Table_6_High Content Analysis Across Signaling Modulation Treatments for Subcellular Target Identification Reveals Heterogeneity in Cellular Response.XLSX

Cellular phenotypes on bioactive compound treatment are a result of the downstream targets of the respective treatment. Here, a computational approach is taken for downstream subcellular target identification to understand the basis of the cellular response. This response is a readout of cellular phenotypes captured from cell-painting-based light microscopy images. The readouts are morphological profiles measured simultaneously from multiple cellular organelles. Cellular profiles generated from roughly 270 diverse treatments on bone cancer cell line form the high content screen used in this study. Phenotypic diversity across these treatments is demonstrated, depending on the image-based phenotypic profiles. Furthermore, the impact of the treatments on specific organelles and associated organelle sensitivities are determined. This revealed that endoplasmic reticulum has a higher likelihood of being targeted. Employing multivariate regression overall cellular response is predicted based on fewer organelle responses. This prediction model is validated against 1,000 new candidate compounds. Different compounds despite driving specific modulation outcomes elicit a varying effect on cellular integrity. Strikingly, this confirms that phenotypic responses are not conserved that enables quantification of signaling heterogeneity. Agonist-antagonist signaling pairs demonstrate switch of the targets in the cascades hinting toward evidence of signaling plasticity. Quantitative analysis of the screen has enabled the identification of these underlying signatures. Together, these image-based profiling approaches can be employed for target identification in drug and diseased states and understand the hallmark of cellular response.

细胞在生物活性化合物处理下的表型变化，是相应治疗下游靶点的结果。本研究采用计算方法对下游亚细胞靶点进行识别，以理解细胞反应的基础。这种反应是通过对基于细胞绘画的光学显微镜图像中捕获的细胞表型进行读数来体现的。这些读数是从多个细胞器中同时测量的形态学特征。约270种不同的处理方法在骨癌细胞系上生成的细胞特征构成了本研究中使用的超高含量筛选。这些处理方法在基于图像的表型特征方面的多样性得到了展示。此外，还确定了治疗对特定细胞器及其相关细胞器敏感性的影响。这表明内质网被靶向的可能性更高。通过多元回归，基于较少的细胞器反应预测整体细胞反应。该预测模型与1,000种新的候选化合物进行了验证。尽管不同的化合物驱动特定的调节结果，但它们对细胞完整性的影响各不相同。令人惊讶的是，这证实了表型反应并非保守的，从而实现了信号异质性的量化。激动剂-拮抗剂信号对在级联反应中靶点的切换表明了信号可塑性的证据。筛选的定量分析使得识别这些潜在的标志物成为可能。总之，这些基于图像的细胞特征分析技术可以应用于药物和疾病状态下的靶点识别，并理解细胞反应的特征。