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An atlas of rabbit development as a model for single-cell comparative genomics

Nature Cell Biology volume 25, pages 1061–1072 (2023)Cite this article

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Abstract

Traditionally, the mouse has been the favoured vertebrate model for biomedical research, due to its experimental and genetic tractability. However, non-rodent embryological studies highlight that many aspects of early mouse development, such as its egg-cylinder gastrulation and method of implantation, diverge from other mammals, thus complicating inferences about human development. Like the human embryo, rabbits develop as a flat-bilaminar disc. Here we constructed a morphological and molecular atlas of rabbit development. We report transcriptional and chromatin accessibility profiles for over 180,000 single cells and high-resolution histology sections from embryos spanning gastrulation, implantation, amniogenesis and early organogenesis. Using a neighbourhood comparison pipeline, we compare the transcriptional landscape of rabbit and mouse at the scale of the entire organism. We characterize the gene regulatory programmes underlying trophoblast differentiation and identify signalling interactions involving the yolk sac mesothelium during haematopoiesis. We demonstrate how the combination of both rabbit and mouse atlases can be leveraged to extract new biological insights from sparse macaque and human data. The datasets and computational pipelines reported here set a framework for a broader cross-species approach to decipher early mammalian development, and are readily adaptable to deploy single-cell comparative genomics more broadly across biomedical research.

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Fig. 1: Rabbit timeline of development and high-resolution imaging series of rabbit embryos.
Fig. 2: Rabbit transcriptional landscape of gastrulation and early organogenesis.
Fig. 3: Chromatin accessibility and gene expression along the trajectory of early SCT differentiation.
Fig. 4: Rabbit and mouse neighbourhood comparisons identify regions of similarity and dissimilarity across species.
Fig. 5: Automated annotation models trained on the rabbit atlas accurately classify cell types in sparse human and macaque data.
Fig. 6: Rabbit YS haematopoiesis exhibits conserved molecular markers with human in vitro development models and reveals signalling role of mesothelium.

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Data availability

Raw sequencing data are available through ArrayExpress with the following accessions: scRNA-seq: E-MTAB-11836; scATAC-seq: E-MTAB-11804. Raw histology and RNAscope imaging files are available through the EBI BioImage archive under accession S-BIAD604. All other links to the data are available at https://marionilab.github.io/RabbitGastrulation2022/. This includes links to processed single-cell transcriptomics and ATAC-seq data in a variety of formats for loading into both R and Python analysis pipelines. The transcriptomics and imaging data are also available to explore interactively via a web app accessible through the same link. Low-resolution thumbnail images for all generated imaging datasets are additionally provided. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Codes are available at https://github.com/MarioniLab/RabbitGastrulation2022 and https://marionilab.github.io/RabbitGastrulation2022/.

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Acknowledgements

We thank K. Katarzyna and the CRUK CI Genomics Core for their help with the 10x Genomics sample processing; R. Argelaguet for bioinformatic support; N. Wilson for assistance with the facilities and technical support; M. Rostovskaya for discussions around eutherian amnion; M. Keller (Gehlenborg lab) for help in setting up the Vitessce visualization platform. We also thank H. D. Farizi and S. D. Høiberg (Novo Nordisk) for help with rabbit embryo sectioning. M.-L.N.T. is funded by a Herchel Smith PhD Fellowship in Science. D.K. is funded by the Wellcome Mathematical Genomics and Medicine Programme at the University of Cambridge (PFZH/158 RG92770). C.G. was funded by the Swedish Research Council (2017-06278) and by a Swedish Childhood Cancer Fund position grant (TJ2021-0009). I.I.-R. was funded by the Wellcome Mathematical Genomics and Medicine Programme at the University of Cambridge (203942/Z/16/Z; RDAG/426RG86191). Work in the Gottgens group is supported by Wellcome, Bloodwise, MRC and CRUK, and by core support grants from Wellcome to the Wellcome-MRC Cambridge Stem Cell Institute. Work in the Marioni group is supported by core funding from CRUK (C9545/A29580) and by the European Molecular Biology Laboratory. Work in the È.B.-G. lab is supported by CRUK (C9545/A29580). This work was funded as part of a Wellcome grant (220379/B/20/Z) awarded to B.G., J.N. and J.C.M. and the WSSS Dev Ext Wellcome Grant (220152_C_20_Z) awarded to B.G. and J.C.M.

