Biology subjects

Trevino, A. E.

Publications and source records attributed to Trevino, A. E..

17 recordsLinked to original sources

bioRxiv2026

HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision Oncology

Hematoxylin and eosin (H&E) stained images are fundamental clinical tools for disease assessment. However, even with advanced computational models, their prognostic capabilities remain limited. Spatial omics characterizes tumor microenvironments (TME) in detail yet remains clinically inaccessible due to cost and complexity. In this study, we present HOPE, a lightweight framework that learns TME signatures from paired H&E and spatial omics data during training, then applies these to H&E alone at inference. Leveraging H&E foundation models, HOPE consistently outperforms identical architectures trained without spatial omics guidance across cancer types and cohorts. It further generates interpretable annotations of TME signature on H&E regions, stratifying patients into biologically coherent groups with different prognostic outcomes. HOPE establishes a practical route to translate high-content spatial omics discoveries into scalable, clinically deployable tools.

bioinformatics↗

bioRxiv2025

Modeling patient tissues at molecular resolution with Eva

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference from H&E to proteomics stains, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

bioinformatics↗

bioRxiv2025

OmicsNavigator: an LLM-driven multi-agent system for autonomous zero-shot biological analysis in spatial omics

Translating high-dimensional, spatially resolved molecular datasets into testable biological findings remains a major research bottleneck. Here, we present Omic-sNavigator, an autonomous large language model-powered system for end-to-end data exploration and hypothesis validation on spatial omics data. OmicsNaviga-tor reasons directly over the multi-modal inputs of spatial omics data, including visual and molecular signatures, to perform knowledge-guided annotation of spatial structures. We show that by transforming high-dimensional data into textual interpretations, OmicsNavigator enables zero-shot semantic retrieval of tissue biomarkers and the reconstruction of patient-level disease profiles from raw omics observations. Furthermore, OmicsNavigator features an objective hypothesis validation engine governed by pre-registered, human-audited blueprints. By validating the system across datasets spanning diverse pathological conditions including diabetic kidney disease, kidney transplant rejection, and COVID-19 pulmonary pathology, we demonstrate that OmicsNavigator generates evidence-based, human-readable insights from spatial omics data, with potential to accelerate spatial biology discoveries.

bioinformatics↗

bioRxiv2025

Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinoma

Despite advances in immunotherapy treatment, nonresponse rates remain high, and mechanisms of resistance to checkpoint inhibition remain unclear. To address this gap, we performed spatial transcriptomic and proteomic profiling on human hepatocellular carcinoma tissues collected before and after immunotherapy. We developed an interpretable, multimodal deep learning framework to extract key cellular and molecular signatures from these data. Our graph neural network approach based on spatial proteomic inputs achieved outstanding performance (ROC-AUC > 0.9) in predicting patient treatment response. Key predictive features and associated spatial transcriptomic profiles revealed the multi-omic landscape of immunotherapy response and resistance. One such feature was an interface niche expressing restrictive extracellular matrix factors that physically separates tumor tissue and lymphoid aggregates in nonresponders. We integrate this and other spatially-resolved signatures into SPARC, a multi-omic "fingerprint" comprising scores for immunotherapy response and resistance mechanisms. This study lays groundwork for future patient stratification and treatment strategies in cancer immunotherapy.

cancer biology↗

bioRxiv2025

Learning single-cell spatial context through integrated spatial multiomics with CORAL

Cellular organization is central to tissue function and homeostasis, influencing development, disease progression, and therapeutic outcomes. The emergence of spatial omics technologies, including spatial transcriptomics and proteomics, has enabled the integration of molecular and histological features within tissues. Analyzing these multimodal data presents unique challenges, including variable resolutions, imperfect tissue alignment, and limited or variable spatial coverage. To address these issues, we introduce CORAL, a probabilistic deep generative model that leverages graph attention mechanisms to learn expressive, integrated representations of multimodal spatial omics data. CORAL deconvolves low-resolution spatial data into high-resolution single-cell profiles and detects functional spatial domains. It also characterizes cell-cell interactions and elucidates disease-relevant spatial features. Validated on synthetic data and experimental datasets, including Stero-CITE-seq data from mouse thymus, and paired CODEX and Visium data from hepatocellular carcinoma, CORAL demonstrates robustness and versatility. In hepatocellular carcinoma, CORAL uncovered key immune cell subsets that drive the failure of response to immunotherapy, highlighting its potential to advance spatial single-cell analyses and accelerate translational research.

