Biology subjects

Sondergaard, J. N.

Publications and source records attributed to Sondergaard, J. N..

4 recordsLinked to original sources

bioRxiv2026

A multimodal framework for immune cell annotation across blood and tissue single-cell data

AbstractAccurate cell annotation remains a central challenge in single-cell analysis, particularly when datasets contain rare populations, transitional states, and tissue-adapted phenotypes. We previously developed scODIN as an expert-guided framework for immune cell annotation in single-cell RNA-sequencing data. Here, we present pyODIN, a major extension of this framework for Python. pyODIN supports multimodal annotation using RNA, antibody-derived tag (ADT), or combined RNA-ADT information. It also substantially expands the annotation database from a CD4 T-cell-centred framework to a broader cell-type reference resource spanning major and minor peripheral blood cell populations as well as tissue-associated subsets. We benchmarked pyODIN against CellTypist, MMoCHi and HiCAT using independently curated immune reference datasets. pyODIN demonstrated the highest overall classification accuracy, and at the top-lineage level, pyODIN reduced cross-lineage errors relative to the benchmark methods. At finer resolution, pyODIN preserved substantially greater CD4 T-cell subtype structure than CellTypist and HiCAT, resolving regulatory, follicular, helper, memory, and cytotoxic states that were collapsed into broader categories by the comparator methods. In a controlled marker-ablation experiment, pyODIN outperformed MMoCHi when key lineage-defining RNA markers were removed from the expression feature set, and the addition of ADT information restored NK and CD8 T-cell annotation, demonstrating the value of multimodal annotation when transcript-level evidence is incomplete. Finally, application to liver fine-needle aspirate data showed that the expanded framework supports annotation beyond PBMC datasets. Together, pyODIN provides an expert-guided, adaptable cell annotation framework for Python, designed for multimodal cell phenotyping across blood and tissue single-cell datasets. pyODIN is freely available at https://github.com/SondergaardLab/pyODIN.

immunology↗

bioRxiv2025

Optimized Detection and Inference of Names in scRNA-seq data

Accurate identification of immune cell subsets in single-cell (sc)RNA-seq data is critical for understanding immune responses in autoimmune diseases, infections, and cancer. One caveat of scRNA-seq is the inability to properly assign rare immune cell subsets due to gene dropout events. To circumvent this caveat, we here developed Optimized Detection and Inference of Names in scRNA-seq data (scODIN). scODIN uses an informed holistic two-step approach combining expert knowledge with machine learning to rapidly assign cell type identities to large scRNA-seq dataset. First, scODIN uses key lineage-defining markers to identify a set of core cell types. Second, scODIN compensates for dropout events by integrating a k-nearest neighbors (kNN) algorithm. We additionally programmed scODIN to detect dual and transitional phenotypes, which are usually overlooked in conventional analyses. Consequently, scODIN may enhance our understanding of immune cell heterogeneity and provides comprehensive insights into immune regulation, with broad implications for immunology and personalized medicine.

systems biology↗

bioRxiv2022

Control of NK cell tolerance in MHC class I-deficiency by regulated SHP-1 localization to the activating immune synapse

Signaling via inhibitory KIR/Ly49 receptors preserves natural killer (NK) cell self-tolerance but also conveys NK cell reactivity towards MHC class-I low target cells in an education process. Here, we demonstrate that mouse NK cell education by H-2Dd regulates transcription of several genes in Ly49A+ NK cells including Ptpn6, encoding the phosphatase SHP-1. SHP-1 was highly expressed in uneducated NK cells, in which knock-out of Ptpn6 increased responsiveness. Following NKp46 triggering of uneducated NK cells, a higher synaptic abundance of phosphorylated SHP-1 was found relative to educated NK cells, concomitant with reduced phosphorylation of several signaling molecules, including PLC-g2, SLP-76, ZAP70/Syk and ERK1/2. SHP-1 overlapped extensively with F-actin and SLP-76 in the uneducated activating synapse of Ly49A+ NK cells, whereas a greater association between Ly49A and SHP-1 was observed in educated NK cells. Thus, our results indicate that in addition to transcriptional regulation, a distinct SHP-1 patterning in NK cell activating synapses can determine their tolerance.

immunology↗

bioRxiv2021

CRISPR/Cas9 deletions induce adverse on-target genomic effects leading to functional DNA in human cells

The CRISPR/Cas9 system is widely used to permanently delete genomic regions by inducing double-strand breaks via dual guide RNAs. However, on-target consequences of Cas9 deletion events have yet to be fully investigated. To characterize Cas9-induced genotypic abnormalities in human cells, we utilized an innovative droplet-based target enrichment approach followed by long-read sequencing and coupled it to a customized de novo sequence assembly. This approach enabled us to dissect the sequence content at kilobase scale within an on-target genomic locus. We here describe extensive genomic disruptions by Cas9, involving a genomic duplication and inversion of the target region as well as integrations of exogenous DNA and interchromosomal DNA fragment rearrangements at the double-strand break sites often at the same time. Although these events altered the genomic composition of the on-target region, we found that the aberrant DNA fragments are still functional, marked by active histones and bound by RNA polymerase III. In HAP1 cells, the integration of the target-derived fragments accelerated cell proliferation in deletion clones. Our findings broaden the consequential spectrum of the Cas9 deletion system, reinforce the necessity of meticulous genomic validations and rationalize extra caution when interpreting results from a deletion event.

molecular biology↗