The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

Ascensión, Alex M.; Araúzo‐Bravo, Marcos J.; Izeta, Ander

doi:10.12688/f1000research.54864.2

Cited by 9 publications

(10 citation statements)

References 39 publications

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“…High variability of gene expression between similar cell types is easily misinterpreted into novel cell subsets ( 64 ). Particularly for human skin, technical artifacts of sample processing and computational correction of the artifacts may hinder the reproducibility of key gene detections ( 65 ).…”

Section: Discussionmentioning

confidence: 99%

The imbalance between Type 17 T-cells and regulatory immune cell subsets in psoriasis vulgaris

2022

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Psoriasis vulgaris is a common inflammatory disease affecting 7.5 million adults just in the US. Previously, psoriasis immunopathogenesis has been viewed as the imbalance between CD4+ T-helper 17 (Th17) cells and regulatory T-cells (Tregs). However, current paradigms are rapidly evolving as new technologies to study immune cell subsets in the skin have been advanced. For example, recently minted single-cell RNA sequencing technology has provided the opportunity to compare highly differing transcriptomes of Type 17 T-cell (T17 cell) subsets depending on IL-17A vs. IL-17F expression. The expression of regulatory cytokines in T17 cell subsets provided evidence of T-cell plasticity between T17 cells and regulatory T-cells (Tregs) in humans. In addition to Tregs, other types of regulatory cells in the skin have been elucidated, including type 1 regulatory T-cells (Tr1 cells) and regulatory dendritic cells. More recently, investigators are attempting to apply single-cell technologies to clinical trials of biologics to test if monoclonal blockade of pathogenic T-cells will induce expansion of regulatory immune cell subsets involved in skin homeostasis.

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Section: Discussionmentioning

confidence: 99%

The imbalance between Type 17 T-cells and regulatory immune cell subsets in psoriasis vulgaris

2022

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“…Tissue dissociation, a necessary step for acquiring samples suitable for scRNA-seq, subjects cells to traumatic conditions. Previous studies have highlighted the emergence of cell clusters exhibiting up-regulated genes associated with stress conditions [22][23][24][25] . However, to date, the presence of cell clusters devoid of immature mRNA content has not been thoroughly investigated as an artifact.…”

Section: Discussionmentioning

confidence: 99%

Revealing the Prevalence of Suboptimal Cells and Organs in Reference Cell Atlases: An Imperative for Enhanced Quality Control

Montserrat-Ayuso,

Esteve-Codina

2024

Preprint

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The advent of droplet-based single-cell RNA-sequencing (scRNA-seq) has dramatically increased data throughput, enabling the release of a diverse array of tissue cell atlases to the public. However, we will show that prominent initiatives such as the Human Cell Atlas, Tabula Muris, and Tabula Sapiens contain a significant amount of contamination products (frequently affecting the whole organ) in their data portals due to suboptimal quality filtering. Our work addresses a critical gap by advocating for more stringent quality filtering, highlighting the imperative for a shift from existing standards, which currently lean towards greater permissiveness. We will show the importance of incorporating cell intronic fraction in quality control -or MALAT1 expression otherwise- showcasing its informative nature and potential to elevate cell atlas data reliability. In summary, here, we unveil the hidden intronic landscape of every tissue and highlight the importance of more rigorous single-cell RNA-sequencing quality assessment in cell atlases to enhance their applicability in diverse downstream analyses.

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“…biological expertise and an adept data analyst, and standardized pipelines are needed for the translation of data analysis [132].…”

Section: The Trade-offs Of Single-cell Sequencingmentioning

confidence: 99%

“…The scarce nature of datasets and the massive amount of data make the computational aspect challenging. The accurate analysis of single‐cell data requires extensive biological expertise and an adept data analyst, and standardized pipelines are needed for the translation of data analysis [132].…”

Section: The Trade‐offs Of Single‐cell Sequencingmentioning

confidence: 99%

Applications of single‐cell omics for chimeric antigen receptor T cell therapy

Ghaffari,

Saleh,

Akbari

et al. 2023

Immunology

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Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single‐cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single‐cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single‐cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single‐cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi‐omics of CAR T cell therapy.

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The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

Cited by 9 publications

References 39 publications

The imbalance between Type 17 T-cells and regulatory immune cell subsets in psoriasis vulgaris

The imbalance between Type 17 T-cells and regulatory immune cell subsets in psoriasis vulgaris

Revealing the Prevalence of Suboptimal Cells and Organs in Reference Cell Atlases: An Imperative for Enhanced Quality Control

Applications of single‐cell omics for chimeric antigen receptor T cell therapy

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