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Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding

Nature Methods volume 20, pages 1244–1255 (2023)Cite this article

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Abstract

A fundamental interest in developmental neuroscience lies in the ability to map the complete single-cell lineages within the brain. To this end, we developed a CRISPR editing-based lineage-specific tracing (CREST) method for clonal tracing in Cre mice. We then used two complementary strategies based on CREST to map single-cell lineages in developing mouse ventral midbrain (vMB). By applying snapshotting CREST (snapCREST), we constructed a spatiotemporal lineage landscape of developing vMB and identified six progenitor archetypes that could represent the principal clonal fates of individual vMB progenitors and three distinct clonal lineages in the floor plate that specified glutamatergic, dopaminergic or both neurons. We further created pandaCREST (progenitor and derivative associating CREST) to associate the transcriptomes of progenitor cells in vivo with their differentiation potentials. We identified multiple origins of dopaminergic neurons and demonstrated that a transcriptome-defined progenitor type comprises heterogeneous progenitors, each with distinct clonal fates and molecular signatures. Therefore, the CREST method and strategies allow comprehensive single-cell lineage analysis that could offer new insights into the molecular programs underlying neural specification.

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Fig. 1: A Cre-driven lineage recording mouse model based on CRISPR barcoding.
Fig. 2: Transcriptional profiling of En1-derived cells in the developing mouse vMB.
Fig. 3: CREST barcode-based lineage relationships among cell clusters in the developing mouse vMB.
Fig. 4: Clonal analyses reveal variable fate outcomes for individual En1-derived vMB progenitors.
Fig. 5: Distinct differentiation trajectories and molecular programs for DA and GLU specification.
Fig. 6: CREST barcoding combined with ex vivo brain organoid culture and clonal splitting strategy reveals fate potentials of transcriptome-defined progenitors.

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Data availability

Sequencing raw data and counts matrices are available under accession number NCBI GEO: GSE210139. The reference genome mm10 is available from the 10X Genomics website https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build. Raw imaging data have been deposited in Mendeley Data at https://data.mendeley.com/datasets/6b36kp78g5. Source data are provided with this paper.

Code availability

Computer code for single-cell transcriptome analysis and lineage analysis is available at https://github.com/Y0NEKO/ChenLab-CREST.

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Acknowledgements

We thank J. Zhou in Institute of Neuroscience, Z. Yang in Fudan University Shanghai Medical College and B. Zhou in Institute of Biochemistry and Cell Biology for providing us En1-Cre, Nkx2.1-Cre and NR1 mouse lines. We thank M. Poo and S. Yang for intensive discussions on the logic and flow of the manuscript’s content. We thank J. He for comments on the manuscript. We thank D. Xiang, X. Chen and Q. Hu in Optical Imaging facility, Z. Zhou, M. Zhang, H. Hai and L. Quan in Molecular and Cellular Biology Core Facility, M. Zhang and P. Bao in Neural Stem Cell Core Facility at the Institute of Neuroscience. This research was supported in part by STI2030-Major Projects (grant no. 2021ZD0200900 to Y.C.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32030200 to Y.C.), the Shanghai Municipal Science and Technology Major Project (grant nos. 2018SHZDZX05 to Y.C. and 2018SHZDZX01 to M.X.), the National Natural Science Foundation of China (grant nos. 32170806 to Y.C.; 82222021, 32270849 and 81974174 to M.X. and 81870187 to W.W.), Program of Shanghai Academic/Technology Research leader (grant no. 22XD1420800 to M.X.) and UniXell R&D Innovation Cooperation Program (grant no. DA001-RD202101 to Y.C.).

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Author notes

  1. These authors contributed equally: Lianshun Xie, Hengxin Liu.

Authors and Affiliations

  1. Institute of Neuroscience, Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China

    Lianshun Xie, Zhiwen You, Yiwen Li, Xinyue Zhang, Hui He, Tingli Yuan, Wenping Zheng & Yuejun Chen

