Jonas Nørskov Søndergaard

Accurate immune cell annotation remains a central challenge in single-cell analysis, particularly when datasets contain rare populations, closely related subtypes, transitional states, and tissue-adapted immune 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 the Python ecosystem. Compared to scODIN, pyODIN supports multimodal annotation using RNA, antibody-derived tag (ADT), or combined RNA-ADT information. It also substantially expands the underlying annotation database from a CD4 T-cell-centered framework to a broader immune reference resource spanning major and minor peripheral blood mononuclear cell populations as well as tissue-associated immune subsets. To evaluate performance, we benchmarked pyODIN against CellTypist and HiCAT using two independently curated immune reference datasets. pyODIN achieved the highest overall classification accuracy on the Allen dataset and matched the top-performing method on Azimuth. At the top-lineage level, pyODIN reduced broad 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 largely collapsed into broader categories by the comparator methods. In a controlled marker-ablation experiment, addition of ADT information restored NK and CD8 T-cell annotation when key lineage-defining RNA markers were removed from the expression feature set, 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 can support both broad and fine-grained annotation beyond PBMC datasets. Together, pyODIN provides an expert-guided and adaptable immune annotation framework for Python that is designed for multimodal immune phenotyping across blood and tissue single-cell datasets.

Over the past decade, thousands of putative human RNA-binding proteins (RBPs) have been identified, increasing the need for methods that define their RNA-binding capacities across diverse biological settings. Existing methods rely either on antibody-based in vivo capture (e.g. CLIP-seq), which depends on cross-linking efficiency and antibody availability, or on synthetic oligonucleotide-based assays (e.g. RNAcompete), which use artificial RNA substrates and cannot assess binding across the native transcriptome. To bridge this gap, we developed RNA affinity purification followed by sequencing (RAPseq), an in vitro method that profiles RBP-binding to native cellular RNA, enabling large-scale transcriptome-wide characterization of RNA–protein interactions without antibodies or synthetic probes. Using RAPseq, we characterized the RNA interactomes of 11 canonical RBPs and 26 non-canonical RBPs, and uncovered novel and specialized moonlighting RNA-binding activities. Applying RAPseq to vertebrate HUR proteins revealed recognition of a conserved RNA-binding motif but showed species-specific binding preferences. Profiling of five pathological IGF2BP family variants exhibited distinct gain- and loss-of-function binding patterns, with implications for cancer biology. Our combinatorial RBP-binding assay (co-RAPseq) uncovered cooperative RNA-binding by HUR and PTBP1, including de novo estimation of the optimal binding distance. Lastly, we introduce a modification-sensitive assay (mod-RAPseq) to distinguish between modification-dependent and -independent RNA-binding sites of YTHDF1 and YBX1. Overall, our simple, scalable, and versatile method enables exploration of complex RNA–protein interactions and the regulatory layers that shape post-transcriptional gene regulation.

Mycobacteria have a cell envelope that can act as a shield against host defense. This study shows that mycobacteria survive in host macrophages by targeting the innate host receptor dectin-1 through a noncanonical ligand. Compared with wild-type (WT) mice, dectin-1–deficient mice were more resistant to infection to mycobacteria. Dectin-1–deficient mice presented with substantially reduced bacterial burdens, inflammatory cytokines, and infiltrating myeloid cells, such as neutrophils and macrophages. Intracellular survival of these bacteria was reduced in macrophages derived from dectin-1–deficient mice compared with those from WT mice. Cellular characterization of mycobacteria-infected macrophages indicated that the presence of dectin-1 altered phagosomal maturation and association with markers of autophagy. Activity-based purification and nuclear magnetic resonance spectrometry identified branched α-glucan as the dectin-1 mycobacterial ligand. This branched glucan was essential for activating dectin-1. These results show that mycobacterial α-glucan targets dectin-1 to facilitate intracellular bacterial survival.

