Biology subjects

Orlova, D.

Publications and source records attributed to Orlova, D..

9 recordsLinked to original sources

bioRxiv2026

Practical quantification of immunohistochemistry antigen concentrations and reaction-diffusion parameters

The immunohistochemistry (IHC) methods widely used in diagnostic medicine and biomedical research are kinetically complex reaction-diffusion processes that, ideally, produce stain intensities correlated with the local antigen concentration. Yet after 75 years of use, practical theoretical tools to rigorously plan and interpret IHC experiments are still lacking. Because modeling the reactions requires time-consuming computer simulation, impractical for regular use, most protocols are optimized empirically, without detailed knowledge of the reaction rates and antigen-antibody equilibria. The resulting stain intensities can be calibrated against standards with known antigen abundance, but they are typically not interpretable in terms of chemical antigen concentrations. To address these limitations, we developed a fast interpolation method to model reaction-diffusion behavior, and experimental methods to characterize IHC kinetic parameters in formalin-fixed paraffin-embedded (FFPE) samples. Used together, these allow experimental measurement of both the chemical concentration of antigen in the sample and the reaction-diffusion parameters consistent with the assay results. Results show 1) direct immunofluorescent detection has low nanomolar sensitivity with >1000-fold dynamic range, and 2) antibody diffusion rates in FFPE samples can be >1000-fold slower than in aqueous solutions, producing diffusion-limited conditions in which the IHC reaction time course may depend on the sample antigen concentration. Awareness of these details is necessary to avoid potential underestimation of both the absolute and relative antigen concentrations in different samples that may occur if staining is stopped before reaching equilibrium. Software tools are provided to allow users to rapidly model IHC reaction time courses and to fit experimental time course data with candidate reaction parameters. The principles described here apply equally to other tissue-based "spatial omics" analyses and should be considered when designing and interpreting experiments requiring any macromolecule to diffuse into and react in a tissue section. SIGNIFICANCEThe theoretical and experimental framework described here advances IHC staining from a qualitative or semi-quantitative method towards a more rigorously quantitative assay. The practical ability to predict IHC reaction kinetics and fit reaction parameters to experimental data has the potential to advance IHC applications in diagnostic medicine and biomedical research in three ways: 1) interpretation of experimental and diagnostic samples stained under different conditions can be more objective, facilitating comparison of results from different protocols and different laboratories; 2) IHC staining can be interpreted as molar chemical antigen-antibody concentrations calculated from the reaction parameters measured in the studied sample; 3) the correlation between antigen concentration and biological behavior can be examined more reliably. Practical software tools are provided.

pathology↗

bioRxiv2025

Predicting specificity of TCR-pMHC interactions using machine learning and biophysical models

Understanding the mechanism of T-cell activation and T-cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains an open problem. Machine learning (ML)-based prediction of TCR specificity has gained considerable recent attention. However, the capacity of current models to generalize to peptides unseen during training is currently unknown. Here, we use a proprietary cancer-patient data set that profiles TCR binding to novel regions of peptide space to show that peptide generalization remains an unsolved problem. Specifically, we show that while ML methods have demonstrable utility in predicting TCR specificity for known peptides, they fail to generalize to novel peptides. We also show that physics-based methods utilizing classical energy functions outperform ML methods when predicting TCR binding to novel peptides but underperform them on known peptides. In light of these observations, we develop a new ML model that leverages general knowledge acquired by protein foundation models to achieve better or comparable performance than either ML or biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore analyze model performance as a function of distance of TCR sequence specificity between the training and test sets to quantitatively characterize the generalization potential of any given TCR-pMHC model. Our analysis sheds light on the status of modeling TCR-pMHC interactions and suggests new paths forward for continued method development and data acquisition.

