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Primitive macrophages induce sarcomeric maturation and functional enhancement of developing human cardiac microtissues via efferocytic pathways

Nature Cardiovascular Research volume 3, pages 567–593 (2024)Cite this article

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Abstract

Yolk sac macrophages are the first to seed the developing heart; however, owing to a lack of accessible tissue, there is no understanding of their roles in human heart development and function. In this study, we bridge this gap by differentiating human embryonic stem (hES) cells into primitive LYVE1+ macrophages (hESC-macrophages) that stably engraft within contractile cardiac microtissues composed of hESC-cardiomyocytes and fibroblasts. Engraftment induces a human fetal cardiac macrophage gene program enriched in efferocytic pathways. Functionally, hESC-macrophages trigger cardiomyocyte sarcomeric protein maturation, enhance contractile force and improve relaxation kinetics. Mechanistically, hESC-macrophages engage in phosphatidylserine-dependent ingestion of apoptotic cardiomyocyte cargo, which reduces microtissue stress, leading hESC-cardiomyocytes to more closely resemble early human fetal ventricular cardiomyocytes, both transcriptionally and metabolically. Inhibiting hESC-macrophage efferocytosis impairs sarcomeric protein maturation and reduces cardiac microtissue function. Together, macrophage-engineered human cardiac microtissues represent a considerably improved model for human heart development and reveal a major beneficial role for human primitive macrophages in enhancing early cardiac tissue function.

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Fig. 1: hESC-macrophages undergo stepwise in vivo yolk sac tissue macrophage programming in bioengineered human cardiac microtissues.
Fig. 2: hESC-macrophages enhance electromechanical properties of human cardiac microtissues across cell sources and platforms.
Fig. 3: hESC-macrophages induce maturation of cardiomyocyte sarcomeric apparatus and alter ECM composition in human cardiac microtissues.
Fig. 4: Multidimensional CyTOF analyses reveal that hESC-macrophages reduce mitochondrial debris, increase ATP production and realign cardiomyocyte metabolism to approximate early fetal ventricular cardiomyocytes.
Fig. 5: hESC-macrophage incorporation into cardiac microtissues results in reduced cytotoxicity and increased differentiation of cardiomyocytes toward in vivo human fetal cardiomyocytes.
Fig. 6: Efferocytic uptake of cardiomyocytes by hESC-macrophages is driven by phosphatidylserine recognition and is required for full macrophage maturation.
Fig. 7: Inhibiting hESC-macrophage efferocytosis of cardiomyocytes leads to blunted cardiomyocyte maturation, increased cytotoxicity and impaired microtissue function.
Fig. 8: Graphical summary of findings.

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Data availability

RNA sequencing data generated in this study were deposited to the Gene Expression Omnibus under accession number GSE261628. Proteomics data generated in this study were deposited to the ProteomeXchange with the identifiers PXD050990 (control versus macrophage dataset) and PXD050996 (efferocytosis inhibition with annexin dataset). Transcriptomics and proteomics data are available to explore through an interactive web browser (Shiny app) at https://www.epelmanlab.com/resources. Accession codes and sample information for publicly available single-cell RNA sequencing data analyzed in this study are summarized in Supplementary Table 9. All data generated in this study are included in the main article and associated source files.

Code availability

The custom-made MATLAB code for quantitative image analysis of cardiac structural properties are described in ref. 95. Scripts for the analysis of bulk RNA sequencing and single-cell RNA sequencing data are available at https://github.com/HomairaH/EpelmanLab_Human-cardiac-microtissues.

