Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cellular recovery after prolonged warm ischaemia of the whole body

Abstract

After cessation of blood flow or similar ischaemic exposures, deleterious molecular cascades commence in mammalian cells, eventually leading to their death1,2. Yet with targeted interventions, these processes can be mitigated or reversed, even minutes or hours post mortem, as also reported in the isolated porcine brain using BrainEx technology3. To date, translating single-organ interventions to intact, whole-body applications remains hampered by circulatory and multisystem physiological challenges. Here we describe OrganEx, an adaptation of the BrainEx extracorporeal pulsatile-perfusion system and cytoprotective perfusate for porcine whole-body settings. After 1 h of warm ischaemia, OrganEx application preserved tissue integrity, decreased cell death and restored selected molecular and cellular processes across multiple vital organs. Commensurately, single-nucleus transcriptomic analysis revealed organ- and cell-type-specific gene expression patterns that are reflective of specific molecular and cellular repair processes. Our analysis comprises a comprehensive resource of cell-type-specific changes during defined ischaemic intervals and perfusion interventions spanning multiple organs, and it reveals an underappreciated potential for cellular recovery after prolonged whole-body warm ischaemia in a large mammal.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the OrganEx technology and the experimental workflow.
Fig. 2: Circulation and blood/perfusate properties during the perfusion protocols.
Fig. 3: Analysis of tissue integrity across experimental conditions and organs.
Fig. 4: Functional characterization and metabolic activity of selected organs.
Fig. 5: Organ- and cell-type-specific transcriptomic changes assessed by snRNA-seq across various warm ischaemia intervals and different perfusion interventions.

Similar content being viewed by others

Data availability

The snRNA-seq dataset was deposited at the NCBI’s Gene Expression Omnibus68 and is accessible through GEO Series accession number GSE183448.

Code availability

The source code used to analyse the data presented in this paper is deposited and publicly available at GitHub (https://github.com/sestanlab/OrganEx).

References

  1. Lee, P., Chandel, N. S. & Simon, M. C. Cellular adaptation to hypoxia through hypoxia inducible factors and beyond. Nat. Rev. Mol. Cell Biol. 21, 268–283 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Daniele, S. G. et al. Brain vulnerability and viability after ischaemia. Nat. Rev. Neurosci. 22, 553–572 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Vrselja, Z. et al. Restoration of brain circulation and cellular functions hours post-mortem. Nature 568, 336–343 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hsia, C. C., Schmitz, A., Lambertz, M., Perry, S. F. & Maina, J. N. Evolution of air breathing: oxygen homeostasis and the transitions from water to land and sky. Compr. Physiol. 3, 849–915 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Eltzschig, H. K. & Eckle, T. Ischemia and reperfusion-from mechanism to translation. Nat. Med. 17, 1391–1401 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Iadecola, C., Buckwalter, M. S. & Anrather, J. Immune responses to stroke: mechanisms, modulation, and therapeutic potential. J. Clin. Invest. 130, 2777–2788 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Trump, B. F. & Harris, C. C. Human tissues in biomedical research. Hum. Pathol. 10, 245–248 (1979).

    Article  CAS  PubMed  Google Scholar 

  8. Brasile, L. et al. Overcoming severe renal ischemia: the role of ex vivo warm perfusion. Transplantation 73, 897–901 (2002).

    Article  PubMed  Google Scholar 

  9. García Sáez, D. et al. Ex vivo heart perfusion after cardiocirculatory death; a porcine model. J. Surg. Res. 195, 311–314 (2015).

    Article  PubMed  Google Scholar 

  10. Schön, M. R. et al. Liver transplantation after organ preservation with normothermic extracorporeal perfusion. Ann. Surg. 233, 114–123 (2001).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Charles, E. J. et al. Ex vivo assessment of porcine donation after circulatory death lungs that undergo increasing warm ischemia times. Transplant Direct 4, e405 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Taunyane, I. C. et al. Preserved brain morphology after controlled automated reperfusion of the whole body following normothermic circulatory arrest time of up to 20 minutes. Eur. J. Cardiothorac. Surg. 50, 1025–1034 (2016).

