Expression data of FFPE peritumoral neocortex tissue
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Wang Charles
PROVIDER: S-ECPF-GEOD-32534 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Epilepsy is a common cause of morbidity affecting approximately one third of patients with primary brain tumors. However, the molecular mechanism underlying the tumor induced epileptogenesis is poorly understood. The alteration in peritumoral microenvironments is believed to play a significant role in inducing epileptogenesis. We hypothesize that the change of gene expression in neighboring astrocytes and neurons may contribute to the increased neuronal excitability and epileptogenesis.
2012-12-11 | GSE32534 | GEO
Project description:Epilepsy is a common cause of morbidity affecting approximately one third of patients with primary brain tumors. However, the molecular mechanism underlying the tumor induced epileptogenesis is poorly understood. The alteration in peritumoral microenvironments is believed to play a significant role in inducing epileptogenesis. We hypothesize that the change of gene expression in neighboring astrocytes and neurons may contribute to the increased neuronal excitability and epileptogenesis. Genome-wide gene expression profiling was conducted using Affymetrix HG U133 plus 2.0 arrays and RNAs derived from formalin-fixed paraffin embedded (FFPE) peritumoral cortex tissue slides from 5-paired (seizure vs. non-seizure) low grade brain tumor patients.
2012-12-11 | E-GEOD-32534 | biostudies-arrayexpress
Project description:Massive formalin-fixed, paraffin-embedded (FFPE) tissue archives exist worldwide, representing an invaluable resource for clinical proteomics research. However, current protocols for FFPE proteomics lack standardization, efficiency, reproducibility, and scalability. Here we present high-yield protein extraction and recovery by direct solubilization (HYPERsol), an optimized workflow using ultrasonication and S-Trap sample processing that enables proteome coverage and quantification from FFPE samples comparable to that achieved from flash-frozen tissue (average R = 0.936). When applied to archival samples, HYPERsol resulted in high-quality data from FFPE specimens in storage for up to 17 years, and may enable the discovery of new immunohistochemical markers.
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Project description:Defining molecular features that can predict the recurrence of colorectal cancer (CRC) for stage II-III patients remains challenging in cancer research. Most available clinical samples are Formalin-Fixed, Paraffin-Embedded (FFPE). NanoString nCounter® and Affymetrix GeneChip® Human Transcriptome Array 2.0 (HTA) are the two platforms marketed for high-throughput gene expression profiling for FFPE samples. In this study, to evaluate the gene expression of frozen tissue-derived prognostic signatures in FFPE CRC samples, we evaluated the expression of 516 genes from published frozen tissue-derived prognostic signatures in 42 FFPE CRC samples measured by both platforms. Based on HTA platform-derived data, we identified both gene (99 individual genes, FDR < 0.05) and gene set (four of the six reported multi-gene signatures with sufficient information for evaluation, P < 0.05) expression differences associated with survival outcomes. Using nCounter platform-derived data, one of the six multi-gene signatures (P < 0.05) but no individual gene was associated with survival outcomes. Our study indicated that sufficiently high quality RNA could be obtained from FFPE tumor tissues to detect frozen tissue-derived prognostic gene expression signatures for CRC patients.
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Project description:Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffin-embedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC-MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.
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Project description:Formalin fixed paraffin-embedded (FFPE) tumor specimens are the conventionally archived material in clinical practice, representing an invaluable tissue source for biomarkers development, validation and routine implementation. For many prospective clinical trials, this material has been collected allowing for a prospective-retrospective study design which represents a successful strategy to define clinical utility for candidate markers. Gene expression data can be obtained even from FFPE specimens with the broadly used Affymetrix HG-U133 Plus 2.0 microarray platform. Nevertheless, important major discrepancies remain in expression data obtained from FFPE compared to fresh-frozen samples, prompting the need for appropriate data processing which could help to obtain more consistent results in downstream analyses. In a publicly available dataset of matched frozen and FFPE expression data, the performances of different normalization methods and specifically designed Chip Description Files (CDFs) were compared. The use of an alternative CDFs together with fRMA normalization significantly improved frozen-FFPE sample correlations, frozen-FFPE probeset correlations and agreement of differential analysis between different tumor subtypes. The relevance of our optimized data processing was assessed and validated using two independent datasets. In this study we demonstrated that an appropriate data processing can significantly improve the reliability of gene expression data derived from FFPE tissues using the standard Affymetrix platform. Tools for the implementation of our data processing algorithm are made publicly available at http://www.biocut.unito.it/cdf-ffpe/.
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Project description:We describe the use of a ligation-based targeted whole transcriptome expression profiling assay, TempO-Seq, to profile formalin-fixed paraffin-embedded (FFPE) tissue, including H&E stained FFPE tissue, by directly lysing tissue scraped from slides without extracting RNA or converting the RNA to cDNA. The correlation of measured gene expression changes in unfixed and fixed samples using blocks prepared from a pellet of a single cell type was R2 = 0.97, demonstrating that no significant artifacts were introduced by fixation. Fixed and fresh samples prepared in an equivalent manner produced comparable sequencing depth results (+/- 20%), with similar %CV (11.5 and 12.7%, respectively), indicating no significant loss of measurable RNA due to fixation. The sensitivity of the TempO-Seq assay was the same whether the tissue section was fixed or not. The assay performance was equivalent for human, mouse, or rat whole transcriptome. The results from 10 mm2 and 2 mm2 areas of tissue obtained from 5 ?m thick sections were equivalent, thus demonstrating high sensitivity and ability to profile focal areas of histology within a section. Replicate reproducibility of separate areas of tissue ranged from R2 = 0.83 (lung) to 0.96 (liver) depending on the tissue type, with an average correlation of R2 = 0.90 across nine tissue types. The average %CVs were 16.8% for genes expressed at greater than 200 counts, and 20.3% for genes greater than 50 counts. Tissue specific differences in gene expression were identified and agreed with the literature. There was negligible impact on assay performance using FFPE tissues that had been archived for up to 30 years. Similarly, there was negligible impact of H&E staining, facilitating accurate visualization for scraping and assay of small focal areas of specific histology within a section.
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Project description:BackgroundInvestigating the expression of candidate genes in tissue samples usually involves either immunohistochemical labelling of formalin-fixed paraffin-embedded (FFPE) sections or immunofluorescence labelling of cryosections. Although both of these methods provide essential data, both have important limitations as research tools. Consequently, there is a demand in the research community to be able to perform routine, high quality immunofluorescence labelling of FFPE tissues.ResultsWe present here a robust optimised method for high resolution immunofluorescence labelling of FFPE tissues, which involves the combination of antigen retrieval, indirect immunofluorescence and confocal laser scanning microscopy. We demonstrate the utility of this method with examples of immunofluorescence labelling of human kidney, human breast and a tissue microarray of invasive human breast cancers. Finally, we demonstrate that stained slides can be stored in the short term at 4 degrees C or in the longer term at -20 degrees C prior to images being collected. This approach has the potential to unlock a large in vivo database for immunofluorescence investigations and has the major advantages over immunohistochemistry in that it provides higher resolution imaging of antigen localization and the ability to label multiple antigens simultaneously.ConclusionThis method provides a link between the cell biology and pathology communities. For the cell biologist, it will enable them to utilise the vast archive of pathology specimens to advance their in vitro data into in vivo samples, in particular archival material and tissue microarrays. For the pathologist, it will enable them to utilise multiple antibodies on a single section to characterise particular cell populations or to test multiple biomarkers in limited samples and define with greater accuracy cellular heterogeneity in tissue samples.
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