Biology subjects

Sidarta-Oliveira, D.

Publications and source records attributed to Sidarta-Oliveira, D..

2 recordsLinked to original sources

bioRxiv2023

Distinct adrenal gland macrophages regulate corticosteroid production

The adrenal glands are hormone secreting glands that sit on top of the kidneys. Adrenal glands produce glucocorticoids, mineralocorticoids, and catecholamines, and are therefore critical regulators of the stress response, the immune response, metabolism, and blood pressure. Despite being identified for more that 30 years, our understanding of adrenal macrophages remains incomplete. In numerous other tissues, macrophages carry out a plethora of physiological and homeostatic roles in addition to their classical immune functions. The aim of this study was to characterise the macrophage compartment of the adrenal gland and assess its contribution to adrenal function. Using an in vivo approach, we herein describe two morphologically and spatially distinct subsets of adrenal macrophages - dendritic-like macrophages that are present throughout the gland in young and old mice, and "foamy" lipid-laden macrophages that accumulate in the murine adrenal cortex in an age and diet-dependent manner. Furthermore, we present data showing that these foamy-like macrophages accumulate cholesterol and thereby regulate adrenal hormonal output, at steady state and in the context of obesity. We hereby provide novel insights into the physiological roles of macrophages in the adrenal gland and the mechanisms by which adrenal hormone production is regulated.

immunology↗

bioRxiv2022

A comprehensive dimensional reduction framework to learn single-cell phenotypic topology uncovers T cell diversity

Reconstructing and investigating the geometry underlying data is a fundamental task in single-cell analysis, yet no unified framework exists for learning, evaluating, and diagnosing representations that faithfully preserve it. We present TopoMetry, a geometry-aware framework that learns intrinsic coordinate systems directly from the data and refines them into high-fidelity spectral scaffolds. These scaffolds capture both local neighborhoods and global structure, supporting downstream analysis such as clustering and visualization. In benchmarks across diverse single-cell datasets, TopoMetry preserved geometry more reliably than standard workflows and revealed biological signals otherwise obscured, including unexpected transcriptional diversity among T cells and links between RNA-defined subpopulations and clonal expansion. The full analysis can be executed with a single line of code to generate a comprehensive report, making the framework both powerful and accessible. Beyond individual findings, TopoMetry warrants a shift of focus from static two-dimensional projections to the systematic learning and evaluation of geometry itself, enabling more accurate exploration of cellular diversity.

bioinformatics↗