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Long-term, in toto live imaging of cardiomyocyte behaviour during mouse ventricle chamber formation at single-cell resolution

Nature Cell Biology volume 22, pages 332–340 (2020)Cite this article

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Abstract

Mapping of the holistic cell behaviours sculpting the four-chambered mammalian heart has been a goal or previous studies, but so far only success in transparent invertebrates and lower vertebrates with two-chambered hearts has been achieved. Using a live-imaging system comprising a customized vertical light-sheet microscope equipped with a mouse embryo culture module, a heartbeat-gated imaging strategy and a digital image processing framework, we realized volumetric imaging of developing mouse hearts at single-cell resolution and with uninterrupted cell lineages for up to 1.5 d. Four-dimensional landscapes of Nppa+ cardiomyocyte cell behaviours revealed a blueprint for ventricle chamber formation by which biased outward migration of the outermost cardiomyocytes is coupled with cell intercalation and horizontal division. The inner-muscle architecture of trabeculae was developed through dual mechanisms: early fate segregation and transmural cell arrangement involving both oriented cell division and directional migration. Thus, live-imaging reconstruction of uninterrupted cell lineages affords a transformative means for deciphering mammalian organogenesis.

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Fig. 1: Live-imaging system for long-term recording of the developing mouse heart at single-cell resolution.
Fig. 2: Ex utero culture of mouse embryos with normal developmental progression for long-term live imaging.
Fig. 3: Accurate cell segmentation and lineage tracing in the developing mouse heart.
Fig. 4: Uninterrupted cell lineages reveal dorsal–ventral gradients during ventricular morphogenesis of the mouse heart.
Fig. 5: Time-resolved transmural gradients of cell division during ventricular wall growth.
Fig. 6: Coordinated cell division and migration during ventricular chamber ballooning.
Fig. 7: Both early cell fate segregation and inward migration sculpt trabeculation.

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Data availability

Additional raw images of key experiments have been deposited in Figshare (https://figshare.com/projects/Long-term_live_imaging_of_mouse_embryonic_heart/74532). All of the other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Codes for image pre-processing are available at https://sourceforge.net/projects/grapebio/.

References

  1. Meilhac, S. M., Lescroart, F., Blanpain, C. & Buckingham, M. E. Cardiac cell lineages that form the heart. Cold Spring Harb. Perspect. Med. 4, a013888 (2014).

    Article  Google Scholar 

  2. Kelly, R. G., Buckingham, M. E. & Moorman, A. F. Heart fields and cardiac morphogenesis. Cold Spring Harb. Perspect. Med. 4, a015750 (2014).

    Article  Google Scholar 

  3. Vincent, S. D. & Buckingham, M. E. How to make a heart: the origin and regulation of cardiac progenitor cells. Curr. Top. Dev. Biol. 90, 1–41 (2010).

    Article  Google Scholar 

  4. Sedmera, D., Pexieder, T., Vuillemin, M., Thompson, R. P. & Anderson, R. H. Developmental patterning of the myocardium. Anat. Rec. 258, 319–337 (2000).

    Article  CAS  Google Scholar 

  5. Hoffman, J. I. E., Kaplan, S. & Liberthson, R. R. Prevalence of congenital heart disease. Am. Heart J. 147, 425–439 (2004).

    Article  Google Scholar 

  6. Keller, P. J., Schmidt, A. D., Wittbrodt, J. & Stelzer, E. H. K. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008).

    Article  CAS  Google Scholar 

  7. Ichikawa, T. et al. Live imaging and quantitative analysis of gastrulation in mouse embryos using light-sheet microscopy and 3D tracking tools. Nat. Protoc. 9, 575–585 (2014).

    Article  CAS  Google Scholar 

  8. Amat, F. et al. Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nat. Methods 11, 951–958 (2014).

    Article  CAS  Google Scholar 

  9. McDole, K. et al. In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Cell 175, 859–876.e33 (2018).

    Article  CAS  Google Scholar 

  10. Chen, B. C. et al. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346, 1257998 (2014).

    Article  Google Scholar 

  11. Royer, L. A. et al. Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat. Biotechnol. 34, 1267–1278 (2016).

    Article  CAS  Google Scholar 

  12. Udan, R. S., Piazza, V. G., Hsu, C. W., Hadjantonakis, A. K. & Dickinson, M. E. Quantitative imaging of cell dynamics in mouse embryos using light-sheet microscopy. Development 141, 4406–4414 (2014).

