Single-nuclei multiomics analysis identifies abnormal cardiomyocytes in a murine model of cardiac development

Leonard, Riley; Zhao, Yi; Eliason, Steven; Zimmerman, Kathy; Batz, Ariana; Hatcher, Cathy J.; Weiss, Robert M.; Sweat, Mason; Li, Xiao; Amendt, Brad A.

doi:10.1038/s41467-025-62208-9

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Published: 29 July 2025

Single-nuclei multiomics analysis identifies abnormal cardiomyocytes in a murine model of cardiac development

Nature Communications volume 16, Article number: 6947 (2025) Cite this article

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Abstract

Transcription factors such as Tbx5, Gata4, Mef2c and Pitx2 are required during cardiac development, and in adult cardiac homeostasis. We demonstrate that the gene dosage and modulation of these factors are mediated in vivo by the miR-200 family. Inhibition of a single miR-200 family member within the cluster results in defects of the left ventricle and cardiomyocyte maturation during development. Inhibition of the entire miR-200 family results in a ventricular septal defect and embryonic lethality by embryonic day (E)16.5. Inhibition of each miR-200 family has distinct heart phenotypes in cell specific differentiation and maturation. snRNA-sequencing reveals an immature cardiomyocyte cell state, suggesting reduced differentiation of these cells. The miR-200 family members are critical regulators of early cardiac development through maintaining cardiomyocyte differentiation and maturation. In this report, we identify several transcription factors regulated by miR-200 during heart development, a role for miR-200 in specific heart defects, and an abnormal cardiomyocyte population.

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Introduction

The field of embryonic cardiac development, historically, has used animal models that recapitulate congenital heart defects (CHDs), observed in human patients^1,2,3,4. Many of these animal models have altered expression of what are known as, cardiogenic transcription factors (Tbx5, Nkx2.5, Gata4, Mef2c, Pitx2 etc.), and mutations in these genes are highly associated with CHDs^{1,2,3,4,5,6,7,8,9}. Deleting any of these factors in murine models can alter the expression of the heart gene regulatory network (GRN) and downstream factors of this network^2,3,5. In particular, loss of Tbx5 in mice leads to atrial and ventricular septal, ventricular wall, and epicardium defects². Tbx5 deletion in mice can also cause upper limb abnormalities like what is observed in humans with Holt-Oram syndrome caused by TBX5 genetic mutations². Using these models, we have come to understand many of the principles, steps and signaling pathways that occur during embryonic cardiac development^5,6,7,8,9. Most notably, the study of the murine models and cardiogenic TFs has revealed not only how cardiomyocytes, the contractile cells of the heart, arise but, additionally, how the endocardium, epicardium, and cardiac fibroblasts contribute to the developing heart^2,5,10,11,12. Numerous genetic and biochemical studies have provided evidence supporting a highly complex GRN, that drives cardiac development^5,6,7,8,9. However, the generation of new animal models to study embryonic cardiac development, the GRN, and CHDs has been limited. Thus, although the contributions of cardiogenic TFs to cardiac development are known, the complex interplay and gene dosage requirements of these TFs and the downstream effects, potentially mediated through microRNAs, have yet to be determined.

The GRN that governs cardiogenesis is comprised of TFs from many different families. In clinics, human genetic mutations in cardiogenic TFs have been known to cause CHD^{2,5,10,13,14,15,16}. Additionally, CHD can occur as both syndromic and non-syndromic^2,5,10,13. The phenotypes of each case and mutations vary between patients; however, the first phenotypes observed of CHD persist today^2,17. As mentioned, cardiogenesis is a highly regulated process, especially at the transcriptional level¹⁸. Over the past two decades, tremendous efforts have provided an understanding of the overly complex GRN and its factors that promote and drive cardiogenesis¹⁸. These findings have been corroborated through the introduction of clinical mutations and/or the loss of a TF in mice^{1,2,3,5,7,8,13,19,20}. These mouse models have shown the TF’s which are necessary for cardiac development and the specific roles and mechanisms of the TFs. Further, results have provided the field of cardiac developmental biology with the tools to ask questions about gene regulation during cardiogenesis^{1,2,3,5,7,8,13,19,20}. Bioinformatics have been applied to understand GRNs using TF mutant mice and human samples^{21,22,23,24,25}. However, the role of microRNAs and the miR-200 family controlling GRNs during early murine heart development is unknown.

The miR-200 family is subdivided into two functional groups: miR-141/200a (miR-200a group) and miR-200b/c/429 (miR-200c group) (Fig. 1A)²⁶. These miR clusters are located on murine chromosomes 4 and 6 (human 12 and 14)²⁶, with group members found on each chromosome (Fig. 1B). We have shown previously that the miR-200 family members are required for embryonic development of craniofacial tissues^27,28,29. In cardiomyocytes (CMs), miR-200 is thought to play a role in CM hypertrophy and hypoxia regulation^30,31,32, however studies have only been performed in vitro. The in vivo investigation of miRs has been hindered by the redundancy of miRs, including the miR-200 family^33,34. To overcome this barrier, we have designed the Plasmid-based microRNA Inhibitor System (PMIS) to study individual miRs within a cluster in vivo (Supplementary Fig. 1)^{28,35,36,37,38,39}. PMIS interacts with Dicer and the RISC complex to mediate miR inhibition. The PMIS system is efficient, specific, shows no off-target effects and is non-toxic^28,35,36. To study the miR-200 family, we generated transgenic mice that inhibit individual groups of the family: PMIS-miR-200a (PMIS-A) and PMIS-miR-200c (PMIS-C)²⁸. PMIS-A and -C mice can be crossed to inhibit the entire miR-200 family (PMIS-AC). These transgenic mice have allowed us to interrogate the molecular mechanisms of miR-200a, miR-200c and the entire family in the context of cardiac development.

**Fig. 1: Inhibition of the *miR-200* family has differential effects during cardiac development.**

In this study, we use mouse models that have increased expression of cardiogenic TFs Tbx5, Gata4, Mef2c and Irx1, to study how gene dosage affects cardiac development. The increased combinatorial expression of the TFs due to miR-200 inhibition has not been observed in other models. Altered expression of TFs was achieved in transgenic mice that express an RNA-based inhibitor to the miR-200 family^{28,35,36,37,38,39}, which targets the 3’ UTR of Tbx5, Gata4, Mef2c, and Irx1^31,40,41. Histological analysis of the mice with reduced miR-200 expression showed phenotypes similar to previous models, and humans with CHD. Single-nuclei multiomics analyses found altered expression of TFs, and resulted in an abnormal cardiomyocyte cell state only in the PMIS-C mice. These cardiomyocytes are marked by the expression of Tbx5. Moreover, these cardiomyocytes have increased expression and more open chromatin around Nppa, a known transcriptional target of Tbx5. Our results from our mouse model provide evidence of the importance of gene dosage during cardiac development. Moreover, these results provide the basis for a major requirement of miRs, specifically the miR-200 family during in vivo cardiac development. Taken together, we found a requirement for miR-200 targeting Tbx5 to allow for cardiac maturation. Our murine models demonstrate the complex interplay of these miRs and TF targets during cardiac development.

Results

The miR-200 family is required during early cardiac development

To determine if miR-200 is a key player in mammalian cardiac development, we profiled miR-200 family expression from embryonic mouse whole heart RNA. Transcripts encoding the miR-200 family members are highly expressed from E12.5 to E16.5, with Ct values between 24.33–26.62 for miR-200a and 24.94–27.25 for miR-200c (Fig. 1C). miR-200 family expression was decreased by P28 (Ct values of 38.25 for miR-200a and 32.26 for miR-200c) (Fig. 1C). The expression profile of the miR-200 family suggests they are active in early heart development.

The function of miR-200 during cardiac development was determined using PMIS-A and PMIS-C mouse models, which significantly inhibit miR-200 expression (Supplementary Fig. 1A, B). These two mouse models allow us to study the impact of individual miR-200 family members. Complete inhibition of the miR-200 family can be achieved by crossing the two mouse lines to yield PMIS-A/C mice. PMIS-A and PMIS-C embryos survive into adulthood, whereas PMIS-A/C embryos are lethal at E16.5, and have severe cardiac abnormalities (Fig. 1D, Supplementary Fig. 2, Supplementary Fig. 3A, B).

Serial sectioning and H&E staining of mouse heart tissue at E12.5, E14.5 and E16.5 revealed unique cardiac phenotypes in PMIS-A, PMIS-C, and PMIS-A/C (Fig. 1D, Supplementary Fig. 2, Supplementary Fig. 3A, B). At E12.5, PMIS-A, PMIS-C, and PMIS-A/C show abnormal morphology of the ventricular chambers (Supplementary Fig. 3A). PMIS-miR-200 heart phenotypes are evident at E14.5 in the ventricle free wall myocardium (Fig. 1D, E, Supplementary Fig. 2). However, the PMIS-A/C compact and trabecular myocardium appear disorganized compared to other hearts (Fig. 1D). PMIS hearts also have abnormal ventricular septal development (Fig. 1D, Supplementary Fig. 2, Supplementary Fig. 3A, B). Serial transverse sections of E14.5 PMIS hearts revealed an atrioventricular septal defect in PMIS-AC (Supplementary Fig. 2D). Additionally, the PMIS hearts have abnormal looping of the heart, based on the positioning of the outflow tract and pulmonary vessels (Supplementary Fig. 2). At E16.5, PMIS-A and PMIS-C retain a non-compacted ventricle free wall myocardium (Supplementary Fig. 3B). The ventricular wall defects observed previously in E14.5 PMIS-A/C embryonic hearts remain at E16.5 and these embryos do not survive beyond this stage (Supplementary Fig. 3B). These defective cardiac phenotypes displayed in the PMIS embryos throughout development demonstrate a role for the miR-200 family in heart development.

