Assessment of RNA in human breast tissue sampled by random periareolar fine needle aspiration and ductal lavage and processed as fixed or frozen specimens

Ductal lavage (DL) and random periareolar fine needle aspiration (RPFNA) have both been proposed as minimally invasive techniques to sample breast tissue during breast cancer prevention trials. Laser capture microdissection (LCM), linear RNA amplification and quantitative real-time polymerase chain reaction (qPCR) theoretically overcome the limitations of small specimen size obtained with DL and RPFNA. In order to test the yield, relative stability and amplifiability of RNA from fixed and archived RPFNA and DL specimens, breast tissue was sampled from individual high risk women (n = 9) by both DL and RPFNA. RPFNA samples showed good RNA/cDNA yield and amplification while only 2 of 9 of the paired DL specimens had cDNA of adequate quality for subsequent PCR. One and two rounds of linear amplification provided approximately a 200- and 20,000-fold enrichment of RNA, respectively. PCR analysis consistently detected ER and COX-1 mRNA in the majority of RPFNA samples examined while pS2, PCNA, VEGF and survivin expression varied with subject. RNA yield and/or stability was greater for fixed and archived RPFNA than DL specimens of breast tissue. In a subsequent study examining an expanded biomarker gene panel in fixed vs. frozen RPFNA samples, mRNA profiles and ranked relative mRNA abundance were similar (r = 0.89) for frozen and fixed RPFNA specimens. In summary, frozen RPFNA samples may be optimal for RNA endpoints in human breast cancer prevention trials but fixed RPFNA specimens allow similar analyses with greater convenience.     

GSTs同工酶基因在乳腺癌中的扩增与基因的表达

采用ＤＮＡ和ＲＮＡ的斑点杂交分析方法,对３２例乳腺癌和相应的癌旁正常组织中ＧＳＴ－π、ＧＳＴ－α和ＧＳＴ－μ基因的ＤＮＡ扩增和ＲＮＡ转录表达情况进行研究,发现ＧＳＴ－π在乳腺癌中存在基因扩增和明显的ｍＲＮＡ表达升高,ＧＳＴ－π基因表达调控主要在转录水平进行的;ＧＳＴ－α和ＧＳＴ－μ在乳腺癌中表达水平较低,但仍可见α和μ类ＧＳＴ同工酶ｍＲＮＡ转录在肿瘤和正常组织中发生了较大的变化。结合乳腺癌中雌激素受体（ＥＲ）表达情况还发现ＧＳＴ－π表达水平与ＥＲ的表达存在负相关性。     

Expression profiling using human tissues in combination with RNA amplification and microarray analysis: assessment of Langerhans cell histiocytosis

Summary. Advances in molecular genetics have led to sequencing of the human genome, and expression data is becoming available for many diverse tissues throughout the body, allowing for exciting hypothesis testing of critical concepts such as development, differentiation, homeostasis, and ultimately, disease pathogenesis. At present, an optimal methodology to assess gene expression is to evaluate single cells, either identified physiologically in living preparations, or by immunocytochemical or histochemical procedures in fixed cells in vitro or in vivo. Unfortunately, the quantity of RNA harvested from a single cell is not sufficient for standard RNA extraction methods. Therefore, exponential polymerase-chain reaction (PCR) based analyses, and linear RNA amplification including amplified antisense (aRNA) RNA amplification and a newly developed terminal continuation (TC) RNA amplification methodology have been used in combination with microdissection procedures such as laser capture microdissection (LCM) to enable the use of microarray platforms within individual populations of cells obtained from a variety of human tissue sources such as biopsy-derived samples {including Langerhans cell histiocytosis (LCH)} as well as postmortem brain samples for high throughput expression profiling and related downstream genetic analyses.     

