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Curated Papers > In recent years, high-impact research on "single-cell sequencing" (IF≥30, past 5 years)

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis. ​

Literature Information ​

DOI10.1186/s13045-022-01280-w
PMID35549970
JournalJournal of hematology & oncology
Impact Factor40.4
JCR QuartileQ1
Publication Year2022
Times Cited55
KeywordsArtificial intelligence, Single-cell sequencing, Targeted therapy, Tumor heterogeneity, Tumor marker
Literature TypeJournal Article, Review, Research Support, Non-U.S. Gov't
ISSN1756-8722
Pages59
Issue15(1)
AuthorsYingying Han, Dan Wang, Lushan Peng, Tao Huang, Xiaoyun He, Junpu Wang, Chunlin Ou

TL;DR ​

This review highlights the transformative role of single-cell sequencing (SCS) in understanding tumor metastasis, revealing its applications in analyzing tumor heterogeneity, drug resistance, and microenvironment, while also enabling the construction of metastasis-related cell maps and identification of therapeutic targets. The integration of SCS with artificial intelligence enhances liquid biopsy techniques for detecting circulating tumor cells, offering promising strategies for improving treatment outcomes in metastatic cancer.

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Artificial intelligence · Single-cell sequencing · Targeted therapy · Tumor heterogeneity · Tumor marker

Abstract ​

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

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Primary Questions Addressed ​

  1. How does single-cell sequencing improve our understanding of tumor heterogeneity compared to bulk sequencing methods?
  2. What are the limitations of single-cell sequencing in studying tumor metastasis, and how might these be addressed in future research?
  3. In what ways can the integration of artificial intelligence enhance the analysis of single-cell sequencing data in cancer research?
  4. What specific therapeutic targets have been identified through single-cell sequencing that could potentially alter the treatment landscape for metastatic tumors?
  5. How can single-cell sequencing be utilized to monitor the dynamics of metastasis in real-time during treatment?

Key Findings ​

Background and Purpose ​

Tumor metastasis, the spread of cancer cells from primary tumors to distant organs, is a major contributor to cancer mortality. Identifying the mechanisms behind metastasis is crucial for improving treatment outcomes. Single-cell sequencing (SCS) has emerged as a powerful tool to explore the genetic, transcriptomic, and epigenetic landscapes of individual cells, offering insights into tumor heterogeneity, drug resistance, and the tumor microenvironment (TME). This review discusses the applications of SCS in understanding tumor metastasis and its potential for developing therapeutic strategies.

Main Methods/Materials/Experimental Design ​

SCS involves several key steps, which can be illustrated in the following workflow:

Mermaid diagram
  1. Sample Processing: Tumor samples are prepared to isolate single cells.
  2. Cell Isolation: Cells are separated from the tumor mass.
  3. Cell Lysis: Isolated cells are lysed to extract RNA or DNA.
  4. cDNA Synthesis: RNA is reverse-transcribed into complementary DNA (cDNA).
  5. Library Preparation: The cDNA is amplified and prepared for sequencing.
  6. Sequencing: High-throughput sequencing is performed to obtain the genetic data.
  7. Data Analysis: Bioinformatics tools are used to analyze the sequencing data and interpret the results.

Key Results and Findings ​

  • SCS has revealed significant differences in the genetic and transcriptomic profiles between primary and metastatic tumors, highlighting tumor heterogeneity.
  • Studies utilizing SCS have identified key markers and gene expression patterns associated with metastasis and drug resistance.
  • SCS has facilitated the construction of cell maps that track the dynamics of metastasis and the interactions within the TME.
  • The integration of SCS with artificial intelligence (AI) has improved the identification of circulating tumor cells (CTCs) and potential therapeutic targets.

Main Conclusions/Significance/Innovation ​

SCS is a transformative technology that enhances our understanding of tumor metastasis at the single-cell level. Its ability to uncover the complexities of tumor heterogeneity, drug resistance, and the TME provides a foundation for developing targeted therapies. The review emphasizes the potential of SCS to inform clinical strategies and improve patient outcomes in metastatic cancer treatment.

