High-resolution RNA capture from tissue by Slide-seq. (A) Left: Schematic of array generation. A monolayer of randomly deposited, DNA barcoded beads (termed a "puck") is spatially indexed by SOLiD sequencing. Top Right: A representative puck with called barcodes shown in black. Bottom Right: A composite image of the same puck colored by the base calls for a single base of SOLiD sequencing. (Scale bar 500 μm) (B) Top Row: Schematic of the sample preparation procedure developed for Slide-seq. Total time for library generation is ~3 hrs. Bottom Row: Schematic of a naïve analysis, in which each bead is clustered by its gene expression, visualized in a tSNE two-dimensional embedding, and by locations in space. (C) Spatial positions of Slide-seq beads, colored by clusters defined purely by gene expression relationships amongst beads, across five tissue types (see Fig. S2 for tSNE embeddings and definitions). (D) Characterization of lateral diffusion of signal on the Slide-seq surface. Top Left: Digital image of a Slide-seq puck with bead color intensity scaled by total transcript counts. Top Right: Image of the adjacent tissue section, stained with DAPI (scale bar 500 μm). Boxes represent regions where an intensity profile was taken across CA1. Bottom left: Profile of pixel intensity across CA1 in Slide-seq. Bottom right: Profile across CA1 in DAPI stained tissue. Red dots represent locations of half max of the distribution. (E) Quantification of full width at half maximum of profiles in (D), from both Slide-seq (red dots) and DAPI-stained tissue (blue dots) (dotted line, mean; N = 10 profiles) (F) Log ratio of total number of quantified RNA transcripts on a puck to the number of cells counted on a serially stained DAPI slice of equal area (dotted line, mean) across five different tissues.

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... first asked whether barcoded oligonucleotides could be arrayed randomly on a surface at high spatial resolution, with locations determined post hoc. We packed uniquely barcoded 10 µm microparticles ('beads')-similar to those used by the Drop-seq approach to scRNA-seq (4)-onto a rubber-coated glass coverslip forming a monolayer we termed a "puck" (Fig. S1, 92.9% ± 2.15% packing). We found that the bead barcode sequences on the puck could be uniquely determined via SOLiD sequencing-by-ligation chemistry (Fig. 1A, Fig. S1). Moreover, pucks could be stored for extended periods of time prior to use, allowing them to be produced in large batches and used as ...

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... uniquely barcoded 10 µm microparticles ('beads')-similar to those used by the Drop-seq approach to scRNA-seq (4)-onto a rubber-coated glass coverslip forming a monolayer we termed a "puck" (Fig. S1, 92.9% ± 2.15% packing). We found that the bead barcode sequences on the puck could be uniquely determined via SOLiD sequencing-by-ligation chemistry (Fig. 1A, Fig. S1). Moreover, pucks could be stored for extended periods of time prior to use, allowing them to be produced in large batches and used as ...

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... transferred onto the dried bead surface via cryosectioning (4). We observed efficient hybridization of tissue-extracted mRNA to polyT capture sequences, after which 3'-end digital expression libraries could be prepared and sequenced (5). The simplicity of the protocol enabled fast, facile generation of libraries in a highly multiplexed fashion (Fig. 1B). To highlight its generalizability, we performed Slide-seq across a range of samples, generating data from three different organs (mouse brain, kidney, liver). Expression measurements by Slide-seq agreed well with those from bulk mRNAseq (r = 0.89) (Fig. S1D), similar to standard single-cell profiling technologies (5), and average mRNA ...

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... the protocol enabled fast, facile generation of libraries in a highly multiplexed fashion (Fig. 1B). To highlight its generalizability, we performed Slide-seq across a range of samples, generating data from three different organs (mouse brain, kidney, liver). Expression measurements by Slide-seq agreed well with those from bulk mRNAseq (r = 0.89) (Fig. S1D), similar to standard single-cell profiling technologies (5), and average mRNA transcript capture per cell was consistent across tissues and experiments (Fig. ...

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... a range of samples, generating data from three different organs (mouse brain, kidney, liver). Expression measurements by Slide-seq agreed well with those from bulk mRNAseq (r = 0.89) (Fig. S1D), similar to standard single-cell profiling technologies (5), and average mRNA transcript capture per cell was consistent across tissues and experiments (Fig. ...

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... clustering of individual bead profiles using single-cell analysis approaches (4) yielded cluster assignments reflecting known positions of cell types in the assayed tissues ( Fig 1C). Specifically, in analyses of three different regions of brain-cerebellum, hippocampus, and olfactory bulb-the different neuronal cell types that form the layered tissue architecture were immediately detectable, as were populations of resident glial types. ...

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... liver, the lobule architecture was visible, with genes known to manifest zonation patterns driving the distinction between two main clusters (6). To quantify diffusion in the tissue, we compared the width of mRNA transcript density in hippocampal CA1 observed in Slide-seq to that observed in an adjacent, DAPI-stained tissue section (Fig. 1D). We estimated the lengthscale of lateral diffusion of transcripts during hybridization to be 1.4um ± 1.3um (Fig. 1E), implying that mRNA is transferred from the tissue to the beads with high spatial ...

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... distinction between two main clusters (6). To quantify diffusion in the tissue, we compared the width of mRNA transcript density in hippocampal CA1 observed in Slide-seq to that observed in an adjacent, DAPI-stained tissue section (Fig. 1D). We estimated the lengthscale of lateral diffusion of transcripts during hybridization to be 1.4um ± 1.3um (Fig. 1E), implying that mRNA is transferred from the tissue to the beads with high spatial ...