B-S.B.58: scLEMBAS: Context-Aware Signaling Pathway Modeling at Single-Cell Resolution
Signaling pathways sense and propagate information from the extracellular environment to dictate a cell's response, governing a range of essential functions. However, signaling pathway activity is difficult to decipher due to the vast combinatorial space of possible interactions, the nonlinearity of these interactions, and pathway crosstalk.
Current data-driven approaches often lack mechanistic interpretability, while mechanistic models typically fail to scale to genome-wide, multi-context settings. Here, we present scLEMBAS, a mechanistically grounded artificial intelligence framework for modeling signaling pathway activity at single-cell resolution. scLEMBAS enables genome-scale inference of transcription factor (TF) activity from extracellular or intracellular perturbations. To do so, we integrate recently established biologically informed neural network approaches with compositional deep learning techniques from single-cell perturbation modeling. The model captures nonlinear signal propagation of perturbation through a learnable adjacency matrix representing the protein-protein interaction (PPI) signaling network.
We demonstrate that scLEMBAS can accurately predict transcription factor activity in diverse datasets representing a variety of tissue and perturbation contexts. Additionally, we show that the model captures cell subtype specific perturbation responses despite being agnostic to such labels. Beyond predictive accuracy, the mechanistic nature of scLEMBAS provides distinct utility: learned PPI weights provide a meaningful representation of the network beyond topology alone, and the model can “self-prune†by downweighting spurious interactions irrelevant to the biological context. Additionally, the learned parameters enable identification of perturbation-specific subnetworks that capture the core proteins driving cell type-specific responses. Together, these capabilities establish scLEMBAS as a framework for dissecting context-specific signaling mechanisms at single-cell resolution.
Co-authors: Nikolaos Meimetis, Olof Nordenstorm, Avlant Nilsson, Douglas Lauffenburger
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