使用生化扰动和高内涵读数的高通量表型筛选受到规模的限制。为了解决这个问题，我们建立了一种汇集外源扰动的方法，然后进行计算反卷积，以减少所需的样本量、劳动力和成本。通过使用生物活性小分子库和高内涵成像读数进行基准测试，我们证明了压缩实验设计与传统方法相比效率的提高。然后，我们在两个生物发现活动中应用压缩筛选。首先，我们使用早期传代的胰腺癌类器官将转录反应映射到重组肿瘤微环境蛋白配体库，揭示由不同于典型参考特征且与临床结果相关的特定配体诱导的可重复表型转变。在第二部分中，我们确定了具有已知作用机制的化合物库对原代人外周血单核细胞免疫反应的多效调节作用。总之，我们的方法为表型筛选提供了信息丰富的读数，以推进药物发现工作和基本的生物学探究。© 2024。作者。

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.© 2024. The Author(s).