Biology subjects

Nazarov, P. V.

Publications and source records attributed to Nazarov, P. V..

13 recordsLinked to original sources

bioRxiv2026

Language models annotate mass cytometry clusters within the range where human experts disagree

Cell-type identification of clusters from unsupervised cytometry pipelines (e.g. FlowSOM, GigaSOM) is a time-consuming, expert-dependent bottleneck, typically requiring an expert to read a heatmap of median marker intensities and assign a label to each cluster. Large language models (LLMs) can annotate single-cell RNA-seq clusters from marker-gene lists, but cytometry cluster data differ in both dimensionality and data type. We present a direct procedure in which cluster medians are scaled per marker and grouped by similarity into a few dozen metaclusters. The continuous marker profiles are then passed to the LLM, which returns a cell-type label and confidence level for each metacluster in a single call, without a gating hierarchy, manually defined marker-specific thresholds, or task-specific training data. Across four human and mouse datasets spanning blood, bone marrow and gut, a single well-chosen model reproduces the reference annotation at the major-lineage level with 85-98% metacluster-level concordance (cloud Claude Sonnet 4.6 85-98%, best local model 83-96%), substantially exceeding majority-class baselines; chance-corrected agreement is Cohen's k = 0.67-0.83. Local open-weight models perform competitively with the cloud model on the independent benchmarks, enabling application to clinical or patient-derived data subject to data-protection restrictions. Reliability is model-dependent: no local model is best everywhere, and the frontier model Claude Opus 5 was no more reliable than Claude Sonnet 4.6, the most consistent in concordance across datasets. Because no annotation is absolute ground truth, we obtained two additional expert annotations of the same continuous heatmaps, blinded to the reference and to all model output, to establish a method-matched agreement ceiling. The experts agreed on ~99% of major-lineage assignments but only ~73% of T-cell maturation states; on that axis the best models fall within the expert disagreement range. LLM-based annotation is therefore a practical, training-free alternative to rule-based annotation, provided its output is checked against the intended panel and species.

bioinformatics↗

bioRxiv2026

Circulating immune profiling reveals impaired monocyte states and trajectories driving immunosuppression in glioblastoma

Glioblastoma (GBM) is an aggressive and lethal brain tumor marked by profound local and systemic immune dysfunction. Yet, the diagnostic and therapeutic relevance of peripheral impairments remains undefined. To clinically dissect their underlying mechanisms and pathological implications, we combined mass and flow cytometry with single-cell RNA-sequencing of peripheral blood mononuclear cells from GBM patients and healthy donors. GBM blood profiles were characterized by heterogeneous changes in classical monocytes, encompassing expanded, reduced and unchanged subsets, presenting distinct functional states, including antigen-presenting, interferon and metabolic subsets. Additional adaptations included myeloid-derived suppressor cell (MDSC) expansion and loss of non-classical monocytes. Trajectory analyses positioned MDSCs as an intermediate state, in continuum with the metabolic subset. Single-cell RNA-sequencing further showed antigen-presenting monocyte propensity to differentiate into tumor-associated macrophages. Circulating monocytes shared a "GBM-classical monocytic signature" exhibiting low MHC class II expression, altered cell-cell communication and increased anti-inflammatory mediators, such as IL1R2 and CD163. Lastly, lymphocyte alterations included decreased proportions of CD4+ T, natural killer (NK) and CD56+ T cells, retaining relatively conserved activation profiles, exemplified by up-regulation of alarmins S100A8/S100A9. These findings map systemic immune reprogramming in GBM, suggesting new avenues for non-invasive biomarker discovery and therapeutic strategies to restore anti-tumor immunity.

cancer biology↗

bioRxiv2025

Integrative multi-omics combined with functional pharmacological profiling in patient-derived organoids identifies personalized therapeutic vulnerabilities of adult high-grade gliomas

