Larsen Vornholz

Cytokines orchestrate immune responses, yet we still lack a comprehensive understanding of their specific effects across human immune cells due to their pleiotropy, context dependence and extensive functional redundancy. Here, we present a Human Cytokine Dictionary, created from high-resolution single-cell transcriptomes of 9,697,974 human peripheral blood mononuclear cells (PBMC) from 12 donors stimulated in vitro with 90 different cytokines. We describe donor-specific response variation and uncover robust consensus cytokine signatures across individuals. We then delineate similarities between cytokine response profiles, and derive cytokine-induced immune programs that organize responsive genes into data-driven, biologically interpretable functional modules. By integrating cell type-specific responses with expression of cytokines, we infer higher-order cell-to-cell and cytokine-to-cytokine communication networks exemplified by an IL-32-β-initiated signaling cascade, which rewires myeloid programs by inducing neutrophil-recruiting factors while suppressing Th1-responses and promoting IL-10-family cytokines. Finally, we show how the Human Cytokine Dictionary enables the interpretation of cytokine-driven immune responses in other studies and disease contexts, including systemic lupus erythematosus, multiple sclerosis, and non-small cell lung carcinoma. Together, the Human Cytokine Dictionary constitutes the first comprehensive cell type-resolved transcriptional screen of human cytokine responses and provides an essential open-access, easy-to-use community resource with accompanying software package to advance our understanding of cytokine biology in human disease and guide therapeutic discovery.

Cytokines orchestrate immune responses, yet we still lack a comprehensive understanding of their specific effects across human immune cells due to their pleiotropy, context dependence and extensive functional redundancy. Here, we present a Human Cytokine Dictionary, created from high-resolution single-cell transcriptomes of 9,697,974 human peripheral blood mononuclear cells (PBMC) from 12 donors stimulated in vitro with 90 different cytokines. We describe donor-specific response variation and uncover robust consensus cytokine signatures across individuals. We then delineate similarities between cytokine response profiles, and derive cytokine-induced immune programs that organize responsive genes into data-driven, biologically interpretable functional modules. By integrating cell type-specific responses with expression of cytokines, we infer higher-order cell-to-cell and cytokine-to-cytokine communication networks exemplified by an IL-32-β-initiated signaling cascade, which rewires myeloid programs by inducing neutrophil-recruiting factors while suppressing Th1-responses and promoting IL-10-family cytokines. Finally, we show how the Human Cytokine Dictionary enables the interpretation of cytokine-driven immune responses in other studies and disease contexts, including systemic lupus erythematosus, multiple sclerosis, and non-small cell lung carcinoma. Together, the Human Cytokine Dictionary constitutes the first comprehensive cell type-resolved transcriptional screen of human cytokine responses and provides an essential open-access, easy-to-use community resource with accompanying software package to advance our understanding of cytokine biology in human disease and guide therapeutic discovery.

Tissue makeup depends on the local cellular microenvironment. Spatial single-cell genomics enables scalable and unbiased interrogation of these interactions. Here we introduce Nicheformer, a transformer-based foundation model trained on both human and mouse dissociated single-cell and targeted spatial transcriptomics data. Pretrained on SpatialCorpus-110M, a curated collection of over 57 million dissociated and 53 million spatially resolved cells across 73 tissues on cellular reconstruction, Nicheformer learns cell representations that capture spatial context. It excels in linear-probing and fine-tuning scenarios for a newly designed set of downstream tasks, in particular spatial composition prediction and spatial label prediction. Critically, we show that models trained only on dissociated data fail to recover the complexity of spatial microenvironments, underscoring the need for multiscale integration. Nicheformer enables the prediction of the spatial context of dissociated cells, allowing the transfer of rich spatial information to scRNA-seq datasets. Overall, Nicheformer sets the stage for the next generation of machine-learning models in spatial single-cell analysis.

The autosomal dominant hyper-IgE syndrome (AD-HIES) is a primary immunodeficiency, which originates from heterozygous missense mutations in the signal transducer and activator of transcription 3 ( STAT3 ) gene. It is accepted that most STAT3 variants causing AD-HIES are dominant negative. Whether haploinsufficient mutations cause a phenotype in humans is still debated. We report on a family with a heterozygous STAT3 nonsense mutation that led to rapid decay of the mutant mRNA and protein, leading to haploinsufficiency. To explore STAT3 heterozygosity, we created a Stat3 haploinsufficient ( Stat3 ^+/− ) mouse model in which we found that Stat3 ^+/− mice had increased IgE serum levels, reduced T _H 17 cell differentiation, and were susceptible to a cutaneous Staphylococcus aureus infection. Together, our findings provide mechanistic evidence for the impact of haploinsufficiency in STAT3 with residual protein expression as an important cause for immune deficiency. The implications extend to the diagnosis of immunodeficiency disorders and to the design of gene therapy in situations where gene dosage matters.