  2. University of Chinese Academy of Sciences, Beijing, China

    Lianshun Xie, Hengxin Liu, Zhiwen You, Luyue Wang & Xinyue Zhang

  3. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

    Hengxin Liu, Luyue Wang & Wu Wei

  4. Department of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China

    Xiaoshan Ji

  5. UniXell Biotechnology, Shanghai, China

    Ziyan Wu

  6. State Key Laboratory of Medical Neurobiology–Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China

    Man Xiong

  7. Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China

    Wu Wei

  8. Lingang Laboratory, Shanghai, China

    Wu Wei

  9. Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China

    Yuejun Chen

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Contributions

Y.C. conceived the project. Y.C. and W.W. supervised the project. Y.C., Z.Y. and L.X. designed CREST mouse lines. L.X. performed bulk experiments, scRNA-seq experiments, library construction and ex vivo culture. L.X. and X.Z. performed transcriptome analysis. H.L., L.W. and L.X. performed lineage analysis. L.X., X.Z., Z.W. and W.Z. performed plasmid construction. Y.L., X.J., H.H., M.X., L.X. and T.Y. performed immunohistochemistry and imaging. L.X., Y.C., H.L. and W.W. wrote the manuscript.

Corresponding authors

Correspondence to Wu Wei or Yuejun Chen.

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The authors declare no competing interests.

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Nature Methods thanks James Gagnon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Madhura Mukhopadhyay, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Generation of CERST mouse lines.

a, Gene targeting of the TIGRE locus. The gRNA cassette and the V1 recorder cassette were targeted into the TIGRE locus of mouse embryonic stem cells (mESCs). The upper diagram shows the wild-type TIGRE locus, the lower shows the donor plasmid used for gene targeting. The probes used for southern blot are shown. Neo, neomycin resistance gene driven by PGK promoter; Frt, recognition site of Flp recombinase; DTA, diphtheria toxin A. b, Southern blot of selected mESC clones. The probes used are indicated in Extended Data Fig. 1a. Successful targeting of clone 3-H03 was confirmed using restriction fragments of different sizes: 19.7 kb for the 5′ probe, 6.3 kb for the 3′ probe, and 11.7 kb for the Neo probe. c, BFP expression was observed in the 3-H03 mESC clone, which was used to generate the TIGRECREST-gRNAs mouse line. d, Gene targeting of Rosa26 locus. The Cas9 cassette and the V2 recorder cassette were targeted into the Rosa26 locus of mouse zygotes. The upper diagram shows the wild-type Rosa26 locus, and the lower shows the donor plasmid used for gene targeting. e, Successful targeting of Rosa26CREST-Cas9 mouse was confirmed through genotyping using the primers indicated in Extended Data Fig. 1d. f, Retrieval of CREST barcodes for individual cells. CREST barcodes are enriched from cDNA libraries of single cells, classified as V1 or V2, and assigned to individual cells by 10X cell barcodes for clonal analysis (see Methods).

Source data

Extended Data Fig. 2 Validation of CERST in vivo.

a, Immunohistochemistry images at E12.5 and P0 showing that En1-driven Cre recombinase activates EGFP expression, which indicates Cas9 expression in the midbrain and anterior hindbrain. The boxed areas are magnified. Scale bars, 200 µm. b, FACS results of En1-Cre-CREST embryos between E7.5–E8.5. c, d, Edited array proportion (c) and CREST potential (d) in targeted (EGFP+) and non-targeted (EGFP−) cells at E8.5. CREST libraries from mice without Cas9 or without sgRNA were used as control for V1 or V2, respectively. Library preparation error is limited (V1, 0.20%; V2, 0.38%). e, InDels (insertions and deletions) of varying lengths were observed in both recorder arrays. The occurrence is calculated as [the number of InDel types with the indicated length in V1 or V2] / [total number of InDel types of V1 or V2]. f, g, The 20 most frequently observed CREST barcodes in V1 (f) and V2 (g) recorders in each dataset before blacklist filtering. Numerical postfixes represent biological replicates; alphabetical postfixes represent technical replicates. Heatmaps (right) show the proportion of the indicated CREST barcode in each sample. h, Edit type frequency at each target site of V1 and V2 among all En1-Cre-CREST bulk samples. i, Frequencies of InDel types spanning different numbers of target sites in all En1-Cre-CREST bulk samples. Data are presented as mean +/− SEM. j, Length (bp) of V1 and V2 barcodes among all En1-Cre-CREST bulk samples. The lower and upper hinges are the first and third quartiles, the central line is the median, and the whiskers extend to ±1.5* IQR. For panels i and j, statistical analyses were performed using the two-sided Student’s t test and p values were shown. k, l, Quantification of proportions of CREST barcodes (k) or InDels (l) with different frequencies in eight biological samples (E9.5–P0). m, Venn diagram showing shared V1 or V2 barcodes and their corresponding cell (UMI) counts in three Nkx2.1-Cre-CREST replicates. 167,763 and 503,571 cells were analyzed for V1 and V2, respectively.