    Article  PubMed  Google Scholar 

  13. Grunau, B. et al. Comparing the prognosis of those with initial shockable and non-shockable rhythms with increasing durations of CPR: informing minimum durations of resuscitation. Resuscitation 101, 50–56 (2016).

    Article  PubMed  Google Scholar 

  14. Lequier, L., Horton, S. B., McMullan, D. M. & Bartlett, R. H. Extracorporeal membrane oxygenation circuitry. Pediatr. Crit. Care Med. 14, S7–S12 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kirino, T. Delayed neuronal death in the gerbil hippocampus following ischemia. Brain Res. 239, 57–69 (1982).

    Article  CAS  PubMed  Google Scholar 

  16. Pulsinelli, W. A., Brierley, J. B. & Plum, F. Temporal profile of neuronal damage in a model of transient forebrain ischemia. Ann. Neurol. 11, 491–498 (1982).

    Article  CAS  PubMed  Google Scholar 

  17. Unal-Cevik, I., Kilinç, M., Gürsoy-Ozdemir, Y., Gurer, G. & Dalkara, T. Loss of NeuN immunoreactivity after cerebral ischemia does not indicate neuronal cell loss: a cautionary note. Brain Res. 1015, 169–174 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Kroemer, G. et al. Classification of cell death: recommendations of the Nomenclature Committee on Cell Death 2009. Cell Death Differ. 16, 3–11 (2009).

    Article  CAS  PubMed  Google Scholar 

  19. Zhang, P. L. et al. Kidney injury molecule-1 expression in transplant biopsies is a sensitive measure of cell injury. Kidney Int. 73, 608–614 (2008).

    Article  CAS  PubMed  Google Scholar 

  20. Nadasdy, T., Laszik, Z., Blick, K. E., Johnson, L. D. & Silva, F. G. Proliferative activity of intrinsic cell populations in the normal human kidney. J. Am. Soc. Nephrol. 4, 2032–2039 (1994).

    Article  CAS  PubMed  Google Scholar 

  21. Dunn, A. F., Catterton, M. A., Dixon, D. D. & Pompano, R. R. Spatially resolved measurement of dynamic glucose uptake in live ex vivo tissues. Anal. Chim. Acta 1141, 47–56 (2021).

    Article  CAS  PubMed  Google Scholar 

  22. Fishbein, M. C., Wang, T., Matijasevic, M., Hong, L. & Apple, F. S. Myocardial tissue troponins T and I. An immunohistochemical study in experimental models of myocardial ischemia. Cardiovasc. Pathol. 12, 65–71 (2003).

    Article  CAS  PubMed  Google Scholar 

  23. Brown, D. J., Brugger, H., Boyd, J. & Paal, P. Accidental hypothermia. N. Engl. J. Med. 367, 1930–1938 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. Guluma, K. Z. et al. Therapeutic hypothermia is associated with a decrease in urine output in acute stroke patients. Resuscitation 81, 1642–1647 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Villa, G., Katz, N. & Ronco, C. Extracorporeal membrane oxygenation and the kidney. Cardiorenal Med. 6, 50–60 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Tujjar, O. et al. Acute kidney injury after cardiac arrest. Crit. Care 19, 169 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Dieterich, D. C. et al. In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons. Nat. Neurosci. 13, 897–905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Movahed, M., Brockie, S., Hong, J. & Fehlings, M. G. Transcriptomic hallmarks of ischemia-reperfusion injury. Cells 10, 1838 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang, J. et al. Effects of ischemia on gene expression. J. Surg. Res. 99, 222–227 (2001).

    Article  CAS  PubMed  Google Scholar 

  30. Molenaar, B. et al. Single-cell transcriptomics following ischemic injury identifies a role for B2M in cardiac repair. Commun. Biol. 4, 146 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Androvic, P. et al. Decoding the transcriptional response to ischemic stroke in young and aged mouse brain. Cell Rep. 31, 107777 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Ferreira, P. G. et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun. 9, 490 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  33. Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C. & Humphreys, B. D. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc. Natl Acad. Sci. USA 117, 15874–15883 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Skinnider, M. A. et al. Cell type prioritization in single-cell data. Nat. Biotechnol. 39, 30–34 (2021).

