Massively parallel quantification of phenotypic heterogeneity in single-cell drug responses.
Massively parallel quantification of phenotypic heterogeneity in single-cell drug responses.
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DOI：
10.1126/sciadv.abf9840
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发表时间：
2021-09-17
期刊：
影响因子：
13.6
通讯作者：
Hammerbacher J
中科院分区：
文献类型：
--
作者：
Yellen BB;Zawistowski JS;Czech EA;Sanford CI;SoRelle ED;Luftig MA;Forbes ZG;Wood KC;Hammerbacher J
活细胞分析平台测量生长速度和表型特性，每次实验多达10万个克隆。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，由于处理活体生物的难度增加，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工具，能够以每次实验接近100,000个克隆的规模测量基于图像的克隆特性。这些进步是通过利用阶梯微流控网络中以前未识别的流动状态来实现的，在适当的条件下，可以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。利用这个平台，我们量化了成千上万的单细胞衍生急性髓系白血病（AML）克隆对靶向治疗的反应，鉴定了罕见的耐药性和形态表型，频率低至0.05%。这种方法可以扩展到更高层次的细胞结构，如细胞对和类器官和芯片上的活细胞荧光分析。
A live-cell analysis platform measures growth rate and phenotypic properties of up to 100,000 clones per experiment. 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.
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