# 单细胞CRISPR扰动的几何一致性揭示调控架构并预测细胞应激

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-17 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo80g0e703bpslmlnw045n8z
- 原文链接：https://arxiv.org/abs/2604.16642

## AI 摘要

研究团队提出名为Shesha的几何稳定性指标，通过计算个体细胞位移向量与平均扰动方向的平均余弦相似度，量化单细胞CRISPR扰动响应的方向一致性。分析涵盖2,200余个扰动的五组数据集发现，稳定性与效应幅度高度相关（Spearman ρ=0.75-0.97），但两者解耦案例暴露关键调控差异：多效性主调控因子CEBPA和GATA1产生大而不一致的"几何代价"，而谱系特异性因子KLF1则呈现紧密协调的响应。几何不稳定性与伴侣蛋白HSPA5/BiP激活升高独立相关，且高稳定性/高应激组合呈系统性缺失。该关系在scGPT基础模型嵌入中依然存在，证实其为生物状态空间固有属性，为功能基因组筛选和细胞制造质控提供新维度。

## 正文

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.