DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

ABSTRACT: Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacte

SUBMITTER: O'Connor OM 

PROVIDER: S-EPMC8797229 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature