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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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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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.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–9 and Tables 1, 2 and 6–8.
Plots of all genes dendritically enriched in Slide-seqV2.
Plots of all genes called as spatially significant in Slide-seqV2.
Dendritically enriched gene sets.
List of all genes called as spatially significant for Slide-seqV2 data in the developing cortex and eye.
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 (2020). https://doi.org/10.1038/s41587-020-0739-1
Nature Methods (2021)