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 "title": "Slide-seq",
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 "excerpt": "Slide-seq is a spatial transcriptomics method, introduced at the Broad Institute in 2019, that transfers RNA from tissue sections onto DNA-barcoded 10 µm beads so gene expression is measured with position preserved.",
 "snippet": "Slide-seq is a spatial transcriptomics method, introduced at the Broad Institute in 2019, that transfers RNA from tissue sections onto DNA-barcoded 10 µm beads so gene expression is measured with position preserved.",
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 "markdown": "# Slide-seq\n\nSlide-seq is a spatial transcriptomics method that transfers RNA from a tissue section onto a dense array of DNA-barcoded 10 µm beads on a slide, so that gene expression is measured while each transcript's position in the tissue is preserved and recovered by sequencing.<sup>[1](https://www.science.org/doi/10.1126/science.aaw1219)</sup> Each bead acts as a miniature capture spot with a known coordinate, producing a per-bead expression matrix at near-single-cell resolution; the Slide-seqV2 refinement reaches an RNA capture efficiency of roughly 50% of single-cell RNA sequencing.<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> It belongs to the sequencing-based family of spatial methods, which are transcriptome-wide and unbiased but less sensitive per transcript than imaging-based methods such as [MERFISH](https://www.edgepedia.org/merfish).<sup>[3](https://mdpi-res.com/d_attachment/cells/cells-12-02042/article_deploy/cells-12-02042.pdf?version=1691679379)</sup>\n\n| Key fact | Value |\n|---|---|\n| Output | Per-bead 3′-end barcoded expression matrix; software assigns each sequencing read to a bead coordinate to map cell types and genes<sup>[4](https://www.broadinstitute.org/news/new-tool-uses-rna-sequencing-chart-rich-maps-cellular-and-tissue-function)</sup> |\n| Capture surface | Uniquely DNA-barcoded 10 µm beads packed on a rubber-coated glass coverslip (a \"puck\")<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup> |\n| Practical resolution | 10 µm features, most commonly capturing one to two cells and no more than three<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup> |\n| Per-bead yield (V2 protocol) | Most beads detect 100–580 UMIs and 75–450 genes<sup>[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC11061798/)</sup> |\n| Sequencing depth | ~200–400 million reads per puck (3,000–5,000 reads per bead) on an Illumina NovaSeq<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> |\n| Cost (2019 estimate) | ~$200–$500 per puck, dominated by short-read sequencing<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup> |\n\n## How it works\n\nThe method rests on three ideas. First, 10 µm microparticles (\"beads\"), similar to those used in the Drop-seq approach to single-cell RNA sequencing, are packed onto a rubber-coated glass coverslip to form a monolayer called a puck; each bead carries a unique DNA barcode with a poly(T) capture tail.<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup> Second, every bead's barcode sequence is determined before tissue is applied, originally via SOLiD sequencing-by-ligation chemistry, so each bead's position is known.<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup> Third, a fresh-frozen tissue section is transferred onto the dried bead surface, and mRNA released from the tissue is captured on the nearby beads for preparation of 3′-end barcoded RNA-seq libraries.<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup>\n\nSpatial fidelity depends on RNA staying put. Comparison with FISH showed no detectable difference in the dimensions of brain structures, implying that mRNA is transferred with minimal lateral diffusion.<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup>\n\n## How it is done\n\nA practitioner's workflow runs as follows.\n\n1. **Bead synthesis and puck fabrication.** Barcoded oligos (UMI, split-pool \"J\" bases, poly(T) tail) are synthesized by split-pool phosphoramidite synthesis, 15 cycles in two blocks of 8 and 7, on 10 µm Agilent PLRP-S particles using an Akta OligoPilot 10, yielding about \\( 10^{9} \\) unique barcodes.<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup> Beads are packed into pucks and indexed up front, which supports reconstructing 3D tissue volumes of tens to hundreds of cubic millimeters with standard lab equipment.<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup>\n2. **Barcode decoding.** Bead positions are imaged in a Bioptechs FCS2 flow cell on a Nikon Eclipse Ti with a Yokogawa CSU-W1 confocal scanner, using SOLiD di-base or monobase sequencing-by-ligation; stitched images were 6,030 × 6,030 pixels for the arrays reported.<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup>\n3. **Tissue transfer and library preparation.** 10 µm cryosections are melted onto the array, hybridized (15 min in 6x SSC with RNase inhibitor), reverse-transcribed (30 min at room temperature, then 90 min at 52 °C), cleared with proteinase K, and beads are released; cDNA proceeds through second strand synthesis with Klenow, whole-transcriptome PCR, and Nextera tagmentation of 600 pg cDNA.<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup>\n4. **Sequencing and processing.