    Article  CAS  PubMed  Google Scholar 

  35. Jurga, A. M., Paleczna, M. & Kuter, K. Z. Overview of general and discriminating markers of differential microglia phenotypes. Front. Cell Neurosci. 14, 198 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lopaschuk, G. D. & Stanley, W. C. Glucose metabolism in the ischemic heart. Circulation 95, 313–315 (1997).

    Article  CAS  PubMed  Google Scholar 

  38. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Markmann, J. F. et al. Impact of portable normothermic blood-based machine perfusion on outcomes of liver transplant: the OCS Liver PROTECT randomized clinical trial. JAMA Surg. 157, 189–198 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  41. De Carlis, R. et al. How to preserve liver grafts from circulatory death with long warm ischemia? A retrospective Italian cohort study with normothermic regional perfusion and hypothermic oxygenated perfusion. Transplantation 105, 2385–2396 (2021).

    Article  PubMed  CAS  Google Scholar 

  42. Smith, D. E. et al. Early experience with donation after circulatory death heart transplantation using normothermic regional perfusion in the United States. J. Thorac. Cardiovasc. Surg. 164, 557–568.e1 (2022).

    Article  PubMed  Google Scholar 

  43. Sellers, M. T. et al. Early United States experience with liver donation after circulatory determination of death using thoraco‐abdominal normothermic regional perfusion: a multi‐institutional observational study. Clin. Transplant. 36, e14659 (2022).

    Article  PubMed  Google Scholar 

  44. De Beule, J. et al. A systematic review and meta-analyses of regional perfusion in donation after circulatory death solid organ transplantation. Transpl. Int. 34, 2046–2060 (2021).

    Article  PubMed  Google Scholar 

  45. De Charrière, A. et al. ECMO in cardiac arrest: a narrative review of the literature. J. Clin. Med. 10, 534 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kobak, D. & Linderman, G. C. Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nat. Biotechnol. 39, 156–157 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Stewart, B. J. et al. Spatiotemporal immune zonation of the human kidney. Science 365, 1461–1466 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Litviňuková, M. et al. Cells of the adult human heart. Nature 588, 466–472 (2020).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  53. Franjic, D. et al. Transcriptomic taxonomy and neurogenic trajectories of adult human, macaque, and pig hippocampal and entorhinal cells. Neuron 110, 452–469 (2021).

    Article  PubMed  CAS  Google Scholar 

  54. MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  55. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Blighe K., Rana, S., Lewis, M. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling (2018); https://github.com/kevinblighe/EnhancedVolcano

  57. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Laposata, M. Laboratory Medicine: The Diagnosis of Disease in the Clinical Laboratory 364 (McGraw-Hill Education, 2012).

  59. Lee, J. W., Chou, C.-L. & Knepper, M. A. Deep sequencing in microdissected renal tubules identifies nephron segment–specific transcriptomes. J. Am. Soc. Nephrol. 26, 2669–2677 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Cavalcante, G. C. et al. A cell’s fate: an overview of the molecular biology and genetics of apoptosis. Int. J. Mol. Sci. 20, 4133 (2019).

    Article  PubMed Central  CAS  Google Scholar 

  61. Yu, P. et al. Pyroptosis: mechanisms and diseases. Signal Transduct. Target. Ther. 6, 128 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Li, J. et al. Ferroptosis: past present and future. Cell Death Dis. 11, 88 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Dhuriya, Y. K. & Sharma, D. Necroptosis: a regulated inflammatory mode of cell death. J. Neuroinflammation 15, 199 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for Gene Ontology. R package version 2.48.0 (2022).