In mice, deletion of cardiogenic transcription factors Tbx5, Gata4, Pitx2 and Mef2c can alter gene expression within the cardiac GRN, and downstream factors required for heart development^{1,2,3,4,5,13,42}. Loss of Tbx5 in mice leads to ventricular septal, ventricular wall, and epicardium defects^2,21. Tbx5 deletion in mice also causes upper limb abnormalities². However, overexpression of Tbx5 during cardiogenesis results in malformations and a VSD^43,44,45. Additionally, overexpression of other cardiogenic T-Box family members results in cardiovascular phenotypes during development^46,47,48,49. Loss of Gata4 is associated with atrioventricular septal defects (AVSD) among other cardiac phenotypes⁴. Loss of Pitx2 leads to defects in left-right asymmetry of the heart and defects in atrial development³. The PMIS embryos present with defective cardiac phenotypes similar to those observed in mouse models demonstrating disruption of transcription factors known to be targeted by miR-200. These findings further support an essential role for the miR-200 family in cardiac development (Fig. 1D, Supplementary Fig. 2, Supplementary Fig. 3A, B).

Previous reports have confirmed that miR-200 members target the 3’UTRs of Tbx5, Gata4, and Pitx2 in cells^31,40,41. To show that miR-200 targets Tbx5, Gata4, Pitx2 and Mef2c in vivo, we performed Western blots, using protein isolated from whole embryonic hearts. Inhibition of the miR-200 family increased the expression of these factors across developmental stages (Fig. 1F, Supplementary Fig. 3C, D). The increased protein expression of the miR-200 targets in PMIS-miR-200 murine cardiac tissues demonstrates a role for miR-200 in heart development.

In addition to these, we identified two binding sites for miR-200a and miR-200c in the 3’ UTR of Mef2c (Fig. 1G). We examined the functionality of these miR-200 sites by cloning the seed sequence site (±25 bp) downstream of a luciferase reporter. The luciferase reporter co-transfected with a plasmid to over express either miR-200a or miR-200c into cells. A significant reduction in luciferase activity occurred for both sites compared to mutant control sites, indicating both sites in the Mef2c 3’UTR are subject to miR-200a and miR-200c regulation.

The miR-200 family members are highly expressed during key stages of embryonic cardiac development. Interestingly, inhibition of the entire miR-200 family results in major cardiac defects, including thinning of the ventricular myocardium, a ventricular septal defect, and embryonic lethality by E16.5, all of which have been observed in ablation of the identified miR-200 targets^2,10. Our mouse models provide in vivo confirmation that the miR-200 family modulates the expression of Tbx5, Gata4, Pitx2, and Mef2c. The requirement of the miR-200 family in cardiac development adds to the growing evidence that correct gene dosage is indispensable during development, especially during cardiogenesis.

Inhibition of miR-200 increases TF expression in CMs, leading to dysregulation of TF targets

We performed immunofluorescence (IF) staining of E14.5 apical left ventricle (LV) sections for Tbx5, Gata4, Mef2c, and Nkx2.5 (Fig. 2A). Our results indicate that Tbx5 and Gata4 were significantly over-expressed in PMIS-C and PMIS-AC LV CMs (Fig. 2A, B). The significantly increased expression of Tbx5 in PMIS-C and PMIS-AC is intriguing as previous reports suggest that Tbx5 is lowly expressed in ventricle CMs and is enriched in the atrial CMs at this stage⁸. This expression offers additional support for the requirement of miR-200c’s targeting Tbx5, and suggests miR-200c may have a role in restricting Tbx5 expression to the atria. Interestingly, Mef2c was significantly increased in all PMIS-miR-200 E14.5 heart CMs (Fig. 2A, B). In addition to the miR-200 targets, the transcription factor Nkx2.5, which is not a direct target of miR-200, was increased significantly in PMIS-A, PMIS-C, and PMIS-AC CMs (Fig. 2A, B). The miR-200 targets have significantly increased expression in apical LV CMs at E14.5, and the miR-200 family specifically regulates Tbx5, Gata4, and Mef2c in LV CMs.

**Fig. 2: Identification of in vivo *miR-200* regulated genes in CMs.**

To evaluate potential downstream effects of increased TF expression, we examined the expression of several transcriptional targets by qPCR (Fig. 2C)^5,7,21,44,50. The transcriptional targets Nppa, Myh7, Myh6, Myl7, Myl2 and Bmp10 were significantly increased in PMIS-miR-200 hearts (Fig. 2C). Moreover, miR-200 targeted TFs are known to drive expression of genes required to assemble sarcomeres. IF stain for sarcomere myosin (MF20) and cardiac troponin T (cTnT) were increased in PMIS embryos (Fig. 2A). However, expression of the atrial CM marker and Tbx5 transcriptional target, Nppa at E14.5 in atrial CMs was unchanged in PMIS-miR-200 hearts compared to WT. These results suggest that miR-200 may selectively regulate ventricle CMs, but not atrial CMs.

The observed regulation of Tbx5, Gata4, and Mef2c in PMIS LV CMs is consistent with the ability of the miR-200 family to target these factors in LV CMs. Additionally, the increased expression of Nkx2.5, which is not targeted by miR-200, provides evidence of the downstream changes in PMIS-miR-200 CMs. However, understanding how the increase in Tbx5, Gata4 and Mef2c expression alters the CMs’ transcriptional profiles at a cellular level requires a high-throughput approach.

snRNA-sequencing of E14.5 PMIS hearts reveals changes in transcription profiles of cardiac cell types

To understand the effect of inhibiting miR-200 at a cellular level during cardiac development, we performed single nuclei multiomics sequencing (sn-multiomics) on E14.5 murine cardiac tissue isolated from WT, PMIS-A, PMIS-C, and PMIS-AC embryos, profiling both gene expression and chromatin accessibility simultaneously at the single cell level (Fig. 3). After rigorous quality controls and scVI-based data integration, we carried out Uniform Manifold Approximation and Projection (UMAP) and graph-based cell clustering⁵¹ (Fig. 3A). Overall, we captured 12,507 nuclei which belonged to 6 cell type clusters that were assigned based on the top differentially expressed genes (DEGs) (Fig. 3A, B, Supplementary. Data File 1). We detected all major cardiac cell types, including cardiomyocytes (CMs), endocardial cells (EndoCs), epicardial cells (Epi), Endothelium (Endo), fibroblasts (CFs), and immune cells (Fig. 3A, Supplementary. Data File 1). We next quantified the proportion of cell types from each sample (Fig. 3B). CMs were the most prevalent making up ∼29% percent of our dataset. EndoCs/Endo cells (31%), CFs (26%), EPI (9%), and immune cells (3%) (Fig. 3B, Supplementary Dataset 9) accounted for the remainder of the detected nuclei. Differences in the proportions for the CMs, EndoC/Endo, Epi, and FBs cell types were detected amongst the different samples (Fig. 3B, C, Supplementary Dataset 9). Each cell type had cluster-specific DEGs compared to the others (Fig. 3D). Additionally, we detected significant DEGs in nuclei originating from PMIS-miR-200 samples compared to the other samples for each of the cell type clusters (Fig. 3E), indicating inhibiting miR-200 may have functional consequences in cells other than CMs. Iterative subclustering within the major cell types identified 15 unique cell state clusters, including 5 CM populations (Fig. 3F). These results are consistent with recent scRNA-seq studies detailing comparable stages of human and murine ontogeny^52,53. High-throughput experiments have revealed cellular heterogeneity^{25,52,54,55,56}, and alteration in TFs can affect the composition of specific cell states. Thus, we sought to determine how inhibiting miR-200 may affect the heterogeneity of CMs.

