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Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

Samuel G. Rodriques, Robert R. Stickels, Aleksandrina Goeva, Carly A. Martin, Evan Murray, Charles R. Vanderburg, Joshua Welch, Linlin M. Chen, Fei Chen, Evan Z. Macosko
doi: https://doi.org/10.1101/563395
Samuel G. Rodriques
1Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139
2MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139
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Robert R. Stickels
3Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, 02138
4Division of Medical Science, Harvard Medical School, Boston, MA, 02115
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Aleksandrina Goeva
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Carly A. Martin
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Evan Murray
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Charles R. Vanderburg
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Joshua Welch
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Linlin M. Chen
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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Fei Chen
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
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  • For correspondence: chenf@broadinstitute.org emacosko@broadinstitute.org
Evan Z. Macosko
5Broad Institute of Harvard and MIT, Cambridge, MA, 02142
6Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114
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  • For correspondence: chenf@broadinstitute.org emacosko@broadinstitute.org
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Abstract

The spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq’s utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.

One Sentence Summary Slide-seq measures genome-wide expression in complex tissues at 10-micron resolution.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 28, 2019.
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Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution
Samuel G. Rodriques, Robert R. Stickels, Aleksandrina Goeva, Carly A. Martin, Evan Murray, Charles R. Vanderburg, Joshua Welch, Linlin M. Chen, Fei Chen, Evan Z. Macosko
bioRxiv 563395; doi: https://doi.org/10.1101/563395
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Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution
Samuel G. Rodriques, Robert R. Stickels, Aleksandrina Goeva, Carly A. Martin, Evan Murray, Charles R. Vanderburg, Joshua Welch, Linlin M. Chen, Fei Chen, Evan Z. Macosko
bioRxiv 563395; doi: https://doi.org/10.1101/563395

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