The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.

Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening syndrome fueled by uncontrolled mononuclear phagocyte activity, yet the innate immune mechanisms driving HLH pathogenesis remain elusive. Germline gain-of-function (GOF) mutations in CARD11, a pivotal regulator of lymphocyte antigen receptor signaling, cause the lymphoproliferative disease B-cell expansion with NF-κB and T-cell anergy, which is frequently associated with HLH development. Given that CARD11 is physiologically expressed not only in lymphocytes but also in dendritic cells (DCs), we explored whether enforced CARD11 signaling in DCs contributes to immunopathology. We demonstrated that exclusive DC-intrinsic expression of CARD11-GOF in mice was sufficient to induce a lethal autoinflammatory syndrome that mimicked human HLH. Mechanistically, DC-intrinsic CARD11-GOF signaling triggered cell-autonomous inflammatory cytokine production via MALT1 paracaspase engagement. Genetic deletion of Malt1 in CARD11-GOF-expressing animals reversed the hyperinflammatory phenotype. These results highlight the significant role of enforced CARD11/MALT1 signaling in DCs as a contributor to HLH pathology and suggest potential therapeutic strategies for HLH treatment.

Tissue makeup relies fundamentally on the cellular microenvironment. Spatial single-cell genomics allows probing the underlying cellular interactions in an unbiased, scalable fashion. To learn a unified cell representation that accounts for local dependencies in the cellular microenvironment, we propose Nicheformer, a transformer-based foundation model that combines human and mouse dissociated single-cell and targeted spatial transcriptomics data. Pretrained on over 57 million dissociated and 53 million spatially resolved cells across 73 tissues on cellular reconstruction, the model is fine-tuned on spatial tasks for spatial omics data to decode spatially resolved cellular information. Nicheformer excels in linear-probing and fine-tuning scenarios for a novel set of downstream tasks, in particular spatial composition prediction and spatial label prediction. We further show that existing foundation models trained on dissociated single-cell data alone are not capable of recapitulating the spatial complexity of cells in their microenvironments, indicating that multiscale models are required to understand complex local dependencies at scale. Nicheformer enables the prediction of the spatial context of dissociated cells, allowing the transfer of rich spatial information to scRNA-seq datasets. Overall, Nicheformer sets the stage for the next generation of machine-learning models in spatial single-cell analysis.

Infections caused by the yeast Candida albicans are among the primary opportunistic fungal nosocomial infections [1, 2]. Systemic candidiasis, characterized by dissemination of fungi to vital organ...

Medicinal chemistry has discovered thousands of potent protein and lipid kinase inhibitors. These may be developed into therapeutic drugs or chemical probes to study kinase biology. Because of polypharmacology, a large part of the human kinome currently lacks selective chemical probes. To discover such probes, we profiled 1,183 compounds from drug discovery projects in lysates of cancer cell lines using Kinobeads. The resulting 500,000 compound–target interactions are available in ProteomicsDB and we exemplify how this molecular resource may be used. For instance, the data revealed several hundred reasonably selective compounds for 72 kinases. Cellular assays validated GSK986310C as a candidate SYK (spleen tyrosine kinase) probe and X-ray crystallography uncovered the structural basis for the observed selectivity of the CK2 inhibitor GW869516X. Compounds targeting PKN3 were discovered and phosphoproteomics identified substrates that indicate target engagement in cells. We anticipate that this molecular resource will aid research in drug discovery and chemical biology.

Although most cancer drugs modulate the activities of cellular pathways by changing posttranslational modifications (PTMs), little is known regarding the extent and the time- and dose-response characteristics of drug-regulated PTMs. In this work, we introduce a proteomic assay called decryptM that quantifies drug-PTM modulation for thousands of PTMs in cells to shed light on target engagement and drug mechanism of action. Examples range from detecting DNA damage by chemotherapeutics, to identifying drug-specific PTM signatures of kinase inhibitors, to demonstrating that rituximab kills CD20-positive B cells by overactivating B cell receptor signaling. DecryptM profiling of 31 cancer drugs in 13 cell lines demonstrates the broad applicability of the approach. The resulting 1.8 million dose-response curves are provided as an interactive molecular resource in ProteomicsDB.