Biology subjects

Povlsen, H. R.

Publications and source records attributed to Povlsen, H. R..

4 recordsLinked to original sources

bioRxiv2026

GxP Single-cell RNA-seq and Spatial Transcriptomics end-to-end pipeline for clinical research

Single-cell/nucleus RNA-sequencing and Spatial Transcriptomics are powerful tools for investigating cellular heterogeneity and tissue architecture that have deepened our disease understanding. Their broader adoption in clinical and regulated settings, however, is hindered by challenges related to data integrity, regulatory compliance, reproducibility, and scalability. To address this gap, we developed NNclinSSOAP (Novo Nordisk Clinical Single-cell Spatial Omics Analytical Pipeline) - a modular, GxP-ready end-to-end computational pipeline, that combines established single-cell workflows with a new Nextflow pipeline for Spatial Transcriptomics. NNclinSSOAP transforms RNA sequencing and Xenium spatial data into integrated, annotated single-cell objects and spatially resolved tissue maps. Designed to support mechanistic studies and clinical endpoint generation, it enables traceable and reproducible processing of large-scale datasets, scalable for both local and HPC environments. Here, we provide a step-by-step guide for using NNclinSSOAP. All code and data are publicly available. Using a standard laptop, the pipeline can be executed within 1.5 hours.

bioinformatics↗

bioRxiv2023

Data-driven filtering for denoising of TCRpMHC single-cell data: a benchmark

Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality by optimizing signal yield from high throughput studies of TCRpMHC-specificity is paramount in increasing our understanding of T cell-mediated immunity.

bioinformatics↗

bioRxiv2022

ATRAP - Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells

Novel single-cell based technologies hold the promise of matching T cell receptor (TCR) sequences with their cognate peptide-MHC recognition motif in a high-throughput manner. Parallel capture of TCR transcripts and peptide-MHC is enabled through the use of reagents labeled with DNA barcodes. However, analysis and annotation of such single-cell sequencing (SCseq) data is challenged by dropout, random noise, and other technical artifacts that must be carefully handled in the downstream processing steps. We here propose a rational, data-driven method termed ATRAP (Accurate T cell Receptor Antigen Paring) to deal with these challenges, filtering away likely artifacts, and enable the generation of large sets of TCR-pMHC sequence data with a high degree of specificity and sensitivity, thus outputting the most likely pMHC target per T cell. We have validated this approach across 10 different virus-specific T cell responses in 16 healthy donors. Across these samples we have identified up to 1494 high-confident TCR-pMHC pairs derived from 4135 single-cells.

immunology↗

bioRxiv2020

SARS-CoV-2 genome-wide mapping of CD8 T cell recognition reveals strong immunodominance and substantial CD8 T cell activation in COVID-19 patients

To understand the CD8+ T cell immunity related to viral protection and disease severity in COVID-19, we evaluated the complete SARS-CoV-2 genome (3141 MHC-I binding peptides) to identify immunogenic T cell epitopes, and determine the level of CD8+ T cell involvement using DNA-barcoded peptide-major histocompatibility complex (pMHC) multimers. COVID-19 patients showed strong T cell responses, with up to 25% of all CD8+ lymphocytes specific to SARS-CoV-2-derived immunodominant epitopes, derived from ORF1 (open reading frame 1), ORF3, and Nucleocapsid (N) protein. A strong signature of T cell activation was observed in COVID-19 patients, while no T cell activation was seen in the non-exposed and high exposure risk healthy donors. Interestingly, patients with severe disease displayed the largest T cell populations with a strong activation profile. These results will have important implications for understanding the T cell immunity to SARS-CoV-2 infection, and how T cell immunity might influence disease development.

immunology↗