Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

ABSTRACT: Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven appr

SUBMITTER: Jin J 

PROVIDER: S-EPMC9910600 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature