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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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References

  1. Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Au, K.F. et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc. Natl. Acad. Sci. USA 110, E4821–E4830 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Oikonomopoulos, S., Wang, Y.C., Djambazian, H., Badescu, D. & Ragoussis, J. Benchmarking of the Oxford Nanopore MinION sequencing for quantitative and qualitative assessment of cDNA populations. Sci. Rep. 6, 31602 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tilgner, H., Grubert, F., Sharon, D. & Snyder, M.P. Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc. Natl. Acad. Sci. USA 111, 9869–9874 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Koren, S. et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat. Biotechnol. 30, 693–700 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tilgner, H. et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat. Biotechnol. 33, 736–742 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tilgner, H. et al. Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome. Genome Res. 28, 231–242 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bolisetty, M.T., Rajadinakaran, G. & Graveley, B.R. Determining exon connectivity in complex mRNAs by nanopore sequencing. Genome Biol. 16, 204 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Roy, C.K., Olson, S., Graveley, B.R., Zamore, P.D. & Moore, M.J. Assessing long-distance RNA sequence connectivity via RNA-templated DNA–DNA ligation. eLife 4, e03700 (2015).

    Article  PubMed Central  Google Scholar 

  10. Treutlein, B., Gokce, O., Quake, S.R. & Südhof, T.C. Cartography of neurexin alternative splicing mapped by single-molecule long-read mRNA sequencing. Proc. Natl. Acad. Sci. USA 111, E1291–E1299 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Schreiner, D. et al. Targeted combinatorial alternative splicing generates brain region–specific repertoires of neurexins. Neuron 84, 386–398 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Karlsson, K. & Linnarsson, S. Single-cell mRNA isoform diversity in the mouse brain. BMC Genomics 18, 126 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Byrne, A. et al. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Song, Y. et al. Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Mol. Cell 67, 148–161 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  17. Lake, B.B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zheng, G.X.Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285–7290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pollen, A.A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fededa, J.P. et al. A polar mechanism coordinates different regions of alternative splicing within a single gene. Mol. Cell 19, 393–404 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Fagnani, M. et al. Functional coordination of alternative splicing in the mammalian central nervous system. Genome Biol. 8, R108 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Mecklenburg, N. et al. Growth and differentiation factor 10 (Gdf10) is involved in Bergmann glial cell development under Shh regulation. Glia 62, 1713–1723 (2014).

    Article  PubMed  Google Scholar 

  25. Koirala, S. & Corfas, G. Identification of novel glial genes by single-cell transcriptional profiling of Bergmann glial cells from mouse cerebellum. PLoS One 5, e9198 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Lein, E.S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Butts, T., Green, M.J. & Wingate, R.J.T. Development of the cerebellum: simple steps to make a 'little brain'. Development 141, 4031–4041 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338–345 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Waterston, R.H. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Tilgner, H. et al. Accurate identification and analysis of human mRNA isoforms using deep long read sequencing. G3 3, 387–397 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mcmanus et al. Global analysis of trans-splicing in Drosophila. Proc. Natl. Acad. Sci. USA 107, 12975–12979 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Karolchik, D. et al. The UCSC Genome Browser database: 2014 update. Nucleic Acids Res. 42, D764–D770 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. O'Leary, N.A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Ge, K. et al. Mechanism for elimination of a tumor suppressor: aberrant splicing of a brain-specific exon causes loss of function of Bin1 in melanoma. Proc. Natl. Acad. Sci. USA 96, 9689–9694 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fugier, C. et al. Misregulated alternative splicing of BIN1 is associated with T tubule alterations and muscle weakness in myotonic dystrophy. Nat. Med. 17, 720–725 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Karni, R. et al. The gene encoding the splicing factor SF2/ASF is a proto-oncogene. Nat. Struct. Mol. Biol. 14, 185–193 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Anvar, S.Y. et al. Full-length mRNA sequencing uncovers a widespread coupling between transcription initiation and mRNA processing. Genome Biol. 19, 46 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Vaquero-Garcia, J. et al. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife 5, e11752 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Li, Y.I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Sharma, K. et al. Cell type– and brain region–resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kang, H.M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  PubMed  Google Scholar 

  46. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wu, T.D. & Watanabe, C.K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

Download references

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

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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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