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. 2020:4:141-148.
doi: 10.1016/j.tma.2020.08.003. Epub 2020 Aug 21.

SPOCK, an R based package for high-throughput analysis of growth rate, survival, and chronological lifespan in yeast

Eric M Small  1 , Daniel P Felker  2 , Olivia C Heath  2 , Ryla J Cantergiani  2 , Mary Ann Osley  1 , Mark A McCormick  2   3
Affiliations

SPOCK, an R based package for high-throughput analysis of growth rate, survival, and chronological lifespan in yeast

Eric M Small et al. Transl Med Aging. 2020.

Abstract

Plate reader-based methods for high-throughput measurement of growth rate, cellular survival, and chronological lifespan are a compelling addition to the already powerful toolbox of budding yeast Saccharomyces cerevisiae genetics. These methods have overcome many of the limits of traditional yeast biology techniques, but also present a new bottleneck at the point of data-analysis. Herein, we describe SPOCK (Survival Percentage and Outgrowth Collection Kit), an R-based package for the analysis of data created by high-throughput plate reader based methods. This package allows for the determination of chronological lifespan, cellular growth rate, and survival in an efficient, robust, and reproducible fashion.

Keywords: R package; budding yeast; chronological lifespan; data-analysis; growth rate; high-throughput; software; survival.

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Conflict of interest statement

DECLARATION OF COMPETING INTERESTS The authors do not have any conflicts of interest to declare.

Figures

Fig 1:
Fig 1:
SPOCK is a package composed of a number of functions. The principle functions are ladder.create(), OGA(), and SurvivalCalc(). ladder.create() is responsible for the creation of OD calibration ladders. OGA() filters and corrects RAW data to calculate doubling times. SurvivalCalc() integrates a number of functions to determine the survival percentage and survival integral for outgrowth analysis. create.plot() and stats() allow the user to visualize SPOCK calibrated data, and to calculate replicate statistics (MEAN, SD, and SE) for doubling-time and chronological aging analyses.
Fig 2:
Fig 2:
Butterworth filtering of growth curve data is an effective way to robustly remove typical sources of noise. A. Fourier transformations of representative unfiltered and Butterworth filtered growth curves, using a 3rd order Butterworth filter with 5[cHz] frequency cutoff. The Fourier transformation of the impulse response underlying the Butterworth filter is also shown. B. Unfiltered, Butterworth filtered and Savitzyk-Golay filtered representative growth curve, suggesting that Butterworth is a better choice than Savitzyk-Golay for these data.
Fig. 3:
Fig. 3:
A representative Butterworth filtered growth curve, overlaid with the first order derivative (multiplied times 10 for visualization), showing that the maximum first order derivative occurs at the inflection point of exponential growth. By this method OGA() picks the limits of exponential growth. The data between these limits is reproducibly highly linear when log transformed.
Fig. 4:
Fig. 4:
A. Aged yeast cultures reach exponential growth at a later time than young cultures. This difference is related to percent cell survival in the aged culture. B. The mean survival of a strain over time is the survival integral or area under the curve formed by plotting survival percentage vs age.
Fig. 5:
Fig. 5:
A. Growth curves of S. cerevisiae strains known to have decreased or increased survival relative to a wild type strain at days 2,7, and 21 after culture incoluation. B. Percent survival is calculated as described in Section 3.2, in which each strain is plotted at each timepoint during aging.
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