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Acknowledgements

We would like to thank the Immune Profiling Team at the Tumor Immunotherapy Program (Princess Margaret Cancer Centre, Toronto, Canada) for processing of mass cytometry samples and guidance on experimental design and analysis (G. Boukhaled, B. Wang and D. Brooks). We would also like to thank the Princess Margaret Genomics Centre (Toronto, Canada) for processing of RNA sequencing samples and the SickKids-UHN Flow Cytometry Facility (Toronto, Canada) for sample sorting. This work was supported by the Canadian Institutes of Health Research (S.E., H.H. and M.R.); the Ted Rogers Centre for Heart Research (S.E. and H.H.); the Peter Munk Cardiac Centre (S.E.); Medicine by Design (S.E., G.K. and M.A.L.); the Stem Cell Network (M.R. (MP-C4R1-3), S.E. and H.H.); and National Institutes of Health (M.R. (2R01 HL076485)). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors jointly supervised this work: Michael A. Laflamme, Gordon Keller, Milica Radisic, Slava Epelman.

Authors and Affiliations

  1. Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada

    Homaira Hamidzada, Simon Pascual-Gil, Qinghua Wu, Crystal Kantores, Yimu Zhao, Jared Nanthakumar, Shabana Vohra, Anthony Gramolini, Peter H. Backx, Milica Radisic & Slava Epelman

  2. Ted Rogers Centre for Heart Research, Translational Biology and Engineering Program, Toronto, Ontario, Canada

    Homaira Hamidzada, Simon Pascual-Gil, Crystal Kantores, Jared Nanthakumar, Shabana Vohra, Anthony Gramolini & Slava Epelman

  3. Department of Immunology, University of Toronto, Toronto, Ontario, Canada

    Homaira Hamidzada, Tracy L. McGaha & Slava Epelman

  4. Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Qinghua Wu, M. Juliana Gomez-Garcia, Naimeh Rafatian, Shira Landau, Yimu Zhao & Milica Radisic

  5. McEwen Stem Cell Institute, University Health Network, Toronto, Ontario, Canada

    Gregory M. Kent, M. Juliana Gomez-Garcia, Michael H. Atkins, Ian Fernandes, Tamilla Valdman Sadikov, Michael A. Laflamme & Gordon Keller

  6. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Gregory M. Kent, Michael H. Atkins, Ian Fernandes & Gordon Keller

  7. Hull Family Cardiac Fibrillation Management Laboratory, Toronto General Hospital, Toronto, Ontario, Canada

    Stéphane Massé, Tasnia Subha & Kumaraswamy Nanthakumar

  8. Department of Physiology, University of Toronto, Toronto, Ontario, Canada

    Uros Kuzmanov, Anthony Gramolini & Peter H. Backx

  9. Department of Biology, York University, Toronto, Ontario, Canada

    Renée A. Gorman, Marianne Wauchop & Peter H. Backx

  10. Scientific Research Center, Second Affiliated Hospital of, Guangdong Medical University, Zhanjiang, China

    Wenliang Chen

  11. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada

    Erika Beroncal

  12. David H. Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA

    Erika Y. Wang

  13. Department of Medicine, Vascular Medicine Institute, University of Pittsburgh School of Medicine and UPMC, Pittsburgh, PA, USA

    Babak Razani

  14. Department of Cardiology, Pittsburgh VA Medical Center, Pittsburgh, PA, USA

    Babak Razani

  15. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Tracy L. McGaha & Gordon Keller

  16. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

    Ana C. Andreazza

  17. Mitochondrial Innovation Initiative, Toronto, Ontario, Canada

    Ana C. Andreazza

  18. Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada

    Michael A. Laflamme & Slava Epelman

  19. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

    Michael A. Laflamme & Slava Epelman

  20. Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

    Milica Radisic

  21. Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Ontario, Canada

    Milica Radisic

Authors
  1. Homaira Hamidzada
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  2. Simon Pascual-Gil
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  3. Qinghua Wu
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  4. Gregory M. Kent
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  5. Stéphane Massé
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  6. Crystal Kantores
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  7. Uros Kuzmanov
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  8. M. Juliana Gomez-Garcia
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  9. Naimeh Rafatian
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  10. Renée A. Gorman
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  13. Shira Landau
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  14. Tasnia Subha
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  15. Michael H. Atkins
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  16. Yimu Zhao
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  17. Erika Beroncal
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  18. Ian Fernandes
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  19. Jared Nanthakumar
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  20. Shabana Vohra
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  21. Erika Y. Wang
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  22. Tamilla Valdman Sadikov
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  23. Babak Razani
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  24. Tracy L. McGaha
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  25. Ana C. Andreazza
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  26. Anthony Gramolini
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  27. Peter H. Backx
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  28. Kumaraswamy Nanthakumar
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  29. Michael A. Laflamme
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  30. Gordon Keller
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  31. Milica Radisic
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  32. Slava Epelman
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Contributions

