Biology subjects

Raaisa, R.

Publications and source records attributed to Raaisa, R..

2 recordsLinked to original sources

bioRxiv2020

Corna - An Open Source Python Tool For Natural Abundance Correction In Isotope Tracer Experiments

BackgroundStable isotope-based approaches are used in the field of metabolomics for quantification and identification of metabolites, discovery of new pathways and measurement of intracellular fluxes. In these experiments, often performed with mass spectrometry (MS), data must be corrected for natural abundance of isotopes. Various stand-alone tools with their own separate data formats and learning curves exist for correction of data collected at different resolutions, for tandem MS, and for different number of tracer elements. ResultsWe present a Python package, Corna, that combines natural abundance correction workflows for several experimental conditions and can be used as a one-stop-shop for stable isotope labeled experiments. We validate the algorithms in Corna with published tools, where available, and include new features, such as correction of two tracer elements, that are not yet implemented in any existing software application as per our knowledge. We also present the integration of Corna with an existing open source peak integration software. The integrated workflow can reduce processing times for a typical stable isotope based workflow from days to hours for a familiar user. ConclusionsAlgorithmic advancements have been keeping up with the developments in mass spectrometry technologies and have been the focus of most existing tools for natural abundance correction. However, in this high throughput era, it is also important to recognize user experience, and integrated and reproducible workflows. Corna has been written in Python and is designed for users who have access to large amounts of data from different kinds of experiments and want to integrate a natural abundance correction tool seamlessly in their pipelines. The latest version of Corna can be accessed at https://github.com/raaisakuk/NA_Correction.

bioinformatics↗

bioRxiv2018

Optimization of Experiment Design for Mass Spectrometric Isotopic Labeling Kinetics

Determination of metabolic fluxes by measurement of time-dependent sampling of isotopic enrichments during the administration of labeled substrates provides rich information. Because such experiments are resource-intensive and frequently push the limits of sensitivity of the measurement techniques, optimization of experiment design can improve feasibility with respect to financial and labor costs, time to completion, and increase precision and accuracy of the results. Here we used a previously published set of data acquired in cultured insulinoma cells to evaluate contributions to the sensitivity and variability of the rate of citrate synthase (CS). Specifically, we calculated changes in uncertainty in CS if sample times were dropped or new ones were added, and we observed that some sampling times can be dropped with little effect, while improvements can be made with a strategic choice of when to add samples. We measured the contributions of data sampled at different times on the sensitivity of CS, finding that CS had greater sensitivity at early time points. We tested the concept that if two estimated parameters are correlated significantly, then refining one might constrain the other. In this case, the rate of Beta-oxs was found to be correlated with CS, and narrower variability in Beta-ox did indeed improve the sensitivity of CS. The tests described here might be applied at the initial design stage and then after a pilot phase to improve sensitivities of targeted fluxes and the reduction of materials, time, labor, and other experimental resources. The correlation analyses can be used to consider what orthogonal measurements might be beneficial for further improvement of measurements. While this study used a specific example of a set of time-dependent kinetic isotopic measurements, the results illustrate some generalizable behaviors that can be tested in other experimental systems.

bioinformatics↗