Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

SUMMARY: Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack's interactive, graphical interface makes it significantly more user friendly. AVAILABILITY AND IMPLEMENTATION: NucliTrack is available as a free, cross platform application and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.