Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development

Nature Methods (2023)Cite this article

Subjects

Abstract

The brain is a complex tissue whose function relies on coordinated anatomical and molecular features. However, the molecular annotation of the spatial organization of the brain is currently insufficient. Here, we describe microfluidic indexing-based spatial assay for transposase-accessible chromatin and RNA-sequencing (MISAR-seq), a method for spatially resolved joint profiling of chromatin accessibility and gene expression. By applying MISAR-seq to the developing mouse brain, we study tissue organization and spatiotemporal regulatory logics during mouse brain development.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of MISAR-seq: data quality and validation.
Fig. 2: Spatial chromatin accessibility, gene expression and combined mapping for mouse brain development at E11.0, E13.5, E15.5 and E18.5.
Fig. 3: Combined analysis of chromatin accessibility and gene expression across development stages of mouse brain.
Fig. 4: The role of Neurod1 and Rfx2 in mouse brain development.
Fig. 5: Molecular dynamic and standard gene network of corticogenesis.

Data availability

MISAR-seq data reported in this paper are available accessed under National Genomics Data Center accession number (OEP003285, www.biosino.org/node/project/detail/OEP003285).

Code availability

Custom scripts used in this study are available from https://github.com/gpenglab/MISAR-seq or https://zenodo.org/record/7714382#.ZAqdYciUf8g (https://doi.org/10.5281/zenodo.7714382; license information: Creative Commons Attribution 4.0 International).

References

  1. Takei, Y. et al. Single-cell nuclear architecture across cell types in the mouse brain. Science 374, 586–594 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. La Manno, G. et al. Molecular architecture of the developing mouse brain. Nature 596, 92–96 (2021).

    Article  PubMed  Google Scholar 

  3. Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Vinsland, E. & Linnarsson, S. Single-cell RNA-sequencing of mammalian brain development: insights and future directions. Development 149, dev200180 (2022).

    Article  CAS  PubMed  Google Scholar 

  6. Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Preissl, S. et al. Deciphering the epigenetic code of cardiac myocyte transcription. Circ. Res. 117, 413–423 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Di Bella, D. J. et al. Molecular logic of cellular diversification in the mouse cerebral cortex. Nature 595, 554–559 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ortiz, C., Carlen, M. & Meletis, K. Spatial transcriptomics: molecular maps of the mammalian brain. Annu Rev. Neurosci. 44, 547–562 (2021).

    Article  CAS  PubMed  Google Scholar 

  11. Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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  PubMed  PubMed Central  Google Scholar 

  13. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).

  15. Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).

    Article  CAS  PubMed  Google Scholar 

  17. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116.e20 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  20. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Skinner, M. K., Rawls, A., Wilson-Rawls, J. & Roalson, E. H. Basic helix-loop-helix transcription factor gene family phylogenetics and nomenclature. Differentiation 80, 1–8 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tutukova, S., Tarabykin, V. & Hernandez-Miranda, L. R. The role of Neurod genes in brain development, function, and disease. Front Mol. Neurosci. 14, 662774 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lemeille, S. et al. Interplay of RFX transcription factors 1, 2 and 3 in motile ciliogenesis. Nucleic Acids Res. 48, 9019–9036 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Westerman, B. A., Chhatta, A., Poutsma, A., van Vegchel, T. & Oudejans, C. B. NEUROD1 acts in vitro as an upstream regulator of NEUROD2 in trophoblast cells. Biochim. Biophys. Acta 1676, 96–103 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Gonda, Y. et al. Expression profiles of Insulin-like growth factor binding protein-like 1 in the developing mouse forebrain. Gene Expr. Patterns 7, 431–440 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Greig, L. C., Woodworth, M. B., Galazo, M. J., Padmanabhan, H. & Macklis, J. D. Molecular logic of neocortical projection neuron specification, development and diversity. Nat. Rev. Neurosci. 14, 755–769 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Englund, C. et al. Pax6, Tbr2, and Tbr1 are expressed sequentially by radial glia, intermediate progenitor cells, and postmitotic neurons in developing neocortex. J. Neurosci. 25, 247–251 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Krieger, T. G. et al. Mutations in thyroid hormone receptor alpha1 cause premature neurogenesis and progenitor cell depletion in human cortical development. Proc. Natl Acad. Sci. USA 116, 22754–22763 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bernal, J. Thyroid hormone receptors in brain development and function. Nat. Clin. Pr. Endocrinol. Metab. 3, 249–259 (2007).

