Biology subjects

Nikulina, N.

Publications and source records attributed to Nikulina, N..

8 recordsLinked to original sources

bioRxiv2026

Loss of systemic anti-viral immunity and LMP1-driven suppressive myeloid tumour niches converge to shape the immunobiology of EBV+ diffuse large B-cell lymphoma

Epstein-Barr virus (EBV)-positive diffuse large B-cell lymphoma (EBVDLBCL) is an aggressive lymphoma with poor outcomes and an incompletely understood pathogenesis, frequently attributed to immunosenescence. However, its occurrence across all age groups suggests alternative mechanisms. Here, we integrate functional profiling of peripheral antiviral T-cell immunity with high-dimensional spatial proteomics and mechanistic in vitro modelling to define the immunological landscape of EBVDLBCL. We show that both EBV and EBV-DLBCL patients exhibit broad impairments in antiviral T-cell responses compared with healthy controls, affecting latent and lytic EBV antigens as well as non-EBV viral targets, with deficits most pronounced in EBV patients. Spatial proteomic analysis revealed that EBVDLBCL harbours a profoundly immunosuppressive tumour microenvironment characterised by relative loss of intratumoural CD8 T cells, expansion of PD-1 regulatory and exhausted T-cell populations, and dense aggregates of PD-L1/IDO1 macrophages. Compared with EBV classical Hodgkin lymphoma and infectious mononucleosis, EBV DLBCL displayed the most marked macrophage-associated immunosuppressive signature and the lowest T-cell density. Suppressive myeloid niches were preferentially enriched around LMP1-expressing tumour cells, a feature not observed in the other EBV-associated conditions. Together, these findings indicate that EBVDLBCL is driven by the convergence of systemic antiviral immune dysfunction and an LMP1-dependent suppressive tumour microenvironment.

cancer biology↗

bioRxiv2025

The spatial landscape of infectious mononucleosis tonsils

Primary Epstein-Barr virus (EBV) infection remains incompletely understood, particularly in relation to the nature of in vivo latency patterns and their influence on the immune microenvironment. Here, we present the first multiomic single-cell spatial atlas of palatine tonsils from individuals with infectious mononucleosis (IM). We identify rare EBV infection of epithelial cells, challenging the prevailing assumption that primary infection is restricted to B- and T-cells. Moreover, our detailed analysis suggests a spatiotemporal transition of EBV latency programs, with distinct viral gene expression patterns corresponding to specific tissue microenvironments-a dynamic process previously inferred but not directly mapped in human tissues. We found that cells expressing the EBV-encoded protein LMP1 form an immunosuppressive niche that is rich in IDO1+PDL1+ macrophages and relatively depleted of T-cells. Furthermore, the T-cells that do infiltrate LMP1+ regions exhibit signatures consistent with an activated and cytotoxic phenotype, providing new insights into how LMP1 shapes immune evasion. Taken together, our data redefine the spatial and functional organisation of primary EBV infection, integrating latency plasticity, epithelial tropism, and microenvironmental immunosuppression into a unified framework, as well as providing a data-rich framework for understanding viral persistence and immune evasion.

immunology↗

bioRxiv2025

CellMAPS: A no-code model-based customisable multiplex image analysis workflow

Although multiplex imaging allows simultaneous mapping of complex tissue architectures and cellular phenotypes, the dearth of user-friendly robust image analysis workflows remains a significant limitation to its widespread use, restricting multiplex imaging to laboratories with image analysis expertise. Here, we introduce a no-code model-based customisable suite, CellMAPS, which allows both image processing and spatial analyses by non-specialists. We also developed new tools for tissue de-arraying, image normalisation and cell segmentation to increase usability and reduce analysis subjectivity. We have also introduce new spatial analysis tools for the partition of tissue architectures and cellular microenvironments. We demonstrate the capabilities of CellMAPS in various diseases captured using different multiplex imaging platforms. Overall, CellMAPS provides a platform for inexperienced laboratories to implement multiplex imaging and complex spatial analyses, which ultimately should lead to the broader adoption of these technologies in the biomedical field.

