Biology subjects

Garcia, M. J.

Publications and source records attributed to Garcia, M. J..

4 recordsLinked to original sources

bioRxiv2026

Phasor analysis of RGB camera data enables fluorescence microscopy unmixing and brightfield segmentation in a commercial microscope

Spectral information plays a crucial role in biological imaging, yet conventional epifluorescence and histological techniques often rely on RGB image acquisition, limiting the resolution of spectrally overlapping components. Here, we present a phasor-based spectral analysis framework adapted for RGB images, enabling unsupervised segmentation and unmixing without the need for hyperspectral systems or sequential acquisition. By applying a discrete Fourier transform to the red, green, and blue intensities at each pixel, we generate a two-dimensional phasor plot where spectral relationships are encoded in modulation and phase. We demonstrate the utility of this approach across three distinct applications: segmentation of lung histology images stained with hematoxylin and eosin to quantify alveolar collapse, analysis of autofluorescence in skin lesions (nevi and melanoma) to highlight pathological spectral signatures, and spectral unmixing in multicolor-labeled U2OS cells to resolve overlapping fluorophores. Our method improves signal separation, reduces noise, and enhances biological interpretability using standard RGB acquisition. These findings establish RGB phasor analysis as a practical and powerful tool for spectral decomposition and segmentation in microscopy, bridging the gap between conventional imaging and advanced spectral analysis.

biophysics↗

bioRxiv2025

Quantitative and unbiased lung alveolar septum assessment in a LPS experimental mouse model using 2D-spatial correlation image analysis from hematoxylin and eosin slides.

Quantitative assessment of lung tissue architecture is essential for evaluating disease progression in experimental models of acute lung injury (ALI). However, conventional methods for measuring alveolar septum (tabique) thickness rely on manual annotation, which is time-consuming and observer-dependent, compromising comparability and reproducibility. In this study, we introduce a novel, fully automated approach based on two-dimensional spatial autocorrelation function (2D-ACF) analysis to quantify septum thickness from hematoxylin and eosin (H&E)-stained lung tissue sections. After validating the method with a simulated data set, the new approach was applied to a murine model of Acute Lung Injury (ALI) induced by intratracheal instillation of lipopolysaccharide (LPS), under two dietary conditions: normal (ND) and high-fat diet (HFD), thereby testing our approach with a double hit protocol for ALI development. The 2D-ACF analysis provided a robust metric of structural organization, allowing for an unbiased estimation of mean septum thickness across entire tissue images. Compared to controls, LPS instillation increased septal thickness more than six-fold, with further thickening observed in the HFD+Instilled group. These findings were consistent across >400 images and captured subtle additive effects of metabolic stress on lung injury. Traditional manual measurements of septum thickness exhibited substantial inter-observer variability, particularly in the LPS-instilled group, where structural heterogeneity made consistent interpretation challenging. This subjectivity limits the reproducibility of histopathological evaluations, especially under pathological conditions with subtle anatomopathological differences. In contrast, the 2D-ACF method offered a standardized and observer-independent approach. A direct comparison between methods revealed a strong and statistically significant correlation in the instilled group (r = 0.89, p < 0.001), indicating that the 2D-ACF captures key structural features aligned with expert assessments while reducing user bias. In summary, the 2D-ACF framework offers a powerful alternative to conventional image analysis for studying lung pathology. Its adaptability to different experimental settings and potential to be extended to other tissues with spatial heterogeneity makes it a valuable tool for translational biomedical research and digital pathology.

biophysics↗

bioRxiv2021

CNpare: matching DNA copy number profiles

Selecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Cell lines are often chosen based on their tissue of origin, however, the results of large-scale pan-cancer studies suggest that matching lines based on molecular features may be more appropriate. Existing approaches are available for matching lines based on gene expression, DNA methylation or low resolution DNA copy number features. However, a specific tool for computing similarity based on high resolution genome-wide copy number profiles is lacking. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number. CNpare compares copy number profiles using four different similarity metrics, quantifies the extent of genome differences between pairs, and facilitates comparison based on copy number signatures. CNpare incorporates a precomputed database of 1,170 human cancer cell line profiles for comparison. In an analysis of separate cultures of 304 cell line pairs, CNpare identified the matched lines in all cases. CNpare provides a powerful solution to the problem of selecting the best cell line models for cancer research, especially in the context of studying chromosomal instability.

bioinformatics↗

bioRxiv2020

Dynamin-like proteins are essential for vesicle biogenesis in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) secretes pathogenicity factors and immunologically active molecules via membrane vesicles. However, nothing is known about the mechanisms involved in mycobacterial vesicle biogenesis. This study investigates molecular determinants of membrane vesicle production in Mtb by analyzing Mtb cells under conditions of high vesicle production: iron limitation and VirR restriction. Ultrastructural analysis showed extensive cell envelope restructuring in association with vesicle release that correlated with downregulation of cell surface lipid biosynthesis and peptidoglycan alterations. Comparative transcriptomics showed common upregulation of the iniBAC operon in association with high vesicle production in Mtb cells. Vesicle production analysis demonstrated that the dynamin-like proteins (DLPs) encoded by this operon, IniA and IniC, are necessary for release of EV by Mtb in culture and in infected macrophages. Isoniazid, a first-line antibiotic, used in tuberculosis treatment, was found to stimulate vesicle release in a DLP-dependent manner. Our results provide a new understanding of the function of mycobacterial DLPs and mechanistic insights into vesicle biogenesis. The findings will enable further understanding of the relevance of Mtb-derived extracellular vesicles in the pathogenesis of tuberculosis and may open new avenues for therapeutic research. IMPORTANCEIron is an essential nutrient that promotes survival and growth of M. tuberculosis, the bacterium that causes human tuberculosis (TB). Limited availability of iron, often encountered in the host environment, stimulates M. tuberculosis to secrete membrane-bound extracellular vesicles containing molecules that may help it evade the immune system. Characterizing the bacterial factors and mechanisms involved in the production of mycobacterial vesicles is important for envisioning ways to interfere with this process. Here, we report the discovery of proteins required by M. tuberculosis for vesicle biogenesis in culture and during host cell infection. We also demonstrate a connection between antibiotic response and extracellular vesicle production. The work provides insights into the mechanisms underlying vesicle biogenesis in M. tuberculosis and permits better understanding of the significance of vesicle production to M. tuberculosis-host interactions and antibiotic stress response.

microbiology↗