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Rapid processing of archival tissue samples for proteomic analysis using pressure-cycling technology

ABSTRACT: Advent of mass spectrometry based proteomics has revolutionized our ability to study proteins from biological specimenin a high-throughput manner. Unlike cell line based studies, biomedical research involving tissue specimen is often challenging due to limited sample availability. In addition, investigation of clinically relevant research questions often requires enormous amount of time for sample collection prospectively. Formalin fixed paraffin embedded (FFPE) archived tissue samples are a rich source of tissue specimen for biomedical research. However, there are several challenges associated with analysing FFPE samples. Protein cross-linking and degradation of proteins particularly affects proteomic analysis We demonstrate that barocycler that uses pressure-cycling technology enables efficient protein extraction and processing of small amounts of FFPE tissue samplesfor proteomic analysis. We identified 3,525 proteins from six 10µm esophageal squamous cell carcinoma (ESCC) tissue sections. Barocycler allows efficient protein extraction and proteolytic digestion of proteins from FFPE tissue sections where conventional methods have limited success

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Esophageal Squamous Epithelium

DISEASE(S): Esophagus Squamous Cell Carcinoma

SUBMITTER: Harsha Gowda

LAB HEAD: Harsha Gowda

PROVIDER: PXD006255 | Pride | 2018-01-08

REPOSITORIES: Pride

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Project description:Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of 5-year-old AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Previously reported 186-gene liver signature of poor prognosis was also analyzed by digital transcript counting technology (nCounter assay, NanoString). Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10-60% and 70-90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4-0.9) compared to FC-FFPE (0.6-1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Switch of prediction to another subclass was observed in 6% or less of the patients. nCounter assay yielded highly confident prediction: p<0.05 in 20/24 samples (83%). Switch of the prediction was observed in 2/24 samples (8%). Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in a quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis. Digital transcript counting is a promising option to measure gene-expression signatures in AS-FFPE tissue. FFPE tissue sections (10 micron-thick) sliced from 5~16-year-old FFPE blocks and archived for 6~7 years on glass slide

Project description:Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10%~60% and 70%~90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4~0.9) compared to FC-FFPE (0.6~1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis. FFPE tissue sections (10 micron-thick) sliced from 5~16-year-old FFPE blocks and archived for 6~7 years on glass slide Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) liver tissues from HCC patients: 47 samples Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) tumor tissues from HCC patients: 83 samples

Project description:In recent years, the use of FFPE tissue in gene expression microarray (GEM) studies has become a topic of increased interest, because pathology departments worldwide contain an invaluable source of biological disease-specific FFPE- tissue material. Thus far, some published studies on FFPE tissue consider the processed tissue amenable, whereas other studies assert that the tissue is not useful in GEM. In general, pancreatic tissue is known to contain large amounts of nucleases, leading to high turnover rates and degradation of RNA in the tissue. This characteristic, in combination with FFPE processing time and its effect on the tissue, does not make FFPE pancreatic tissue the obvious choice for GEM. The basis for using FFPE pancreatic tissue in RNA-based assays seems suboptimal. Previous GEM studies have used different FFPE tissue types, but GEM studies on FFPE pancreatic tissue have not been published. In most studies, the tissue was specially processed for use with GEMs. Here we demonstrate the usefulness of randomly archived FFPE pancreatic tissues for GEMs. For this purpose we included FFPE pancreatic tissue from patients with congenital hyperinsulinism or insulinoma; In these patients, pancreas resection is performed when medical treatment is not adequate to prevent hyperinsulinemic hypoglycemia or when patients do not respond to medical treatment. Although ribonuclease-rich, we obtained biologically relevant and disease-specific, significant genes; cancer-related genes and genes involved in a) the regulation of insulin secretion and synthesis, b) amino acid metabolism, and c) calcium ion homeostasis. This application may extend the possibilities of gene expression studies to many tissue types, especially in rare diseases, for which fresh frozen tissue is not readily available. A total of 15 samples were included in this study using a 58K chip. 29,134 human 60mer oligonucleotide targets were arrayed on the chips in duplicates. Each chip is hybridized with sample material (labelled with Cy5) from one patient and reference material (labelled with Cy3). Reference material contains pooled RNA from four different cancer cell lines; HeLa (cervical epithelium), SK-BR-3 (mammary gland), HT29 (colon) and A431 (skin) cells. Seven patients with congenital hyperinsulinism (five FFPE and two frozen samples), one patient with insulinoma and five controls were included in the study. Inclusion criteria for controls was that their cause of death was not pancreas-related. Samples were obtained from archived FFPE pancreatic material. The insulinoma sample was included in the study, because the biopsy was originally divided with one part being frozen and the other part being routinely formalin-fixed and paraffin-embedded. In the study we therefore include a frozen insulinoma sample and a FFPE insulinoma sample that was processed in duplicate.

			Action	DRS
	FFPE_RPS_1.raw	Raw
	FFPE_RPS_2.raw	Raw
	FFPE_RPS_3.raw	Raw
	FFPE_RPS_4.raw	Raw
	FFPE_RPS_5.raw	Raw

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Rapid processing of archival tissue samples for proteomic analysis using pressure-cycling technology

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