CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context

2026-06-10

AI Summary

This study addresses the challenging problem of predicting transcriptional responses to unseen gene perturbations in single cells under a zero-shot setting, where perturbation effects arise from complex interactions among cellular states, gene functions, and regulatory mechanisms. To tackle this, the authors propose CisTransCell, a novel framework that, for the first time, incorporates both cis-regulatory sequences and trans-encoded protein sequences as multimodal priors alongside cellular expression states. CisTransCell models the cascade from gene function through regulatory logic to downstream transcriptional changes. By integrating genomic, proteomic coding, and single-cell transcriptomic data, the method substantially outperforms existing approaches across multiple benchmark datasets, demonstrating exceptional performance in zero-shot gene perturbation prediction.

Abstract

Predicting cellular transcriptional responses to genetic perturbations is a central problem in single-cell biology, especially in the zero-shot setting where the perturbed gene or gene combination is unseen during training. A major difficulty is that perturbation effects are not determined by expression state alone: they depend on how the perturbed gene product influences other genes and proteins, how those downstream factors act on cis-regulatory elements, and which regulatory programs are active in the current cell state. To better capture this biological complexity, we propose CisTransCell, a cell-conditioned multi-modal framework for single-cell perturbation prediction that augments each gene with two complementary priors: a regulatory-sequence prior that captures how the gene is controlled, and a coding-sequence prior that captures what the gene product does. By integrating these priors with cellular expression state, CisTransCell models perturbation response as a cascade from gene function to regulatory control to downstream transcriptional change. Experiments on benchmark single-cell perturbation datasets show that CisTransCell achieves strong performance in zero-shot perturbation prediction.

Problem

• single-cell perturbation prediction
• zero-shot learning
• transcriptional response
• gene regulation
• cellular context

Innovation

• zero-shot perturbation prediction
• regulatory-sequence prior
• coding-sequence prior
• multi-modal framework
• single-cell transcriptomics