Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Abstract

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.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

“…Self-supervised methods focus on information that can be learnt from different documentations of the same object (perturbation, sample or cell type) (panel c of the figure in Box 3 ). For example, if the same microscopy field is documented with two views with different stains, a method can be trained to predict one view from the other; in the process, it learns to filter out the noise in either view to leave only the robust signal 46 . Related methods train to differentiate multiple cells from a single view or multiple views (that is, replicates) that document a specific perturbation (for example, chemical or genetic) from those documenting any other perturbations 47 , 48 .…”

Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact‐free and denoised images

“…The key idea of RoAR is to use self‐supervised learning , illustrated in Figure 2B, to train the parameters θ of the model . The idea of using self‐supervised learning has recently gained popularity in several distinct imaging applications for addressing the lack of ground‐truth training data 26‐30 . Recent work in MRSI spectral quantification has seen the integration of CNN’s with physical models as a means to avoid dependence on ground‐truth labels 31 .…”

“…The idea of using self-supervised learning has recently gained popularity in several distinct imaging applications for addressing the lack of ground-truth training data. [26][27][28][29][30] Recent work in MRSI spectral quantification has seen the integration of CNN's with physical models as a means to avoid dependence on ground-truth labels. 31 The self-supervised learning in RoAR is enabled through our knowledge of the analytical biophysical model connecting the mGRE signal with biological tissue microstructure.…”

Roadmap to Building a Cell: An Evolutionary Approach

“…Other mechanisms could be exploitation of exaptation, where a structure that was performing a certain function can switch to performing a different function; and compositional systems, where a new or enhanced function emerges from the combination of smaller part types. Finally, to aid in the discovery of difficult-to-identify features, one can explore the utilization of unsupervised machine learning ( Lu et al, 2019 ). Open ended evolution could provide new unexpected solutions to evolutionary challenges.…”