bioinformatics↗

bioRxiv2025

SAME: Topology-flexible transforms enable robust integration of multimodal spatial omics

Spatial omics technologies provide complementary and layered molecular insights that span proteins, transcripts, and metabolites. However, aligning and integrating these modalities across serial tissue sections remains a computational challenge. Existing alignment methods are primarily unimodal and assume preserved topology, often failing with tissue distortions like tears, folds, or anatomical changes. Here, we present SAME (Spatial Alignment of Multimodal Expression) that introduces space-tearing transforms, a framework for controlling localized topological disruptions during cross-sectional alignment. Using integer linear programming to maximize cell type matches across the modalities, we enhance cell-type alignment accuracy by 20% compared to existing methods while preserving biologically meaningful spatial relationships. Applied to protein-RNA integration in healthy tongue tissue and lung adenocarcinoma, SAME revealed cryptic immune subpopulations that were otherwise missed by RNA-only or protein-only classification. In a separate lung adenocarcinoma study, we assayed and integrated protein and metabolomic profiles, uncovering localized mevalonic acid upregulation specifically within tumor-macrophage spatial niches and identifying targeted metabolic crosstalk invisible to single-modality approaches. SAME enables unprecedented experimental designs that leverage each modality independently while computationally recovering cross-modal spatial structure, unlocking multimodal discoveries in complex tissues.

bioinformatics↗

bioRxiv2023

Single-cell Spatial Metabolic and Immune Phenotyping of Head and Neck Cancer Tissues Identifies Tissue Signatures of Response and Resistance to Immunotherapy

Head and neck squamous cell carcinomas (HNSCC) are the seventh most common cancer and represent a global health burden. Immune checkpoint inhibitors (ICIs) have shown promise in treating recurrent/metastatic cases, with durable benefit in [~]30% of patients. Current biomarkers for head and neck tumors are limited in their dynamic ability to capture tumor microenvironment (TME) features, with an increasing need for deeper tissue characterization. Therefore, new biomarkers are needed to accurately stratify patients and predict responses to therapy. Here, we have optimized and applied an ultra-high plex, single-cell spatial protein analysis in HNSCC. Tissues were simultaneously analyzed with a panel of 101 antibodies that targeted biomarkers related to tumor immune, metabolic and stress microenvironments. Our data uncovered a high degree of intra-tumoral heterogeneity intrinsic to head and neck tumors and provided unique insights into the biology of the tumor. In particular, a cellular neighborhood analysis revealed the presence of 6 unique spatial tumor-immune neighborhoods enriched in functionally specialized immune cell subsets across the patient tissue. Additionally, functional phenotyping based on key metabolic and stress markers identified four distinct tumor regions with differential protein signatures. One tumor region was marked by infiltration of CD8+ cytotoxic T cells and overexpression of BAK, a proapoptotic regulator, suggesting strong immune activation and stress. Another adjacent region within the same tumor had high expression of G6PD and MMP9, known drivers of tumor resistance and invasion respectively. This dichotomy of immune activation-induced death and tumor progression in the same sample demonstrates the heterogenous niches and competing microenvironments that underpin clinical responses of therapeutic resistance. Our data integrate single-cell ultra-high plex spatial information with the functional state of the tumor microenvironment to provide insights into a partial response to immune checkpoint inhibitor therapy in HNSCC. We believe that the approach outlined in this study will pave the way towards a new understanding of TME features associated with response and sensitivity to ICI therapies.

cancer biology↗

bioRxiv2023

A Machine Learning One-Class Logistic Regression Model to Predict Stemness in Single Cell Transcriptomics and Spatial Omics Datasets

