SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data

Quantitative liquid chromatography–mass spectrometry
(LC-MS)-based
metabolomics is becoming an important approach for studying complex
biological systems but presents several technical challenges that
limit its widespread use. Computing metabolite concentrations using
standard curves generated from standard mixtures of known concentrations
is a labor-intensive process that is often performed manually. Currently,
there are few options for open-source software tools that can automatically
calculate metabolite concentrations. Herein, we introduce SCALiR (standard
curve application for determining linear ranges), a new web-based
software tool specifically built for this task, which allows users
to automatically transform LC-MS signals into absolute quantitative
data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation
of the line of best fit for each standard curve and uses this equation
to calculate compound concentrations from the LC-MS signal. Using
a standard mix containing 77 metabolites, we show a close correlation
between the concentrations calculated by SCALiR and the expected concentrations
of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate
that SCALiR reproducibly calculates concentrations of midrange standards
across ten analytical batches (average coefficient of variation 0.091).
SCALiR can be used to calculate metabolite concentrations either using
external calibration curves or by using internal standards to correct
for matrix effects. This open-source and vendor agnostic software
offers users several advantages in that (1) it requires only 10 s
of analysis time to compute concentrations of >75 compounds, (2)
it
facilitates automation of quantitative workflows, and (3) it performs
deterministic evaluations of compound quantification limits. SCALiR
therefore provides the metabolomics community with a simple and rapid
tool that enables rigorous and reproducible quantitative metabolomics
studies.