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Systematic discovery of potential regulators of transcriptional variability reveals NAP1L1 as a transcriptional stabilizer in mouse ESCs

ABSTRACT: 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.

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ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Cell Culture, Embryonic Stem Cell

SUBMITTER: Tamar Segal

LAB HEAD: Tamar Segal

PROVIDER: PXD071600 | Pride | 2026-08-06

REPOSITORIES: Pride

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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.

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Systematic discovery of potential regulators of transcriptional variability reveals NAP1L1 as a transcriptional stabilizer in mouse ESCs

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