A5052899938- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
- T-cell and B-cell Immunology
- Gene expression and cancer classification
- Monoclonal and Polyclonal Antibodies Research
- Immune Cell Function and Interaction
- Genomics and Chromatin Dynamics
- Cancer Immunotherapy and Biomarkers
- Immunotherapy and Immune Responses
- CAR-T cell therapy research
- Respiratory Support and Mechanisms
- vaccines and immunoinformatics approaches
- Advanced Fluorescence Microscopy Techniques
- Microfluidic and Bio-sensing Technologies
- Bioinformatics and Genomic Networks
- Advanced Biosensing Techniques and Applications
- Cancer Genomics and Diagnostics
- COVID-19 Clinical Research Studies
- Nuclear Structure and Function
- DNA Repair Mechanisms
- Gene Regulatory Network Analysis
- RNA Interference and Gene Delivery
- Immune cells in cancer
- Chemotherapy-induced cardiotoxicity and mitigation
- Biomedical Text Mining and Ontologies
Cell Signaling Technology (United States)
2024-2025
National Research Mordovia State University
2023
Stanford University
2014-2019
Institute of Chemical Kinetics and Combustion
2007-2015
Czech Academy of Sciences
2009-2012
Czech Academy of Sciences, Institute of Biophysics
2009-2012
Masaryk University
2011
Siberian Branch of the Russian Academy of Sciences
2007
Novosibirsk State University
2007
Changes in the frequencies of cell subsets that (co)express characteristic biomarkers, or levels biomarkers on subsets, are widely used as indices drug response, disease prognosis, stem reconstitution, etc. However, although currently available computational “gating” tools accurately reveal subset and marker expression levels, they fail to enable statistically reliable judgements whether these differ significantly between/among subject groups. Here we introduce flow cytometry data analysis...
Abstract Deciphering cell signaling pathways is essential for advancing our understanding of basic biology, disease mechanisms, and the development innovative therapeutic interventions. Recent advancements in multi-omics technologies enable us to capture information a more meaningful context. However, omics data inherently complex—high-dimensional, heterogeneous, extensive—making it challenging human interpretation. Currently, computational tools capable inferring from are very limited,...
Abstract Troubling disparities in COVID-19–associated mortality emerged early, with nearly 70% of deaths confined to Black/African American (AA) patients some areas. However, targeted studies on this vulnerable population are scarce. Here, we applied multiomics single-cell analyses immune profiles from matching airways and blood samples Black/AA during acute SARS-CoV-2 infection. Transcriptional reprogramming infiltrating IFITM2+/S100A12+ mature neutrophils, likely recruited via the...
Polycomb group (PcG) proteins, organized into bodies, are important regulatory components of epigenetic processes involved in the heritable transcriptional repression target genes. Here, we asked whether acetylation can influence nuclear arrangement and function BMI1 protein, a core component complex, PRC1. We used time-lapse confocal microscopy, micro-irradiation by UV laser (355 nm) GFP technology to study dynamics protein. observed that was recruited UV-damaged chromatin simultaneously...
Abstract Cancer 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 tumor microenvironment determine if density, spatial distribution, cellular composition immune cell infiltrates provide prognostic and/or predictive information. Attempts have been made develop standardized methods evaluate routine...
Understanding the mechanism of T-cell activation and receptor (TCR) discrimination MHC-presented epitope peptides (pMHCs) remains an open problem. Machine learning (ML)-based prediction TCR specificity has gained considerable recent attention. However, capacity current models to generalize unseen during training is currently unknown. Here, we use a proprietary cancer-patient data set that profiles binding novel regions peptide space show generalization unsolved Specifically, while ML methods...
We studied the elastic light-scattering properties of human blood neutrophils, both experimentally and theoretically. The experimental study was performed with a scanning flow cytometer measuring patterns (LSPs) individual cells over an angular range 5–60 deg. determined absolute differential cross sections neutrophils. also proposed optical model for neutrophil as sphere filled by small spheres prolate spheroids that correspond to granules segmented nucleus, respectively. This used in...
Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by curse dimensionality, or fail when population patterns significantly vary Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) efficient and accommodates cases where locations differ (or even disappear appear) from sample to sample....
Abstract Unsupervised clustering is a powerful machine-learning technique widely used to analyze high-dimensional biological data. It plays crucial role in uncovering patterns, structure, and inherent relationships within complex datasets without relying on predefined labels. In the context of biology, data may include transcriptomics, proteomics, variety single-cell omics Most existing algorithms operate directly space, their performance be negatively affected by phenomenon known as curse...
Abstract When examining datasets of any dimensionality, researchers frequently aim to identify individual subsets (clusters) objects within the dataset. The ubiquity multidimensional data has motivated replacement user-guided clustering with fully automated clustering. methods are designed make more accurate, standardized and faster. However, adoption these is still limited by lack intuitive visualization cluster matching that would allow users readily interpret automatically generated...
ABSTRACT Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with promise enabling a deeper understanding biology either homeostasis or disease. The wealth data generated by these technologies has recently driven development wide range computational methods. These methods requirement advanced coding fluency to be applied integrated across full spatial omics analysis process thus presenting...
Although it is well known that chromosomes are non-randomly organized during interphase, not completely clear whether higher-order chromatin structure transmitted from mother to daughter cells. Therefore, we addressed the question of how rearranged interphase and heterochromatin pattern after mitosis. We additionally tested similarity arrangement in sister nuclei. noticed a very active cell rotation especially when histone hyperacetylation was induced or transcription inhibited. This natural...
Abstract The ability to predict T-cell receptor (TCR) specificity computationally could revolutionize personalized immunotherapies, vaccine development, and the understanding of immunology autoimmune diseases. While progress depends on obtaining training data that represent vast range possible TCR-ligand pairs, systematic assessment modeling assumptions is equally important can begin with existing data. We illustrate this by evaluating two ideas currently present in field 1,2 : treating TCR...
<title>Abstract</title> The ability to predict T-cell receptor (TCR) specificity computationally could revolutionize personalized immunotherapies, vaccine development, and the understanding of immunology autoimmune diseases. While progress depends on obtaining training data that represent vast range possible TCR-ligand pairs, systematic assessment modeling assumptions is equally important can begin with existing data. We illustrate this by evaluating two ideas currently present in field:...
2626 Background: Based on the clinical success of immuno-oncology therapeutics, tissue-based, biomarker analyses have shifted from a focus tumor cell phenotypes to spatial and functional immune environment. Distribution composition infiltrates shown prognostic or predictive value in various studies; however, standardized approaches categorize tumors into “Desert”, “Excluded” “Inflamed” immunophenotypes based density pattern are missing. This categorization is typically visual inspection...
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the ensuing COVID-19 pandemic have caused ∼40 million cases over 648,000 deaths in United States alone. Troubling disparities COVID-19-associated mortality emerged early, with nearly 70% of confined to Black/African-American (AA) patients some areas, yet targeted studies within this demographic are scant. Multi-omics single-cell analyses immune profiles from airways matching blood samples Black/AA revealed low viral...
Summary The immune phenotype of a tumor is key predictor its response to immunotherapy 1–4 . Patients who respond checkpoint blockade generally present with tumors that are infiltrated by activated T cells, tumor-immune referred as ‘immune inflamed’ 5–7 However, not all inflamed therapy, and in addition the majority patients presents lack cells (‘immune desert’) or exclude periphery islet excluded’) 8 Despite importance these phenotypes patients, little known about their development,...