A high-resolution spatial and temporal atlas of the postnatal mouse heart is generated by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics, revealing dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation.
Abstract
Understanding how spatial organization and cell−cell interactions shape gene regulatory programs is central to decoding tissue development and function. The transition at birth, marked by increased circulatory demands and rapid tissue growth, requires precise spatiotemporal coordination of cardiac maturation. In this study, we generated a high-resolution spatial and temporal atlas of the postnatal mouse heart by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics. This framework revealed dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation. To functionally test prioritized regulators in vivo and at scale, we developed PIP-seq (probe-based indel-detectable Perturb-seq), a high-throughput platform that detects single guide RNA identity, infers gene editing and profiles transcription from fixed nuclei. Applying PIP-seq to the developing postnatal heart, we identified 21 previously uncharacterized regulators of cardiomyocyte maturation, including genes essential for sarcomere assembly, metabolic reprogramming and electrophysiological transitions. Together, our findings define how microenvironmental signals and intrinsic gene programs cooperate to guide heart maturation and establish a broadly applicable framework for functional genomics in complex tissues. By integrating single-nucleus RNA sequencing and spatial transcriptomics, Wang, Dong, Song et al. generated a high-resolution spatiotemporal atlas of the postnatal mouse heart, identifying 21 regulators of cardiomyocyte maturation and a spatially coordinated regulatory network underlying heart development.
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Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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