CoChat
Sign Up
Cited by 244Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesRNA Research and Splicing

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

High throughput error corrected Nanopore single cell transcriptome sequencing

Open access
Lebrigand, Kevin, Magnone, Virginie, Barbry, Pascal, Waldmann, Rainer·2020·Nature Communications
DOI 10.1038/s41467-020-17800-6PMC PMC7423900

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Droplet-based high throughput single cell sequencing techniques tremendously advanced our insight into cell-to-cell heterogeneity. However, those approaches only allow analysis of one extremity of the transcript after short read sequencing. In consequence, information on splicing and sequence heterogeneity is lost. To overcome this limitation, several approaches that use long-read sequencing were introduced recently. Yet, those techniques are limited by low sequencing depth and/or lacking or inaccurate assignment of unique molecular identifiers (UMIs), which are critical for elimination of PCR bias and artifacts. We introduce ScNaUmi-seq, an approach that combines the high throughput of Oxford Nanopore sequencing with an accurate cell barcode and UMI assignment strategy. UMI guided error correction allows to generate high accuracy full length sequence information with the 10x Genomics single cell isolation system at high sequencing depths. We analyzed transcript isoform diversity in embryonic mouse brain and show that ScNaUmi-seq allows defining splicing and SNVs (RNA editing) at a single cell level.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-020-17800-6

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Efficiency and accuracy of cellBC and UMI assignment. a CellBC and UMI assignment strategy (Detailed in Supplementary Fig. 2). b Efficiency of cellBC and UMI identification for eight PromethION sequencing runs (n = 8): both poly(A) and adapter, 57 ± 11% (SD) of total reads; cellBC, 68 ± 4% (SD) of reads with identified poly(A) and adapter; UMI, 76 ± 3% (SD) of reads with identified cellBC. Boxes and error bars indicate the means and standard deviations, respectively, for n = 8 flow cells. c UMI sequencing depth (reads/UMI) for the Illumina and Nanopore dataset. d Accuracy of cellBC and UMI assignment. Number of cellBC and UMI assigned reads before and after replacement of cellBC or UMIs with random sequences and after cellBC and UMI identification guided by an unrelated Illumina dataset (24 × 106 reads of the 190 cell sample were scanned). e, f Edit distance distribution of the cellBC and UMI assignment.
Fig. 1 Efficiency and accuracy of cellBC and UMI assignment. a CellBC and UMI assignment strategy (Detailed in Supplementary Fig. 2). b Efficiency of cellBC and UMI identification for eight PromethION sequencing runs (n = 8): both poly(A) and adapter, 57 ± 11% (SD) of total reads; cellBC, 68 ± 4% (SD) of reads with identified poly(A) and adapter; UMI, 76 ± 3% (SD) of reads with identified cellBC. Boxes and error bars indicate the means and standard deviations, respectively, for n = 8 flow cells. c UMI sequencing depth (reads/UMI) for the Illumina and Nanopore dataset. d Accuracy of cellBC and UMI assignment. Number of cellBC and UMI assigned reads before and after replacement of cellBC or UMIs with random sequences and after cellBC and UMI identification guided by an unrelated Illumina dataset (24 × 106 reads of the 190 cell sample were scanned). e, f Edit distance distribution of the cellBC and UMI assignment.CC BY · PubMed Central
Fig. 2 Comparison of Nanopore long read with short-read scRNA-seq. Number of genes a and number of UMIs b detected for each cell of the 190 and 951 cell datasets after Illumina and Nanopore sequencing. Median genes a and UMIs b per cells for both datasets are shown in the histograms. c Correlation of gene expression between Illumina and Nanopore sequencing data for each cell. Boxes represent the 25% quantile to 75% quantile range, upper and lower edges of notches are median +/− 1.58 * IQR/sqrt(n) (IQR: inter quantile range, n = 1121). t-SNE plots of Illumina gene expression d and Nanopore isoform expression e for 1121 cells (integration of the 190 and 951 cell datasets). Colors for individual cells are the same in d and e. Clustering details are in Supplementary Fig. 8 and Supplementary Data 1.
Fig. 2 Comparison of Nanopore long read with short-read scRNA-seq. Number of genes a and number of UMIs b detected for each cell of the 190 and 951 cell datasets after Illumina and Nanopore sequencing. Median genes a and UMIs b per cells for both datasets are shown in the histograms. c Correlation of gene expression between Illumina and Nanopore sequencing data for each cell. Boxes represent the 25% quantile to 75% quantile range, upper and lower edges of notches are median +/− 1.58 * IQR/sqrt(n) (IQR: inter quantile range, n = 1121). t-SNE plots of Illumina gene expression d and Nanopore isoform expression e for 1121 cells (integration of the 190 and 951 cell datasets). Colors for individual cells are the same in d and e. Clustering details are in Supplementary Fig. 8 and Supplementary Data 1.CC BY · PubMed Central
Fig. 3 Nanopore scRNA-seq reveals transcript isoform diversity. a–c Clta isoform expression switch during neuronal maturation visualized on the t-SNE plot of Fig. 2d. a Clta-204 (ENSMUST00000107849.9), b Clta-206 (ENSMUST00000170241.7), c Overlay Clta-204/Clta- 206. d Principal Clta splice variants and supporting genome-aligned reads for two UMIs. A read not consistent with the UMI consensus (likely PCR artifact) is labeled with an “*”. e Relative expression of known Ensembl Clta transcript isoforms in different cell types of the brain (clusters of Fig. 2d).
Fig. 3 Nanopore scRNA-seq reveals transcript isoform diversity. a–c Clta isoform expression switch during neuronal maturation visualized on the t-SNE plot of Fig. 2d. a Clta-204 (ENSMUST00000107849.9), b Clta-206 (ENSMUST00000170241.7), c Overlay Clta-204/Clta- 206. d Principal Clta splice variants and supporting genome-aligned reads for two UMIs. A read not consistent with the UMI consensus (likely PCR artifact) is labeled with an “*”. e Relative expression of known Ensembl Clta transcript isoforms in different cell types of the brain (clusters of Fig. 2d).CC BY · PubMed Central
Fig. 4 Nanopore scRNA-seq reveals sequence diversity. a Principal splice variants of Gria2. The 3′ end of genome aligned reads for two UMIs are shown (Integrated Genome Viewer, UCSC Santa Cruz). b Zoom onto the R/G A- > I editing site for the reads shown in e. c Q/R and R/G editing. Editing rates at the Q/R site are 89.1 and 82% for RNAs edited or unedited at the R/G site respectively. R/G site editing is 25.1% and 15.8% for Q/R site edited and unedited RNAs respectively. d Extend of A− > I editing of the R/G site increases during neuronal maturation. Progenitors, 9.3%, clusters 1,2,5 in Fig. 2d; GABA immature, 14.3%, cluster 6; Glutamatergic immature, 16.4%, cluster 3; GABA mature, 26.7%, cluster 7; Glutamatergic mature, 26%, cluster 4.
Fig. 4 Nanopore scRNA-seq reveals sequence diversity. a Principal splice variants of Gria2. The 3′ end of genome aligned reads for two UMIs are shown (Integrated Genome Viewer, UCSC Santa Cruz). b Zoom onto the R/G A- > I editing site for the reads shown in e. c Q/R and R/G editing. Editing rates at the Q/R site are 89.1 and 82% for RNAs edited or unedited at the R/G site respectively. R/G site editing is 25.1% and 15.8% for Q/R site edited and unedited RNAs respectively. d Extend of A− > I editing of the R/G site increases during neuronal maturation. Progenitors, 9.3%, clusters 1,2,5 in Fig. 2d; GABA immature, 14.3%, cluster 6; Glutamatergic immature, 16.4%, cluster 3; GABA mature, 26.7%, cluster 7; Glutamatergic mature, 26%, cluster 4.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 19 references