Limitations and Future Directions ​

  • Limitations: Challenges in SCS include the difficulty of single-cell isolation, variability in amplification efficiency, high costs, and complex data analysis.
  • Future Directions: Continued advancements in SCS technology and integration with AI will likely enhance its application in clinical settings. Future research should focus on overcoming current limitations and expanding the understanding of metastasis mechanisms to identify novel therapeutic targets.
AspectDetails
BackgroundMetastasis is a leading cause of cancer mortality.
MethodsSCS workflow includes sample processing, cell isolation, cDNA synthesis, library preparation, sequencing, and data analysis.
Key FindingsRevealed tumor heterogeneity and identified markers for metastasis and drug resistance.
ConclusionsSCS enhances understanding of metastasis and informs targeted therapy development.
LimitationsChallenges in single-cell analysis and data interpretation.
Future DirectionsFocus on technological advancements and clinical integration.

References ​

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Literatures Citing This Work ​

  1. The Hippo Pathway Effector Transcriptional Co-activator With PDZ-Binding Motif Correlates With Clinical Prognosis and Immune Infiltration in Colorectal Cancer. - Yutong Wang;Hui Nie;Huiling Li;Zhiming Liao;Xuejie Yang;Xiaoyun He;Jian Ma;Jianhua Zhou;Chunlin Ou - Frontiers in medicine (2022)
  2. Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies. - Haruko Takahashi;Daisuke Kawahara;Yutaka Kikuchi - Cancers (2022)
  3. Lipid nanoparticle-based mRNA vaccines in cancers: Current advances and future prospects. - Tao Huang;Lushan Peng;Yingying Han;Dan Wang;Xiaoyun He;Junpu Wang;Chunlin Ou - Frontiers in immunology (2022)
  4. A novel epithelial-mesenchymal transition (EMT)-related gene signature of predictive value for the survival outcomes in lung adenocarcinoma. - Yimeng Cui;Xin Wang;Lei Zhang;Wei Liu;Jinfeng Ning;Ruixue Gu;Yaowen Cui;Li Cai;Ying Xing - Frontiers in oncology (2022)
  5. Minichromosome maintenance gene family: potential therapeutic targets and prognostic biomarkers for lung squamous cell carcinoma. - Xuejie Yang;Chunrong Wang;Hui Nie;Jianhua Zhou;Xiaoyun He;Chunlin Ou - Aging (2022)
  6. Targeting macrophages: a novel treatment strategy in solid tumors. - Mengmeng Liu;Lina Liu;Yongping Song;Wei Li;Linping Xu - Journal of translational medicine (2022)
  7. Expression profile and prognostic values of GATA family members in kidney renal clear cell carcinoma. - Xuejie Yang;Cheng Mei;Hui Nie;Jianhua Zhou;Chunlin Ou;Xiaoyun He - Aging (2023)
  8. RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding. - Jun Komatsu;Alba Cico;Raya Poncin;Maël Le Bohec;Jörg Morf;Stanislav Lipin;Antoine Graindorge;Hélène Eckert;Azadeh Saffarian;Léa Cathaly;Frédéric Guérin;Sara Majello;Damien Ulveling;Anaïs Vayaboury;Nicolas Fernandez;Dilyana Dimitrova;Xavier Bussell;Yannick Fourne;Pierre Chaumat;Barbara André;Elodie Baldivia;Ulysse Godet;Mathieu Guinin;Vivien Moretto;Joy Ismail;Olivier Caille;Natacha Roblot;Carine Beaupère;Alexandrine Liboz;Ghislaine Guillemain;Bertrand Blondeau;Pierre Walrafen;Stuart Edelstein - Scientific reports (2023)
  9. RNA helicase DDX5-induced circPHF14 promotes gastric cancer cell progression. - Jia Wang;Chunjie Han;Jinsheng Wang;Qiu Peng - Aging (2023)
  10. CD31 promotes diffuse large B-cell lymphoma metastasis by upregulating OPN through the AKT pathway and inhibiting CD8+ T cells through the mTOR pathway. - Zhengchang He;Shaoxian Shen;Yuyao Yi;Lingli Ren;Huan Tao;Fujue Wang;Yongqian Jia - American journal of translational research (2023)

... (45 more literatures)

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