- Open Access
YODA: Software to facilitate high-throughput analysis of chronological life span, growth rate, and survival in budding yeast
© Olsen et al; licensee BioMed Central Ltd. 2010
- Received: 3 December 2009
- Accepted: 18 March 2010
- Published: 18 March 2010
The budding yeast Saccharomyces cerevisiae is one of the most widely studied model organisms in aging-related science. Although several genetic modifiers of yeast longevity have been identified, the utility of this system for longevity studies has been limited by a lack of high-throughput assays for quantitatively measuring survival of individual yeast cells during aging.
Here we describe the Yeast Outgrowth Data Analyzer (YODA), an automated system for analyzing population survival of yeast cells based on the kinetics of outgrowth measured by optical density over time. YODA has been designed specifically for quantification of yeast chronological life span, but can also be used to quantify growth rate and survival of yeast cells in response to a variety of different conditions, including temperature, nutritional composition of the growth media, and chemical treatments. YODA is optimized for use with a Bioscreen C MBR shaker/incubator/plate reader, but is also amenable to use with any standard plate reader or spectrophotometer.
We estimate that use of YODA as described here reduces the effort and resources required to measure chronological life span and analyze the resulting data by at least 15-fold.
- Genetic Modifier
- Aging Culture
- Chronological Aging
- Issue Tracker
The ability to accurately monitor survival and growth rate of cells is essential for many assays employed in studies of the budding yeast. Changes in growth rate and survival over time are often monitored in response to a chemical treatment, environmental change (e.g. temperature, starvation, etc.), or genetic variant. For example, the yeast ORF deletion collection, which consists of >5000 unique single-gene deletion strains in an isogenic background, has been queried for more than 100 unique phenotypes by monitoring growth or viability under different conditions . Growth rate (doubling time) of yeast cells can be quantified by monitoring the change in optical density at 600 nm (OD600) of a yeast culture under specified conditions. Survival of yeast cells has traditionally been quantified by plating the cells onto rich growth medium (yeast peptone dextrose, YPD) and counting colony forming units (CFUs) before and after treatment.
One important assay that involves monitoring survival of yeast cells over time is measurement of chronological life span (CLS), which is defined as the length of time a yeast cell is able to maintain viability during post-diauxic growth arrest . Yeast CLS has emerged as a useful paradigm in aging-related research and has led to the identification of several dozen genetic modifiers of yeast longevity, some of which play a conserved aging-related function in multicellular eukaryotes . CLS has traditionally been performed by culturing cells in a synthetically defined medium and monitoring survival over time (every 2-3 days) by periodically removing an aliquot of the aging culture, serially diluting that aliquot, plating the cells onto YPD and counting CFUs .
where sn is the survival percentage, Δtn is the time shift, and δn is the doubling time. Detailed methodology and a video protocol describing the CLS assay have been published, and we refer the interested reader to these references [5, 6].
In order to automate analysis of the data generated during a CLS experiment, we developed a software package called YODA, the Yeast Outgrowth Data Analyzer. YODA accepts as input single or multiple text files containing OD values as a function of time (one file for each age-point) and returns several useful parameters, including maximal growth rate for each well, survival at each age-point for each aging culture, and the survival integral (SI) for each strain, which is defined as the area under the survival curve. YODA also has the capacity to group data from replicate cultures and perform simple statistical analyses of each group of replicates both individually and relative to experiment-matched control replicates. YODA is provided to the scientific community as a freely available utility on our website at http://www.kaeberleinlab.org/yoda and on the SAGEWEB website at http://www.sageweb.org/yoda. We have also provided the YODA source code for download along with an issue tracker at http://code.google.com/p/sageweb-yoda/.
Here we describe the key features of YODA and provide a demonstration of how YODA can be used to analyze data from a typical CLS experiment. We also provide examples of how YODA can be used for additional types of experiments in yeast where quantitation of cell survival or growth rate are desired and describe how data generated from sources other than a Bioscreen C MBR machine can be analyzed with YODA.
Uploading outgrowth data from a CLS experiment
Detailed instructions for performing chronological aging experiments using the Bioscreen C MBR machine are published [5, 6] and are also available on our website. At each age-point, the Bioscreen "EZExperiment" software produces a comma-delimited file containing OD420-580 values for each of the 200 wells corresponding to the maximal loading capacity of the machine (2 × 100 well plates). The first column of the resulting table contains the "Time" data (30 minute intervals by our protocol [5, 6], although this can be varied) and each subsequent column contains the data for a single well. In order to upload your data into YODA, the outgrowth data for each age-point should be saved in a separate comma-delimited (.csv) or Microsoft Excel (.xls) file. It is important that well position be maintained for each aging culture throughout the entire experiment (e.g. wild type replicate #1 always in well #1, mutant A replicate #1 always in well #2, wild type replicate #2 always in well #3, etc.). This is ensured by loading the wells in the same order at each age-point. Save each file with an appropriate identifying name, such as 'name_date.csv'.
Quantifying growth rate and survival with YODA
YODA export output options.
Outputs info for each well or well position.
