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

Nature Methods volume 20, pages 1048–1057 (2023)Cite this article

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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.

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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.

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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).

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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

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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.

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Correspondence to Guangdun Peng.

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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.

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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.

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Jiang, F., Zhou, X., Qian, Y. et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat Methods 20, 1048–1057 (2023). https://doi.org/10.1038/s41592-023-01884-1

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