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Cell competition acts as a purifying selection to eliminate cells with mitochondrial defects during early mouse development

Nature Metabolism volume 3, pages 1091–1108 (2021)Cite this article

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Abstract

Cell competition is emerging as a quality-control mechanism that eliminates unfit cells in a wide range of settings from development to the adult. However, the nature of the cells normally eliminated by cell competition and what triggers their elimination remains poorly understood. In mice, 35% of epiblast cells are eliminated before gastrulation. Here we show that cells with mitochondrial defects are eliminated by cell competition during early mouse development. Using single-cell transcriptional profiling of eliminated mouse epiblast cells, we identify hallmarks of cell competition and mitochondrial defects. We demonstrate that mitochondrial defects are common to a range of different loser cell types and that manipulating mitochondrial function triggers cell competition. Moreover, we show that in the mouse embryo, cell competition eliminates cells with sequence changes in mt-Rnr1 and mt-Rnr2, and that even non-pathological changes in mitochondrial DNA sequences can induce cell competition. Our results suggest that cell competition is a purifying selection that optimizes mitochondrial performance before gastrulation.

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Fig. 1: Cells eliminated during early mouse embryogenesis have a distinct transcriptional profile.
Fig. 2: A cell competition transcriptional signature is identified in cells eliminated during mouse embryonic development.
Fig. 3: Cells eliminated during early mouse embryogenesis have mitochondrial defects.
Fig. 4: Mitochondrial defects are a common feature of cells eliminated by cell competition.
Fig. 5: Manipulating mitochondria biology is sufficient to trigger cell competition.
Fig. 6: Intermediate and loser epiblast cells accumulate polymorphisms in mtDNA sequences.
Fig. 7: Changes in mtDNA sequence can determine the competitive ability of a cell.
Fig. 8: Model of cell competition.

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Data availability

Data were analysed with standard programmes and packages, as detailed above. All relevant data are included in the paper and/or its Supplementary Information files. RNA-seq raw data as well as processed data are available through ArrayExpress, under accession numbers E-MTAB-8640, for scRNA-seq data, and E-MTAB-8692, for bulk RNA-seq data. Source data are provided with this paper.

Code availability

Code used to generate the figures in the paper is available at https://github.com/ScialdoneLab/Cell-Competition-Paper-Figures/.

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Acknowledgements

We thank S. Rothery for guidance and advice with confocal microscopy. The FILM at Imperial College London is supported in part by funding from the Wellcome Trust (grant no. 104931/Z/14/Z) and BBSRC (grant no. BB/L015129/1). We thank J. Elliot and B. Patel from the LMS/NIHR Imperial Biomedical Research Centre Flow Cytometry Facility for support. We are thankful to G. Chennell and A. Sardini for guidance and support with Seahorse experiments. Research in the laboratory of T.A.R. was supported by the MRC project grant (MR/P018467/1) and the BBSRC project grant (BB/S008284/1) and by the British Heart Foundation (BHF) PhD studentships (FS/14/62/31288 and FS/17/64/33476). Work in the laboratory of A.S. is funded by the Helmholtz Association. A.L. was funded by a BHF centre of excellence PhD studentship. S.S. was funded through Wellcome awards 103788/Z/14/Z and 108438/Z/15/Z.

Author information

Author notes

  1. These authors contributed equally: Ana Lima, Gabriele Lubatti.

  2. These authors jointly supervised this work: Antonio Scialdone, Tristan A. Rodriguez.

Authors and Affiliations

  1. National Heart and Lung Institute, Imperial College London, London, UK

    Ana Lima, Aida Di Gregorio, Tamzin Zawadzki, Barbara Pernaute, Salvador Perez-Montero, Juan Miguel Sanchez, Sarah Bowling, Margarida Sancho & Tristan A. Rodriguez

