这些研究聚焦于细胞和组织生物学的因果基础模型，旨在通过高通量扰动筛选和人工智能/机器学习（AI/ML）技术来理解复杂的分子回路。文章讨论了诸如Perturb-seq和光学池化筛选（OPS）等实验方法如何从单一表型读取发展到高内容、多模态的读数，从而能更全面地捕捉细胞状态。随着实验规模的扩大和计算能力的增强，特别是在处理非线性遗传相互作用方面，现在有可能构建能够预测未测试扰动结果的生成模型。最终，这些进展为创建一个全面的扰动细胞图谱奠定了基础，该图谱将补充现有的观测性人类细胞图谱，并统一我们对基因、细胞和组织运作方式的理解，从而推动生物学和医学的发现。References: Rood J E, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas[J]. Cell, 2024, 187(17): 4520-4545.
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