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

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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

Code availability

Code related to this manuscript can be found at and The following package version numbers were used for data processing and associated analyses: (Drop-seq-tools-2.3.0), (picard-2.18.14), (STAR-2.5.2a), (0.1.25), (beta) and (2.3.4). MATLAB 2017a, R3.5.3 and Python 3.7 were used for processing data.


  1. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  Google Scholar 

  2. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  Google Scholar 

  3. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

  4. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    Article  CAS  Google Scholar 

  5. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    Article  CAS  Google Scholar 

  6. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  Google Scholar 

  7. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  Google Scholar 

  8. Drmanac, R. et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010).

    Article  CAS  Google Scholar 

  9. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    Article  CAS  Google Scholar 

  10. Hughes, T. K. et al. Second-strand synthesis-based massively parallel scRNA-seq reveals cellular states and molecular features of human inflammatory skin pathologies. Immunity 53, 878–894 (2020).

    Article  CAS  Google Scholar 

  11. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    Article  CAS  Google Scholar 

  12. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  Google Scholar 

  13. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    Article  CAS  Google Scholar 

  14. Choi, H. M. T. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development 145, dev165753 (2018).

  15. Govindarajan, A., Israely, I., Huang, S.-Y. & Tonegawa, S. The dendritic branch is the preferred integrative unit for protein synthesis-dependent LTP. Neuron 69, 132–146 (2011).

    Article  CAS  Google Scholar 

  16. Richter, J. D. & Klann, E. Making synaptic plasticity and memory last: mechanisms of translational regulation. Genes Dev. 23, 1–11 (2009).

    Article  CAS  Google Scholar 

  17. Huber, K. M., Kayser, M. S. & Bear, M. F. Role for rapid dendritic protein synthesis in hippocampal mGluR-dependent long-term depression. Science 288, 1254–1257 (2000).

    Article  CAS  Google Scholar 

  18. Kosik, K. S. Life at low copy number: how dendrites manage with so few mRNAs. Neuron 92, 1168–1180 (2016).

    Article  CAS  Google Scholar 

  19. Ainsley, J. A., Drane, L., Jacobs, J., Kittelberger, K. A. & Reijmers, L. G. Functionally diverse dendritic mRNAs rapidly associate with ribosomes following a novel experience. Nat. Commun. 5, 4510 (2014).

    Article  CAS  Google Scholar 

  20. Tushev, G. et al. Alternative 3′ UTRs modify the localization, regulatory potential, stability, and plasticity of mRNAs in neuronal compartments. Neuron 98, 495–511 (2018).

    Article  CAS  Google Scholar 

  21. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  CAS  Google Scholar 

  22. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  Google Scholar 

  23. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  Google Scholar 

  24. Welch, J. D., Hartemink, A. J. & Prins, J. F. SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17, 106 (2016).

    Article  Google Scholar 

  25. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  Google Scholar 

  26. Lodato, S. & Arlotta, P. Generating neuronal diversity in the mammalian cerebral cortex. Annu. Rev. Cell Dev. Biol. 31, 699–720 (2015).

    Article  CAS  Google Scholar 

  27. Telley, L. et al. Temporal patterning of apical progenitors and their daughter neurons in the developing neocortex. Science 364, eaav2522 (2019).

  28. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

  29. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. (2020).

  30. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  Google Scholar 

  31. Ruediger, T. et al. Integration of opposing semaphorin guidance cues in cortical axons. Cereb. Cortex 23, 604–614 (2013).

    Article  Google Scholar 

  32. Polleux, F., Giger, R. J., Ginty, D. D., Kolodkin, A. L. & Ghosh, A. Patterning of cortical efferent projections by semaphorin–neuropilin interactions. Science 282, 1904–1906 (1998).

    Article  CAS  Google Scholar 

  33. Heavner, W. & Pevny, L. Eye development and retinogenesis. Cold Spring Harb. Perspect. Biol. 4, a008391 (2012).

  34. Ashery-Padan, R., Marquardt, T., Zhou, X. & Gruss, P. Pax6 activity in the lens primordium is required for lens formation and for correct placement of a single retina in the eye. Genes Dev. 14, 2701–2711 (2000).

    Article  CAS  Google Scholar 

  35. Barbieri, A. M. et al. A homeobox gene, vax2, controls the patterning of the eye dorsoventral axis. Proc. Natl Acad. Sci. USA 96, 10729–10734 (1999).

    Article  CAS  Google Scholar 

  36. Andley, U. P. Crystallins in the eye: function and pathology. Prog. Retin. Eye Res. 26, 78–98 (2007).

    Article  CAS  Google Scholar 

  37. Niederreither, K., Subbarayan, V., Dollé, P. & Chambon, P. Embryonic retinoic acid synthesis is essential for early mouse post-implantation development. Nat. Genet. 21, 444–448 (1999).

    Article  CAS  Google Scholar 

  38. Fan, X. et al. Targeted disruption of Aldh1a1 (Raldh1) provides evidence for a complex mechanism of retinoic acid synthesis in the developing retina. Mol. Cell. Biol. 23, 4637–4648 (2003).

    Article  CAS  Google Scholar 

  39. Snead, M. P. et al. Stickler syndrome, ocular-only variants and a key diagnostic role for the ophthalmologist. Eye 25, 1389–1400 (2011).

    Article  CAS  Google Scholar 

  40. Fares-Taie, L. et al. ALDH1A3 mutations cause recessive anophthalmia and microphthalmia. Am. J. Hum. Genet. 92, 265–270 (2013).

    Article  CAS  Google Scholar 

  41. Martin, H. C. et al. Quantifying the contribution of recessive coding variation to developmental disorders. Science 362, 1161–1164 (2018).

    Article  CAS  Google Scholar 

  42. Kaplanis, J. et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature 586, 757–762 (2020).

    Article  CAS  Google Scholar 

  43. McKernan, K. J. et al. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res. 19, 1527–1541 (2009).

    Article  CAS  Google Scholar 

  44. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  45. Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. B Stat. Methodol. 64, 479–498 (2002).

    Article  Google Scholar 

  46. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  Google Scholar 

  47. Carlson genome wide annotation for mouse. R package version 3.8.2 (2019).

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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.

Author information

Authors and Affiliations



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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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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).

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