Biology subjects

Lund-Andersen, C.

Publications and source records attributed to Lund-Andersen, C..

4 recordsLinked to original sources

bioRxiv2026

Single-cell immune repertoire atlas maps coordinated circulating adaptive immune states in inflammatory bowel disease

Single-cell studies have defined immune states in inflammatory bowel disease (IBD), but how adaptive receptor histories organize circulating immunity remains unclear. We generated a single-cell transcriptomic atlas of peripheral blood from 249 participants with Crohn's disease, ulcerative colitis, or non-IBD control status, including 182 with productive TCR and BCR recovery. Expanded TCR clonotypes marked inflammatory-memory and cytotoxic states, while distinct but similar paired TCRs shared inflammatory programs across participants. BCR lineage maturation linked IgA-associated mucosal and plasma B cell programs to somatic mutation and class switching, distinguishing maturation-associated biology from clonal expansion. Helper, regulatory, and cytotoxic T-cell programs covaried with B-cell states, and inferred interactions nominated reciprocal antigen-presentation and helper pathways. Repertoire-based machine learning distinguished diagnosis, inflammation, and contemporaneous six-month treatment-response status. Together, this atlas connects receptor architecture to coordinated systemic immune remodeling, establishes a foundation for repertoire-informed patient stratification, and prioritizes candidate mechanisms of IBD pathogenesis.

immunology↗

bioRxiv2026

Unsupervised Machine Learning for Adaptive Immune Receptors with immuneML

Machine learning (ML) enables adaptive immune receptor repertoires (AIRRs) analyses for biomarker identification and therapeutic development. With the majority of AIRR data partially or imperfectly labeled, unsupervised ML is essential for motif discovery, biologically meaningful clustering, and generation of novel receptor sequences. However, no unified framework for unsupervised ML exists in the AIRR field, hindering the assessment of model robustness and generalizability. Here, we present an immuneML release advancing unsupervised ML in the AIRR field through unified clustering workflows, interpretable generative modeling, integration with protein language model embeddings, dimensionality reduction, and visualization. We demonstrate immuneMLs utility in three use cases: (i) benchmarking generative models for epitope-specific sequence generation, assessing specificity and novelty, (ii) systematic evaluation of clustering approaches on experimental receptor sequences against biological properties, such as epitope specificity and MHC, and (iii) unsupervised analysis of an experimental AIRR dataset to examine potential confounding, a practice widespread in related fields but unexplored in AIRR analyses.

bioinformatics↗

bioRxiv2021

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

bioinformatics↗

bioRxiv2020

Towards a consensus microRNA signature of primary and metastatic colorectal cancer

Although microRNAs (miRNA) are involved in all hallmarks of cancer, miRNA dysregulation in metastasis remains poorly understood and contradictory results have been published. The aim of this work was to identify miRNAs associated with metastatic progression of colorectal cancer (CRC). Novel and previously published next generation sequencing (NGS) datasets generated from 268 samples with primary (pCRC) and metastatic CRC (mCRC; liver, lung and peritoneal metastases) and tumor adjacent tissues were analyzed. Differential expression analysis was performed using a meticulous bioinformatics pipeline, including only bona fide miRNAs, utilizing miRNA-tailored quality control and processing, and applying a physiologically meaningful cut-off value (100 reads per million). The results were adjusted for host tissue background expression and samples from the different metastatic sites were independently analyzed. A metastatic signature containing five miRNAs up-regulated at multiple metastatic sites was identified (Mir-210_3p, Mir-191_5p, Mir-8-P1b_3p (mir-141-3p), Mir-1307_5p, and Mir-155_5p) along with a number of miRNAs that were differentially expressed at individual metastatic sites. Several of these have previously been implicated in metastasis through involvement in epithelial-to-mesenchymal transition and hypoxia, while other identified miRNAs represent novel findings. The identified differentially expressed miRNAs confirm known associations and contribute novel insights into miRNA involvement in the metastatic process. The use of open science practices facilitates reproducibility, and new datasets may easily be added to the publicly available pipeline to continuously improve the knowledge in the field. The identified set of miRNAs provides a reliable starting-point for further research into the role of miRNAs in metastatic progression.

cancer biology↗