T follicular regulatory cells (Tfr) play a pivotal role in maintaining immune self-tolerance and prevention of autoantibody induction. However, the stages of Tfr formation have still not been clarified. Here, we found that 30 to 50% of circulating Tfr in human blood have a naïve-like phenotype (preTfr, CD45RA ⁺ CD45RO ⁻ Foxp3 ⁺ CXCR5 ⁺ ). PreTfr are expandable in vitro while retaining suppressive capacity. They are transcriptionally similar to naïve T _regs (nT _regs ) ex vivo, but after stimulation, they gain increased expression of Tfr-related suppressive molecules such as IL-1RA, suggesting that they are primed for full differentiation into mature Tfr. Further, we demonstrate that preTfr but not nT _reg are significantly reduced in the blood of patients with COVID-19 and patients with sepsis and that this loss is correlated with increased anti–interferon-γ antibodies and activated atypical B cells. Together, these data suggest that Tfr are disrupted at the earliest stage of their formation during severe disease and may be an important therapeutic target in future vaccine developments.

Accurate identification of immune cell subsets in single-cell RNA sequencing (scRNA-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 2-step approach combining expert knowledge with machine learning to rapidly assign cell type identities to large scRNA-seq datasets. 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 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 provide comprehensive insights into immune regulation, with broad implications for immunology and personalized medicine.

Regulatory T cells (Treg) play an important role in regulating immune homeostasis in health and disease. Traditionally their suppressive function has been assayed by mixing purified cell populations, which does not provide an accurate picture of a physiologically relevant response. To overcome this limitation, we here develop ‘single cell suppression profiling of human Tregs’ (scSPOT). scSPOT uses a 52-marker CyTOF panel, a cell division detection algorithm, and a whole PBMC system to assess the effect of Tregs on all other cell types simultaneously. In this head-to-head comparison, we find Tregs having the clearest suppressive effects on effector memory CD8 T cells through partial division arrest, cell cycle inhibition, and effector molecule downregulation. Additionally, scSPOT identifies a Treg phenotypic split previously observed in viral infection and propose modes of action by the FDA-approved drugs Ipilimumab and Tazemetostat. scSPOT is thus scalable, robust, widely applicable, and may be used to better understand Treg immunobiology and screen for therapeutic compounds.

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.

Resting memory B cells can be divided into classical or atypical groups, but the heterogenous marker expression on activated memory B cells makes similar classification difficult. Here, by longitudinal analysis of mass cytometry and CITE-seq data from cohorts with COVID-19, bacterial sepsis, or BNT162b2 mRNA vaccine, we observe that resting B cell memory consist of classical CD45RB⁺ memory and CD45RB^lo memory, of which the latter contains of two distinct groups of CD11c⁺ atypical and CD23⁺ non-classical memory cells. CD45RB levels remain stable in these cells after activation, thereby enabling the tracking of activated B cells and plasmablasts derived from either CD45RB⁺ or CD45RB^lo memory B cells. Moreover, in both COVID-19 patients and mRNA vaccination, CD45RB^lo B cells formed the majority of SARS-CoV2 specific memory B cells and correlated with serum antibodies, while CD45RB⁺ memory are activated by bacterial sepsis. Our results thus identify that stably expressed CD45RB levels can be exploited to trace resting memory B cells and their activated progeny, and suggest that atypical and non-classical CD45RB^lo memory B cells contribute to SARS-CoV-2 infection and vaccination.

Regulatory T cells (Tregs) play an important role in regulating immune homeostasis in health and disease. Traditionally their suppressive function has been assayed by mixing purified cell populations, which does not provide an accurate picture of a physiologically relevant immune response. To overcome this limitation, we here developed “single cell suppression profiling of human Tregs” (scSPOT). scSPOT is more informative and physiological than traditional assays, using a 52-marker CyTOF panel, a cell division detection algorithm, and a whole PBMC system to assess the function of Tregs on all cell types simultaneously. By employing scSPOT, we observed Tregs having the clearest suppressive effects on CD8-EM through division arrest, cell cycle inhibition, and effector molecule downregulation. Additionally, we demonstrate cellular differentiation tracking and drug screening applications of scSPOT and use it to identify the potential origin of a Treg phenotypic split in critical viral infection and further propose modes of action by the FDA-approved drugs Ipilimumab and Tazemetostat. scSPOT is scalable, robust, widely applicable, and may be used to better understand Treg immunobiology and screen for therapeutic compounds.