Biology subjects

Mayer, A. T.

Publications and source records attributed to Mayer, A. T..

14 recordsLinked to original sources

bioRxiv2026

Generalizable virtual staining from histopathology to spatial proteomics across biological scales

Large histology archives capture morphological correlates of molecular tissue states but lack direct spatial protein measurements. In this study, we show that routine hematoxylin and eosin (H&E) histology can provide a queryable, morphology-based representation of spatial protein expression that remains informative from pixels to patients. To achieve this, we developed QUEST, a model framework that represents protein markers as semantic queries for flexible multiplex immunofluorescence (MIF) prediction. Trained on a heterogeneous atlas of paired H&E and MIF images comprising over 10,000 tissue regions, QUEST captured morphological patterns associated with protein markers and reconstructed their expression intensities across diverse tissue types and cohorts. Its utility extended beyond image reconstruction and preserved biologically meaningful information across multiple scales: virtual staining preserved protein marker expression at the cellular and tissue scales, supported cell phenotyping, cellular neighborhood analysis, and characterization of spatial tissue organization, retained clinically relevant signals for patient stratification, and enabled H&E-based queries to retrieve MIF references with similar molecular profiles. These findings position virtual staining not simply as image generation but as a flexible interface between routine histology and spatial proteomics, expanding the analytical value of existing H&E archives.

bioinformatics↗

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

High-Dimensional Spatiotemporal Single-Cell and 3D Atlas of the Bone Marrow Microenvironment during Leukemic Progression

The bone marrow microenvironment (BMME) is essential for hematopoiesis and immunity, yet spatiotemporal single-cell analysis during leukemogenesis remains challenging. We characterized the BMME in femurs from wild-type and chronic myeloid leukemia (CML) mice at 7, 14 and 21 days post-induction by highly multiplexed and 3D microscopy. Using a 54-marker CODEX panel, we profiled 2,033,725 cells in 55 tissue regions of interest and identified 41 cell types through unsupervised clustering and supervised annotation. During leukemic progression, we observed an expansion of myeloid and progenitor cell populations, increased PD-L1+ leukemic cells, the upregulation of PD-1 on CD4+ and CD8+ T cells, and a profound loss of B cells, plasma cells and bone cells. Advanced CML exhibited a striking expansion of immature, pericyte-deficient vasculature that disrupted vascular niches and impaired hematopoietic stem/progenitor cell positioning. Spatial mapping revealed leukemia-specific cellular neighborhoods enriched in PD-1+CD8+ T cells, suggesting localized immune cell exhaustion. Early-stage CML showed increaseds between plasmacytoid dendritic cells and megakaryocytes, whereas advanced CML featured heightened megakaryocyte emperipolesis of non-leukemic granulocytes. Megakaryocytes were morphologically irregular in CML mice and BM trephine biopsies from CML patients. Laser-capture microdissected megakaryocytes from newly diagnosed CML patients had reduced expression of cytoskeleton genes, which was reversed in advanced cases treated with tyrosine kinase inhibitors. 3D imaging revealed vascular disorganization and depleted megakaryocytes in the diaphysis, underscoring region-specific pathology. Together, this study provides a spatiotemporal single-cell atlas of the BMME during leukemic progression, showing how leukemic cells reprogram the niche to support their expansion and immune evasion.

cancer biology↗

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↗

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

A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence Images

Due to the rapid increase and importance of multiplexed immunofluorescence (mIF) imaging data in spatial biology, there is a pressing need to develop efficient image-to-image search pipelines for both diagnostic and research purposes. While several image search methods have been introduced for conventional images and digital pathology, mIF images present three main challenges: (1) high dimension-ality, (2) domain-specificity, and (3) complex additional molecular information. To address this gap, we introduce the MIISS framework, a Multi-granularity mIF Image Similarity Search pipeline that employs self-supervised learning models to extract features from mIF image patches and an entropy-based aggregation method to enable similarity searches at higher, multi-granular levels. We then benchmarked various feature generation approaches to handle high dimensional images and tested them on various foundation models. We conducted evaluations using datasets from different tissues on both patch- and patient-level, which demonstrate the frame-works effectiveness and generalizability. Notably, we found that domain-specific models consistently outperformed other models, further showing their robustness and generalizability across different datasets. The MIISS framework offers an effective solution for navigating the growing landscape of mIF images, providing tangible clinical benefits and opening new avenues for pathology research.

bioinformatics↗

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↗

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

Injectable Nanoparticle-Based Hydrogels Enable the Safe and Effective Deployment of Immunostimulatory CD40 Agonist Antibodies

When properly deployed, the immune system can eliminate deadly pathogens, eradicate metastatic cancers, and provide long-lasting protection from diverse diseases. Unfortunately, realizing these remarkable capabilities is inherently risky as disruption to immune homeostasis can elicit dangerous complications or autoimmune disorders. While current research is continuously expanding the arsenal of potent immunotherapeutics, there is a technological gap when it comes to controlling when, where, and how long these drugs act on the body. Here, we explore the ability of a slow-releasing injectable hydrogel depot to reduce the problematic dose-limiting toxicities of immunostimulatory CD40 agonist (CD40a) while maintaining their potent anti-cancer efficacy. We leverage a previously described polymer-nanoparticle (PNP) hydrogel system that exhibits shear-thinning and yield-stress properties that we hypothesized would improve locoregional delivery of the CD40a immunotherapy. Using PET imaging, we demonstrate that prolonged hydrogel-based delivery redistributes CD40a exposure to the tumor and the tumor draining lymph node (TdLN), thereby reducing weight loss, hepatotoxicity, and cytokine storm associated with standard treatment. Moreover, CD40a-loaded hydrogels mediate improved local cytokine induction in the TdLN and improve treatment efficacy in the B16F10 melanoma model. PNP hydrogels, therefore, represent a facile, drug-agnostic method to ameliorate immune-related adverse effects and explore locoregional delivery of immunostimulatory drugs.

bioengineering↗

bioRxiv2020

Activating Immune Recognition in Pancreatic Ductal Adenocarcinoma Using Autophagy Inhibition, MEK blockade and CD40 Agonism

Pancreatic ductal adenocarcinoma (PDA) patients have not yet benefitted from the revolution in cancer immunotherapy due in large part to the dominantly immunosuppressive tumor microenvironment (TME). MEK inhibition combined with autophagy inhibition leads to transient tumor responses in some PDA patients. We find that co-inhibition of MEK (using cobimetinib, COBI) and autophagy (using mefloquine, MFQ), but not either treatment alone, activates the Type I Interferon/STING pathway in tumor cells which in turn reprogram tumor associated macrophages (TAMs) in paracrine to foster an immunogenic switch. This effect is augmented by a CD40 agonist (aCD40). Triple therapy (COBI+MFQ+aCD40) achieved cytotoxic T cell activation in an immunologically "cold" mouse PDA model, leading to enhanced anti-tumor immunity. Collectively, MEK and autophagy co-inhibition coupled with CD40 agonism invokes immuno-reprograming and is an attractive therapeutic approach for PDA immunotherapy development.

cancer biology↗