Alexandro E. Trevino

This study introduces QUEST, a model that uses H&E histology to predict spatial protein expression, enabling virtual staining and analysis across biological scales.

Eva is a foundation model for tissue imaging data that learns multi-scale spatial representations and performs various tasks like cross-modal inference and patient stratification.

HOPE learns tumor microenvironment signatures from paired H&E and spatial omics data to improve prognostic stratification using H&E images alone.

ROSIE generates multiplex immunofluorescence staining from H&E images by imputing protein expression and localization using deep learning.

OmicsNavigator translates spatial omics data into testable biological findings using a large language model for hypothesis validation and annotation.

This study uses spatial multi-omics and deep learning to identify immunotherapy response and resistance signatures in hepatocellular carcinoma.

The paper introduces CORAL, a deep generative model for spatial multiomics data that deconvolves low-resolution data into single-cell profiles and identifies spatial domains and cell-cell interactions.

The paper uses spatial proteomics to analyze protein expression changes in human diabetic kidney disease across different stages, revealing compartment-specific cellular and inflammatory changes.

The paper introduces a metric to measure surface protein polarity from immunofluorescence imaging and shows it identifies immune cell states linked to tumor engagement, improving survival prediction models.

The paper introduces MIISS, a framework for similarity search in multiplexed immunofluorescence images, showing domain-specific models outperform others.

This work introduces SCGP, a method for unsupervised identification of tissue structures from spatial omics data, and demonstrates its application to spatial transcriptomics and proteomics datasets.

The paper introduces emObject, a domain-specific data abstraction for spatial biology, demonstrating its utility for various spatial omics data types.

7-UP generates in silico 40-plex CODEX data from a 7-plex mIF panel using cellular morphology, enabling cell type classification and survival prediction across diverse samples.

SPACE-GM identifies spatial motifs in cancer samples linked to recurrence and immunotherapy outcomes using geometric deep learning.

The paper maps gene-regulatory dynamics in the developing human cerebral cortex using single-cell data, identifying key transcription factors and disrupted regulatory elements in ASD.

This study examines the 3D genome organization of dinoflagellates, finding that transcription-induced supercoiling drives domain-scale structure without chromatin loops.

The paper introduces a method to profile off-target specificity of CRISPR enzymes using ssDNA mapping, applicable to both catalytically dead and active Cas9 variants.

The paper identifies off-target effects in CRISPR-Cas9 screens for non-coding elements and shows that strict filtering with specificity scores improves the identification of essential regulatory regions.

Omni-ATAC improves ATAC-seq by reducing background noise and enabling analysis of frozen tissues, revealing disease-associated DNA element activities in human brain structures.