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      High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

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          Abstract

          The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

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          Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

          A high-capacity system was developed to monitor the expression of many genes in parallel. Microarrays prepared by high-speed robotic printing of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. Because of the small format and high density of the arrays, hybridization volumes of 2 microliters could be used that enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA. Differential expression measurements of 45 Arabidopsis genes were made by means of simultaneous, two-color fluorescence hybridization.
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            Quantitative single-cell RNA-seq with unique molecular identifiers.

            Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency.
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              The technology and biology of single-cell RNA sequencing.

              The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                05 February 2020
                2020
                : 11
                : 19
                Affiliations
                [1] 1 Guangxi Key Laboratory of Medicinal Resources Protection and Genetic Improvement, Guangxi Botanical Garden of Medicinal Plants , Nanning, China
                [2] 2 Dana-Farber Cancer Institute, Harvard Medical School , Brookline, MA, United States
                [3] 3 School of Life Sciences, Jiangsu University , Zhenjiang, China
                [4] 4 College of Biological Big Data, Yunnan Agricultural University , Kunming, China
                [5] 5 State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University , Kunming, China
                [6] 6 School of Pharmacy, Guangxi Medical University , Nanning, China
                Author notes

                Edited by: Yanqiang Li, Houston Methodist Research Institute, United States

                Reviewed by: Jinyuan Ma, Boston University, United States; Guangyu Wang, Houston Methodist Research Institute, United States

                *Correspondence: Jianhua Miao, mjh1962@ 123456163.com ; Yang Dong, loyalyang@ 123456163.com

                This article was submitted to Toxicogenomics, a section of the journal Frontiers in Genetics

                †These authors have contributed equally to this work

                Article
                10.3389/fgene.2020.00019
                7013098
                32117438
                d5296748-0b26-4727-af6f-1e88a1f315dd
                Copyright © 2020 Yang, Kui, Tang, Li, Wei, Chen, Miao and Dong

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 16 October 2019
                : 07 January 2020
                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 119, Pages: 12, Words: 6506
                Categories
                Genetics
                Review

                Genetics
                transcriptome,gene expression microarray,rna-seq,natural drug discovery,biomarker discovery

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