Source data

Extended Data Fig. 3 Marker genes, pseudo-time ranks, and validations of cell types in E11.5 and E15.5 vMB.

a, b, Representative FACS gating strategies for En1-Cre-CREST vMB at E11.5 and E15.5. Cells in Q2 (EGFP+/BFP+) were sorted and collected for scRNA-seq. c, d, Violin plots showing selected marker genes for each cell cluster in E11.5 (c) and E15.5 (d) datasets. Mean expression levels (color shade) in groups are shown. All marker genes used to define cell types are listed in Supplementary Tables 4 and 5. e, Pseudo-time ranks (calculated by Monocle 2) of individual cells are visualized on the merged UMAP space. f, Quantification of mean pseudo-time ranks in each cluster. Earlier state and later state cell types are defined manually based on pseudo-time ranks. Early progenitor types at E11.5 are highlighted in blue. The late neuronal types of E15.5 are highlighted in red. The lower and upper hinges are the first and third quartiles, the central line is the median, and the whiskers extend to ±1.5* IQR (inter-quartile range). Cell number for each cell type is shown in the figure. g, h, In situ hybridization of coronal sections of mice brains E11.5 (g) and E15.5 (h), showing expression of marker genes of different domains and GLU neurons in the midbrain. Data are collected from the Allen Brain Atlas. Scale bar, 100 µm. i, In situ hybridization of sagittal sections of E15.5 mouse brains, showing the expression of marker genes in the mid-hindbrain boundary. Data are collected from Allen Brain Atlas. Scale bar, left 100 µm.

Source data

Extended Data Fig. 4 Cell type comparisons across different samples.

a, Comparison of cell compositions in datasets from this study versus previously published datasets33. Heatmaps showing the distribution of the densities of cells sampled at E11.5 in this study (upper left), at E15.5 in this study (lower left), at E11.5 from the published literature (upper right), and at E15.5 from published literature (lower right) mapped in the UMAP space. b, Cell types in panel a are indicated by canonical markers of neural progenitor (Sox2), neuroblast (Neurod1), and GABAergic neuroblast (Otx2+/Gata3+). c, Co-staining of Nkx6-1 and Nkx6-2 using in situ hybridization (RNAscope) in E11.5 vMB. Representative regions are magnified to show NPBL (Nkx6-1+/Nkx6-2+) and NPBM (Nkx6-1+/Nkx6-2−) cells. BL, basal lateral; BM, basal medial; FP, floor plate. Scale bar, left 50 µm, right 10 µm. d, Hierarchical clustering of pairwise correlations between cell types in of E11.5 and E15.5 datasets based on transcriptomic similarities. Neuroblasts captured at both timepoints are highlighted in red. Cell type names are prefixed with ‘E11’ or ‘E15’ to indicate their sources from the E11.5 or E15.5 vMB datasets, respectively. e, Comparison of cell type compositions between En1-Cre-CREST and normally developed (En1-Cre/Ai9) mouse vMB. Two-sided paired t-test was used to examine the correlations between two groups. f, Gene expression correlation of each cell type between En1-Cre-CREST and normally developed (En1-Cre/Ai9) vMB datasets. Each dot represents a gene.

Source data

Extended Data Fig. 5 Additional details of lineage correlation analyses.

