Back

Three-dimensional Virtual Adult Cardiomyocyte Transcriptomics

Luo, C.; Lyu, Y.; Guo, X.; Cheng, L.; Liang, Q.; Wang, S.; Wang, Y.; Zhang, S.; Wang, S.; Liu, T.; Luo, Y.; Lu, F.; Ran, B.; Zhang, Y.; Liu, X.; Wang, Y.; Qin, G.; Wu, J.; Lyu, Q. R.

2026-04-16 bioinformatics
10.64898/2026.04.14.718375 bioRxiv
Show abstract

Adult cardiomyocytes are large, rod-shaped, and often multinucleated, which makes them challenging for current single-cell or single-nucleus RNA-sequencing platforms 1-4. Current spatial transcriptomics (ST) relies on nuclear-based cell segmentation, which performs poorly when identifying adult cardiomyocytes 5-8. Moreover, single-section ST of adult myocardium is insufficient to capture the cellular transcriptomic information of intact cardiomyocytes 9-11. Thus, there is an urgent need for novel technology that accurately profiles the transcriptome of adult cardiomyocytes in situ at the single-cell level. Here, we report the first three-dimensional virtual cardiomyocyte (3D-VirtualCM) transcriptome atlas by reconstructing multi-layer ST spanning a 100{micro}m depth of the adult mouse heart. Using membrane-based cell segmentation and similarity-guided cross-sectional contour matching, 3D-VirtualCM delineates individual cardiomyocyte 3D contours and integrates in situ transcriptome. 3D-VirtualCM identifies cardiomyocytes in the cell cycle using proliferative markers in the context of myocardial infarction (MI) and reveals the asymmetric intracellular RNA distribution along the longitudinal axis of cardiomyocytes. Using 3D RNA fluorescence in situ hybridization (FISH), we validated the longitudinal asymmetry of Glul and Gja1 mRNA in adult cardiomyocytes. In summary, 3DVirtualCM provides a workflow that advances the study of cardiac pathophysiology at a bona fide single-cell level while preserving spatial context.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.