H.H. designed and performed experiments, with the help of S.P.-G., Q.W., S.M., C.K., U.K., N.R., R.A.G., M.W., W.C., S.L., T.S., Y.Z., E.B., J.N. and S.V. H.H. performed the bioinformatics analyses. Q.W., G.M.K., M.H.A., M.J.G.-G., E.Y.W., I.F. and T.V.S. generated bioengineering platforms and produced and differentiated cells. B.R., T.M., A.C.A., A.G., P.B., K.N., M.A.L., G.K. and M.R. contributed to data interpretation and provided expertise. S.E. and M.R. conceived the study and obtained funding. H.H. and S.E. wrote the manuscript. All authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Slava Epelman.

Ethics declarations

Competing interests

M.R., Y.Z. and Q.H. are inventors on patents related to the Biowire II cardiac tissue cultivation and maturation protocols that are used as a main experimental system in this manuscript (all figures). These patents and applications cover platform fabrication, cell seeding and tissue cultivation (patent 10,034,738; patent 10,254,274; patent 11,913,940; application 17,520,303 filed on 5 November 2021; application 17798047, publication date 9 March 2023; application 17520303, publication date 12 May 2022; application 17798047, publication date 9 March 2023; and application 17520303, publication date 12 May 2022). Patents 10,254,274 and 11,913,940 are licensed to Valo Health. M.R. and Y.Z. receive royalty payments and annual fees for licensing of these inventions. M.R. and Y.Z. are also eligible for milestone payments from Valo Health related to successful discovery and translation of molecules using the Biowire II platform. All other authors declare no competing interests.

Peer review

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Nature Cardiovascular Research thanks Charles Murry and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Integration of hESC-macrophages into bioengineered human cardiac microtissues.

(a) Flow cytometry of hESC-cardiomyocytes on day 16 post-differentiation. (b) qPCR of generic or cardiac lineage-specific genes in human primary cardiac fibroblasts compared to human primary dermal fibroblasts at passages 3 or 5. n = 3 replicates per group from one experiment. (c-d) Immunofluorescence confocal imaging of microtissues 14 days post-seeding with or without hESC-macrophages. Images were acquired at the surface or deep within the tissue. Scale bar: 100 µm (C, left), 50 µm (C, right), 100 µm (D). (e) hESC-macrophages were seeded either alone or with a range of abundances of human primary cardiac fibroblasts. The number of hESC-macrophages were counted over three weeks. n = 3 per group, representative experiment shown, repeated two times. Diagram made with BioRender.com. (f) hESC-macrophages were incubated with control or conditioned media from human primary cardiac fibroblasts. The number of hESC-macrophages were counted over three weeks. n = 3 per group, representative experiment shown, repeated two times. cTnT: cardiac troponin T; CM: cardiomyocyte; FB: fibroblast; MF: macrophage. One-way ANOVA with P values adjusted for multiple comparisons using the Tukey-Kramer test: *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 2 Defining contamination and dissociation-induced gene expression in hESC-macrophages.