  2. MRC London Institute of Medical Sciences (LMS), Institute of Clinical Sciences, Imperial College London, London, UK

    Ana Lima, Marian Dore, Mohammad M. Karimi & David Carling

  3. Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München, Munich, Germany

    Gabriele Lubatti, Elmir Mahammadov & Antonio Scialdone

  4. Institute of Functional Epigenetics, Helmholtz Zentrum München, Neuherberg, Germany

    Gabriele Lubatti, Elmir Mahammadov & Antonio Scialdone

  5. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

    Gabriele Lubatti, Elmir Mahammadov & Antonio Scialdone

  6. Institute of Animal Breeding and Genetics, University of Veterinary Medicine, Vienna, Austria

    Jörg Burgstaller

  7. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK

    Di Hu & Shankar Srinivas

  8. EPSRC Centre for the Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK

    Alistair P. Green & Nick Jones

  9. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Barbara Pernaute

  10. Orchard Therapeutics, London, UK

    Juan Miguel Sanchez

  11. Biomodels Austria (Biat), University of Veterinary Medicine Vienna, Vienna, Austria

    Thomas Kolbe

  12. Department IFA-Tulln, University of Natural Resources and Life Sciences, Vienna, Austria

    Thomas Kolbe

  13. Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, UK

    Mohammad M. Karimi

Authors
  1. Ana Lima
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  2. Gabriele Lubatti
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  20. Antonio Scialdone
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  21. Tristan A. Rodriguez
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Contributions

A.L. performed most of the experimental wet-lab work. J.B. and A.L. derived heteroplasmic mESC lines. J.B. performed heteroplasmy measurements in heteroplasmic mESCs. B.P. generated Mfn2−/− and Drp1−/− mESCs, and J.M.S. conducted characterization of mitochondria shape and pluripotency status. S.P.-M. participated in the metabolic characterization of Drp1−/− cells. D.H. performed embryo dissections, treatments and cell dissociation before scRNA-seq experiments. G.L. did the bioinformatic analysis of scRNA-seq data. E.M., N.J. and A.P.G. participated in the analysis of mtDNA heteroplasmy. A.D.G. performed the metabolomic studies using the Metabolon platform and participated in embryo dissections and immunohistochemistry stainings for validation of results obtained by scRNA-seq. T.K. collected the embryos for derivation of the mESCs with different mtDNA content. M.D. and M.K. performed the bioinformatic analysis of bulk RNA-seq experiments. N.J., S.S. and D.C. participated in the design of experimental work and analysis of results. A.L., G.L., A.S and T.A.R. interpreted results and wrote the paper. T.A.R. and A.S. directed and designed the research.

Corresponding authors

Correspondence to Antonio Scialdone or Tristan A. Rodriguez.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Anna-Katerina Hadjantonakis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Christoph Schmitt; Elena Bellafante.

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

Extended data

Extended Data Fig. 1 Quality controls of scRNA-seq and clustering robustness analysis.

a, Selection criteria for quality control (QC) of all cells. A total of 723 passed the quality control (723 good quality cells) and were considered for downstream analysis. All these parameters were computed for each cell. Log10 total number of reads (top left): log10 of the sum of the number of reads that were processed in every cell; Fraction of mapped reads (top central): number of reads that are confidentially mapped to the reference genome divided by total number of reads that were processed for each cell. This number is automatically provided by Salmon v0.8.2; Fraction of genes (top right): number of reads mapped to endogenous genes divided by the total sum of reads that were processed; Fraction of mt-genes (bottom left): number of reads mapped to mitochondrial genes divided by the total sum of reads that were processed; Fraction of spikes (bottom central): number of reads mapped to ERCC spike-ins divided by the total sum of reads that were processed; Number of genes above 10 RPM (bottom right): number of genes with expression level above 10 reads per million. b, Number of good quality cells in each condition (rows) and batch (columns). c, Number of good quality cells per cluster (rows) and batch (columns). d, UMAP plot of the data with cells coloured by batch. In each batch there is a balanced distribution of cells in the two conditions and across the five clusters. e, The Pearson’s gamma (left panel) and the Average Silhouette Width (right panel) was calculated for each set of clusters obtained with 100 random subsamples of 60% of highly variable genes and different values of the deepSplit parameter (see Methods). The most robust clusters correspond to deepSplit values of 0 and 1. f, The changes in composition and number of clusters between the clustering obtained with deepSplit 0 (top) and 1 (bottom) are shown using the library ‘clustree’80. See methods for details on statistical analysis.

