CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation
- 1Johns Hopkins University
Abstract
Cardiomyocyte maturation spans embryogenesis through postnatal life and involves coordinated transcriptional transitions that are disrupted in disease and incompletely recapitulated by stem cell-derived cardiomyocytes. However, no unified framework exists for assigning absolute transcriptional maturation age across datasets, platforms, and experimental conditions. Here, we developed CMAC (Cardiomyocyte Maturation Age Clock), integrating 18 murine single-cell RNA-sequencing datasets spanning embryonic and postnatal timepoints, into a batch-invariant probabilistic atlas using single cell variational inference (scVI) with a 100-dimensional latent representation. Analysis of the developmental trajectory revealed highly non-uniform transcriptional change, with 63.9% of the total transcriptional journey completed by birth and the neonatal transition representing the largest single transcriptional step. An XGBoost regressor trained on the CMAC latent space predicted absolute chronological age with a leave-one-dataset-out mean absolute error (MAE) of 4.09 days (median 3.27 days), Pearson r = 0.903, and Spearman {rho} = 0.908 across 18 datasets and six sequencing platforms, outperforming linear regression (MAE = 6.18 days) and PCA-based representations (MAE = 6.35 days). In an independent P14 cohort comprising 98,163 cardiomyocytes from six biological replicates, CMAC achieved a mouse-level MAE of 0.33 days, with all six mice predicted within 1.1 days of chronological age. Application of CMAC to a previously published PGC1/{beta} double-knockout model with established delayed cardiomyocyte maturation independently recapitulated this phenotype, quantifying maturation deficits of 4.77 to 8.60 days across P7 to P28, with the largest deficit at P14. Together, these results establish CMAC as a quantitative, deep learning framework for assigning absolute transcriptional maturation age and quantifying deviations from normal cardiomyocyte development.
Related
- Kinematic Analysis of Fine Motor Skills with the Montoya Staircase and Supervised Machine Learning: A framework from Video Acquisition to Behavioral Analysis Using DeepLabCut for Markerless Pose Estimation
- Transcriptional control of higher alcohol formation in Saccharomyces cerevisiae: a systems framework linking nutrient sensing, precursor allocation, Ehrlich output, and stress adaptation
- ImmuneLens: linking transcriptional states and TCR clonotypes through disentangled multimodal learning
- From pose estimation accuracy to motor biomarker reliability: a reliability-centred evaluation framework for markerless movement analysis
- Machine learning guided design of strong transcriptional activation domains
BibTeX
@misc{quansah2026cmac,
title = {{CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation}},
author = {K. Quansah and N. Zureick and E. Chen and S. Murphy and E. Anderson and E. Kwon and D. Suh and G. Stein O'Brien and P. Cahan and C. Kwon},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.19.752897},
url = {https://www.biorxiv.org/content/10.64898/2026.09.19.752897v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation
AU - K. Quansah
AU - N. Zureick
AU - E. Chen
AU - S. Murphy
AU - E. Anderson
AU - E. Kwon
AU - D. Suh
AU - G. Stein O'Brien
AU - P. Cahan
AU - C. Kwon
PY - 2026
DA - 2026/09/19
PB - bioRxiv
DO - 10.64898/2026.09.19.752897
UR - https://www.biorxiv.org/content/10.64898/2026.09.19.752897v1
AB - Cardiomyocyte maturation spans embryogenesis through postnatal life and involves coordinated transcriptional transitions that are disrupted in disease and incompletely recapitulated by stem cell-derived cardiomyocytes. However, no unified framework exists for assigning absolute transcriptional maturation age across datasets, platforms, and experimental conditions. Here, we developed CMAC (Cardiomyocyte Maturation Age Clock), integrating 18 murine single-cell RNA-sequencing datasets spanning embryonic and postnatal timepoints, into a batch-invariant probabilistic atlas using single cell variational inference (scVI) with a 100-dimensional latent representation. Analysis of the developmental trajectory revealed highly non-uniform transcriptional change, with 63.9% of the total transcriptional journey completed by birth and the neonatal transition representing the largest single transcriptional step. An XGBoost regressor trained on the CMAC latent space predicted absolute chronological age with a leave-one-dataset-out mean absolute error (MAE) of 4.09 days (median 3.27 days), Pearson r = 0.903, and Spearman {rho} = 0.908 across 18 datasets and six sequencing platforms, outperforming linear regression (MAE = 6.18 days) and PCA-based representations (MAE = 6.35 days). In an independent P14 cohort comprising 98,163 cardiomyocytes from six biological replicates, CMAC achieved a mouse-level MAE of 0.33 days, with all six mice predicted within 1.1 days of chronological age. Application of CMAC to a previously published PGC1/{beta} double-knockout model with established delayed cardiomyocyte maturation independently recapitulated this phenotype, quantifying maturation deficits of 4.77 to 8.60 days across P7 to P28, with the largest deficit at P14. Together, these results establish CMAC as a quantitative, deep learning framework for assigning absolute transcriptional maturation age and quantifying deviations from normal cardiomyocyte development.
ER -