bioinformatics↗

bioRxiv2024

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.

pathology↗

bioRxiv2023

PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement

Subcellular protein localization is important for understanding functional states of cells, but measuring and quantifying this information can be difficult and typically requires high-resolution microscopy. In this work, we develop a metric to define surface protein polarity from immunofluorescence (IF) imaging data and use it to identify distinct immune cell states within tumor microenvironments. We apply this metric to characterize over two million cells across 600 patient samples and find that cells identified as having polar expression exhibit characteristics relating to tumor-immune cell engagement. Additionally, we show that incorporating these polarity-defined cell subtypes improves the performance of deep learning models trained to predict patient survival outcomes. This method provides a first look at using subcellular protein expression patterns to phenotype immune cell functional states with applications to precision medicine.

bioinformatics↗

bioRxiv2023

Characterizing tissue structures from spatial omics with spatial cellular graph partition

Spatial transcriptomic and proteomic measurements enable high-dimensional characterization of tissues. However, understanding organizations of cells at different spatial scales and extracting tissue structures of interest remain challenging tasks that require extensive human annotations. To address this need for consistent identification of tissue structures, in this work, we present a novel annotation method Spatial Cellular Graph Partitioning (SCGP) that allows unsupervised identification of tissue structures that reflect the anatomical and functional units of human tissues. We further present a reference-query extension pipeline SCGP-Extension that enables the generalization of existing reference tissue structures to previously unseen samples. Our experiments demonstrate reliable and robust partitionings of both spatial transcriptomics and proteomics datasets encompassing different tissue types and profiling techniques. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease and skin disorder, underscoring its potential in facilitating spatial analysis and driving new discoveries.

bioinformatics↗

bioRxiv2023

emObject: domain specific data abstraction for spatial omics

Recent advances in high-parameter spatial biology have yielded a rapidly growing new class of biological data, allowing researchers to more comprehensively characterize cellular state and morphology in native tissue context. However, spatial biology lacks a cohesive data abstraction on which to build novel computational tools and algorithms, making it difficult to fully leverage these emergent data. Here, we present emObject, a domain-specific data abstraction for spatial biology data and experiments. We demonstrate the simplicity, flexibility, and extensibility of emObject for a range of spatial omics data types, including the analysis of Visium, MIBI, and CODEX data, as well as for integrated spatial multiomic experiments. The development of emObject is an essential step towards building a unified data science ecosystem for spatial biology and accelerating the pace of scientific discovery.

bioinformatics↗

bioRxiv2023

Spatial proteomics of human diabetic kidney disease, from health to class III

Aims/HypothesisDiabetic kidney disease (DKD) remains a significant cause of morbidity and mortality in people with diabetes. Though animal models have taught us much about the molecular mechanisms of DKD, translating these findings to human disease requires greater knowledge of the molecular changes caused by diabetes in human kidneys. Establishing this knowledge base requires building carefully curated, reliable, and complete repositories of human kidney tissue, as well as tissue proteomics platforms capable of simultaneous, spatially resolved examination of multiple proteins. MethodsWe used the multiplexed immunofluorescence platform CO-Detection by indexing (CODEX) to image and analyze the expression of 21 proteins in 23 tissue sections from 12 individuals with diabetes and healthy kidneys (DM, 5 individuals), DKD classes IIA, and IIB (2 individuals per class), IIA-B intermediate (2 individuals), and III (one individual). ResultsAnalysis of the 21-plex immunofluorescence images revealed 18 cellular clusters, corresponding to 10 known kidney compartments and cell types, including proximal tubules, distal nephron, podocytes, glomerular endothelial and peritubular capillaries, blood vessels, including endothelial cells and vascular smooth muscle cells, macrophages, cells of the myeloid lineage, broad CD45+ inflammatory cells and the basement membrane. DKD progression was associated with co-localized increase in collagen IV deposition and infiltration of inflammatory cells, as well as loss of native proteins of each nephron segment at variable rates. Compartment-specific cellular changes corroborated this general theme, with compartment-specific variations. Cell type frequency and cell-to-cell adjacency highlighted (statistically) significant increase in inflammatory cells and their adjacency to tubular and SMA+ cells in DKD kidneys. Finally, DKD progression was marked by substantial regional variability within single tissue sections, as well as variability across patients within the same DKD class. The sizable intra-personal variability in DKD severity impacts pathologic classifications, and the attendant clinical decisions, which are usually based on small tissue biopsies. Conclusions/InterpretationsHigh-plex immunofluorescence images revealed changes in protein expression corresponding to differences in cellular phenotypic composition and microenvironment structure with DKD progression. This initial dataset demonstrates the combined power of curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools in revealing pathophysiology of human DKD.

molecular biology↗

bioRxiv2022

7-UP: generating in silico CODEX from a small set of immunofluorescence markers

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex imaging can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UPs imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.

bioinformatics↗

bioRxiv2022

SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles.

