Search bioRxiv⌕ Search

Biology subjects

Kitanovski, S.

Publications and source records attributed to Kitanovski, S..

7 recordsLinked to original sources

Uncertainty-aware quantitative analysis of the structure and dynamics of T cell receptor repertoires

Diversity and dynamics of immune cell receptor repertoires (IRRs) are two factors at the functional heart of adaptive immunity that together make IRRs difficult to grasp. Moreover, measurements are compounded by various sources of experimental noise. Here we propose a computational framework (ClustIRR) for uncertainty-aware quantitative analysis of IRR structure and dynamics. ClustIRR maps multiple IRRs across replicates, time points, or conditions onto a joint graph induced by immune receptor sequence similarity. It then detects communities on the joint graph (CJs). Based on CJs as reference structures across IRRs, ClustIRR then performs quantitative Bayesian analyses of differential CJ occupancy. Additionally, ClustIRR integrates single-cell gene expression data to link community expansion with transcriptional activation signatures. We demonstrate the capabilities of ClustIRR with the joint analysis of multiple T cell receptor repertoires in several example applications: (1) quantitative changes due to antigen challenge, (2) longitudinal dynamics during cancer immunotherapy, (3) V(D)J recombination biases in human vs murine repertoires that pre-adapt IRRs for pathogen responses. ClustIRR is freely available as open source software from the bioconductor repository.

immunology↗

Spatial Compartmentalization of TCR Repertoires Between Primary Melanomas and Sentinel Lymph Nodes Reveals Distinct Clonal Architectures and Shared Antigen Recognition

Primary tumors and their sentinel lymph nodes are functionally linked sites of anti-tumor immunity, yet how T cell receptor (TCR) repertoires are organized across these compartments remains incompletely understood. We thus performed TCR{beta} sequencing on paired primary melanoma tumors and sentinel lymph nodes from 24 treatment-naive patients to quantify TCR repertoire diversity and clonal architecture as markers of antigen-driven selection. Primary tumors exhibited markedly reduced TCR diversity and pronounced clonal dominance compared with matched lymph nodes, consistent with selective expansion of tumor-reactive T cells. Tumor-associated clonotypes displayed significantly longer CDR3 sequences driven by increased non-templated nucleotide insertions, a feature associated with neoantigen recognition, and showed biased TRBV and TRBJ gene usage indicative of CD8+ T cell enrichment. Annotation against known melanoma differentiation and cancer testis antigens identified only a small fraction of clonotypes with characterized specificities, and fewer than 10% of these were shared between tumors and lymph nodes, suggesting that most expanded tumor clonotypes recognize patient-specific antigens. To identify shared immune features beyond individual clonotypes, we applied ClustIRR to build a joint graph linking clonotypes across repertoires by CDR3 sequence similarity and detected Communities on the Joint graph (CJs) to uncover recurrent TCR sequence motifs. Differential occupancy analysis identified distinct CJs segregating tumors and lymph nodes, including a limited number of recurrent CJs targeting MART-1 epitopes, while the majority were patient-specific. Together, these data define spatially structured TCR repertoire architectures in human melanoma and establish a scalable framework for interrogating tumor-immune interactions. eLife DigestMelanoma is a skin cancer harboring many UV-associated mutations, making it recognizable to the immune system. T cells attack cancer using specialized molecular structures called T cell receptors (TCRs). They are frequently referred to as specific keys that only fit into the respective locks, i.e. HLA molecules presenting the target antigen. Surgeons often remove both the tumor and the tumor draining lymph node (aka sentinel lymph node). The tumor is where T cells directly fight cancer, while the sentinel lymph node is where new anti-tumor T cells are first activated. How these two sites coordinate immune surveillance is not completely understood. Here, Kitanovski and colleagues sequenced TCRs from paired tumors and sentinel lymph nodes obtained from melanoma patients. These analyses demonstrated that primary tumors have dramatically reduced TCR diversity, dominated by a few massively expanded T cell clones, whereas sentinel lymph nodes contain broad, balanced repertoires. This reveals intense local selection within the tumor-a small set of tumor-specific T cells expand repeatedly while the lymph node preserves diverse immune reserves. Examining TCR structure revealed a second key difference. Tumor-infiltrating T cells possess on average slightly longer CDR3 loops-the region contacting antigens-especially among dominant clones. These longer loops arise from random nucleotide insertions. Prior work shows that self-antigen-specific T cells have shorter CDR3s due to thymic constraints, whereas T cells recognizing tumor-specific mutation-derived neoantigens escape these restrictions and have longer CDR3s. Thus, tumor TCRs appear tailored for recognizing patient-specific neoantigens. The authors could only use TCRs matching known databases for shared melanoma antigens like MART-1 to identify TCR-specific epitopes; most tumor-expanded TCRs could not be annotated because patient-specific neoantigens are not in public databases. This limitation itself is informative- most expanded tumor TCRs likely target private neoantigens. Grouping TCRs by sequence similarity into "communities" revealed striking patterns: sentinel lymph nodes showed conserved community composition across patients, consistent with responses to common viruses, while tumor repertoires were highly individualized, reflecting each tumors unique mutations. These findings establish how anti-melanoma immunity is spatially organized: sentinel lymph nodes contain diverse T cell landscapes, while in primary tumors melanoma-infiltrating T cells appear to have undergone intense selection for neoantigen recognition. This provides a foundation for using TCR features as biomarkers and designing personalized T cell therapies.

