Measurement of DNA Concentration as a Normalization Strategy for Metabolomic Data from Adherent Cell Lines
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 frankl...
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| Published in | Analytical chemistry (Washington) Vol. 85; no. 20; pp. 9536 - 9542 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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United States
American Chemical Society
15.10.2013
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| Abstract | 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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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] 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.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. |
| Author | Lorenzi, Philip L. Purwaha, Preeti Weinstein, John N. Silva, Leslie P. Hawke, David H. Yong, Valeda |
| AuthorAffiliation | University of Texas MD Anderson Cancer Center Department of Pathology Department of Bioinformatics and Computational Biology |
| AuthorAffiliation_xml | – name: University of Texas MD Anderson Cancer Center – name: Department of Pathology – name: Department of Bioinformatics and Computational Biology – name: Department of Pathology, University of Texas MD Anderson Cancer Center, 7435 Fannin St., Houston, TX, 77054, United States – name: Department of Bioinformatics & Computational Biology, University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030, United States |
| Author_xml | – sequence: 1 givenname: Leslie P. surname: Silva fullname: Silva, Leslie P. – sequence: 2 givenname: Philip L. surname: Lorenzi fullname: Lorenzi, Philip L. email: pllorenzi@mdanderson.org – sequence: 3 givenname: Preeti surname: Purwaha fullname: Purwaha, Preeti – sequence: 4 givenname: Valeda surname: Yong fullname: Yong, Valeda – sequence: 5 givenname: David H. surname: Hawke fullname: Hawke, David H. – sequence: 6 givenname: John N. surname: Weinstein fullname: Weinstein, John N. |
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| Title | Measurement of DNA Concentration as a Normalization Strategy for Metabolomic Data from Adherent Cell Lines |
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