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A suite of MATLAB-based computational tools for automated analysis of COPAS Biosort data
Elizabeth Morton and Todd Lamitina
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Herein, we describe a suite of MATLAB algorithms—COPAquant, COPAmulti, and COPAcompare—which extract, filter, normalize, graph, statistically analyze, and compare intra and interplate values from COPAS Biosort data files acquired with the Advanced Acquisition Software Package (Union Biometrica). COPAquant analyzes data generated in the single-sample mode, whereas COPAmulti and COPAcompare analyze data obtained in the 96-well autosampling ReFLXmode. Automation of this step within the context of a high-throughput RNAi screen allowed us to rapidly move from secondary validations to hit identification. Although we have used it primarily for screens in C. elegans, the standard file format of COPAS data files, our simple GUI for multiwell plate analyses, and the freely available nature of the algorithms make it widely useful for analysis of any type of COPAS-generated data.

Materials and methods


The C. elegans strain TJ375 (hsp-16.2p::GFP) was used in this study and was obtained from the Caenorhabditis Genetics Center (University of Minnesota, Minneapolis, MN, USA). RNAi was conducted as described (7). Worms were dispensed to wells as L1s and given 4 days to grow to adulthood at 16°C. Worms were visually screened for basal GFP fluorescence, heat-shocked at 35°C for 3 h, allowed to recover at 16°C for 3 h, and then visually screened again for wells whose RNAi treatment prevented activation of the heat-shock promoter. Clones identified as hits from the primary screen were rescreened in quadruplicate and compared with an empty vector control by quantitative analysis on the COPAS. Hits were considered verified if their normalized values were ≤60% of the empty vector.

COPAS Biosort

A COPAS Biosort with Advanced Acquisition Software Version 5.2.69 was utilized. Systems without Advanced Acquisition Software or earlier versions of the COPAS software that do not output data in 26-column format are not compatible with the software as written. Young adult animals fed either empty vector RNAi or gene-specific RNAi were sorted through the COPAS for quantification of GFP fluorescence. Worms were washed from plates with 5–10 mL deionized water, placed in the COPAS sample cup, and analyzed in the single-sample format. COPAS settings were as follows: gain ext, 1; green, 5; yellow, 1; red, 1; threshold signal, 30; TOF minimum, 1; photomultiplier tube (PMT) settings control green, 600; yellow, 0; red, 0. Worms were gated based on TOF to select for adults, and MATLAB analysis was performed specifically on this gated population. Although we prefiltered our data during screening, COPAquant allows users to filter raw data files based on gating status (gated, nongated, or all data). COPAmulti also filters based on gating status and will additionally filter on any COPAS measured parameter [TOF, EXT, fluorescent channel 1 (Ch1), fluorescent channel 2 (Ch2), or fluorescent channel 3 (Ch3)].


MATLAB version was used in the creation of this program. MATLAB M-files for COPAquant, COPAmulti, and COPAcompare, as well as sample data files and instructional documentation are freely available through our web site (


Bar graphs indicate mean values ± SD. In COPAmulti, we implement the mean ± k SD method for hit identification by calculating the plate mean ± plate SD and then determining which wells exceed this minimum SD threshold. The median absolute deviation (MAD) test was conducted using the MAD function in the MATLAB library. Multiple comparison t-tests were conducted using the t-test function in the MATLAB library. It should be noted that user-defined P values must be corrected for multiple comparisons by dividing the selected P value by the number of samples analyzed (Bonferroni correction). A table of standard P values and Bonferroni-corrected P values for 96-well plate samples can be found in Table 1.

Results and discussion

Many RNAi screens performed in C. elegans are based on the in vivo expression of GFP reporters. One such screen under investigation in our laboratory involves the temperature-dependent regulation of an hsp-16p::GFP reporter. In this strain, GFP expression within young adult hermaphrodites (TOF = 400–1000) is negligible under basal conditions (Figure 1A), but is highly induced in almost all cells after a brief heat shock and recovery period (Figure 1B) (see the “Materials and methods” section for a more detailed description of the experiment). Quantification of this induction among young adult animals revealed a wide distribution of GFP expression levels between individuals (Figure 1C), as has been previously reported (9,10). However, the population means accurately reflect the behavior of the transgene (Figure 1D). In order to identify regulators of the heat-shock response pathway in C. elegans, we conducted a genome-wide RNAi screen for suppressors and enhancers of heat shock–dependent hsp-16p::GFP expression (Morton and Lamitina, unpublished data). GFP reporter expression was initially quantified by visual inspection. During the secondary validation screen, RNAi treatments were quantified using the COPAS Biosort in the single-sample mode of screening.

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