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Mattiazzi Usaj, M.

Publications and source records attributed to Mattiazzi Usaj, M..

3 recordsLinked to original sources

Dissipation of lysosome pH impairs formation and collapses existing LPS-induced lysosome tubules in macrophages

While lysosomes are typically globular in morphology, lipopolysaccharide-activated macrophages reorganize lysosomes into an expanded tubular network. Here, we sought to determine if the V-ATPase and the lysosomal pH contribute to LPS-mediated lysosome tubulation in macrophages. We found that inhibition of the V-ATPase prevented lysosome tubulation and collapsed preformed tubules. However, the V-ATPase also controls mTORC1 activity, which itself is needed for tubulation. To distinguish between lysosomal pH and mTORC1, we turned to NH4Cl, which alkalinized lysosomal pH but did not interfere with mTORC1; yet NH4Cl blocked tubulation showing that an acidic lysosomal pH is needed for lysosome remodelling. Moreover, clamping the pH to either acidic or alkaline pH caused tubules to collapse, indicating that the pH gradient promotes tubulation, rather than a specific pH. These effects were not due to altered microtubule organization or impaired lysosome motility, suggesting that motors remained associated with lysosomes. On the other hand, while LPS did not alter the average pH of spherical or tubular lysosomes, growing tubules displayed a more acidic peripheral end relative to the pericentral end. Based on this observation, we propose that a localized pH gradient along the tubule may enable tubulation by modulating factors that catalyse tubule growth.

cell biology↗

PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data

Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA, (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell.

bioinformatics↗

Exploring endocytic compartment morphology with systematic genetics and single cell image analysis

Endocytosis is a conserved process that mediates the internalization of nutrients and plasma membrane components, including receptors, for sorting to endosomes and the vacuole (lysosome). We combined systematic yeast genetics, high-content screening, and neural network-based image analysis of single cells to screen for genes that influence the morphology of four main endocytic compartments: coat proteins, actin patches, late endosome, and vacuole. This unbiased approach identified 17 mutant phenotypes and [~]1600 genes whose perturbation affected at least one of the four compartments. Numerous mutants were associated with multiple phenotypes, indicating that morphological pleiotropy is often seen within the endocytic pathway. Morphological profiles based on the 17 aberrant phenotypes were highly correlated for functionally related genes, enabling prediction of gene function. Incomplete penetrance was prevalent, and single-cell analysis enabled exploration of the mechanisms underlying cellular heterogeneity, which include replicative age, organelle inheritance, and stress response.

cell biology↗