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Digital Barcodes for High-Throughput Screening

Authors: Ze Yang; Jingyi Chen; Yao Xiao; Chenjing Yang; Chun-Xia Zhao; Dong Chen; David A. Weitz;

Digital Barcodes for High-Throughput Screening

Abstract

High-throughput screening is an indispensable technology in drug discovery, cancer therapy, and disease diagnosis, and it could greatly reduce time cost, reagent consumption, and labor expense. Here, four high-throughput screening methods with high sensitivity and accessibility are discussed in detail. Fluorescence, DNA, heavy metal, and nonmetal isotope barcodes, which generally label antibodies, proteins, and saccharides to identify cells, are detected by flow cytometry, second-generation DNA sequencing, mass cytometry, and second-ion mass spectrometry, respectively. Encoding binary information in barcodes, labeling individual cells by barcodes, performing the characterization of cells together, and identifying the result belonging to individual cells via barcodes are the main steps for high-throughput screening. Applications of the four digital barcodes in high-throughput screening for both in vitro and in vivo tests are described in detail, and their advantages and disadvantages are also summarized. High-throughput screening has provided a powerful platform widely accessible for multidisciplinary studies and has greatly sped up the progress of drug discovery, disease diagnosis, and cancer therapy.

Related Organizations
Keywords

heavy metal barcode, Chemical engineering, 1500 Chemical Engineering, fluorescence barcode, nonmetal isotope barcode, DNA barcode, 2204 Biomedical Engineering, TP155-156, high-throughput screening, TP248.13-248.65, Biotechnology

  • BIP!
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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    33
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 1%
Green
hybrid
Related to Research communities
Cancer Research