RESUMO
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 beta4), luminal secretory (dipeptidyl peptidase IV), stromal fibromuscular (integrin alpha 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.
Assuntos
Perfilação da Expressão Gênica , Próstata/metabolismo , Proteínas Morfogenéticas Ósseas/metabolismo , Células Cultivadas , Análise por Conglomerados , Perfilação da Expressão Gênica/métodos , Humanos , Separação Imunomagnética , Masculino , Modelos Biológicos , Análise de Sequência com Séries de Oligonucleotídeos , Especificidade de Órgãos , Fenótipo , Neoplasias da Próstata/metabolismo , RNA Mensageiro/metabolismo , Transdução de Sinais , Células Estromais/metabolismo , Fator de Crescimento Transformador beta/metabolismoRESUMO
PURPOSE: Prostate cancer cells differ from their normal counterpart in gene expression. Therefore, cancer and normal tissue/cells would show differences in their proteomes. From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. We used ProteinChip Array (Ciphergen Biosystems, Fremont, California) surface enhanced laser desorption/ionization (SELDI) time of flight mass spectrometry to profile prostate tissue samples to generate phenomic fingerprints. We used quantitative proteomics based on glycopeptide capture followed by tandem mass spectrometry to identify differentially expressed proteins. MATERIALS AND METHODS: Patient matched cancer and noncancer specimens were digested by collagenase to single cells. After digestion the cells were pelleted and the cell-free supernatant was used for analysis. A reversed phase hydrophobic ProteinChip Array was used to generate SELDI patterns from 43 primary prostate tumors, including 26 with matched noncancer specimens. Quantitative proteomics was applied to 1 specimen and the expression pattern was verified by Western blotting and immunohistochemistry. RESULTS: SELDI profiles showed that cancers of similar TNM stages were more likely to have similar profiles. On quantitative proteomics tissue metalloproteinase inhibitor-1 was identified to be down-regulated in cancer. Tissue metalloproteinase inhibitor-1 expression was localized to secretory cells. CONCLUSIONS: Protein profiling by SELDI is relatively easy to perform and it has great potential in prostate cancer diagnosis through pattern recognition. Quantitative proteomics can potentially determine the identity of many biomarkers specific for prostate cancer.
Assuntos
Biomarcadores Tumorais/sangue , Neoplasias da Próstata/genética , Proteômica/métodos , Western Blotting , Humanos , Imuno-Histoquímica , Lasers , Masculino , Espectrometria de Massas , Valor Preditivo dos Testes , Próstata/patologia , Neoplasias da Próstata/sangue , Neoplasias da Próstata/metabolismo , Neoplasias da Próstata/patologia , Análise Serial de Proteínas , Inibidor Tecidual de Metaloproteinase-1/metabolismo , Células Tumorais CultivadasRESUMO
BACKGROUND: LNCaP and its derivative cell lines, which include C4-2 (and the related C4-2B) and CL1, are used as models of prostate cancer. Unlike LNCaP, the other cell lines show features of progressed disease such as metastatic capability and hormone independence. Analyses were done to determine if C4-2 or CL1 cells were selected from pre-existent subpopulations in LNCaP. METHODS: Prostate cancer cells were characterized by cluster designation (CD) phenotyping. Specific cell populations were sorted by flow cytometry. DNA array analysis was used to probe differential gene expression. RESULTS: CD phenotyping showed that CL1 and C4-2 (and C4-2B) were very dissimilar, and C4-2 was more similar to LNCaP. One common difference between LNCaP and its derivatives was CD26, in which virtually all C4-2 or CL1 cells were CD26(+) but only approximately 10% of LNCaP cells were CD26(+). The CD26(+) subpopulation of LNCaP was isolated and cultured in vitro. After culture, a high percentage of the cells (descended from the sorted cells) were CD26(+), in contrast to those sorted by CD13 or CD44. The cultured CD13 and CD44 populations did not show a high percentage of CD13(+) and CD44(+) cells, respectively. CD13 and CD44 are markers, in addition to CD26, for CL1 but not for C4-2. CONCLUSIONS: C4-2 arose probably from CD26(+) LNCaP cells, while CL1 arose de novo.