Author information

Author notes

  1. These authors contributed equally: Mai-Linh N. Ton, Daniel Keitley.

Authors and Affiliations

  1. Department of Haematology, University of Cambridge, Cambridge, UK

    Mai-Linh Nu Ton, Bart Theeuwes, Fernando J. Calero-Nieto, Ivan Imaz-Rosshandler & Berthold Göttgens

  2. Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Mai-Linh Nu Ton, Bart Theeuwes, Fernando J. Calero-Nieto & Berthold Göttgens

  3. Department of Zoology, University of Cambridge, Cambridge, UK

    Daniel Keitley & Èlia Benito-Gutiérrez

  4. Inst. Biomedicine, Dept. Microbiology and Immunology, Sahlgrenska Center for Cancer Research, University of Gothenburg, Gothenburg, Sweden

    Carolina Guibentif

  5. Global Discovery & Development Sciences, Novo Nordisk A/S, Måløv, Denmark

    Jonas Ahnfelt-Rønne & Thomas Kjærgaard Andreassen

  6. Medical Research Council Laboratory of Molecular Biology, Cambridge, UK

    Ivan Imaz-Rosshandler

  7. Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    Blanca Pijuan-Sala

  8. MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Jennifer Nichols

  9. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK

    John C. Marioni

  10. European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK

    John C. Marioni

  11. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    John C. Marioni

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  1. Mai-Linh Nu Ton
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Contributions

C.G., M.-L.N.T. and J.N. performed embryo dissections and generated the scRNA-seq atlas dataset. M.-L.N.T. and F.J.C.-N. generated the scATAC-seq atlas dataset. J.A.-R. And T.K.A. supplied the animals for the experiments and conducted histology and imaging of the embryos. M.-L.N.T. performed pre-processing and initial low-level analysis for both atlases. D.K. performed batch correction and global visualization of the scRNA-seq dataset. D.K. performed cross-species comparisons between the rabbit mouse, macaque and human datasets. D.K. implemented the website. B.T. performed analysis on the scATAC-seq dataset. D.K., M.-L.N.T., B.P.-S., C.G., B.T. and I.I.-R. annotated atlas cell types. M.-L.N.T., B.T. and D.K. analysed the atlas trophoblast cells. J.N., È.B.-G., J.C.M. and B.G. supervised the study. M.-L.N.T., D.K., B.T., B.G., J.C.M. and È.B.-G. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Èlia Benito-Gutiérrez, John C. Marioni or Berthold Göttgens.

Ethics declarations

Competing interests

Provision of time-mated rabbit embryos by Labcorp Early Development Laboratories Limited as well as scRNA-seq costs were supported by a research contract agreement with J.A.-R. and T.K.A. at Novo Nordisk A/S. È.B.-G. and J.C.M. have been employees of Genentech since September 2022.

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Nature Cell Biology thanks Janet Rossant and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Histology and dissection images reveals early dynamics of early germ layers of rabbit flat-disk embryo.

a) Representative RNAscope image of GD7 rabbit sagittal in utero section showing beginning of gastrulation. TFAP2C and OCT4 mark trophoblast and epiblast-derived cell types, respectively. Arrowheads indicate implantation on the anti-mesometrial side of the embryo. b) Representative RNAscope image of GD8 rabbit sagittal in utero sections showing germ layer differentiation. (i) Arrowheads indicate implantation sides in the mesometrial direction. TFAP2C marks trophoblast and trophoblast progenitors. (ii) Chorioamnion fold formation can be seen alongside early germ layer differentiation. Chorion consists of a mesoderm inner layer, and amnion has an mesoderm outer layer as captured in the RNAscope image. c) Representative histology images of rabbit cross-sections in utero and magnification of the embryo to show details and orientation of the embryo in the uterus. cm, cranial mesoderm. d) Summary of over 650 + H&E and RNAscope images of rabbit sections generated in this study of which the representative images are pulled from. e) RNAscope image of the vitelline artery (VA) and dorsal aorta (DA) region showing the RUNX1 + CDH5+ presumptive haemogenic endothelium in the ventral portion of the dorsal aorta. f) Representative images of the rabbit embryos and their extraembryonic tissues utilized in this study g) 13 somite and 16 somite GD9 rabbit embryos microdissected into anterior, mid, and posterior regions for scRNA-seq.

