ArticleScientific reports2020
Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 40 citations in OpenAlex.
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- Integrating multi-omic QTLs and predictive models reveals regulatory architectures at immune related GWAS loci in CD4+ T cells.medRxiv : the preprint server for health sciences · 2026Article
- Identification of immune signatures associated with both SARS-CoV-2 infection and lung transplantation.Cell reports. Medicine · 2025Article
- Single-cell chromatin accessibility profiling reveals regulatory mechanisms and evolution in pig brains.BMC biology · 2025Article
- Tracking HIV persistence across T cell lineages during early ART-treated HIV-1-infection using a reservoir-marking humanized mouse model.Nature communications · 2025Article
- Single cell analysis identifies distinct CD4 + T cells associated with the pathobiology of pediatric obesity related asthma.Scientific reports · 2025Article
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- Exploring the Expression and Function of T Cell Surface Markers Identified through Cellular Indexing of Transcriptomes and Epitopes by Sequencing.Yonsei medical journal · 2024Article
- Uncovering the Cellular Microenvironment in Chronic Rhinosinusitis via Single-Cell RNA Sequencing: Application and Future Directions.Clinical reviews in allergy & immunology · 2024Review
- Single-cell resolution of longitudinal blood transcriptome profiles in rheumatoid arthritis, systemic lupus erythematosus and healthy control pregnancies.Annals of the rheumatic diseases · 2024Article
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Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
CD4+ T-cells represent a heterogeneous collection of specialised sub-types and are a key cell type in the pathogenesis of many diseases due to their role in the adaptive immune system. By investigating CD4+ T-cells at the single cell level, using RNA sequencing (scRNA-seq), there is the potential to identify specific cell states driving disease or treatment response. However, the impact of sequencing depth and cell numbers, two important factors in scRNA-seq, has not been determined for a complex cell population such as CD4+ T-cells. We therefore generated a high depth, high cell number dataset to determine the effect of reduced sequencing depth and cell number on the ability to accurately identify CD4+ T-cell subtypes. Furthermore, we investigated T-cell signatures under resting and stimulated conditions to assess cluster specific effects of stimulation. We found that firstly, cell number has a much more profound effect than sequencing depth on the ability to classify cells; secondly, this effect is greater when cells are unstimulated and finally, resting and stimulated samples can be combined to leverage additional power whilst still allowing differences between samples to be observed. While based on one individual, these results could inform future scRNA-seq studies to ensure the most efficient experimental design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.