** Libraries are sequenced with a 42 bp Read 1, 8 bp i7, 50 bp Read 2 structure; software assigns locations to each read, and bead-level counts are deconvolved into cell types.<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup><sup> • </sup><sup>[4](https://www.broadinstitute.org/news/new-tool-uses-rna-sequencing-chart-rich-maps-cellular-and-tissue-function)</sup> The original paper's NMFreg method reconstructs each bead as a weighted combination of scRNA-seq cell-type signatures.<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup>\n\n## Origin\n\nSlide-seq was introduced by Samuel G. Rodriques and colleagues in \"Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution\", published in Science in 2019.<sup>[1](https://www.science.org/doi/10.1126/science.aaw1219)</sup> The method was developed in the labs of Evan Macosko and [Fei Chen](https://www.edgepedia.org/fei-chen) at the [Broad Institute](https://www.edgepedia.org/broad-institute).<sup>[4](https://www.broadinstitute.org/news/new-tool-uses-rna-sequencing-chart-rich-maps-cellular-and-tissue-function)</sup> It built on an earlier array-based strategy, \"spatial transcriptomics\", reported by Patrik L. Ståhl and colleagues in Science in 2016, which placed expression data on array spots in tissue sections.<sup>[8](https://doi.org/10.1126/science.aaf2403)</sup> A related high-density array format, HDST, was reported by Sanja Vickovic and colleagues in [Nature Methods](https://www.edgepedia.org/nature-methods) in 2019.<sup>[9](https://doi.org/10.1038/s41592-019-0548-y)</sup>\n\n## Variants\n\n**Slide-seqV2** was reported by Robert R. Stickels and colleagues in [Nature Biotechnology](https://www.edgepedia.org/nature-biotechnology) (2020).<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> It combines improvements in library generation, bead synthesis, and array indexing to reach an RNA capture efficiency of about 50% of single-cell RNA sequencing data, roughly 10 times greater than the original Slide-seq.<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> Barcode decoding moved from proprietary SOLiD di-base chemistry to an open-source monobase encoding scheme using sequencing by ligation with offset primers, chosen because di-base-to-base conversion is not error robust.<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> An added second strand synthesis step contributed 4.6-fold of the gain; on E12.5 mouse embryos V2 obtained about 9.3 times more UMIs per bead than the original protocol (median 550 versus 59), and replicate reproducibility was \\( \\rho = 0.98 \\).<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup>\n\n**Slide-tags**, reported by Andrew J. C. Russell and colleagues in Nature (2023), inverts the principle: spatial barcodes are photocleaved from decoded 10 µm bead arrays and diffuse into 20 µm fresh-frozen sections to tag individual nuclei, which are then profiled with standard single-nucleus assays.<sup>[10](https://doi.org/10.1038/s41586-023-06837-4)</sup> It positioned mouse hippocampus nuclei at less than 10 µm resolution with 2,000–10,000 UMIs per cell, at quality indistinguishable from ordinary snRNA-seq and without deconvolution or segmentation.<sup>[10](https://doi.org/10.1038/s41586-023-06837-4)</sup>\n\nLater developments include the Broad's WARP processing pipeline and commercialization as Curio Seeker,<sup>[11](https://broadinstitute.github.io/warp/docs/Pipelines/SlideSeq_Pipeline/README)</sup> a computational array reconstruction approach that removes the imaging step, reported by Chenlei Hu and colleagues in Nature Biotechnology (2025),<sup>[12](https://doi.org/10.1038/s41587-025-02612-0)</sup> and SlideCNA, which infers spatial copy-number alterations by binning neighboring beads with a weighted spatial-expression distance matrix.<sup>[13](https://link.springer.com/article/10.1186/s13059-025-03573-y)</sup>\n\n## Applications\n\nIn mouse brain, the original method resolved gene expression in the Purkinje layer of the cerebellum and was used to dissect the temporal evolution of cell type–specific responses in a model of traumatic brain injury.<sup>[1](https://www.science.org/doi/10.1126/science.aaw1219)</sup> It resolved the single-cell ependymal layer between the central ventricle and the habenula, detected hepatocyte zonation in liver and nephron constituents in kidney, and worked on postmortem human cerebellum.<sup>[5](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)</sup>\n\nSlide-seqV2 extended this to development and disease: it profiled E12.5 mouse embryos<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> and, applied to nine human kidneys and mouse models of diabetic kidney disease and toxic proteinopathy, identified [LYVE1](https://www.edgepedia.org/lyve1)+ macrophage-centered cell neighborhoods and perturbation of 77 unfolded-protein-response genes in a spatially restricted epithelial subpopulation.<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup>\n\n## Limitations and alternatives\n\n**Sensitivity.** Slide-seqV2 detects approximately 44% ± 26% of the counts of Drop-seq for equivalent CA1 cells, so capture remains the main gap relative to dissociated single-cell RNA-seq.<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> The data have limited power for detecting low-expression genes and rare cell types.