<noautolink> ---+ Measuring Microbial Growth Rates in a Plate Reader The following protocol can be used to determine the growth rate of a bacterial culture using a plate reader by measuring the optical density (OD600) of the culture over time. This protocol is written specifically for a Tecan Infinite M200 Pro plate reader combined with the Magellan data analysis software and thus may require alteration for other machines. ---++Program set-up The parameters used by the Barrick Lab are: * Temperature: Appropriate for growth of your organism * Kinetic Cycle: <p style="margin-left:5%">Duration: 16-24 hours (as appropriate for your experiment)</p> <p style="margin-left:5%">Kinetic interval: every 10 minutes</p> <p style="margin-left:10%">Orbital Shaking: 420 seconds at amplitude 3</p> <p style="margin-left:10%">Wait: 5 seconds</p> <p style="margin-left:10%">Absorbance reading: 600 nm, 25 flashes, 50 ms settle time</p> It's important for the program to shake for most of the time that you are not making measurements. Less shaking leads to slower growth. ---++Reviving cultures (2 days before experiment) 1) Grow an overnight culture of each strain being tested. Inoculate 2 μl of frozen glycerol stocks of each strain into 5 ml of media. Prepare a separate tube of uninoculated media as a control for contamination. Incubate overnight. ---++Preconditioning cultures (1 day before experiment) Preconditioning acclimates the strains to the media. Additionally, on this day you should pre-warm your media as it takes a long time for the plate reader to warm up media. Not doing this will lead to inconsistent lag time. 2) Inoculate 5 μl of each overnight culture into fresh media to precondition. Prepare a separate tube of uninoculated media as a control for contamination. Incubate overnight.<br> 3) Place the media you will use for the assay in an incubator at the correct growth temperature overnight to pre-warm. ---++ Growing cells in plate reader and measuring OD600 All strains tested should have at least have 3 replicates, although more replicates should be performed as long as there is available space on the plate. Evaporation can occur in the outermost wells, so if there are few enough samples the outer ring of wells should be skipped. Due to small variations in temperature throughout a 96-well plate, best results will be obtained if replicates are distributed randomly across the plate. Clear Costar 96-well plates are a good brand to use. 4) Add 195 μl of pre-warmed media to each well being used for cultures and additional wells for blanks.<br> 5) Inoculate each test well with 5 μl of overnight culture.<br> 6) Place the plate into the plate reader. The lid can be removed (we've had no problems with contamination, as seen on wells with LB blanks, with this).<br> 7) Start your program.<br> 8) Once the program has finished, export the data as an Excel spreadsheet.<br> ---++ Fitting growth curves with Growthcurver in R Growthcurver is an R package that analyzes the optical density data by fitting it to a logistic function from which the growth rate, doubling time, and carrying capacity can be calculated. Growthcurver can be installed with the command: <code> install.packages("growthcurver") </code> Growthcurver can be run with the following code. The input file should be a tab-delimited file, which can be created from the Excel spreadsheet. The variable "r" contains the growth rate and is extracted once the model has been fit to the data. <pre> <code> library("growthcurver") #import data growth = read.table("/path/to/your/file/data.tsv", header=T, sep="\t", stringsAsFactors=F) #calculate growth rate gc_out = SummarizeGrowthByPlate(growth, bg_correct="blank") gc_out$r </code> </pre> Quality control can be performed by checking for notes on the model fitting. Any low quality fits should be identified by the following code: <pre> <code> gc_out %>% filter(note != "") gc_fit$vals$note </code> </pre> Additionally, a histogram of the variance of the data can be produced with the following code. Data points should produce a fit similar to a bell curve, with most data points of average variance. Poorly fitting points will appear as outliers on the histogram. <pre> <code> library(dplyr) gc_out = as_data_frame(gc_out) hist(gc_out$sigma, main="Histogram of sigma values", xlab = "sigma") </code> </pre> ---++ Calculating Growth Rates using Grofit R package Growth curves can also be fit using [[ProtocolGrofit][Grofit]], however this package is no longer supported by the current version of R. ---++ References 1 Growthcurver publication: https://link.springer.com/article/10.1186/s12859-016-1016-7 1 Growthcurver manual: https://cran.r-project.org/web/packages/growthcurver/vignettes/Growthcurver-vignette.html ---++ *Contributors* * Isaac Gifford * Julie Perreau * Gabriel Suárez
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Topic revision: r9 - 2021-11-03 - IsaacGifford