Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma

Wiśniewski, Jacek R.; Ostasiewicz, Paweł; Duś, Kamila; Zielińska, Dorota; Gnad, Florian; Mann, Matthias

doi:10.1038/msb.2012.44

Cited by 229 publications

(275 citation statements)

References 61 publications

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“…We analyzed seven mass spectrometry datasets covering the proteomes of different mammalian tissues (Geiger et al , 2013), cell types (Azimifar et al , 2014; Sharma et al , 2015), healthy and diseased states (Wiśniewski et al , 2012; Guo et al , 2015; Tyanova et al , 2016), and cancer development stages (Wisńiewski et al , 2015). In these experiments, the abundance fold change (FC) of thousands of proteins has been calculated using standard differential analysis approaches (see Materials and Methods section).…”

Section: Resultsmentioning

confidence: 99%

“…Percentage of protein with (i) no GO annotation, (ii) any GO cellular component annotation, (iii) annotation to ten major cellular compartments (nucleus, cytoplasm, mitochondrion, extracellular space, endoplasmic reticulum, Golgi apparatus, cell membrane, nuclear membrane, lysosome, and peroxisome), and (iv) annotation to four major cellular compartments (nucleus, cytoplasm, mitochondrion, and extracellular space) are reported for each dataset.Percentage of proteins annotated to one, two, three, four, five, or more compartments in each dataset, in order (Geiger et al , 2012; Wiśniewski et al , 2012; Azimifar et al , 2014; Guo et al , 2015; Sharma et al , 2015; Wisńiewski et al , 2015; Tyanova et al , 2016). …”

Section: Resultsmentioning

confidence: 99%

“…Percentage of proteins annotated to one, two, three, four, five, or more compartments in each dataset, in order (Geiger et al , 2012; Wiśniewski et al , 2012; Azimifar et al , 2014; Guo et al , 2015; Sharma et al , 2015; Wisńiewski et al , 2015; Tyanova et al , 2016). …”

Section: Resultsmentioning

confidence: 99%

“…Scatter plot of average abundance shift of mitochondrial proteins ( x ‐axis) and peroxisomal proteins ( y ‐axis) in seven different datasets (Geiger et al , 2012; Wiśniewski et al , 2012; Azimifar et al , 2014; Guo et al , 2015; Sharma et al , 2015; Wisńiewski et al , 2015; Tyanova et al , 2016); a gray line represents the line fitted using the resulting points (Pearson's R = 0.97, P = 3.5 × 10 −4 ). Proteins shared between the two compartments (annotated as both mitochondrial and peroxisomal) were excluded for this analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Protein intensities were log 2 ‐transformed. (vi) Proteome quantification of normal colon tissue (eight samples) and adenocarcinoma (eight samples; Wiśniewski et al , 2012). Protein intensities were log 2 ‐transformed.…”

Section: Methodsmentioning

confidence: 99%

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Quantifying compartment‐associated variations of protein abundance in proteomics data

Parca

Beck

Bork

et al. 2018

Molecular Systems Biology

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Quantitative mass spectrometry enables to monitor the abundance of thousands of proteins across biological conditions. Currently, most data analysis approaches rely on the assumption that the majority of the observed proteins remain unchanged across compared samples. Thus, gross morphological differences between cell states, deriving from, e.g., differences in size or number of organelles, are often not taken into account. Here, we analyzed multiple published datasets and frequently observed that proteins associated with a particular cellular compartment collectively increase or decrease in their abundance between conditions tested. We show that such effects, arising from underlying morphological differences, can skew the outcome of differential expression analysis. We propose a method to detect and normalize morphological effects underlying proteomics data. We demonstrate the applicability of our method to different datasets and biological questions including the analysis of sub‐cellular proteomes in the context of Caenorhabditis elegans aging. Our method provides a complementary perspective to classical differential expression analysis and enables to uncouple overall abundance changes from stoichiometric variations within defined group of proteins.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Quantifying compartment‐associated variations of protein abundance in proteomics data

Parca

Beck

Bork

et al. 2018

Molecular Systems Biology

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Diabetes‐mediated promotion of colon mucosa carcinogenesis is associated with mitochondrial dysfunction

et al. 2019

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Type 2 diabetes mellitus (T2DM) has been associated with an increased risk of cancer, including colon cancer (CC). However, we recently reported no influence of T2DM on CC prognosis, suggesting that any effect might be at the early stages of tumor development. We hypothesized that T2DM may create an environment in the healthy tissue, which acts as a carcinogenesis driver in agreement with the field of cancerization concept. Here, we focused on early carcinogenesis by analyzing paired tumor and normal colonic mucosa samples from the same patients. The proteome of CC and paired mucosa was quantitatively analyzed in 28 individuals (12 diabetics and 16 nondiabetics) by mass spectrometry with isobaric labeling. Out of 3076 identified proteins, 425 were differentially expressed at the tumor in diabetics compared with nondiabetics. In the adjacent mucosa, 143 proteins were differentially expressed in diabetics and nondiabetics. An enrichment analysis of this signature pointed to mitochondria, ribosome, and translation. Only six proteins were upregulated by diabetes both in tumor and mucosa, of which five were mitochondrial proteins. Differential expression in diabetic versus nondiabetic mucosa was confirmed for MRPL53, MRPL18, and TIMM8B. Higher levels of MRPL18, TIMM8B, and EIF1A were also found in normal colon epithelial cells exposed to high‐glucose conditions. We conclude that T2DM is associated with specific molecular changes in the normal mucosa of CC patients, consistent with field of cancerization in a diabetic environment. The mitochondrial protein signature identifies a potential therapeutic target that could underlie the higher risk of CC in diabetics.

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High‐throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

et al. 2019

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Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh-frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored Abbreviations BPH, benign prostatic hyperplasia; CRYAB, crystallin alpha Bbetween 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

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Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma

Abstract: In-depth proteomic analysis of microdissected colorectal cancer identifies extensive alterations in the cell-surface and nuclear proteomes between normal mucosa and adenocarcinoma, but observes strikingly little proteomic change between cancer and metastases.

Cited by 229 publications

References 61 publications

Quantifying compartment‐associated variations of protein abundance in proteomics data

Quantifying compartment‐associated variations of protein abundance in proteomics data

Diabetes‐mediated promotion of colon mucosa carcinogenesis is associated with mitochondrial dysfunction

High‐throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

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