RNA subcellular localization is a fundamental biological phenomenon that underlies processes such as cell polarity formation and differentiation. Importantly, localization patterns change in response to intracellular and extracellular environments as well as cellular states, and their disruption is known to contribute to neurological disorders and cancer. Therefore, elucidating the mechanisms of RNA localization is an important challenge for understanding complex biological regulatory networks and overcoming disease. To date, methods for predicting RNA localization have primarily relied on RNA sequence information. However, these approaches have mainly focused on predicting population-averaged (bulk-level) RNA localization across multiple cells, making single-cell-level prediction difficult. As a result, they have been unable to fully capture cell-state-dependent variations in RNA localization. In this study, we developed a deep learning method to predict RNA localization at the single-cell level by integrating RNA sequence information, cellular imaging data from Cell Painting, a multiplexed morphological profiling assay, and spatial transcriptomics data. Compared with a baseline model that outputs the average localization proportion in the training data, our method predicted RNA localization proportions more accurately. In addition, the model predicted with high correlation the localization proportions of individual cells averaged across the gene dimension, suggesting that it can capture cell-specific average RNA localization tendencies reflective of cellular state. This study demonstrates the potential of integrating RNA sequence and cellular state information to enable single-cell-level prediction of RNA localization and contribute to a better understanding of cell-state-dependent mechanisms of RNA localization control.

Co-authors: Chikara Mizukoshi, Haruhiko Morita, Teppei Shimamura