Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines
Open accessAbstract
Metabolomics is a rapidly advancing field, and much of our understanding of the subject has come from research on cell lines. However, the results and interpretation of such studies depend on appropriate normalization of the data; ineffective or poorly chosen normalization methods can lead to frankly erroneous conclusions. That is a recurrent challenge because robust, reliable methods for normalization of data from cells have not been established. In this study, we have compared several methods for normalization of metabolomic data from cell extracts. Total protein concentration, cell count, and DNA concentration exhibited strong linear correlations with seeded cell number, but DNA concentration was found to be the most generally useful method for the following reasons: (1) DNA concentration showed the greatest consistency across a range of cell numbers; (2) DNA concentration was the closest to proportional with cell number; (3) DNA samples could be collected from the same dish as the metabolites; and (4) cell lines that grew in clumps were difficult to count accurately. We therefore conclude that DNA concentration is a widely applicable method for normalizing metabolomic data from adherent cell lines.
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The DNA amount in a sample is determined by the number of cells.
“which in turn depends on the number of cells.”
From A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards (2014) · cites this paper as [25]
DNA concentration is proportional to cell count and has been used to normalize enzyme activities, transcript and protein amounts, and metabolome data.
“DNA concentration was shown to be proportional to the cell count and was successfully used to normalize enzyme activities, transcript and protein amounts, and metabolome data (23–25).”
From A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards (2014) · cites this paper as [25]
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- Authors
- Silva, L. P., Lorenzi, P. L., Purwaha, P., Yong, V., Hawke, D. H., & Weinstein, J. N.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Lorenzi, Philip L.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-10-02
- Volume
- 85
- Issue
- 20
- Pages
- 9536-9542
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- PMC
- PMC3868625
- OpenAlex
- W2054932082
- Cited by
- 111
- References
- 25
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 6 · 2023: 12 · 2022: 9 · 2021: 9 · 2020: 9 · 2019: 16 · 2018: 11 · 2017: 12 · 2016: 5 · 2015: 6 · 2014: 5
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Analytical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Michael and Susan Dell Foundation
- National Cancer Institute (CA083639, CA143883, U24CA143883, P50CA083639, P30CA016672)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- adherent cell lines, cell number estimation, total protein concentration, cell number, cell extracts
- MeSH terms
- Cell Adhesion, Cell Count, DNA, Humans, Time Factors, Linear Models, Cell Line, Tumor, Metabolomics
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