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Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types

Nature Genetics volume 54, pages 1711–1720 (2022)Cite this article

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Abstract

Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.

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Fig. 1: Zebrafish and Drosophila cell landscapes constructed using Microwell-seq.
Fig. 2: Cross-species analysis of eight species.
Fig. 3: Comparison of regulatory TFs within and across species using scRNA-seq data.
Fig. 4: Application and interpretation of the deep-learning model framework.
Fig. 5: Interpretation of the deep-learning model framework.

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Data availability

Raw data files for the RNA sequencing analysis reported in this paper have been deposited in is the NCBI Gene Expression Omnibus under accession number GSE178151. Digital expression matrices are available at https://figshare.com/s/ecc05b1051fb5678fd3e. Nvwa data can be accessed at http://bis.zju.edu.cn/nvwa/.

Code availability

The source code for reproducing our analysis and running and training the Nvwa models is available at GitHub (https://github.com/JiaqiLiZju/Nvwa/) and Zenodo (https://zenodo.org/record/6806748) (JiaqiLiZju/Nvwa: release v.1.0, 2022).

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Acknowledgements

G.G. is a participant in the Human Cell Atlas Project. We thank M. Chen, Y. Zhou, F. Gu, D. Wang, P. Xu, C. Li, K. Li and H. Wu for support on the project. We thank M20 (Hangzhou), G-BIO (Hangzhou), BGI (Shenzhen) and CNGB (Shenzhen) for supporting the sequencing experiments; and Vazyme (Nanjing) for supplying the customized enzymes used in the study. We also thank the core facility platform of Zhejiang University School of Medicine and the Center of Cryo-Electron Microscopy at Zhejiang University for computational resources, and the core facilities of Zhejiang University Medical Center and the Liangzhu Laboratory for technical support. This work was supported by National Natural Science Foundation of China grants 31930028 to G.G., 31922049 to X.H., 91842301 to G.G., 32000461 to J.W. and 62088101 to J.C.; National Key Research and Development Program grants 2018YFA0800503 to G.G., 2018YFA0107804 to G.G. and 2018YFA0107801 to X.H.; Fundamental Research Funds for the Central Universities (G.G.); and Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare.

Author information

Author notes

  1. These authors contributed equally: Jiaqi Li, Jingjing Wang, Peijing Zhang, Renying Wang.

Authors and Affiliations

  1. Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Jiaqi Li, Jingjing Wang, Peijing Zhang, Renying Wang, Yuqing Mei, Zhongyi Sun, Lijiang Fei, Mengmeng Jiang, Lifeng Ma, Weigao E, Haide Chen, Xinru Wang, Yuting Fu, Hanyu Wu, Daiyuan Liu, Xueyi Wang, Jingyu Li, Yuan Liao, Chengxuan Yu, Danmei Jia, Xiaoping Han & Guoji Guo

  2. Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

    Jiaqi Li, Jingjing Wang, Peijing Zhang, Mengmeng Jiang, Haide Chen & Guoji Guo

  3. Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China

    Qile Guo & Guoji Guo

  4. Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, China

    Yuan Liao, Xiaoping Han & Guoji Guo

  5. Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital School of Medicine, Zhejiang University, Hangzhou, China

    Jian Wu

  6. College of Control Science and Engineering, Zhejiang University, Hangzhou, China

    Shibo He & Jiming Chen

  7. Women’s Hospital and Institute of Genetics, Zhenjiang University School of Medicine, Hangzhou, China

    Huanju Liu & Jun Ma

  8. Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China

    Kai Lei

  9. Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China

    Guoji Guo

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  1. Jiaqi Li
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  2. Jingjing Wang
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Contributions

G.G., X.H. and J. Wang conceived the study. G.G. and X.H. supervised the study. Jiaqi Li designed the model. X.H., R.W., M.J., X.H., H.C., Xinru Wang, Xueyi Wang, Y.L., D.J. and T.Z. performed all the experiments. Jiaqi Li, J. Wang, P.Z., Y.M., Z.S., L.F., L.M., W.E., Y.F., H.W., D.L., H.W., Jingyu Li, Q.G. and C.Y. performed all computational analyses. H.L., J.M. and K.L. helped with the cell-type annotation. J. Wu., S.H. and J.C. guided model design and parameter optimization. G.G., J. Wang., Jiaqi Li and P.Z. wrote the initial draft of the manuscript. All authors participated in discussion of results and manuscript editing.