  65. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the staff at HbO2 Therapeutics for providing the Hemopure product; S. G. Waxman for providing us with insights into central nervous system assessments; N. Guerrera, C. Hawley, M. Mamarian and C. Romero for their help in the operating room; T. Wing for assistance with the EEG; C. Booth, A. Brooks, A. Nugent, G. Terwilliger and M. Schadt for help with histopathology and staining; T. Rajabipour for help with the perfusion circuit; P. Heerdt for help with animal perfusions; K. Henderson for assistance with slide imaging; R. Khozein for providing us with EEG equipment; the members of the external advisory and ethics committee for assistance and guidance throughout this research; various members of our laboratory community for their comments on the manuscript; and the staff at the Yale Macaque Brain Resource (grant to A. Duque, NIMH R01MH113257) for the use of the Aperio CS2 scanner. This work was supported by the NIH BRAIN Initiative grants MH117064, MH117064-01S1, R21DK128662, T32GM136651, F30HD106694 and Schmidt Futures.

Author information

Authors and Affiliations

Authors

Contributions

D.A., Z.V. and N.S. designed the OrganEx technology and the research described here. Z.V. and D.D. assembled the OrganEx perfusion system. D.A., Z.V., T.L., S.L.T., A.J.S., G.T.T., D.D. and K.T.G. were involved in the planning and preparation for the perfusion studies. D.A. and T.L. performed surgical procedures. D.A., Z.V., T.L. and D.D. conducted perfusion experiments. D.A., Z.V., T.L., D.D., S.Z., S.G.D. and K.T.G. collected and processed tissue samples for subsequent analyses. S.L.T., A.J.S., D.A. and Z.V. performed fluoroscopic and ultrasound imaging and analysis. D.A., Z.V., P.Q.D., S.Z., T.L., A.U.I. and S.G.D. conducted histological and immunohistological studies, imaged and analysed the data. D.A., Z.V., S.Z., D.D., T.L., S.P., K.B., M.C.M., A.S. and A.H. analysed and quantified the histological data. S.Z. and Z.V. performed organotypic slice culture experiments. K.T.G., H.P.Z. and R.B.D. performed the EEG studies and analysed the data. M.S. and S.-K.K. generated snRNA-seq data. A.S., S.M., D.L. and M.L. conducted post-processing and analysis of the snRNA-seq data. D.A., Z.V., A.S. and N.S. interpreted results of the snRNA-seq findings. S.R.L. contributed to the bioethical aspects of the research and interacted with the external advisory committee. N.S. conceived and supervised the project. D.A., Z.V., S.Z. and N.S. wrote the first draft of the manuscript and prepared figures. All of the authors discussed and commented on the data.

Corresponding author

Correspondence to Nenad Sestan.

Ethics declarations

Competing interests

D.A., Z.V. and N.S. have disclosed these findings to the Yale Office of Cooperative Research, which has filed a patent to ensure broad use of the technology. All protocols, methods, perfusate formulations and components of the OrganEx technology remain freely available for academic and non-profit research. Although the Hemopure product was provided in accordance with a material transfer agreement between HbO2 Therapeutics and Yale University through N.S., the Company had no influence on the study design or interpretation of the results. No author has a financial stake in, or receives compensation from, HbO2 Therapeutics.

Peer review

Peer review information

Nature thanks Amir Bashan, Rafael Kramann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Analysis of circulation and blood/perfusate properties after 1h of warm ischaemia and perfusion interventions.

a, Representative fluoroscopy images of autologous blood flow (ECMO intervention, up) or a mixture of autologous blood and the perfusate (OrganEx intervention, below) in the head captured after 3 and 6 h respectively of perfusion, showing robust restoration of the circulation in the OrganEx group. A contrast catheter was placed in the left common carotid artery (CCA), except in the ECMO group at 6 h timepoint where contrast catheter could not be advanced beyond aortic arch in to the left CCA due to pronounced vasoconstriction, thus resulting in bilateral CCA filling. n = 6. b, Representative colour Doppler images of the CCA demonstrating robust flow in OrganEx group. Ultrasound waveform analysis demonstrated that OrganEx produced pulsatile, biphasic flow pattern (lower panel). SCM, sternocleidomastoid muscle; RI, resistive index. n = 6. c, Longitudinal change in arterial and venous cannula pressures throughout the perfusion demonstrating robust perfusion in OrganEx group. d, Time-dependent changes in oxygen delivery and consumption demonstrating increased oxygen delivery and stable oxygen consumption over the perfusion period in OrganEx group. n = 6. e, Presence of classical signs of death (rigor and livor mortis) in ECMO as compared to OrganEx group at the experimental endpoint. Data presented are mean ± s.e.m. Two-tailed unpaired t-test was performed. For more detailed information on statistics and reproducibility, see methods. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 2 Nissl staining and immunohistochemical analysis of the hippocampal CA1 region and the prefrontal cortex (PFC).

