Vacuum-Forced Agroinfiltration for In planta Transformation of Recalcitrant Plants: Cacao as a Case Study.

Transient in planta transformation is a fast and cost-effective alternative for plant genetic transformation. Most protocols for in planta transformation rely on the use of Agrobacterium-mediated transformation. However, the protocols currently in use are standardized for small-sized plants due to the physical and economic constraints of submitting large-sized plants to a vacuum treatment. This work presents an effective protocol for localized vacuum-based agroinfiltration customized for large-sized plants. To assess the efficacy of the proposed method, we tested its use in cacao plants, a tropical plant species recalcitrant to genetic transformation. Our protocol allowed applying up to 0.07 MPa vacuum, with repetitions, to a localized aerial part of cacao leaves, making it possible to force the infiltration of Agrobacterium into the intercellular spaces of attached leaves. As a result, we achieved the Agrobacterium-mediated transient in planta transformation of attached cacao leaves expressing for the RUBY reporter system. This is also the first Agrobacterium-mediated in planta transient transformation of cacao. This protocol would allow the application of the vacuum-based agroinfiltration method to other plant species with similar size constraints and open the door for the in planta characterization of genes in recalcitrant woody, large-size species.

GridMass: a fast two-dimensional feature detection method for LC/MS.

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community.