Biology subjects

Kristensen, N. P.

Publications and source records attributed to Kristensen, N. P..

5 recordsLinked to original sources

bioRxiv2025

ITRAP2, a flexible and robust strategy to assign antigen recognition of T-cells in a coupled single-cell TCR-pMHC assay

Determining T-cell specificity forms a crucial step toward understanding T-cell involvement in health and disease. Single-cell sequencing technologies allow for co-capture of TCR alpha and beta chains, and their antigen specificity can be determined through peptide-MHC (pMHC) multimer binding and capture of a co-attached barcode oligo. However, SC sequencing often includes a high level of dropouts and risk of cross-contamination. Similarly, barcoded pMHC readouts often suffer from significant background noise. These issues complicate the automatic assignment of pMHC recognition to TCR clonotypes. To overcome these challenges, we developed a method for data denoising - Improved T-cell Receptor Antigen Paring 2 (ITRAP2). This approach significantly reduces noise in single-cell pMHC readouts and allows for accurate identification of TCR specificity. ITRAP2 incorporates statistical tests and confidence metrics for each TCR-pMHC pairing, offering user flexibility in pairing rigor, and allows multiple pMHC assignments to the same T-cell clone in the event of cross-binding within the pMHC multimer library. We tested this method on an in-house generated dataset of 8141 single cells, screened for CD8 T-cell binding using a panel of 100 different barcode-labelled pMHC multimers holding virus-derived peptides, and on a larger public dataset from 10x Genomics with 208,589 T-cells evaluated for recognition using a panel of 50 different pMHCs. In both datasets, ITRAP2 was able to recover TCR-pMHC hits that were missed either when investigating the raw data or analyzing the data using alternative tools. Importantly, we demonstrate that the size of the pMHC multimer library is crucial for accurate pMHC-TCR pairing and that a minimum of 25 pMHC multimer should be included to optimally determine background characteristic, and assigning true positive events.

bioinformatics↗

bioRxiv2025

Mapping of CD8 T-cell recognition to latent EBV infection and neuroantigens reveals HLA-specific depletion of T-cell responses in multiple sclerosis

Multiple sclerosis (MS) is a complex autoimmune disease with an unclear contribution from antigen-specific CD8 T cells. To enumerate adaptive immune responses associated with MS and to identify MS-related epitopes recognized by CD8 T cells, we evaluated T-cell recognition of 1,158 predicted major histocompatibility complex (MHC)-binding peptides derived from latent EBV antigens (laEBVs) and selected neuroantigens (NAgs) using DNA barcode-labelled pMHC multimers. HLA-B*07:02+ individuals living with relapsing-remitting MS (RRMS) and progressive MS (PMS) were characterized by a relative reduction in EBNA6-specific and MAG-specific CD8 T cells compared to healthy donors. Furthermore, both RRMS and PMS patients exhibited a delayed expansion of CMV- and laEBV-specific CD8 T cells compared to healthy donors indicating an age-specific interaction with disease. Female RRMS patients were moreover characterized by a specific reduction of laEBV-specific CD8 T cells compared to male RRMS patients. NAg-specific CD8 T cells recognized heterogeneous peptides, were of low cellular frequency in peripheral blood, and exhibited a circulating, naive-like, anergic phenotype. In contrast, virus-specific CD8 T cells were characterized by high-frequency cellular responses with an effector-like, tissue-homing phenotype. These immunophenotypes were largely shared between MS and healthy donors. Collectively, our data provide novel insights into CD8 T-cell recognition of both NAg and laEBV antigens in MS and constitute a key reference map of epitopes for further immunomonitoring of EBV- and self-specific CD8 T cells in health and disease.

immunology↗

bioRxiv2025

TCR activation impairs CAR-T cytotoxicity against separate target cells

Chimeric antigen receptor T cells (CAR-T) are effective therapeutics against cancer and autoimmunity, but whether the endogenous T-cell receptor (TCR) is beneficial, detrimental or irrelevant for CAR-T function and patient outcome remains unclear. We here traced anti-CD19 CAR-T clonotypes in patients with B-cell malignancies pre- and post-infusion using single-cell RNA-, TCR-, and CITE-sequencing. A cytotoxic phenotype, but not CAR-mediated in vitro reactivity to tumor cells, predicted CAR-T persistence. To test the functional impact of endogenous TCR activity on CAR-T behavior, we combined CAR transduction with orthotopic TCR replacement. This revealed that TCR signaling adds to activation of CAR-T cells, but gradually compromises CAR-mediated cytotoxicity, when TCR and CAR antigens are presented by different target cells. Therefore, spatial antigen separation alters TCR/CAR interplay with implications for therapeutic CAR-T design.