**Fig. 3: Single nuclei transcript profiling of E14.5 *miR-200* inhibited cardiac tissue.**

PMIS-miR-200 hearts are enriched for a unique population of CMs

As our previous results found inhibiting miR-200 family members increased expression of target TFs in ventricle CMs (Fig. 1F, Fig. 2A., Supplementary Fig. 3C, D)⁸, we sought to further understand the cellular changes across PMIS-miR-200 CMs. Gene ontology (GO) enrichment analysis of PMIS-miR-200 CMs showed the top 10 common upregulated terms related to heart, cardiac, and muscle development and differentiation (Supplementary Fig. 4A). Several down-regulated GO terms are related to metabolism and the mitochondria (Supplementary Fig. 4A), processes associated with CM maturation⁵⁷. Granular sub-clustering of CMs found 5 cell states (CM1, CM2, CM3, CM4, CM5) (Fig. 4A), with each state exhibiting specific signature gene expression (Supplementary Fig. 4D, Supplementary Dataset 10). The fraction of each CM population across samples showed that the CM4 cell state was enriched in PMIS-miR-200 samples (WT- 1.44%, PMIS-A, 16.82%, PMIS-C- 22.37%, PMIS-AC- 13.35%) (Fig. 4B, C). The CM4 cluster exhibits a unique expression profile and DEGs compared to other CM cell states (Fig. 4F, Supplementary Fig. 4B–D, Supplementary Data File 2). Tnni1 shows robust expression across all clusters, confirming CM identity (Fig. 4D). Additionally, Tbx5 and Mef2c were shown to be enriched in PMIS-miR-200 samples across all CMs (Fig. 4E). Based on DEGs from each cluster we annotated the different cell states, including CM1 as ventricle CMs (vCMs) (Hey2, Cacna1c, Ryr2, Gja1), CM2 as proliferating CMs (pCMs) (mKi67) and CM3 as atrial CMs (aCMs) (Pitx2, Tbx3, Myl7)^25,54,58,59 (Supplementary Data File 2). The CM4 cluster shows expression of many aCM specific genes (Nppa, Nppb, Myl7, and Myh7) (Fig. 4F)^25,58. Additionally, the cluster expresses miR-200 targets Tbx5 and Mef2c (Fig. 4F) (Supplementary Data File 2). Other markers for this PMIS-miR-200 enriched cluster include Sox5 and Fgf12 (Fig. 4F, Supplementary Fig. 4B)^60,61,62,63. Moreover, the CM4 cluster also has differential expression of contractile smooth muscle cell markers Tangln, Acta1, and others (Supplementary Fig. 4B, C, E, Supplementary Data File 2)⁶⁴. Lastly, the CM5 cluster appears to be a more mature CM state as these cells are marked by Tbx20, Tbx2, and Tnnt2 (Supplementary Data File 2)^52,65. Analysis of DEGs in CMs of individual PMIS-miR-200 samples vs WT revealed significant DEGs across all 5 CMs clusters (Supplementary Data File 3). Markers of the CM4 cluster, Nppa, Sox5, and Fgf12 show enriched expression in the CM4 cells and minor expression in a subset of CM3 cells (Fig. 4F). As CM3 are likely aCMs, these genes are known to be expressed in aCMs^{19,58,59,66,67}. To validate the in vivo existence of CM4 cells, we stained E14.5 apical LV sections for Nppa and cTnT. PMIS-miR-200 apical LV CMs had increased Nppa expression, which was nearly undetectable in WT. The induced expression at E14.5 of Nppa in LV CMs of PMIS hearts provides supporting evidence that the CM4 cells are present and located in the apical LV. However, the development properties of this CM population must be further investigated.

**Fig. 4: Subclustering of CMs reveals an enriched CM population in *PMIS-miR-200* hearts.**

The CM4 cell state exhibits immature cell properties

To gain insight into the potential developmental trajectory of the CM populations, we performed pseudotime inference based on RNA velocity (Fig. 5)⁶⁸. These analyses showed the CM2 cell state appears to transition towards the CM1 and CM3 clusters (Fig. 5A). The CM4 cluster however, did not show a strong likelihood of transitioning any particular CM state. The result could suggest a potential immature cell state (Fig. 5A), as latent time inference clearly shows CM4 as the initiating population. Plotting of the CMs across latent pseudotime revealed that CM4 and CM5 represent terminal cell states in the latent space and we therefore inferred their identity as immature and mature cells, respectively (Fig. 5B), which align with DEGs expressed in the clusters (Supplementary Data File 2). Additionally, these analyses found DEGs across the RNA velocity pseudotime (Fig. 5C). Of note, Tbx5 was found to be expressed at an early pseudotime and correlated with CM4 cells (Fig. 5C)^25,52,54. The pseudotime plot of Tbx5 showed increased expression in CM4 and CM3 cells at early time points (Fig. 5D). Expression of Tbx5 is highest in these two populations. As CM3 is most likely aCM⁵⁹, expression of Tbx5 in these cells was expected⁶⁷. A similar pattern of expression was found for Mef2c however, this factor is more broadly expressed across all CMs (Fig. 5D)⁶⁹. Moreover, Tbx5 transcripts in CM4 cells showed an increased ratio of unspliced to spliced RNAs. The presence of more unspliced Tbx5 transcripts suggests the gene is actively being transcribed, and thus, an increase in more immature RNAs (Supplementary Fig. 6A–C). DEGs in CM4 suggested Nppa and Sox5 as markers of these cells (Fig. 4F, Supplementary Data File 2)^59,60, therefore we analyzed the expression of CM4 markers across pseudotime. Indeed, Nppa and Sox5 expression were enriched in CM4 cells at early pseudotime (Fig. 5E). Both Nppa and Sox5 have increased expression in CM4 cells compared to more mature CM1 cells. In addition, both Nppa, Sox5 and Fgf12 have an increased ratio of unspliced to spliced, suggesting active transcription of these genes (Supplementary Fig. 5D). These results suggest the CM4 population may be an immature population of CMs activated by miR-200 inhibition, marked by Nppa and Sox5 expression.

**Fig. 5: In-depth snRNA-seq. analysis predicts CM4 as a progenitor population of CMs.**

To validate Nppa and Sox5 as in vivo markers of CM cells, we stained E14.5 LV apical sections to confirm the presence of CM4 cells. We found the presence of dual Nppa + /Sox5+ cells in PMIS-miR-200 embryos, which appear more frequently in PMIS-C and PMIS-AC hearts (Fig. 5F). The bioinformatic and in vivo results suggest that CM4 has an immature cell transcriptome, and is marked by Tbx5, Nppa, and Sox5 expression. These cells are found in the apical LV of PMIS-miR-200 embryos at E14.5, providing evidence for their existence in vivo.

Analyses of early embryonic CM maturation suggest PMIS-miR-200 CM4 cells are a unique cellular state

To understand and further characterize the predicted immature CMs within the PMIS embryonic heart, we determined if CM4 cells could be found at earlier stages of cardiogenesis. In the LV at E12.5, Nppa and Sox5 expression was observed in WT and PMIS hearts (Supplementary Fig. 6). Across all genotypes, Nppa is expressed in CMs (cTnT + ) within the LV. However, expression of Sox5, in WT and PMIS-A, is found in non-CMs (cTnT-) cells of the LV. Whereas PMIS-C and PMIS-AC CMs show co-expression of Nppa and Sox5 (Supplementary Fig. 6). The result that a subpopulation of WT CMs is not Nppa + /Sox5+ at an earlier developmental time point, suggests an abnormal cellular state is acquired when inhibiting miR-200. To further validate CM4 as an abnormal cell state, we used publicly available scRNA-Seq and snRNA-Seq data from CMs at various developmental time points (Supplementary Fig.7)⁵⁵. In our model, we identified maturation-correlated genes of CMs from E10.5, E11.5, E12.5, E13.5, and E14.5 CMs, and computed a maturation score (Supplementary Fig. 7, Supplementary Data File 4). The maturation score was calculated based on gene modules classified as “down in development” or “up in development” based on their continuous expression trend from E10.5 to E14.5 (Supplementary Fig. 8, Supplementary Data File 4). Based on this metric, CM clusters CM3 and CM4 had the lowest maturation score (Supplementary Fig. 7B). Additionally, overall PMIS-miR-200 CMs had a lower maturation score versus WT, suggesting a more immature cellular state (Supplementary Fig. 7C). Using the list of genes for maturation scoring (Supplementary Data File 4), we examined genes classified as “down in development”, as these genes have higher expression in immature (i.e. E10.5) CMs versus mature (E14.5) CMs. We compared “down in development” and significantly upregulated PMIS-miR-200 genes to identify a potential set of common markers. These analyses yielded just five genes (Alcam, Crb2, Bex2, Diras2, and Bcar3) (Supplementary Fig. 8A). Of these genes, Alcam has been previously described as a marker of progenitor CMs and is robustly expressed within our dataset (Supplementary Fig. 8B)⁷⁰. Specifically, Alcam is highly expressed in CM4 cells and increased in PMIS-miR-200 CMs versus WT (Supplementary Fig. 8C). To confirm Alcam expression in CM4 cells, we stained E12.5 and E14.5 hearts for Alcam and Nppa (Supplementary Figs. 9 and 10). At E12.5 and E14.5, WT hearts displayed minimal expression of Alcam (Supplementary Figs. 9 and 10). However, PMIS-miR-200 hearts did express Alcam in CMs within the LV (Supplementary Figs. 9 and 10). Alcam expression was enriched within PMIS-C and PMIS-AC hearts and was co-expressed with Nppa in CMs (cTnT + ) (Supplementary Figs. 9 and 10).

Taken together, the bioinformatic and in vivo results are suggestive that the CM4 cell state is one with unique immature properties. Additionally, the data suggest that this CM4 state is found only when low levels of miR-200 are present in the CM. If the CM4 population was a reversion to an earlier common cell state during normal development, WT hearts would contain Nppa + /Sox5+ and/or Nppa + /Alcam+ CMs at earlier developmental stages. We find that there is little overlap in gene expression between WT cells, and cells of the CM4 cluster found in PMIS-miR-200c mice within the sequencing and processing of the sn-multiomics data. In a non-biased approach, the CM maturity analysis did not show a cell population that had a similar transcriptome to that of our CM4. One would expect if CM4 were indeed a cell-state that is present early in cardiogenesis that the CM4 cell state and WT CMs would share many common genes. We found only a few genes that are common between WT CMs and the CM4 cluster in PMIS-miR-200 hearts.