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches are often biased and manually curated. Here we harness an existing stemness model for assessing oncogenic states to transform its application to single cell and spatial omic datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic or proteomic features from non-transformed stem cells to identify dedifferentiated cell states. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. Finally these stemness indices are applicable across a variety of spatial transcriptomic and proteomic technologies for the identification of oncogenic cell types in the tumor microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/539461v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@f2973forg.highwire.dtl.DTLVardef@a7aa9corg.highwire.dtl.DTLVardef@1b1ea27org.highwire.dtl.DTLVardef@183b4a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

A spatially resolved single cell genomic atlas of the adult human breast

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

genomics↗

bioRxiv2022

Multiplex imaging of human induced pluripotent stem cell-derived neurons with CO-Detection by indEXing (CODEX) technology

BackgroundHuman induced pluripotent stem cell (iPSC) models have been hailed as a breakthrough for understanding disease and developing new therapeutics. The major advantage of iPSC-derived neurons is that they carry the genetic background of the donor, and as such could be more predictive for clinical translation. However, the development of these cell models is time-consuming and expensive and it is thus critical to maximize biomarker readout from every model that is developed. One option is to use a highly multiplexed biomarker imaging assay, like CO-Detection by indEXing (CODEX), which allows detection of 50+ targets in situ at single-cell resolution. New MethodThis paper describes the development of CODEX in neuronal cell cultures derived from human iPSCs. ResultsWe differentiated human iPSCs into mixed neuronal and glial cultures on glass coverslips. We then developed and optimized a panel of 21 antibodies to phenotype iPSC-derived neuronal subtypes of cortical, dopaminergic, and striatal neurons, as well as astrocytes, and pre-and postsynaptic proteins. Comparison with existing methodsCompared to standard immunocytochemistry, CODEX oligoconjugated fluorophores circumvent antibody host interactions and allow for highly customized multiplexing. ConclusionWe show that CODEX can be applied to iPSC neuronal cultures and developed fixation and staining protocols for the neurons to sustain the multiple wash-stain cycles of the technology. Furthermore, we demonstrate both cellular and subcellular resolution imaging of multiplexed biomarkers in the same samples. CODEX is a powerful technique that complements other single-cell omics technologies for in-depth phenotype analysis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/479039v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@68d49dorg.highwire.dtl.DTLVardef@5ea424org.highwire.dtl.DTLVardef@16a763forg.highwire.dtl.DTLVardef@7950fc_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOC_FLOATNO CODEX(R) Multiplex Imaging in human iPSC neurons [A-D] Schematic drawings of the tools and steps used for Co-Detection by indEXing (CODEX) imaging. [A] Target-specific antibodies are conjugated to unique DNA oligonucleotide barcodes. Fluorescent reporter (excitation wavelength at 488 nm, 550 nm, or 647 nm/Cy5) carrying the complementary DNA (to the barcode) enables barcode-specific binding of the reporter to the antibody and detection by fluorescence microscopy. [B] Neuronal cell cultures are prepared for the CODEX staining and imaging by several fixation steps with different PFA concentrations followed by incubation with 100% acetone. Residual acetone is removed by drying the sample. After rehydration with PBS, autofluorescence is quenched by exposure to broad-spectrum LED light. Following a pre-staining fixation step, the sample is incubated with a mix of all conjugated primary antibodies. Excessive, unbound antibodies are removed by a washing step, leaving only the bound antibodies followed by a final post-staining fixation. [C] The CODEX Instrument Manager performs the multicycle run and controls the microscope software for automated addition of reporters, imaging, and washing of the samples (pre-stained with primary antibodies) to remove reporters from each cycle. After imaging, bound reporters are removed without damaging the tissue using a solvent, and the next set of reporters (conjugated to different barcodes) are added. [D] CODEX(R) Processor processes raw files and performs stitching, deconvolution, background subtraction, and cell segmentation. The processed images can be viewed and analyzed with the CODEX(R) Multiplex Analysis Viewer (MAV) plugin using Fiji software. C_FIG

neuroscience↗