Research works printed but not linked: 1

  1. Volden, R. & Vollmers, C. Highly multiplexed single-cell full-length cDNA sequencing of human immune cells with 10X genomics and R2C2. bioRxiv https://www.biorxiv.org/content/10.1101/2020.01.10.902361v1 (2020). · DOI

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Lebrigand, K., Magnone, V., Barbry, P., & Waldmann, R.
Affiliations
Centre National de la Recherche Scientifique (FR)
Corresponding author
  • Barbry, Pascal
  • Waldmann, Rainer
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-08-12
Volume
11
Issue
1
Pages
4025-4025
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-17800-6
PMC
PMC7423900
OpenAlex
W3048389895
Cited by
236
References
19
Citations by year
2026: 28 · 2025: 44 · 2024: 41 · 2023: 41 · 2022: 36 · 2021: 36 · 2020: 10
FWCI
10.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Institut National Du Cancer (PLBIO2018-156)
  • Conseil Départemental des Alpes Maritimes (2016-294DGADSH-CV)
  • Fondation pour la Recherche Médicale (DEQ20180339158)
  • Institut National de la Santé et de la Recherche Médicale (HuDeCA 2018)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
single-cell transcriptomics, Oxford Nanopore sequencing, unique molecular identifiers, error correction, cell barcoding, long-read sequencing, alternative splicing, RNA editing, single-cell RNA sequencing, 10x Genomics, embryonic mouse brain, cellular heterogeneity
MeSH terms
Nanopore Sequencing, Animals, Brain, Mice, Inbred C57BL, Gene Expression, Sequence Analysis, DNA, Sequence Analysis, RNA, Receptors, AMPA, Protein Isoforms, Gene Expression Profiling, Genomics, Mice, Nanopores, High-Throughput Nucleotide Sequencing, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review