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 viral infections.
Watch a recording of the talk here.