    Article  CAS  Google Scholar 

  13. Skylaki, S., Hilsenbeck, O. & Schroeder, T. Challenges in long-term imaging and quantification of single-cell dynamics. Nat. Biotechnol. 34, 1137–1144 (2016).

    Article  CAS  Google Scholar 

  14. Massarwa, R. & Niswander, L. In toto live imaging of mouse morphogenesis and new insights into neural tube closure. Development 140, 226–236 (2013).

    Article  CAS  Google Scholar 

  15. Tyser, R. C. et al. Calcium handling precedes cardiac differentiation to initiate the first heartbeat. eLife 5, e17113 (2016).

    Article  Google Scholar 

  16. Ivanovitch, K., Temino, S. & Torres, M. Live imaging of heart tube development in mouse reveals alternating phases of cardiac differentiation and morphogenesis. eLife 6, e30668 (2017).

    Article  Google Scholar 

  17. Udan, R. S. & Dickinson, M. E. Imaging mouse embryonic development. Methods Enzymol. 476, 329–349 (2010).

    Article  Google Scholar 

  18. Kelly, D. P. & Scarpulla, R. C. Transcriptional regulatory circuits controlling mitochondrial biogenesis and function. Genes Dev. 18, 357–368 (2004).

    Article  CAS  Google Scholar 

  19. Zamir, L. et al. Nkx2.5 marks angioblasts that contribute to hemogenic endothelium of the endocardium and dorsal aorta. eLife 6, e20994 (2017).

    Article  Google Scholar 

  20. Tian, X. et al. Identification of a hybrid myocardial zone in the mammalian heart after birth. Nat. Commun. 8, 87 (2017).

    Article  Google Scholar 

  21. De Boer, B. A., van den Berg, G., de Boer, P. A. J., Moorman, A. F. M. & Ruijter, J. M. Growth of the developing mouse heart: an interactive qualitative and quantitative 3D atlas. Dev. Biol. 368, 203–213 (2012).

    Article  CAS  Google Scholar 

  22. MacGrogan, D., Nus, M. & de la Pompa, J. L. Notch signaling in cardiac development and disease. Curr. Top. Dev. Biol. 92, 333–365 (2010).

    Article  CAS  Google Scholar 

  23. Grego-Bessa, J. et al. Notch signaling is essential for ventricular chamber development. Dev. Cell 12, 415–429 (2007).

    Article  CAS  Google Scholar 

  24. Del Monte-Nieto, G. et al. Control of cardiac jelly dynamics by NOTCH1 and NRG1 defines the building plan for trabeculation. Nature 557, 439–445 (2018).

    Article  CAS  Google Scholar 

  25. Li, J. et al. Single-cell lineage tracing reveals that oriented cell division contributes to trabecular morphogenesis and regional specification. Cell Rep. 15, 158–170 (2016).

    Article  CAS  Google Scholar 

  26. Chen, H. et al. BMP10 is essential for maintaining cardiac growth during murine cardiogenesis. Development 131, 2219–2231 (2004).

    Article  CAS  Google Scholar 

  27. Harris, L., Zalucki, O. & Piper, M. BrdU/EdU dual labeling to determine the cell-cycle dynamics of defined cellular subpopulations. J. Mol. Histol. 49, 229–234 (2018).

    Article  CAS  Google Scholar 

  28. Christoffels, V. M. et al. Chamber formation and morphogenesis in the developing mammalian heart. Dev. Biol. 223, 266–278 (2000).

    Article  CAS  Google Scholar 

  29. Foudi, A. et al. Analysis of histone 2B-GFP retention reveals slowly cycling hematopoietic stem cells. Nat. Biotechnol. 27, 84–90 (2009).

    Article  CAS  Google Scholar 

  30. Liebling, M., Forouhar, A. S., Gharib, M., Fraser, S. E. & Dickinson, M. E. Four-dimensional cardiac imaging in living embryos via postacquisition synchronization of nongated slice sequences. J. Biomed. Opt. 10, 054001 (2005).

    Article  Google Scholar 

  31. Taylor, J. M. Optically gated beating-heart imaging. Front. Physiol. 5, 481 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. Takahashi, M., Makino, S., Kikkawa, T. & Osumi, N. Preparation of rat serum suitable for mammalian whole embryo culture. J. Vis. Exp. 2014, e51969 (2014).

    Google Scholar 

  33. Yue, Y. et al. Long-term, in toto live imaging of the developing mouse heart. Protoc. Exch. https://doi.org/10.21203/rs.2.21499/v1 (2020).

  34. Liu, Z. et al. Fscn1 is required for the trafficking of TGF-β family type I receptors during endoderm formation. Nat. Commun. 7, 12603 (2016).

    Article  CAS  Google Scholar 

  35. Reinhard, E. et al. High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (Elsevier Science, 2010).

  36. Barber, C. B., Dobkin, D. P. & Huhdanpaa, H. The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. 22, 469–483 (1996).

    Article  Google Scholar 

  37. Schroeder, W., Martin, K. & Lorensen, B. The Visualization Toolkit: An Object-oriented Approach to 3D Graphics (Kitware, 2006).

Download references

Acknowledgements

We thank R. H. Harvey for reviewing this manuscript, I. C. Bruce for manuscript editing, and Y. Xu, L. Yuan and E. Yao for technical assistance. A.H. was supported by grants from the National Basic Research Program of China (2017YFA0103402 and 2019YFA0801802), National Natural Science Foundation of China (31571487, 31771607 and 31327901), Peking-Tsinghua Center for Life Sciences and 1000 Youth Talents Program of China. H.C. was supported by grants from the National Key Technologies R&D Program (SQ2011SF11B01041), National Basic Research Program of China (2016YFA0500403) and National Natural Science Foundation of China (31521062). W.Z. was supported by grants from the National Key Technologies R&D Program (2018YFA0109600). R.W. was supported by grants from the National Postdoctoral Program for Innovative Talents (8206200030).

Author information

Author notes

  1. These authors contributed equally: Yanzhu Yue, Weijian Zong, Xin Li, Jinghang Li, Youdong Zhang, Runlong Wu.

Authors and Affiliations

  1. State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Yanzhu Yue, Weijian Zong, Xin Li, Jinghang Li, Youdong Zhang, Runlong Wu, Qianhao Wang, Yunkun Bian, Xianhong Yu, Liangyi Chen, Heping Cheng & Aibin He

  2. State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronic Engineering and Computer Science, Peking University, Beijing, China

    Runlong Wu & Yunfeng Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Yazui Liu, Jiahao Cui, Gang Zhao & Wenlei Xiao

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Yao Liu & Guangming Tan

  5. State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, University of the Chinese Academy of Sciences, Shanghai, China

    Bin Zhou

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Contributions

A.H. and H.C. supervised the study. A.H. designed the experiments. W.Z. and R.W. designed the vLSFM and heartbeat-gated imaging system under the supervision of L.C., Yunfeng Z., A.H. and H.C. Y.Y., X.L. and W.Z. designed the advanced embryo culture system under the supervision of A.H. Y.Y., X.L., Q.W., Y.B. and X.Y. performed the experiments. J.L., Youdong Z., Yazui L., J.C. and G.Z. developed the computational algorithms under the supervision of W.X., H.C. and A.H. Yao L. and G.T. contributed to the integration of TGMM. B.Z. provided the reagents. A.H., H.C., Y.Y., X.L. and YoudongZ. wrote the paper with input from all other authors. All authors participated in data discussion and interpretation.

Corresponding authors

Correspondence to Wenlei Xiao, Heping Cheng or Aibin He.

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Extended data

Extended Data Fig. 1 The vLSFM microscope equipped with embryo culture system and imaging synchronization module.

a, Close-up of the vLSFM imaging system, including the laser light source (1), two illumination arms (2), one detection arm with a sCMOS camera (3), near infrared heart-beating detection path (4), embryo culture and imaging chamber (5), and the 3-axis stage (6). b, 3D model about how to image a mouse embryo by vLSFM. The anterior, posterior and dorsal view of the mouse sheltered the light path so only the lateral and ventral view of the heart could use for illumination and detection. c, Schematic of the optical tracking of heartbeats using near infrared (850 nm) bright-field imaging at 50 Hz. d, On-line processing to sum absolute pixel intensity changes between two temporally adjacent images. A trigger for light-sheet fluorescence image acquisition was sent when the sum was below a designated threshold, indicating that the heart was entering diastole. For a volumetric image stack of 160 layers at 2.5-μm steps, the entire stack was completed within 2 min at a heart rate of 107 ± 18 bpm.

Extended Data Fig. 2 Agarose holder with triangular hollow designed for mouse embryonic heart culture and imaging.

a, Component B inserted with component A is filled with 2% low melting-temperature agarose. Triangular hollow is formed after removing component A. b, Sucking the mouse embryo with culture medium to the hollow in the agarose holder. c, Comparison of embryos cultured in a Petri dish (n = 19) versus in a triangular hollow agarose (n = 11). Embryonic hearts were evaluated by two parameters, the morphology and size. Hearts at 3.0 exhibited the similar morphology and size with the freshly dissected counterparts. Heart at 2.0 exhibited either the similar morphology or size with the freshly dissected counterparts. Hearts at 1.0 exhibited less developmental progression compared with the freshly dissected counterparts. Data are mean ± s.d. Source data for c are available online.

Source data

Extended Data Fig. 3 Controlling and monitoring of temperature and oxygen in culture medium.

a, Real-time monitoring of the dissolved oxygen (green) and temperature (orange) of culture medium for a long period of time. Data are representative of three independent experiments. O2 is added to 5%, 10%, 20% and 40% at the indicated time of 5 h, 8 h, 11 h and 14 h. The medium is heated to 37 °C at 0.8 h. b, A close-up as shown in (a). The medium temperature goes up to 37 °C in 0.2 h. c, Dissolved oxygen concentration in the culture medium. A good linear relationship has been observed when increasing oxygen concentration from 5% to 40%. Source data for a–c are available online.

Source data

Extended Data Fig. 4 Characterization of Nppa reporter mice at different developmental stages.

a, Schematic of the cardiomyocyte-nuclei (H2B-GFP) induction scheme in the Nppa reporter mouse line. Doxycycline (2 mg, i.p.) was injected into pregnant mice 6 h prior to embryo harvesting. b–i, Immunofluorescence staining of mouse embryos from E8.0 to E10.5. The images are representative of three independent experiments. Transverse sections 5 μm thick were stained for the cardiomyocyte marker TNNI3 and nuclei were counterstained with DAPI. OFT, out flow tract. RV, right ventricle. LV, left ventricle. RA, right atrium. LA, left atrium. S, somites. E, embryo stage. Scale bars, 200 μm in embryos and 100 μm in sections.

Extended Data Fig. 5 Comparison between the imaged and freshly dissected embryonic hearts.

a, The chart showing the imaging duration from 14 imaged mouse embryos in this study. The bars with the darker shades indicate the embryos shown as representative images. b, Quality control of imaged embryos. This evaluation is based on scoring long axis length, short axis length and cell number of left ventricle (LV) by arbitrary value (highest to lowest, 4.0 to 1.0) (Online methods). c, Quantitative analysis of imaged and freshly dissected embryos by the long axis, short axis and cell number of LV. Of note, E8.5 + 24 h (after 24 h imaging, n = 3) embryos closely matched E9.5 mouse heart (n = 3) from these aspects. Data are mean ± s.d. d, 3D-reconstructions of a representative E8.5 + 24 h imaged embryo and a freshly dissected E9.5 counterpart. The images are representative of three independent experiments. e, Comparison of optical sections from live imaging data and cryo-sections of mouse heart at the corresponding developmental stages. Representative immunostaining of cryo-sections of three independent experiments at indicated developmental stages matching imaging time points (right panels). Gray spheres, cell nuclei from raw image data; blue spheres, digital nuclei of outer layer cells (left panels); red spheres, digital nuclei of inner layer cells (middle panels); magenta in the cryosections, cytoplasmic cardiomyocytes (right panel); green spheres, Nppa positive nuclei. Z-stack at 5 μm. For c, n represents the number of independent animals. Source data for c are available online. Scale bars, 50 μm.

Source data

Extended Data Fig. 6 Imaging pre-processing framework and evaluation of errors in cell segmentation and lineage tracking.

a, Types of imaging analysis challenges (magenta, segmented image results) on orthogonal image slices. SNR, signal-to-noise. b, The image pre-processing framework comprising background subtraction, median filter de-noising, single image HDR (siHDR), light balance among stacks, and spatio-temporal alignment in between layers and between stacks. HDR, high dynamic range. c, Examples of segmentation errors, including missing-segmentation, false-segmentation, over- and under-segmentations represented. d–g, Different types of lineaging errors. The xy, xz and yz planes are shown in the figures. The higher magnification image is on the top left corner. Lineaging error due to adding one irrelevant cell to the lineage (d). Lineaging error owing to missing one cell at either non-dividing (e) or dividing phase (f). Lineaging error due to erroneously linking to a different lineage (g). Note, evaluation of these errors is applied only when obvious image drift is observed during imaging. Source data for b are available online. Scale bars, 10 μm in a,c, 100 μm in d–g.

Source data

Extended Data Fig. 7 EdU/BrdU dual labeling of compact and trabecular cardiomyocytes.

a, Schematic of EdU and BrdU sequential labeling experimental design. Pregnant wildtype animals at E9.5 and E10.0 were sequentially pulsed using 5-ethynyl-2′-deoxyuridine (EdU) and 5-bromo-2′-deoxyuridine (BrdU), two thymidine analogs, spaced 1 h apart. b, Immunofluorescence of EdU+ (magenta) and BrdU+ (green) cells in compact (Com) and trabecular (Tra) layers. TNNI3 (gray) and DAPI (blue) were used to identify cardiomyocytes. The representative images of three independent experiments depict cardiomyocytes at different phases of cell cycle containing EdU+ (magenta arrowhead), BrdU+ (green arrowhead) and EdU+ BrdU+ (white arrow). c, Quantification of the TNNI3 + cells (cardiomyocytes) that were EdU+ (left), BrdU+ (middle) or EdU+BrdU+ (right) in compact and trabecular layers of E9.5 (n = 4) and E10.0 embryos (n = 5). Two-tailed Student’s t test was used to determine the statistical significance. **, P < 0.05; ***, P < 0.001. Source data for c are available online. Scale bars, 100 μm.

Source data

Supplementary information

Supplementary Information

Supplementary Notes.

Reporting Summary

Supplementary Table 1

This table provides the different characteristics about our vLSFM and other light sheet microscopes, containing light path, sample types, imaging mode, z depth, circulation system, mounting methods, imaging periods, sample monitoring and imaging beating heart.

Supplementary Tables 2

Table showing the advantages and disadvantages about real-time gating and image registration (our work), retrospective gating and prospective gating.

Supplementary Video 1

vLSFM recording of E7.75 mouse embryo cardiogenesis. Representative video showing the early developing heart of Nkx2–5Cre::Rosa26H2B-mCherry mice from the cardiac crescent stage (E7.75) to heart tube formation. Images were recorded at 3-min intervals, at an imaging depth of 400 μm, starting from E7.75 for an imaging time of ~12 h. The maximum-intensity Z projections were made to demonstrate the process of early mouse development. Scale bar: 100 μm.

Supplementary Video 2

3D reconstructions of cultured and freshly dissected embryos. Representative video showing the 3D morphology of Nkx2–5Cre::Rosa26H2B-mCherry embryonic hearts under cultured and freshly dissected conditions. Images were acquired at an imaging depth of 550 μm and a step size of 1 μm, using our vLSFM. The 3D reconstructions showed the marker gene expression pattern and heart gross morphology of E8.5 + 24 h imaged embryos and freshly dissected E9.5 counterparts. Scale bar: 100 μm.

