Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells
Imaging flow cytometry combines the fluorescence sensitivity and high throughput capabilities of flow cytometry with single cell imaging, and hence provides high volume data well matched to the strengths of deep learning. We present DeepFlow, a data analysis workflow for imaging flow cytometry that combines deep convolutional neural networks with non linear dimension reduction. DeepFlow uses learned features of the neural network to visualize, organize and biologically interpret single cell data. Dissecting the cell cycle as a source of cell to cell variability is crucial for quantitative single cell biology. We demonstrate DeepFlow for a large dataset of cell cycling Jurkat cells. First, we reconstruct the cells' continuous progression through cell cycle from raw image data. This shows that DeepFlow can learn a continuous distance measure between categorical phenotypes. Second, we are able to detect and separate a subpopulation of dead cells, although the data set had been cleaned using established approaches. DeepFlow detects this morphologically abnormal subpopulation in an unsupervised manner. Third, in label free classification of cell cycle phases, we reach a 6 fold reduction in error rate as compared to a recent approach based on boosting on a series of image features. In contrast to previous methods, DeepFlow's predictions are fast enough to consider integration with the imaging flow cytometry measurement process. Author Summary We present DeepFlow, a deep learning based data analysis workflow optimized for the requirements of imaging flow cytometry. We use it to analyze a large data set of a certain type of human T cells (Jurkat cells) which undergo cell cycle. DeepFlow enables reconstructing the continuous cell cycle progression of these cells, and separates dead from living cells. We show how learned features of the neural network can be visualized and biologically interpreted. When used to classify the cell cycle stage, DeepFlow performs significantly better than previous approaches.