Chromatin accessibility reveals changes in CM4 genes and Tbx5 transcriptional targets

The cardiogenic TFs regulated by miR-200 have been well documented to respond to epigenetic chromatin modifiers^{55,58,59,71,72,73,74,75,76,77,78,79,80}. We sought to understand if increased expression of TFs by miR-200 inhibition would result in changes to the cellular chromatin landscape. We further leveraged the chromatin accessibility modality in our data measured by ATAC sequencing of murine cardiac tissue derived from WT, PMIS-A, PMIS-C, and PMIS-AC embryos (Fig. 6). After computational processing⁸¹, we embedded the cells in an ATAC modality-based UMAP, independent of gene expression⁵¹. Clustering showed the same 6 cell types from all major cardiac cell types, mirroring clusters found in analysis based on gene expression. The 5 unique populations of CMs identified in snRNA-Seq (CM1, CM2, CM3, CM4, CM5) are clearly separated in the ATAC UMAP, with enrichment of PMIS samples in the CM4 cluster. To understand the chromatin structure across the CM population, we looked at reads near the locus of genes that mark CM4 cells (Fig. 6A, B, Supplementary Fig. 11, Supplementary Fig. 12). Indeed, CM4 cells had more open chromatin surrounding Nppa (Fig. 6A, B), which was a significant DAR (differentially accessible region) peak between CM clusters (Supplementary File 5) and other CM4 genes Tbx5, Sox5, and Fgf12 (Supplementary Fig. 12A, B). These data suggest the CM4 population has a unique epigenetic structure compared to other CMs. Additionally, the miR-200c targets of Mef2c and Gata4 had increased accessibility in PMIS-C and PMIS-AC samples (Supplementary Fig. 11B, C). Taken together, the epigenetic changes observed in PMIS-miR-200 CM4 cells further support this cluster as an immature cell state. As CM4 marker genes had an increased unspliced to spliced ratio, significantly increased expression in PMIS-miR-200 embryos, and more open chromatin, suggests active transcription of these genes marking CM4. Furthermore, the results showing changes in chromatin access in the PMIS embryos demonstrate that inhibition of miR expression can cause changes at the chromatin level. This notion that inhibition of miR-200 results in epigenetic landscape changes adds a layer of regulation to the functional role of miRs in modulating the expression of mRNA targets.

**Fig. 6: ATAC-sequencing shows changes in the CM4 subcluster and changes in chromatin access near TFs.**

Based on the previous data, we hypothesized that the increase in miR-200 target TF expression, specifically Tbx5, could result in increased downstream gene expression. We observed a significant increase in Nppa and other Tbx5 transcriptional targets in PMIS-miR-200 embryos compared to WT (Fig. 2B), to support this hypothesis. We identified the significantly DARs in each CM population and performed motif enrichment analyses (Supplementary Fig. 13, Supplementary Data Files 5 and 6)^81,82. Each CM population had a unique enrichment of motifs, interestingly the CM4 population showed significant enrichment of multiple T-Box family members, including the Tbx5 motif (Supplementary Fig. 13, Supplementary Data File 6). Along with T-Box members, CM4 had a significant enrichment of GATA, MADS, and NK motifs (Fig. 6C, D, Supplementary Fig. 14, Supplementary Data File 6). However, Tbx5/Tbx5 gene expression and motif enrichment was the only TF that showed a strong correlation to CM4 cells (Fig. 6D, Supplementary Fig. 14).

To further confirm that Tbx5/Tbx5 RNA and motif are correlated in CM4 cells, we performed SCENIC+ analysis^83,84. The pipeline leverages both snATAC data to identify TF binding sites in open chromatin regions and snRNA data to identify TFs with high expression in given cell states to infer gene regulatory networks (eRegulons) enriched in different cell states (Fig. 6E, F, Supplementary Fig. 15, Supplementary Data File 7)^83,84. Results from SCENIC+ showed the CM4 cell state had greater enrichment for an eRegulon driven by Tbx5 compared to other cell states (Fig. 6E, F, Supplementary Fig. 15). Taken together the increased accessibility near known Tbx5 transcriptional targets (i.e. Nppa), and the results of a high-throughput bioinformatic analysis SCENIC+ are suggestive that Tbx5 plays a role in regulating the transcriptome of the CM4 cell state.

As Tbx5, and other target TFs, are known to drive the expression of Nppa², this result supports previous results and solidifies the role of Tbx5 as a transcriptional activator during cardiac development^58,59,77. Moreover, Tbx5 and Mef2c motifs were enriched in CM4 cells (Fig. 6C, D). Finally, we sought to confirm the bioinformatic results of potential Tbx5 increased transcriptional activity. We transfected AC16 cells, that stably express PMIS-miR-200, with a luciferase reporter plasmid driven by Nppa’s proximal promoter¹⁹ which contain Tbx5, Gata4, and Mef2c, binding sites. PMIS-miR-200 AC16 cells were found to have significantly increased luciferase activity compared to controls, suggesting activation of Tbx5 transcriptional activity upon inhibiting miR-200 (Fig. 6G). To understand which motif may be responsible for the increased activity of the Nppa promoter region, we cloned each TF motif in front of a luciferase reporter construct^{19,85,86,87,88}. Results show that the Tbx5, Gata4, and Mef2c motifs had significantly increased luciferase activity in PMIS-A, PMIS-C and PMIS-AC cells (Supplementary Fig. 16). To demonstrate binding specificity each motif was mutated and TF activity was accessed in the luciferase reporter assay (Supplementary Fig. 16). We found that mutation of each motif abrogated TF activity through decreased luciferase activity (Supplementary Fig. 16).

Transcriptional regulation of the miR-200 family in CMs provides insight into the complex mechanisms of miR expression during cardiogenesis

We have shown that members of the miR-200 family are highly expressed during cardiogenesis (Fig. 1C). However, based on our multiomics analysis, we wanted to understand if miR-200 may be regulated across CMs populations. Our previous work found that miR-200 was expressed in cells differentiating from a stem cell niche²⁸, as these miRs target stem cell TFs to promote differentiation. However, their expression pattern in cardiogenesis are not well characterized. As sc/sn RNA-sequencing utilizes a 3’ polyA-based sequence, this excludes miRs from being captured in these experiments. To understand expression of the miR-200 family, we visualized open chromatin in our multiomics experiments (Supplementary Fig. 17). Open chromatin at both the chromosome 4 and chromosome 6 loci (Fig. 1A), were different across cell types within the heart (Supplementary Fig. 17). Specifically, at the chromosome 4 loci (miR-200b, -200a, -429) the DNA was more open in CMs versus other cell types. However, the chromosome 6 loci (miR-200c, -141) was open in endothelial and endocardial cells, but closed elsewhere. The result displays a potential location specific mechanism and expression pattern of the miR-200 family across CMs (Supplementary Fig. 17). To add further evidence of cell type expression of the miR-200 family, we profiled the expression of the 5 family members across E14.5 whole hearts, AC16 cardiomyocytes, C166 endothelial cells, MEC1 epicardial cells, and human cardiac fibroblasts (Supplementary Fig. 18), and found increased expression of miR-200b, miR-200c in AC 16 cardiomyocytes compared to other cell types. We additionally utilized the bioinformatic pipeline of miReact to predict the activity of the miR-200 family across our dataset⁸⁹. Predictive results from miReact showed a significant increase of miR-200a and miR-200c subfamilies in CMs versus other cell types (Supplementary Fig. 19). Additionally, the program predicted a significantly lower miR-200 activity across PMIS samples (Supplementary Figs. 19 and 20). As our lab has previously described the miR-200 family as a regulator of stemness factors (Sox2, Klf, Bim), we hypothesized that the miR-200 family would be more highly expressed in more mature CMs, such as CM1 and CM5. However, miReact predicted a significantly increased activity in CM1, CM3, and CM4 clusters (Supplementary Fig. 20). The miReact results support an expression pattern of miR-200 within the CM4 cluster, however miR-200 may have a role in other populations of CMs. Additionally, across samples, miR-200 activity was predicted to be significantly decreased in PMIS samples versus WT (Supplementary Fig. 20).

As the bioinformatic results show miR-200 activity in CMs within the heart, we sought to test if expression of the PMIS-miR-200 inhibitors could induce expression of CM4 markers in different cells. We found that in AC16 cells that express PMIS-C and PMIS-AC, they have significantly increased expression of target TFs (Tbx5, Gata4, and Mef2c) and CM4 markers (Nppa, Sox5, and Fgf12) (Supplementary Figs. 21–24). However, PMIS-miR-200 inhibitors did not endogenously increase the expression of these factors in other cell types. This is due to the low expression of the miR-200 family in the other cell types, showing that the miR-200 family is predominantly expressed in CMs.

To gain further insight into the transcriptional regulation of the miR-200 family we tested a potential feedback loop between CM TFs and the miR-200 family. Indeed, over-expression of Tbx5, Gata4, Pitx2, or a combination of these TFs caused increased endogenous miR-200a and miR-200c expression in AC16 cells, providing evidence for a feedback loop (Supplementary Fig. 25). Interestingly, ChIP-Seq data from Akerberg et al. 2019 shows binding of Mef2a, Mef2c, Tbx5, and Nkx2.5 at the chromosome 4 locus but no clear peak at the chromosome 6 locus⁵⁸ (Supplementary Fig. 26). These results provide evidence of the expression profile of the miR-200 family members during cardiogenesis. Additionally, the results show that the CM4 cell state appears only when miR-200 is inhibited within CMs. Lastly, our results provide insight into the potential regulatory mechanism of miR-200 by various cardiogenic TFs.

