Transcriptomes of human prostate cells

BACKGROUND: The gene expression profiles of most human tissues have been studied by determining the transcriptome of whole tissue homogenates. Due to the solid composition of tissues it is difficult to study the transcriptomes of individual cell types that compose a tissue. To overcome the problem of heterogeneity we have developed a method to isolate individual cell types from whole tissue that are a source of RNA suitable for transcriptome profiling. RESULTS: Using monoclonal antibodies specific for basal (integrin β4), luminal secretory (dipeptidyl peptidase IV), stromal fibromuscular (integrin α 1), and endothelial (PECAM-1) cells, respectively, we separated the cell types of the prostate with magnetic cell sorting (MACS). Gene expression of MACS-sorted cell populations was assessed with Affymetrix GeneChips. Analysis of the data provided insight into gene expression patterns at the level of individual cell populations in the prostate. CONCLUSION: In this study, we have determined the transcriptome profile of a solid tissue at the level of individual cell types. Our data will be useful for studying prostate development and cancer progression in the context of single cell populations within the organ

Molecular and cellular characterization of ABCG2 in the prostate

BACKGROUND: Identification and characterization of the prostate stem cell is important for understanding normal prostate development and carcinogenesis. The flow cytometry-based side population (SP) technique has been developed to isolate putative adult stem cells in several human tissue types including the prostate. This phenotype is mainly mediated by the ATP-binding cassette membrane transporter ABCG2. METHODS: Immunolocalization of ABCG2 was performed on normal prostate tissue obtained from radical prostatectomies. Normal human prostate SP cells and ABCG2(+ )cells were isolated and gene expression was determined with DNA array analysis and RT-PCR. Endothelial cells were removed by pre-sorting with CD31. RESULTS: ABCG2 positive cells were localized to the prostate basal epithelium and endothelium. ABCG2(+ )cells in the basal epithelium constituted less than 1% of the total basal cell population. SP cells constituted 0.5–3% of the total epithelial fraction. The SP transcriptome was essentially the same as ABCG2(+ )and both populations expressed genes indicative of a stem cell phenotype, however, the cells also expressed many genes in common with endothelial cells. CONCLUSION: These results provide gene expression profiles for the prostate SP and ABCG2(+ )cells that will be critical for studying normal development and carcinogenesis, in particular as related to the cancer stem cell concept

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases

Molecular and cellular characterization of ABCG2 in the prostate

Abstract Background Identification and characterization of the prostate stem cell is important for understanding normal prostate development and carcinogenesis. The flow cytometry-based side population (SP) technique has been developed to isolate putative adult stem cells in several human tissue types including the prostate. This phenotype is mainly mediated by the ATP-binding cassette membrane transporter ABCG2. Methods Immunolocalization of ABCG2 was performed on normal prostate tissue obtained from radical prostatectomies. Normal human prostate SP cells and ABCG2+ cells were isolated and gene expression was determined with DNA array analysis and RT-PCR. Endothelial cells were removed by pre-sorting with CD31. Results ABCG2 positive cells were localized to the prostate basal epithelium and endothelium. ABCG2+ cells in the basal epithelium constituted less than 1% of the total basal cell population. SP cells constituted 0.5–3% of the total epithelial fraction. The SP transcriptome was essentially the same as ABCG2+ and both populations expressed genes indicative of a stem cell phenotype, however, the cells also expressed many genes in common with endothelial cells. Conclusion These results provide gene expression profiles for the prostate SP and ABCG2+ cells that will be critical for studying normal development and carcinogenesis, in particular as related to the cancer stem cell concept.</p

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

Abstract Background Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. Methods Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. Results Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. Conclusion Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases.</p

Development of the Minimum Information Specification for in situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)

We describe the creation process of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE). Modeled after the existing minimum information specification for microarray data, we created a new specification for gene expression localization experiments, initially to facilitate data sharing within a consortium. After successful use within the consortium, the specification was circulated to members of the wider biomedical research community for comment and refinement. After a period of acquiring many new suggested requirements, it was necessary to enter a final phase of excluding those requirements that were deemed inappropriate as a minimum requirement for all experiments. The full specification will soon be published as a version 1.0 proposal to the community, upon which a more full discussion must take place so that the final specification may be achieved with the involvement of the whole community. This paper is part of the special issue of OMICS on data standards

Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE)

One purpose of the biomedical literature is to report results in sufficient detail that the methods of data collection and analysis can be independently replicated and verified. Here we present reporting guidelines for gene expression localization experiments: the minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). MISFISHIE is modeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarray experiments. Both guidelines define what information should be reported without dictating a format for encoding that information. MISFISHIE describes six types of information to be provided for each experiment: experimental design, biomaterials and treatments, reporters, staining, imaging data and image characterizations. This specification has benefited the consortium within which it was developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal