Biology subjects

Mogollon, I.

Publications and source records attributed to Mogollon, I..

5 recordsLinked to original sources

bioRxiv2025

Self-supervised learning enables unbiased patient characterization from multiplexed microscopy images

Multiplexed immunofluorescence microscopy offers detailed insights into the spatial architecture of cancer tissue. However, classical single-cell analysis approaches are limited by segmentation accuracy, reliance on predefined features, and the inability to capture spatial interrelationships among cells. We developed a hierarchical self-supervised deep learning framework that learns spatial protein marker patterns from multiplexed microscopy images by encoding the tissue at both local (cellular) and global (tissue architecture) levels. Applied to lung, prostate, and renal cancer tissue microarray cohorts, our method stratified patients into prognostically distinct groups with significantly different survival outcomes. These groupings were consistent with prior expert-driven single-cell analyses, demonstrating the validity of our approach. Furthermore, attention maps extracted from these models highlighted biologically relevant tissue regions associated with specific marker patterns. Overall, our framework effectively profiles complex multiplexed microscopy images and offers a scalable, interpretable tool for improved biomarker discovery, with potential to support more informed cancer treatment decisions.

bioinformatics↗

bioRxiv2024

Achieving high-resolution single-cell segmentation in convoluted cancer spheroids via Bayesian optimization and deep-learning

Accurate quantification of drug responses in 3D tumor-immune co-cultures remains challenging because complex spatial architecture and cellular heterogeneity limit the interpretability of bulk viability assays. Here, we present MorphoNavigator-3D ('Morphological Navigator in 3D';MoNa-3D), an automated framework for high-resolution, annotation-free single-cell analysis in complex 3D co-cultures. The approach integrates optimized live-cell staining, deep learning-based segmentation, and Bayesian optimization (BO) to adapt end-to-end image-analysis workflows across diverse experimental conditions. MoNa-3D was applied to clear cell renal cell carcinoma (ccRCC)-immune cell 3D-spheroid co-cultures, exposed to PI3K/mTOR pathway inhibitors and immunomodulatory compounds in a high-content imaging-based drug screen. The pipeline was used to extract multiscale phenotypic features encompassing ATP-based cell viability, morphology, nuclear remodeling, spatial dispersion, and immune infiltration. This analysis resolved distinct drug-induced phenotypes: PI3K/mTOR inhibitors promoted spheroid disintegration, nuclear enlargement, and immune exclusion, whereas immunomodulators preserved spheroid architecture and T-cell engagement. Multivariate phenotypic integration distinguished drug classes and revealed intra-class variation, including divergent spatial responses to dual PI3K/mTOR versus mTORC1 inhibition. These phenotypes were consistent with known drug mechanisms, supporting the biological interpretability of the framework. Together, these findings establish MoNa-3D as a generalizable platform for multidimensional phenotypic profiling across complex 3D multicellular systems, supporting applications in drug discovery, tumor-immune interaction studies, and precision oncology.

cancer biology↗

bioRxiv2024

Morphological single-cell analysis of peripheral blood mononuclear cells from 390 healthy blood donors with Blood Cell Painting

The morphological diversity of blood immune cells of healthy individuals, critical for recognizing disease-related phenotypes, remains largely uncharacterized. To address this gap, we developed Blood Cell Painting (BCP): a high content, high throughput fluorescence imaging assay for peripheral blood mononuclear cells. We generated a BCP Atlas with images of 50 million cells from 390 healthy blood donors, identifying 18 distinct immune cell morphology clusters. A genome-wide association study of BCP-derived imaging-based cellular features revealed 93 significant associations across 30 genetic loci. These loci include genes linked to mast cell function, inflammation, immune signaling, mitochondrial maintenance and circadian immune modulation. We also observed correlations between immune cell morphological features and clinical traits, such as respiratory conditions and healthcare visits related to contraceptive management, potentially reflecting hormonal influences on immune cell phenotypes. As a proof of concept for clinical application, acute myeloid leukemia subtypes were distinguished by BCP. Our study establishes BCP as a versatile method for immune cell profiling to uncover genetic, phenotypic and clinical determinants of immune cell morphology in health and disease.

cell biology↗

bioRxiv2022

Foxi3 Suppresses Signaling Center Fate and is Necessary for the Early Development of Mouse Teeth

Tooth morphogenesis is regulated by ectodermal-mesenchymal interactions which are controlled by reiteratively used, evolutionarily conserved pathways. How these signals translate into different cellular behaviors is beginning to be understood. Embryonic cell behaviors are regulated by signaling centers that define organ position, size, and shape. The recently identified initiation knots (IKs) and the well-known enamel knots (EK) are tooth epithelial signaling centers that drive proliferation of the neighboring cells and are involved at different stages of morphogenesis, from the early epithelial thickening to the final formation of shape. Signaling center cell fate and maintenance can be regulated by numerous factors. Here, we studied the early stages of murine incisor and molar epithelial morphogenesis and overcame the previous shortage of early-stage mutant models to functionally manipulate the initiation processes of tooth morphogenesis. We achieved the early conditional knock down of the forkhead-box transcription factor Foxi3 during tooth initiation and used imaging approaches to explore cellular and molecular disease mechanisms, specifically those related to signaling center dysfunction in tooth dysplasia. We show that early deficiency of Foxi3 in incisors and molars frequently arrests growth at bud stage, whereas later knockdown of Foxi3 affects tooth downgrowth and shape. Cell-level analyses revealed a decrease in epithelial proliferation and the ectopic appearance of cells with hallmarks of signaling centers: quiescent cell state and canonical Wnt activity. However, the distribution of these cells was wider all over the tooth buds with abnormal decrease of apoptosis. We postulate that, depending on Foxi3 expression levels, the bud cells shift commitment into signaling center fate, ultimately leading to growth arrest or growth/shape changes, implicating Foxi3 as a regulator of cell fates between the signaling centers and proliferating bud cells.

developmental biology↗

bioRxiv2020

The initiation knot is a signaling center required for molar tooth development

Signaling centers, or organizers, regulate many aspects of embryonic morphogenesis. In the mammalian molar tooth, reiterative signaling in specialized centers called enamel knots (EKs) determine tooth patterning. Preceding the first, primary EK, a transient epithelial thickening appears whose significance remains debated. Here, using tissue confocal fluorescence imaging with laser ablation experiments, we show that this transient thickening is an earlier signaling center, the molar initiation knot (IK) that is required for the progression of tooth development. IK cell dynamics manifest the hallmarks of a signaling center; cell cycle exit, condensation, and eventual silencing through apoptosis. IK initiation and maturation are defined by the juxtaposition of high Wnt activity cells to Shh-expressing non-proliferating cells, the combination of which drives the growth of the tooth bud, leading to the formation of the primary EK as an independent cell cluster. Overall, the whole development of the tooth, from initiation to patterning, is driven by the iterative use of signaling centers.

cell biology↗