Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model
Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies.
Figure 4c, obtained from the simulation of human colitis model is highlighted here.
This model is described in the article:
Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9
Abstract:
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we 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 mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.
Project description:Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterized by repetitive alveolar injuries with excessive deposition of extracellular matrix (ECM) proteins. A crucial need in understanding IPF pathogenesis is identifying cell types associated with histopathological regions, particularly local fibrosis centers known as fibroblast foci. To address this, we integrated published spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) transcriptomics and adopted the Query method and the Overlap method to determine cell type enrichments in histopathological regions. Distinct fibroblast cell types are highly associated with fibroblast foci, and transitional alveolar type 2 and aberrant KRT5-/KRT17+ epithelial cells are associated with morphologically normal alveoli in human IPF lungs. Furthermore, we employed laser capture microdissection directed mass spectrometry to profile proteins. By comparing with another published similar dataset, common differentially expressed proteins and enriched pathways related to ECM structure organization and collagen processing were identified in fibroblast foci. Importantly, cell type enrichment results from innovative spatial proteomics and scRNA-seq data integration accord with those from spatial transcriptomics and scRNA-seq data integration, supporting the capability and versatility of the entire approach. In summary, we integrated spatial multi-omics with scRNA-seq data to identify disease-associated cell types and potential targets for novel therapies in IPF intervention. The approach can be further applied to other disease areas characterized by spatial heterogeneity.
Project description:Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy and understanding disease progression. Redox factor-1 (Ref-1), a redox signaling protein, regulates the DNA binding activity of several transcription factors, including HIF-1. The conversion of HIF-1 from an oxidized to reduced state leads to enhancement of its DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia.Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions. Experimental Methods: scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model. Matched samples were also collected for bulk proteomic analysis of the four conditions. scRNA-seq data was validated using proteomics and qRT-PCR. Ref-1’s role in mitochondrial function was confirmed using mitochondrial function assays and qRT-PCR. Results: We also integrated the scRNA data analysis with the proteomic analysis and found that the differentially expressed genes and pathways identified from the scRNA-seq data are highly consistent to the significant proteins observed in the proteomics data, especially for the upregulated cell cycle and transcription pathways and downregulated metabolic, apoptosis and signaling pathways under hypoxia. Conclusion: The scRNA-seq and proteomics data consistently demonstrated down-regulated central metabolism pathways in APE1/Ref-1 knockdown vs scrambled control under both normoxia and hypoxia conditions.
Project description:Energy metabolism and extracellular matrix function are closely connected to orchestrate and maintain tissue organization, but the crosstalk is poorly understood. Here, we used scRNA-seq analysis to uncover the importance of respiration for extracellular matrix homeostasis in mature cartilage. Genetic inhibition of respiration in cartilage results in the expansion of a central area of 1-month-old mouse femur head cartilage showing disorganized chondrocytes and increased deposition of extracellular matrix material. scRNA-seq analysis identified a cluster-specific decrease in mitochondrial DNA-encoded respiratory chain genes and a unique regulation of extracellular matrix-related genes in nonarticular chondrocyte clusters. These changes were associated with alterations in extracellular matrix composition, a shift in the collagen/non-collagen protein content and an increase of collagen crosslinking and ECM stiffness. The results demonstrate, based on findings of the scRNA-seq analysis, that respiration is a key factor contributing to ECM integrity and mechanostability in cartilage and presumably also in many other tissues.
Project description:Embryonic stem cells (ESCs) exhibit gene expression heterogeneity of key pluripotency factors (e.g. Nanog), suggesting functional relevance. To systematically identify potential regulators of transcriptional variability, we integrated single-cell RNA-seq (scRNA-seq) with ChIP-seq datasets in ESCs. One of the potential ‘stabilizer’ proteins we identified is Nucleosome Assembly Protein 1-Like 1 (NAP1L1), a member of the NAP1 family. To directly test the function of NAP1L1 as a transcriptional stabilizer, we first recruited NAP1L1 to the Nanog promoter via fusion with dCas9. NAP1L1 recruitment stabilized Nanog expression in ESCs. To further explore NAP1L1’s activity genome-wide, we analyzed transcriptional heterogeneity in WT and Nap1l1-knock-out (KO) ESCs using scRNA-seq, revealing increased transcriptional variability in Nap1l1-KO ESCs. Finally, co-IP of NAP1L1 followed by Mass-Spectrometry (LC-MS/MS) identified DPPA3 as a key interacting partner of NAP1L1. Taken together, we provide a systematic discovery platform for regulators of transcriptional variability and identify NAP1L1 as a transcriptional stabilizer in ESCs.
Project description:Aging is a universal biological phenomenon linked to many diseases, such as cancer or neurodegeneration. However, the molecular mechanisms underlying aging, or how lifestyle interventions such as cognitive stimulation can ameliorate this process, are yet to be clarified. Here, we performed a multi-omic profiling, including RNA-seq, ATAC-seq, ChIP-seq, EM-seq, SWATH-MS and single cell Multiome scRNA and scATAC-seq, in the dorsal hippocampus of young and old mouse subjects which were subject to cognitive stimulation using the paradigm of environmental enrichment. In this study we were able to describe the epigenomic landscape of aging and cognitive stimulation.
Project description:Intra-tumor heterogeneity of tumor-initiating cell (TIC) activity drives colorectal cancer (CRC) progression and therapy resistance. Here, we used single-cell mRNA-sequencing (scRNA-seq) of patient-derived CRC models to decipher distinct cell subpopulations based on their transcriptional profiles. Cell type-specific expression modules of stem-like, transit amplifying-like, and differentiated CRC cells resemble differentiation states of normal intestinal epithelial cells. Strikingly, identified subpopulations differ in proliferative activity and metabolic state. In summary, we here show at single-cell resolution that transcriptional heterogeneity identifies functional states during TIC differentiation. Targeting transcriptional states associated to cancer cell differentiation might unravel vulnerabilities in human CRC.
Project description:This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput Data Independent Acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This optimized DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1,300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by integrating SCP and single-cell RNA-seq (scRNA-seq) data, we identified six upregulated and ten downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.
Project description:Objective Increased neutrophil extracellular trap-osis (NETosis) is a hallmark of inflammatory bowel disease (IBD). However, the mechanisms of NETosis formation and its detrimental effect on the intestinal epithelium remain poorly understood. Design Dextran sulfate sodium (DSS) induce colitis in peptidyl-arginine deiminase-4 deficient (PAD4-/-) and wild-type (WT) mice. PAD4, NETosis and citrullination levels were detected in mice and human volunteers. Citrullination analysis and single-cell RNA-sequencing (scRNA-seq) were performed to explore the target of PAD4 during NETosis formation. The effect of this citrullinated target in the intestinal epithelium during the pathogenesis of IBD was validated both in vivo and in vitro. Results The levels of NETosis and citrullination were improved in IBD patients, which was positively associated with endoscopic inflammation scores. Deleting PAD4 alleviated colonic inflammation and intestinal barrier dysfunctions. In citrullination analysis, R243 of mitochondrial creatine kinase 1 (CKMT1) was identified as the potential target of PAD4 during NETosis. The scRNA-seq data further indicated that PAD4 in neutrophils contributed to the increase of epithelia (Nos2+ Clca3b+) and the decrease of distal immature epithelia. PAD4-mediated NETosis contributed to the destruction of intestinal barriers and increase of apoptosis as well as lower level of CKMT1 protein in vitro. Finally, mice with intestinal epithelial-specific ablation of CKMT1 were used to verify its detrimental role in the development of IBD, which was also validated by the moderate correlation between CKMT1 and endoscopic scores in IBD patients.