Single-cell analysis: Advances and future perspectives.
In recent years, innovative technologies for single-cell genomics, transcriptomics, and proteomics, with a particular emphasis on quantification and multiplicity that can perform measurements on many single cells in a given experimental run, include microfluidics, optical tweezers, transcriptome in vivo analysis, and mass cytometry [4,8-10].
Applications: Advances in whole-genome and whole-transcriptome amplification have permitted the gene expression profiling and sequencing of the minute amounts of DNA and RNA present in a single cell, offering a window into the extent and nature of genomic and transcriptomic heterogeneity which occurs in both normal development and disease. Each cell in the body has a unique genomic structure, which allows the reconstruction of cell lineage trees with very high precision that can predict the existence of small population of steam cells. This information is important for cancer research for detection of rare tumor cells, preimplantation, and genetic diagnosis [11-13]. Single-cell approaches have been utilized for understanding the intricate cellular interplay involved in immune response that requires single-cell resolution, especially with rare antigen-specific T- or B-cells [14,15]. In addition, protein expression analysis is vital to understand the true metabolic or functional state of cells and the single-cell approach enables simultaneous analysis of more than 35 proteins of individual cells [16,17]. Currently, researchers are starting to combine single-cell genomics with single-cell proteomics to tackle important questions in fields including cancer, stem cell biology, neuroscience, developmental biology, and infectious disease. As more investigators explore heterogeneity in cell populations, knowledge of intricate biological cellular networks will empower researchers to discover new ways to diagnose and treat disease.
Challenges: The advances of single-cell analysis over the past 5 years have happened at a lightning pace, and the potential for their use in various fields is high. However, the novelty of these single-cell techniques also implies various limitations. There is a lack of cross-disciplinary collaboration to more effectively utilize advantage of single-cell analysis. Another caveat is that algorithms for the analysis of single-cell data are even less mature than the experimental platforms, and effective interpretations of what are increasingly large datasets remain challenging, with techniques that vary across research groups [4,11,17].
DECLARATION OF INTERESTS
The author declares no conflict of interests.
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Emir Hodzic *
Real-time PCR Research and Diagnostic Core Facility, School of Veterinary Medicine, University of California at Davis, California, United States of America
* Corresponding author: Emir Hodzic, Real-time PCR Research and Diagnostic Core Facility, School of Veterinary Medicine, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA. E-mail: email@example.com
Submitted: 05 May 2016/Accepted: 06 May 2016
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|Title Annotation:||NEW AND EMERGING MEDICAL ENTITIES|
|Publication:||Bosnian Journal of Basic Medical Sciences|
|Date:||Nov 1, 2016|
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