美国麻省理工和哈佛布罗德研究所Shantanu Singh和Marzieh Haghighi共同合作近期取得重要工作进展，他们研究绘制了跨越28000个遗传和化学扰动的细胞的高维基因表达和形态图谱。相关论文于2022年11月7日在线发表于《自然—方法学》杂志上。

研究人员提供了四个数据集的集合，这些数据集包括基因表达和形态特征数据，可用于开发和测试多模态方法。在28000多个化学和遗传扰动中，两种数据类型中的每一种都测量了大约1000个特征。研究人员定义了在这两种数据模式中使用共享和互补信息的生物学问题，为多组学应用提供基线分析和评估指标，并将数据资源公开（https://broad.io/rosetta/）。

据介绍，细胞可以受到各种化学和遗传处理的干扰，对基因表达和形态的影响可以分别通过转录组分析和基于图像的分析来测量。在这些高维剖面数据中观察到的模式可以为药物开发和基础生物学研究中的十几个应用提供动力，但这两种类型的剖面很少用于大规模实验。

附：英文原文

Title: High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations

Author: Haghighi, Marzieh, Caicedo, Juan C., Cimini, Beth A., Carpenter, Anne E., Singh, Shantanu

Issue&Volume: 2022-11-07

Abstract: Cells can be perturbed by various chemical and genetic treatments and the impact on gene expression and morphology can be measured via transcriptomic profiling and image-based assays, respectively. The patterns observed in these high-dimensional profile data can power a dozen applications in drug discovery and basic biology research, but both types of profiles are rarely available for large-scale experiments. Here, we provide a collection of four datasets with both gene expression and morphological profile data useful for developing and testing multimodal methodologies. Roughly a thousand features are measured for each of the two data types, across more than 28,000 chemical and genetic perturbations. We define biological problems that use the shared and complementary information in these two data modalities, provide baseline analysis and evaluation metrics for multi-omic applications, and make the data resource publicly available (https://broad.io/rosetta/).

DOI: 10.1038/s41592-022-01667-0

Source: https://www.nature.com/articles/s41592-022-01667-0