Compressed phenotypic screening empowers scalable biological discovery

High-throughput phenotypic screens leveraging biochemical perturbations and high-content readouts are poised to advance therapeutic discovery, yet they remain constrained by limitations of scale. To address 2 this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to compress a screen’s required sample, labor, and financial input. We benchmark the approach with a bioactive small molecule library and a high-content imaging readout, demonstrating the feasibility and increased efficiency of compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening in two different biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of tumor-microenvironment recombinant protein ligands. We uncover reproducible phenotypic shifts induced by specific ligands that are distinct from canonical reference signatures and uniquely correlate with clinical outcome. In the second, we examine the modulatory effects of a known mechanism of action chemical compound library on primary human peripheral blood mononuclear cell immune responses. Through contrastive analyses, we identify molecules that potentiate and/or inhibit cell-type specific transcriptional features, uncover pleiotropic effects for individual compounds across diverse cell types, and realize a systems-level view of drug responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts as well as basic biological inquiry.借助生化扰动与高内涵检测（high-content readouts）的高通量表型筛选（high-throughput phenotypic screens）有望推动治疗学发现，但目前仍受限于规模瓶颈。为解决这一问题，本研究提出一种将外源性扰动（exogenous perturbations）混合后辅以计算反卷积（computational deconvolution）的方法，以压缩筛选所需的样本量、人力与资金投入。 2 本研究使用生物活性小分子库（bioactive small molecule library）与高内涵成像检测（high-content imaging readout）对该方法开展基准测试，证实相较于传统实验方案，压缩式实验设计具备可行性且效率显著提升。为验证方法的普适性，我们在两项不同的生物发现研究中应用压缩筛选技术。 第一项研究中，我们使用早期传代胰腺癌类器官（early-passage pancreatic cancer organoids），绘制肿瘤微环境重组蛋白配体库诱导的转录响应图谱。我们发现特定配体可诱导出可重复的表型变化，该变化不同于经典参考特征（canonical reference signatures），且与临床结局存在独特相关性。 第二项研究中，我们针对已知作用机制的小分子化合物库，探究其对原代人外周血单个核细胞（peripheral blood mononuclear cell）免疫响应的调控作用。通过对比分析，我们筛选出可增强或抑制细胞类型特异性转录特征的分子，揭示了单个化合物在不同细胞类型中的多效性效应（pleiotropic effects），并构建了药物响应的系统级视图（systems-level view）。 综上，本研究提出的方法可为搭载高信息量检测的表型筛选提供赋能，助力药物开发与基础生物学研究。