Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Xu, Chuanliang; Zheng, Yunxi; Xu, Kai

doi:10.1007/s12033-023-00777-0

Cited by 16 publications

(6 citation statements)

References 159 publications

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“…As such, data about the developmental potential and hierarchical relationships among early human haematopoietic progenitors is well‐protected. 59 However, scRNA‐seq enables date mapping and lineage tracing. This could raise potential ethical concerns due to an unintentional breach of privacy.…”

Section: Resultsmentioning

confidence: 99%

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Exploring the current landscape of single‐cell RNA sequencing applications in gastric cancer research

Awuah,

Roy,

Tan

et al. 2024

J Cellular Molecular Medi

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Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer‐related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single‐cell RNA sequencing (scRNA‐seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA‐seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA‐seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA‐seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.

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Section: Resultsmentioning

confidence: 99%

“…This could raise potential ethical concerns due to an unintentional breach of privacy. 59 Prioritizing this aspect promotes ethical practice of genomic medicine.…”

Section: Resultsmentioning

confidence: 99%

Exploring the current landscape of single‐cell RNA sequencing applications in gastric cancer research

Awuah,

Roy,

Tan

et al. 2024

J Cellular Molecular Medi

View full text Add to dashboard Cite

show abstract

“…Combined with artificial intelligence technology, scRNA-seq can generate and analyze models with physiological relevance and gene diversity at singlecell resolution, providing new insights into SL research. 297 These means of adequate screening and prediction before experimentation facilitate the discovery and validation of SL in different genetic backgrounds in a shorter time, avoiding the waste of researchers' time and effort. In addition, the recent emergence of organoid technology provides an effective bridge between preclinical and clinical research.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

The biological essence of synthetic lethality: Bringing new opportunities for cancer therapy

Ge,

Luo,

et al. 2024

MedComm – Oncology

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Synthetic lethality (SL), a genetic concept, has revolutionized the development of antitumor therapies by providing avenues to target previously “undruggable” targets with enhanced specificity for tumor cells over normal tissue. The principles of SL have expanded beyond genetic definitions to encompass biological functions, including genome stability, cell cycle regulation, cell death mechanisms, cellular metabolism, cell–cell interactions, and the tumor microenvironment (TME). Tumor cells with inactivated survival pathways are sensitive to therapeutic inhibition of compensatory mechanisms, while normal cells remain unaffected. Exploiting SL based on functional contexts has the potential to significantly improve cancer patient survival by reducing resistance to targeted therapies and enhancing antitumor efficacy when combined with other treatment modalities. This review explores the underlying mechanisms of synthetic lethality interactions (SLI) characterized by biological functions in individual cancer cells and the TME. We also provide a comprehensive summary of strategies for leveraging the dynamic nature of SLI to overcome therapeutic resistance. Additionally, we discuss various approaches and models for screening and designing potent SL agents tailored to the specific needs of cancer patients, as well as strategies for combining SL drugs in tumor treatment. This review offers valuable insights into harnessing SL as a promising avenue for precision cancer therapy.

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“…scRNA-seq is a powerful tool that allows for in-depth exploration of cellular heterogeneity and gene expression profiles at a single-cell resolution [ 250 – 253 ]. While scRNA-seq is primarily used to study gene expression patterns and cellular dynamics in various diseases, it also holds the potential to aid in the diagnosis and understanding of TB [ 254 , 255 ].…”

Section: Application Of ML In Discriminating Diagnosis Of Ltbimentioning

confidence: 99%

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Li,

Yang,

Zhuang

et al. 2023

Military Med Res

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Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

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Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Cited by 16 publications

References 159 publications

Exploring the current landscape of single‐cell RNA sequencing applications in gastric cancer research

Exploring the current landscape of single‐cell RNA sequencing applications in gastric cancer research

The biological essence of synthetic lethality: Bringing new opportunities for cancer therapy

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

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