Multimodal Single-Cell Death Recognition Based on Optically Induced Electrokinetics Cell Manipulation

Abstract

The understanding of how a single-cell dies is one of the most important issues to multi-celled organisms. Bio-scientists have been investigating the functional outcomes of dead cells through three classical directions, including morphological manifestations, biochemical features, and biophysical considerations. Usually, only one or two characteristics from one of these directions can be studied in a single experimental setup, providing a narrow view on the complex cell death process. We present here a new technology that could collect 84 kinds of characteristics of cells simultaneously from all of the three classical directions in a single experimental system. The technology essentially combines the method of manipulating individual cells using optically induced electrokinetics (OEK) and analysis of experimental data using data mining techniques. Using this technology, we have revealed a broader connection between the cell variability and its morphological, biophysical, and biochemical manifestations. Unlike other detection methods [1]–[5], as shown in the Supporting Table, our method can be used to quantify many parameters and recognize the living states of 20,000 cells per hour.