Supplementary Video 3

vLSFM recording of E8.5 mouse embryonic heart ventricle morphogenesis. Representative video showing the early developing heart of NppartTA::Col1a1tetO-H2BGFP/+ mice. This inducible reporter is CM specific, predominantly in the left ventricle and sporadically in the right ventricle for this imaging time of 32 h, starting at E8.5. The volumetric images were recorded at 3-min intervals and nuclear GFP was excited at 488 nm. Image stacks of 160 planes encompassing the embryo heart were acquired at a step size of 2.5 μm. In total, 644 time points are shown in this video. The movie displays maximum-intensity Z and Y projections corresponding to ventral and anterior views of the embryonic heart. The field of view was cropped to reduce the video size. Scale bar: 100 μm.

Supplementary Video 4

Comparison between spatiotemporal alignment data and raw data for the E8.5 mouse embryonic heart. The representative video displayed both spatiotemporal alignment data after pre-processing (left panel) related to Extended Data Fig. 6b and raw data (right panel) for the consecutive time points of imaging the early developing heart of NppartTA::Col1a1tetO-H2BGFP/+ mice. Scale bar: 100 μm.

Supplementary Video 5

An example validating the corrected cell tracks in the rotated images. Two crossing cells in two-dimensional data can be recognized accurately in our 3D image data generated from time-lapse light-sheet microscopy with high lineage linkage accuracy.

Supplementary Video 6

Visualization and colour indexing of cell lineage tracing in early mouse heart development. Semi-automated cell lineage tracing is shown in ventral, anterior and rotating views of the digital embryo from the microscopy imaging data in Supplementary Video 3. Each cell lineage is encoded with one random colour at the starting point, and the colours are propagated forwards based on the automated cell lineage information. The solid lines indicate cell movements within ten time points (0.5 h).

Supplementary Video 7

Visualization and colour indexing of cell movement directions in early mouse heart development. Visualization of cell movement directions at different time points from the microscopy imaging data in Supplementary Video 3. The directions were analysed at 3-min intervals (movements: yellow: anterior; red: posterior; cyan: left; green: right; magenta: ventral; blue: dorsal).

Supplementary Video 8

Visualization and colour indexing of cell movement speed in early mouse heart development. Visualization of speeds of cell movement at different time points from the microscopy imaging data in Supplementary Video 3. Speeds ranged from 0–2 μm min−1.

Supplementary Video 9

Visualization and colour indexing of cell division rates in early mouse heart development. Visualization of cell division rates at different time points from the microscopy imaging data in Supplementary Video 3. Colour codes indicate the rates of cell division.

Supplementary Video 10

Representation of semi-automatic computational separation of trabecular (inner layer) and compact (outer layer) cells. Microscopy imaging data from Supplementary Video 3 were used. Trabecular cells are shown in red and compact cells are shown in blue. Scale bar: 100 μm.

Supplementary Video 11

An example of the intercalation (type I) mechanism by which the surface layer is filled. The cells from the secondary outer layer were intercalated to the surface layer.

Supplementary Video 12

An example of the horizontal dividing (type II) mechanism by which the surface layer is filled. The cells from the surface layer divided horizontally to fill the surface layer.

Supplementary Video 13

An example of trabeculation via the type I mechanism. One cell inhabits the trabecular layer and self-expands through cell division, indicating the earlier segregation of differentiated CMs from cardiac progenitor cells.

Supplementary Video 14

An example of trabeculation via the type 2a mechanism. One trabecular cell originated from the compact layer. Shortly after division, one daughter cell stayed in the compact layer while the other contributed to the trabecular layer.

Supplementary Video 15

An example of trabeculation via the type 2b mechanism. Two daughter cells did not invade the inner layer immediately after the last cell division of their parental cell in the compact layer, but subsequently migrated into and populated trabecular cells, suggesting that the cellular mechanism of directional cell migration contributes to trabecular formation.

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Yue, Y., Zong, W., Li, X. et al. Long-term, in toto live imaging of cardiomyocyte behaviour during mouse ventricle chamber formation at single-cell resolution. Nat Cell Biol 22, 332–340 (2020). https://doi.org/10.1038/s41556-020-0475-2

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