Multivariate approaches in plant science.

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

Explorative data analysis of two-dimensional electrophoresis gels.

Methods for classification of two-dimensional (2-DE) electrophoresis gels based on multivariate data analysis are demonstrated. Two-dimensional gels of ten wheat varieties are analyzed and it is demonstrated how to classify the wheat varieties in two qualities and a method for initial screening of gels is presented. First, an approach is demonstrated in which no prior knowledge of the separated proteins is used. Alignment of the gels followed by a simple transformation of data makes it possible to analyze the gels in an automated explorative manner by principal component analysis, to determine if the gels should be further analyzed. A more detailed approach is done by analyzing spot volume lists by principal components analysis and partial least square regression. The use of spot volume data offers a mean to investigate the spot pattern and link the classified protein patterns to distinct spots on the gels for further investigation. The explorative approach in analysis of 2-D gels makes it possible, in a fast and convenient way, to screen many gels in order to determine the protein patterns that form clusters and could be selected for further examination.

Determination of wheat quality by mass spectrometry and multivariate data analysis.

Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis. Proteome analysis of storage proteins from the wheat gluten complex based on two-dimensional electrophoresis and analysis of the N-terminal sequence has revealed a protein homologous to gamma-gliadins, tentatively associated with quality and within the molecular weight range 27-35 kDa. Further examinations of gliadin data based on mass spectrometry revealed that quality among wheat varieties could be determined by means of principal component analysis. Further examinations by interval partial least squares made it possible to encircle an overall optimal molecular weight interval from 31.5 to 33.7 kDa. The use of multivariate analysis on data from mass spectrometry has thus shown to be a promising technique to minimize the number of two-dimensional gels within the field of proteome analysis.