Limits...
A high confidence, manually validated human blood plasma protein reference set.

Schenk S, Schoenhals GJ, de Souza G, Mann M - BMC Med Genomics (2008)

Bottom Line: Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3).The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Bioinformatics, University of Southern Denmark, Campusvej 55, 5230 Odense M,Denmark. sschenk@bmb.sdu.dk

ABSTRACT

Background: The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods: To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results: Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion: Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.

No MeSH data available.


Related in: MedlinePlus

Pie chart representation of all validated proteins which were categorized as GO cellular component. Of the 697 plasma proteins identified, 540 of these fell into the 'GO cellular component' category. Of the 540 'GO cellular component' proteins, 392 (65%) were classified as 'cellular' and 208 (35%) were classified as 'extracellular'. 44 of the proteins from the 'extracellular' category were classified as 'extracellular matrix' proteins. Note that because some of the proteins have been reported more than once, the total number of proteins reported for the two categories shown is actually higher than the total number of proteins for the 'GO cellular component'. We have therefore normalized the sum of the cellular and extracellular components to 100%.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2563020&req=5

Figure 4: Pie chart representation of all validated proteins which were categorized as GO cellular component. Of the 697 plasma proteins identified, 540 of these fell into the 'GO cellular component' category. Of the 540 'GO cellular component' proteins, 392 (65%) were classified as 'cellular' and 208 (35%) were classified as 'extracellular'. 44 of the proteins from the 'extracellular' category were classified as 'extracellular matrix' proteins. Note that because some of the proteins have been reported more than once, the total number of proteins reported for the two categories shown is actually higher than the total number of proteins for the 'GO cellular component'. We have therefore normalized the sum of the cellular and extracellular components to 100%.

Mentions: According to GoMiner, of the 540 proteins recognized as being part of the cellular component from our list of 697, 208 of the proteins were categorized as 'extracellular' and 392 proteins as 'cellular', although some proteins fall into both categories. Because of this redundancy we have chosen to normalize the sum of the cellular and extracellular categories to 100% for comparison purposes; thus, 35% of the proteins were categorized as extracellular and 65% as cellular (Figure 4). 44 proteins from the extracellular protein group were classified as 'extracellular matrix' proteins, leaving the remaining 164 proteins from the extracellular protein category as possible plasma proteins. As for the remaining 157 proteins not categorized by GoMiner as cellular components, their subsequent classification could potentially alter the present profile.


A high confidence, manually validated human blood plasma protein reference set.

Schenk S, Schoenhals GJ, de Souza G, Mann M - BMC Med Genomics (2008)

Pie chart representation of all validated proteins which were categorized as GO cellular component. Of the 697 plasma proteins identified, 540 of these fell into the 'GO cellular component' category. Of the 540 'GO cellular component' proteins, 392 (65%) were classified as 'cellular' and 208 (35%) were classified as 'extracellular'. 44 of the proteins from the 'extracellular' category were classified as 'extracellular matrix' proteins. Note that because some of the proteins have been reported more than once, the total number of proteins reported for the two categories shown is actually higher than the total number of proteins for the 'GO cellular component'. We have therefore normalized the sum of the cellular and extracellular components to 100%.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2563020&req=5

Figure 4: Pie chart representation of all validated proteins which were categorized as GO cellular component. Of the 697 plasma proteins identified, 540 of these fell into the 'GO cellular component' category. Of the 540 'GO cellular component' proteins, 392 (65%) were classified as 'cellular' and 208 (35%) were classified as 'extracellular'. 44 of the proteins from the 'extracellular' category were classified as 'extracellular matrix' proteins. Note that because some of the proteins have been reported more than once, the total number of proteins reported for the two categories shown is actually higher than the total number of proteins for the 'GO cellular component'. We have therefore normalized the sum of the cellular and extracellular components to 100%.
Mentions: According to GoMiner, of the 540 proteins recognized as being part of the cellular component from our list of 697, 208 of the proteins were categorized as 'extracellular' and 392 proteins as 'cellular', although some proteins fall into both categories. Because of this redundancy we have chosen to normalize the sum of the cellular and extracellular categories to 100% for comparison purposes; thus, 35% of the proteins were categorized as extracellular and 65% as cellular (Figure 4). 44 proteins from the extracellular protein group were classified as 'extracellular matrix' proteins, leaving the remaining 164 proteins from the extracellular protein category as possible plasma proteins. As for the remaining 157 proteins not categorized by GoMiner as cellular components, their subsequent classification could potentially alter the present profile.

Bottom Line: Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3).The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biochemistry and Molecular Biology, Bioinformatics, University of Southern Denmark, Campusvej 55, 5230 Odense M,Denmark. sschenk@bmb.sdu.dk

ABSTRACT

Background: The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods: To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results: Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion: Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.

No MeSH data available.


Related in: MedlinePlus