Our results show CM4 cells have a unique epigenetic landscape compared to other CMs. Inhibiting miR-200 resulted in CM4 cells with more open chromatin compared to WT, with PMIS-C and PMIS-AC being most enriched. Lastly, motif and functional analysis showed that Tbx5 can be a potential transcriptional driver of the CM4 population, further supporting its role as a major transcriptional regulator during cardiogenesis. Although we have found a potential de novo Tbx5-miR-200c pathway in cardiac development, we hypothesized the PMIS-miR-200 embryos and the CM4 cells may be marked by other TFs.

miR-200c targets Irx1, which is expressed in CM4 cells

The miR-200 family, and more specifically, miR-200c is known to target “stemness” TFs such as Sox2 and Klf4^28,90. In the heart, miR-200c’s targeting of early cardiogenic TFs Tbx5, Gata4, and Mef2c^31,40 complements this miRs function in modulating cell progenitor states. We are also identifying other potential genes regulated by the miR-200 family. We have found miR-200c binds the 3’UTR of Irx1 (Fig. 7A). Supporting miR-200c’s ability to target Irx1, expression of Irx1 is significantly increased in E14.5 PMIS-C and PMIS-AC hearts (Fig. 7B). Previous work has shown Irx1 is expressed during early cardiac development throughout the myocardium and becomes restricted to the ventricular septum CMs by E14.5^91,92. Staining for Irx1 in apical LV sections showed PMIS-C and PMIS-AC have increased expression of Irx1 in LV CMs (Fig. 7C). Expression of Irx1 was confirmed in septal CMs in wild-type, PMIS-A, PMIS-C, and PMIS-AC embryonic hearts^91,92. Thus, miR-200c can specifically target Irx1 in CMs during cardiac development. To understand potential changes at the epigenetic levels, we examined open chromatin reads surrounding the Irx1 locus (Fig. 7D). Open chromatin around Irx1 in CM1 and CM2 were enriched compared to other CM states (Fig. 7D). In addition, changes in chromatin landscape were observed across PMIS samples in CM1, CM2, and CM4 (Supplementary Fig. 27). PMIS-C samples had more open chromatin, especially in CM1 and CM2 cells (Supplementary Fig. 27). Moreover, several regions upstream and downstream of the transcription start site (Fig. 7D), showed increased accessibility suggesting a role for potential cis-regulatory elements in Irx1 (Fig. 7D, Supplementary Fig. 27). Similar results were found for Irx4, which is located near Irx1, and thought to have a similar mechanism of transcriptional regulation⁹³. To understand which CM population expresses Irx1, we analyzed its expression pattern across pseudotime (Fig. 7E). Based on this analysis, Irx1’s expression is correlated with early to middle pseudotime and with CM1 and CM2 populations (Fig. 7E). Finally, to validate the multiomics analysis of Irx1, we stained E14.5 LVs for Irx1, Tbx5, and Nppa. PMIS-C and PMIS-AC CMs show some co-expression of Irx1 with CM4 markers, however Irx1 was found in other CMs. Based on this data, we propose a role for Irx1 in assisting the progression of CM-progenitors toward a more mature state. (Fig. 7F). Further investigation of Irx1, its co-factors, and transcriptional targets may reveal new mechanisms that regulate cardiogenesis.

**Fig. 7: *miR-200c* regulates the TF *Irx1* in CM4 cells.**

Discussion

The contribution of miRs to cardiac development is essential⁹⁴. However, the role and function of a single miR within the broader miR-200 family cluster are not understood. A limited number of miRs and their targets, and the downstream process they modulate have been identified in the context of development⁹⁵. Our study has identified a layer of regulation of the cardiogenic GRN that drives and promotes cardiac development. The miR-200 family is required during cardiac development to modulate the gene dosage of TFs Tbx5, Gata4, and Mef2c^31,40,41. Reduced expression of the family increased expression of targets and lethal developmental defects. Moreover, snMulti-Omic sequencing found inhibition of miR-200 caused transcriptional changes in the developing heart. In CMs, miR-200 promotes maturation through its ability to target Tbx5, Gata4, Mef2c, and Irx1. In PMIS-miR-200 embryos, we found an abnormal population of CMs. Additionally, induced expression of target TFs changed the epigenetic landscape in PMIS-miR-200 hearts, specifically in PMIS-C and PMIS-AC murine embryos. Thus, inhibiting a miR can lead to epigenetic changes demonstrating how essential miRs are to development and the downstream effects of miR suppression. Lastly, our results identified Irx1 as a potential regulator of CM4 cells and mediating maturation progression. Overall, these results provide a unique outlook on the complex nature and requirement miRs have in regulating gene dosage in a developmental context.

miR-1 and miR-133 are part of the same cluster and have roles in cardiac muscle cell development^96,97,98. Studies have found miR-1 targets the transcripts of Hand2, Irx5, and Hdac4, while miR-133 targets CyclinD2 and Srf⁹⁶. This miR cluster has been found to inhibit cardiac growth by regulating their targets^96,98. Our group and others have shown that the miR-200 family acts similarly to this cluster with miR-200a targeting Pitx2 and Mef2c⁴¹ and miR-200c targeting Tbx5, Gata4 and Mef2c^31,40. Interestingly, Srf and Mef2c drive transcription of this cluster adding a feedback loop to further regulate the pathway⁹⁶. We found a similar result in which expression of Tbx5, Gata4, or Pitx2 in cardiomyocytes increased expression of the miR-200 family, again demonstrating a feedback loop. Other miRs regulate different processes across heart development as well. miR-138 is involved in cardiac patterning by targeting cspg2, miR-126 has a role in endothelial cell angiogenesis, and miR-208 has a role in cardiac maturation and myosin isoform switching⁹⁵. We demonstrate that inhibition of miR-200 increases the expression of cardiogenic TF targets and downstream pathways that prevent CM maturation. Thus, the seemingly homologous role of miRs in cardiac development, is to decrease the expression of factors that are associated with immature CMs^{32,99,100,101,102}.

Tbx5 is well-known for its association with Holt-Oram syndrome¹. Individuals with Holt-Oram have mutations in the TBX5 gene that cause severe congenital heart defects (CHDs) and upper limb abnormalities. Whereas homozygous deletion of Tbx5 in mice caused early embryonic lethality due to major cardiac defects, heterozygous loss of Tbx5 serves as a murine model of human Holt-Oram syndrome². While increased expression of the TF results in similar phenotypes in cardiac development. Gata4 is one of the earliest transcription factors expressed during cardiac development⁴. Mutations in Gata4 are known to cause CHD⁵, including mutation in the 3’ UTR¹⁴. Loss of Gata4 in embryos results in failure of the heart to form and early embryonic lethality, making these knockout embryos difficult to study in later stages⁴. However, familial studies have found that mutations in Gata4 cause both atrial and ventricular septal defects in human patients⁵. Loss of Mef2c leads to early embryonic lethality, with defective heart looping and dysregulation of cardiac gene expression¹⁰³. Further investigation has shown a genetic interaction of Tbx5 with Mef2c¹⁰⁴. Loss of a single copy of both Tbx5 and Mef2c causes abnormal cardiac development and embryonic lethality²¹. Taken together, the roles of Tbx5, Gata4 and Mef2c interactions are essential for cardiac development.

We observe similar phenotypes in the PMIS-C and PMIS-A/C embryos which have increased Tbx5 expression. These findings support the idea that there is an exquisite sensitivity of the developing heart to alterations in Tbx5 gene dosage¹⁰⁵. Strict maintenance of TBX5 gene dosage is necessary for proper cardiac development to occur in mice as well as in humans where TBX5 haploinsufficiency or duplication is causative for Holt-Oram syndrome¹⁰⁶. Gata4 and Mef2c regulate downstream effectors, such as Nppa, Myh7, and Bmp10 in cardiomyocytes. The expression of Nppa in PMIS-miR-200 hearts also marks the immature population of CMs. Furthermore, PMIS-miR-200 CMs have increased chromatin accessibility near genes regulated by Tbx5, Gata4, and Mef2c^58,59,77. Motif analysis provided supporting evidence that Tbx5 may initiate these changes because open regions were enriched for T-box binding motifs. As Tbx5, Gata4 and Mef2c are known to interact with chromatin remodelers^{25,52,54,55,74,79,107,108,109,110,111}, and act as pioneer TFs, the change in the epigenetic landscape in PMIS-miR-200 CMs is supported by previous work. Thus, miR-200 targeting of Tbx5, Gata4, and Mef2c is necessary to maintain CM gene expression and chromatin accessibility as development progresses.

In cardiac development, the role of Irx1 appears to be associated with progenitor-like CMs^91,92. As our lab and others have defined Irx1 as a regulator of progenitor/stem cell niches¹¹², these results are in line with these reports. However, we do not hypothesize that Irx1+ cells are CM “stem cells,” but rather that Irx1+ CMs happen to be more immature proliferative cells, with Irx1 potentially working to push CMs towards a more mature cell state. Further studies will look to understand the expression of Irx1 in cardiac development, how inhibiting miR-200c can affect Irx1 expression, and the location of Irx1+ CMs in early cardiac development. Further, it would be of benefit to determine if Irx1 has a role during cardiac regeneration. Studies have found that increased Irx1 expression in a disease state is associated with better outcomes, and prevention of heart failure¹¹³, suggesting a role for Irx1 in disease. Outside the heart, we have shown that Irx1 functions in regulating the murine incisor and lung stem cell niche¹¹². Additionally, induced Irx1 expression in numerous cancers results in more aggressive and invasive tumors¹¹⁴. Based on Irx1 expression and function, we hypothesize that Irx1 may mark CMs that are less differentiated, immature, and potentially primed for regeneration.

Further experiments will seek to understand the potential of inhibiting the miR-200 family in promoting cardiac regeneration following injury. As PMIS-A and PMIS-C embryos survive into adulthood without developing cancer or toxic metabolic activities, PMIS-miR-200 may have a potential role in regeneration. Interestingly, miR-200 is nearly absent in adult homeostasis, preliminary data indicates that following injury, when CMs revert to an immature state¹¹⁵, miR-200 expression increases. We have identified a unique regulatory loop where, over expression of Tbx5, Gata4, and Pitx2 increases expression of the miR-200 family. Furthermore, we observe open chromatin surrounding the genomic location of the miR-200 family in CM4 cells as well as other CMs. As miR-200 TF targets are known to be activated following injury^{116,117,118,119} and are associated with regenerative potential, we hypothesize that inhibiting the miR-200 family may mitigate some pathological outcomes following a serious myocardial injury.