(a) hESC-macrophages were stimulated with LPS in the presence or absence of the transcription inhibitor Flavopiridol. qPCR was performed on IL6 and TNF with expression normalized to the housekeeping gene b2M. n = 3 replicates per group from one experiment. (b) In control experiments, hESC-macrophages were incubated either (1) alone, (2) with fibroblasts or (3) with fibroblasts and hESC-cardiomyocytes during a 40-minute digestion period at 37 degrees Celsius. hESC-macrophages were sorted for bulk RNA sequencing (n = 3 replicates per group from one experiment). (c) Representative gating strategy for fluorescence activated cell sorting (FACS) isolation of hESC-macrophages from each digestion control group in (B). CD14 + RFP + CD45 + DAPI- live single cells were sorted for bulk RNA sequencing. (d) Principal component analysis. (e) Volcano plots showing differentially expressed genes between CM + FB + MF vs. MF and FB + MF vs. MF. (f-g) Microtissues (HT-Biowires) were seeded in combinations of hESC-cardiomyocytes, human primary cardiac fibroblasts and/or hESC-macrophages. On day 14, hESC-macrophages were sorted for bulk RNA sequencing. (f) Representative gating strategy for fluorescence activated cell sorting (FACS) isolation of hESC-macrophages from microtissues. CD14 + RFP + CD45 + DAPI- live single cells were sorted for bulk RNA sequencing (n = 3 microtissues per group from one experiment). (g) Principal component analysis of hESC-macrophages sorted from each group in microtissues in (F). (h) CM + FB + MF vs. FB + MF DEGs compared in Biowire vs. digestion controls, or FB + MF vs. MF DEGs compared in Biowires vs. in digestion controls. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. One-way ANOVA with P values adjusted for multiple comparisons using the Šídák test: *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 3 hESC-macrophages improve electromechanical function and reduce electrical instability in iCell-cardiomyocyte containing Biowires.

Microtissues were seeded with iPSC-cardiomyocytes (iCells) and primary human cardiac fibroblasts with or without hESC-macrophages in the Biowire II platform. (a) Independent experiments of distinct iPSC-cardiomyocyte and hESC-macrophage batches. Biowires were seeded with or without hESC-macrophages. Force and electrical properties were measured day 11 post-seeding. Batch 2: n = 3 (control) or n = 5 (hESC-macrophage) microtissues per group from one experiment, except for excitation threshold and maximum capture rate where n = 5 control microtissues. Batch 3: n = 4 microtissues per group from one experiment. (b) Microtissues were stimulated at increasing frequencies from 1 Hz to 4 Hz. Graph shows the tracking of pixel movement during contraction and relaxation. n = 4 per group. (c) Schematic depicting the categorization of weak amplitude beats during mechanical alternans. (d) Electrical instability threshold representing the minimum frequency at which an alternating reduced force amplitude pattern was observed. n = 4 microtissues per group from one experiment. We defined a reduced force amplitude based on whether the amplitude was at least 15% less than the prior measured amplitude. (e) Percentage of peaks at 1 Hz or 2 Hz that contain alternating amplitudes were quantified. n = 4 microtissues per group, one experiment. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. Unpaired two-tailed t-test (A, D-E): *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 4 Baseline heart rate correlates with the effect of hESC-macrophages on the heart rate of cardiac microtissues.

(a) Microtissues (HT-Biowires) were seeded with hESC-cardiomyocytes and fibroblasts with or without hESC-macrophages. Active force was measured on day 14 post-seeding. n = 26 (control) or n = 24 (hESC-macrophage) microtissues per group pooled from two independent experiments. Unpaired t-test was performed. (b) Relationship between the baseline heart rate of microtissues (HT-Biowires, except for iCell data point from Biowire II platform) without hESC-macrophages to the change in heart rate upon addition of hESC-macrophages. Each data point represents the average of a distinct experiment, with the batch of cardiomyocytes used indicated. Simple linear regression was performed, reporting R-squared and P value indicating whether the slope is significantly non-zero. (c) Fold-change in active force in microtissues (HT-Biowires, except for iCell data from Biowire II platform) with hESC-macrophages relative to controls in each experiment performed. n = 14, 20, 6, 9, 11, 2, 6, 18, 6 control microtissues (left to right) or n = 8, 30, 5, 11, 14, 9, 16, 18, 6 microtissues with hESC-macrophages (left to right). Each group represents an independent experiment. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. Unpaired two-tailed t-test (A, C): *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 5 Expression of proteins in mass spectrometry-based proteomics of cardiac microtissues.

Liquid chromatography mass spectrometry was performed on total protein isolated from individual microtissues (HT-Biowires) on day 3 and day 14. (a) The number of proteins detected in each sample. (b) Histogram showing the number of proteins that are shared across multiple samples as indicated on the x-axis. (c) LFQ intensity of contractile machinery proteins in microtissues with or without hESC-macrophages on day 3. (d) LFQ intensity of JHP2 in microtissues with or without hESC-macrophages on day 14. n = 9 microtissues per group from one experiment. (e) Immunofluorescence and confocal microscopy was performed on microtissues with or without hESC-macrophages stained with a-actinin and MLC2v (as in Fig. 3G). Myofibril alignment was quantified. n = 8 microtissues per group from one experiment. (f) LFQ intensity of collagen proteins in microtissues with or without hESC-macrophages on day 3. n = 7 (control) or n = 8 (hESC-macrophage) microtissues per group from one experiment. (g) LFQ intensity of extracellular matrix proteins in microtissues with or without hESC-macrophages on day 3. n = 7 (control) or n = 8 (hESC-macrophage) microtissues per group from one experiment. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. Multiple unpaired (two-tailed) t-tests were conducted with P values adjusted for multiple comparisons using the Holm-Šídák method (C, F-G). Unpaired two-tailed t-test was performed for pairwise comparisons of two groups (D-E). *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 6 hESC-macrophages increase calcium amplitude in human cardiac microtissues without changes in calcium transients or in single hESC-cardiomyocyte ion channel function.

(a) Liquid chromatography mass spectrometry was performed on total protein isolated from individual microtissues (HT-Biowires). LFQ intensity of calcium handling proteins in microtissues with or without hESC-macrophages on day 14. n = 8 (control) n = 9 (hESC-macrophage) microtissues per group from one experiment. Multiple unpaired (two-tailed) t-tests were conducted with P values adjusted for multiple comparisons using the Holm-Šídák method. (b-g) Microtissues were seeded with hESC-cardiomyocytes and human primary cardiac fibroblasts with or without hESC-macrophages in a 24-well based HT-Biowire platform. (b) Microtissues were incubated with a calcium indicator dye (Fluo-4). Calcium amplitude relative to baseline intensity was measured 14 days post-seeding either during spontaneous beating or while pacing at 3 Hz. n = 12, 13, 12 13 microtissues per group (left to right) from one experiment. (c) Conduction velocity in microtissues with or without hESC-macrophages paced at 3 Hz. n = 28 (control) or n = 19 (hESC-macrophage) microtissues per group pooled from two independent experiments. Unpaired two-tailed t-test was performed. (d-e) Microtissues were stimulated at increasing frequencies. Calcium transient duration from depolarization to either 50% (CaTD50) or 80% (CaTD80) decay in microtissues with or without hESC-macrophages paced at 2–5 Hz 14 days post-seeding. n = 29, 17, 34, 18, 11, 12, 8, 10 microtissues per group (left to right) pooled from two independent. (f) Relaxation time (peak to baseline) in microtissues with or without hESC-macrophages paced at 2–5 Hz 14 days post-seeding. n = 29, 17, 34, 19, 11, 12, 8, 10 microtissues per group (left to right) pooled from two independent experiments. (g) Time to peak from 90% depolarization to 10% repolarization in microtissues with or without hESC-macrophages paced at 2–5 Hz 14 days post-seeding. n = 29, 16, 36, 17, 12, 11, 8, 9 microtissues per group (left to right) pooled from two independent experiments. (h) Schematic of experimental design made with BioRender.com. Microtissues were seeded with hESC-cardiomyocytes and fibroblasts with or without hESC-macrophages. On day 14, microtissues were dissociated and cells were plated for single cardiomyocyte patch clamp recordings. (i) Representative ICa tracings (left). Peak ICa amplitude at 0 mV (right). n = 11 (control) or n = 8 (hESC-macrophage) cardiomyocytes per group pooled from two independent experiments. (j) Representative INa tracings (left). Peak INa amplitude at −20 mV (right). n = 8 (control) or n = 11 (hESC-macrophage) cardiomyocytes per group from one experiment. (k) INa+ Current density-voltage (I-V) plot and activation curve with the least-square fits to Boltzmann function. n = 8–11 per group, one experiment. (l) Representative action potential (AP) tracings along with peak AP amplitude, AP duration at 50% repolarization (APD50) and resting membrane potential (RMP) (left to right). n = 3 cardiomyocytes per group from one experiment. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. Two-way ANOVA with P values adjusted for multiple comparisons using the Holm-Šídák method (B, D-G). Unpaired two-tailed t-test (C, I, J, L). *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 7 hESC-macrophages reduce accumulation of mitochondrial proteins in cardiac microtissues.

