Hur, E. | Search bioRxiv

bioRxiv2026

Shape Factor Analysis as a Quantitative Framework for Assessing Spheroid and Organoid Morphology and Invasiveness

Morphological changes of spheroids and organoids are widely used as in vitro indicators of healthy and diseased tissue functions; however, quantitative methods to classify spheroid and organoid morphology are limited. In clinical breast imaging, radiologists use tumor shape as a prognostic marker, with irregular margins associated with invasive disease and increased malignancy. Here, we adapted this approach for translational research and developed a custom MATLAB algorithm to quantify the variance in radial lengths of invasive protrusions in spheroids and organoids. First, we analyzed digital phantoms by both ImageJ/FIJI shape descriptors and our radial length analysis to evaluate the capabilities of each measurement method. Subsequently, we performed the same comparisons with images from experimental spheroid and organoid datasets. We demonstrate that multivariate shape factor analysis, including radial length analysis, enables more reliable and comprehensive quantification of spheroid and organoid morphologies than standard shape descriptors alone. By enabling numerical morphological readouts, shape factor analysis can enhance phenotypic profiling of spheroids and organoids and provide valuable metrics for in vitro studies including high-throughput and drug screening workflows.

bioengineering↗

bioRxiv2025

BoltzGen: Toward Universal Binder Design

We introduce BoltzGen, an all-atom generative model for designing proteins and peptides across all modalities to bind a wide range of biomolecular targets. BoltzGen builds strong structural reasoning capabilities about target-binder interactions into its generative design process. This is achieved by unifying design and structure prediction, resulting in a single model that also reaches state-of-the-art folding performance. BoltzGens generation process can be controlled with a flexible design specification language over covalent bonds, structure constraints, binding sites, and more. We experimentally validate these capabilities in eight diverse design campaigns with functional and affinity readouts across 26 targets. In our experiments, binder modalities span from nanobodies to disulfide-bonded peptides, and targets from disordered proteins to small molecules. In particular, we identify nanobody binders for novel targets with low similarity to proteins with already known bound structures. We release model weights, data, and both inference and training code at: https://github.com/HannesStark/boltzgen.

bioengineering↗