This research demonstrates that miR-200 is a critical modulator of gene expression during cardiac development. Our study provides significant evidence of the importance and necessity of the miR-200 family in in vivo cardiac development. These data support and advance the knowledge of previous studies on cardiogenic TFs and provide further evidence of the highly complex GRN that governs cardiac development.

An animal model is required to understand the direct role of each miR in development, proliferation, differentiation, and organ function. One disadvantage of using miR conditional knockout mice to map gene pathways in specific tissues is that they are unable to differentiate the effect of each individual miR within a cluster such as the miR-200 family, and a knockout of both clusters is required to establish a phenotype. We observe that knocking out miRs at later stages of development with tissue-specific Cre drivers has little effect on miR-regulated development. Only by inhibiting miRs very early during development do we observe significant anomalies, demonstrating that miRs regulate early gene networks required for the development of the body and organs. The PMIS system was engineered to be stable, highly specific, with no off-target effects, and non-toxic, as it is not an oligonucleotide. The PMIS transgenic mice express the specific PMIS inhibitor molecule from a Pol III promoter to produce low levels of the transcript. This transcribed RNA molecule is 122nt in length and can physically interact with Dicer and the Ago:RISC complex³⁵. The PMIS-miR transgene can be expressed from a plasmid for use in in vitro studies and therapeutic treatments³⁶. Because the miR-200a/141 group and the miR-200b/200c/429 group are differentiated by one nucleotide, we demonstrate through the mouse models that PMIS-A mice have different heart phenotypes than PMIS-C mice, demonstrating cell-specific differences in these miRs. Thus, we can specifically inhibit miRs with only one nucleotide change in their seed sequence using the highly efficient PMIS system. While we have tried to generate “conditional PMIS-miR mice” due to the Pol III promoter, it is difficult to achieve tissue-specific expression. However, we have found this is not required; (1) as explained, the Cre drivers are expressed after the requirement for specific miRs during development, and (2) the only tissues affected are the tissues that express high levels of the miR, such as the heart with high levels in cardiomyocytes, due to the specificity of the PMIS system towards its target miR. Lastly, we engineered the PMIS system as a potential therapeutic molecule that can be delivered to cells and tissues for regeneration and repair.

Methods

Animals

Mice were housed and handled in accordance with guidelines established by the University of Iowa Institutional Animal Care and Use Committee (IACUC). All experimental techniques were approved by the University of Iowa IACUC guidelines. Mice (C57BL/6) male and female mice and embryos were used in the experiments. Sex does not play a role in phenotypes of the mice. Mice are housed in BSL2 barrier facilities with 12:12 light;dark cycles, at 68–79 degrees F and feed normal hard pellets. Euthanasia was accomplished by anesthesia followed by cervical dislocation. This method is consistent with the recommendations of the Panel on Euthanasia of the American Veterinary Medical Association. For developmental studies the mice are euthanized by CO2 asphyxiation and decapitation and the embryos excised from the mother. Embryos are euthanized by rapid cervical dislocation or decapitation.

To construct the miR inhibitor (PMIS) clone vector, we used two BsmBI sites in the most effective inhibitor design. AscI and PmeI sites were inserted between ApaI and XbaI sites before the U6 promoter. A SmaI site was inserted before XhoI after the polIII terminator. This vector is termed pmiRI for plasmid of miR inhibitor. After digestion by BsmBI, pmiRi can be used to clone different miR inhibitors into it after annealing and ligation of different miR binding sites with a central bulge³⁵. To obtain double inhibitor mice, we crossed the PMIS-miR-200a (PMIS-A) and PMIS-miR-200c (PMIS-C) mouse lines to produce PMIS-miR-200a/c (PMIS-AC). E0.5 was designated upon observation of a vaginal plug after mating.

RNA isolation from tissue

RNA was isolated from freshly dissected tissues or cells using Trizol. RNA quality was assessed by gel electrophoresis prior to reverse transcription (RT) applications, 0.5-1 μg of total RNA was used in an RT reaction with the 5x primescript RT kit (Takara Bio). cDNA was diluted 1:5 and 1 μl was used per qPCR reaction. qPCR reactions were normalized to β-actin levels. For miRs, RT was performed using the Qiagen miRScript kit. The resultant cDNA was diluted 1:5 and 1 μl was used per qPCR reaction. qPCR probes for specific genes were provided by Qiagen, as well as the normalization set for murine U6. PCR products were examined by melting curve analysis and the sequences were confirmed. Fold changes was calculated using the 2^–ΔΔCT method.

Gene	Forward primer	Reverse primer
PMIS-miR-200a	CTAAACATCGTTACCAATCAGACAGTG	GTCAGCTCTTAGTATTCATGAGATG
PMIS-miR-200c	CTAATCCATCATTACCCATCAGACAGTATTA	GTCAGCTCTTAGTATTCATGAGATG
miR-200a	TAACACTGTCTGGTAACGATGATG
miR-200b	TAATACTGCCTGGTAATGATGA
miR-200c	TAATACTGCCGGGTAATGATGGA
miR-141	TAACACTGTCTGGTAAAGATGG
miR429	TAATACTGTCTGGTAAAACCGT
Nppa	GCTTCCAGGCCATATTGGAG	GGGGGCATGACCTCATCTT
Sox5	TTTTCCCAACAAGCCTCACTC	TTGCCATCGACTTCCATTGTG
Fgf12	GTACCATTGATGGGACCAAGG	ACGCAGTCCTACAGGAATTAGAT
Myh7	ACTGTCAACACTAAGAGGGTCA	TTGGATGATTTGATCTTCCAGGG
Myh6	GCCCAGTACCTCCGAAAGTC	GCCTTAACATACTCCTCCTTGTC
Myl2	ATCGACAAGAATGACCTAAGGGA	ATTTTTCACGTTCACTCGTCCT
Myl7	GGCACAACGTGGCTCTTCTAA	TGCAGATGATCCCATCCCTGT
Bmp10	ATGGGGTCTCTGGTTCTGC	CAATACCATCTTGCTCCGTGAA
Gja5	GGTCCACAAGCACTCCACAG	CTGAATGGTATCGCACCGGAA
Irx1 (mouse)	ACACCTGACAGCACCACCA	GCAAAAGTAAAAGATGACCCC
IRX1 (human)	GCTCTTCGGCAGCGACAC	GCTCTGGGGCCTCCTTTG
Ttll10	CCTGCATACCAACTGTGAAGT	CACAAAGCGACGCTGTGAC
β-actin	CTCTTCCAGCCTTCCTTC	ATCTCCTTCTGCATCCTGTC

Paraffin embedding, sectioning, and staining

After embryo isolation and dissection, embryos were fixed for 30 min at 4 °C in 4% paraformaldehyde (PFA) and dehydrated through an ethanol gradient. Dehydrated tissues were pre-cleared with xylene and incubated with three changes of paraffin, and then embedded into blocks. Paraffin-embedded tissues were sectioned at 7μm and mounted on slides. Prior to staining, slides were de-paraffinized and rehydrated through a reverse ethanol gradient. Hematoxylin and eosin (H&E) staining, was performed to assess tissue morphology²⁷.

Histology and fluorescent immunohistochemistry

Sections to be used for fluorescence immunohistochemistry were rehydrated and treated with 10 mM Sodium Citrate solution for 15 min at a slow boil or microwaved for antigen retrieval²⁸. In brief, sections were rehydrated in xylene and ethanol series and then incubated in Block Buffer (1% BSA, 2.5% goat serum, 1% phosphate-buffered saline with 0.25% Triton X-100, PBST) for 30 min at room temperature, washed, followed by overnight incubation at 4 °C with primary antibody (Ab). The antibodies recognized one of the following proteins: Tbx5 (1:100) (Thermo Fisher), Gata4 (1:100) (Santa Cruz), Mef2c (1:100) (Cell Signaling), Nkx2.5 (1:200) (Abcam), MF20 (1:100) (MF 20 was deposited to the DSHB by Fischman, D.A. (DSHB Hybridoma Product MF 20), cTnT (1:50) (CT3 was deposited to the DSHB by Lin, J.J.-C. (DSHB Hybridoma Product CT3), cTnT (1:500) (Proteintech), Nppa (1:100) (Proteintech), Sox5 (1:25) (PCRP-SOX5-1E3 was deposited to the DSHB by Common Fund – Protein Capture Reagents Program (DSHB Hybridoma Product PCRP-SOX5-1E3), and Irx1 (1:100) (Atlas). After the incubation, the slides were treated with goat anti-mouse Alexa-488 or Alexa-594 and/or goat anti-rabbit Alexa-488, Alexa-555, Alexa-594 or Alexa-647 labeled secondary antibody (Invitrogen) at a concentration of 1:300 for 60 min. Each antibody incubation was followed by 3–6 PBST washes. Nuclei were counterstained with DAPI prior to adding coverslips with mounting media (Vector Laboratories). For subsequent IF primary rabbit Ab was added for overnight incubation at 4 °C. Sections were incubated with secondary Ab for 60 min. Sections were incubated in Blocking Buffer plus 2.5% rabbit serum for 30 min, followed by overnight incubation at 4 °C with primary Ab. Sections were incubated with secondary Ab for 60 min and washed. Nuclei were counterstained with DAPI mounting media.

