Morphology and gene expression profiling provide complementary information for mapping cell state.
Morphology and gene expression profiling provide complementary information for mapping cell state.
复制标题
DOI：
10.1016/j.cels.2022.10.001
复制
发表时间：
2022-11-16
期刊：
影响因子：
9.3
通讯作者：
中科院分区：
文献类型：
--
作者：
形态和基因表达谱分析可以经济有效地捕获数千个样本中的数千个特征，跨越疾病、突变或药物治疗的扰动，但尚不清楚这两种模式在多大程度上捕获重叠与互补的信息。在这里，我们分别使用L1000和Cell Painting检测来分析基因表达和细胞形态，用来自药物再利用中心的1327个小分子在6个剂量下干扰A549个肺癌细胞，提供了包括两种检测的剂量反应数据的数据资源。这两种检测方法捕获了用于映射细胞状态的共享和互补信息。复合扰动的细胞绘画轮廓更具可重复性，显示出更多的多样性，但测量的不同组的特征较少。应用无监督和监督方法来预测复合作用机制（MOA）和基因靶点，我们发现这两种方法提供了部分共享的药物机制，但也是互补的观点。鉴于基因图谱在生物学中的众多应用，我们的分析为规划实验提供了指导，这些实验可以检测不同的细胞类型、疾病表型以及对化学或遗传扰动的反应。我们在两种分析分析中测试了1,327种药物和工具化合物，分为六种剂量：细胞绘画和L1000。分别从两种检测中提取细胞形态和基因表达读数，我们对检测的可重复性、信号多样性和信息含量进行了表征，揭示了它们在大规模药物分析中的互补性。
Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity, but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOA) and gene targets, we find that the two assays provide a partially shared, but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations. We tested 1,327 drug and tool compounds across six doses in two profiling assays: Cell Painting and L1000. Extracting cell morphology and gene expression readouts from the two assays, respectively, we characterized the assays’ reproducibility, signal diversity, and information content, revealing their complementarity for large-scale drug profiling.
登录
查看更多内容
影响因子：
14.9
作者：
Gene Ontology Consortium
通讯作者：
Gene Ontology Consortium
DOI：
10.1038/s41573-020-00117-w
发表时间：
2021-03
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
Chandrasekaran SN;Ceulemans H;Boyd JD;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
48
作者：
Caicedo JC;Cooper S;Heigwer F;Warchal S;Qiu P;Molnar C;Vasilevich AS;Barry JD;Bansal HS;Kraus O;Wawer M;Paavolainen L;Herrmann MD;Rohban M;Hung J;Hennig H;Concannon J;Smith I;Clemons PA;Singh S;Rees P;Horvath P;Linington RG;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
5.8
作者：
Boyd, Joseph C.;Pinheiro, Alice;Walter, Thomas
通讯作者：
Walter, Thomas
影响因子：
64.5
作者：
Dixit, Atray;Pamas, Oren;Li, Biyu;Chen, Jenny;Fulco, Charles P.;Jerby-Amon, Livnat;Marjanovic, Nemanja D.;Dionne, Danielle;Burks, Tyler;Raychowdhury, Raktima;Adamson, Britt;Norman, Thomas M.;Lander, Eric S.;Weissman, Jonathan S.;Friedman, Nir;Regev, Aviv
通讯作者：
Regev, Aviv