Spotsizer: High-throughput quantitative analysis of microbial growth

A Matlab-based application for quantification of yeast cell growth on solid media

Quantitative assessment of growth and survival is a suitable technique in studying biochemical, genetic and physiological processes in the cells. The budding yeast Saccharomyces cerevisiae is one of the most widely used eukaryotic model organisms for studying cellular mechanisms and processes in evolutionarily distant species, including humans. Yeast growth can be evaluated on both liquid and solid media by measuring cell suspension turbidity and colony forming units, respectively. Several software tools utilizing different parameters have been proposed to quantify yeast growth on solid media. Here, we developed a Matlab-based application which provides a rapid and robust quantitative yeast growth analysis from spot plating assay. Spot plating assay is a typical procedure to evaluate yeast growth in low-throughput laboratory settings, including growth on different nutrient sources or treatment with specific stressors. The app has a one-step installation process, a self-explanatory interface and shorter analysis steps compared with previous established methods, providing a useful tool for both expert and non-expert yeast researchers.

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

ScreenGarden: a shinyR application for fast and easy analysis of plate-based high-throughput screens

BACKGROUND

Colony growth on solid media is a simple and effective measure for high-throughput genomic experiments such as yeast two-hybrid, synthetic dosage lethality and Synthetic Physical Interaction screens. The development of robotic pinning tools has facilitated the experimental design of these assays, and different imaging software can be used to automatically measure colony sizes on plates. However, comparison to control plates and statistical data analysis is often laborious and pinning issues or plate specific growth effects can lead to the detection of false-positive growth defects.

RESULTS

We have developed ScreenGarden, a shinyR application, to enable easy, quick and robust data analysis of plate-based high throughput assays. The code allows comparisons of different formats of data and different sized arrays of colonies. A comparison of ScreenGarden with previous analysis tools shows that it performs, at least, equivalently. The software can be run either via a website or offline via the RStudio program; the code is available and can be modified by expert uses to customise the analysis.

CONCLUSIONS

ScreenGarden provides a simple, fast and effective tool to analyse colony growth data from genomic experiments.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

A Quantitative Imaging-Based Protocol for Yeast Growth and Survival on Agar Plates

We present a detailed protocol that describes the evaluation of the growth and survival of yeast cells by quantitatively analyzing spotting assays. This simple method reproducibly detects and quantifies subtle differences in growth by measuring the density of cells within a single spot of defined size on an image of a spotting assay. Our protocol is tailored specifically for low-throughput applications, can be easily adapted for specific experimental conditions, and is accessible to yeast experts and non-experts alike.

For an example of the execution of this protocol, please refer to DiGregorio et al. (Di Gregorio et al., 2020).

•

Experimental considerations for enabling spotting assay quantification

•

A step-by-step procedure for spotting yeast cultures on agar plates

•

Quantification and statistical analysis of spotting assay data

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.

RNA metabolism is the primary target of formamide in vivo

The synthesis, processing and function of coding and non-coding RNA molecules and their interacting proteins has been the focus of a great deal of research that has boosted our understanding of key molecular pathways that underlie higher order events such as cell cycle control, development, innate immune response and the occurrence of genetic diseases. In this study, we have found that formamide preferentially weakens RNA related processes in vivo. Using a non-essential Schizosaccharomyces pombe gene deletion collection, we identify deleted loci that make cells sensitive to formamide. Sensitive deletions are significantly enriched in genes involved in RNA metabolism. Accordingly, we find that previously known temperature-sensitive splicing mutants become lethal in the presence of the drug under permissive temperature. Furthermore, in a wild type background, splicing efficiency is decreased and R-loop formation is increased in the presence of formamide. In addition, we have also isolated 35 formamide-sensitive mutants, many of which display remarkable morphology and cell cycle defects potentially unveiling new players in the regulation of these processes. We conclude that formamide preferentially targets RNA related processes in vivo, probably by relaxing RNA secondary structures and/or RNA-protein interactions, and can be used as an effective tool to characterize these processes.

Genetic interactions and functional analyses of the fission yeast gsk3 and amk2 single and double mutants defective in TORC1-dependent processes

The Target of Rapamycin (TOR) signalling network plays important roles in aging and disease. The AMP-activated protein kinase (AMPK) and the Gsk3 kinase inhibit TOR during stress. We performed genetic interaction screens using synthetic genetic arrays (SGA) with gsk3 and amk2 as query mutants, the latter encoding the regulatory subunit of AMPK. We identified 69 negative and 82 positive common genetic interactors, with functions related to cellular growth and stress. The 120 gsk3-specific negative interactors included genes functioning in translation and ribosomes. The 215 amk2-specific negative interactors included genes functioning in chromatin silencing and DNA damage repair. Both amk2- and gsk3-specific interactors were enriched in phenotype categories related to abnormal cell size and shape. We also performed SGA screen with the amk2 gsk3 double mutant as a query. Mutants sensitive to 5-fluorouracil, an anticancer drug are under-represented within the 305 positive interactors specific for the amk2 gsk3 query. The triple-mutant SGA screen showed higher number of negative interactions than the double mutant SGA screens and uncovered additional genetic network information. These results reveal common and specialized roles of AMPK and Gsk3 in mediating TOR-dependent processes, indicating that AMPK and Gsk3 act in parallel to inhibit TOR function in fission yeast.