Outputs average value and standard deviation for each well group described in well info.
Outputs median value and standard deviation for each well group described in well info.
Curve Parameter Options:
The min and max background normalized OD values to use in doubling time calculation.
Doubling time Interval OD
The min and max background normalized OD values to calculate "doubling time interval". The program will calculate the average doubling time over this interval.
Survival time shift OD
The background normalized OD value to calculate time shift between curves at different age points.
Run Export Options:
Whether or not the OD readings for a well should be appended to output.
Outputs the OD readings with the file's background subtracted
doubling time inflection
Outputs the doubling time calculated where the curve has the steepest slope between min and max OD thresholds.
doubling time interval
Outputs the average doubling time calculated at all points between min and max doubling time threshold ODs.
doubling time correction
Outputs empirically corrected doubling times.
Lineage Export Options:
Outputs the survival fractions for each age point.
Outputs the integral of the survival fractions over all age points.
Survival fractions are cleaned so that the survival curve has no increases or gasping (spikes at end).
show time shifts
Outputs the time shift relative to reference curve (first age point).
doubling time method
The doubling time method used to calculate survival fraction (uses doubling time of first age point curve).
% change versus reference
Calculates the percent change in survival integral of each well versus a reference.
log2 ratio versus reference
Calculates the log base 2 ratio of survival integrals of each well versus a reference.
t-test versus reference
Calculates the t-statistic and p-value for each well group versus a reference (grouping option must be selected).
Using YODA for non-aging assays
YODA can be easily adapted for analysis of data for a variety of assays in which growth rate or survival is the parameter of interest. To quantify growth rate from OD420-580 readings obtained with a Bioscreen C MBR machine, simply upload a single comma-delimited "EZExperiment" output file as described above and export the data using the "runs" option from the "output" drop-down menu under the Export window. Quantification of survival in response to different experimental stimuli can be performed in a manner analogous to that described for CLS above, with the control treatment assigned as the first age-point in the CLS experiment and each experimental group assigned as a subsequent age-point. Specific examples are provided in the Results section below.
Using YODA with alternative methods of optical density determination
Although YODA is designed for simplified analysis of OD data obtained from the Bioscreen C MBR machine, OD measurements obtained from alternative sources, such as a standard plate reader or spectrophotometer, can also be analyzed with YODA. For such uses, simply ensure that the OD data is entered into .csv or .xls files in the manner described above for data obtained from the Bioscreen machine. All subsequent steps in the analysis are identical.
Quantitation of yeast CLS with YODA
Quantitation of yeast survival following heat shock
In addition to determining the CLS of yeast strains, YODA can also be used to quantify cell viability following an environmental stressor such as heat shock. As an example experiment, an overnight culture of wild type cells was split into 100 μL aliquots and subjected to varying lengths of 55°C heat shock (0, 5, 10, 15, and 20 minutes). Following heat shock, 5 μL of each treatment was inoculated into 145 μL YPD in individual wells of a Bioscreen Honeycomb plate. Each treatment was assayed in triplicate.
Quantitation of growth inhibition by rapamycin
YODA provides a web-based platform for quantifying survival and growth inhibition in the budding yeast. YODA was designed to facilitate high-throughput studies of yeast CLS, but is equally suitable for identifying genetic variants or environmental interventions that modify cell survival or growth rate. In addition to providing information not attainable using the traditional CFU method, such as growth rate of each strain, the use of YODA with a machine such as the Bioscreen C MBR enhances efficiency in the laboratory. For example, the method described here can accommodate up to three biological replicates of 66 strains in a single experiment (198 out of 200 wells used). We estimate that an entire experiment of this nature requires approximately 4 hours of effort for an experienced researcher, including preparation time, strain handling, and data analysis. In contrast, we estimate that the traditional CFU method with serial dilutions would require at least 15-fold longer for the same researcher to obtain equivalent data. In terms of resources, both methods require equal volumes of media for aging cultures; however, the method described here requires only 30 mL of liquid media and two multi-well plates per age point, whereas the traditional CFU method requires approximately 5 L of agar-based media and 198 Petri dishes per age-point (25 mL per plate × 198 strains). Thus we conclude that YODA in combination with the methods described here results in a significant reduction in time and resources required to measure CLS or perform other survival-based assays in yeast.
YODA is provided to the scientific community as a freely available utility on our website at http://www.kaeberleinlab.org/yoda and on the SAGEWEB website at http://www.sageweb.org/yoda. YODA can be used anonymously without a username and password. We have also provided the YODA source code for download along with an issue tracker at http://code.google.com/p/sageweb-yoda/. Installation requires a web server with PHP 5.2 support and MySQL 5.1 or above.
This work was supported by NIH grant R21AG031965, an infrastructure award from the Ellison Medical Foundation supporting SAGEWEB: the Science of Aging Electronic Resource Center, and a Pilot Project Grant from the University of Washington Nathan Shock Center of Excellence in the Basic Biology of Aging (NIH Grant P30AG013280) to MK. MK is an Ellison Medical Foundation New Scholar in Aging.
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