Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers

The type of carbon source not the growth rate it supports can determine diauxie in Saccharomyces cerevisiae

How cells choose between carbon sources is a classic example of cellular decision-making. Microbes often prioritise glucose, but there has been little investigation of whether other sugars are also preferred. Here we study budding yeast growing on mixtures of sugars with palatinose, a sucrose isomer that cells catabolise with the MAL regulon. We find that the decision-making involves more than carbon flux-sensing: yeast prioritise galactose over palatinose, but sucrose and fructose weakly if at all despite each allowing faster growth than palatinose. With genetic perturbations and transcriptomics, we show that the regulation is active with repression of the MAL genes via Gal4, the GAL regulon's master regulator. We argue, using mathematical modelling, that cells enforce their preference for galactose through weakening the MAL regulon's positive feedback. They do so through decreasing intracellular palatinose by repressing MAL11, the palatinose transporter, and expressing the isomaltases IMA1 and IMA5. Supporting these predictions, we show that deleting IMA1 abolishes diauxie. Our results demonstrate that budding yeast actively prioritises carbon sources other than glucose and that such priorities need not reflect differences in growth rates. They imply that carbon-sensing strategies even in model organisms are more complex than previously thought.

Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal-oxide-semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications.

Inferring fungal growth rates from optical density data

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration.

Parsley: a web app for parsing data from plate readers

SUMMARY

As demand for the automation of biological assays has increased over recent years, the range of measurement types implemented by multiwell plate readers has broadened and the list of published software packages that caters to their analysis has grown. However, most plate readers export data in esoteric formats with little or no metadata, while most analytical software packages are built to work with tidy data accompanied by associated metadata. 'Parser' functions are therefore required to prepare raw data for analysis. Such functions are instrument- and data type-specific, and to date, no generic tool exists that can parse data from multiple data types or multiple plate readers, despite the potential for such a tool to speed up access to analysed data and remove an important barrier for less confident coders. We have developed the interactive web application, Parsley, to bridge this gap. Unlike conventional programmatic parser functions, Parsley makes few assumptions about exported data, instead employing user inputs to identify and extract data from data files. In doing so, it is designed to enable any user to parse plate reader data and can handle a wide variety of instruments (10+) and data types (53+). Parsley is freely available via a web interface, enabling access to its unique plate reader data parsing functionality, without the need to install software or write code.

AVAILABILITY AND IMPLEMENTATION

The Parsley web application can be accessed at: https://gbstan.shinyapps.io/parsleyapp/. The source code is available at: https://github.com/ec363/parsleyapp and is archived on Zenodo: https://zenodo.org/records/10011752.

Nunchaku: optimally partitioning data into piece-wise contiguous segments

MOTIVATION

When analyzing 1D time series, scientists are often interested in identifying regions where one variable depends linearly on the other. Typically, they use an ad hoc and therefore often subjective method to do so.

RESULTS

Here, we develop a statistically rigorous, Bayesian approach to infer the optimal partitioning of a dataset not only into contiguous piece-wise linear segments, but also into contiguous segments described by linear combinations of arbitrary basis functions. We therefore present a general solution to the problem of identifying discontinuous change points. Focusing on microbial growth, we use the algorithm to find the range of optical density where this density is linearly proportional to the number of cells and to automatically find the regions of exponential growth for both Escherichia coli and Saccharomyces cerevisiae. For budding yeast, we consequently are able to infer the Monod constant for growth on fructose. Our algorithm lends itself to automation and high throughput studies, increases reproducibility, and should facilitate data analyses for a broad range of scientists.

AVAILABILITY AND IMPLEMENTATION

The corresponding Python package, entitled Nunchaku, is available at PyPI: https://pypi.org/project/nunchaku.