Analysis of differential gene expression in colorectal cancer and stroma using fluorescence-activated cell sorting purification
Myles SmithAedín C. CulhaneMaria D. DonovanJ. Calvin CoffeyB. D. BarryMarie KellyDesmond G. HigginsJiafeng WangW. O. KirwanThomas G. CotterH. P. Redmond
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Abstract:
Tumour stroma gene expression in biopsy specimens may obscure the expression of tumour parenchyma, hampering the predictive power of microarrays. We aimed to assess the utility of fluorescence-activated cell sorting (FACS) for generating cell populations for gene expression analysis and to compare the gene expression of FACS-purified tumour parenchyma to that of whole tumour biopsies. Single cell suspensions were generated from colorectal tumour biopsies and tumour parenchyma was separated using FACS. Fluorescence-activated cell sorting allowed reliable estimation and purification of cell populations, generating parenchymal purity above 90%. RNA from FACS-purified and corresponding whole tumour biopsies was hybridised to Affymetrix oligonucleotide microarrays. Whole tumour and parenchymal samples demonstrated differential gene expression, with 289 genes significantly overexpressed in the whole tumour, many of which were consistent with stromal gene expression (e.g., COL6A3, COL1A2, POSTN, TIMP2). Genes characteristic of colorectal carcinoma were overexpressed in the FACS-purified cells (e.g., HOX2D and RHOB). We found FACS to be a robust method for generating samples for gene expression analysis, allowing simultaneous assessment of parenchymal and stromal compartments. Gross stromal contamination may affect the interpretation of cancer gene expression microarray experiments, with implications for hypotheses generation and the stability of expression signatures used for predicting clinical outcomes.Keywords:
Cell Sorting
Parenchyma
Tissue microarray
To study the technology for establishing DNA microarrays for the diagnosis of HPV. HPV6, 11, 16 and 18 gene fragments were isolated and printed onto aminosilane-coated glass slides by PixSys 5500 microarray printer as probes to prepare the HPV. HPV samples, after labeled with Cy3 or Cy5, were hybridaized with the microarray followed by scanning for analysis. The experimental condition for preparing the HPV gene chips was investigated and the possibility of HPV genotying using DNA microarrays was discussed. The technique established in this study for preparing HPV DNA microarrays is applicable and has potential clinical application significance.
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Gene chip analysis
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Microarrays include cDNA microarray and oligonucleotide microarray. A lot of known partial sequences DNA probes were spotted on microarrays.Using principle of molecular hybridization,the compared samples (labelled with fluorescent or radioactivation)were hybridized parallelly with microarrays, then the intensity of hybridized signals were detected and transformed to proficiency of target genes by deta extraction system,the different gene expression of samples were compared in global. The technology is a efficient way to study genomic functions.
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genomic DNA
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High‐throughput microarray technologies have the potential to impact significantly on the practice of histopathology over the coming years. Global gene expression profiling allows for a systematic search of all human genes for novel diagnostic and prognostic markers and for potential therapeutic targets. Likewise, gene copy number changes can be determined on a gene‐by‐gene basis using microarrays. Tissue microarrays are an efficient method to extend and validate the findings obtained from the initial ‘discovery’ phase of the research, done using cDNA microarrays. In addition, tissue microarrays can be used for quality assurance for immunohistochemical and in situ hybridization procedures. In this review we give a brief overview of microarray technology and research uses, and discuss potential applications of microarrays in the practice of diagnostic histopathology.
Histopathology
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Abstract More than 110 000 publications have used microarrays to decipher phenotype-associated genes, clinical biomarkers and gene functions. Microarrays rely on digital assaying the fluorescence signals of arrays. In this study, we retrospectively constructed raw images for 37 724 published microarray data, and developed deep learning algorithms to automatically detect systematic defects. We report that an alarming amount of 26.73% of the microarray-based studies are affected by serious imaging defects. By literature mining, we found that publications associated with these affected microarrays have reported disproportionately more biological discoveries on the genes in the contaminated areas compared to other genes. 28.82% of the gene-level conclusions reported in these publications were based on measurements falling into the contaminated area, indicating severe, systematic problems caused by such contaminations. We provided the identified published, problematic datasets, affected genes and the imputed arrays as well as software tools for scanning such contamination that will become essential to future studies to scrutinize and critically analyze microarray data.
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Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations. PICKY 2.0 can design shared probes for sets of genes that cannot be individually identified using unique probes. PICKY 2.0 uses novel algorithms to track sharable regions among genes and to strictly distinguish them from other highly similar but nontarget regions during thermodynamic comparisons. Therefore, PICKY does not sacrifice the quality of shared probes when choosing them. The latest PICKY 2.1 includes the new capability to reanalyze existing microarray probes against updated gene sets to determine probes that are still valid to use. In addition, more precise nonlinear salt effect estimates and other improvements are added, making PICKY 2.1 more versatile to microarray users. Shared probes allow expressed gene family members to be detected; this capability is generally more desirable than not knowing anything about these genes. Shared probes also enable the design of cross-genome microarrays, which facilitate multiple species identification in environmental samples. The new nonlinear salt effect calculation significantly increases the precision of probes at a lower buffer salt concentration, and the probe reanalysis function improves existing microarray result interpretations.
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Tissue microarray
Proteome
Protein microarray
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Objective To exploethe significance of each type of tissue microarrary on the examine of associated genes production in breast carcinoma.Methods Tissue microarray technology and immunohistochemistry were used to detect the expression of ER,PR,nm23,Her-2 and p53 in 31 cases of breast carcinoma and compared theresult with the traditional pathological investigation.Results The difference was no found detween the immunohistochemistric result by 1.0 mm tissue microarray and by 0.5 mm tissue microarray.2 cases of ER and 1 of PR were negative by traditional pathological technology,but were positive by tissue microarray.1 case of nm23 and 1 of Her-2 were positive by traditional pathological technology,but were negative by tissue microarray.On the expressional strength of ER,PR,nm23,Her-2 and p53,the nigligible difference was found detween by traditional pathological technology and by tissue microarray in some cases.Conclusion On the positive rate of ER,PR,nm23,Her-2 and p53,the significant difference was no found detween the result with the traditional pathological investigation and with the tissue microarray(P0.05).It has important pracyical significance and broad application prospect in pathology.
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Gene chip analysis
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Clinical Significance
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Microarray databases
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