a, Representative images of Nissl staining of the CA1 (up) and PFC (below). b, c, Quantification of the number of cells per standardized area (b) and percentage of ellipsoid cells per area (c) in the CA1 between the experimental groups. d, e, Quantification of the number of cells per standardized area (d) and percentage of ellipsoid cells per area (e) in the PFC between the experimental groups. n = 3. f, h, Representative confocal images of immunofluorescent staining for neurons (NeuN), astrocytes (GFAP), and microglia (IBA1) counterstained with DAPI nuclear stain in CA1 (f) and PFC (h). g, Quantification of GFAP immunoreactivity in hippocampal CA1 region depicting comparable immunoreactivity between OrganEx and 0h WIT group, with a significant increase compared to the other groups. i, j, k, l, Quantification of NeuN immunolabeling intensity (i), number of GFAP+ fragments (j), and number of GFAP+ cells (k) depict similar trends between the groups as seen in the CA1. Microglia number (l) shows comparable results between OrganEx and 0h WIT with different dynamics seen in the ECMO group. n = 3. Scale bars, 50 μm. Data presented are mean ± s.e.m. One-way ANOVA with post-hoc Dunnett’s adjustments was performed. For more detailed information on statistics and reproducibility, see methods. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 3 Representative images of H&E staining across assessed peripheral organs and kidney periodic acid-Schiff (PAS) staining and immunolabeling for HACVR1 and Ki-67.

a, Representative images of the H&E staining in heart, kidney, liver, lungs, and pancreas. Arrows point to nuclear damage, asterisks point to disrupted tissue integrity, empty arrowheads point to haemorrhage, full arrowheads point to cell vacuolization, double arrows point to tissue oedema. b, c, H&E histopathological scores in lungs (b) and pancreas (c). d, Representative images of PAS staining of the kidney. Arrows point to disrupted brush border, full arrowheads point to the presence of casts, asterisks point to tubular dilation, double arrows point to the Bowman space dilation. e, Kidney PAS histopathological damage score. n = 5. f, h, Representative confocal images of immunofluorescent staining for HAVCR1 and Ki-67 in kidney, respectively. g, Quantification of HAVCR1 immunolabeling signal intensity. i, j, Quantification of the kidney Ki-67 positive staining. HACVR1 and Ki-67 immunolabeling quantification results follow a similar pattern seen with other organs with comparable results between 0h WIT and OrganEx group and significant decrease in the 7h WIT and ECMO groups. n = 3. Scale bars,100 μm. Data presented are mean ± s.e.m. One-way ANOVA with post-hoc Dunnett’s adjustments was performed. For more detailed information on statistics and reproducibility, see methods. *P < 0.05, **P < 0.01.

Extended Data Fig. 4 Analysis of cell death across experimental conditions and organs.

a, f, k, n, Representative confocal images of immunofluorescent staining for activated caspase 3 (actCASP3) and TUNEL assay in heart, liver, kidney, pancreas and brain. b-e, Quantification of actCASP3 immunolabeling signal intensity in heart (b), liver (c), kidney (d), and pancreas (e). n = 3. g-j, Normalized total intensity of TUNEL signal in heart (g), liver (h), kidney (i), and pancreas (j). n = 3. l, m, Percentage of actCASP3 positively stained nuclei in the CA1 (l) and PFC (m). n = 5. o, p, Normalized total intensity of TUNEL signal in CA1 (o) and PFC (p). n = 5. Scale bars, 50 μm. Data presented are mean ± s.e.m. One-way ANOVA with post-hoc Dunnett’s adjustments was performed. For more detailed information on statistics and reproducibility, see methods. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 5 Evaluation of different cell death pathways by immunohistochemical staining for important molecules in pyroptosis (IL-1B), necroptosis (RIPK3) and ferroptosis (GPX4) across the experimental conditions.

