Single-cell analysis tools have made substantial advances in characterizing genomic heterogeneity; however, tools for measuring phenotypic heterogeneity have lagged due to the increased difficulty of handling live biology. Here, we report a single-cell phenotyping tool capable of measuring image-based clonal properties at scales approaching 100,000 clones per experiment. These advances are achieved by exploiting a previously unidentified flow regime in ladder microfluidic networks that, under appropriate conditions, yield a mathematically perfect cell trap. Machine learning and computer vision tools are used to control the imaging hardware and analyze the cellular phenotypic parameters within these images. Using this platform, we quantified the responses of tens of thousands of single cell–derived acute myeloid leukemia (AML) clones to targeted therapy, identifying rare resistance and morphological phenotypes at frequencies down to 0.05%. This approach can be extended to higher-level cellular architectures such as cell pairs and organoids and on-chip live-cell fluorescence assays.

中文翻译：

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单细胞药物反应中表型异质性的大规模平行量化

单细胞分析工具在表征基因组异质性方面取得了实质性进展；然而，由于处理活体生物学的难度增加，用于测量表型异质性的工具已经滞后。在这里，我们报告了一种单细胞表型分析工具，能够在每个实验接近 100,000 个克隆的规模上测量基于图像的克隆特性。这些进步是通过在梯形微流体网络中利用以前未识别的流态来实现的，该流态在适当的条件下会产生数学上完美的细胞陷阱。机器学习和计算机视觉工具用于控制成像硬件并分析这些图像中的细胞表型参数。使用这个平台，我们量化了数万个单细胞衍生的急性髓性白血病 (AML) 克隆对靶向治疗的反应，以低至 0.05% 的频率识别出罕见的耐药性和形态学表型。这种方法可以扩展到更高级别的细胞结构，例如细胞对和类器官以及片上活细胞荧光测定。