a, Workflow of single-cell lineage analysis adapted to the CREST system. b, Proportions of cell barcode overlap across three CREST barcode amplicons for each scRNA-seq sample. For E11.5 (n = 6), 82.7 ± 2.3% and 72.8 ± 2.7% of cell barcodes were shared across three amplicons for V1 and V2, respectively; the numbers were 78.3 ± 0.9% and 81.5 ± 3.6% for E15.5 (n = 3); 76.4 ± 0.5% and 80.3 ± 0.9% for organoids (n = 3). c, Barcode recovery among samples. 77.3 ± 1.2% E11.5 cells (45.7 ± 8.1% for V1, 74.1 ± 0.9% for V2, and 42.5 ± 8.0% for both V1 and V2; n = 6), 70.9 ± 3.4% E15.5 cells (32.0 ± 4.5% for V1, 69.3 ± 3.6% for V2, and 30.4 ± 4.6% for both V1 and V2; n = 3), and 56.6 ± 4.1% organoid cells (31.2 ± 9.8% for V1, 52.5 ± 5.5% for V2, and 27.1 ± 11.2% for both V1 and V2; n = 3) were recovered with at least one CERST barcodes. d, e, Bar plots showing the proportions of cells recovered with only V1 barcodes, only V2 barcodes, both V1 and V2 barcodes, or no CREST barcode in each cell type in six E11.5 (d) and three E15.5 (e) datasets. f, Pairwise lineage correlation similarities among replicates. Each dot represents the lineage correlation (calculated as the absolute Pearson correlation based on V1 + V2) of a pair of cell types. g, Summary of the correlation coefficient in panel f. The p values were calculated using the two-sided ANOVA test. h, Pairwise lineage correlation similarities across lineage relationships obtained based on V1 + V2 CREST barcodes, V1 CREST barcodes, and V2 CREST barcodes (see Methods). Each dot represents the pairwise correlation calculated from the combined dataset of all three replicates for each group of experiments. i, Summary of the correlation coefficient in panel h. In panels b, c, g, data are presented as mean +/− SEM. In panels f and h, the p values were calculated using the two-sided Pearson’s correlation test.

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Extended Data Fig. 6 Benchmarking, lineage validation, and details of clonal properties in vivo.

a, Workflow for benchmarking CRISPR barcoding lineage analysis methods. Phylogenic trees were first divided into several given numbers (leaf resolution) of subtrees (upper). These subtrees were then taken as leaves (that is, clone groups) to calculate the pairwise lineage correlations. The correlations among the pairwise lineages of three replicates were calculated for each leaf resolution. b, Benchmarking results of different lineage reconstruction methods, including two top scoring phylogenic tree reconstruction methods in the DREAM challenge (Cassiopeia and DCLEAR)36, one traditional phylogenic tree reconstruction method based on Hamming distance and the clonal analysis method. The consistency of lineage results across replicates peaks at single leaf resolution (that is, clonal analysis). For each method at each resolution, three snapCREST E15.5 replicate datasets were used (n = 3 for each box). c, Schematics of fate mapping of floor plate (Lmx1a+) cells. Lmx1a-iCreER mice were crossed with Ai9 (Rosa26-LSL-tdT) reporter mice. Tamoxifen was administrated at E10.5. Embryos were collected at E15.5. d, Fate mapping results of floor plate cells. Representative images are magnified to show the floor plate-derived DA neurons and GLU neurons (GLUFP). Scale bars, 100 µm (upper left) and 10 µm (upper right and lower right). e, Boxplots showing the size distributions of all clones in the E11.5 and E15.5 datasets. f, g, Boxplots showing the size distributions of all clones in each domain. Clones in the E11.5 (f) and E15.5 (g) datasets are classified and grouped based on the dominant regional identities of the clonal cells. h-m, Statistical analysis of InDel number vs. regional identity at E11.5 (h) and E15.5 (i), vs. proliferative potential at E11.5 (j) and E15.5 (k), and vs. progenitor type at E11.5 (l) and E15.5 (m). Statistics: two-sided Student’s t for each indicated pair in panels e-g, l-m; two-sided ANOVA test for panels h-i; two-sided Mann–Kendall trend test for panels f-g, j-k. For each boxplot in this figure, the lower and upper hinges are the first and third quartiles, the central line is the median, and the whiskers extend to ±1.5* IQR.

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Extended Data Fig. 7 Additional data of regional analyses and clustering of clones at E11.5.

a, Visualization of dispersion behaviors of clones along the dorsal-ventral axis in the E11.5 dataset, where circles, sectors, and boxes are colored by regional identities as described in Fig. 4b. b, Distribution of dispersion scores in E11.5 clones. c, Relationships between dispersion score and maximum percentage (calculated as [number of cells with the most dominant regional identity in each clone] / [total number of cells in each clone (referred to as clone size)]); between clone size and cell type complexity (calculated as the Shannon-Wiener Index based on the clonal cell type compositions); and between clone size and dispersion score of clones in E11.5 (upper) and E15.5 (lower) datasets. d, e, Clustering of pairwise lineage correlations as determined by CREST barcodes shared between midbrain domains in the E11.5 dataset (d) and the E15.5 dataset (e). Cell types with a same regional identity in each dataset (as described in Fig. 2f–j) were merged into a domain cluster. For example, NbFP and Rgl1 in the E11.5 dataset were merged as the E11.5 floor plate (FP) cluster. Cell types with undefined regional identities were discarded. f, Quantification of the number of clonal cell types in clones from the E11.5 (left) and E15.5 (right) datasets. Multipotent progenitors (producing at least two distinct cell types) were predominant, giving rise to 85.8% and 88.8% of individual clones at E11.5 and E15.5, respectively. g, Clones in the E11.5 dataset are clustered based on the cell type composition matrix. h, Visualization of E11.5 clone clusters mapped on the UMAP space. The UMAP reference is colored based on spatial identities.

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Extended Data Fig. 8 Additional data for analysis and validation of floor plate lineages.

a, Visualization of fate-biased NbFP cells on the UMAP space. b, Loadings of principal component 1 (PC1) and principal component 2 (PC2). c, Design of sgRNAs targeting Pbx1 and primers for genotyping, also see Methods. d, Representative TA cloning results of Pbx1-KO embryos. Each panel shows an allele of the Pbx1 gene. The upper sequence in each panel shows the sequence of exon I of Pbx1 in a wild-type mouse. Embryos with both alleles being out-of-frame mutated were used in Pbx1-KO group. e, f, Statistical analysis of total DA cell (TH+) counts and total GLU cell (TH−Vglut2+) counts in control (n = 6) and Pbx1-KO (n = 6) embryos. E14.5 mouse embryos were analyzed. For each boxplot, p values were calculated using two-sided Student’s t test. Lower and upper hinges are the first and third quartiles, the central line is the median, and the whiskers extend to the minima and maxima.

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Extended Data Fig. 9 Transcriptional similarities between sister subclones split from the E11.5 dataset and between cell types in the organoid and E15.5 vMB.

a, b, Cells from split clone pairs (that is, sister subclones) are transcriptionally similar. The replicate 1 of snapCREST-E11.5 dataset was randomly divided for analyses. Variations in integrated clonal transcriptomes (a) and integrated clonal progenitor transcriptomes (b) between sister subclones were visualized and analyzed using: 1) UMAP clustering of integrated transcriptomes; 2) typical examples of sister subclones/progenitors pairs, colored by the clone cluster defined in Extended Data Fig. 7g, and sister subclones/progenitors are linked by colored lines; 3) distributions of UMAP distances between sister and random subclone pairs; and 4) quantification of pairwise Pearson’s correlations of integrated transcriptomes between random, nearest neighbors in UMAP spaces, and sister subclone pairs. See also Methods. For each boxplot, the lower and upper hinges are the first and third quartiles, the central line is the median, and the whiskers extend to ±1.5* IQR; p values were calculated using two-sided Student’s t test and were shown in the figure. c, d, Sister subclones (c) and sister progenitors (d) prefer to localize in the same or nearby transcriptome-based clusters. Left, cumulative probability that subclone pairs will lie within the same cluster or across N nearest clusters of panel a; right, heatmaps showing the probability that a sister subclone pair lies in which two clusters of panel a. See also Methods. e, Heatmaps showing cell distribution of the densities in the organoid and E15.5 datasets on the integrated UMAP space. f, Hierarchical clustering of pairwise correlations between cell types in E15.5 and organoid datasets based on transcriptomes. The names of cell types are prefixed with ‘E15’ or ‘OGN’ to indicate their sources. g, Cell types in (e) are indicated by canonical markers of Rgl (Slc1a3), DA (Pitx3), GABA (Gad1), GLU (Slc17a6), BM (Pitx2), AL/BM (Pou4f1), AL (Lhx2), and MHB (Pax5). h, Gene expression correlation of each cell type between ex vivo organoids and in vivo E15.5 vMB datasets.

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Extended Data Fig. 10 Lineage similarities between in vivo and ex vivo datasets and fate maps for E11.5 progenitors.

a, PCA analysis of clonal cell-type compositions for in vivo and ex vivo clones. b, Pairwise lineage correlation (calculated as absolute Pearson correlation) of cell types captured both in vivo and ex vivo. The upper right shows the lineage relationship in vivo. The lower left shows the lineage relationship ex vivo. c, Examples of the fate of individual NPBM-restricted clones in Fig. 6f are shown on UMAP spaces of the E11.5 and OGN datasets. d, Fate maps for individual NPBL-restricted clones. An example of each fate is shown on the UMAP spaces of the E11.5 and OGN datasets.

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Xie, L., Liu, H., You, Z. et al. Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding. Nat Methods 20, 1244–1255 (2023). https://doi.org/10.1038/s41592-023-01947-3

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