Our group studies how living systems and their respective viruses encode and decode information about their internal state and their environment by combining ideas from cell biology and physics with recent advances in imaging, machine learning, and genomics to make novel measurements. Active research directions in our group include:
Host-virus interactions during viral decision making
How do viruses access information about their host cell's environment and internal state? How does this information flow to key decision points in the viral lifecycle? We are answering this question for temperate bacteriophage using functional genomics and high-throughput single-cell imaging.
Deep learning for single-cell biology
One of the major computational challenges of analyzing modern imaging experiments is image segmentation, that is determining which parts of a microscope image correspond to which individual cell. Our prior work has demonstrated that deep learning is a natural solution for this problem. We are currently developing the next generation of deep learning algorithms that can analyze dynamic data from live-cell imaging experiments as well as multi-dimensional data from spatial genomics experiments.
Integrated measurements of signaling and gene expression
Mammalian cells use dynamics to expand the information encoding capacity of their signaling networks, but how these dynamics are decoded into patterns of gene expression is less clear. Recent technological advances have made it possible to measure signaling dynamics and genome wide gene expression profiles in the same individual cell. We are working to merge live-cell imaging and spatial genomics data to quantify information transmission in signaling networks involved in the anti-viral response.
Publications
- Laubscher, Emily;Wang, Xuefei (Julie) et al. (2024) Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learningCell Systems
- Ma, Jun;Xie, Ronald et al. (2024) The multimodality cell segmentation challenge: toward universal solutionsNature Methods
- Israel, Uriah;Marks, Markus et al. (2024) A Foundation Model for Cell SegmentationbioRvix
- Sockell, Alexandra;Wong, Wing et al. (2023) A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoidsCell Systems
- Greenbaum, Shirley;Averbukh, Inna et al. (2023) A spatially resolved timeline of the human maternal–fetal interfaceNature
- Scott, Ryan T.;Sanders, Lauren M. et al. (2023) Biomonitoring and precision health in deep space supported by artificial intelligenceNature Machine Intelligence
- Sockell, Alexandra;Wong, Wing et al. (2022) A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids
- Andrews, Brenda;Chang, Jae-Byum et al. (2022) Imaging cell biologyNature Cell Biology
- Miguel, Amanda;Zietek, Matylda et al. (2022) Modulation of bacterial cell size and growth rate via activation of a cell envelope stress response
- Greenwald, Noah F.;Miller, Geneva et al. (2022) Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learningNature Biotechnology