A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast

被引:0
作者
Lam, Ulysses Tsz-Fung [1 ]
Nguyen, Thi Thuy Trang [1 ]
Raechell, Raechell [1 ]
Yang, Jay [2 ]
Singer, Harry [2 ]
Chen, Ee Sin [1 ,3 ,4 ,5 ]
机构
[1] Natl Univ Singapore, Dept Biochem, Singapore 117596, Singapore
[2] Singer Instruments, Roadwater TA23 0RE, Watchet, England
[3] Natl Univ Singapore, NUS Ctr Canc Res, Singapore 117599, Singapore
[4] Natl Univ Singapore, Life Sci Inst, NUS Synthet Biol Clin & Technol Innovat SynCTI, Singapore 117456, Singapore
[5] Natl Univ Hlth Syst NUHS, Singapore 119228, Singapore
关键词
drug screening; yeast; edge effect; high-throughput screening; Schizosaccharomyces pombe; fission yeast; high-density array; hydroxyurea; SCHIZOSACCHAROMYCES-POMBE; LIFE-SPAN; GENOME; TRANSCRIPTION; IDENTIFICATION; CYTOTOXICITY; COMPOUND; EFFICACY; TARGETS; RNAS;
D O I
10.3390/biomedicines11102829
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.
引用
收藏
页数:15
相关论文
共 72 条
[1]  
Aguiar-Cervera J, 2019, Access Microbiology, V1, DOI [10.1099/acmi.byg2019.po0017, 10.1099/acmi.byg2019.po0017, DOI 10.1099/ACMI.BYG2019.PO0017]
[2]   Long non-coding RNA-mediated transcriptional interference of a permease gene confers drug tolerance in fission yeast [J].
Ard, Ryan ;
Tong, Pin ;
Allshire, Robin C. .
NATURE COMMUNICATIONS, 2014, 5
[3]  
Auld DouglasS., 2004, Assay Guidance Manual
[4]  
Baek ST, 2008, J MICROBIOL BIOTECHN, V18, P263
[5]   PHENOS: a high-throughput and flexible tool for microorganism growth phenotyping on solid media [J].
Barton, David B. H. ;
Georghiou, Danae ;
Dave, Neelam ;
Alghamdi, Majed ;
Walsh, Thomas A. ;
Louis, Edward J. ;
Foster, Steven S. .
BMC MICROBIOLOGY, 2018, 18
[6]   Quantitative analysis of fitness and genetic interactions in yeast on a genome scale [J].
Baryshnikova, Anastasia ;
Costanzo, Michael ;
Kim, Yungil ;
Ding, Huiming ;
Koh, Judice ;
Toufighi, Kiana ;
Youn, Ji-Young ;
Ou, Jiongwen ;
San Luis, Bryan-Joseph ;
Bandyopadhyay, Sunayan ;
Hibbs, Matthew ;
Hess, David ;
Gingras, Anne-Claude ;
Bader, Gary D. ;
Troyanskaya, Olga G. ;
Brown, Grant W. ;
Andrews, Brenda ;
Boone, Charles ;
Myers, Chad L. .
NATURE METHODS, 2010, 7 (12) :1017-U110
[7]   Development of Ultra-High-Density Screening Tools for Microbial "Omics" [J].
Bean, Gordon J. ;
Jaeger, Philipp A. ;
Bahr, Sondra ;
Ideker, Trey .
PLOS ONE, 2014, 9 (01)
[8]   Differential analysis of high-throughput quantitative genetic interaction data [J].
Bean, Gordon J. ;
Ideker, Trey .
GENOME BIOLOGY, 2012, 13 (12) :R123
[9]   A three-hybrid approach to scanning the proteome for targets of small molecule kinase inhibitors [J].
Becker, F ;
Murthi, K ;
Smith, C ;
Come, J ;
Costa-Roldán, N ;
Kaufmann, C ;
Hanke, U ;
Degenhart, C ;
Baumann, S ;
Wallner, W ;
Huber, A ;
Dedier, S ;
Dill, S ;
Kinsman, D ;
Hediger, M ;
Bockovich, N ;
Meier-Ewert, S ;
Kluge, AF ;
Kley', N .
CHEMISTRY & BIOLOGY, 2004, 11 (02) :211-223
[10]   Spotsizer: High-throughput quantitative analysis of microbial growth [J].
Bischof, Leanne ;
Prevorovsky, Martin ;
Rallis, Charalampos ;
Jeffares, Daniel C. ;
Arzhaeva, Yulia ;
Bahler, Jurg .
BIOTECHNIQUES, 2016, 61 (04) :191-201