Deep learning for single cell biology
The study of living systems is challenging because of their high dimensionality, spatial and temporal heterogeneity, and high degree of variability in the fundamental unit of life the living cell. Recently, advances in genomics, imaging, and machine learning are enabling researchers to tackle all of these challenges. In this talk, I describe my research group's efforts to use machine learning to connect imaging and genomics measurements to enable high-dimensional measurements of living systems. We show how deep learning-based image segmentation enables the quantification of dozens of protein markers in spatial proteomics measurements of breast cancer and describe a new method for deep learning-based cell tracking which will enable information-theoretic measurements of cell signaling. Lastly, we relay our efforts in deploying deep learning models in the cloud for large-scale deep learning-enabled image analysis. By using single-cell imaging as the read out for a genetic screen, we show how we can identify deep connections between host cell energetics and viral decision making in a model system of latent viral infections.