ANALYTICALBIOCHEMISTRY

Analytical Biochemistry 344 (2005) 138–140

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Notes & Tips

Resolution and repeatability of phenotypic assays by automated growth curve analysis in yeast and bacteria

Lukasz Jasnos, Piotr Sliwa, Ryszard Korona ¤

Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland

Received 22 February 2005Available online 17 May 2005

Development of functional genomics is to a largedegree dependent on the availability of techniques allow-ing for eYcient and precise phenotypic assays of exten-sive collections of mutants [1]. In the budding yeast,mixtures of strains with gene knock-outs are routinelyassayed in massive competition experiments. Manymutants were found to have either none or hardlydetectable growth eVects [2]. However, accuracy of suchestimates is limited both technically, because of insuY-cient sensitivity of the accessible microarray hybridiza-tion techniques, and statistically, because of the arisingmultiplicity of comparisons [3]. Recently, an alternativeapproach in which numerous but separate strains areplaced in multiwell plates and their maximum growthrates are assayed by an automat has been proposed [4,5].In this work, we employ previous developments [4,5] andadd some new suggestions with the objective to minimizepotential errors arising in the whole-plate automaticgrowth curve assays. We concentrate on the buddingyeast but we show that our Wndings apply to bacteriaalso.

We used the Bioscreen C automated microbiologicalworkstation that incubates, shakes, and periodically esti-mates optical density (OD)1 of liquid microbial micro-cultures. The device is able to hold two 100-well“honeycomb” plates that were designed speciWcally forit. We applied either minimum synthetic dextrose (SD)with the required supplements or typical complexmedium (YPD) and programmed incubation regimes inthe same way as in two former studies. Warringer and

* Corresponding author. Fax: +48 12 6646912.E-mail address: korona@eko.uj.edu.pl (R. Korona).

1 Abbreviations used: OD, optical density; SD, synthetic dextrose;YPD, typical complex medium.

0003-2697/$ - see front matter 2005 Elsevier Inc. All rights reserved.doi:10.1016/j.ab.2005.04.034

Blomberg [4], abbreviated later as WB, measured ODevery 20 min in a volume of 350 �l, temperature 30 °C,and applied intense shaking turned on/oV every 1 min. Inthe study by Weiss et al. [5] (WDG), the correspondingsettings were 30 min, 350 �l, 30 °C, low and continuousshaking. An example of a curve obtained from a singlewell is given in Fig. 1A. To extract a single parameter ofthe curve, both WB and WDG calculated growth ratesover each three consecutive measurements, ranked them,and chose the second highest as an estimate of the maxi-mum growth rate. In our analysis, we calculated themaximum growth rate over longer time periods. We sub-tracted the background absorbance from the OD read-ing, ln transformed the resulting values, and thenregressed them over time. We corrected our calculationsfor the eVects of nonlinearity using the correction func-tion developed by WB. We intended to use densities highenough that the readings were repeatable but lowenough that the growth rate was still at maximum. Thiscorresponds to the OD interval between 0.1 and 0.5 aftersubtracting the background absorbance. We found thatlinear regression of ln(OD) within this interval alwaysexplained over 99% of variation, that is, r2 > 0.99, where ris Pearsons’s coeYcient of correlation.

Table 1 provides examples of repeatability of thewhole-plate assays. In all trials, we used a single strain(BY 4742) to inoculate whole plates and therefore anygenetic variation was absent. We assayed the platesunder the WB or WDG regimes using SD or YPDmedium with four replicates for each combination ofregime and medium. The mean, range, and coeYcient ofvariation was calculated from 100 maximum growthrates per plate. The diVerences between the lowest andthe highest estimates within a plate were about 10% ormore. There were also diVerences in the mean growth

Notes & Tips / Anal. Biochem. 344 (2005) 138–140 139

rates in the plates (Table 1). They were more pronouncedand statistically signiWcant for the SD medium, both forthe WB (F D 63.359, df D 397, p ¿ 0.001) and for theWDG (F D 77.954, df D 397, p ¿ 0.001) shaking regime.The plate’s means were more homogeneous for YPD(WDG: F D 5.163, df D 397, p D 0.002; WB:F D 1.148,df D 397, p D 0.330).

To test whether the problems with variation of maxi-mum growth rate estimates are not restricted to yeastmicrocultures, we repeated our experiments using a stan-dard laboratory strain of Escherichia coli, JM109. A cul-ture was derived from a single cell, diluted, dispensedinto 300-�l aliquots of Luria–Bertani broth per well, andincubated at 37 °C with continuous shaking of low inten-sity. The maximum growth rates were calculated in thesame way as for the yeast microcultures. Results aresummarized in Table 1. They demonstrate that the esti-mates are even more variable than those for yeast.

We compared spatial distribution of the yeast growthrates across several plates incubated under the same con-ditions. Figs. 1B and C present examples of two suchplates. There are several reasons to expect minimal vari-

Fig. 1. (A) Typical growth curve of the BY 4742 strain in YPD. Thevertical axis shows OD values without subtracting the backgroundabsorbance. The two horizontal lines mark the range of OD that wasused for calculating the maximum growth rate. (B and C) Two platesexemplifying spatial variation in the exponential growth rate. A singleculture of BY 4742 was used to inoculate all wells in both plates. Thesame incubation regime, WB, was applied for both plates. (D)Expected values for the plate shown in (C). The expected values arelocated on a surface of best Wt that was obtained by Wtting cubic poly-nomial function to the data.

ation in the growth rate within and between them. Theinitial microcultures were very homogeneous becausethey were obtained by dilution of a single overnight cul-ture about 1:3000 with fresh medium followed by rapiddistribution to wells. The Wrst readings included in anal-yses were obtained after 4–5 h of incubation, that is, afterthe cells entered true exponential growth phase. We alsoknew that mixing of cultures was good because we visu-ally inspected all individual growth curves and foundthem smooth and typically shaped. Nevertheless, theestimates of the maximum growth rate are far fromhomogeneous (Figs. 1B and C). The distribution of lowand high estimates appears to show plate-speciWc pat-terns of clustering. Such observations were made repeat-edly for much more than the two plates presented here.

We tested whether these apparent nonrandom pat-terns were real. We calculated a surface that would bestWt individual estimates on a plate. We used a cubic poly-nomial function with two variables in the “Non-linearestimation” procedure of the Statistica 6.1 package. Fig.1D presents such a surface of best Wt for the plate shownin Fig. 1C. We then calculated the coeYcient of variationusing residuals between the obtained values and thosepredicted by the surface estimation. For the plates listedin Table 1, the coeYcient always decreased by as muchas 25–40%, conWrming that the spatial patterns madesubstantial contribution to the total variation.

The four plates containing E. coli cultures also exhib-ited spatial clusters of low and high estimates of themaximum growth rate. Similarly as in case of yeast, the

Table 1Maximum growth rate estimated through automated phenotypicassays

Minimum, maximum, mean, and coeYcient of variation (V, standarddeviation over mean) calculated for individual 100-well plates.

Conditions Min Max Mean V

WB synthetic 0.366 0.397 0.385 0.0170.354 0.395 0.379 0.0200.370 0.406 0.386 0.0180.371 0.419 0.393 0.019

WDG synthetic 0.392 0.435 0.411 0.0240.354 0.427 0.406 0.0270.374 0.418 0.392 0.0220.316 0.429 0.395 0.030

WB YPD 0.424 0.458 0.443 0.0170.424 0.460 0.444 0.0170.423 0.463 0.444 0.0210.421 0.462 0.444 0.017

WDG YPD 0.400 0.444 0.422 0.0230.404 0.449 0.427 0.0230.404 0.439 0.424 0.0170.384 0.444 0.422 0.023

E. coli LB 0.699 0.919 0.805 0.0610.725 0.926 0.813 0.0640.706 0.947 0.841 0.0670.747 0.908 0.828 0.048

140 Notes & Tips / Anal. Biochem. 344 (2005) 138–140

patterns of clustering were evident and tended to changebetween individual plates (not shown).

Random error of estimates would not produce theobserved clusters. On the other hand, the patterns of clus-tering were often dissimilar and therefore it would bediYcult to suggest what systematic factor(s) were respon-sible for their origin. We tested several volumes and shak-ing regimes diVerent from those used by WB and WDG.The variation in the maximum growth rate was not lowerand was often higher. In other tests, we supplementedmedia with a detergent or an antifoam agent to augmentmixing. To facilitate aeration, we incubated and mea-sured OD with lids taken oV plates. None of these experi-mentations produced more homogeneous results. We canoVer only a vague and hypothetical explanation that tinyindividual diVerences between plates, or their placingwithin the holders, or slightly irreproducible operation ofthe apparatus is probably responsible for the occurrenceof spatial diVerences in agitation/precipitation, incuba-tion, or accuracy of OD reading.

Because the observed spatial variation cannot be rem-edied experimentally, it has to be taken into accountwhen experiments are designed and analyzed. In generalterms, the problem is how to assays samples placed in aheterogeneous environment. The usual recommenda-tion is to arrange the compared classes of samples inblocks. In a simple example study, we compared the wildtype BY 4742 and its derivative ssb1�. Deletion of thisgene is thought neutral to Wtness because the buddingyeast has another closely related gene of this type, SSB2.We alternated 50 cultures of BY and 50 of ssb1� alongthe numbered well’s positions. There was no diVerence inthe maximum growth rate, m, of the strains when a sim-ple comparison was made (mBY D 0.456, mssb1 D 0.440,t D 1.712, df D 98, p D 0.090). But, the diVerence turnedout to be statistically signiWcant (t D 2.507, df D 49,p D 0.015) when the t test for pairs (well 1 vs 2, 3 vs 4, ƒ)was applied. Clearly, the pairwise arrangement and anal-ysis helped to eliminate errors caused by the spatial vari-ation. It is an encouraging result because plate assaysallow for easy replication and therefore may be espe-cially useful when only a few strains are compared andthe expected diVerences are small.

Limited replication combined with randomization ofstrains’ positions is recommended when many strains arecompared. Another possibility is to divide the plate’swells into those for assays of the studied strains andthose for estimation of the spatial patterns. The latterwells would have to be spaced appropriately over the

whole plate and Wlled with a single “reference” strain.For example, a simple rule “odd wells in odd columns,even wells in even columns” would result in a dense andregular distribution of the reference wells. The estimatesof the studied strains could be then compared to those ofthe reference strain in neighboring wells and this shouldalleviate the eVect of spatial variation.

To conclude, we conWrmed that the discriminatorypower of the automated growth curve assays can be con-siderable [4,5] but we also demonstrated that the spatialvariation across multiwell plates must be taken intoaccount. We believe that these Wndings apply not only toBioscreen C but to any automat working with multiwellplates. The necessity to replicate experiments or sacriWcea substantial share of wells to identify spatial gradientsmay be considered a drawback. However, it will often beunavoidable because phenotypic changes are frequentlysmall and this is true not only when the studied geneticvariants are generated by systematic gene deletions [6]but also when they appear spontaneously or are inducedchemically [7].

Acknowledgment

This study was supported by Grant 0231/P04/2002/22from the State Committee for ScientiWc Research ofPoland.

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