    Article  CAS  Google Scholar 

  31. Moffitt, J. R., Lundberg, E. & Heyn, H. The emerging landscape of spatial profiling technologies. Nat. Rev. Genet. 23, 741–759 (2022).

    Article  CAS  PubMed  Google Scholar 

  32. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhang, Y. et al. Model-based analysis of ChIP–Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Key R&D Program of China (grant no. 2018YFA0801402 to G.P.), the ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences (grant no. XDA16020404 to G.P.), National Natural Science Foundation of China (grant nos. 32270854 and 32161160322 to G.P., 32100483 to G.C.), Guangdong Basic and Applied Basic Research Foundation (grant nos. 2019B151502054 to G.P., 2019A1515110985 to G.C. and 2020A1515110517 to F.Q.), Frontier Research Program of Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory, grant no. 2018GZR110105013), Jiazi Research Innovative Project of Bioland Laboratory (grant no. 2019GZR110108001) and the Science and Technology Planning Project of Guangdong Province (grant no. 2020B1212060052). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank P. Tam for discussions and critical reading of this study.

Author information

Author notes

  1. These authors contributed equally: Fuqing Jiang, Xin Zhou, Yingying Qian, Miao Zhu.

Authors and Affiliations

  1. Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, University of Chinese Academy of Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

    Fuqing Jiang, Yingying Qian, Miao Zhu, Li Wang, Zhuxia Li, Minhan Wang & Guangdun Peng

  2. Center for Cell Lineage and Atlas, Bioland Laboratory, Guangzhou, China

    Fuqing Jiang, Xin Zhou, Qingmei Shen, Fangfang Qu, Guizhong Cui & Guangdun Peng

  3. Guangzhou Laboratory, Guangzhou, China

    Fuqing Jiang, Fangfang Qu & Guizhong Cui

  4. Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Yingying Qian

  5. Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China

    Kai Chen

  6. Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China

    Guangdun Peng

Authors
  1. Fuqing Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingying Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhuxia Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qingmei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Minhan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fangfang Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Guizhong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Guangdun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.P. and F.J. designed the study. F.J., X.Z., Y.Q., L.W., Q.S. and M.W. performed the experiments. F.J. and M.Z. analyzed the data with help from F.Q., G.C. and Z.L. K.C. provided reagents and suggestions. F.J. and G.P. wrote the manuscript with the help of others.

Corresponding author

Correspondence to Guangdun Peng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Lei Tang and Nina Vogt, in collaboration with the Nature Methods team.

Additional information

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

Extended data

Extended Data Fig. 1 MISAR-seq: workflow, design and library distribution.

a, MISAR-seq workflow. The tissue section was first subjected to Tn5 tagmentation, followed by reverse transcription (RT) and finally two rounds of barcode (50 kinds each) ligation on chip. R1, Read1 adaptor. R2, Read2 adaptor. b, The composition of microfluidic devices used in this study, including PDMS slab, barcode inlets, acrylic clamp and suction hole. c, AutoCAD design of PDMS chip with 50 μm channel width. d, Verification of leakage or diffusion by Cy3 and Fam dye. e-g, Size distributions of ATAC library (e), cDNA amplicons (f) and cDNA library (g).

Extended Data Fig. 2 Data quality of spatial ATAC in MISAR-seq.

a-c, Comparison of percentage for mitochondrial, TSS fragments and FRiP value between MISAR-seq and 10x scATAC-seq, S1, Section1. S2, Section2. Dashed lines indicate the results from Spatial-ATAC-seq14 (a). Comparison of insert fragments size distribution (b) and TSS enrichment profiles (c) between MISAR-seq and 10x scATAC-seq. The violin plots were drawn from lower quartile (Q1) to upper quartile (Q3), with middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers indicated with dots. (n = 3,598 (10x scATAC-seq (E18.0 brain)), 1,263 (E11.0 brain (S1)), 1,353 (E11.0 brain (S2)), 1,777 (E13.5 brain (S1)), 2,183 (E13.5 brain (S2)), 1,949 (E15.5 brain (S1)), 1,939 (E15.5 brain (S2)), 2,129 (E18.5 brain (S1)) and 2,248 (E18.5 brain (S2)) cells or grids, respectively).