Cytometry by time-of-flight (CyTOF) was performed on microtissues (HT-Biowires) with or without hESC-macrophages on day 3 post-seeding. n = 6 replicates per group, each replicate represented 8 pooled microtissues, experiment performed once. (a) UMAP visualization of 580,776 live and dead cells following standard quality control filtering, split by replicate in each group. (b) Percentage of Dead ATP5hi group relative to all events in each replicate. (c) Number of cardiomyocytes or fibroblasts acquired in each replicate in microtissues with or without hESC-macrophages. (d) Percentage of ATP5Amid cells relative to the number of cardiomyocytes (left) or the number of cardiomyocytes and fibroblasts (right) in microtissues with or without hESC-macrophages. (e) Pathways downregulated in microtissues with hESC-macrophages on day 14 from mass spectrometry-based proteomics data (as in Fig. 3). (f) Normalized expression of DNA 1 or DNA 2 in each cardiomyocyte subcluster (averaged in each replicate) in microtissues with or without hESC-macrophages. (g) Normalized expression live-dead label in each replicate (averaged) in CM-4 from microtissues with or without hESC-macrophages. (h) Microtissues were seeded with hESC-cardiomyocytes and fibroblasts with or without hESC-macrophages. On day 3 post-seeding, microtissues were labelled with the MitoTracker dye and flow cytometry was performed. Geometric mean fluorescence intensity (MFI) of MitoTracker in total cells, cardiomyocytes (CD45−CD14−), or fibroblasts (CD45dimCD14dim) is shown. n = 6 microtissues per group, two experiments shown (MitoTracker Green and MitoTracker Deep Red). (i) Normalized expression of cardiac troponin T (cTnT) in each replicate (averaged) for all cardiomyocyte subclusters (CyTOF data) in microtissues with or without hESC-macrophages. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. Two-way ANOVA with P values adjusted for multiple comparisons using the Šídák method (B, F, I), or unpaired two-tailed t-test (C, D, E, G, H). *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 8 Global downregulation of metabolic proteins in fibroblasts in cardiac microtissues with hESC-macrophages.

(a) CyTOF data of fibroblasts re-clustered showing 5 subclusters. n = 6 replicates per group, each replicate represented 8 pooled microtissues, experiment performed once. (A-F). (b) Frequency of each subcluster of fibroblasts in microtissues with or without hESC-macrophages. (c) Expression of each marker in each subcluster of fibroblasts. (d-f) Expression of each marker in microtissues with or without hESC-macrophages for FB-1 versus FB-2 (d), FB-3 € or FB-4 (f). CM: cardiomyocyte; FB: fibroblast; MF: macrophage. Multiple unpaired two-tailed t-tests were conducted with P values adjusted for multiple comparisons using the Bonferroni-Dunn method (D-F), or two-way ANOVA was performed with P values adjusted with Šídák method (B). *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 9 Gating strategy for sorting hESC-cardiomyocytes from microtissues for bulk RNA sequencing.

