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.

翻译：预测细胞对遗传扰动的转录响应是单细胞生物学中的核心问题，尤其在零样本场景下，被扰动的基因或基因组合在训练中未曾出现。主要困难在于扰动效应并非仅由表达状态决定：它们取决于被扰动基因产物如何影响其他基因和蛋白质，这些下游因子如何作用于顺式调控元件，以及当前细胞状态下哪些调控程序处于激活状态。为更好地捕捉这种生物复杂性，我们提出CisTransCell——一种面向单细胞扰动预测的细胞条件多模态框架，为每个基因附加两种互补先验知识：捕捉基因受调控方式的调控序列先验，以及捕捉基因产物功能的编码序列先验。通过将这些先验与细胞表达状态相整合，CisTransCell将扰动响应建模为从基因功能到调控控制、再到下游转录变化的级联过程。在基准单细胞扰动数据集上的实验表明，CisTransCell在零样本扰动预测中取得了优越性能。