Cell culture

AC16 cardiomyocytes, MEC1 epicardial cells (Millipore Sigma), C166 endothelial, and 293T cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin and transfected by polyethyleneimine (PEI). HCFs (Sigma) were cultured in Cardiac Fibroblast Growth Media (Sigma). 293 T cells were transfected with Pax2 (psPAX2 was a gift from Didier Trono (Addgene plasmid #12260; http://n2t.net/addgene:12260; RRID:Addgene_12260) and MD2G (pMD2.G was a gift from Didier Trono (Addgene plasmid # 12259; http://n2t.net/addgene:12259; RRID:Addgene_12259) along with one of PMIS-miR-Empty Vector, PMIS-miR-200a, PMIS-miR-200c, or PMIS-miR-200ac to make lentivirus. Media was collected after 48 hours and filtered through 0.45 μm filters. AC16 cells (gift from Dr. James F. Martin) were cultured in lentiviral-containing media in the presence of polybrene. Stable infected cells were selected by puromycin.

In Vitro transfection- Cell lines were cultured prior to and during transfections. Cells were transfected in 100 mm plates with either PMIS plasmids expressing PMIS-miR-Empty Vector, PMIS-miR-200a, PMIS-miR-200c, or PMIS-miR-200ac or plasmid over expression of Tbx5, Gata4, Mef2c, and MTG. After 12 hours media was changed. RNA was collected from cells 3 days post transfection.

Luciferase assays

The TK-Luc plasmid containing 638 bp upstream of the Nppa transcriptional start site, containing a Tbx5 (-100), a Gata4 (-1), and a A/T rich region (Mef2c)(-) has been previously described¹⁹. Each transcription factors motif ± 20 bps was cloned individually upstream of the luciferase reporter in the TK-Luc plasmid using BamHI and HDIII sites. Mutant sequences did not contain the motif, but contained identical sequences outside the motif. Various TK-Luc clones, SV-40 β-Gal, and overexpression plasmids with transfected into AC16 cells. Media was changed after 24 hours. At 48 hours, cells were lysed, and luciferase and β-Gal activity was measured. The 3’ UTR of Mef2c or Irx1 which contain a miR-200 seed sequence (±20 bps) were cloned downstream of the Renilla Luciferase reporter in the pPsiCheck2 vector. Mutant sequences did not contain a miR-200 seed sequence. Reporter plasmid and a plasmid overexpressing miR-200a or miR-200c were transfected into 293T cells. At 48 h, cells were lysed, and luciferase activity was using a luminometer¹²⁰.

All cloned constructs were confirmed by DNA sequencing. All plasmids used for transfection were purified by double banding in cesium chloride.

	Forward Oligo	Reverse Oligo
Mef2c WT	GATCctgacttcatactctaaaaaaaaaataatagctctttcacctgact	AGCTagtcaggtgaaagagctattattttttttttagagtatgaagtcag
Mef2c Mut	GATCctgacttcatactctaaaaaaaatcacaccgctctttcacctgact	AGCTagtcaggtgaaagagcggtgtgattttttttagagtatgaagtcag
Gata4 WT	GATCcaagctttgccgaactgataactttaaaagggcat	AGCTatgcccttttaaagttatcagttcggcaaagcttg
Gata4 Mut	GATCcaagctttgccgaatacgtaactttaaaagggcat	AGCTatgcccttttaaagttacgtattcggcaaagcttg
Tbx5 WT	GATCctcctctcacacctttgaagtgggggcctcttgaggcaaatcagca	AGCTtgctgatttgcctcaagaggcccccacttcaaaggtgtgagaggag
Tbx5 Mut	GATCctcctctcacacctttgaagtgggggcctccctaacaaatcagca	AGCTtgctgatttgttagggaggcccccacttcaaaggtgtgagaggag

Western blot

Immunoblots were performed as previously described²⁸. In brief, tissue or cell lysates were analyzed on SDS-PAGE gels. For embryonic cardiac tissue, 10-15 hearts for each stage and genotype were pooled. Following electrophoresis, the protein was transferred to PVDF membrane (Millipore), immunoblotted, and detected with an HRP-conjugated secondary antibody and ECL reagents (GE Healthcare/Amersham Biosciences). The following polyclonal antibodies were used to detect the proteins: Tbx5 (1:750) (Thermo Fisher), Gata4 (1:1000) (Santa Cruz), Pitx2 (1:2000) (Capra Sciences), Mef2c (1:1000) (Cell Signaling) and Gapdh (1:8000) (Santa Cruz).

Single-nuclei isolation

Isolation of E14.5 single-nuclei from murine hearts was adapted from methods previously described¹²¹. Pregnant dams were euthanized by CO₂ inhalation and embryos were removed and placed in ice-cold 1x PBS. Hearts were dissected from embryos under a dissection microscope. Hearts were placed in a 1.5 mL tube and rinsed with ice-cold 1x PBS to remove excess blood and placed on ice. The following steps were done at 4 °C. Hearts were rinsed twice in ice-cold PBS and collected by low-speed centrifugation before homogenization in Lysis Buffer (10 mM Tris-HCl (pH7.4), 10 mM NaCl, 3 mM MgCl₂, 0.1% Tween-20, 0.1% NP40, 0.1% Digitonin, 1% BSA, and 0.5U/μL RNase inhibitor in Nuclease-free H₂0). Hearts were pipetted repetitively in Lysis Buffer to dissociate nuclei, and tubes were centrifuged for 60 sec to pellet nuclei. Nuclei were rinsed in wash buffer (10 mM Tris-HCl (pH7.4), 10 mM NaCl, 3 mM MgCl₂, 0.1% Tween-20, 5% BSA, and 1U/μL RNase inhibitor in Nuclease-free H₂0). Nuclei in wash buffer were placed through a pre-wet 40μm cell strainer to remove excess cellular debris. Nuclei were collected in a tabletop centrifuge for 60 sec, and fresh wash buffer was added prior to counting and diluting nuclei for snMulti-omics experiments.

Single-nuclei multiome library preparation

Gene expression using single-nuclei RNA-Seq (snRNA-Seq) and snATAC-Seq sequencing libraries were prepared by the Iowa Institute for Human Genetics (IIHG), Genomics Division Laboratory. Briefly, 10,000 nuclei were targeted for bulk transposition before individual nuclei were encapsulated in oil droplets along with the 10X GEM code beads in the Chip J cartridge (P/N: 1000234) using the 10X Genomics Single Cell iX Chromium Controller. Generation of gel beads in emulsion (GEMs), barcoding, pre-amplification PCR, and ATAC library construction were all performed as recommended by the manufacturer (10X Genomics, Chromium Next GEM Single Cell Multiome + ATAC Reagent Kit, Rev F, P/N:1000283). The gene expression libraries were pooled and sequenced on a NovaSeq 6000 to give at least 20,000 reads per nuclei. The ATAC-Seq libraries were pooled and sequenced on a NovaSeq 6000 to give at least 25,000 reads per nuclei.

Single-nuclei multiomics data processing and integration

All newly snMultiomics datasets underwent processing using a standardized pipeline. The raw sequencing reads were aligned to the mm10 genome build using the 10X Genomics toolkit CellRanger-ARC version 2.0.2 (cellranger-arc count), with default settings. Quality control metrics, generated by CellRanger, were examined for each library. To eliminate background signals originating from ambient transcripts in the RNA modality, the raw RNA UMI count matrices underwent further processing using CellBender {doi.org/10.1038/s41592-023-01943-7} version 0.1.0 (cellbender remove-background). The following parameters were used: “--total-droplets-included = 25000”, “--low-count-threshold = 10”, and “--epochs = 200”. To minimize the loss of valid cell barcodes identified by CellRanger, we also set the “--expected-cells” parameter to 1.5 times the number of nuclei output by CellRanger. The output matrices from CellBender were filtered to include only valid cell barcodes that were also identified by CellRanger.

Additional quality controls were conducted at the single nucleus level for each library. Initially, low-quality nuclei were identified based on predetermined cut-off values: UMI count per nucleus > 200, gene count per nucleus > 150, and mitochondria gene-derived UMI < 25%. For the ATAC modality, we set cut-offs for nucleosome signal <1.2, transcription start site (TSS) enrichment >2 and percent reads in blacklisted regions <5%. All nuclei that failed to meet these criteria were subsequently removed from the datasets. Subsequently, dynamic cut-off values were calculated based on the per-library data distribution to account for inherent heterogeneity across samples. For each library, upper and lower bounds were established at the 75th percentile plus 1.5 times the interquartile range (IQR) and the 25th percentile minus 1.5 times the IQR, respectively, for UMI count, gene count, ATAC fragment count, and the percentage of ATAC fragment in peaks (FRiP) per nucleus. The nuclei fell outside of these bounds and were consequently excluded from the datasets. Next, the remaining nuclei underwent evaluation using the Scrublet tool¹²² to identify potential doublets, utilizing the following parameters: expected_doublet_rate = 0.15 and call_doublets threshold = 0.25. Around 20% of the nuclei were labeled as potential doublets but were retained in the dataset.

Finally, all samples were integrated, and batch effects were corrected using the probabilistic modeling tool, scVI {doi:10.1038/s41592-018-0229-2}. The 50 dimensions of scVI latent space were subsequently reduced to generate the final global UMAP. We performed an additional round of integration of cardiomyocytes using only the ATAC modality to maximize the resolution provided by the chromatin accessibility independent of RNA expression. Harmony was used for correcting batch effects embedded in the ATAC data.

Clustering and annotation

We applied FindNeighbors function of the Seurat version 4.3.0 package¹²³ to generate the shared nearest-neighbor graph (SNN) using 50 dimensions of the scVI latent space or the harmonized principal components. We defined clusters based on the SNN using Louvain algorithm with an optimized resolution of 1.5. In addition to scVI modeling, we also trained an scANVI model {10.15252/msb.20209620} with previously published mouse cardiac snRNA-seq data sets as a reference. We examined the scANVI-predict cell type identities for each cell cluster. Based on both scANVI-predicted labels and the expression of known cardiac cell type marker genes, we annotated all main clusters.

