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Published October 7, 2021 | Supplemental Material + Submitted + Published
Journal Article Open

Isoform cell-type specificity in the mouse primary motor cortex


Full-length SMART-seq single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types—including isoform shifts between cell types that are masked in gene-level analysis—as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.

Additional Information

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 09 March 2020; Accepted 27 August 2021; Published 06 October 2021. We thank members of the BICCN consortium, especially the Mini-MOp analysis group, for helpful conversations related to transcriptome analysis of the MOp. We thank N. Volovich, V. Ntranos and P. Melsted for help with a preliminary quantification of the SMART-seq data. Figure 1 was created from scratch using the tools available on Biorender.com. Extended Data Fig. 6a was obtained from http://atlas.brain-map.org/atlas. This work was funded by the NIH Brain Initiative via grant U19MH114930 to H.Z. and L.P. Data availability: The single-cell RNA-seq data used in this study were generated as part of the BICCN consortium22. The 10xv3 and SMART-seq data can be downloaded from http://data.nemoarchive.org/biccn/lab/zeng/transcriptome/scell/. The MERFISH data are available at https://caltech.box.com/shared/static/dzqt6ryytmjbgyai356s1z0phtnsbaol.gz. All cell annotations and cluster labels are available at https://github.com/pachterlab/BYVSTZP_2020/tree/master/reference. Code availability: The software used to generate the results and figures of the paper is available at https://github.com/pachterlab/BYVSTZP_2020. Author Contributions: A.S.B. and L.P. conceived the study. A.S.B. implemented the methods and produced the results and figures. A.S.B. and L.P. analysed the data and wrote the manuscript. Z.Y., C.v.V., K.S., B.T. and H.Z. produced the SMART-seq and 10xv3 data. The authors declare no competing interests. Peer review information: Nature thanks Chris Burge and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Attached Files

Published - s41586-021-03969-3.pdf

Submitted - 2020.03.05.977991v3.full.pdf

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August 20, 2023
December 22, 2023