Biology subjects

Kilian, C.

Publications and source records attributed to Kilian, C..

4 recordsLinked to original sources

bioRxiv2025

Single-nucleus mRNA-sequencing reveals dynamics of lipogenic and thermogenic adipocyte populations in murine brown adipose tissue in response to cold exposure

Brown adipose tissue (BAT) comprises a heterogeneous population of adipocytes and non-adipocyte cell types. To characterize these cellular subpopulations and their adaptation to cold, we performed single-nucleus mRNA-sequencing (snRNA-seq) on interscapular BAT from mice maintained at room temperature or exposed to acute (24h) or chronic (10 days) cold (6{degrees}C). To investigate the role of the de novo lipogenesis (DNL)-regulating transcription factor carbohydrate response element-binding protein (ChREBP), we analyzed control and brown adipocyte-specific ChREBP knockout mice. We identified different cell populations, including seven brown adipocyte subtypes with distinct metabolic profiles. One of them highly expressed ChREBP and DNL enzymes. Notably, these lipogenic adipocytes were highly sensitive to acute cold exposure, showing a marked depletion in BAT of control mice that was compensated by other brown adipocyte subtypes maintaining DNL. Chronic cold exposure resulted in an expansion of basal brown adipocytes and adipocytes putatively derived from stromal and endothelial precursors. In ChREBP-deficient mice, lipogenic adipocytes were almost absent under all conditions, identifying the transcription factor as a key determinant of this adipocyte subtype. Pathway and cell-cell interaction analyses implicated a Wnt-ChREBP axis in the maintenance of lipogenic adipocytes, with Wnt ligands from stromal and muscle cells providing instructive cues. Our findings provide a comprehensive atlas of BAT cellular heterogeneity and reveal a critical role for ChREBP in lipogenic adipocyte identity, with implications for BAT plasticity and metabolic function.

cell biology↗

bioRxiv2023

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell mRNA sequencing (scRNA-seq) allows deep molecular and cellular profiling of immunological processes. Longitudinal scRNA-seq datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. Here, we derived longitudinal changes in the abundance of six colonic cell types during inflammatory bowel disease (IBD) from scRNA-seq data of a mouse model of colitis using ODE-based models. We then predicted the immune dynamics of a different mouse colitis protocol and confirmed these scRNA-seq-based predictions with our previously published single-cell-based flow cytometry data. We further hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the model simulations by predicting genes indicative of treatment response in human IBD patients. The predictive power of IBD deterministic modelling from scRNA-seq data highlights its potential to advance our understanding of immune dynamics in health and disease.

systems biology↗

bioRxiv2023

Emergence and suppressive function of Tr1 cells in glomerulonephritis

T regulatory type 1 (Tr1) cells, which are defined by their regulatory function, lack of Foxp3, high expression of IL-10, CD49b, and LAG3, are known to be able to suppress Th1 and Th17 in the intestine. Th1 and Th17 cells are also the main drivers of crescentic glomerulonephritis, the most severe form of renal autoimmune disease. However, whether Tr1 cells emerge in renal inflammation and moreover, whether they exhibit regulatory function during glomerulonephritis has not been thoroughly investigated yet. To address these questions, we used a mouse model of experimental crescentic glomerulonephritis and double Foxp3mRFP IL-10eGFP reporter mice. We found that Foxp3neg IL-10-producing CD4+ T cells infiltrate the kidneys during glomerulonephritis progression. Using single-cell RNA- sequencing, we could show that these cells express the core transcriptional factors characteristic of Tr1 cells. In line with this, Tr1 cells showed a strong suppressive activity ex vivo and were protective in experimental crescentic glomerulonephritis in vivo. Finally, we could also identify Tr1 cells in the kidneys of patients with anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis and define their transcriptional profile. Tr1 cells are currently used in several immune-mediated inflammatory diseases, e.g. as T- cell therapy. Thus, our study provides proof of concept for Tr1 cell-based therapies in experimental glomerulonephritis.

immunology↗

bioRxiv2018

Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here we propose the use of conditional single cell Generative Adversarial Neural Networks (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGANs outperform existing methods for single cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

bioinformatics↗