Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.
Anal Chem. 2021 Mar 2;93(8):4108-4117. doi: 10.1021/acs.analchem.0c05352. Epub 2021 Feb 18.
Biophysical markers of cells such as cellular electrical and mechanical properties have been proven as promising label-free biomarkers for studying, characterizing, and classifying different cell types and even their subpopulations. Further analysis or manipulation of specific cell types or subtypes requires accurate isolation of them from the original heterogeneous samples. However, there is currently a lack of cell sorting ability that could actively separate a large number of individual cells at the single-cell level based on their multivariate biophysical makers or phenotypes. In this work, we, for the first time, demonstrate label-free and high-throughput acoustic single-cell sorting activated by the characterization of multivariate biophysical phenotypes. Electrical phenotyping is implemented by single-cell electrical impedance characterization with two pairs of differential sensing electrodes, while mechanical phenotyping is performed by extracting the transit time for the single cell to pass through microconstriction from the recorded impedance signals. A real-time impedance signal processing and triggering algorithm has been developed to identify the target sample population and activate a pulsed highly focused surface acoustic wave for single-cell level sorting. We have demonstrated acoustic single-particle sorting solely based on electrical or mechanical phenotyping. Furthermore, we have applied the developed microfluidic system to sort live MCF-7 cells from a mixture of fixed and live MCF-7 population activated by a combined electrical and mechanical phenotyping at a high throughput >100 cells/s and purity ∼91.8%. This demonstrated ability to analyze and sort cells based on multivariate biophysical phenotyping provides a solution to the current challenges of cell purification that lack specific molecular biomarkers.
细胞的生物物理标志物，如细胞的电学和力学性质，已被证明是有前途的无标记生物标志物，可用于研究、表征和分类不同的细胞类型，甚至是它们的亚群。进一步分析或操作特定的细胞类型或亚型需要从原始异质样本中准确地分离它们。然而，目前缺乏能够根据细胞的多变量生物物理标志物或表型主动地在单细胞水平上分离大量单个细胞的细胞分选能力。在这项工作中，我们首次展示了基于多变量生物物理表型特征的无标记和高通量声学生物细胞分选。电表型通过使用两对差分传感电极对单细胞阻抗特性进行测量来实现，而力学表型则通过从记录的阻抗信号中提取单细胞穿过微缩流的传输时间来实现。开发了一种实时阻抗信号处理和触发算法，以识别目标样本群体并激活用于单细胞水平分选的脉冲高度聚焦表面声波。我们已经证明了仅基于电或机械表型即可进行声学生物细胞分选。此外，我们已经应用了所开发的微流控系统，以基于组合的电和力学表型从固定和活 MCF-7 群体的混合物中以 >100 个细胞/s 的高通量和约 91.8%的纯度分选活 MCF-7 细胞。这种基于多变量生物物理表型分析和分选细胞的能力为解决当前缺乏特异性分子生物标志物的细胞纯化挑战提供了一种解决方案。
