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Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps.

Guo T, Kouvonen P, Koh CC, Gillet LC, Wolski WE, Röst HL, Rosenberger G, Collins BC, Blum LC, Gillessen S, Joerger M, Jochum W, Aebersold R - Nat. Med. (2015)

Bottom Line: The method combines pressure cycling technology (PCT) and sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS.The resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples.From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland.

ABSTRACT
Clinical specimens are each inherently unique, limited and nonrenewable. Small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry (MS)-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. The resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from nine patients with renal cell carcinoma into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The measured proteins clearly distinguished tumorous kidney tissues from healthy tissues and differentiated distinct histomorphological kidney cancer subtypes.

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Comparative proteomic analysis of clear cell RCC tissues(a) Unsupervised clustering of 2,317 proteins quantified in ccRCC as measured by PCT-SWATH in tumorous and non-tumorous tissues. (b) Unsupervised clustering of 2276 proteins quantified in pRCC and ccRCC tumorous tissues. (c) Volcano plots of all regulated proteins and proteins belonging to specific protein classes, biological processes, and pathways. P values were calculated using paired two-tailed t-test. Regulated proteins were defined as fold change higher than 2 with P value lower than 0.05. Up-regulated and down-regulated proteins are shown in purple and yellow, respectively.
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Figure 6: Comparative proteomic analysis of clear cell RCC tissues(a) Unsupervised clustering of 2,317 proteins quantified in ccRCC as measured by PCT-SWATH in tumorous and non-tumorous tissues. (b) Unsupervised clustering of 2276 proteins quantified in pRCC and ccRCC tumorous tissues. (c) Volcano plots of all regulated proteins and proteins belonging to specific protein classes, biological processes, and pathways. P values were calculated using paired two-tailed t-test. Regulated proteins were defined as fold change higher than 2 with P value lower than 0.05. Up-regulated and down-regulated proteins are shown in purple and yellow, respectively.

Mentions: Reproducible quantification of clinically relevant proteins as outlined above suggests a high potential for using this method for tumor classification based on proteome profiling. We found that the proteomic profile is useful to categorize the tissue samples according to their degree of malignancy and histomorphological subtypes. Unsupervised clustering of more than 2,000 consistently quantified proteins resulted in the separation of tumor biopsies from neighboring non-tumorous biopsies from ccRCC patients as shown in Fig.6a. In total, 317 proteins were down-regulated, and 296 proteins were upregulated in tumor compared to non-tumorous biopsies (Supplementary Table 4). These included protein kinases and transcription factors, in addition to other proteins involved in the regulation of biological processes or pathways including apoptosis, metabolic pathways, signaling, and immune response (Fig. 6c).


Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps.

Guo T, Kouvonen P, Koh CC, Gillet LC, Wolski WE, Röst HL, Rosenberger G, Collins BC, Blum LC, Gillessen S, Joerger M, Jochum W, Aebersold R - Nat. Med. (2015)

Comparative proteomic analysis of clear cell RCC tissues(a) Unsupervised clustering of 2,317 proteins quantified in ccRCC as measured by PCT-SWATH in tumorous and non-tumorous tissues. (b) Unsupervised clustering of 2276 proteins quantified in pRCC and ccRCC tumorous tissues. (c) Volcano plots of all regulated proteins and proteins belonging to specific protein classes, biological processes, and pathways. P values were calculated using paired two-tailed t-test. Regulated proteins were defined as fold change higher than 2 with P value lower than 0.05. Up-regulated and down-regulated proteins are shown in purple and yellow, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4390165&req=5

Figure 6: Comparative proteomic analysis of clear cell RCC tissues(a) Unsupervised clustering of 2,317 proteins quantified in ccRCC as measured by PCT-SWATH in tumorous and non-tumorous tissues. (b) Unsupervised clustering of 2276 proteins quantified in pRCC and ccRCC tumorous tissues. (c) Volcano plots of all regulated proteins and proteins belonging to specific protein classes, biological processes, and pathways. P values were calculated using paired two-tailed t-test. Regulated proteins were defined as fold change higher than 2 with P value lower than 0.05. Up-regulated and down-regulated proteins are shown in purple and yellow, respectively.
Mentions: Reproducible quantification of clinically relevant proteins as outlined above suggests a high potential for using this method for tumor classification based on proteome profiling. We found that the proteomic profile is useful to categorize the tissue samples according to their degree of malignancy and histomorphological subtypes. Unsupervised clustering of more than 2,000 consistently quantified proteins resulted in the separation of tumor biopsies from neighboring non-tumorous biopsies from ccRCC patients as shown in Fig.6a. In total, 317 proteins were down-regulated, and 296 proteins were upregulated in tumor compared to non-tumorous biopsies (Supplementary Table 4). These included protein kinases and transcription factors, in addition to other proteins involved in the regulation of biological processes or pathways including apoptosis, metabolic pathways, signaling, and immune response (Fig. 6c).

Bottom Line: The method combines pressure cycling technology (PCT) and sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS.The resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples.From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland.

ABSTRACT
Clinical specimens are each inherently unique, limited and nonrenewable. Small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry (MS)-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. The resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from nine patients with renal cell carcinoma into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The measured proteins clearly distinguished tumorous kidney tissues from healthy tissues and differentiated distinct histomorphological kidney cancer subtypes.

Show MeSH
Related in: MedlinePlus