Biology subjects

Buytenhuijs, F.

Publications and source records attributed to Buytenhuijs, F..

4 recordsLinked to original sources

bioRxiv2025

Differential T cell receptor gene expression analysis using the Wilcoxon test with similarity-based weighting

To better understand immune responses, comparing the abundance of T cell receptors (TCRs) between conditions can provide insights into which T cells have proliferated or were involved in immune activation. This requires methods that can accurately identify significant differences in TCR-seq data. For conventional RNA-seq data, well-established differential gene expression (DGE) analysis tools such as DESeq2 and edgeR have been developed. However, applying these methods to TCR sequencing (TCR-seq) data presents additional challenges. TCR-seq data is highly sparse, overdispersed, and contains many artificial zeros, which can lead to inflated false-positive rates when using traditional approaches. While non-parametric methods like the Wilcoxon test better control false positives, they may suffer from lower statistical power. To address these issues, we propose a novel pre-processing step for TCR-seq data using network-based local weighting based on TCR sequence similarity. This pre-processing step improves the sensitivity and reduces the false-positive rates of methods like DESeq2 and edgeR while enhancing the power of the Wilcoxon test. Through empirical analysis of both simulated and real datasets, we show that combining our pre-processing step with the Wilcoxon test achieves robust performance, outperforming traditional RNA-seq methods. This simple but powerful approach to TCR-seq analysis could help advance our understanding of adaptive immune responses.

bioinformatics↗

bioRxiv2025

Compartment size and proliferation shape the murine T cell receptor repertoire in splenic T- and B cell zones during an immune response

The adaptive immune system recognizes billions of unique antigens using highly variable T cell receptors (TCRs). To what extent TCR features shape the spatial distribution of the TCR repertoire (TCR-R) within lymphoid organs remains unclear. Here, we compared the TCR-R composition in T cell zones (TCZ), B cell zones (BCZ), and germinal centers (GC) of the spleen by analyzing the complementarity-determining region 3 of the beta chain (CDR3{beta}) in microdissected splenic compartments from naive and immunized mice. While we found substantial differences in TCR-R composition between these compartments, these differences largely disappeared once the effects of compartment size and proliferation were accounted for: During steady state, the few T cells found in BCZs strongly resemble random subsets of those found in TCZs. After immunization, expanded clones started appearing in the TCZ at day 3 and later shifted to the BCZ. GCs harbored even further expanded clones that were grouped into clusters with high sequence similarity. TCR-R differences during the immune response were well explained by a Galton-Watson model of T cell proliferation, suggesting that this could be the central factor shaping the observed TCR-R differences. Similarly, while TCR length and closeness to germline appeared to differ between the compartments, our models of random distribution and proliferation explained these differences almost entirely. Hence, our findings suggest that splenic TCR-Rs are shaped predominantly by compartment size and proliferation rates rather than TCR sequence features. This would imply that quantitative assessment of TCR-R dynamics across heterogeneous spatial compartments during an immune response is only possible when these effects are explicitly accounted for.

immunology↗

bioRxiv2022

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

The T cell receptor (TCR) determines the specificity and affinity for both foreign and self-peptides presented by MHC. It is established that self-pMHC reactivity impacts T cell function, but it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-pMHC reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that predicts self-reactivity directly from TCR{beta} sequences. This approach revealed that n-nucleotide additions and acidic amino acids weaken self-reactivity. We tested our ML predictions of TCR{beta} sequence self-reactivity using retrogenic mice. Extrapolating our analyses to independent datasets, we found high predicted self-reactivity for regulatory CD4+ T cells and low predicted self-reactivity for T cells responding to chronic infection. Our analyses suggest a potential trade-off between repertoire diversity and self-reactivity intrinsic to the architecture of a TCR repertoire.

immunology↗

bioRxiv2021

A Segmentation-Free Machine Learning Architecture for Immune Landscape Phenotyping in Solid Tumors by Multichannel Imaging

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treat-ment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here we introduce the machine learning pipeline "ImmuNet" that identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker. This approach al-lowed us to annotate 34,458 cells. We show that ImmuNet consistently outperforms a state-of-the-art segmentation-based pipeline for multiplex immunohistochemistry analysis across tissue types, cell types and tissue densities, achieving error rates below 5-10% on challenging detection and phenotyping tasks. We externally validate Im-muNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze multiplex tissue images more easily and accurately.

immunology↗