Subsequent re-clustering of each major cell type was performed using an iterative approach. Within each major cell type, we applied FindNeighbors function using 50 dimensions of the scVI latent space and run FindCluster function with various resolution settings between 0.1 to 1.5. The optimal resolutions were determined by clear separation in the UMAP dimension, and robust identification of >30 significantly differentially expressed genes across subclusters. Subclusters enriched with previously called doublets were examined for the expression of main cell type marker genes and were collectively labeled as ambiguous cells. Only the non-ambiguously annotated nuclei were used for subsequent analyses.

Gene set expression scoring

We used AddModuleScore function provided by the Seurat suite to score each cell in snRNA-seq data for the expression of genes in each gene set. AddModuleScore is an implementation of a scoring approach previously described¹²⁴. In each cell or spot, this function computes a score equal to the mean expression of genes in the given gene set, subtracted by the average expression of genes in a background gene set. For each gene, the method randomly selects 100 background genes with similar average expressions (computed over all cells).

Detection of differentially expressed genes (DEG)

The detection of signature genes among clusters was performed using the FindAllMarkers function provided by Seurat. Specifically, we specified the return of only upregulated genes with a log2 fold-change cut-off of 0.25 and adjusted P values < 0.01, corrected using the Benjamini-Hochberg method.

Gene ontology and pathway enrichment analysis

GO and pathway were performed using clusterProfiler package (version 4.8.1) for R. We extracted enriched GO terms in the biological process category. Pathway enrichment and the related gene network were determined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database. All enriched terms and pathways were filtered at a threshold of false discovery rate (FDR, Benjamini-Hochberg correction) at <0.05.

RNA velocity inference

RNA velocity is a metric that utilizes the detection of spliced and unspliced transcripts to forecast single-cell development directionality⁶⁸. To determine the direction in which cells were transitioning along various potential trajectories, we employed the velocyto tool. Specifically, we followed the R implementation velocyto.R (version 0.6), utilizing the CellRanger output BAM file and the GENCODE v38 GTF file as the source to calculate the RNA velocity of individual cells. Subsequently, we visualized the RNA velocities atop the original clustering of the cells using scVelo (version 0.2.5)¹²⁵. For velocity computation, the “dynamic mode” was used for different cell types. Latent time vector was computed and used to represent cell-cell transition pseudotime.

Mature CM scoring

We used a computational method to measure cardiomyocyte (CM) maturity based on gene expression levels. For unbiased identification of genes indicative of CM maturity, we utilized two sets of single-nuclei RNA sequencing (snRNA-seq) data from murine developing hearts, covering developmental stages E10.5, E11.5, E12.5, E13.5, and E14.5. We integrated data from 12,333 CMs (3,848 C57 CMs and 8485 CD1 CMs) reported by Feng et al.⁵⁵. We aggregated the normalized expression values of all genes within each age group and computed their mean pseudobulk expression. Next, we calculated the Spearman’s correlation coefficient (SCC) of each gene against the E10.5 to E14.5 timeline. This analysis identified 777 positively correlated genes with SCC = 1 and 1257 negatively correlated genes with SCC = -1. For the current study, we created a single metric representing maturity. For a given CM i, we calculated gene set expression scores P_i and N_i based on the positively and negatively correlated gene sets, respectively (detailed above). P_i and N_i were then transformed into Z-scores based on their distributions within the entire CM population from all samples. Since the P_i score represents the expression of genes upregulated during CM maturation and the N_i score represents those downregulated, we calculated the final maturation score (M_i) for each cell as the difference between the two scores (M_i = P_i - N_i).

miReact

To estimate miR-200a/c activity for each single nucleus, we utilized the miReact software {Sci Rep. 2021 Apr 28;11(1):9170. doi: 10.1038/s41598-021-88480-5}⁸⁹, with default parameters. miReact leverages motif enrichment analysis to derive miR activity estimates from snRNA-seq data based on well-defined sequence motifs that miRNAs bind to and their typical downregulation of target genes. In this study, we used the seed sequences CAGTGTT for miR-200a and CAGTATT for miR-200c, along with the expression matrix from the snRNA-seq dataset as input for miReact. As a result, per-cell miR-200a and miR-200c activities were calculated and their distributions across different cell types and conditions were compared.

SCENIC +

To run the SCENIC+ pipeline^83,84 the count matrix, cell metadata matrix, and gene names from the Seurat analysis were used to create an anndata object, followed by snATAC data preprocessing performed using pycisTopic based on cell state identities annotated from snRNA-seq clustering. Preprocessing steps included generating psuedobulk ATAC-seq profiles for each cell state, calling peaks, creating a consensus peak set, performing QC on the snATAC-seq barcodes, and performing topic modeling to identify sets of co-accessible regions (16 topics were used). Barcode QC for snATAC data was performed by determining TSS enrichment, the number of reads in peaks, the duplicate ratio and the number of unique fragments per barcode, with similar values being used for each of the multiome runs. After pycisTopic preprocessing was complete, enhancer-driven Gene Regulatory Networks (eGRNs) were inferred using the SCENIC+ pipeline. The first step in eGRN inference was to create a Scenic+ object containing both gene expression and chromatin accessibility data as well as the motif enrichment results obtained from pycisTopic analysis, containing only barcodes passing QC standards for both modalities. Then, the original Scenic+ pipeline was followed to identify eGRNs (https://github.com/aertslab/scenicplus/blob/old/docs/pbmc_multiome_tutorial.ipynb). Based on the calculated eGRN enrichment scores, dimensionality reduction was performed to produce a UMAP and tSNE plot, and the original cell states determined by snRNA-seq were projected onto the UMAP. In order to produce a dotplot-heatmap, high quality eRegulons were selected by taking the correlation between transcription factor expression and target region enrichment scores (correlation coefficient>0.5) and the plot was created, with color corresponding to TF expression values and dot size correlating to region enrichment scores. Next, TF cooperativity was inferred by determining the overlap of predicted target regions for TF binding (Top 5 TFs for each cell state).

ChIP-Seq

To determine binding patterns of core cardiac TFs near the genomic loci containing the miR-200 family, embryonic (E12.5) bioChIP-Seq data from Akerberg et al. (2019) were used (GSE124008)⁵⁸. Briefly,.bw files were input into IGV to visualize TF binding patterns.

Statistical methods

P values were calculated using two tailed, unpaired t tests with a minimum of three biological replicates for each group. Error bars are presented as ± standard error of the mean in every figure. * indicates a P value of <0.05, **<0.01, ***<0.001, ****<0.0001.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data supporting the findings of this study are available in the article, its Supplementary information and the Source Data file. snRNA-seq dataset has been deposited in the NCBI Gene Expression Omnibus and are accessible through the accession number GSE272154. PMIS constructs, mice and data is available from the corresponding author upon request. Source data are provided with this paper.

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Acknowledgements

The authors thank members of the Amendt laboratory for their expertise and helpful discussions and previous lab members for contributing to the study. We thank the Iowa Institute of Human Genetics (IIHG) Genomics Division for their help with sequencing, and the Flow Cytometry core for cell sorting to establish our stable cell lines. We thank the University of Iowa Cardiovascular Research Center and Cardiovascular Phenotyping Core for advice and support of this project. We also thank the University of Iowa Craniofacial Anomalies Research Center for their support. This work was supported by funding from the University of Iowa to BAA.

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Authors and Affiliations

The University of Iowa, Department of Anatomy and Cell Biology, Iowa City, IA, USA
Riley Leonard, Steven Eliason & Brad A. Amendt
Craniofacial Anomalies Research Center, The University of Iowa, Iowa City, IA, USA
Riley Leonard, Steven Eliason & Brad A. Amendt
Texas Heart Institute, Houston, TX, USA
Yi Zhao & Xiao Li
The University of Iowa, Department of Internal Medicine, Iowa City, IA, USA
Kathy Zimmerman, Ariana Batz & Robert M. Weiss
Philadelphia College of Osteopathic Medicine, Philadelphia, PA, USA
Cathy J. Hatcher
Department of Veteran Affairs, Iowa City, IA, USA
Robert M. Weiss
Harvard University, Boston, MA, USA
Mason Sweat

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Riley Leonard
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Yi Zhao
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Steven Eliason
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Kathy Zimmerman
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Ariana Batz
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Contributions

R.L. performed the experimental studies, analyzed data and prepared figures and a draft of the manuscript; Y.Z. performed computational analyses, prepared figures; S.E. performed the experimental studies, analyzed data and managed funds and reagents; K.Z. performed initial studies, analyzed data; A.B. performed initial studies, analyzed data; C.J.H. analyzed data, edited the manuscript, provided critical input on experimental design; R.M.W. analyzed data, edited the manuscript, provided critical input on experimental design; M.S. performed computational analyses, prepared figures, edited the manuscript; X.L. performed computational analyses, prepared figures, edited the manuscript; B.A.A. project development, funding, analyzed data, prepared the manuscript.

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Correspondence to Brad A. Amendt.

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Brad A. Amendt is the CSO and founder of NaturemiRI, LLC. All other authors have no disclosures to report.

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Nature Communications thanks Irfan Kathiriya, and Hiroki Kurihara for their contribution to the peer review of this work. A peer review file is available.

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Leonard, R., Zhao, Y., Eliason, S. et al. Single-nuclei multiomics analysis identifies abnormal cardiomyocytes in a murine model of cardiac development. Nat Commun 16, 6947 (2025). https://doi.org/10.1038/s41467-025-62208-9

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Received: 09 October 2024
Accepted: 12 July 2025
Published: 29 July 2025
DOI: https://doi.org/10.1038/s41467-025-62208-9