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Scaling of Morphogen Gradient in Drosophila Embryogenesis

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Quantum-Classical Correspodence by Wave Packets

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D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response Permalink

Published in Cell, 2026

D-SPIN builds quantitative, interpretable gene regulatory network models from large-scale single-cell perturbation datasets. The framework reveals key regulators of cell-state decisions and organizing principles of cellular responses to genetic and drug perturbations.

Recommended citation: Jiang, J., Chen, S., Tsou, T., McGinnis, C. S., Khazaei, T., Zhu, Q., Park, J. H., Strazhnik, I.-M., Vielmetter, J., Gong, Y., Hanna, J., Chow, E. D., Sivak, D. A., Gartner, Z. J., & Thomson, M. (2026). "D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response." Cell, 189(13), 4159–4184.e35. https://doi.org/10.1016/j.cell.2026.04.028 https://www.cell.com/cell/fulltext/S0092-8674(26)00463-0

Tunable population diversity through a noise-driven differentiation switch Permalink

Published in bioRxiv (preprint), 2026

Single-cell RNA sequencing across 40 signaling conditions and D-SPIN regulatory network models reveal how combinations of signals tune the proportions of neurons and glia. The study identifies a candidate noise-driven differentiation switch that links transcriptional variability to probabilistic cell-fate decisions.

Recommended citation: Jiang, J., Chen, S., Park, J. H., Tsou, T., Yang, V., Rivaud, P., & Thomson, M. (2026). "Tunable population diversity through a noise-driven differentiation switch." bioRxiv [Preprint], version 3. https://doi.org/10.1101/2025.09.20.677552 https://www.biorxiv.org/content/10.1101/2025.09.20.677552v3.full