Cancer detection from stained biopsies using high-speed spectral imaging.

The escalating demand for diagnosing pathological biopsies requires the procedures to be expedited and automated. The existing imaging systems for measuring biopsies only measure color, and even though a lot of effort is invested in deep learning analysis, there are still serious challenges regarding the performance and validity of the data for the intended medical setting. We developed a system that rapidly acquires spectral images from biopsies, followed by spectral classification algorithms.

Single-particle tracking for studying the dynamic properties of genomic regions in live cells.

The appropriate functioning of living cells depends on a variety of dynamic processes that necessitate delicate motion, transportation, association, and disassociation in time and space. Different dynamic patterns such as directed motion, normal diffusion, and restricted diffusion take part at different length scales, and their identification serves as a tool for exploring biochemical processes. Here we describe single-particle tracking which is a powerful method that allows the characterization of dynamic processes on the single-molecule or single-particle level with nanometer spatial and sub-second temporal precision.