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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

Nature Biotechnology volume 39, pages 313–319 (2021)Cite this article

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Abstract

Measurement of the location of molecules in tissues is essential for understanding tissue formation and function. Previously, we developed Slide-seq, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 μm. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq), approaching the detection efficiency of droplet-based single-cell RNA-seq techniques. First, we leverage the detection efficiency of Slide-seqV2 to identify dendritically localized mRNAs in neurons of the mouse hippocampus. Second, we integrate the spatial information of Slide-seqV2 data with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex, identifying underlying genetic programs that were poorly sampled with Slide-seq. The combination of near-cellular resolution and high transcript detection efficiency makes Slide-seqV2 useful across many experimental contexts.

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Fig. 1: Highly improved mRNA detection sensitivity in Slide-seqV2.
Fig. 2: Slide-seqV2 reveals spatial patterning of dendritically enriched mRNAs.
Fig. 3: Slide-seqV2 of developing mouse cortex reconstructs spatial developmental trajectories.

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Data availability

All data are available at https://singlecell.broadinstitute.org/single_cell/study/SCP815/sensitive-spatial-genome-wide-expression-profiling-at-cellular-resolution#study-summary.

Code availability

Code related to this manuscript can be found at https://github.com/MacoskoLab/slideseq-tools and https://github.com/rstickels/Slide_seqv2. The following package version numbers were used for data processing and associated analyses: https://github.com/broadinstitute/Drop-seq (Drop-seq-tools-2.3.0), https://broadinstitute.github.io/picard/ (picard-2.18.14), https://github.com/alexdobin/STAR (STAR-2.5.2a), https://github.com/theislab/scvelo (0.1.25), https://github.com/cole-trapnell-lab/monocle3 (beta) and https://github.com/satijalab/seurat (2.3.4). MATLAB 2017a, R3.5.3 and Python 3.7 were used for processing data.

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Acknowledgements

We thank J. Dimidschstein and G. Fishell for their helpful advice on the developmental trajectory analysis. This work was supported by an NIH New Innovator Award (DP2 AG058488-01 to E.Z.M.), an NIH Early Independence Award (DP5, 1DP5OD024583 to F.C.), the NHGRI (R01, R01HG010647 to E.Z.M. and F.C.), the Burroughs Wellcome Fund CASI award (to F.C.) and the Schmidt Fellows Program at the Broad Institute and the Stanley Center for Psychiatric Research.

Author information

Author notes

  1. These authors contributed equally: Robert R. Stickels, Evan Murray.

  2. These authors jointly supervised this work: Evan Z. Macosko, Fei Chen.

Authors and Affiliations

  1. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Robert R. Stickels, Evan Murray, Pawan Kumar, Jilong Li, Jamie L. Marshall, Evan Z. Macosko & Fei Chen

  2. Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA

    Robert R. Stickels

  3. Division of Medical Science, Harvard Medical School, Boston, MA, USA

    Robert R. Stickels

  4. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Daniela J. Di Bella & Fei Chen

  5. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Paola Arlotta & Evan Z. Macosko

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Contributions

F.C. and E.Z.M. supervised the work. R.R.S. and E.M. performed experiments. D.J.D. and P.A. contributed to experiments on the developing neocortex. R.R.S., F.C. and E.Z.M. analyzed the data. J.L. developed the Slide-seq tools software package. P.K. developed the bead synthesis protocol. J.L.M. performed optimization experiments. F.C., E.Z.M., R.R.S. and E.M. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Evan Z. Macosko or Fei Chen.

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Competing interests

R.R.S., F.C. and E.Z.M. are listed as inventors on a pending patent application related to the development of Slide-seq.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Tables 1, 2 and 6–8.

Reporting Summary

Supplementary Dataset 1

Plots of all genes dendritically enriched in Slide-seqV2.

Supplementary Dataset 2

Plots of all genes called as spatially significant in Slide-seqV2.

Supplementary Table 3

Dendritically enriched gene sets.

Supplementary Table 4

List of all genes called as spatially significant for Slide-seqV2 data in the developing cortex and eye.

Supplementary Table 5

List of genes unique to each method regarding the trajectory inference.

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Stickels, R.R., Murray, E., Kumar, P. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol 39, 313–319 (2021). https://doi.org/10.1038/s41587-020-0739-1

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