Back

A Robust Isotope Ratio LC-MS/MS Workflow for High-Throughput Metabolic Profiling of Bacteria

Rocha, C.; Pinto, S. P.; Jensen, S. I.; Nielsen, L. K.; Donati, S.

2025-08-11 systems biology
10.1101/2025.08.08.669109 bioRxiv
Show abstract

Stable isotope dilution mass spectrometry (IDMS) has become a cornerstone of quantitative metabolomics, enabling accurate intracellular metabolite quantification across a range of biological systems. However, the broader adoption of IDMS in high-throughput studies remains limited by the high costs of commercially available 13C-labeled internal standards (IS), labor-intensive in-house IS production, and the narrow applicability of existing methods to different organisms. Here, we present a robust and scalable IDMS-based LC-MS/MS workflow for high-throughput metabolic profiling of diverse bacteria. The analytical method couples ion-pairing liquid chromatography with multiple reaction monitoring (MRM) to quantify 96 intracellular metabolites in under 16 minutes, with an average RSD of 21%. We developed a protocol for large-scale production of high-quality 13C-labeled IS, considerably lowering the cost for high-throughput IDMS studies. We then applied the workflow to 5 bacterial species in different cultivation conditions. This work provides a versatile platform for microbial metabolomics, supporting systems biology and data-driven metabolic engineering at scale. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/669109v2_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1f3e9e9org.highwire.dtl.DTLVardef@81eb0eorg.highwire.dtl.DTLVardef@16efe09org.highwire.dtl.DTLVardef@1e83a06_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in Analytical Chemistry (predicted rank #2) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.