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Single-cell transcriptomics uncovers conserved cardiac cell types and regulatory adaptation across birds and mammals

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    1. Eleven major cardiac cell types were identified in adult and neonatal tree sparrows.

      Adult and neonatal birds exhibited a substantial difference in cell abundance and transcriptional divergence.

      Cardiac cell types and transcriptional programs were conserved across birds and mammals.

      Overexpression of insulin-like growth factor 1 receptor (IGF1R) in avian cardiomyocytes induced hypertrophy.

  • Birds and mammals possess highly efficient four-chambered hearts to meet the elevated metabolic demands of endothermy, yet the cellular and molecular basis of this convergent evolution remains poorly understood. To address this gap, we generated the first single-cell heart transcriptomic atlas of a wild bird, the Eurasian Tree Sparrow (Passer montanus), across neonatal and adult stages, and integrated it with existing human and mouse data. This cross-species comparison reveals broadly conserved transcriptional profiles: cardiomyocytes are the most transcriptionally conserved cell type across species and developmental stages, whereas non-myocyte populations (fibroblasts and endothelial cells) show greater transcriptional plasticity. These findings suggest that cardiac diversification and adaptation may occur primarily through regulatory changes within a shared cellular repertoire. Extending this framework, we investigate cardiac adaptation to high-elevation hypoxia in tree sparrows. Strikingly, the transcriptional programs significantly overlapped with genes previously linked to cardiac remodeling and disease-related phenotypes in humans and mice. To test the functional relevance of this overlap, we performed in vitro assays and found that IGF1R overexpression drives hypertrophy-like growth in avian cardiomyocytes, accompanied by PI3K-AKT-mTOR-associated transcriptional reprogramming, a pathway critically implicated in mammalian cardiac hypertrophy. Collectively, our findings demonstrate that birds and mammals share a conserved cardiac cellular blueprint, wherein regulatory programs are recurrently co-opted during development, adaptation to hypoxia, and pathological remodeling.
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  • [1] Hillenius Willem J. and Ruben John A. (2004). The evolution of endothermy in terrestrial vertebrates: Who? when? why? Physiol. Biochem. Zool. 77:1019−1042. DOI:10.1086/425185

    View in Article CrossRef Google Scholar

    [2] Stephenson A., Adams J. W. and Vaccarezza M. (2017). The vertebrate heart: An evolutionary perspective. J. Anat. 6:787−797. DOI:10.1111/joa.12687

    View in Article CrossRef Google Scholar

    [3] Shiels H. A. (2022). Avian cardiomyocyte architecture and what it reveals about the evolution of the vertebrate heart. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 377:20210332. DOI:10.1098/rstb.2021.0332

    View in Article CrossRef Google Scholar

    [4] Ivy C. M. and Scott G. R. (2015). Control of breathing and the circulation in high-altitude mammals and birds. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 186:66−74. DOI:10.1016/j.cbpa.2014.10.009

    View in Article CrossRef Google Scholar

    [5] Monge C. and Leon-Velarde F. (1991). Physiological adaptation to high altitude: Oxygen transport in mammals and birds. Physiol. Rev. 71:1135−1172. DOI:10.1152/physrev.1991.71.4.1135

    View in Article CrossRef Google Scholar

    [6] Ostadal B. and Kolar F. (2007). Cardiac adaptation to chronic high-altitude hypoxia: Beneficial and adverse effects. Respir. Physiol. Neurobiol. 158:224−236. DOI:10.1016/j.resp.2007.03.005

    View in Article CrossRef Google Scholar

    [7] She H. and Qu Y. (2025). Cardiovascular plasticity and adaptation of high-altitude birds and mammals. Integr. Zool. DOI:10.1111/1749-4877.12996

    View in Article Google Scholar

    [8] Velotta J. P., Ivy C. M., Wolf C. J., et al. (2018). Maladaptive phenotypic plasticity in cardiac muscle growth is suppressed in high-altitude deer mice. Evolution 72:2712−2727. DOI:10.1111/evo.13626

    View in Article CrossRef Google Scholar

    [9] Rong C., Yan M., Zhen-Zhong B., et al. (2012). Cardiac adaptive mechanisms of Tibetan antelope (Pantholops hodgsonii) at high altitudes. Am. J. Vet. Res. 73:809−813. DOI:10.2460/ajvr.73.6.809

