LNCaP human prostate cancer cell line, passage 31-32
Biomaterial provider
ATCC (Manassas, VA)
Treatment protocol
No specific treatment
Growth protocol
LNCaP cells were routinely maintained in a complete growth medium consisting of phenol red-positive RPMI1640 (Invitrogen; Carlsbad, CA) supplemented with 10% fetal bovine serum (FBS; Invitrogen) and 100 units/ml penicillin-100 µg/ml streptomycin (Invitrogen) (24). LNCaP cells were passaged once a week, whereas PC-3 cells were passaged twice a week. LNCaP cells were harvested between passages 31 and 34 for total RNA extraction
Extracted molecule
total RNA
Extraction protocol
Total RNA was isolated from prostate LNCaP and PC-3 cells for probe preparation and microarray hybridization. Briefly, 5x105 prostate cells were seeded in 60 mm tissue culture plates and cultured for 48 hours in their growth media. Total RNA was isolated from 4 consecutive passages from each cell line using RNeasy® Mini total RNA isolation kit following the manufacturer’s recommendations (Qiagen, Valencia, CA). The concentration of the isolated total RNA was determined with a Nanodrop scanning spectrophotometer, and then qualitatively assessed for their integrity using the ratio of 28:18s rRNA by a capillary gel electrophoresis system (Agilent 2100 Bioanalyzer, Agilent Technologies, Santa Clara CA).
Label
Cy3
Label protocol
A total of 250 ng of total RNA from each passage and from each cell line was labeled using the Illumina Total Prep RNA Amplification Kit following manufacturer’s directions (Ambion, Austin. TX). Briefly, cDNA was reverse transcribed from the total RNA after priming with T7-oligo-dT, and cRNA was synthesized in vitro from the T7 promoter with the incorporation of biotinylated UTP.
Hybridization protocol
Biotinylated cRNA was hybridized overnight to Illumina human Ref-8 version 3 BeadChips containing probes for a total of 24,526 transcripts.
Scan protocol
Microarray chips were washed to high stringency and labeled with streptavidin -Cy3 (Amersham Biosciences, Piscataway, NJ) prior to scanning on an Illumina BeadArray Reader.
Description
No other descriptions
Data processing
The data were first normalized based on normal distribution of low-level expressed genes using the previously described procedure (Dozmorov, Knowlton et al. 2004; Knowlton, Dozmorov et al. 2004). Normalization was performed for each array by first plotting a frequency histogram of the raw expression data for all genes. The histograms showed a right-skewed unimodal distribution curve with the mode around zero. Normal distribution curve representing variability of the data around zero was then fitted around the mode, mirroring Gaussian profile of the left part of the histogram. Two parameters [mean and standard deviation (SD)] were defined for this distribution; and the data were normalized to the SD of the noise after subtraction of the zero point. The arrays were than adjusted to each other by robust linear regression (Hua, Balagurunathan et al. 2006; Gusnanto, Calza et al. 2007).Genes with expression value less than 3.0 were considered to be not expressed. This is equivalent of setting a threshold at 3 SD above noise level, and each experiment accounted approximately 10,000 genes expressed below noise.
To identify differentially expressed genes between two experimental groups, we used associative analysis as described by Dozmorov et al. (Dozmorov and Centola 2003). Briefly, in pooled microarray datasets, a reference group of genes expressed above background with low variability of expression identified by F-test was identified. A t-test termed "associative t-test" was applied to test if a given gene belongs to or differs from this group. The associative t-test is a standard t-test applied to the comparison of expression variability. Due to a large number of genes that are differentially expressed between LNCaP and PC3 cells, a very stringent criterion was applied to identify genes with the most dramatic differences. Only genes expressed below noise level in one group and greater than 50 SD above noise level in another group were selected for further analysis. For genes that were expressed in both cell lines, genes that have greater than 10-fold differences in expression levels and expressed greater than 100 SD above noise level in at least in one cell line were also selected. These genes can be considered to be “beacons” that point to pathways or gene networks that are altered.
Dozmorov, I. and M. Centola (2003). "An associative analysis of gene expression array data." Bioinformatics 19(2): 204-211.
Dozmorov, I., N. Knowlton, et al. (2004). "Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays." BMC Bioinformatics 5(1): 53.
Gusnanto, A., S. Calza, et al. (2007). "Identification of differentially expressed genes and false discovery rate in microarray studies." Curr Opin Lipidol 18(2): 187-93.
Hua, J., Y. Balagurunathan, et al. (2006). "Normalization benefits microarray-based classification." EURASIP J Bioinform Syst Biol: 43056.
Knowlton, N., I. M. Dozmorov, et al. (2004). "Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data." Bioinformatics 20(18): 3687-90.