a, f, k, Representative confocal images of immunofluorescent staining for pyroptosis marker IL-1B, necroptosis marker RIPK3, and ferroptosis marker GPX4, each co-stained with DAPI nuclear stain in CA1, heart, liver, and kidney. b-e, Quantification of IL-1B immunolabeling signal intensity in CA1 (b), heart (c), liver (d), and kidney (e). n = 3. g-j, Quantification of RIPK3 positive intranuclear co-staining in CA1 (g), and immunolabeling signal intensity heart (h), liver (i), kidney (j). n = 3. l-o, Quantification of GPX4 immunolabeling signal intensity in CA1 (l), heart (m), liver (n), and kidney (o). n = 3. Scale bars, 50 μm left and right panels. Data presented are mean ± s.e.m. One-way ANOVA with post-hoc Dunnett’s adjustments was performed. For more detailed information on statistics and reproducibility, see methods. *P < 0.05, **P < 0.01, ***P < 0.001. IN, intranuclear.

Extended Data Fig. 6 EEG setup and recordings, click-iT chemistry and immunohistochemical analysis of factor V and troponin I.

a, Placement of EEG electrodes on the porcine scalp. b, Representative snapshot of the EEG recordings after administration of anaesthesia and before the induction of cardiac arrest by ventricular fibrillation. c, Representative snapshot of the EEG recordings immediately following the ventricular fibrillation. d, Representative snapshot of the EEG during ECMO intervention at around 2h of perfusion protocol. e, Representative snapshot of the EEG during OrganEx intervention at around 2h of perfusion protocol, showing a light pulsatile artefact. f, g, Representative snapshot of the EEG recordings following contrast injection at 3h in ECMO and OrganEx animals, respectively. OrganEx EEG snapshot is consistent with a possible muscle-movement artefact. GND, ground electrode; REF, reference electrode. h, i, Representative confocal images of AHA through Click-iT chemistry in newly synthesized proteins with DAPI nuclear stain in the long-term organotypic hippocampal slice culture in CA3 (h) and DG (i) subregions. j, k, Relative intensity of nascent protein around nuclei in hippocampal CA3 (j) and DG (k) region showing comparable protein synthesis between OrganEx and 0h WIT up to 14 days in culture. n = 3-5. l, Representative confocal images of immunofluorescent staining for troponin I in the heart. m, Quantification of troponin I immunolabeling signal intensity in heart. A decreased trend in immunolabeling intensity was observed with ischaemia time and a significant decrease in immunolabeling intensity in ECMO compared to the OrganEx group. n = 3. n, Representative confocal images of immunofluorescent staining for factor V in liver. o, Quantification of factor V immunolabeling signal intensity in liver follows a similar pattern seen with other organs with comparable results between 0h WIT, 1h WIT, and OrganEx group and a significant decrease in 7h WIT and ECMO groups. n = 3. Scale bars, 50 μm. Data presented are mean ± s.e.m. For more detailed information on statistics and reproducibility, see methods. *P < 0.05, **P < 0.01. AU, arbitrary units.

Extended Data Fig. 7 Quality control of snRNA-seq data in healthy and varying ischaemic conditions in the hippocampus, heart, liver, and kidney.

