Naas, J. | bioRxiv Science

bioRxiv2024

MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy

With recent advances in multi-color super-resolution light microscopy it has become possible to simultaneously visualize multiple subunits within complex biological structures at nanometer resolution. To opti-mally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. Here, we present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observ-able particle chains. We showcase that MultiMatch is able to consistently recover all existing chain structures in three-color STED images of DNA origami nanorulers and outperforms established geometry-uninformed triplet colocalization methods in this task in a simulation study. Further-more, MultiMatch also excels in the evaluation of simulated four-color STED images and generalizations to even more color channels can be immediately derived from our analysis. MultiMatch is provided as a user-friendly Python package comprising intuitive colocalization visual-izations and a computationally efficient network flow implementation.

bioinformatics↗

bioRxiv2022

Cellular stress in brain organoids is limited to a distinct and bioinformatically removable subpopulation.

Organoids enable disease modeling in complex and structured human tissue, in vitro. Like most 3D models, they lack sufficient oxygen supply, leading to cellular stress. These negative effects are particularly prominent in complex models, like brain organoids, where they can prevent proper lineage commitment. Here, we analyze brain organoid and fetal single cell RNA sequencing (scRNAseq) data from published and new datasets totaling over 190,000 cells. We describe a unique stress signature found in all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined organoid cell population, and present Gruffi, an algorithm that uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. Our data show that adverse effects of cell stress can be corrected by bioinformatic analysis, improving developmental trajectories and resemblance to fetal data.

neuroscience↗