A Quantitative Fitness Analysis Workflow

The aim of this procedure is to capture quantitative microbial culture fitnesses in parallel on a genome wide scale in a precise and high throughput manner. This is accomplished by first Turing and array library of microbial colonies in 96 well plates and growing the culture to saturation. Next, the saturated cultures are diluted in water and then spotted onto solid agar plates.

Then the solid a garf plates are photographed periodically and until growth of the microbial colony is saturated. Finally, the time course photographs of the growth of each culture are converted into quantitative measures of cell density and the observed growth curves with growth models and quantitative measures of microbial fitness are summarized. Ultimately, results can be obtained that show evidence for genetic interactions through demonstrating that microbial strains have different fitnesses either when combined with different mutations or grown under different conditions.

The main advantage of this technique over existing methods like high density pinning methods such as SGA or barcode competition experiments, is that fitness measurements are more accurate. This method can help answer key questions in the cancer drug development field, such as which genes and processes are important in the response to DNA damage and cell cycle control, understanding and translating such research and basic microorganisms in the form of potential drug targets In human cancer cells. Though this method can provide insight into telomere function and visi.

It can also be applied to other areas of yeast biology or indeed any area where fitness phenotypes can provide insight into microbial biology. We hope that QFA can be applied to many other microbial organisms for which array libraries exist. For example, povi e coli, orilla surplus Starting from saturated overnight cultures played up to 96 independent yeast strains in 200 microliter volumes of rich liquid medium.

In a 96 well cultured dish sterilize a 96 pin, one eighth of an inch diameter manual replica plater by first dipping it into 100%ethanol, then flaming it and leaving it to cool array, 200 microliters of sterile water in the wells of a 96 Well plate vortex has saturated yeast cultures and dilute by dipping the sterilized pin tool into the strains three times. Then once into the plate containing the water, sterilize the pin tool, and allow it to cool before dipping it three times into the diluted cultures. Then transfer the pintle to a solid agar plate.

After incubating the agar plates, use an s and p robotics SP imager to manually image them for automated culturing. Begin with a rectangular array of independent yeast strains grown on solid agar. In this example, the starting strains are the result of crossing a temperature sensitive query mutation with the yeast deletion collection.

1, 536 cultures representing four replicates of 384 independent yeast strains are grown on SGA plates using a robot with a 96 pin, one millimeter pin diameter sterile pin tool transfer 384 of the 1, 536 strains into 4 96. Well culture plates containing 200 microliters of selective medium grow the cultures at 20 degrees Celsius to saturation. To spot the saturated yeast cultures begin by transferring them to a liquid handling robot and Resus suspending them on a magnetic shaker dilute the cultures to around one to 70 by using a sterile 96 pin robotic pin tool to pin into 200 microliters of sterile water.

After cleaning and sterilizing the pin tool, spot the diluted cultures onto agar plates. In 384, well format transfer the plates to a temperature controlled humidified incubator with an automatic carousel and access hatch. To image the cultures use A robotic automated imager, which repeatedly removes the plates from the incubator, removes the lids, transfers them to a uniformly illuminated space beneath a 35 millimeter camera, captures an image at 5, 184 by 3, 456.

Pixel resolution replaces the lid, then returns them to the incubator carousel to track culture locations on plates and treatments applied to them. Plates are automatically barcoded according to the incubator name, temperature, unique sequential batch number and position in the incubator. Image names are a concatenation of the plate name and timestamp and are automatically generated upon image capture.

Plates are often also manually labeled to ensure that they remain in the same order when transferring between robot carousels. The metadata files are tab delimited text files, which are manually generated. The experimental description file FT two.

Table one is unique to each experiment, but the library description FT three, table one can be reused extensively. Once constructed, the experiment is described in the experimental description file FT two table one containing columns for barcode or automatically generated plate name experiment starting timestamp plate treatment contents of solid agar medium screen name, library plate number for libraries with multiple plates, and a repeat quadrant number for scaling down from 1, 536, well format to 384. Well format the e strain library is described with a library description file FT three, table one stating the genotype grown in each culture location.

In each plate of a library, it contains columns four, library name, ORF, plate number plate, row plate column, and an optional notes column. An optional standard gene name file can be provided FT four, table one describing the standard gene name associated with each systematic ORFY number, identifying strains being screened. This file contains two columns, ORF and gene name for analyzing the data.

The QFA computational workflow requires access to a reasonably powerful multi-core computer workstation on which is installed. The colonizer image analysis software tool and the QFAR package, both of which are documented online, are freely available and run on a range of operating systems. Run colonizer with each of the captured plate images as input generating one colonizer output file FT five, table one for each image captured colonizer output files, specify culture density estimates, culture, area, shape, and color.

For each of the 384 locations on the image plate, an output file name is automatically copied from the source image file name. The QFAR package contains functions seldens to time courses for each culture to fit generalized logistic population models to observations and to plot. Both see figures two and three for examples.

Fitted parameter values are written to QFA logistic parameter files. The QFAR package also contains functions for quality control. Edge colonies are discarded due to the greater availability of nutrients at the plate edges and difficulty with image analysis.

Near plate walls and cultures, which failed the SGA and genotypes displaying linkage with screen specific marker genes are stripped from analysis. Several quantitative fitness measures are derived from logistic population model parameters for each culture. These include maximum population doubling rates and number of divisions from inoculation to saturation for each set of replicate cultures and for each fitness definition.

Several summary statistics of fitness estimates are computed mean and medium fitness, fitness standard deviation and number of replicates observed. Summary statistics are output to QFA fitness summary files FT eight, table one for further analysis. For example, calculations of genetic interaction scores FT nine table one.

This figure shows one typical growth curve on a log scale OUTTA 384 QFA. Cultures growing at 20 degrees Celsius on solid agar as quantified by colonizer with an increase in cell density first detectable after approximately two days. This delay is likely a combined effect of a culture lag phase after inoculation onto solid medium, and a lower limit of detection for cell density.

Here, 308 similar growth curves were captured from a single plate. Note that strains that produced a flat line were very sick or dead. This figure demonstrates the comparison of two genome wide QFA screens to infer genetic interaction strengths.

The QFAR package also includes functions to produce the previous growth curve figure and to output ranked lists of examined genotypes and estimates of genetic interaction strength. Together with a Q value for the significance of the observed GIS Once mastered a complete genome-wide four replicate QF.A experiment can be completed inside three weeks. While attempting this procedure, it’s important to remember to be organized in the tracking and naming of all plates and images.

All plates must inoculated and spot in the correct orientation and order further. The data analysis relies on the correctness of the auxiliary text files built by the experimental scientist. After watching this video, you should have a good understanding of how to estimate the fitness of thousands of independent microbial cultures.

Fitnesses are estimated by repeatedly photographing spotted cultures, growing on solid agar plates, converting the captured images into estimates of cell density and generating growth curves. Mathematical modeling of growth curves provides quantitative fitness estimates, which can then be used to deduce functional relationships between gene products on a genome wide scale.