Measurement and Analytical Platforms
Many important biological processes remain difficult to study because existing measurement tools lack either the spatial resolution, throughput, or analytical frameworks needed to interpret complex biological data. Our lab develops experimental platforms and analytical methods that expand what can be measured in biological systems. These technologies are built in direct response to conceptual limitations in current measurement approaches. By integrating experimental design with analytical frameworks, we aim to ensure that new measurements reveal biologically meaningful structure rather than simply generating larger datasets.
Droplet Microfluidics
Droplet microfluidics provides a powerful strategy for isolating and manipulating individual biological entities in massively parallel experiments. Our lab develops droplet-based platforms that enable quantitative measurements at the level of single viruses, individual molecules, or synthetic biomimetic systems. We design microfluidic devices and droplet architectures that support high-throughput genomic measurements, biochemical screening, and controlled assembly of functional biomolecular structures. These platforms allow thousands to millions of parallel experiments to be performed with precise control over reaction environments. Students and postdocs in the lab work on both the engineering and application of droplet systems, including device design, fluidic control strategies, and integration with molecular assays and sequencing technologies.
High-Throughput, High-Resolution Imaging
Spatial organization is a fundamental aspect of biological systems. Our lab develops imaging-based spatial omics technologies that measure molecular distributions across large biological structures while maintaining high spatial resolution. Our imaging platforms are designed to capture large-scale molecular maps across tissues and model organisms while preserving spatial relationships between cells, circuits, and biological structures. By developing custom hardware, imaging protocols, and analysis pipelines, we maintain full control over the measurement process—from experimental design to image processing and quantitative analysis. These technologies enable large-scale spatial transcriptomics experiments that reveal molecular organization across entire biological systems, including whole-brain molecular maps used in our comparative spatial omics studies. Researchers in the lab work on instrument development, imaging chemistry, computational image processing, and quantitative spatial data analysis.
Theoretical and Analytical Framework Development
High-dimensional biological measurements require new analytical frameworks to extract interpretable biological insight. Our lab develops mathematical and computational approaches that translate complex experimental data into quantitative understanding of biological systems. A key focus of our work is identifying information that conventional analysis methods discard or overlook. We develop analytical frameworks that recover hidden molecular signals, define coordinate systems for comparing spatial datasets, and infer interaction patterns from high-dimensional spatial measurements. Examples include analytical methods for recovering gene expression signals beyond reference cell types (RESCUE), constructing coordinate systems for comparative spatial omics datasets (SpaceExpress), and inferring molecular interaction networks from spatial colocalization patterns (InSTAnT). Students and postdocs in this area work at the interface of mathematical modeling, statistical analysis, and biological data interpretation, developing new frameworks that shape how complex biological datasets are analyzed.