Corresponding authors

Correspondence to Jingjing Wang, Xiaoping Han or Guoji Guo.

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Extended data

Extended Data Fig. 1 Zebrafish cell landscape was constructed using Microwell-seq.

(a-b) t-SNE visualization of 635,228 single cells from whole bodies across three stages of zebrafish, colored by stage (a) and cell lineage (b). (c) Heatmap showing the scaled average expression levels of zebrafish cell type-specific marker genes (left), and relative gene expression of representative cell type-specific markers for each cell type overlaid on t-SNE plots (right). (d) Heatmap showing the correspondence between zebrafish cell landscape in this study (row) and tissue-specific zebrafish dataset from Jiang et al., 2021 (column). Blue refers to a mean AUROC greater than 0.9. (e-f) t-SNE showing 24 subclusters from zebrafish C1 (neuron) (e), and violin plot showing the scale normalized expression levels of representative markers for each subcluster (f).

Extended Data Fig. 2 Drosophila cell landscape was constructed using Microwell-seq.

(a-b) t-SNE visualization of 276,706 single cells from whole bodies across two stages of Drosophila, colored by stage (a) and cell lineage (b). (c) Heatmap showing the scaled average expression levels of Drosophila cell type-specific marker genes (left), and relative gene expression of representative cell type-specific markers for each cluster overlaid on t-SNE plots (right). (d) Heatmap showing the correspondence between Drosophila cell landscape in this study (row) and tissue-specific fly cell atlas construed by Li et al., 2021 (column). Blue refers to a mean AUROC greater than 0.9. (e-f) t-SNE showing 21 subclusters from Drosophila C1 (central nerve cell) (e), violin plot showing the scale normalized expression levels of representative markers for each subcluster (f).

Extended Data Fig. 3 Earthworm cell landscape was constructed using Microwell-seq.

(a) t-SNE visualization of 95,020 single cells from whole bodies of earthworm, colored by cell type (left) and cell lineage (right). (b) Heatmap showing the scaled average expression levels of earthworm cell type-specific marker genes (left), and relative gene expression of representative cell type-specific markers for each cluster overlaid on t-SNE plots (right). (c) The hierarchical clustering tree (right) showing the similarity among earthworm 62 cells, and the histogram plot (left) showing the subtypes of each cell type. The similarity refers to the AUROC score from MetaNeighbor analysis. (d-e) t-SNE showing 11 subclusters from earthworm C24 (neuron) (d), violin plot showing the scale normalized expression levels of representative markers for each subcluster (e).

Extended Data Fig. 4 Characteristics of immune-related structure cells in zebrafish, Drosophila, and earthworm.

(a) Ridge plots showing the scale normalized expression levels of known zebrafish epithelial markers and immune-related markers in zebrafish epithelial cells. (b) Ridge plots showing the the scale normalized expression levels of known Drosophila MT markers and immune-related markers in Drosophila MT cells (C34 and C69). (c) Ridge plots showing the scale normalized expression levels of two earthworm immune-related markers, evm.TU.Chr04.2499 and evm.TU.ctg2984.2 in earthworm coelomocytes (earthworm C0, C5, C6, C16, C19, C30, C32, C35, and C37). (d-e) t-SNE visualization of 11 subtypes of MT cells from Drosophila C34 and C69, and violin plot showing the scale normalized expression levels of representative markers for each subtype (e).

Extended Data Fig. 5 Cross-species analysis among eight species.

(a) Sankey diagrams showing homologous cell-type pairs between human and mouse obtained from SAMap analyses based on different datasets. (b) The Venn diagram showing the number of overlapping homologous cell-type pairs obtained from SAMap analyses based on different datasets. (c-e) The information of homologous cell types among human, mouse, zebrafish, Ciona, Drosophila, earthworm, C. elegans, and planarian, including the number of homologous cell-type pairs (c), aligned score (d), and number of enriched gene pairs (e). Boxplots represent the median, 25th percentile, and 75th percentile, and whiskers correspond to 1.5 times the interquartile range. N of cell types: human: 434, mouse: 432, zebrafish: 378, Ciona: 291, Drosophila: 288, earthworm: 110, C. elegans: 321, and planarians: 173.