BackgroundPrecision medicine has transformed cancer treatment by tailoring therapies to specific molecular aberrations. Integrating high-resolution multi-omics with high-throughput functional profiling in patient-derived organoids of-fers a powerful strategy to further refine patient stratification. While (epi)genetic profiling has drastically improved the classification in diffuse adult gliomas, these advances have not yet translated into effective therapeutic interventions and precision medicine approaches remain to be established. Material and MethodsWe investigated a panel of 48 patient-derived organoid and orthotopic xenograft models of adult high-grade gliomas, comprehensively characterized at genomic, epigenomic and transcriptomic levels. A functional drug screen was performed on 27 organoid models using a 202-compound library targeting cancer-related pathways and epigenetic regulators. Unsupervised multi-omics factor analysis was employed to identify patient-specific therapeutic vulnerabilities. Validation included dose-dependent drug efficacy assessments, as well as biomarker assessment in patient tumors across molecular subgroups. ResultsMulti-omics analysis revealed a broad spectrum of molecular profiles capturing the genetic, epigenetic, and transcriptomic diversity of high-grade gliomas. Multi-omics factor analysis, integrating multi-omics and drug response profiles, identified distinct subgroups associated with IDH1 mutation and MYCN amplification. IDH1 mutant grade 4 astrocytomas showed selective sensitivity to histone deacetylase 3 inhibitors, while a MYCN-amplified glioblastoma responded preferentially to histone methyltransferase inhibitors. The differential drug responses were linked to specific (epi)genetic and transcriptomic biomarkers. While other glioblastomas exhibited heterogeneous treatment responses, no robust biomarker-defined responder subgroups were identified. ConclusionOur findings highlight the value of integrating multi-omics and functional profiling to inform precision medicine strategies. This approach enables the stratification of distinct patient subgroups in preclinical models, paving the way for tailored therapeutic interventions. While we observed distinct pharmacogenomic profiles in IDH1 mutant grade 4 astrocytomas and a MYCN-amplified glioblastoma, implementing precision medicine in other glioblastoma subtypes remains a substantial challenge. Key pointsO_LIIntegrating drug screening in a panel of patient-derived organoids with multi-omics enables pharmacogenomic profiling in adult diffuse high-grade gliomas C_LIO_LIIDH1 mutant grade 4 astrocytomas are sensitive to histone deacetylase 3 inhibitors C_LIO_LIMYCN-amplified glioblastoma exhibits distinct DNA methylation pattern and drug responses C_LI Study importanceTo date, attempts to develop effective precision medicine in adult high-grade gliomas failed. Here, we provide a preclinical framework for identifying personalized therapeutic by integrating multi-omics profiling with functional drug screening in patient-derived organoids. We show that IDH1 mutant high-grade astrocytomas present distinct therapeutic vulnerabilities compared to glioblastomas, linked to sensitivity to histone deacetylase 3 inhibitors. Within glioblastomas, we identified a distinct MYCN-amplified tumor, sensitive to histone methyltransferase inhibitors. Applying pharmacogenomic approaches using novel drug libraries holds promise for uncovering additional clinically relevant patient subgroups in the future. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/675145v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@12c04f9org.highwire.dtl.DTLVardef@fa8872org.highwire.dtl.DTLVardef@141de6org.highwire.dtl.DTLVardef@b72086_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Comprehensive multi-omics profiling of a healthy human cohort

Multi-omics approaches can offer powerful insights into personalized biomarker profiles relevant for disease diagnosis, prognosis, and therapeutics. However, separating meaningful biological variability from technical noise remains a major challenge. The EATRIS-Plus consortium analyzed blood samples from 127 healthy adults across six omics layers using twelve platforms, resulting in one of the most comprehensive multi-omics profiling datasets of healthy individuals available to date. We applied reproducible workflows to analyze and integrate these data, revealing several key findings. Sex significantly influenced all omics layers, emphasizing the importance of sex-balanced study designs. Age could be accurately predicted using epigenetic clocks, achieving high performance with our high-resolution enzymatic methylation sequencing data (R2 = 0.90), whereas candidate aging biomarkers were identified across all omics layers. The resulting dataset provides reference ranges in healthy individuals for abundance and variability of omics features, enabling robust power analyses, sample size estimations, and benchmarking of multi-omics integration methods. This resource can guide future biomarker discovery and personalized health research and was made FAIR-compliant and publicly available via the ClinData Portal (https://clindata.imtm.cz) and a Zenodo repository (https://doi.org/10.5281/zenodo.17514796).

bioinformatics↗

bioRxiv2023

Glioblastoma-instructed microglia transit to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts

BackgroundA major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials. MethodsHere, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA-sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors. ResultsWe show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components. ConclusionOur data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/531162v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1549a3corg.highwire.dtl.DTLVardef@159f16aorg.highwire.dtl.DTLVardef@1f89500org.highwire.dtl.DTLVardef@fe6b67_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Splice sites obey the power-law during splicing in leukemia cells

Alternative splicing is an essential characteristic of living cells that usually infers a various exon-exon junction governed by different splice sites. The traditional classification based on the mode of use designates splice site to one of the two groups, constitutive or alternative. Here, we considered another criterion and reorganized splice sites into "unisplice" and "multisplice" groups according to the number of undertaken splicing events. This approach provided us with a new insight in the organization and functionality of leukemia cells. We determined features associated with uni- and multisplice sites and found that combinatorics of these sites follows strict rules of the power-law in the t(8;21)-positive leukemia cells. We also found that system splicing characteristics of the transcriptome of leukemia cells remained persistent after drastic changes in the transcript composition caused by knockdown of the RUNX1-RUNX1T1 oncogene. In this work, we show for the first time that leukemia cells possess a sub-set of unisplice sites with a hidden multisplice potential. These findings reveal a new side in organization and functioning of the leukemic cells and open up new perspectives in the study of the t(8;21)-positive leukemia.

bioinformatics↗

bioRxiv2021

ORFhunteR: an accurate approach for the automatic identification and annotation of open reading frames in human mRNA molecules