immunology↗

bioRxiv2024

A novel sequential sampling algorithm for the application of mechanistic models to species occurrence patterns

AimSpecies occurrence patterns are typically analysed using data-randomisation approaches, which reveal when observed patterns deviate from random expectation, but give little insight why. When non-randomness is detected, the analysis reaches a dead end. Mechanistic models, such as neutral models, offer an alternative: when their predictions fail to match data, the specific nature of each mismatch can implicate candidate mechanisms, turning null-model rejection into a diagnostic process. However, mechanistic models can be computationally expensive. Here, we use an efficient method to simulate such models and explore possible mechanisms governing the occurrence patterns of birds on islands. LocationRiau archipelago, Indonesia. TaxonBirds. MethodsWe used species richness and island-area data to fit a niche-neutral model, where species obey neutral dynamics within non-overlapping discrete niches. We used a sequential sampling algorithm that can efficiently sample presence-absence matrices under the niche-neutral model, and used mismatches to identify which mechanisms were potentially important to occurrence patterns. In particular, we compared model to observed data using standardised effect sizes on segregation (C-score) and nestedness (NODF) metrics. ResultsBirds were more segregated and less nested than expected from both data randomisation and the niche-neutral model. Further, while the niche-neutral model reproduced the mean relationship between island size and species richness, it could not produce sufficient variability to account for richness variation across islands. However, while the niche-neutral model was rejected as a null, it was possible to reproduce the species-occurrence patterns by allowing niche diversity and per-capita immigration rate to vary across islands, which increased segregation and decreased nestedness, respectively. Main conclusionWhile the species-area relationship could be explained by a model with constant per-capita immigration rates and number of niches across islands, inter-island heterogeneity was needed to explain species-occurrence patterns. Unlike data randomisation, which would have identified the patterns as non-random but offered no further insight, the mechanistic approach identified habitat diversity and immigration-rate variation as candidate mechanisms, demonstrating the diagnostic value of using niche-neutral models as an exploratory framework. The sequential sampling algorithm allowed us to explore different scenarios efficiently and may be useful for identifying potential mechanisms structuring patterns in other systems.

ecology↗

bioRxiv2023

Simultaneous analysis of pMHC binding and reactivity unveils virus-specific CD8 T cell immunity to a concise epitope set

Knowledge of widely recognized T-cell epitopes against common virus infections are vital for immune monitoring and characterization of relevant antigen-specific CD8 T cells and their antigen receptors. We therefore aimed to establish a concise and validated epitope panel for monitoring human virus-specific immunity complete with data on both prevalence of recognition and reactivity in humans. To achieve this, we first establish TCR downregulation, and loss of peptide major histocompatibility (pMHC) multimer-binding, as an early and sensitive marker of T cell reactivity after peptide stimulation. We next applied TCR downregulation in a high-throughput assay by monitoring binding, and loss of binding (i.e. reactivity), to libraries of DNA-barcode labelled pMHC multimers in paired unstimulated/stimulated samples. This novel method allowed us to access T-cell responses in 48 donors towards 929 epitopes recorded in the Immune Epitope Database (IEDB) encompassing 29 virus common infections and 25 different HLA alleles. This yielded a concise panel of 137 virus epitopes, many of which were underrepresented in the public domain, recognized by T cells in peripheral blood. 84% of these epitopes exhibited prevalent reactivity to peptide stimulation, which was associated with effector and long-term memory phenotypes. Conversely, non-reactive responses correlated with naive and immunosenescence phenotypes. This study represents the largest effort to unbiasedly assess T-cell recognition and reactivity to common virus infections in healthy individuals providing a minimal epitope panel for monitoring adaptive immune responses in humans. Significance StatementCD8 T-cell epitopes are widely available in public databases yet many are not recognized in the general population. Here we undertook an exhaustive screening process using "state-of-the-art" methods to assess both T-cell recognition and reactivity against common virus infections, which holds significant implications for shaping T-cell immunity and disease protection. We identify 137 commonly recognized epitopes from common virus infections to which T cell responses are expected to occur in human donors. Importantly, several of the verified epitopes were underreported in public databases compared to their observed prevalence of recognition and high cellular frequency making this an important reference dataset and resource for immunologists studying antigen-specific T cells across different immunopathologies and contexts including autoimmunity, infectious disease and cancer immunotherapy.

immunology↗