Multiplexed immunofluorescence imaging enables high-dimensional molecular profiling at subcellular resolution. However, learning disease-relevant cellular environments from these rich imaging data is an open challenge. We developed SPAtial CEllular Graphical Modeling (SPACE-GM), a geometric deep learning framework that flexibly models tumor microenvironments (TMEs) as cellular graphs. We applied SPACE-GM to 658 head-and-neck and colorectal human cancer samples assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and patient survival after immunotherapy. SPACE-GM is substantially more accurate in predicting patient outcomes than previous approaches for modeling spatial data using neighborhood cell-type compositions. Computational interpretation of the disease-relevant microenvironments identified by SPACE-GM generates insights into the effect of spatial dispersion of tumor cells and granulocytes on patient prognosis.

bioinformatics↗

bioRxiv2021

Direct profiling of genome-wide dCas9 and Cas9 specificity using ssDNA mapping (CasKAS)

Detecting and mitigating off-target activity is critical to the practical application of CRISPR-mediated genome and epigenome editing. While numerous methods have been developed to map Cas9 binding specificity genome-wide, they are generally time-consuming and/or expensive, and not applicable to catalytically dead CRISPR enzymes. We have developed a rapid, inexpensive, and facile assay for identifying off-target CRISPR enzyme binding and cleavage by chemically mapping the unwound single-stranded DNA structures formed upon binding of a sgRNA-loaded Cas9 protein ("CasKAS"). We demonstrate this method in both in vitro and in vivo contexts.

genomics↗

bioRxiv2020

Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

Genetic perturbations of cerebral cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory into glutamatergic neurons, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Basepair-resolution neural network models identified strong cell-type specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD subjects and identified frequently disrupted TF binding sites. This approach illustrates how cell-type specific mapping can provide insights into the programs governing human development and disease.

neuroscience↗

bioRxiv2020

Transcription-dependent domain-scale 3D genome organization in dinoflagellates

Dinoflagellate chromosomes represent a unique evolutionary experiment, as they exist in a permanently condensed, liquid crystalline state, are not packaged by histones, and contain genes organized into polycistronic arrays, with minimal transcriptional regulation. We analyze the 3D genome of Breviolum minutum, and find large topological domains without chromatin loops, demarcated by convergent gene array boundaries ("dinoTADs). Transcriptional inhibition degrades dinoTADs, implicating transcription-induced supercoiling as the primary topological force in dinoflagellates.

genomics↗

bioRxiv2019

Identification and mitigation of pervasive off-target activity in CRISPR-Cas9 screens for essential non-coding elements

Pooled CRISPR-Cas9 screens have recently emerged as a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we conducted a genome-scale screen for essential CTCF loop anchors in the K562 leukemia cell line. Surprisingly, the primary drivers of signal in this screen were single guide RNAs (sgRNAs) with low specificity scores. After removing these guides, we found that there were no CTCF loop anchors critical for cell growth. We also observed this effect in an independent screen fine-mapping the core motifs in enhancers of the GATA1 gene. We then conducted screens in parallel with CRISPRi and CRISPRa, which do not induce DNA damage, and found that an unexpected and distinct set of off-targets also caused strong confounding growth effects with these epigenome-editing platforms. Promisingly, strict filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and allowed for the identification of essential enhancers, which we validated extensively. Together, our results show off-target activity can severely limit identification of essential functional motifs by active Cas9, while strictly filtered CRISPRi screens can be reliably used for assaying larger regulatory elements.

genomics↗

bioRxiv2017

An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues

We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility profiling that works across multiple applications with substantial improvement of signal-to-background ratio and information content. The Omni-ATAC protocol enables chromatin accessibility profiling from archival frozen tissue samples and 50 m sections, revealing the activities of disease-associated DNA elements in distinct human brain structures. The Omni-ATAC protocol enables the interrogation of personal regulomes in tissue context and translational studies.

genomics↗