(a) Microtissues (HT-Biowires) were seeded with hESC-cardiomyocytes and fibroblasts with or without hESC-macrophages for bulk RNA sequencing of hESC-cardiomyocytes on day 3 post-seeding. Representative gating strategy shown. DAPI− live single cells were first gated, followed by CD45−CD14−CD90−RFP− cells. (b) hESC-cardiomyocytes, fibroblasts and hESC-macrophages were independently stained and acquired for flow cytometry. Data was overlaid into a single plot. CD45−CD14− cells were hESC-cardiomyocytes, CD45dimCD14dim cells were fibroblasts, and CD45+CD14+ cells were hESC-macrophages, confirming the gating strategy utilized in (A). (c) CD45 expression of populations shown in (B), showing that hESC-cardiomyocytes are CD45−, fibroblasts are CD45dim and hESC-macrophages are CD45+. (d) hESC-cardiomyocytes or fibroblasts were pre-labeled with CFSE prior to seeding microtissues. On day 3, flow cytometry was performed showing that the CFSE+ hESC-cardiomyocytes were CD14−RFP−, whereas CFSE+ fibroblasts were CD14dimRFPdim, confirming the gating strategy utilized in (A). CM: cardiomyocyte; FB: fibroblast; MF: macrophage.

Extended Data Fig. 10 hESC-macrophage efferocytosis alters the total proteome in cardiac microtissues.

(a) Expression of proliferation genes in bulk RNA sequencing data of hESC-macrophages as in Fig. 1. n = 3 replicates per group from one experiment. (b) Number of hESC-cardiomyocytes or hESC-macrophages in microtissues with hESC-macrophages in PBS-treated versus Annexin-treated microtissues assessed using flow cytometry. n = 4 microtissues per group from one experiment. (c) Pathways enriched in microtissues with hESC-macrophages in PBS treated (with efferocytosis) versus Annexin treated (without efferocytosis) in liquid chromatography mass spectrometry-based proteomics data. Fisher’s one-tailed test was performed with correction for multiple comparisons using the g:SCS method as implemented in gProfiler. (d) Human cytokine array (96-plex Discovery Assay) was performed on culture supernatants from microtissues with or without hESC-macrophages on days 1, 3 and 7. Volcano plots show upregulated versus downregulated cytokines at each timepoint. n = 7 replicates (collected from n = 7 microtissues) per group at each timepoint from one experiment. (e) Concentration of key cytokines in culture supernatants at each timepoint in microtissues with or without hESC-macrophages. n = 7 replicates (collected from n = 7 microtissues) per group at each timepoint from one experiment. (f) Microtissues were seeded with or without hESC-macrophages, containing PBS pre-treated versus Annexin pre-treated hESC-cardiomyocytes. On day 14 post-seeding, active force, contraction slope and relaxation slope were measured. n = 17, 14, 14, 10 microtissues per group (left to right) from one experiment, first experiment shown in Fig. 7. CM: cardiomyocyte; FB: fibroblast; MF: macrophage. One-way ANOVA (F) or two-way ANVOA (A, E) with P values adjusted for multiple comparisons using the Šídák method. Unpaired two-tailed t-test (B, D). *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± s.e.m.

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Supplementary information

Supplementary Figs. 1 and 2 and legends

Reporting Summary

44161_2024_471_MOESM3_ESM.xlsx

Supplementary Table 1: Human yolk sac and fetal heart scRNA-seq analysis. Supplementary Table 2: Differential gene expression analysis of bulk RNA-seq of contamination controls. Supplementary Table 3: Differential gene expression analysis of bulk RNA-seq of sorted hESC-macrophages from microtissues. Supplementary Table 4: Proteomics of microtissues with or without hESC-macrophages. Supplementary Table 5: scRNA-seq of human fetal ventricular cardiomyocytes. Supplementary Table 6: Differential gene expression analysis of bulk RNA-seq of sorted hESC-cardiomyocytes from microtissues. Supplementary Table 7: Proteomics of Annexin-treated microtissues with or without hESC-macrophages. Supplementary Table 8: Human cytokine array. Supplementary Table 9: Sample information and accession codes for publicly available scRNA-seq datasets.

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Hamidzada, H., Pascual-Gil, S., Wu, Q. et al. Primitive macrophages induce sarcomeric maturation and functional enhancement of developing human cardiac microtissues via efferocytic pathways. Nat Cardiovasc Res 3, 567–593 (2024). https://doi.org/10.1038/s44161-024-00471-7

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