immunology↗

bioRxiv2025

Cell signaling pathways discovery from multi-modal data

Deciphering cell signaling pathways is key to understanding biology, disease mechanisms, and developing new therapies. Although advances in multi-omics technologies provide richer insight into signaling, the data remain high-dimensional, heterogeneous, and difficult to interpret, and current computational tools for inferring signaling pathways are limited. To address this, we developed Incytr, a method for efficient discovery of cell signaling pathways through integration of diverse data modalities, including transcriptomics, ATAC-seq, proteomics, phosphoproteomics, and kinomics. We demonstrate its application in COVID-19, Alzheimers disease, and cancer, where it successfully recovers known pathways and generates novel, cell-type-specific hypotheses supported by multiple data types. We further show how integrating Incytr-derived pathways with biomarker and drug databases can support target and drug discovery. Finally, we show that using Incytr-derived signaling pathways as training data for simple natural language processing models can deepen our understanding of cell-cell communication and immune cell dynamics, while helping identify new therapeutic targets.

bioinformatics↗

bioRxiv2024

Tricked by Edge Cases: Can Current Approaches Lead to Accurate Prediction of T-Cell Specificity with Machine Learning?

The ability to predict T cell receptor (TCR) specificity from sequence could transform immunotherapy, vaccine development, and our understanding of immune recognition. While machine learning approaches have shown promise, progress is limited by the quality of training data and underlying modeling assumptions. Historically, equilibrium binding assays using multimeric pMHCs have been a dominant source of data, but these assays often conflate high-affinity binding with true functional specificity, introducing noise into predictive models. Here, we critically examine two commonly discussed ideas in the field: that TCR specificity prediction can be separated from functional activation modeling, and that unsupervised sequence-based approaches can generalize across diverse antigen contexts. We introduce a cell-based assay for directly quantifying TCR-pMHC binding kinetics using monomeric ligands, while simultaneously assessing early activation via CD3{zeta} phosphorylation. These kinetic parameters provide a mechanistic basis for specificity that avoids the artifacts of equilibrium-based measurements. We propose a predictive modeling framework that integrates biophysical measurements with machine learning, and outline strategies for generating high-throughput training data to support this approach. Our findings highlight the need for functionally informed, mechanistically grounded models to advance generalizable TCR specificity prediction.

immunology↗

bioRxiv2024

Lifting the curse from high dimensional data: Automated projection pursuit clustering for the variety of biological data modalities

Unsupervised clustering is a powerful machine-learning technique widely used to analyze high-dimensional biological data. It plays a crucial role in uncovering patterns, structure, and inherent relationships within complex datasets without relying on predefined labels. In the context of biology, high-dimensional data may include transcriptomics, proteomics, and a variety of single-cell omics data. Most existing clustering algorithms operate directly in the high-dimensional space, and their performance may be negatively affected by the phenomenon known as the curse of dimensionality. Here, we show an alternative clustering approach that alleviates the curse by sequentially projecting high-dimensional data into a low-dimensional representation. We validated the effectiveness of our approach, named APP, across various biological data modalities, including flow and mass cytometry data, scRNA-seq, multiplex imaging data, and T-cell receptor repertoire data. APP efficiently recapitulated experimentally validated cell-type definitions and revealed new biologically meaningful patterns.

bioinformatics↗

bioRxiv2023

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy

BackgroundCancer immunotherapy has transformed the clinical approach to patients with malignancies as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment (TME) to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. MethodsWe developed approaches to categorize solid tumors into "Desert", "Excluded" and "Inflamed" types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. ResultsWe show that categorization based on "manual" observation is predictive for clinical benefit from anti-programmed cell death ligand-1 (PD-L1) therapy in two large cohorts of patients with non-small cell lung cancer (NSCLC) or triple-negative breast cancer (TNBC). For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based read-outs and patient response to therapy. ConclusionsOur findings suggest tumor immunophenotype (IP) generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. What is already known on this topicClinical benefit from checkpoint inhibitor-targeted therapies is realized only in a subset of patients. Robust biomarkers to identify patients who may respond to such therapies are needed. What this study addsWe have developed manual and automated approaches to categorize tumors into immunophenotypes based on the spatial distribution of CD8+ T effector cells that predict clinical benefit from anti-PD-L1 immunotherapy for patients with advanced non-small cell lung cancer or triple-negative breast cancer. How this study might affect research, practice or policyTumor immunophenotypes should be further validated as predictive biomarker for checkpoint inhibitor-targeted therapies in prospective clinical studies.