Extended Data Fig. 2 Updated transcriptome annotation improves the quality of processed scRNA-seq data.

a, The distances of intergenic reads to their nearest annotated gene in a single GD8 sample. i) The distances for a subset of intergenic reads aligned to a region of chromosome 1 are shown. The vertical line indicates the 600 bp extension added to the 3′ ends (see Methods). ii) Distances are also shown for intergenic reads whose closest annotated gene is GATA1 and SOX9. b) The read coverage for the same GD8 sample shows that many reads associated with FOXA2 transcripts are positioned off the 3′ end of the OryCun 2.0 reference annotation. These missing reads are captured by our 600 bp extension. c) The 3′ extended gene annotations substantially improve the signal obtained after processing with Cell Ranger. d) Examples of new annotations added from aligning human gene annotations with the rabbit transcriptome. These new annotations overlap positions with high read coverage. e) The signal captured as a result of the new human gene annotations are consistent with the known expression patterns of SIX3 (forebrain marker) and FOXC1 (mesodermal marker). Intergenic read distances and normalised expression values are available in the source data.

Source data

Extended Data Fig. 3 RNA-seq quality controls.

a) Number of high-quality cells sampled from each of the 26 scRNA-seq samples. Annotations refer to the anatomical dissections performed for GD9 samples. b) Distribution of UMIs (c) genes detected and (d) percentage of mitochondrial reads recorded across cells sampled from each of the 26 scRNA-seq samples (N = 19 embryos), coloured by developmental stage. The lower and upper hinges of the boxplots relate to the first and third quartiles respectively. The center line denotes the median and the whiskers extend to the largest/smallest value no further than 1.5 times the inter-quartile range from the upper/lower hinge respectively. Outlier points are plotted if they exceed the ranges defined by the the upper and lower whiskers. The number of cells used to calculate each statistic are shown in A) and are provided in the source data. e) Cells from samples taken at each developmental stage and anatomical dissection are highlighted in the UMAP embedding. Following batch correction, the samples are well mixed within the UMAP visualization. A - anterior section, M - mid section; P - posterior section; YS -yolk-sac/extraembryonic tissues; EP - embryo proper. QC values for each cell are available in the source data.

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Extended Data Fig. 4 Automated cell type annotation.

a, SingleR predicted cell type annotations for each cell of the rabbit atlas (n = 146,133 cells), following training on the mouse atlas. Only a subset of predicted labels are annotated for visual clarity. Predicted labels for all cells are more easily visualized via the interactive web app, accessible at https://marionilab.github.io/RabbitGastrulation2022/. b) Normalised SingleR correlation scores for each rabbit cell against all possible mouse labels. Cells are ordered according to the label assigned by SingleR. The final annotated cell types are also displayed to show how the model predictions relate with those assigned after validation. c) Cells of the rabbit (top) and mouse (bottom) (Imaz-Rosshandler et al.23) datasets in a SAMap integrated UMAP embedding (n = 576,472 cells). Cells are coloured and annotated according to the cell type labels of each respective atlas. The rabbit (r) and mouse (m) label numberings refer to those in the legends of Fig. 2c and Extended Data Fig. 4a respectively. The full list of IDs can also be found in Supplementary Tables 3–7. Shortened labels for the rabbit extra-embryonic ectoderm cell types have also been added. rT = Trophoblast; rA1 = Amnion 1; rA2 = Amnion 2; rA3 = Amnion 3; rCY = Cytotrophoblast; rSP = SCT progenitors; rES = Early SCT. All panels depict rabbit cells integrated across all samples (N = 19 embryos). SingleR correlation scores and cell type predictions are provided in the source data, along with the integrated UMAP coordinates and final cell type annotations.

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Extended Data Fig. 5 Cell type validation.

a) Cell type proportions across each scRNA-seq sample (N = 19 embryos). Cell types labels are organised based on the stage and dissection they are most abundant. Cell types with low abundance are not shown to preserve visual clairity. b) Row-normalised gene expression plot showing genes differentially expressed within each cell type. c) RNAscope images of SERPINC1 and DKK1 helped identify cells of the parietal endoderm (PE). Scale bars = 500 μm. d) Row-normalised gene expression plot of marker genes used to annotate rabbit extra-embryonic ectoderm cell types. e) Histology and RNAscope images of a synctial trophoblastic knob expressing TFAP2C at gestational day 8. Scale bar = 100μm. Normalised expression values and cell type annotations (across cells/samples) are available in the Source Data.

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Extended Data Fig. 6 Whole embryo scATAC-seq and trophoblast annotation.

a) An illustration of the 8 samples (N = 4 embryos) processed for single-cell ATAC-sequencing. b) Quality control of whole embryo scATAC-seq nuclei based on transcription start site (TSS) enrichment score, total number of fragments per cell, and fraction of reads in peaks (FRIP) across samples. The lower and upper hinges of the boxplots relate to the first and third quartiles respectively. The center line denotes the median and the whiskers extend to the largest/smallest value no further than 1.5 times the inter-quartile range from the upper/lower hinge respectively. Number of nuclei per sample - BGRGP1:26250; BGRGP2 12246; BGRGP3 13008; BGRGP4 4887; BGRGP5 12192; BGRGP6 11982; BGRGP7 11313; BGRGP8 10368. c) The fragment size distribution plotted for each scATAC-seq sample. d) UMAP of 34,082 nuclei that passed quality control for scATAC-seq, colored by predicted cell type inferred from label transfer (see Methods). Inset shows the UMAP colored by developmental stage. e) Linear regression between mean RNA expression and mean ATAC gene scores across cell-types, for top 50 marker genes of annotated cell-types. Top left panel shows linear regression along with values across cell-types for SCT marker genes, other panels only show the linear model itself. Red dotted lines indicate a slope of 1 (see Methods). f) Gene Activity scores of marker genes for refined trophoblast to SCT cell-type annotation. g) Motif enrichment in marker peaks for refined trophoblast to SCT cell-type annotation. h) Genome browser views of the regions surrounding EOMES, CDH5, FOXA2, GATA1 & HDAC6, HBG1/2, PAX6, TBX6 for selected cell-types. N = 4 embryos in all panels. QC values, fragment size distributions, UMAP coordinates, predicted annotations, linear model parameters and heatmap values are all available in the source data.

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Extended Data Fig. 7 Rabbit-mouse neighborhood comparisons.

a) Distribution of the number of cells within neighbourhoods of the rabbit (n = 5,253 neighborhoods) and mouse atlas (n = 14,034 neighborhoods). b) Same plot as in Fig. 3d shown for all cell types. c) Neighbourhoods of the mouse atlas (n = 14,034 neighborhoods) coloured according to their maximum correlation with any rabbit neighbourhood. General UMAP positions for a subset of cell types are shown. d) Maximum correlation scores, grouped by the scRNA-seq sample (N = 19 embryos) of each neighbourhood index cell. Samples are ordered by mean maximum correlation and coloured by the developmental stage.Annotations above the GD9 samples refer to the anatomical dissection. The lower and upper hinges of the boxplots relate to the first and third quartiles respectively. The center line denotes the median and the whiskers extend to the largest/smallest value no further than 1.5 times the inter-quartile range from the upper/lower hinge respectively. Outlier points are plotted if they exceed the ranges defined by the the upper and lower whiskers. The number of neighbourhoods linked to each sample and thus used to calculate each statistic is written along the x-axis. A - anterior section, M - mid section; P - posterior section; YS -yolk-sac/extraembryonic tissues; EP - embryo proper. Neighbourhood sizes, annotations and maximum correlation values are provided in the source data.

