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

Christoph Kilian, Hanna Ulrich, Viktor A. Zouboulis, Paulina Sprezyna, Jasmin Schreiber, Tomer Landsberger, Maren Büttner, Moshe Biton, Eduardo J. Villablanca, Samuel Huber, Lorenz Adlung*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number69
Journalnpj Systems Biology and Applications
Volume10
DOIs
Publication statusPublished Online - 24 Jun 2024

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • General Biochemistry,Genetics and Molecular Biology
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics

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