Extended Data Fig. 2 Cell cycle analysis and cluster connectivity.

a, Cell cycle analysis of epiblast cells from clusters 1, 3 and 4. Cell cycle phase was predicted with cyclone algorithm81 and shows that there are cells in S and G2M phase also in the loser and intermediate clusters. b, PAGA plot showing the connectivity of the five clusters of cells from CI-treated embryos. c-d, Diffusion map analysis in all epiblast cells (from DMSO and CI-treated embryos): cells are coloured according to the condition (c) and to the cluster (d). e, The pseudotime coordinate of the CI-treated epiblast cells obtained from the diffusion map including all epiblast cells correlates extremely well (with the pseudo-time coordinate obtained in the diffusion map calculated only from CI-treated epiblast cells (Fig. 2a). See methods for details on statistical analysis.

Extended Data Fig. 3 Analysis on epiblast cells from DMSO and CI-treated embryos.

a, Heatmap showing the expression pattern of all genes differentially expressed along the trajectory from winning to losing cells in Fig. 2d. b-c, Overlap of genes differentially expressed along the trajectory joining winning and losing epiblast cells in CI-treated embryos (Fig. 2a and panel d) and genes targeted by p53. Pie charts show the percentage of genes up- or down-regulated in loser cells within the group of target genes that are activated (b) or repressed (c) by p53. There is an enrichment of activated/repressed targets among genes upregulated/downregulated in losing cells respectively (p-value=1E-4). The list of p53 targets is taken from58. d, Scatter plots of the expression levels of different marker genes plotted against each other in loser epiblast cells (cluster 4). Loser cells have higher expression of pluripotency markers as well as higher expression of some lineage-specific markers and the co-expression of these markers is only weakly correlated - the Spearman’s correlation coefficient is shown. e-g Our scRNA-seq data from epiblast cells is projected on top of previously published data from epiblast collected from freshly isolated embryos at different stages (E5.5, E6.25 and E6.5; data from26). First, a diffusion map (e) and a pseudotime coordinate (f) is computed for the epiblast cells from freshly isolated embryos. Then, a pseudotime coordinate is estimated for our data after projecting it onto the diffusion map. Panel g shows the pseudotime coordinates for both datasets, split by stage, treatment and cluster. See methods for details on statistical analysis.

Extended Data Fig. 4 Cells eliminated during early mouse embryogenesis have activated stress responses.

a, Overlap of genes differentially expressed along the trajectory joining winning and losing epiblast cells in CI-treated embryos (Fig. 2a and Extended Data Fig. 3a) and genes related to the unfolded protein response and integrated protein response pathways (UPR_ISR, see Supplementary Table 3). From the 32 genes related to the UPR & ISR pathways, 12 are down-regulated in loser cells, 8 genes are up-regulated in loser cells, and 12 genes are not differentially expressed between loser and winner cells. There is a statistically significant enrichment of UPR&ISR genes among the up-regulated genes in loser cells (odds ratio=3.0, p-value=0.012). The intersection between UPR-ISR genes and the down regulated genes is not significant (odds ratio=1.2, p value=0.69). b-c, List of genes from UPR-ISR pathways that are statistically significantly up-regulated (b) or down-regulated (c) in loser cells. d, Scatterplots with the expression levels of genes involved in stress responses in epiblast cells from CI-treated embryos as a function of cells’ losing score. e, Experimental design with the approach taken to validate the expression of the stress response marker DDIT3 in epiblast cells from DMSO or CI-treated embryos. f, Representative micrographs of DMSO (upper panel) or CI-treated embryos (100 μM, lower panel) stained for DDIT3, quantified in (g). Nuclei are labelled with Hoechst. In control embryos (DMSO-treated), dying cells in the cavity show very high DDIT3 expression (arrow), while live cells in the epiblast of the CI-treated embryos show more modest levels of DDIT3 expression (arrowheads). Scale bar = 20 μm. g, Quantification of the percentage of epiblast cells with nuclear DDIT3 expression. N = 10 DMSO and N = 9 CI-treated embryos. Data shown as mean ± SEM. See methods for details on statistical analysis.