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Extended Data Fig. 8 Neighbourhood comparisons along differentiation trajectories.

a–c) Neighbourhoods associated with a) allantois, b) amnion and c) blood differentiation trajectories are shown for the rabbit and mouse. Maximally correlated neighbourhood pairs are linked, with the strength of correlation indicated with line colour. Neighbourhoods are coloured by annotated cell type. Neighborhoods in b, c are represented in the UMAP embedding, whereas in a, neighborhoods are positioned according to a ForceAtlas2 graph layout. d) Alluvial plot showing the proportion of rabbit gut tube neighbourhoods which form maximally correlated mappings with mouse neighbourhoods of different cell types. Rabbit neighborhoods represented in all panels were constructed from cells integrated across all samples (N = 19 embryos). Details of the neighbourhood mappings are provided in the source data.

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Extended Data Fig. 9 Human and macaque cell type prediction with SingleR.

a) Alluvial plot showing the changes to the original cell type labels after predicting annotations with a SingleR model trained on the rabbit atlas. Mappings only shown for predicted cell types with more than 10 cells. b) UMAP of the CS7 human dataset (Tyser et al.20) coloured according to the SingleR cell type predictions trained on the mouse atlas (1,195 cells, N = 1 embryo). c) Same as in a for the classification of cells from the macaque in-vitro dataset (Yang et al.44). d) Same as in b for the Yang et al.44 dataset (7,194 cells, N = 12 embryos). The predicted cell type annotations are available in the source data.

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Extended Data Fig. 10 Ligand-receptor interactions and gene ontology reveal roles of visceral endoderm and mesothelium in yolk sac hematopoietic niche.

a) Extended dot plot of CellphoneDB output reveals a large network of extracellular matrix protein interactions between the mesothelium and endothelium, as well as VEGF and FGF complexes. The order of the cell types along the x-axis indicates the order of the molecule1:molecule2 expression along the y axis. The size of the dot indicates the -log10(pvalue), while the color of the dot indicates the log2 mean of the mean expression of the molecules. Of note, YS endothelium and mesothelium show a significant interaction between FLT1 and FLT family complexes with VEGFA, which plays a known role in haematopoiesis. Statistical analyses were performed using the one-sided permutation test as described in Efremova et al.49 b) Gene ontology analysis of the differential transcriptional expression of the visceral endoderm clusters and the mesothelium clusters reveal different roles in the yolk sac niche. Visceral endoderm cells score highly on nutrient transport roles like chylomicron assembly, cholesterol efflux, and cholesterol homeostasis (left). Mesothelium transcripts interact highly with the endothelium according to (A), and score highly on extracellular matrix organization and collagen fibril organization. This indicates a closer interaction with endothelium that is critical for the function of the yolk sac hematopoietic niche and the mesothelium provides a role in structure separate from the visceral endoderm cells. P-values are adjusted for multiple-testing. Differential expression results are available in the source data. Statistical analyses were performed using Fisher’s exact test on cells integrated across all samples (N = 19 embryos) where those cell types were present.

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Supplementary information

Reporting Summary

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Supplementary Tables

Supplementary Table 1. scRNA-seq sample submission data. Supplementary Table 2. scATAC sample submission data. Supplementary Table 3. List of cell type annotations with a one-to-one correspondance between the rabbit and mouse Imaz-Rosshandler et al.23 dataset. Supplementary Table 4. Cell type annotations with a one-to-many correspondance with cell types defined in the Imaz-Rosshandler23 mouse dataset. Supplementary Table 5. Cell type labels that are variations of those defined in the Imaz-Rosshandler et al.23 mouse dataset. Supplementary Table 6. Cell types defined exclusively in the rabbit dataset. Supplementary Table 7. Cell types defined exclusively in the Imaz-Rosshandler et al.23 mouse dataset.

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Ton, ML.N., Keitley, D., Theeuwes, B. et al. An atlas of rabbit development as a model for single-cell comparative genomics. Nat Cell Biol 25, 1061–1072 (2023). https://doi.org/10.1038/s41556-023-01174-0

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