Biology subjects

Douglass, K. M.

Publications and source records attributed to Douglass, K. M..

4 recordsLinked to original sources

bioRxiv2026

Quantitative Expansion Microscopy for In Situ Estimation of Endogenous Target Abundance

Spatially mapping protein abundance in situ can offer important insights into molecular mechanisms and the physiological functions of protein complexes. This is typically achieved by combining super-resolution microscopy to image fluorescently-labeled protein locations with statistical estimators to retrieve abundances, where accuracy is strongly impacted by labeling efficiency. We introduce quantitative expansion microscopy (qExM) as a method to estimate endogenous protein abundance on ExM data, which offers improved antibody targeting through molecular decrowding. Using cryo-fixation, we preserved ultrastructure and enhanced labeling efficiency to improve accuracy in abundance estimations. We benchmark the effectiveness of qExM by quantifying the stoichiometry of well-characterized nuclear pore complex subunits, and find a mean percent error of 9.4%. We further apply qExM to investigate the abundance of mitochondrial respiratory chain complexes in functionally distinct organelle subpopulations, and of mitochondrial respiratory chain super-complexes in differentially activated human T-cells. qExM provides a robust methodological framework for quantifying endogenous protein abundance in expanded samples in situ.

cell biology↗

bioRxiv2025

Smart hybrid microscopy for cell-friendlydetection of rare events

Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to tradeoffs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase contrast with the functional specificity of fluorescence. We developed surveillance for events of interest in label-free microscopy by novel dynamics-informed neural networks that trigger smart acquisitions in fluorescence upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable new insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging.

cell biology↗

bioRxiv2020

Homogeneous multifocal excitation for high-throughput super-resolution imaging

Super-resolution microscopies, which allow features below the diffraction limit to be resolved, have become an established tool in biological research. However, imaging throughput remains a major bottleneck in using them for quantitative biology, which requires large datasets to overcome the noise of the imaging itself and to capture the variability inherent to biological processes. Here, we develop a multi-focal flat illumination for field independent imaging (mfFIFI) module, and integrate it into an instant structured illumination microscope (iSIM). Our instrument extends the field of view (FOV) to >100x100 {micro}m2 without compromising image quality, and maintains high-speed (100 Hz), multi-color, volumetric imaging at double the diffraction-limited resolution. We further extend the effective FOV by stitching multiple adjacent images together to perform fast live-cell super-resolution imaging of dozens of cells. Finally, we combine our flat-fielded iSIM setup with ultrastructure expansion microscopy (U-ExM) to collect 3D images of hundreds of centrioles in human cells, as well as of thousands of purified Chlamydomonas reinhardtii centrioles per hour at an effective resolution of [~]35 nm. We apply classification and particle averaging to these large datasets, allowing us to map the 3D organization of post-translational modifications of centriolar microtubules, revealing differences in their coverage and positioning.

biophysics↗

bioRxiv2018

Design Principles for Autonomous Illumination Control in Localization Microscopy

Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning molecule density estimator that is accurate over an extended range. This system obviates the need to fine-tune parameters and demonstrates the design of modular illumination control for localization microscopy.

biophysics↗