Research Directions

Our lab operates at the intersection of quantitative imaging, molecular biology, and data science. We design and build custom imaging instruments, develop new molecular labeling and detection chemistries, and write the software that controls our microscopes and analyzes our data. We are driven by the conviction that new biology requires new tools — and that the most impactful tools are designed in close conversation with specific unsolved biological problems.

Each of our major platforms — single-molecule tracking in live cells, 3D ATAC-PALM, and cycleHCR — was built because an existing instrument could not answer the question in front of us. And each biological discovery we made with a new tool revealed the next question that demanded the next instrument. This cycle of invention and discovery is the organizing principle of the lab.

At Johns Hopkins, we are building the next generation of tools in three areas:

Next-generation spatial omics instrumentation. We are extending cycleHCR toward higher gene multiplexing, faster acquisition, and compatibility with live and lightly fixed tissues. This includes custom microscope builds, new fluidics architectures, and integration with expansion microscopy and tissue-clearing methods for whole-organ spatial profiling.

Multiscale integration: connecting genome structure to tissue maps. We are developing workflows that link quantitative measurements of chromatin organization in single cells with cycleHCR spatial transcriptomics in the same tissue. Combined with systematic genetic perturbations, including CRISPR knockouts, degron-mediated protein depletion, and intersectional genetic strategies. These integrated measurements will let us ask how specific regulators reshape chromatin architecture, alter spatial gene-expression patterns, and ultimately change cell fate within a tissue context.

Computational tools and AI. As our datasets grow in dimensionality (hundreds of genes × millions of cells × 3D volumes), we are building machine-learning pipelines for automated cell segmentation, gene quantification, spatial clustering, and regulatory network inference. We develop and release our analysis software as open-source tools.

We are committed to making our tools available to the research community. We believe the impact of a new technology is measured not only by the discoveries it enables in our own lab, but by the discoveries it enables everywhere.