MotivationModern methods of whole transcriptome sequencing accurately recover nucleotide sequences of RNA molecules present in cells and allow for determining their quantitative abundances. The coding potential of such molecules can be estimated using open reading frames (ORF) finding algorithms, implemented in a number of software packages. However, these algorithms show somewhat limited accuracy, are intended for single-molecule analysis and do not allow selecting proper ORFs in the case of long mRNAs containing multiple ORF candidates. ResultsWe developed a computational approach, corresponding machine learning model and a package, dedicated to automatic identification of the ORFs in large sets of human mRNA molecules. It is based on vectorization of nucleotide sequences into features, followed by classification using a random forest. The predictive model was validated on sets of human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. The developed methods and pre-trained classification model were implemented in a powerful ORFhunteR computational tool that performs an automatic identification of true ORFs among large set of human mRNA molecules. Availability and implementationThe developed open-source R package ORFhunteR is available for the community at GitHub repository (https://github.com/rfctbio-bsu/ORFhunteR), from Bioconductor (https://bioconductor.org/packages/devel/bioc/html/ORFhunteR.html) and as a web application (http://orfhunter.bsu.by).

bioinformatics↗

bioRxiv2020

A comprehensive motifs-based interactome of the C/EBPα transcription factor

The pioneering transcription factor C/EBP coordinates cell fate and cell differentiation. C/EBP represents an intrinsically disordered protein with multiple short linear motifs and extensive post-translational side chain modifications (PTM), reflecting its modularity and functional plasticity. Here, we combined arrayed peptide matrix screening (PRISMA) with biotin ligase proximity labeling proteomics (BioID) to generate a linear, isoform specific and PTM-dependent protein interaction map of C/EBP in myeloid cells. The C/EBP interactome comprises promiscuous and PTM-regulated interactions with protein machineries involved in gene expression, epigenetics, genome organization, DNA replication, RNA processing, and nuclear transport as the basis of functional C/EBP plasticity. Protein interaction hotspots were identified that coincide with homologous conserved regions of the C/EBP family and revealed interaction motifs that score as molecular recognition features (MoRF). PTMs alter the interaction spectrum of multi-valent C/EBP-motifs to configure a multimodal transcription factor hub that allows interaction with multiple co-regulatory components, including BAF/SWI-SNF or Mediator complexes. Combining PRISMA and BioID acts as a powerful strategy to systematically explore the interactomes of intrinsically disordered proteins and their PTM-regulated, multimodal capacity. Key pointsO_LIIntegration of proximity labeling and arrayed peptide screen proteomics refines the interactome of C/EBP isoforms C_LIO_LIHotspots of protein interactions in C/EBP mostly occur in conserved short linear motifs C_LIO_LIInteractions of the BAF/SWI-SNF complex with C/EBP are modulated by arginine methylation and isoform status C_LIO_LIThe integrated experimental strategy suits systematic interactome studies of intrinsically disordered proteins C_LI

molecular biology↗

bioRxiv2020

Primary and recurrent glioma patient-derived orthotopic xenografts (PDOX) represent relevant patient avatars for precision medicine

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

cancer biology↗

bioRxiv2020

XAB2 prevents abortive recombinational repair of replication-associated DNA double-strand breaks and its loss is synthetic lethal with RAD52 inhibition

Unrepaired O6-methylguanine lesions induced by the alkylating chemotherapy agent temozolomide lead to replication-associated single-ended DNA double-strand breaks (seDSBs) that are repaired predominantly through RAD51-mediated homologous recombination (HR). Here, we show that loss of the pre-mRNA splicing and DNA repair protein XAB2 leads to increased temozolomide sensitivity in glioblastoma cells, which reflects abortive HR due to Ku retention on resected seDSBs. XAB2-dependent Ku eviction also occurred at seDSBs generated by the topoisomerase I poison campthotecin and operated in parallel to an ATM-dependent pathway previously described. Although Ku retention elicited by loss of XAB2 did not prevent RAD51 focus formation, the resulting RAD51-ssDNA associations were unproductive, leading to increased engagement of non-homologous-end-joining in S/G2 and genetic instability. Overexpression of RAD51 or the single-stranded DNA annealing factor RAD52 rescued the XAB2 defects. RAD52 depletion led to severe temozolomide sensitivity, whereas a synthetic lethality interaction was observed between RAD52 and XAB2.

molecular biology↗

bioRxiv2019

Reference-free deconvolution of complex DNA methylation data - a detailed protocol

Epigenomic profiling enables unique insights into human development and diseases. Often the analysis of bulk samples remains the only feasible option for studying complex tissues and organs in large patient cohorts, masking the signatures of important cell populations in convoluted signals. DNA methylomes are highly cell type-specific, and enable recovery of hidden components using advanced computational methods without the need for reference profiles. We propose a three-stage protocol for reference-free deconvolution of DNA methylomes comprising: (i) data preprocessing, confounder adjustment and feature selection, (ii) deconvolution with multiple parameters, and (iii) guided biological inference and validation of deconvolution results. Our protocol simplifies the analysis and integration of DNA methylomes derived from complex samples, including tumors. Applying this protocol to lung cancer methylomes from TCGA revealed components linked to stromal cells, tumor-infiltrating immune cells, and associations with clinical parameters. The protocol takes less than four days to complete and requires basic R skills.

bioinformatics↗

bioRxiv2018

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.

genomics↗

bioRxiv2017

Dr.Paso: Drug response prediction and analysis system for oncology research

The prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

bioinformatics↗