Dataset Information

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery.

ABSTRACT: Effective, unbiased, high-throughput methods to functionally identify both class II and class I HLA-presented T cell epitopes and their cognate T cell receptors (TCRs) are essential for and prerequisite to diagnostic and therapeutic applications, yet remain underdeveloped. Here, we present T-FINDER [T cell Functional Identification and (Neo)-antigen Discovery of Epitopes and Receptors], a system to rapidly deconvolute CD4 and CD8 TCRs and targets physiologically processed and presented by an individual's unmanipulated, complete human leukocyte antigen (HLA) haplotype. Combining a highly sensitive TCR signaling reporter with an antigen processing system to overcome previously undescribed limitations to target expression, T-FINDER both robustly identifies unknown peptide:HLA ligands from antigen libraries and rapidly screens and functionally validates the specificity of large TCR libraries against known or predicted targets. To demonstrate its capabilities, we apply the platform to multiple TCR-based applications, including diffuse midline glioma, celiac disease, and rheumatoid arthritis, providing unique biological insights and showcasing T-FINDER's potency and versatility.

SUBMITTER: Cetin M

PROVIDER: S-EPMC10836725 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery.

Cetin Miray M Pinamonti Veronica V Schmid Theresa T Boschert Tamara T Mellado Fuentes Ana A Kromer Kristina K Lerner Taga T Zhang Jing J Herzig Yonatan Y Ehlert Christopher C Hernandez-Hernandez Miguel M Samaras Georgios G Torres Claudia Maldonado CM Fisch Laura L Dragan Valeriia V Kouwenhoven Arlette A Van Schoubroeck Bertrand B Wils Hans H Van Hove Carl C Platten Michael M Green Edward W EW Stevenaert Frederik F Felix Nathan J NJ Lindner John M JM

Science advances 20240202 5

Effective, unbiased, high-throughput methods to functionally identify both class II and class I HLA-presented T cell epitopes and their cognate T cell receptors (TCRs) are essential for and prerequisite to diagnostic and therapeutic applications, yet remain underdeveloped. Here, we present T-FINDER [T cell Functional Identification and (Neo)-antigen Discovery of Epitopes and Receptors], a system to rapidly deconvolute CD4 and CD8 TCRs and targets physiologically processed and presented by an ind ...[more]

PMID: 38306432

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Project description:Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is of significant importance in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates the efficient prioritization of samples for subsequent, costly analyses such as short-read and long-read whole genome sequencing. We focus on five genes-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), alpha-Synuclein (SNCA), and Amyloid Beta Precursor Protein (APP)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using both short-read and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied sparsity, noise, and size. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

Dataset Information

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery.

Publications

T-FINDER: A highly sensitive, pan-HLA platform for functional T cell receptor and ligand discovery.

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