Through transcriptomic integration and iterative clustering, we generated a taxonomy of t-types in healthy organs and brain, heart, liver, and kidney that experienced ischaemia (1h WIT, 7h WIT, ECMO and OrganEx), representing presumptive major cell types across organs of interest. These major t-types were further subdivided into high-resolution subclusters that were transcriptomically comparable to publicly available human single-cell datasets and that were marked by distinct expression profiles (c-f)51,52,53,54. a, Bar plot showing the number of cells/nuclei across organs and biological replicates. b, Violin plot showing the distribution of the number of unique molecular identifiers – UMIs (upper panel) and genes (lower panel) detected across all biological replicates. c-f, respective analyses of snRNA-seq in the hippocampus (c), heart (d), liver (e), and kidney (f). The left upper corner depicts detailed UMAP layout showing all t-types in the respective organs. The right side depicts the expression of top t-type markers. The left lower corner depicts transcriptomic correlation between the t-type taxonomy defined in this study and that of previous human and mouse datasets51,52,53,54. c., cells; LSECs, liver sinusoidal endothelial cells; end., endothelium; prog., progenitor; perit., peritubular; TAL, thick ascending limb; dend., dendritic; CNT, connecting tubule.

Extended Data Fig. 8 Single-nucleus transcriptome analysis in healthy and varying ischaemic conditions in the hippocampus, heart, liver, and kidney.

a-d, From left to right: UMAP layout showing major t-types; UMAP layout, coloured by Augur cell type prioritization (AUC) between 0h WIT compared to 1h (up) and 7h WIT (down); statistical comparison of Augur AUC scores between 0h WIT and 1h (up) and 7h (down) of WIT; Volcano plot showing top DEGs in major annotated t-types between 0h and 1h WIT (up), or 0h and 7h WIT (down); GO terms associated with the genes up and downregulated in detected nuclei between 0h and 1h WIT (up), or 0h and 7h WIT (down) with their nominal P-value in respective major annotated t-types.

Extended Data Fig. 9 Hippocampal single-nucleus transcriptome analysis comparing OrganEx to other experimental conditions.

a, AUC scores of the Augur cell type prioritization between OrganEx and other groups. b, Volcano plot showing DEGs in hippocampal neurons between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. c, Trajectories of hippocampal neurons. Colour indicates different experimental groups. d, Sankey plot showing perfusate components and violin plots showing their effects on hippocampal neurons between the OrganEx and ECMO groups. e, Hierarchical clustering of the top DEGs across experimental groups and derived functional gene modules (upper left). Eigengene average expression trends exhibit distinct trends between ECMO and OrganEx groups (lower left) of modules whose enriched GO terms are predominantly related to cellular function (right) (Supplementary Table 5). f, Expression of the genes involved in cell-death pathways in neurons. g, Gene expression enrichment of the genes involved in cell-death pathways in neurons. h, Stacked bar plot showing relative information flow for each signalling pa pathway across experimental group pairs. Significant signalling pathways were ranked based on differences in the overall information flow within the inferred networks between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. Genes important in inflammation are highlighted grey. i, Overall signalling patterns across all experimental conditions. Genes important in inflammation are highlighted grey. Necro-1, necrostatin-1; Mino, minocycline; DEXA, dexamethasone; Met. B, methylene blue; GEE, Glutathione Ethyl Ester. *P < 0.05, **P < 0.01, ***P < 0.001, NS: not significant.

Extended Data Fig. 10 Heart single-nucleus transcriptome analysis comparing OrganEx to other experimental conditions.

a, AUC scores of the Augur cell type prioritization between OrganEx and other groups. b, Volcano plot showing the DEGs in cardiomyocytes between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. c, Trajectories of heart cardiomyocytes. Colour indicates different experimental groups. d, Sankey plot showing perfusate components and violin plots showing their effects on cardiomyocytes between the OrganEx and ECMO groups. e, Hierarchical clustering of the top DEGs across experimental groups and derived functional gene modules (upper left). Eigengene average expression trends exhibit distinct trends between ECMO and OrganEx groups (lower left) of modules whose enriched GO terms are predominantly related to cellular function (right) (Supplementary Table 5). f, Expression of the genes involved in cell-death pathways in cardiomyocytes. g, Gene expression enrichment of the genes involved in cell-death pathways in cardiomyocytes. h, Stacked bar plot showing relative information flow for each signalling pathway across experimental group pairs. Significant signalling pathways were ranked based on differences in the overall information flow within the inferred networks between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. Genes important in inflammation are highlighted grey. i, Overall signalling patterns across all experimental conditions. Genes important in inflammation are highlighted grey. Necro-1, necrostatin-1; Mino, minocycline; DEXA, dexamethasone; Met. B, methylene blue; GEE, Glutathione Ethyl Ester. *P < 0.05, **P < 0.01, ***P < 0.001, NS: not significant.