pathology↗

bioRxiv2022

SPEX: A modular end-to-end analytics tool for spatially resolved omics of tissues

Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEXs infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEXs ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

bioinformatics↗

bioRxiv2021

Pathogenic neutrophilia drives acute respiratory distress syndrome in severe COVID-19 patients

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the ensuing COVID-19 pandemic have caused [~]40 million cases and over 648,000 deaths in the United States alone. Troubling disparities in COVID-19-associated mortality emerged early, with nearly 70% of deaths confined to Black/African-American (AA) patients in some areas, yet targeted studies within this demographic are scant. Multi-omics single-cell analyses of immune profiles from airways and matching blood samples of Black/AA patients revealed low viral load, yet pronounced and persistent pulmonary neutrophilia with advanced features of cytokine release syndrome and acute respiratory distress syndrome (ARDS), including exacerbated production of IL-8, IL-1{beta}, IL-6, and CCL3/4 along with elevated levels of neutrophil elastase and myeloperoxidase. Circulating S100A12+/IFITM2+ mature neutrophils are recruited via the IL-8/CXCR2 axis, which emerges as a potential therapeutic target to reduce pathogenic neutrophilia and constrain ARDS in severe COVID-19. Graphical AbstractThe lung pathology due to severe COVID-19 is marked by a perpetual pathogenic neutrophilia, leading to acute respiratory distress syndrome (ARDS) even in the absence of viral burden. Circulating mature neutrophils are recruited to the airways via IL-8 (CXCL8)/CXCR2 chemotaxis. Recently migrated neutrophils further differentiate into a transcriptionally active and hyperinflammatory state, with an exacerbated expression of IL-8 (CXCL8), IL-1{beta} (IL1B), CCL3, CCL4, neutrophil elastase (NE), and myeloperoxidase (MPO) activity. Airway neutrophils and recruited inflammatory monocytes further increase their production of IL-8 (CXCL8), perpetuating lung neutrophilia in a feedforward loop. MdCs and T cells produce IL-1{beta} and TNF, driving neutrophils reprogramming and survival. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/446468v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@81fd3aorg.highwire.dtl.DTLVardef@181e63org.highwire.dtl.DTLVardef@172fedcorg.highwire.dtl.DTLVardef@ba55a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

Surveillance of in situ tumor arrays reveals early environmental control of cancer immunity

The immune phenotype of a tumor is a key predictor of its response to immunotherapy1-4. Patients who respond to immune checkpoint blockade generally present with tumors that are infiltrated by activated T cells, a tumor-immune phenotype referred to as immune inflamed5-7. However, not all immune inflamed tumors respond to therapy, and in addition the majority of patients presents with tumors that lack T cells ( immune desert) or that exclude T cells in the periphery of the tumor islet ( immune excluded)8. Despite the importance of these tumor-immune phenotypes in patients, little is known about their development, heterogeneity or dynamics due to an inability to model these features pre-clinically. Here, we describe an approach designated STAMP (skin tumor array by micro-poration), which combines in vivo noninvasive, high-throughput time-lapse imaging with excisional biopsies and next generation sequencing to characterize the establishment of the immunological niche and follow its evolution during immunotherapy. STAMP involves the seeding of dozens to hundreds clonal tumors in the superficial dermis of a single mouse ear that can be visualized in situ over weeks to months. Using this approach, we found that genetically identical tumors could display surprisingly different immune phenotypes. Although individual tumors of the same array were populated by the same T cell clonotypes, regression or progression of individual tumors were associated with distinct patterns of spatial organization of the T cells. In situ imaging of 14K tumors revealed that immune phenotypes were not static over-time but could rather evolve with tumor growth and response to treatment. Therapy-induced or spontaneous early conversion to the immune inflamed phenotype correlated with tumor regression and enhanced cytotoxic T cell activity. Therefore, STAMP provides a flexible approach to study the relationship between tumor evolution, immune cell dynamics, and tumor microenvironment with therapeutic response.

cancer biology↗