    View in Article CrossRef Google Scholar

    [10] Cueva S., Sillau H., Valenzuela A., et al. (1974). High altitude induced pulmonary hypertension and right heart failure in broiler chickens. Res. Vet. Sci. 16:370−374. DOI:10.1016/S0034-5288(18)33737-8

    View in Article CrossRef Google Scholar

    [11] Penaloza D. and Arias-Stella J. (2007). The heart and pulmonary circulation at high altitudes: Healthy highlanders and chronic mountain sickness. Circulation 115:1132−1146. DOI:10.1161/CIRCULATIONAHA.106.624544

    View in Article CrossRef Google Scholar

    [12] Qu Y., Chen C., Xiong Y., et al. (2020). Rapid phenotypic evolution with shallow genomic differentiation during early stages of high elevation adaptation in Eurasian Tree Sparrows. Natl. Sci. Rev. 7:113−127. DOI:10.1093/nsr/nwz138

    View in Article CrossRef Google Scholar

    [13] Laguë S. L., Ivy C. M., York J. M., et al. (2020). Cardiovascular responses to progressive hypoxia in ducks native to high altitude in the Andes. J. Exp. Biol. 223:jeb211250. DOI:10.1242/jeb.211250

    View in Article CrossRef Google Scholar

    [14] Heineke J. and Molkentin J. D. (2006). Regulation of cardiac hypertrophy by intracellular signaling pathways. Nat. Rev. Mol. Cell Biol. 7:589−600. DOI:10.1038/nrm1983

    View in Article CrossRef Google Scholar

    [15] She H., Scott G. R., Fang Y., et al. (2025). Divergent cardiovascular adaptations and gene regulation in high-elevation natives and recent colonizers of the Qinghai-Tibetan Plateau. Mol. Biol. Evol. 42:msaf103. DOI:10.1093/molbev/msaf103

    View in Article CrossRef Google Scholar

    [16] Burggren W. W., Christoffels V. M., Crossley Ii D. A., et al. (2014). Comparative cardiovascular physiology: Future trends, opportunities and challenges. Acta Physiol. 210:257−276. DOI:10.1111/apha.12170

    View in Article CrossRef Google Scholar

    [17] Burggren W. W. and Reyna K. S. (2011). Developmental trajectories, critical windows and phenotypic alteration during cardio-respiratory development. Respir. Physiol. Neurobiol. 178:13−21. DOI:10.1016/j.resp.2011.05.001

    View in Article CrossRef Google Scholar

    [18] Sun Y.-F., Ren Z.-P., Wu Y.-F., et al. (2016). Flying high: Limits to flight performance by sparrows on the Qinghai-Tibet Plateau. J. Exp. Biol. 219:3642−3648. DOI:10.1242/jeb.142216

    View in Article CrossRef Google Scholar

    [19] Dura B., Choi J.-Y., Zhang K., et al. (2019). scFTD-seq: Freeze-thaw lysis based, portable approach toward highly distributed single-cell 3’ mRNA profiling. Nucleic Acids Res. 47:e16. DOI:10.1093/nar/gky1173

    View in Article CrossRef Google Scholar

    [20] Martin M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17. DOI:10.14806/ej.17.1.200

    View in Article Google Scholar

    [21] Dobin A., Davis C. A., Schlesinger F., et al. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29:15−21. DOI:10.1093/bioinformatics/bts635

    View in Article CrossRef Google Scholar

    [22] Liao Y., Smyth G. K. and Shi W. (2014). featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923−930. DOI:10.1093/bioinformatics/btt656

    View in Article CrossRef Google Scholar

    [23] Zheng G. X. Y., Terry J. M., Belgrader P., et al. (2017). Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8:14049. DOI:10.1038/ncomms14049

    View in Article CrossRef Google Scholar

    [24] Hao Y., Hao S., Andersen-Nissen E., et al. (2021). Integrated analysis of multimodal single-cell data. Cell 184:3573−3587.e3529. DOI:10.1016/j.cell.2021.04.048

    View in Article CrossRef Google Scholar

    [25] Young M. D. and Behjati S. (2020). SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience 9:giaa151. DOI:10.1093/gigascience/giaa151

    View in Article CrossRef Google Scholar

    [26] McGinnis C. S., Murrow L. M. and Gartner Z. J. (2019). DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8:329−337.e324. DOI:10.1016/j.cels.2019.03.003

    View in Article CrossRef Google Scholar

    [27] Ahlmann-Eltze C. and Huber W. (2021). glmGamPoi: Fitting Gamma-Poisson generalized linear models on single cell count data. Bioinformatics 36:5701−5702. DOI:10.1093/bioinformatics/btaa1009

    View in Article CrossRef Google Scholar

    [28] Litviňuková M., Talavera-López C., Maatz H., et al. (2020). Cells of the adult human heart. Nature 588:466−472. DOI:10.1038/s41586-020-2797-4

    View in Article CrossRef Google Scholar

    [29] Zhang Y., Gago-Lopez N., Li N., et al. (2019). Single-cell imaging and transcriptomic analyses of endogenous cardiomyocyte dedifferentiation and cycling. Cell Discov. 5:30. DOI:10.1038/s41421-019-0095-9

    View in Article CrossRef Google Scholar

    [30] Cao J., O’Day D. R., Pliner H. A., et al. (2020). A human cell atlas of fetal gene expression. Science 370:eaba7721. DOI:10.1126/science.aba7721

    View in Article CrossRef Google Scholar

    [31] Feng W., Bais A., He H., et al. (2022). Single-cell transcriptomic analysis identifies murine heart molecular features at embryonic and neonatal stages. Nat. Commun. 13:7960. DOI:10.1038/s41467-022-35691-7

    View in Article CrossRef Google Scholar

    [32] Liu X. Y., Shen Q. L. and Zhang S. H. (2023). Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network. Genome Res. 33:96−111. DOI:10.1101/gr.276868.122

    View in Article CrossRef Google Scholar

    [33] Ma S., Skarica M., Li Q., et al. (2022). Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science 377:eabo7257. DOI:10.1126/science.abo7257

    View in Article CrossRef Google Scholar

    [34] Emms D. M. and Kelly S. (2019). OrthoFinder: Phylogenetic orthology inference for comparative genomics. Genome Biol. 20:238. DOI:10.1186/s13059-019-1832-y

    View in Article CrossRef Google Scholar

    [35] Geirsdottir L., David E., Keren-Shaul H., et al. (2019). Cross-species single-cell analysis reveals divergence of the primate microglia program. Cell 179:1609−1622.e1616. DOI:10.1016/j.cell.2019.11.010

    View in Article CrossRef Google Scholar

    [36] Warnes G., Bolker B., Lumley T., et al. (2022). gmodels: Various R programming tools for model fitting. https://CRAN.R-project.org/package=gmodels

    View in Article Google Scholar

    [37] Bindea G., Mlecnik B., Hackl H., et al. (2009). ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091−1093. DOI:10.1093/bioinformatics/btp101

    View in Article CrossRef Google Scholar

    [38] Piñero J., Ramírez-Anguita J. M., Saüch-Pitarch J., et al. (2020). The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48:D845−D855. DOI:10.1093/nar/gkz1021

    View in Article CrossRef Google Scholar

    [39] Gargano M. A., Matentzoglu N., Coleman B., et al. (2024). The Human Phenotype Ontology in 2024: Phenotypes around the world. Nucleic Acids Res. 52:D1333−D1346. DOI:10.1093/nar/gkad1005

    View in Article CrossRef Google Scholar

    [40] Smith C. L. and Eppig J. T. (2009). The Mammalian Phenotype Ontology: Enabling robust annotation and comparative analysis. WIREs Syst. Biol. Med. 1:390−399. DOI:10.1002/wsbm.44

    View in Article CrossRef Google Scholar

    [41] Liu C., Xue R., Wu D., et al. (2014). REDD1 attenuates cardiac hypertrophy via enhancing autophagy. Biochem. Biophys. Res. Commun. 454:215−220. DOI:10.1016/j.bbrc.2014.10.079

    View in Article CrossRef Google Scholar

    [42] Chen S., Zhou Y., Chen Y., et al. (2018). fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884−i890. DOI:10.1093/bioinformatics/bty560

    View in Article CrossRef Google Scholar

    [43] Li B. and Dewey C. N. (2011). RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12:323. DOI:10.1186/1471-2105-12-323

    View in Article CrossRef Google Scholar

    [44] Robinson M. D., McCarthy D. J. and Smyth G. K. (2010). edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139−140. DOI:10.1093/bioinformatics/btp616

    View in Article CrossRef Google Scholar

    [45] Xu S., Hu E., Cai Y., et al. (2024). Using clusterProfiler to characterize multiomics data. Nat. Protoc. 19:3292−3320. DOI:10.1038/s41596-024-01020-z

    View in Article CrossRef Google Scholar

    [46] Mantri M., Scuderi G. J., Abedini-Nassab R., et al. (2021). Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat. Commun. 12:1771. DOI:10.1038/s41467-021-21892-z

    View in Article CrossRef Google Scholar

    [47] Cousin M. A., Creighton B. A., Breau K. A., et al. (2021). Pathogenic SPTBN1 variants cause an autosomal dominant neurodevelopmental syndrome. Nat. Genet. 53:1006−1021. DOI:10.1038/s41588-021-00886-z

    View in Article CrossRef Google Scholar

    [48] Yoshioka N., Kabata Y., Kuriyama M., et al. (2020). Diverse dystonin gene mutations cause distinct patterns of Dst isoform deficiency and phenotypic heterogeneity in Dystonia musculorum mice. Dis. Model. Mech. 13:dmm041608. DOI:10.1242/dmm.041608

    View in Article CrossRef Google Scholar

    [49] Jiang Y. L., Jiao B., Liao X. X., et al. (2020). Analyses mutations in GSN, CST3, TTR, and ITM2B genes in Chinese patients with Alzheimer’s disease. Front. Aging. Neurosci. 12:581524. DOI:10.3389/fnagi.2020.581524

    View in Article CrossRef Google Scholar

    [50] Myllyharju J. and Kivirikko K. I. (2001). Collagens and collagen-related diseases. Ann. Med. 33:7−21. DOI:10.3109/07853890109002055

    View in Article CrossRef Google Scholar

    [51] Zhang L., Smyth D., Al-Khalaf M., et al. (2022). Insulin-like growth factor-binding protein-7 (IGFBP7) links senescence to heart failure. Nat. Cardiovasc. Res. 1:1195−1214. DOI:10.1038/s44161-022-00181-y

    View in Article CrossRef Google Scholar

    [52] Harris L., Genovesi L. A., Gronostajski R. M., et al. (2015). Nuclear factor one transcription factors: Divergent functions in developmental versus adult stem cell populations. Dev. Dyn. 244:227−238. DOI:10.1002/dvdy.24182

    View in Article CrossRef Google Scholar

    [53] Nakamura M. and Sadoshima J. (2018). Mechanisms of physiological and pathological cardiac hypertrophy. Nat. Rev. Cardiol. 15:387−407. DOI:10.1038/s41569-018-0007-y

    View in Article CrossRef Google Scholar

    [54] Kim J., Wende A. R., Sena S., et al. (2008). Insulin-like growth factor I receptor signaling is required for exercise-induced cardiac hypertrophy. Mol. Endocrinol. 22:2531−2543. DOI:10.1210/me.2008-0265

    View in Article CrossRef Google Scholar

    [55] West J. B. (2009). Comparative physiology of the pulmonary blood-gas barrier: The unique avian solution. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 297:R1625−R1634. DOI:10.1152/ajpregu.00459.2009

    View in Article CrossRef Google Scholar

    [56] Parr N., Wilkes M. and Hawkes L. A. (2019). Natural climbers: Insights from avian physiology at high altitude. High Alt. Med. Biol. 20:427−437. DOI:10.1089/ham.2019.0032

    View in Article CrossRef Google Scholar

  • Cite this article:

    Hao Y., Zhou X., Zhao Q., et al. (2026). Single-cell transcriptomics uncovers conserved cardiac cell types and regulatory adaptation across birds and mammals. The Innovation Life 4:100251. https://doi.org/10.59717/j.xinn-life.2026.100251
    Hao Y., Zhou X., Zhao Q., et al. (2026). Single-cell transcriptomics uncovers conserved cardiac cell types and regulatory adaptation across birds and mammals. The Innovation Life 4:100251. https://doi.org/10.59717/j.xinn-life.2026.100251

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