Extended Data Fig. 11 Liver single-nucleus transcriptome analysis comparing OrganEx to other experimental conditions.

a, AUC scores of the Augur cell type prioritization between OrganEx and other groups. b, Volcano plot showing DEGs in hepatocytes between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. c, Trajectories of liver hepatocytes. Colour indicates different experimental groups. d, Sankey plot showing perfusate components and violin plots showing their effects on hepatocytes between the OrganEx and ECMO. e, Hierarchical clustering of the top DEGs across experimental groups and derived functional gene modules (upper left). Eigengene average expression trends exhibit distinct trends between ECMO and OrganEx groups (lower left) of modules whose enriched GO terms are predominantly related to cellular function or cell death (right) (Supplementary Table 5). f, Expression of the genes involved in cell-death pathways in hepatocytes. g, Gene expression enrichment of the genes involved in cell-death pathways in hepatocytes. h, Stacked bar plot showing relative information flow for each signalling pathway across experimental group pairs. Significant signalling pathways were ranked based on differences in the overall information flow within the inferred networks between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. Genes important in inflammation are highlighted grey. i, Overall signalling patterns across all experimental conditions. Genes important in inflammation are highlighted grey. Necro-1, necrostatin-1; Mino, minocycline; DEXA, dexamethasone; Met. B, methylene blue; GEE, Glutathione Ethyl Ester. *P < 0.05, **P < 0.01, ***P < 0.001, NS: not significant.

Extended Data Fig. 12 Kidney single-nucleus transcriptome analysis comparing OrganEx to other experimental conditions.

a, AUC scores of the Augur cell type prioritization between OrganEx and other groups. b, Volcano plot showing DEGs in PCT between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. c, Trajectories of kidney PCTs. Colour indicates pseudotime progression and different cell states, respectively. d, Sankey plot showing perfusate components and violin plots showing their effects on PCT between the OrganEx and ECMO groups. e, Hierarchical clustering of the top DEGs across experimental groups and derived functional gene modules (upper left). Eigengene average expression trends exhibit distinct trends between ECMO and OrganEx groups (lower left) of modules whose enriched GO terms are predominantly related to cellular function or cell death (right) (Supplementary Table 5). f, Expression of the genes involved in cell-death pathways in PCT. g, Gene expression enrichment of the genes involved in cell-death pathways in PCT. h, Stacked bar plot showing relative information flow for each signalling pathway across experimental group pairs. Significant signalling pathways were ranked based on differences in the overall information flow within the inferred networks between OrganEx and 0h WIT, 1h WIT, 7h WIT, and ECMO. Genes important in inflammation are highlighted grey. i, Overall signalling patterns across all experimental conditions. Genes important in inflammation are highlighted grey. PCT, proximal convoluted tubules; DCT, distal convoluted tubules; Necro-1, necrostatin-1; Mino, minocycline; DEXA, dexamethasone; Met. B, methylene blue; GEE, Glutathione Ethyl Ester. *P < 0.05, **P < 0.01, ***P < 0.001, NS: not significant.

Supplementary information

Reporting Summary

Supplementary Table 1

List of the components with respective concentrations that are included in the OrganEx perfusate.

Supplementary Table 2

List of the components with respective concentrations that are included in the priming solution.

Supplementary Table 3

List of the components with respective concentrations that are included in the haemodiafiltration exchange solution.

Supplementary Table 4

List of genes used for comparing their average expression in the analysis of the perfusate effect.

Supplementary Table 5

List of enriched GO terms derived from hierarchical clustering of the top DEGs across experimental groups.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Andrijevic, D., Vrselja, Z., Lysyy, T. et al. Cellular recovery after prolonged warm ischaemia of the whole body. Nature 608, 405–412 (2022). https://doi.org/10.1038/s41586-022-05016-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-05016-1

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing