Cell Type Classification and Discovery across Diseases, Technologies and Tissues Reveals Conserved Gene Signatures of Immune Phenotypes

Chamberlain, Mathew; Nouri, Nima; Kurlovs, Andre; Hanamsagar, Richa; Nestle, Frank O.; Rinaldis, Emanuele de; Savova, Virginia

doi:10.26502/jbsb.5107057

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ScRNAseq data and TCR libraries from the trachea of healthy patients (n = 3) were sourced from online repositories. 11 T cell identity was assigned with Signac 40,41 (Fig. 1A).…”

Section: Highly Expanded T Cell Clones Are Present Throughout the Air...mentioning

confidence: 99%

Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

Clark,

Talatala,

et al. 2024

The Laryngoscope

View full text Add to dashboard Cite

ObjectivesRecent immunologic study of the adaptive immune repertoire in the subglottic airway demonstrated high‐frequency T cell clones that do not overlap between individuals. However, the anatomic distribution and antigenic target of the T cell repertoire in the proximal airway mucosa remain unresolved.MethodsSingle‐cell RNA sequencing of matched scar and unaffected mucosa from idiopathic subglottic stenosis patients (iSGS, n = 32) was performed and compared with airway mucosa from healthy controls (n = 10). T cell receptor (TCR) sequences were interrogated via similarity network analysis to explore antigenic targets using the published algorithm: Grouping of Lymphocyte Interactions by Paratope Hotspots (GLIPH2).ResultsThe mucosal T cell repertoire in healthy control airways consisted of highly expressed T cell clones conserved across anatomic subsites (trachea, bronchi, bronchioles, and lung). In iSGS, high‐frequency clones were equally represented in both scar and adjacent non‐scar tissue. Significant differences in repertoire structure between iSGS scar and unaffected mucosa was observed, driven by unique low‐frequency clones. GLIPH2 results suggest low‐frequency clones share targets between multiple iSGS patients.ConclusionHealthy airway mucosa has a highly conserved T cell repertoire across multiple anatomic subsites. Similarly, iSGS patients have highly expressed T cell clones present in both scar and unaffected mucosa. iSGS airway scar possesses an abundance of less highly expanded clones with predicted antigen targets shared between patients. Interrogation of these shared motifs suggests abundant adaptive immunity to viral targets in iSGS airway scar. These results provide insight into disease pathogenesis and illuminate new treatment strategies in iSGS.Level of EvidenceLevel NA Laryngoscope, 2024

show abstract

“…ScRNAseq data and TCR libraries from the trachea of healthy patients (n = 3) were sourced from online repositories. 11 T cell identity was assigned with Signac 40,41 (Fig. 1A).…”

Section: Highly Expanded T Cell Clones Are Present Throughout the Air...mentioning

confidence: 99%

Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

Clark,

Talatala,

et al. 2024

The Laryngoscope

View full text Add to dashboard Cite

show abstract

Scaling up single-cell RNA-seq data analysis with CellBridge workflow

Nouri,

Kurlovs,

Gaglia

et al. 2023

Bioinformatics

Self Cite

View full text Add to dashboard Cite

Summary Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility. Availability and implementation The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.

show abstract

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis

Rahman,

Kurlovs,

Vodnala

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled intricate disease- specific and common immune cell chemoattraction and extravasation patterns. Our findings delineate disease-specific L-R networks and shed light on shared immune responses across tissues and diseases. Insights gleaned from this analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.

show abstract

Cell Type Classification and Discovery across Diseases, Technologies and Tissues Reveals Conserved Gene Signatures of Immune Phenotypes

Cited by 3 publications

References 0 publications

Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway

Scaling up single-cell RNA-seq data analysis with CellBridge workflow

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis

Contact Info

Product

Resources

About