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：基因组工程已实现序列级别的精确编辑，但预测细胞受扰动后占据的转录组状态仍是未解难题。单细胞CRISPR筛选可测量细胞偏离未扰动状态的距离，但这种效应幅度忽略了一个根本问题：细胞是否协同移动？两个幅度相同的扰动可能产生截然不同的生物学结局：一个驱动细胞沿共享轨迹一致移动，另一个则使其在表达空间中离散分布。我们提出几何稳定性度量指标Shesha，通过计算单个细胞位移向量与平均扰动方向间的平均余弦相似度，量化单细胞扰动响应的方向一致性。在五个CRISPR数据集（涵盖CRISPRa、CRISPRi及混合文库筛选的2200余种扰动）中，稳定性与效应幅度呈强相关（Spearman $ρ=0.75-0.97$），经校准的跨数据集相关系数达0.97。关键的是，当两种指标解耦的异常案例即可揭示调控结构：CEBPA、GATA1等多效性主调控因子需支付"几何税"，产生幅度大但一致性差的位移；而KLF1等谱系特异性因子则引发紧密协调的细胞响应。在控制效应幅度后，几何不稳定性与分子伴侣激活（HSPA5/BiP；跨数据集偏相关$ρ_{partial}$分别为-0.34和-0.21）独立关联，且高稳定性/高应激象限系统性地缺失。幅度-稳定性关系在scGPT基础模型嵌入中依然存在，证实其为生物学状态空间的内在属性而非线性投影的人为产物。扰动稳定性为筛选中的先导化合物优选、细胞生产中的表型质控及计算机扰动预测评估提供了互补性维度。