AI Insight
Researchers developed U-Pert, a computational framework that predicts how cells respond to genetic or drug perturbations by accounting for both changes in gene expression and changes in cell population size due to processes like cell death and division. Unlike previous models that only track gene expression shifts, U-Pert incorporates the "unbalanced" nature of real experiments where perturbations alter both what cells do and how many survive. The system can both predict cellular responses to new perturbations and work backwards to identify interventions that achieve desired cellular outcomes.
Why it matters
This approach could accelerate drug discovery and cellular therapy design by enabling researchers to computationally screen thousands of potential interventions before testing them experimentally. By accounting for cell survival and proliferation alongside molecular changes, U-Pert provides a more realistic model of therapeutic outcomes where both cell composition and cell state matter.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
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Large-scale single-cell perturbation sequencing provides an unprecedented opportunity to construct virtual cells for the in silico simulation of cellular responses and the inverse design of optimal interventions. However, most perturbation-response models treat cellular responses primarily as mass-preserving shifts in transcriptomic state, whereas single-cell perturbation measurements are inherently unbalanced: the recovered endpoint population is shaped by technical sampling as well as biological perturbation-induced proliferation, apoptosis and selection. Here we introduce U-Pert, an unbalanced generative framework that learns condition- and context-dependent perturbation dynamics from unpaired single-cell snapshots. U-Pert jointly models transcriptomic state transitions and cell-number dynamics, enabling scalable and robust forward prediction of unseen perturbations and contexts, as well as inverse design to screen for desired genetic or pharmacological interventions that achieve user-defined transcriptomic or population-level outcomes. Across controlled simulations, genetic perturbation benchmarks, sciPlex3 drug responses and PBMC cytokine perturbations, U-Pert predicts unseen responses, captures both molecular and abundance changes, and performs inverse design for target gene-expression programs and cell-type compositions. These results show that cell abundance is an integral component of the perturbation phenotype, providing a mass-aware framework for virtual-cell modeling and perturbation cell fate design.
Source: Unbalanced Perturbation Dynamics For Cell Fate Design