Early Prediction of Single-Cell Derived Sphere Formation Rate Using Convolutional Neural Network Image Analysis.

ABSTRACT: Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive and grow to tumorspheres in harsh suspension culture environment. Recent advances in microfluidic technology have enabled isolating and culturing thousands of single cells on a chip. However, tumorsphere assay takes a relatively long period of time, limiting the throughput of this assay. In this work, we incorporated machine learning with single-cell analysis to expedite tumorsphere assay. We collected 1,710 single-cell events as the database and trained a convolutional neural network model that predicts whether a single cell could grow to a t

SUBMITTER: Chen YC 

PROVIDER: S-EPMC9552208 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature