Credentialed E. coli Cell Extract Kits

Metabolomics generates large, complex datasets. Due to this complexity, it is challenging to assess method performance, to identify analytes, and to compare data across laboratories and experiments. The diversity of metabolite analytes and metabolomic study designs precludes the use of one or a few standards to address these challenging questions. A solution, developed by Patti and colleagues, is the credentialing protocol. This utilizes E. coli cell extracts and novel Credentialing software (GPL3 open source license at or Compound Discoverer™ version 3.0 or greater) to discriminate features of biological origin from contaminants (e.g., plastic leachable, solvent impurities, carryover) and artifacts (e.g., informatic errors) without having to identify metabolite structures.

Using E. coli as the representative complex biological sample, the Credentialed E. coli Extract Kits are offered by CIL for untargeted metabolomic developments. These kits are supplied as a solution (MSK-CRED-KIT) or dried down (MSK-CRED-DD-KIT) extracts (see below for kit contents). They are designed to assist in optimizing methods for untargeted, metabolomic profiling studies (see references below for example applications).

Kit Contents

  • U-13C-labeled (at 99% enrichment) E. coli cell extract (dried down or 100 µL)

  • Unlabeled E. coli cell extract (dried down or 100 µL)

  • Document package (supplied via QR code). This includes a user manual, which contains instructions/illustrations on example sample preparations and data analysis approaches.

Related Resources

Credentialed E. coli Cell Extract Kits

Related Products

Frequently Asked Questions

What types of optimizations or comparison could these MSK-CRED kits be used for? Using an untargeted metabolomics platform, these kits could be used for performance comparison (e.g., column, instrument) or parameter optimization (e.g., instruments, informatics). The kits could also be used to rule out sources of peak contamination (e.g., materials arising from syringe, tubing, connections) and artifacts (e.g, informatic errors).

What is the procedure for extract reconstitution? The dry extracts in MSK-CRED-DD-KIT should be reconstituted in 100 μL of ACN/H2O (1:1, v/v) then sonicated (for 10 minutes) and centrifuged (e.g., for 15 minutes at 13,000 rpm and 4°C) before overnight incubation at 4ºC. This results in a clear solution. Aliquots of the 13C-labeled and unlabeled extracts should then be mixed (e.g., 1:2 and 1:1 v/v) and vortexed. Upon transfer to LC vials, the sample is ready for LC-MS or LC-MS/MS analysis.

Can the dried down extracts be reconstituted in alternate solvent ratios? It is reasonable to alter the reconstitution recommendations of 1:1 v/v ACN:water. The recommended solvent ratio was chosen in an attempt to capture both water-soluble and lipid-soluble metabolites. Changing the ratio to increase ACN or water will increase the coverage of organic or water-soluble metabolites, respectively. The effectiveness of the credentialing approach, however, will not be affected by the solvent ratio employed.

How many sample runs will each kit allow? Under typical LC-MS protocols (involves 5 μL injection; example provided in the user manual, supplied with kit shipment via QR code), the extracts (both solution and dried down) enable 20 runs.

Example References

Li, S.; Zheng, S. 2023. Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data. Anal Chem, 95(15), 6212-6217. PMID: 37018697
Dodds, J.N.; Wang, L.; Patti, G.J.; et al. 2022. Combining isotopologue workflows and simultaneous multidimensional separations to detect, identify, and validate metabolites in untargeted analyses. Anal Chem, 94(5), 2527-2535.  PMID: 35089687 
Giné, R.; Capellades, J.; Badia, J.M.; et al. 2021. HERMES: a molecular-formula-oriented method to target the metabolome. Nat Methods, 18(11), 1370-1376. PMID: 34725482 
Cho, K.; Schwaiger-Haber, M.; Naser, F.J.; et al. 2021. Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics. Anal Chim Acta, 1149, 338210-338227. PMID: 33551064 
Sindelar, M.; Patti, G.J. 2020. Chemical discovery in the era of metabolomics. J Am Chem Soc, 142(20), 9097-9105. PMID: 32275430 
Wang, L.; Naser, F.J.; Spalding, J.L.; et al. 2019. A protocol to compare methods for untargeted metabolomics. Methods Mol Biol, 1862, 1-15. PMID: 30315456 
Spalding, J.L.; Naser, F.J.; Mahieu, N.G.; et al. 2018. Trace phosphate improves ZIC-pHILIC peak shape, sensitivity, and coverage for untargeted metabolomics. J Proteome Res, 17(10), 3537-3546. PMID: 30160483 
Naser, F.J.; Mahieu, N.G.; Wang, L.; et al. 2018. Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome. Anal Bioanal Chem, 410(4), 1287-1297. PMID: 29256075 
Mahieu, N.G.; Patti, G.J. 2017. Systems-level annotation of a metabolomics data set reduces 25,000 features to fewer than 1,000 unique metabolites. Anal Chem, 89(19), 10397-10406. PMID: 28914531 
Benton, H.P.; Ivanisevic, J.; Mahieu, N.G.; et al. 2015. Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem, 87(2), 884-891. PMID: 25496351 
Mahieu, N.G.; Huang, X.; Chen, Y.; et al. 2014. Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods. Anal Chem, 86(19), 9583-9589. PMID: 25160088