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Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse.

Geiger T, Velic A, Macek B, Lundberg E, Kampf C, Nagaraj N, Uhlen M, Cox J, Mann M - Mol. Cell Proteomics (2013)

Bottom Line: The proportion of strictly tissue-specific proteins appeared to be small.We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue.Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.

ABSTRACT
Identifying the building blocks of mammalian tissues is a precondition for understanding their function. In particular, global and quantitative analysis of the proteome of mammalian tissues would point to tissue-specific mechanisms and place the function of each protein in a whole-organism perspective. We performed proteomic analyses of 28 mouse tissues using high-resolution mass spectrometry and used a mix of mouse tissues labeled via stable isotope labeling with amino acids in cell culture as a "spike-in" internal standard for accurate protein quantification across these tissues. We identified a total of 7,349 proteins and quantified 6,974 of them. Bioinformatic data analysis showed that physiologically related tissues clustered together and that highly expressed proteins represented the characteristic tissue functions. Tissue specialization was reflected prominently in the proteomic profiles and is apparent already in their hundred most abundant proteins. The proportion of strictly tissue-specific proteins appeared to be small. However, even proteins with household functions, such as those in ribosomes and spliceosomes, can have dramatic expression differences among tissues. We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue. Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species.

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Related in: MedlinePlus

Reproducibility of proteomic data. Comparison of triplicate analysis of lung and liver tissues shows high correlation between replicates. A, heat map of the Pearson correlations of ratios relative to super-SILAC. B, C, scatter plots comparing replicate lung (B) and liver (C) samples. Color code represents density as indicated in the bar at the bottom.
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Figure 2: Reproducibility of proteomic data. Comparison of triplicate analysis of lung and liver tissues shows high correlation between replicates. A, heat map of the Pearson correlations of ratios relative to super-SILAC. B, C, scatter plots comparing replicate lung (B) and liver (C) samples. Color code represents density as indicated in the bar at the bottom.

Mentions: LC-MS/MS measurements were performed on an Easy-nano-LC (Thermo Fisher Scientific) coupled to an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific). Peptides were separated on a reverse-phase column (15 cm, 75 μm inner diameter and 3 μm Reprosil resin) using a 100-min gradient of water–acetonitrile. All MS measurements were performed in the positive ion mode. Precursor ions were measured in the Orbitrap analyzer at 60,000 resolution (at 400 m/z) and a target value of 106 ions. The five most intense ions from each MS scan (with a target value of 5,000 ions) were isolated, fragmented, and measured in the linear ion trap. Replicate analysis (Fig. 2) was performed in single runs on an EASY-nLC1000 (22) coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific) (23). Peptides were separated on a reverse-phase column (30 cm, 75 μm inner diameter and 1.8 μm Reprosil resin) using a 200-min gradient of water–acetonitrile. All MS measurements were performed in the positive ion mode. Precursor ions were measured in the Orbitrap analyzer at 70,000 resolution (at 200 m/z) and a target value of 106 ions. The 10 most intense ions from each MS scan were isolated, fragmented, and measured in the Orbitrap with a resolution of 17,500.


Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse.

Geiger T, Velic A, Macek B, Lundberg E, Kampf C, Nagaraj N, Uhlen M, Cox J, Mann M - Mol. Cell Proteomics (2013)

Reproducibility of proteomic data. Comparison of triplicate analysis of lung and liver tissues shows high correlation between replicates. A, heat map of the Pearson correlations of ratios relative to super-SILAC. B, C, scatter plots comparing replicate lung (B) and liver (C) samples. Color code represents density as indicated in the bar at the bottom.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675825&req=5

Figure 2: Reproducibility of proteomic data. Comparison of triplicate analysis of lung and liver tissues shows high correlation between replicates. A, heat map of the Pearson correlations of ratios relative to super-SILAC. B, C, scatter plots comparing replicate lung (B) and liver (C) samples. Color code represents density as indicated in the bar at the bottom.
Mentions: LC-MS/MS measurements were performed on an Easy-nano-LC (Thermo Fisher Scientific) coupled to an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific). Peptides were separated on a reverse-phase column (15 cm, 75 μm inner diameter and 3 μm Reprosil resin) using a 100-min gradient of water–acetonitrile. All MS measurements were performed in the positive ion mode. Precursor ions were measured in the Orbitrap analyzer at 60,000 resolution (at 400 m/z) and a target value of 106 ions. The five most intense ions from each MS scan (with a target value of 5,000 ions) were isolated, fragmented, and measured in the linear ion trap. Replicate analysis (Fig. 2) was performed in single runs on an EASY-nLC1000 (22) coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific) (23). Peptides were separated on a reverse-phase column (30 cm, 75 μm inner diameter and 1.8 μm Reprosil resin) using a 200-min gradient of water–acetonitrile. All MS measurements were performed in the positive ion mode. Precursor ions were measured in the Orbitrap analyzer at 70,000 resolution (at 200 m/z) and a target value of 106 ions. The 10 most intense ions from each MS scan were isolated, fragmented, and measured in the Orbitrap with a resolution of 17,500.

Bottom Line: The proportion of strictly tissue-specific proteins appeared to be small.We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue.Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.

ABSTRACT
Identifying the building blocks of mammalian tissues is a precondition for understanding their function. In particular, global and quantitative analysis of the proteome of mammalian tissues would point to tissue-specific mechanisms and place the function of each protein in a whole-organism perspective. We performed proteomic analyses of 28 mouse tissues using high-resolution mass spectrometry and used a mix of mouse tissues labeled via stable isotope labeling with amino acids in cell culture as a "spike-in" internal standard for accurate protein quantification across these tissues. We identified a total of 7,349 proteins and quantified 6,974 of them. Bioinformatic data analysis showed that physiologically related tissues clustered together and that highly expressed proteins represented the characteristic tissue functions. Tissue specialization was reflected prominently in the proteomic profiles and is apparent already in their hundred most abundant proteins. The proportion of strictly tissue-specific proteins appeared to be small. However, even proteins with household functions, such as those in ribosomes and spliceosomes, can have dramatic expression differences among tissues. We describe a computational framework with which to correlate proteome profiles with physiological functions of the tissue. Our data will be useful to the broad scientific community as an initial atlas of protein expression of a mammalian species.

Show MeSH
Related in: MedlinePlus