Mass spectrometry can help researchers analyze chemical and biological samples (Cravatt B F, 2007; Bantscheff M, 2012). Mass spectrometry techniques can allow for measurement of the mass and concentration of atoms and molecules. Analysis of samples can provide insight into the molecular makeup of samples obtained from one or more populations, and can help facilitate studies aimed at investigating a biological activity. In particular, quantitative mass spectrometry can provide relatively specific and sensitive data that can allow comparison of biological samples taken at various time points. Such quantitation can allow for comparison of biological variation, and can foster understanding of the molecular machinery of cellular activity and disease progression.
Intensity-based label free relative quantification using high performance liquid chromatography coupled with electrospray ionization and tandem mass spectrometry (HPLC-ESI-MS/MS) can help researchers reveal biological variation by employing large scale comparative experiments in which two or more populations are compared (Oberg A L, 2009). In the context of label free relative quantification, a population can be comprised of biological and/or technical replicates from a biological state in common, e.g., healthy or diseased.
These large scale comparative experiments require normalization in order to allow for meaningful comparison of data from different experiments. Sample measurements can be biased by effects such as the efficiency of sample extraction or systematic effects due to characteristics of the chromatographic quantification itself. Accordingly, normalization attempts to compensate for such effects.
Present normalization methods can include the regression analysis model which can be used to efficiently calibrate sample variance. It can be used to estimate a scaling factor between two populations, to account for variance in coverage.
Another analysis model can include the LOESS (“LOcal regrESSion”) normalization method, which is a form of regression modeling method. The LOESS method can combine more than one regression model into meta-model. The LOESS method can take into account intensity dependent effects, and in some cases can partially correct for background effects. A variant of this model can take into account local effects.
The quantile regression method is another method that can complement the classical linear regression analysis, by allowing a user to make a more subtle inference of the effect of an explanatory variable on a dependent variable. The median scale method can be used for data normalization by adjusting the scale of the data, such as by setting the median of differences to 0. In this method of normalization, all of the various datasets are adjusted, not just the median quantile. As such, a potential drawback to the scale normalization method is that the method does not consider any region or intensity dependent effects.
Known normalization methods can be adequate for use with current label-free relative quantification paradigms for detecting biological variation within HPLC-ESI-MS/MS workflows in the absence of extraneous variability. However, extraneous variability is inherent in HPLC-ESI-MS/MS workflows. Known global normalization methods can mitigate systematic bias somewhat, but when complex variability is present, known methods do not perform well. In fact, known global normalization methods can work well to mitigate systemic bias, but can also increase variability in data rather than reduce it.
Becker et al., U.S. Pat. Nos. 7,087,896 and 6,835,927, are both directed toward obtaining relative quantitative information regarding components of chemical or biological samples that can be obtained from mass spectra, such as by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species.
Hashiba et al., U.S. Pat. No. 7,626,162, is directed toward relative quantitative analysis of a liquid mixture of two samples, such as biological samples, labeled with stable isotopes using a liquid chromatography-tandem mass spectrometry system.
Sachs et al., U.S. Pat. No. 6,906,320, is directed toward mass spectrometry data analysis techniques that can be employed to selectively indentify analytes differing in abundance between different sample sets.
Grace et al., U.S. Pat. No. 6,334,099, is directed toward methods for normalization of experimental data with experiment-to-experiment variability.
The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.
Bantscheff. “Mass spectrometry-based chemoproteomic approaches.” Methods Mol Biol. 803:3-13, 2012.
Bland, Altman. “Statistical methods for assessing agreement between two methods of clinical measurement.” Lancet. 1:307-10, 1986.
Bondarenko, Chelius, Shaler. “Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry.” Anal Chem. 74:4741-9, 2002.
Cravatt, Simon, Yates. “The biological impact of mass-spectrometry-based proteomics.” Nature. 13:991-1000, 2007.
Griffin, Gyfi, Ideker, Rist, Eng, Hood, Aebersold. “Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae.” Mol Cell Proteomics. 1:323-33, 2002.
Jung, Effelsberg, Tallarek. “Microchip electrospray: cone-jet stability analysis for water-acetonitrile and water-methanol mobile phases.” J Chromatogr A. 1218:1611-9, 2011.
Karpievitch, Taverner, Adkins, Callister, Anderson, Smith, Dabney. “Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.” Bioinformatics. 25:2573-80, 2009.
Kultima, Nilsson, Scholz, Rossbach, Fäith, Andrén. “Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides.” Mol Cell Proteomics. 8:2285-95, 2009.
Oberg, Vitek. “Statistical design of quantitative mass spectrometry-based proteomic experiments.” J Proteome Res. 8:2144-56, 2009.
Ramanathan, Zhong, Blumendrantz, Chowdhury Alton. “Response normalized liquid chromatography nanospray ionization mass spectrometry.” J Am Soc Mass Spectom. 18:1891-9, 2007.
Rudnick, Clauser, Kilpatrick, Tchekhovskoi, Neta, Blonder, Billheimer, Blackman, Bunk, Cardasis, Ham, Jaffe, Kinsinger, Mesri, Neuber, Schilling, Tabb, Tegeler, Vega-Montoto, Variyath, Wang, Wand, Whiteaker, Zimmerman, Carr, Fisher, Gibson, Paulovich, Regnier, Robriquez, Spiegelman, Tempst, Leibler, Stein. “Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses.” Mol Cell Proteomics. 9:225-41, 2010.
Voyksner, Lee. “Investigating the use of an octupole ion guide for ion storage and high-pass mass filtering to improve the quantitative performance of electrospray ion trap mass spectrometry.” Rapid Commun Mass Spectrom. 13:1427-37, 1999.