Wiesner, L. W. | bioRxiv Science

bioRxiv2025

Deep Learning of Functional Perturbations from Condensate Morphology

Biomolecular condensates compartmentalize the interior of living cells to spatiotemporally organize complex functions, yet linking molecular interactions within condensates to their mesoscale organization remains a major challenge. To bridge this gap, we developed a neural network-based framework - Deep-Phase - that uses microscopy images to quantitatively classify condensate morphology changes resulting from pharmacological alterations in associated biochemical processes. We use Deep-Phase to precisely quantify time- and concentration-dependent structural perturbations to the multiphase nucleolus and show that they are tightly coupled to potencies of drugs inhibiting rRNA transcription and processing. Applying Deep-Phase in a chemical screen, we identify a unique nucleolar morphology and discover a role for a DNA topoisomerase in rRNA processing. Mechanistic studies of this morphology provide insights into how interfaces between nucleolar subcompartments are maintained. We demonstrate Deep-Phases adaptability to diverse cell lines, labels, and condensates, offering a powerful platform for uncovering cellular organizing principles and therapeutic targets. HighlightsO_LIA deep learning framework, Deep-Phase, classifies and quantifies drug-induced changes in morphologies of nucleoli, nucleolar speckles, and viral cytoplasmic condensates, directly from images. C_LIO_LITime- and concentration-dependent morphological responses to perturbation predict associated disruptions in RNA transcription and processing. C_LIO_LIUsing Deep-Phase in a high-content small molecule screen reveals a unique nucleolar morphology induced by TOP1 inhibition. C_LIO_LITOP1 inhibition leads to reduced levels and processing of large ribosomal subunit precursors and provides a mechanism for maintenance of nucleolar phase boundaries. C_LI

molecular biology↗

bioRxiv2024

Mapping and engineering RNA-controlled architecture of the multiphase nucleolus

Biomolecular condensates are key features of intracellular compartmentalization. As the most prominent nuclear condensate in eukaryotes, the nucleolus is a layered multiphase liquid-like structure and the site of ribosome biogenesis. In the nucleolus, ribosomal RNAs (rRNAs) are transcribed and processed, undergoing multiple maturation steps that ultimately result in formation of the ribosomal small subunit (SSU) and large subunit (LSU). However, how rRNA processing is coupled to the layered nucleolar organization is poorly understood due to a lack of tools to precisely monitor and perturb nucleolar rRNA processing dynamics. Here, we developed two complementary approaches to spatiotemporally map rRNA processing and engineer de novo nucleoli. Using sequencing in parallel with imaging, we found that rRNA processing steps are spatially segregated, with sequential maturation of rRNA required for its outward movement through nucleolar phases. Furthermore, by generating synthetic de novo nucleoli through an engineered rDNA plasmid system in cells, we show that defects in SSU processing can alter the ordering of nucleolar phases, resulting in inside-out nucleoli and preventing rRNA outflux, while LSU precursors are necessary to build the outermost layer of the nucleolus. These findings demonstrate how rRNA is both a scaffold and substrate for the nucleolus, with rRNA acting as a programmable blueprint for the multiphase architecture that facilitates assembly of an essential molecular machine.

cell biology↗