Stellate cells, hepatocytes, and endothelial cells imprint the Kupffer cell identity on monocytes colonizing the liver macrophage niche

Macrophages are strongly adapted to their tissue of residence. Yet, little is known about the cell-cell interactions that imprint the tissue-specific identities of macrophages in their respective niches. Using conditional depletion of liver Kupffer cells, we traced the developmental stages of monocytes differentiating into Kupffer cells and mapped the cellular interactions imprinting the Kupffer cell identity. Kupffer cell loss induced tumor necrosis factor (TNF)- and interleukin-1 (IL-1) receptor-dependent activation of stellate cells and endothelial cells, resulting in the transient production of chemokines and adhesion molecules orchestrating monocyte engraftment. Engrafted circulating monocytes transmigrated into the perisinusoidal space and acquired the liver-associated transcription factors inhibitor of DNA 3 (ID3) and liver X receptor-alpha (LXR-alpha). Coordinated interactions with hepatocytes induced ID3 expression, whereas endothelial cells and stellate cells induced LXR-alpha via a synergistic NOTCH-BMP pathway. This study shows that the Kupffer cell niche is composed of stellate cells, hepatocytes, and endothelial cells that together imprint the liver-specific macrophage identity

Modeling intercellular communication from transcriptomics data

Ontcijferen hoe cellen communiceren is nodig om betere inzichten te verwerven in fundamentele biologie en in ziektes waarin cel-cel-communicatieprocessen ontregeld zijn (bv. kanker en COVID-19). Het bestuderen van intercellulaire communicatie is echter zeer uitdagend. Dankzij transcriptomics technologieën is het nu mogelijk om de genexpressie van interagerende cellen te bepalen. Maar, het achterhalen van cel-cel communicatie uit deze transcriptomics data vereist geavanceerde algoritmes. Tijdens dit doctoraat werd een nieuw algoritme, NicheNet, ontwikkeld dat toelaat om te bestuderen hoe signalen geproduceerd door de ene cel de genexpressie kunnen beïnvloeden in een andere cel. Hierdoor kan NicheNet hypotheses genereren over welke communicatiepatronen cruciaal zijn in een bepaald biologische systeem. Dit werd geïllustreerd tijdens een studie over Kupffer cellen waarin verschillende hypotheses van NicheNet gevalideerd konden worden. Hoewel NicheNet een nuttige methode is gebleken, heeft het meerdere beperkingen. Daarom werd in het laatste deel van dit doctoraat een nieuw algoritme ontwikkeld, MultiNicheNet. MultiNicheNet bouwt verder op NicheNet om datasets van grote cohorten patiënten beter te kunnen analyzeren. Hierdoor kunnen betere hypotheses over de rol van cel-cel communicatie in verschillende ziektes gegenereerd worden. Samengevat beschrijft deze thesis dus de ontwikkeling en toepassing van nieuwe algoritmes om cel-cel communicatie te bestuderen o.b.v. transcriptomics data

NicheNet input networks

NicheNet input networks The directory should be unzipped and placed within the 'data/' folder </p

NicheNet : modeling intercellular communication by linking ligands to target genes

Computational methods that model how gene expression of a cell is influenced by interacting cells are lacking. We present NicheNet (https://github.com/saeyslab/nichenetr), a method that predicts ligand-target links between interacting cells by combining their expression data with prior knowledge on signaling and gene regulatory networks. We applied NicheNet to tumor and immune cell microenvironment data and demonstrate that NicheNet can infer active ligands and their gene regulatory effects on interacting cells

Data and code to reproduce all analyses described in the NicheNet paper

Here you can find a directory containing the data and code to reproduce all analyses described in the NicheNet paper. This will be open access after publication. The subdirectories contain following information: *networks: data and scripts to create the integrated ligand-receptor, signaling and gene regulatory networks *evaluation: data and scripts to validate the NicheNet model. Includes scripts for optimization and characterization of data sources as well. *application: data and scripts used to apply NicheNet to single-cell data from Puram et al. and Medaglia et al. *data_nichenet: final networks, models and expression dataset

ILC3s control splenic cDC homeostasis via lymphotoxin signaling

International audienceThe spleen contains a myriad of conventional dendritic cell (cDC) subsets that protect against systemic pathogen dissemination by bridging antigen detection to the induction of adaptive immunity. How cDC subsets differentiate in the splenic environment is poorly understood. Here, we report that LTα 1 β 2-expressing Rorgt + ILC3s, together with B cells, control the splenic cDC niche size and the terminal differentiation of Sirpα + CD4 + Esam + cDC2s, independently of the microbiota and of bone marrow pre-cDC output. Whereas the size of the splenic cDC niche depended on lymphotoxin signaling only during a restricted time frame, the homeostasis of Sirpα + CD4 + Esam + cDC2s required continuous lymphotoxin input. This latter property made Sirpα + CD4 + Esam + cDC2s uniquely susceptible to pharmacological interventions with LTβR agonists and antagonists and to ILC reconstitution strategies. Together, our findings demonstrate that LTα 1 β 2-expressing Rorgt + ILC3s drive splenic cDC differentiation and highlight the critical role of ILC3s as perpetual regulators of lymphoid tissue homeostasis

A Complement Atlas identifies interleukin 6 dependent alternative pathway dysregulation as a key druggable feature of COVID-19

To improve COVID-19 therapy, it is essential to understand the mechanisms driving critical illness. The complement system is an essential part of innate host defense that can also contribute to injury. All complement pathways have been implicated in COVID-19 pathogenesis, however the upstream drivers and downstream consequences on tissue injury remain ill-defined. Here, we demonstrate that complement activation is mediated by the alternative pathway and we provide a comprehensive atlas of the alterations in complement around the time of respiratory deterioration. Proteome and single-cell sequencing mapping across cell types and tissues reveals a division of labor between lung epithelial, stromal and myeloid cells in the production of complement, in addition to liver-derived factors. Upstream, IL-6 drives complement responses, linking complement dysregulation to approved COVID-19 therapies. In an exploratory proteomic study, C5 inhibition improves epithelial damage and markers of disease severity. Collectively, these results identify complement dysregulation as a key druggable feature of COVID-19

A complement atlas identifies interleukin-6–dependent alternative pathway dysregulation as a key druggable feature of COVID-19

Improvements in COVID-19 treatments, especially for the critically ill, require deeper understanding of the mechanisms driving disease pathology. The complement system is not only a crucial component of innate host defense but can also contribute to tissue injury. Although all complement pathways have been implicated in COVID-19 pathogenesis, the upstream drivers and downstream effects on tissue injury remain poorly defined. We demonstrate that complement activation is primarily mediated by the alternative pathway, and we provide a comprehensive atlas of the complement alterations around the time of respiratory deterioration. Proteomic and single-cell sequencing mapping across cell types and tissues reveals a division of labor between lung epithelial, stromal, and myeloid cells in complement production, in addition to liver-derived factors. We identify IL-6 and STAT1/3 signaling as an upstream driver of complement responses, linking complement dysregulation to approved COVID-19 therapies. Furthermore, an exploratory proteomic study indicates that inhibition of complement C5 decreases epithelial damage and markers of disease severity. Collectively, these results support complement dysregulation as a key druggable feature of COVID-19.</p