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Related Experiment Video

Updated: Apr 21, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

High-throughput phenomics.

Carlo Viti¹, Francesca Decorosi, Emmanuela Marchi

¹Dipartimento di Scienze delle Produzioni Agroalimentari e dell'Ambiente (DISPAA), University of Florence, P.le delle Cascine, 24, Florence, 50144, Italy, carlo.viti@unifi.it.

Methods in Molecular Biology (Clifton, N.J.)

|October 26, 2014

Summary

This summary is machine-generated.

This study details optimizing Phenotype MicroArray (PM) technology for reproducible microbial characterization. Key steps include careful selection of dye mixes, carbon sources, and buffered media for accurate phenotypic profiling.

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Area of Science:

Microbiology
Molecular Biology
Biotechnology

Background:

Phenotype MicroArray (PM) technology is a powerful tool for microbial characterization.
Obtaining high-quality, reproducible data from PM requires careful attention to experimental details.
Standard protocols exist, but optimization is needed for specific microbial groups and experimental designs.

Purpose of the Study:

To provide detailed protocols for obtaining defined and reproducible phenotypic profiles of bacteria and yeasts using PM technology.
To highlight crucial steps and considerations for optimizing PM experiments.
To introduce innovative software tools for the analysis of PM kinetic data and pan-genome description.

Main Methods:

Systematic evaluation of auxotrophies in microbial strains prior to PM experiments.
Optimization of critical parameters including dye mix selection, carbon source type and concentration, and buffered media.
Application of detailed protocols for bacteria and yeasts.
Utilizing opm R packages and DuctApe suite for data analysis.

Main Results:

Established detailed protocols for reproducible phenotypic profiling using PM technology.
Identified key factors influencing data quality and reproducibility in PM experiments.
Demonstrated the utility of opm R packages and DuctApe suite for analyzing PM kinetic data and pan-phenome description.

Conclusions:

The presented protocols and optimization strategies enable high-quality, reproducible microbial phenotyping via PM technology.
Careful consideration of experimental variables and the use of advanced software tools are essential for successful PM applications.
This work facilitates deeper understanding of microbial physiology and metabolic capabilities.