowledgements

This work used the Genomics Resources Core Facility and owes special thanks to J. Xiang and A. Wan. This work was supported by start-up funds (Weill Cornell Medicine) and a Leon Levy Fellowship in Neuroscience to H.U.T. as well as an R01 from the National Institute of Neurological Disorders and Stroke (1R01NS105477) to M.E.R.

Author information

Author notes

  1. Ben Barres: Deceased.

  2. Ishaan Gupta and Paul G Collier: These authors contributed equally to this work.

Authors and Affiliations

  1. Brain and Mind Research Institute and Center for Neurogenetics, Weill Cornell Medicine, New York, New York, USA

    Ishaan Gupta, Paul G Collier, Ahmed Mahfouz, Anoushka Joglekar, Taylor Floyd, M Elizabeth Ross & Hagen U Tilgner

  2. The Rockefeller University, New York, New York, USA

    Bettina Haase & Olivier Fedrigo

  3. Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands

    Ahmed Mahfouz

  4. Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands

    Ahmed Mahfouz

  5. Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University, Amsterdam, the Netherlands

    Frank Koopmans & August B Smit

  6. Department of Neurobiology, Stanford University, Stanford, California, USA

    Ben Barres & Steven A Sloan

  7. Brain and Mind Research Institute and Appel Alzheimer's Research Institute, Weill Cornell Medicine, New York, New York, USA

    Wenjie Luo

Authors
  1. Ishaan Gupta
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Contributions

P.G.C., I.G., S.A.S. and H.U.T. devised the experiments. P.G.C., B.H., I.G., S.A.S., O.F. and W.L. performed the experiments. I.G., A.B.S. and H.U.T. devised the analyses. I.G., A.M., A.J., T.F., F.K. and H.U.T. performed the analyses. All of the authors discussed and interpreted the results throughout the project. I.G. and H.U.T. wrote the paper with inputs from all of the other authors. B.B., M.E.R. and H.U.T. supervised the project.

Corresponding author

Correspondence to Hagen U Tilgner.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Figure S1

(A) Comparison of ScISOr-Seq approach to sorting methods. (B) Expression patterns of selected marker genes across each cell-type cluster.

Supplementary Figure 2 Figure S2: Illumina Short-read 3’seq statistics.

Distribution of (A) short reads (B) short-read UMIs and (C) genes per cell. (D) Number of total cells, clustered cells and unclassified cells. (E) Number of cells per cluster. Distribution of (F) short reads (G) short-read UMIs and (H) detected genes for each cell-type cluster.

Supplementary Figure 3 Figure S3: Comparison of single cell biological replicates.

Pairwise Jaccard distance between different identified cell-types calculated as the ratio of the number of genes in the intersection to the number of genes in the union: (A) Within replicate 1, (B) within replicate 2, C) replicate 1 vs. replicate 2. D) Fraction of cells each cell-type cluster contains in single cell replicates 1 and 2.

Supplementary Figure 4 Figure S4: Long-read statistics.

Distribution of (A) long reads (B) long-read UMIs and (C) genes per cell. (D) Number of cells >1,>10,>100,>250 long-reads (E) Dotplot and correlation between long-read UMIs and short-read UMIs per cell. Distribution of (F) long reads (G) long-read UMIs and (H) long-read detected genes for each cell-type cluster.

Supplementary Figure 5 Figure S5: PolyA and barcode identification statistics for ScISOr-Seq using Nanopore.

Detection of polyA tail is based on finding the first window of 30 bps with >=25 bps annotated as “A” or a “T” in the appropriate direction (A) Distribution of read length for reads with (red) and without (black) polyA tail for 1D2 pass (left), 1D2 failed (center) and 1D reads(right). (B) Histogram of polyA tail position for 1D2 pass (left), 1D2 failed(center) and 1D reads(right). We expect the polyA tail to begin at about 121 bps from the beginning of a read, however due to higher error rates in reads the detected position of polyA tail was fuzzy and therefore we found a wide spread of ~90bps around the expected position of ~121 bps unlike in PacBio reads. (C) Barplot for the percentage of reads with polyA tails (D) Barplot for the percentage of reads with a polyA tail for which a unique 10x cell barcode was found (E) Barplot for the percentage of reads with a polyA tail, for which multiple or 1-mismatch 10x cell barcode were found in 1D2 pass (green), 1D2 failed(purple) and 1D reads(gray).

Supplementary Figure 6 Figure S6: Mapping statistics.

(A) Distribution of read length for long-reads (B) Number of molecules submitted for mapping (left bar) and number (and percentage) of molecules that could be mapped to the mm10 genome using STARlong. (C) Number and percentage of molecules for which we could determine a single high-confidence-mapping (well-mapped, left bar) and those that did not overlap ribosomal RNA genes (right, percentage with respect to previous bar). (D) Number of molecules falling entirely into one intergenic, intronic or exonic region. Note that the definition of intergenic used here is based on the Gencode-vM10 annotation, which defines lncRNA genes, ribosomal RNA genes and many other kinds of short RNA genes as ”genes”. (E) Intron length distribution for introns in consensus-split-molecule-mappings, showing only introns of up to 1.5kb. The red dashed line (at 70bps) indicates a cutoff under which very few annotated human introns could be found (see reference 8), suggesting a minimal intron-size of this length, under which human introns might be difficult to process for the splicing machinery.

Supplementary Figure 7 Figure S7

(A) Distributions of coverage with astrocytic (obtained by immune-panning) short reads for junctions in the enhanced annotation that were exclusively observed in one cell type (indicated by name under the x-axis), with at least one observation in that cell type. (B) Distributions of coverage with astrocytic short reads for junctions in the enhanced annotation that were exclusively observed in one cell type (indicated by name under the x-axis), with at least three observations in that cell type. Horizontal red lines indicate the median value for junctions exclusively observed in astrocytic long-reads.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 2001 kb)

Life Sciences Reporting Summary (PDF 162 kb)

Supplementary Dataset 1

Complete Annotation of RNA Isoforms and their cell-type specific expression in the mammalian cerebellum from P1 mouse (ZIP 18439 kb)

Supplementary Dataset 2

Annotation of Novel RNA Isoforms, which are novel with respect to UCSC and RefSeq annotation, and their cell-type specific expression in the mammalian cerebellum from P1 mouse (ZIP 113 kb)

Supplementary Note 1

Supplementary note detailing methodology of defining trustworthy alignments to genes and detection of novel isoforms (PDF 199 kb)

Supplementary Code

R markdown detailing the single cell analysis pipeline (TXT 11 kb)

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Gupta, I., Collier, P., Haase, B. et al. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat Biotechnol 36, 1197–1202 (2018). https://doi.org/10.1038/nbt.4259

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