Genome engineering has achieved remarkable sequence-level precision, yet predicting the transcriptomic state that a cell will occupy after perturbation remains an open problem. Single-cell CRISPR screens measure how far cells move from their unperturbed state, but this effect magnitude ignores a fundamental question: do the cells move together? Two perturbations with identical magnitude can produce qualitatively different outcomes if one drives cells coherently along a shared trajectory while the other scatters them across expression space. We introduce a geometric stability metric, Shesha, that quantifies the directional coherence of single-cell perturbation responses as the mean cosine similarity between individual cell shift vectors and the mean perturbation direction. Across five CRISPR datasets (2,200+ perturbations spanning CRISPRa, CRISPRi, and pooled screens), stability correlates strongly with effect magnitude (Spearman $ρ=0.75-0.97$), with a calibrated cross-dataset correlation of 0.97. Crucially, discordant cases where the two metrics decouple expose regulatory architecture: pleiotropic master regulators such as CEBPA and GATA1 pay a "geometric tax," producing large but incoherent shifts, while lineage-specific factors such as KLF1 produce tightly coordinated responses. After controlling for magnitude, geometric instability is independently associated with elevated chaperone activation (HSPA5/BiP; $ρ_{partial}=-0.34$ and $-0.21$ across datasets), and the high-stability/high-stress quadrant is systematically depleted. The magnitude-stability relationship persists in scGPT foundation model embeddings, confirming it is a property of biological state space rather than linear projection. Perturbation stability provides a complementary axis for hit prioritization in screens, phenotypic quality control in cell manufacturing, and evaluation of in silico perturbation predictions.

翻译：基因组工程已实现卓越的序列级精度，但预测扰动后细胞占据的转录组状态仍是一个未解难题。单细胞CRISPR筛选可测量细胞偏离其未扰动状态的距离，但这种效应幅度忽略了一个根本问题：细胞是否发生集体性移动？若一种扰动沿共享轨迹驱动细胞产生相干性移动，而另一种扰动使细胞在表达空间中离散分布，即使两种扰动的幅度相同，也可能产生本质不同的结果。我们提出一种几何稳定性指标Shesha，通过计算单个细胞位移向量与平均扰动方向之间的平均余弦相似性，量化单细胞扰动响应的方向相干性。在五个CRISPR数据集（涵盖CRISPRa、CRISPRi和混合文库筛选的2,200余种扰动）中，稳定性与效应幅度呈强相关性（Spearman $ρ=0.75-0.97$），跨数据集校准后相关系数达0.97。关键的是，当两种指标解耦时，不一致案例揭示了调控架构：多效性主调控因子如CEBPA和GATA1需支付"几何代价"，产生幅度大但非相干性的位移，而谱系特异性因子如KLF1则引发紧密协调的响应。在控制幅度效应后，几何不稳定性与分子伴侣活化（HSPA5/BiP；跨数据集偏相关系数$ρ_{partial}=-0.34$与$-0.21$）独立关联，且高稳定性-高应激象限呈现系统性缺失。幅度-稳定性关系在scGPT基础模型嵌入中得到保持，证实其为生物状态空间的固有属性而非线性投影的产物。扰动稳定性为筛选中的命中优先排序、细胞制造中的表型质量控制以及计算扰动预测评估提供了互补性维度。