Decoding life – one cell at a time
Our lab explores the biology of tissues and organs through the lens of single-cell genomics. We develop cutting-edge technologies and computational tools to uncover the cellular and molecular mechanisms that drive development, regeneration, and cancer. Our goal is to enable early detection, personalized therapies, and advances in regenerative medicine by understanding biology at single-cell resolution.
Fields of Interest
Gene regulatory networks in tissues and tumors
Mechanisms of alternative mRNA splicing in tissues and tumors
Genomic time series analysis
Recent Publications
Maimon, T., Trink, Y., Goldberger, J., & Kalisky, T. (2025). Unsupervised Machine Learning Reveals Temporal Components of Gene Expression in HeLa Cells Following Release from Cell Cycle Arrest. International Journal of Molecular Sciences, 26(19), Article 9491. https://doi.org/10.3390/ijms26199491
Trink, Y., Urbach, A., Dekel, B., Hohenstein, P., Goldberger, J., & Kalisky, T. (2024). Characterization of Alternative Splicing in High-Risk Wilms’ Tumors. International Journal of Molecular Sciences, 25(8), Article 4520. https://doi.org/10.3390/ijms25084520
Omer, D., Zontag, O. C., Gnatek, Y., Harari-Steinberg, O., Pleniceanu, O., Namestnikov, M., Cohen, A. H., Nissim-Rafinia, M., Tam, G., Kalisky, T., Meshorer, E., & Dekel, B. (2023). OCT4 induces long-lived dedifferentiated kidney progenitors poised to redifferentiate in 3D kidney spheroids. Molecular Therapy Methods and Clinical Development, 29, 329-346. https://doi.org/10.1016/j.omtm.2023.04.005
Trink, Y., Urbach, A., Dekel, B., Hohenstein, P., Goldberger, J., & Kalisky, T. (2023). Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms’ Tumors Using Unsupervised Machine Learning. International Journal of Molecular Sciences, 24(4), Article 3532. https://doi.org/10.3390/ijms24043532