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Extended Data Fig. 5 Mitochondrial function in wild-type, Bmpr1a−/− and 4n mESCs.

a-d, Metabolic flux analysis of wild-type and Bmpr1a−/− mESCs. OCR profile and metabolic parameters assessed during the mitochondria stress test performed in pluripotency conditions (a). ECAR profile and metabolic parameters assessed during the glycolysis stress test performed in pluripotency conditions (b). Metabolic parameters from the mitochondria stress test found to be similar between wild-type and Bmpr1a−/− mESCs during differentiation – day 3 (c). Metabolic parameters from the glycolysis stress test found to be similar between wild-type and Bmpr1a−/− mESCs during differentiation – day 3 (d). Data obtained from 3 (a,b) or 5 (c,d) independent experiments, with 5 replicates per cell type in each assay. e-f, Analysis of mitochondrial membrane potential (Δψm) in defective mESCs maintained in pluripotency conditions, in separate or co-culture. Representative histograms of TMRM fluorescence and quantification for wild-type and Bmpr1a−/− (e) and wild-type and 4n (f). g, Analysis of mitochondrial ROS in wild-type and Bmpr1a−/− mESCs undergoing differentiation in separate or co-culture: representative histograms of mitoSOX Red fluorescence and quantification of the percentage of mitoSOX positive cells. Data shown as mean ± SEM from 3 (e-f) or 5 (g) independent experiments. See methods for details on statistical analysis.

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Extended Data Fig. 6 Effect of actinonin in OPA1 expression in wild-type and Drp1−/− cells.

a, Western blot analysis of OPA1 expression in wild-type and Drp1−/− cells treated with actinonin (Act, 150 μM) during 6 hours on the third day of differentiation, quantified in (b-c). b-c, Expression levels of L-OPA1 (b) and S-OPA1 (c) relative to ɑ-tubulin. Data shown as mean ± SEM of 3 independent experiments.

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Extended Data Fig. 7 Analysis of SNPs in mtDNA in epiblast cells.

a-e, mtDNA heteroplasmy (plotted as Heteroplasmy = 1- frequency of most common allele) in epiblast cells from CI-treated embryos for five positions within the mt-Rnr2 gene. All these positions have an heteroplasmy that increases with the cells’ losing scores in a statistically significant way - the adjusted p-values are indicated at the top of each plot. f-k, The variation in the heteroplasmy across the CI-treated cells is not due to a batch effect for the 6 significant positions within the mt-Rnr1 gene. The number of cells analysed per cluster (and batch) is as follows: number of cells in Normal Epiblast :42 (1),16 (2),18 (3),0 (4),2 (5); number of cells in Intermediate: 42 (1), 28 (2), 28(3), 12 (4), 5 (5); number of cells in Loser Epiblast: 22 (1), 15(2), 20 (3), 2 (4), 7 (5). l, Correlation between the mtDNA heteroplasmy at all the statistically significant positions, six within the gene mt-Rnr1 and five within the gene mt-Rnr2. m, Schematic representation of the mitochondrial genome showing in red the positions that passed our filtering based on coverage and were considered for the heteroplasmy analysis. Only the genes that include these positions are indicated. See methods for details on statistical analysis.

Extended Data Fig. 8 Changes in mtDNA sequence are enough to trigger cell competition.

a, Illustration of the process of derivation of the mESCs lines from mice that are hybrid between the wild-caught strains (BG, HB or ST) and the lab mouse (C57BL/6N). These hybrid mice were generated elsewhere16 by ooplasmic transfer: the zygote of a C57BL/6N mouse was injected with ooplasm from a wild-caught mouse (orange, HB pictured). Therefore, these hybrid mice contain the nuclear background of the C57BL/6N strain and the mtDNA of wild-caught strain and potentially C57BL/6N mtDNA (heteroplasmic mice strains). mESCs lines were derived from the hybrid mice and characterised. b-f, Characterisation of the derived cell lines by flow cytometry, during pluripotency, in comparison to the wild-type cell line used in previous experiments (E14, 129/Ola background). Heteroplasmy analysis of the derived mESC lines from the hybrid mice, indicating the percentage of wild-derived mtDNA (b). Cell granularity (internal complexity) given as median fluorescence intensity of SSc-A laser (c). Cell size given as median fluorescence intensity of FSc-A laser (d). Analysis of the expression of mitochondrial markers: representative western blot and quantification of markers of mitochondrial mass (ATPB, mt-CO1 and TOMM20) and mitochondrial dynamics (DRP1, MFN1and MFN2), relative to vinculin, in cells derived from hybrid mice (e). f, Representative histograms and quantification of median TMRM fluorescence, indicative of Δψm, for the hybrid cell lines derived, in comparison to the wild-type cell line used in previous experiments (E14, 129/Ola background). g-i, Cell competition assays between hybrid cell lines maintained in pluripotency culture conditions. The ratio of final/initial cell numbers in separate or co-culture is shown. j, Experimental design for RNA-Seq and gene set enrichment analysis (GSEA). The isolation of RNA from winner HB(24%) and loser BG(95%) cells was performed after three days in separate or co-culture conditions, once cells have been subjected to FACS to isolate the two populations form mixed cultures. Data shown as mean ± SEM of 3 independent experiments. See methods for details on statistical analysis.

