BRIDGE: A Multimodal Dataset for Biological Representation Learning across Imaging and Gene Expression

Cellular responses to perturbation include molecular and phenotypic changes that cannot be captured in a single assay. Multimodal approaches use two or more assays to capture complementary data on these changes from the same experimental setting. For example, single-cell RNA sequencing (scRNA-seq) provides detailed but destructive measurements of transcriptomic state, while live-cell imaging non-destructively measures phenotype changes. A multimodal dataset that uses both assays captures more features of the system. Here, we introduce a multimodal dataset combining time-series live-cell imaging at 20-min resolution with endpoint long-read scRNA-seq comprising 16,296 cells from experimentally matched populations of human fibroblasts. We transiently perturb cell-cycle synchronized fibroblasts using modified mRNA (mmRNA)-mediated MYOD1 overexpression, RNA interference (RNAi)-mediated PRRX1 suppression, and their combination. Since the imaging and sequencing measurements were obtained from different cells, direct cell-to-cell supervision is unavailable. We therefore formulate the integration as a cross-modal prediction problem, using cell-cycle state as a shared biological anchor across modalities. The resulting model predicts population-level gene programs from imaging-derived features, an improvement over prediction based on cell-cycle phase alone. Together, this dataset enables the development and benchmarking of methods that integrate time-resolved cellular phenotypes with transcriptomic states, while our results demonstrate the potential of non-destructive imaging for inferring transcriptional responses to cellular perturbations.