Clinical Perspectives of Single-Cell RNA Sequencing

Kim, Nayoung; Eum, Hye Hyeon; Lee, Hae‐Ock

doi:10.3390/biom11081161

Cited by 15 publications

(12 citation statements)

References 109 publications

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“…With these advances in mind, the ophthalmologist can gain unprecedented insight into diseases affecting the ocular surface. While scRNA-seq is not currently utilized for the purpose of ocular diagnostics, we believe that there is potential to use such data to create molecular diagnostic screening panels based on a select number of biomarkers in a disease state [ 93 , 94 ], particularly for diseases that lack animal models, such as keratoconus. As described earlier, Wang et al’s identification of loss of NEAT1 function in corneal endothelial cells in FECD could serve as an early-stage diagnostic biomarker in the disease process [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Ahsanuddin

2023

Eye

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Elucidating the cellular and genetic composition of ocular tissues is essential for uncovering the pathophysiology of ocular diseases. Since the introduction of single-cell RNA sequencing (scRNA-seq) in 2009, vision researchers have performed extensive single-cell analyses to better understand transcriptome complexity and heterogeneity of ocular structures. This technology has revolutionized our ability to identify rare cell populations and to make cross-species comparisons of gene expression in both steady state and disease conditions. Importantly, single-cell transcriptomic analyses have enabled the identification of cell-type specific gene markers and signalling pathways between ocular cell populations. While most scRNA-seq studies have been conducted on retinal tissues, large-scale transcriptomic atlases pertaining to the ocular anterior segment have also been constructed in the past three years. This timely review provides vision researchers with an overview of scRNA-seq experimental design, technical limitations, and clinical applications in a variety of anterior segment-related ocular pathologies. We review open-access anterior segment-related scRNA-seq datasets and illustrate how scRNA-seq can be an indispensable tool for the development of targeted therapeutics.

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Section: Discussionmentioning

confidence: 99%

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Ahsanuddin

2023

Eye

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“…Clinical application of scRNA-seq has mostly been for studying malignancy in areas such as hematology and oncology [11]. By enabling analysis of tumor microenvironments and detailed genetic changes, scRNA-seq allows for an improved understanding of cancer heterogeneity [12][13][14]. For example, in a recent clinical trial of multiple myeloma (MM), scRNA-seq was used to characterize cellular heterogeneity between patients with MM and recent treatment failure compared to a group of untreated MM patients and healthy adults [15].…”

Section: Clinical Applications Of Single-cell Rna Sequencingmentioning

confidence: 99%

Cardiovascular utility of single cell RNA-Seq

Safabakhsh

Feng

Miller

et al. 2022

Current Opinion in Cardiology

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Purpose of reviewCardiovascular diseases remain the leading causes of morbidity and mortality globally. Single-cell RNA sequencing has the potential to improve diagnostics, risk stratification, and provide novel therapeutic targets that have the potential to improve patient outcomes.Recent findingsHere, we provide an overview of the basic processes underlying single-cell RNA sequencing, including library preparation, data processing, and downstream analyses. We briefly discuss how the technique has been adapted to related medical disciplines, including hematology and oncology, with short term translational impact. We discuss potential applications of this technology within cardiology as well as recent innovative research within the field. We also discuss future directions to translate this technology to other high impact clinical areas.SummaryThe use of single-cell RNA sequencing technology has made significant advancements in the field of cardiology, with ongoing growth in terms of applications and uptake. Most of the current research has focused on structural or atherosclerotic heart disease. Future areas that stand to benefit from this technology include cardiac electrophysiology and cardio-oncology.

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“…Additionally, the intrinsic and extrinsic characteristics of tumor cells can be precisely captured using scRNA‐seq. Furthermore, distinct cell subpopulations can be identified along with the key elements of tumor heterogeneity and clonal diversity can be characterized 22–24 …”

Section: Introductionmentioning

confidence: 99%

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single‐cell data

Jin

et al. 2023

The Journal of Gene Medicine

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Background: We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS.Methods: Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication.Results: OS was classified into three subtypes based on lipid metabolic pathways.Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2.In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells.Conclusions: Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

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Clinical Perspectives of Single-Cell RNA Sequencing

Cited by 15 publications

References 109 publications

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Cardiovascular utility of single cell RNA-Seq

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single‐cell data

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