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Extended Data Fig. 9 Metabolic flux analysis of the cells with different mtDNA variants: HB(100%), HB(24%), BG(95%) and C57BL/6N.

a, OCR profile during mitochondria stress test performed in pluripotency maintenance conditions. b-i, Metabolic parameters assessed during the during the mitochondria stress test performed in pluripotency conditions. Data obtained from 3 independent experiments, with 5 replicates per cell type in each assay. Error bars represent SEM. See methods for details on statistical analysis.

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Extended Data Fig. 10 Common features of scRNA-seq and bulk RNA-seq datasets.

a, Terms significantly enriched among genes downregulated in BG(95%) (loser) ESCs in vitro when co-cultured with HB(24%) cells. The loss of mitochondrial activity emerges as a common feature between loser cells in vivo and in vitro. The gene enrichment analysis was performed using g-profiler tool (see Methods) and p-values were adjusted for multiple comparisons using the g:Profiler algorithm g:SCS (10.1093/nar/gkm226). b, Intersection between differentially expressed genes along the trajectory from winning to losing epiblast cells (‘in_vivo_scRNA-seq’; Fig. 2a and Extended Data Fig. 3a, and genes differentially expressed between co-cultured HB(24%) (winner) and BG(95%) (loser) ESCs (‘in_vitro_bulk_RNA-seq’). ‘Up’ and ‘Down’ here refer to genes up- or down-regulated in loser cells. For the intersection between down-regulated genes from scRNA-seq (in vivo) and down-regulated genes from bulk RNA-seq (in vitro): p-value, 1.71E-12; odds ratio 1.80. For the intersection between down-regulated genes from scRNA-seq (in vivo) and up-regulated genes from bulk RNA-seq (in vitro): p-value, 5.20E-3; odds ratio 0.67. For the intersection between up-regulated genes from scRNA-seq (in vivo) and down-regulated genes from bulk RNA-seq (in vitro): p-value, 4.87E-3; odds ratio 0.80. The intersection between up-regulated genes from sc-RNA-seq (in vivo) and up-regulated genes from bulk RNA-Seq (in vitro) is not statistically significant: p-value: 0.30, odds ratio 1.14. c, Intersection between the significantly enriched terms in genes upregulated or downregulated in loser cells in the epiblast of CI-treated embryos (‘in_vivo_scRNA-Seq’) or in our in vitro model of competition between co-cultured HB(24%) (winner) and BG(95%) (loser) ESCs (‘in_vitro_bulk_RNA-seq’). All the terms enriched among downregulated genes in vitro are also enriched in vivo. See methods for details on statistical analysis.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8

Reporting Summary

Supplementary Tables 1–10

Supplementary Table 1. List of downregulated genes along the winner-to-loser trajectory in the embryo. Supplementary Table 2. List of upregulated genes along the winner-to-loser trajectory in the embryo. Supplementary Table 3. Genes related to the UPR and IPR pathways. Supplementary Table 4. List of background genes for the winner-to-loser trajectory in the embryo. Supplementary Table 5. List of downregulated genes in BG (95%) cells when co-cultured with HB (24%) cells. Supplementary Table 6. List of upregulated genes in BG (95%) cells when co-cultured with HB (24%) cells. Supplementary Table 7. List of background genes used for the analysis of genes differentially expressed between co-cultured BG (95%) and HB (24%) cells. Supplementary Table 8. Imaging equipment and settings. Supplementary Table 9. BG/HB wild-type mouse mtDNA and C57BL/6N-specific detection by ARMS–qPCR. Supplementary Table 10. ST wild-type mouse mtDNA and C57BL/6N-specific detection by ARMS–qPCR assay.

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Lima, A., Lubatti, G., Burgstaller, J. et al. Cell competition acts as a purifying selection to eliminate cells with mitochondrial defects during early mouse development. Nat Metab 3, 1091–1108 (2021). https://doi.org/10.1038/s42255-021-00422-7

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