Extended Data Fig. 3 Data quality of spatial RNA-seq and spatial cluster similarity comparison in MISAR-seq.

a, Comparison of percentage of mitochondrial and ribosome protein per grid between MISAR-seq and DBiT-seq. The violin plots were drawn from lower quartile (Q1) to upper quartile (Q3), with middle line denoting the median, whiskers with maximum 1.5 interquartile range (IQR) and outliers indicated with dots. (n = 902 (DBiT-seq (E11.0 50 μm)), 1,263 (E11.0 brain (S1)), 1,353 (E11.0 brain (S2)), 1,777 (E13.5 brain (S1)), 2,183 (E13.5 brain (S2)), 1,949 (E15.5 brain (S1)), 1,939 (E15.5 brain (S2)), 2,129 (E18.5 brain (S1)) and 2,248 (E18.5 brain (S2)) girds, respectively). b, Unsupervised clustering of two sections from E15.5 mouse brain for spatial ATAC, RNA and combined modalities. c, RMSE (root mean squared error) similarity for two E15.5 brain sections of spatial ATAC, RNA and combined cluster pattern.

Extended Data Fig. 4 Spatial chromatin accessibility, gene expression and combined mapping for mouse brain development at E11.0, E13.5, E15.5 and E18.5 from section 2.

a-c, Spatial-ATAC (a), RNA (b) and combined (c) UMAP visualization of different mouse brain development stage, colored by different clusters. d, Combined spatial ATAC and RNA UMAP visualization of integrated different mouse brain development stage, colored by different sample sections. e-g, Unsupervised clustering of mouse brain sections for spatial ATAC (e), RNA (f) and combined (g). h, Anatomic annotation of major tissue regions based on the H&E images for different mouse brain stages. DPall, dorsal pallium.

Extended Data Fig. 5 Comparison of spatial expression of selected genes with in situ hybridization data from Allen Mouse Brain Atlas.

a-f, Spatial mapping of gene expression (RNA) and gene score (ATAC) for selected marker genes, and in situ hybridization of corresponding genes at E11.0, E13.5, E15.5 and E18.5 mouse brain.

Extended Data Fig. 6 Marker genes analysis for each cluster.

a, Peak annotation and proportion plot for each cluster. b, Heatmap of spatial ATAC marker peaks across all clusters.

Extended Data Fig. 7 The UMAP embedding and spatial mapping of TF deviation scores, gene scores, gene expression for represented gene.

a, Tn5 bias-adjusted transcription factor footprints for Rfx2 motifs. b, UMAP embedding and spatial mapping of TF deviation scores, gene scores, gene expression and in situ hybridization results from Allen Mouse Brain Atlas at different stages of mouse brain for Rfx2. c-d, UMAP embedding and spatial mapping of gene scores and gene expression for Igfbpl1 (c) and Neurod2 (d).

Extended Data Fig. 8 The genome tracks of representative target genes.

a-c, The genome tracks showing the chromatin accessibility (top), peak sites, peak coaccessibility (middle), peak-gene linkages, gene tracks (bottom), gene expression (right) for Pou2f2 (a), Snhg11 (b), Kndc1 (c) in each cluster. Neurod1 and Rfx2 motif were shown as gray box.

Extended Data Fig. 9 The UMAP embedding and spatial mapping of gene scores, gene expression for represented gene.

a, UMAP embedding and spatial mapping of gene scores, gene expression and in situ hybridization results from Allen Mouse Brain Atlas at different stages of mouse brain for Pou2f2. b,c, UMAP embedding and spatial mapping of gene scores and gene expression for Snhg11 (b) and Kndc1 (c).

Extended Data Fig. 10 Molecular dynamic and gene regularly network of corticogenesis.

a, Distribution of pseudotime value across development stages. b, Scatterplot showing pseudotime value versus distances to the inner layer of cortex, colored by development stages. c, GO enrichment of top 200 enhancer-regulated genes. d,e, DER accessibility score and gene expression of Sox2, Neurod2, Satb2 (d), Mef2c, Neurod6 and Thra (e) in the pseudotime axis. f-h, Spatial mapping of DER Accessibility score, gene scores, gene expression and in situ hybridization results from Allen Mouse Brain Atlas at different stages of mouse brain for Mef2c (f), Thra (g), and Neurod6 (h). i, Gene regulatory network visualization for Neurod6- Mef2c -Thra cascade. The width of edges represents regulation score. j, Schematic of regulatory relationship among Mef2c, Neurod6 and Thra in the corticogenesis.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Table 1

Oligo and mapping information.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, F., Zhou, X., Qian, Y. et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat Methods (2023). https://doi.org/10.1038/s41592-023-01884-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41592-023-01884-1

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research