Extended Data Fig. 6 Screening conserved regulators underlying cell lineages.

(a) The bar chart showing the percentage of cell lineage-specific TFs in humans, mice, zebrafish, Ciona, Drosophila, earthworm, C. elegans, and planarians across different conservative levels based on homologous genes (left) obtained from SAMap and 1-to-1 orthologous genes (right). The group number refers to the conservative level. TFs were divided into eight different conservative levels (Level 1–8) based on the conversion of homologous TFs among eight species. Level 1 means that TFs have no homologous genes in other species, and Level 8 means that TFs have homologous genes in all other seven species. (b) The bar chart showing the conserved neuron-related TFs between human and other species. The blue bar refers to the number of homologous TF pairs between human and other species, the yellow bar refers to the number of human neuron-related TFs involved in homologous gene pairs, and the grey bar refers to the number of other species neuron-related TFs involved in homologous gene pairs. (c) Conserved neuron-related regulators across eight species showing a comprehensive homologous link from lower to higher organisms. (d) Sankey diagram showing homologous relationships among vertebrates’ immune-related TFs. (e) Sankey diagram showing homologous relationships of immune-related TFs between Drosophila and human.

Extended Data Fig. 7 The performance of model training for eight species.

(a) The AUROC values of major cell lineages for humans, mice, zebrafish, Ciona, Drosophila, earthworm, C. elegans, and planarians. (b) Boxplots of AUPR values for eight species. Boxplots represent the median, 25th percentile, and 75th percentile, and whiskers correspond to 1.5 times the interquartile range. N of cells: humans: 134,557, mice: 179,344, zebrafish: 241,233, Ciona: 12,489, Drosophila: 77,337, earthworm: 29,609, C. elegans: 30,515, and planarians: 50,562.

Extended Data Fig. 8 Benchmark of prediction performance.

(a) The AUROC values of Nvwa, Basset, DeepSEA, Beluga, Basenji, SVM, random labels and random features on human (n = 134,557) and Drosophila (n = 77,337) specific datasets. (b) The AUROC of multiple genome training for zebrafish (n = 241,233) and C. elegans (n = 30,515). Boxplots represent the median, 25th percentile, and 75th percentile, and whiskers correspond to 1.5 times the interquartile range. (c) The heatmaps showing the correlation between observed and Nvwa-predicted cell type-specific transcription for eight species.

Extended Data Fig. 9 Overview of sequence patterns recognized by Nvwa.

(a) Mean saliency scores show transcriptional start site and important information-rich region recognized by Nvwa. (b) Comparison of the first-layer convolution filters derived from feature map-based approaches and gradient-based TF-MoDISco on Drosophila-specific model. (c) Examples of known TFBS compared with the PWMs of Nvwa first-layer in humans, mice, zebrafish, Ciona, Drosophila, C. elegans, and planarians.

Extended Data Fig. 10 Overview of sequence motifs and their influence recognized by Nvwa.

(a) Volcano plot of Nvwa first-layer filters for humans, mice, zebrafish, Ciona, Drosophila, earthworm, C. elegans, and planarians. The x-axis represents the information contents (IC) of a Filter, the y-axis represents the overall influence on of a Filter, Filters with high influence are tagged as up, and Filters with low influence are tagged as down. Those reductant Filters are tagged as triangle and non-reductant Filters are tagged as dots, the size of elements represents the reproducibility in each independent cross-validation run. (b) Barplot of the Nvwa and single-cell ATAC cell type specific motifs for mouse. Neuronal cells (C12, n=29 and C5, n=169 for Nvwa and sci-ATAC data respectively) and endothelial cells (C50, n=31 and C22, n=136 for Nvwa and sci-ATAC data respectively) were shown. Hit indicates the same motifs, NotHit indicates the different motifs identified by TomTom. X-axis indicates the percentage. (c) Barplot of the Nvwa and single -cell ATAC cell type specific transcription factor for Drosophila. Neuronal cells (C1, n=293, CB, n=639 and OL, n=484 for Nvwa and Flybrain (GSE163697) data respectively) were shown. Hit indicates the same TFs, and NotHit indicates the different TFs annotated by TomTom. X-axis indicates the percentage.

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Li, J., Wang, J., Zhang, P. et al. Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types. Nat Genet 54, 1711–1720 (2022). https://doi.org/10.1038/s41588-022-01197-7

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