Back

Nanoflow ion-pairing LC-MS for ultra-low-input polar metabolomics and isotope tracing

Ellis, A. E.; Deshpande, R.; Cook, A.; Dufresne, C. P.; Bailey, M.; Bird, S. S.; Sheldon, R. D.

2026-06-08 biochemistry
10.64898/2026.06.03.729938 bioRxiv
Show abstract

Low-input and single-cell metabolomics remain constrained by the poor retention of polar metabolites in conventional reversed-phase nanoflow LC-MS workflows. Here, we establish nanoflow tributylamine (TBA) ion-pairing LC-MS as a platform for ultra-low-input polar metabolomics and stable isotope tracing. By adapting an analytical-flow TBA ion-pairing method to the nanoflow scale, this workflow extends the sensitivity and inline concentration advantages of nanoflow chromatography to charged metabolites involved in central carbon metabolism. Using mouse liver metabolite extracts, we show that the nanoflow method preserves chromatographic retention and separation of chemically diverse metabolite classes, including adenine nucleotides, nucleotide cofactors, TCA cycle intermediates, acyl-CoAs, and bile acid isomers. Despite loading 20-fold less tissue-equivalent material on column, nanoflow LC-MS produced higher signal intensity than the analytical-flow method for many metabolites. Across representative compounds, the nanoflow workflow reduced the biomass required for detection by approximately 20- to >600-fold, with pronounced gains for low-abundance metabolites such as NADPH and acetyl-CoA. TBA ion-pairing also enabled trap-and-elute nanoflow analysis of retained polar metabolites from single-cell-equivalent inputs. ATP was detected from one cell equivalent using both full-scan and targeted parallel reaction monitoring acquisition, with targeted acquisition further increasing signal over blank. Finally, we applied the workflow to stable isotope tracing in uniformly labeled 13C-glucose-treated cells. 13C-labeled ATP isotopologues were detectable from single-cell-equivalent input, and targeted acquisition improved isotopologue measurement near the detection limit. Together, these results demonstrate that nanoflow TBA ion-pairing LC-MS enables retained, high-sensitivity analysis of polar metabolites from ultra-low inputs and provides a foundation for extending central carbon metabolite analysis and isotope tracing toward single-cell-scale applications.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Analytical Chemistry
218 papers in training set
Top 0.1%
39.3%
2
Nature Communications
5641 papers in training set
Top 11%
16.9%
50% of probability mass above
3
Analytica Chimica Acta
17 papers in training set
Top 0.1%
4.0%
4
The Analyst
16 papers in training set
Top 0.1%
3.2%
5
Journal of Proteome Research
234 papers in training set
Top 0.9%
2.7%
6
Communications Chemistry
48 papers in training set
Top 0.3%
2.6%
7
Angewandte Chemie International Edition
93 papers in training set
Top 0.9%
1.9%
8
PLOS ONE
5266 papers in training set
Top 48%
1.7%
9
JACS Au
43 papers in training set
Top 0.5%
1.5%
10
Cell Reports Methods
165 papers in training set
Top 3%
1.1%
11
Molecular & Cellular Proteomics
158 papers in training set
Top 1%
1.1%
12
Journal of the American Society for Mass Spectrometry
37 papers in training set
Top 0.4%
1.1%
13
Communications Biology
993 papers in training set
Top 25%
1.0%
14
Scientific Reports
3612 papers in training set
Top 70%
1.0%
15
Talanta
14 papers in training set
Top 0.3%
1.0%
16
Nature Methods
385 papers in training set
Top 6%
1.0%
17
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 39%
1.0%
18
Nature Chemical Biology
119 papers in training set
Top 3%
0.8%
19
Journal of Biological Chemistry
690 papers in training set
Top 9%
0.8%
20
Analytical Biochemistry
26 papers in training set
Top 0.5%
0.8%
21
Metabolomics
14 papers in training set
Top 0.4%
0.6%
22
Metabolites
53 papers in training set
Top 1%
0.6%
23
ACS Chemical Biology
167 papers in training set
Top 3%
0.6%
24
Water Research
79 papers in training set
Top 1%
0.6%
25
Journal of the American Chemical Society
217 papers in training set
Top 3%
0.6%