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:Background: Congenital cytomegalovirus (cCMV) is a common intrauterine infection, leading to neurodevelopmental disabilities. Early prognostic assessment of the severity of cCMV has remained an ongoing challenge. We aimed to identify amniotic fluid biomarkers of the severity of cCMV-related fetal brain injury. Methods: Global proteome analysis was performed in mid-gestation amniotic fluid samples, comparing fetuses with severe cCMV to asymptomatic CMV-infected fetuses (the discovery cohort). The levels of selected differentially-excreted proteins were further determined by specific immunoassays, and evaluated in an independent validation cohort, including clinically-blinded amniotic fluid of CMV-infected fetuses from unrelated centers. Findings: Proteome analysis identified 29 amniotic fluid proteins with distinct abundance in fetuses with severe-, compared to asymptomatic, cCMV. Pathway analysis of the differentially-excreted proteins revealed enriched pathways of inflammatory response, cellular compromise, immunological disease, organismal injury, and neurological disease, underlining a link between excessive inflammation at the maternal-fetal interface and the development of cCMV-related fetal brain damage. Importantly, the levels of two of these proteins, chemerin and galectin-3-binding protein (Gal-3BP), selected for validation, demonstrated 88.2% sensitivity (each), 100-96.2% specificity, 100-93.8% positive predictive value, and 92.9-92.6% negative predictive value, with 0.98-0.97 area under the curve (for chemerin - Gal-3BP, respectively) in differentiating 17 fetuses with severe cCMV from 26 asymptomatic CMV-infected fetuses. Interpretation: These results identify the immunomodulatory proteins chemerin and Gal-3BP as new highly predictive amniotic fluid biomarkers of cCMV severity, which could guide early prognostic stratification and potential personalized treatment of cCMV-infected fetuses. Funding: Israel Science Foundation; Research Fund of the Hadassah Medical Organization

Project description:Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and diseasee. LncRNAs exhibit cell type- and tissue-specific expression, but little is known about the expression and function of lncRNAs in the developing human brain. Here, we deeply profiled lncRNAs from polyadenylated and total RNA obtained from human neocortex at different stages of development and integrated this resource to analyze the transcriptomes of single cells. While lncRNAs were generally detected at low levels in whole tissues, single cell transcriptomics revealed that many lncRNAs are abundantly expressed in individual cells and are cell type-specific. Furthermore, we used CRISRPi to show that LOC646329, a lncRNA enriched in radial glia but detected at low abundance in tissues, regulates cell proliferation. The discrete and abundant expression of lncRNAs among individual cells has important implications for both their biological function and utility for distinguishing neural cell types. 16 Bulk Tissue Samples from GW13-23; 226 Single Cells from GW19.5-23.5 ------------------ bulk_tpm.polya.txt: bulk RNA-seq expression; using polyA full reference scell_ncounts.genes.thresh.txt: single cell RNA-seq expression; using polyA stringent reference; includes 50 GW16, GW21, GW21p3 cells previously analyzed (Pollen et. al. 2014) polya_RNA_stringent_ref.gtf: bulk RNA-seq polyA stringent transcriptome reference polya_RNA_full_ref.gtf: bulk RNA-seq polyA full transcriptome reference total_RNA_stringent_ref.gtf: bulk RNA-seq total stringent transcriptome reference total_RNA_full_ref.gtf: bulk RNA-seq total full transcriptome reference GW13_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal

Project description:Purpose: Next generation bulk RNA sequencing of a large group (37 individuals) of matched tissues (fetal brain and fetal placenta) from human fetuses collected via elective termination at 72-82 days post conception (d.p.c.), a timeframe that closely matches the embryonic developmental window used in our mouse studies (14.5-16.5) Methods: Human placenta (maternal decidua separated from fetal placenta) and fetal brain tissues were obtained from the NIH-supported Laboratory of Developmental Biology at the University of Washington between the years 1999 and 2010 and used under a protocol approved by the Duke University Institutional Review Board (Pro00014066). Tissue specimens were stored at -80°C until required for RNA or protein extraction. Prior to collection, informed consent was obtained from all individuals undergoing the elective termination. RNA from human tissue was prepared for sequencing using an Illumina TrueSeq RNA Exome kit and sequenced on an Illumina NovaSeq 6000 configured for a S2 flow cell at 50bp PE. Results: PCR for the Y chromosome (SRY) identified 17 male and 20 female fetal tissue sets, with the average age of 78.2 d.p.c. for both sexes. No maternal data were available, so we assessed decidual triglyceride accumulation to use as a proxy for maternal weight, given that women with overweight/obesity exhibit triglyceride accumulation in the placenta. Triglyceride levels trended higher in pregnancies with female fetuses, but there was no significant difference between triglyceride accumulation in decidua associated with male or female pregnancies. Pearson's Correlation of gene expression with maternal triglyceride accumulation from bulk RNA-seq from 16 matched male and 19 matched female brain and placenta samples revealed a strong dimorphic trend in gene expression. In male pregnancies, differentially correlated genes were predominantly over-expressed with increasing maternal weight (i.e. gene expression increased as maternal triglycerides increased); the opposite effect was seen in female pregnancies. In the male brain, only 45 genes significantly correlated with maternal triglyceride accumulation (either positive or negative), whereas 781 genes were significantly correlated in females.

			Action	DRS
	SRR23350300_1.fastq.gz	Fastqsanger.gz
	SRR23350300_2.fastq.gz	Fastqsanger.gz
	SRR23350301_1.fastq.gz	Fastqsanger.gz
	SRR23350301_2.fastq.gz	Fastqsanger.gz
	SRR23350302_1.fastq.gz	Fastqsanger.gz

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