Abstract
With the development of high-throughput metabolic technologies, a plethora of primary and secondary compounds have been detected in the plant cell. However, there are still major gaps in our understanding of the plant metabolome. This is especially true with regards to the compartmental localization of these identified metabolites. Non-aqueous fractionation (NAF) is a powerful technique for the determination of subcellular metabolite distributions in eukaryotic cells, and it has become the method of choice to analyze the distribution of a large number of metabolites concurrently. However, the NAF technique produces a continuous gradient of metabolite distributions, not discrete assignments. Resolution of these distributions requires computational analyses based on marker molecules to resolve compartmental localizations. In this article we focus on expanding the computational analysis of data derived from NAF. Along with an experimental workflow, we describe the critical steps in NAF experiments and how computational approaches can aid in assessing the quality and robustness of the derived data. For this, we have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Furthermore, using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected. Finally, we discuss caveats and benefits of NAF analysis in the context of the compartmentalized metabolome.
Highlights
The main biochemical pathways in plants have been resolved by classical biochemical approaches in the last century (Fernie, 2007; Stitt et al, 2010a), many aspects of cellular metabolism and its regulatory functions are still not well understood, mostly due to technical limitations in gathering a more holistic insight into the cell’s biochemistry
We have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected
From the computational point of view as well, this type of data analysis is mostly underexplored. Using both experimentally derived and simulated data, we investigated the effects of computationally modulating parameters important for the analysis of Non-aqueous fractionation (NAF) gradients in order to address several technically and biologically relevant questions, such as: How many fractions are required to produce a good compartmental separation? Does the fraction number or the marker choice influence the estimated compartmental abundances? How good must the compartmental separation be in order to get reasonable estimates of compartmental abundances? How accurate must an estimate of compartmental abundances be in order to be considered valid? Taken together, the answers to these questions give a solid theoretical basis for the planning and execution of NAF experiments
Summary
The main biochemical pathways in plants have been resolved by classical biochemical approaches in the last century (Fernie, 2007; Stitt et al, 2010a), many aspects of cellular metabolism and its regulatory functions are still not well understood, mostly due to technical limitations in gathering a more holistic insight into the cell’s biochemistry. The abundance of metabolites and compartment-specific markers, which are used as anchors to computationally estimate subcellular metabolite distributions, are analyzed throughout the collected gradient fractions.
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