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Single-Cell Technologies for Cancer Therapy

  • Geng-Ming Hu
  • Victor Daniel Lee
  • Hung-Yu Lin
  • Pu-Wei Mao
  • Hsin-Yi Liu
  • Jih-Hou Peh
  • Chih-Wei ChenEmail author
Living reference work entry
  • 13 Downloads

Abstract

Routine sequencing techniques used in cancer diagnosis have primarily focused on bulk tissue genomic analysis. However, cell heterogeneity is a commonly observed phenomenon in several types of cancer, and the current methods do not properly disclose information of rare cell types that may contribute to the progression of the disease as well as play a role in drug resistance and metastasis. The lack of techniques for detecting differences in the genomic expression of a heterogeneous group of cells in clinical practice has been problematic for the selection of targeted therapies. Recent developments in single-cell analysis techniques in combination with next generation sequencing (NGS) are providing the tools to analyze and detect polymorphisms at the whole genome level of individual targeted cells. Moreover, single cell sequencing (SCS) analysis can assist in predicting cancer progression and be used as a diagnostic tool for early detection of cancer through circulating tumor cells (CTCs). In addition to the current methods used for SCS research, we also organize systems biology, pharmacy, multi-omics, bioinformatics, and deep leaning/AI-assisted clinical care, all of which, for meta-analysis, can take advantage of single-cell technology as the core source of information.

Keywords

NGS CTCs SCS Bioinformatics Systems biology Immunotherapy 

Notes

Acknowledgements

We thank Dr. Fan-Gang Tseng and Dr. Tuhin Subhra Santra for their support on the microfluidic device, Dr. Ming-Hsien Chien at the Institute of Clinical Medicine for the advice on the cancer treatment, Dr. Chien-Wen Chung for nanoparticle delivery instruction, Mr. Ding Wang from Linkou Chang Gung Memorial Hospital for the advice on clinical trial, Mr. Min-Han Lee from the Taiwan Semiconductor Manufacturing Company for the advice on AI/deep leaning and database, Dr. Chao-Lung Chiang from National Synchrotron Radiation Research Center (NSRRC) for materials chemistry and chapter revision, King & Queen Entertainment Inc. for funding, and Academia Sinica and BIOGEN Inc. for the NGS guidance.

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Copyright information

© Springer Nature Singapore Pte Ltd 2019

Authors and Affiliations

  • Geng-Ming Hu
    • 1
  • Victor Daniel Lee
    • 2
  • Hung-Yu Lin
    • 3
  • Pu-Wei Mao
    • 4
  • Hsin-Yi Liu
    • 5
  • Jih-Hou Peh
    • 6
  • Chih-Wei Chen
    • 7
    Email author
  1. 1.Department of PhysicsNational Taiwan Normal UniversityTaipeiTaiwan
  2. 2.Bio-Gen Inc.TegucigalpaHonduras
  3. 3.NeuroSky, Inc.San JoseUSA
  4. 4.Genomics Research CenterAcademia SinicaTaipeiTaiwan
  5. 5.Department of PharmacyHsinchu National Military HospitalHsinchuTaiwan
  6. 6.BSL3 core facility LaboratoryNational University of SingaporeSingaporeSingapore
  7. 7.Department of Molecular and Cellular BiologyNational Tsing Hua UniversityHsinchuTaiwan

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