<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup>\n\n**Error sources.** Expression sparsity is substantial, with beads under 100 counts capped at 35% of beads in one tumor analysis.<sup>[13](https://link.springer.com/article/10.1186/s13059-025-03573-y)</sup> Molecular diffusion affects the resolution of spatial methods generally, and its profile varies considerably with tissue type.<sup>[14](https://www.genomeweb.com/sequencing/benchmarking-study-points-strengths-challenges-spatial-transcriptomics-platforms)</sup>\n\n**Comparison with other methods.** Standard Visium (v1/v2) uses 55 µm spots at 100 µm center-to-center spacing that capture multiple cells per spot, though sensitivity exceeds 10,000 transcripts per spot, and Visium HD uses a lawn of roughly 11 million barcoded squares to map whole-transcriptome expression from tissue sections at single-cell scale.<sup>[3](https://mdpi-res.com/d_attachment/cells/cells-12-02042/article_deploy/cells-12-02042.pdf?version=1691679379)</sup> At equal feature size in mouse hippocampus, Slide-seqV2 exceeded Visium in sensitivity (mean 45,772 versus 27,952 UMIs) while maintaining about 30 times better spatial resolution by area (10 µm versus 55 µm features), and it recovered 44.9-fold more transcripts per 10 µm feature than HDST in olfactory bulb (494 versus 11.5 UMIs).<sup>[2](https://www.nature.com/articles/s41587-020-0739-1)</sup> Imaging-based methods such as MERFISH and seqFISH+ detect about 10,000 genes at sub-cellular resolution, but they are targeted panel assays, whereas sequencing-based methods are transcriptome-wide.<sup>[3](https://mdpi-res.com/d_attachment/cells/cells-12-02042/article_deploy/cells-12-02042.pdf?version=1691679379)</sup> In the cadasSTre benchmark of 11 sequencing-based methods, Slide-seq v2 showed better capture efficiency at normalized sequencing depth, no method reached read saturation, and Slide-tags was the only technology achieving true single-cell resolution.<sup>[14](https://www.genomeweb.com/sequencing/benchmarking-study-points-strengths-challenges-spatial-transcriptomics-platforms)</sup> For cell-type assignment, the original NMFreg approach and Seurat [label transfer](https://www.edgepedia.org/label-transfer) have both been used; in kidney Slide-seqV2 data, Seurat mapped all cell types better.<sup>[6](https://doi.org/10.1016/j.isci.2022.104097)</sup>\n\n## References\n\n1. [Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution (Science, publisher page)](https://www.science.org/doi/10.1126/science.aaw1219)\n2. [Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Nature Biotechnology, publisher page)](https://www.nature.com/articles/s41587-020-0739-1)\n3. [Spatial Transcriptomic Technologies (Cells, 2023)](https://mdpi-res.com/d_attachment/cells/cells-12-02042/article_deploy/cells-12-02042.pdf?version=1691679379)\n4. [New tool uses RNA sequencing to chart rich maps of cellular and tissue function (Broad Institute)](https://www.broadinstitute.org/news/new-tool-uses-rna-sequencing-chart-rich-maps-cellular-and-tissue-function)\n5. [Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution (PMC full text)](https://pmc.ncbi.nlm.nih.gov/articles/PMC6927209/)\n6. [High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways (iScience, 2022)](https://doi.org/10.1016/j.isci.2022.104097)\n7. [Identification and Localization of Cell Types in the Mouse Olfactory Bulb using Slide-SeqV2 (2024 protocol paper)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11061798/)\n8. [Patrik L. Ståhl and colleagues (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science.](https://doi.org/10.1126/science.aaf2403)\n9. [Sanja Vickovic and colleagues (2019). High-definition spatial transcriptomics for in situ tissue profiling. Nature Methods.](https://doi.org/10.1038/s41592-019-0548-y)\n10. [Andrew J. C. Russell and colleagues (2023). Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature.](https://doi.org/10.1038/s41586-023-06837-4)\n11. [Slide-seq Overview | WARP](https://broadinstitute.github.io/warp/docs/Pipelines/SlideSeq_Pipeline/README)\n12. [Chenlei Hu and colleagues (2025). Scalable spatial transcriptomics through computational array reconstruction. Nature Biotechnology.](https://doi.org/10.1038/s41587-025-02612-0)\n13. [SlideCNA: spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data (Genome Biology, 2025)](https://link.springer.com/article/10.1186/s13059-025-03573-y)\n14. [Benchmarking Study Points to Strengths, Challenges of Spatial Transcriptomics Platforms (GenomeWeb, 2024)](https://www.genomeweb.com/sequencing/benchmarking-study-points-strengths-challenges-spatial-transcriptomics-platforms)\n\n---\n*Topic: Encyclopedia › Life and health › Biological foundations › RNA and gene regulation › RNA elements, catalytic RNAs, and technologies › RNA methods, databases, and resources*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
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 "speakable": "Slide-seq is a spatial transcriptomics method, introduced at the Broad Institute in 2019, that transfers RNA from tissue sections onto DNA-barcoded 10 µm beads so gene expression is measured with position preserved."
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