Biology subjects

Loza Lopez, M. d. J.

Publications and source records attributed to Loza Lopez, M. d. J..

3 recordsLinked to original sources

bioRxiv2026

VINTER: a generative vision-language model for annotating and interrogating spatial tissues

Spatial transcriptomics links gene expression to tissue structure, yet current models learn embeddings whose meaning is assigned afterwards or read histology without measured expression. Tissue decisions are therefore rarely made from morphology, genes and neighbourhood together, or examined through them. Here we introduce VINTER, a generative vision-language model aligned on 1.45 million histology-caption pairs that reads histology, measured gene expression and spatial neighbourhood as one sequence. It returns the probability of every tissue class, and because genes enter as text, its molecular evidence can be edited with the image held fixed. Reading all three sources together, VINTER annotated tissue more accurately than spatial-domain methods and a spatial foundation model, and transferred across cohorts without target labels. Editing only the neighbourhood resolved the follicular B-cell and plasma-cell programmes behind its reading of tertiary lymphoid structure (TLS) maturation in lung and kidney cancer. Its class probabilities exposed what single labels conceal in triple-negative breast cancer: immune heterogeneity within stroma and cancer hidden beneath benign labels. Without retraining, the same model transferred to Visium HD and single-cell Xenium data and resolved follicular architecture within colorectal cancer TLS. VINTER thus turns spatial annotation into an interrogable reading of tissue across platforms and resolutions.

bioinformatics↗

bioRxiv2024

Th17 cells with a pathogenic signature in joints of ZAP-70 mutant arthritic mice harbor unique TCR repertoires that share features with WT Tregs

Mutations in the ZAP-70 gene that cause moderate attenuation of T cell receptor (TCR) signaling in mice can result in autoimmune manifestations. One explanation for this pathology is a shift in the regulatory-conventional (Treg-Tconv) T cell repertoire composition. To test this hypothesis, we characterized the single-cell gene expression profiles and TCR repertoires of Tconv and Treg CD4+ T cells of arthritic (ZAC), poised (SKG) ZAP-70 mutant, and wild-type (WT) mice. We identified a group of Th17 cells which exhibited a pathogenic signature and occurred exclusively in inflamed joints of ZAC mice. Such pathogenic signature was uniquely detected in CD4+ T cells obtained from inflamed joints of RA patients. Overall, the Tconv repertoires of ZAP-70 mutant mice were increasingly similar to the repertoires of WT Tregs, and this effect was most notable in the subset of pathogenic Th17 cells. Our results support a model where, upon moderate ZAP-70-mediated signal weakening, T cells that would normally develop into Tregs, instead develop into self-reactive Tconvs, resulting in a breakdown in self-tolerance and susceptibility to autoimmune arthritis.

immunology↗

bioRxiv2023

STAIG: Spatial Transcriptomics Analysis via Image-Aided Graph Contrastive Learning for Domain Exploration and Alignment-Free Integration

Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a novel deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it was designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrated the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.

bioinformatics↗