cancer biology↗

Uncertainty-aware quantitative analysis of high-throughput live cell migration data

Accurate quantification of cell migration velocity is essential for understanding biological processes such as development, immune function, and cancer metastasis. High-throughput migration assays generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing uncertainty into velocity estimates that current methods often fail to quantify. To address this, we present cellmig, a computational tool using Bayesian hierarchical modeling to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity. cellmig provides a robust framework for analyzing migration assays, including dose-response studies and large-scale screens with biological and technical replicates. By modeling biological variability and technical confounders within a unified Bayesian framework, it estimates condition-specific effects (e.g., drug effects) on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls of null-hypothesis testing. Its generative models simulate migration under various assumptions, aiding experimental planning. Overall, cellmig improves reproducibility and comparability across studies, offering deeper biological insight.

bioinformatics↗

scBubbletree: quantitative visualization of single cell RNA-seq data

MotivationVisualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. ResultsWe present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. Availability and Implementationthe R package scBubbletree is freely available at: https://bioconductor.org/packages/scBubbletree/ Contactsimo.kitanovski@uni-due.de, daniel.hoffmann@uni-due.de

bioinformatics↗

Increasing information gain in animal research by improving statistical model accuracy

Reduction of the numbers of laboratory animals is one of the three pillars of ethical animal research. Equivalently, information gain per animal should be maximized. A road towards this goal that is barely taken in current animal research is the more accurate statistical modeling of experiments. Here we show for a typical experiment ("open field test") with outcomes that are non-normally distributed count data, how this can be implemented and what information gain is achieved. We contrast the state of the art - the use of confidence intervals based on null-hypothesis significance testing (NHST) -, with a Bayesian approach with the same underlying normal model, and a Bayesian approach with a more accurate negative binomial model. We find that the more accurate model leads to a marked improvement of knowledge gained with the experiment, especially for small sample sizes. As experimental data that violate assumptions of simple, conventional models are frequent, our findings have wider implications.

bioinformatics↗

Comprehensive comparison of transcriptomes in SARS-CoV-2 infection: alternative entry routes and innate immune responses

The pathogenesis of COVID-19 emerges as complex, with multiple factors leading to injury of different organs. Several studies on underlying cellular processes have produced contradictory claims, e.g. on SARS-CoV-2 cell entry or innate immune responses. However, clarity in these matters is imperative for therapy development. We therefore performed a meta-study with a diverse set of transcriptomes under infections with SARS-CoV-2, SARS-CoV and MERS-CoV, including data from different cells and COVID-19 patients. Using these data, we investigated viral entry routes and innate immune responses. First, our analyses support the existence of cell entry mechanisms for SARS and SARS-CoV-2 other than the ACE2 route with evidence of inefficient infection of cells without expression of ACE2; expression of TMPRSS2/TPMRSS4 is unnecessary for efficient SARS-CoV-2 infection with evidence of efficient infection of A549 cells transduced with a vector expressing human ACE2. Second, we find that innate immune responses in terms of interferons and interferon simulated genes are strong in relevant cells, for example Calu3 cells, but vary markedly with cell type, virus dose, and virus type.

bioinformatics↗

Epigenetic mechanisms mediating cell state transitions in chondrocytes.

Epigenetic modifications play critical roles in regulating cell lineage differentiation, but the epigenetic mechanisms guiding specific differentiation steps within a cell lineage have rarely been investigated. To decipher such mechanisms, we used the defined transition from proliferating (PC) into hypertrophic chondrocytes (HC) during endochondral ossification as a model. We established a map of activating and repressive histone modifications for each cell type. ChromHMM state transition analysis and Pareto-based integration of differential levels of mRNA and epigenetic marks revealed that differentiation associated gene repression is initiated by the addition of H3K27me3 to promoters still carrying substantial levels of activating marks. Moreover, the integrative analysis identified genes specifically expressed in cells undergoing the transition into hypertrophy. Investigation of enhancer profiles detected surprising differences in enhancer number, location, and transcription factor binding sites between the two closely related cell types. Furthermore, cell type-specific upregulation of gene expression was associated with a shift from low to high H3K27ac decoration. Pathway analysis identified PC-specific enhancers associated with chondrogenic genes, while HC-specific enhancers mainly control metabolic pathways linking epigenetic signature to biological functions.

developmental biology↗