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Performance evaluation of Arabidopsis scRNA-seq sample processing strategies

ABSTRACT: Plant cells display significant heterogeneity, complicating the isolation and profiling of diverse cell populations from complex tissues. Optimizing methodologies for cell enrichment and single-cell transcriptomics is therefore critical for accurately capturing cellular diversity. Here, we systematically compared protoplast enrichment technologies (including conventional and image-based flow cytometry, as well as magnetic cell sorting) and single-cell RNA sequencing (scRNA-seq) platforms (10X Genomics Chromium, BD Rhapsody) using Arabidopsis roots. Image-based flow cytometry offered greater precision due to customizable gating strategies, while magnetic sorting provided faster processing and better representation of cell size heterogeneity. Both scRNA-seq platforms captured root cell heterogeneity and yielded reproducible gene expression profiles, but we observed platform-specific biases in cell type composition. Notably, single nucleotide polymorphism analysis of a mixed ecotype sample revealed that computational doublet detection algorithms misclassified two-thirds of the cells as doublets. Since the Arabidopsis root contains a wide range of cell types and developmental stages, our findings have broad implications. In summary, these insights can guide the end user to optimise their scRNA-seq workflows and improve data quality across plant species.

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE300264 | GEO | 2026/06/24

REPOSITORIES: GEO

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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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Performance evaluation of Arabidopsis scRNA-seq sample processing strategies

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