Microarray data generated from formalin-fixed, paraffin-embedded (FFPE) uninvolved small bowel (SB) ileal tissue.
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ABSTRACT:
PROVIDER: PRJNA494513 | ENA |
REPOSITORIES: ENA
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Project description:Transcriptomic data was generated on uninvolved SB tissue from formalin-fixed paraffin-embedded (FFPE) small bowel resection margins of subjects requiring surgery at Cedars-Sinai Medical Center for Crohn’s disease.
2019-12-23 | GSE120782 | GEO
Project description:Formalin-fixed paraffin-embedded (FFPE) ring trial
| PRJEB32144 | ENA
Project description:Genome wide DNA methylation profiling of formalin-fixed paraffin-embedded (FFPE) meningioma samples. The Illumina Infinium MethylationEPIC BeadChip was used to obtain DNA methylation profiles across approximately 850,000 methylation sites across the genome at single-nucleotide resolution.
2022-04-27 | GSE200321 | GEO
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.
| S-EPMC2288605 | biostudies-literature
Project description:Hereditary breast cancer constitutes only 5-10% of all breast cancer cases and is characterized by strong family history of breast and/or other associated cancer types. Only ~ 25% of hereditary breast cancer cases carry a mutation in BRCA1 or BRCA2 gene, while mutations in other rare high and moderate-risk genes and common low penetrance variants may account for additional 20% of the cases. Thus the majority of cases are still unaccounted for and designated as BRCAX tumors. MicroRNAs are small non-coding RNAs that play important roles as regulators of gene expression and are deregulated in cancer. To characterize hereditary breast tumors based on their miRNA expression profiles we performed global microarray miRNA expression profiling on a retrospective cohort of 80 FFPE breast tissues, including 66 hereditary breast tumors (13 BRCA1, 10 BRCA2 and 43 BRCAX), 10 sporadic breast carcinomas and 4 normal breast tissues, using Exiqon miRCURY LNA™ microRNA Array v.11.0. Here we describe in detail the miRNA microarray expression data and tumor samples used for the study of BRCAX tumor heterogeneity (Tanic et al., 2013) and biomarkers associated with positive BRCA1/2 mutation status (Tanic et al., 2014). Additionally, we provide the R code for data preprocessing and quality control.
| S-EPMC4535901 | biostudies-literature
Project description:N- and O-glycans are attractive clinical biomarkers as glycosylation changes in response to diseases. The limited availability of defined clinical specimens impedes glyco-biomarker identification and validation in large patient cohorts. Formalin-fixed paraffin-embedded (FFPE) clinical specimens are the common form of sample preservation in clinical pathology, but qualitative and quantitative N- and O-glycomics of such samples has not been feasible to date. Here, we report a highly sensitive and glycan isomer selective method for simultaneous N- and O-glycomics from histopathological slides. As few as 2000 cells isolated from FFPE tissue sections by laser capture microdissection were sufficient for in-depth histopathology-glycomics using porous graphitized carbon nanoLC ESI-MS/MS. N- and O-glycan profiles were similar between unstained and hematoxylin and eosin stained FFPE samples but differed slightly compared with fresh tissue. This method provides the key to unlock glyco-biomarker information from FFPE histopathological tissues archived in pathology laboratories worldwide.
| S-EPMC5383776 | biostudies-literature
Project description:Transcription profiling by array of formalin-fixed and paraffin-embedded (FFPE) tumor samples.
2013-06-21 | E-MTAB-990 | biostudies-arrayexpress
Project description:Epigenome analysis of formalin-fixed paraffin-embedded (FFPE) meningioma samples
| PRJNA824053 | ENA
Project description:BackgroundArchived tissues from previously completed prospective trials represent invaluable resource for biomarker development. However, such specimens are often stored as sections on glass slides, in which RNA is severely degraded due to prolonged air exposure. We evaluated whether a proportion of archived sectioned formalin-fixed paraffin-embedded (AS-FFPE) tissues yield transcriptome profiles comparable to freshly cut (FC) FFPE tissues, which can be used for retrospective class prediction analysis.MethodsGenome-wide transcriptome profiles of 6 to 7-year-old AS-FFPE tissue sections (generated from 5 to 16-year-old blocks) of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina) and digital transcript counting (nCounter) assay (NanoString), and gene signature-based prediction of HCC subclasses and prognosis was compared with previously generated FC-FFPE profiles from the same tissue blocks.ResultsRNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate to poor quality FC-FFPE samples (RNA Integrity Number: up to 2.50, R-square for technical replicates: up to 0.93). Analyzable transcriptome profiles were obtained in 64 (77%) HCC and 36 (77%) cirrhosis samples. Statistically more confident predictions based on random resampling-based method (nearest template prediction) were obtained in 37 (58%) HCC and 13 (36%) cirrhosis samples. Predictions made in FC-FFPE profiles were reproduced in 36 (97%) HCC and 11 (85%) cirrhosis AS-FFPE profiles. nCounter assay was tested in 24 cirrhosis samples, which yielded confident prediction in 15 samples (63%), of which 10 samples (67%) showed concordant predictions with FC-FFPE profiles.ConclusionsAS-FFPE tissues yielded poorer quality RNA and transcriptome profiles compared to FC-FFPE tissues. Statistically more confident class prediction was feasible in 37 of 83 HCC samples and 13 of 47 cirrhosis samples. These results suggest that AS-FFPE tissues can be regarded as a resource for retrospective transcriptome-based class prediction analysis when they are the only available materials.
| S-EPMC3907407 | biostudies-literature
Project description:Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.
2022-08-31 | GSE211797 | GEO