Multiplex standardized RT-PCR for expression analysis of many genes in small samples

Standardized RT-PCR (StaRT-PCR) enables numerical quantification as well as intra- and inter-laboratory comparison of gene expression. Multiplex StaRT-PCR, using two rounds of amplification, was conducted on Stratagene Universal Reference RNA. In the first round, cDNA, competitive template (CT) mix, and primers for up to 96 genes were amplified for varying numbers of cycles. Next, products from round one were diluted, combined with primers for one gene, and amplified for an additional 35 cycles. No additional cDNA or CT mix was added. Expression values obtained by uniplex and multiplex StaRT-PCRs were highly correlated (R=0.993, p<0.001). Products from round one could be diluted as much as 100,000-fold and still be quantified following round two amplification. Thus, using multiplex StaRT-PCR, 96 genes were measured in the same amount of cDNA typically used to measure one gene with uniplex StaRT-PCR. Multiplex StaRT-PCR was also used to measure 18 genes in the fine needle biopsy of a primary lung carcinoma.     

High-fidelity mRNA amplification for gene profiling

The completion of the Human Genome Project has made possible the comprehensive analysis of gene expression, and cDNA microarrays are now being employed for expression analysis in cancer cell lines or excised surgical specimens. However, broader application of cDNA microarrays is limited by the amount of RNA required: 50-200 microg of total RNA (T-RNA) and 2-5 microg poly(A) RNA. To broaden the use of cDNA microarrays, some methods aiming at intensifying fluorescence signal have resulted in modest improvement. Methods devoted to amplifying starting poly(A) RNA or cDNA show promise, in that detection can be increased by orders of magnitude. However, despite the common use of these amplification procedures, no systematic assessment of their limits and biases has been documented. We devised a procedure that optimizes amplification of low-abundance RNA samples by combining antisense RNA (aRNA) amplification with a template-switching effect (Clonetech, Palo Alto, CA). The fidelity of aRNA amplified from 1:10,000 to 1:100,000 of commonly used input RNA was comparable to expression profiles observed with conventional poly(A) RNA- or T-RNA-based arrays.     

Chum-RNA allows preparation of a high-quality cDNA library from a single-cell quantity of mRNA without PCR amplification

Linear RNA amplification using T7 RNA polymerase is useful in genome-wide analysis of gene expression using DNA microarrays, but exponential amplification using polymerase chain reaction (PCR) is still required for cDNA library preparation from single-cell quantities of RNA. We have designed a small RNA molecule called chum-RNA that has enabled us to prepare a single-cell cDNA library after four rounds of T7-based linear amplification, without using PCR amplification. Chum-RNA drove cDNA synthesis from only 0.49 femtograms of mRNA (730 mRNA molecules) as a substrate, a quantity that corresponds to a minor population of mRNA molecules in a single mammalian cell. Analysis of the independent cDNA clone of this library (6.6 × 10⁵ cfu) suggests that 30-fold RNA amplification occurred in each round of the amplification process. The size distribution and representation of mRNAs in the resulting one-cell cDNA library retained its similarity to that of the million-cell cDNA library. The use of chum-RNA might also facilitate reactions involving other DNA/RNA modifying enzymes whose Michaelis constant (K_m) values are around 1 mM, allowing them to be activated in the presence of only small quantities of substrate.     

MicroRNA-125a is over-expressed in insulin target tissues in a spontaneous rat model of Type 2 Diabetes

Background

The methods used for sample selection and processing can have a strong influence on the expression values obtained through microarray profiling. Laser capture microdissection (LCM) provides higher specificity in the selection of target cells compared to traditional bulk tissue selection methods, but at an increased processing cost. The benefit gained from the higher tissue specificity realized through LCM sampling is evaluated in this study through a comparison of microarray expression profiles obtained from same-samples using bulk and LCM processing.

Methods

Expression data from ten lung adenocarcinoma samples and six adjacent normal samples were acquired using LCM and bulk sampling methods. Expression values were evaluated for correlation between sample processing methods, as well as for bias introduced by the additional linear amplification required for LCM sample profiling.

Results

The direct comparison of expression values obtained from the bulk and LCM sampled datasets reveals a large number of probesets with significantly varied expression. Many of these variations were shown to be related to bias arising from the process of linear amplification, which is required for LCM sample preparation. A comparison of differentially expressed genes (cancer vs. normal) selected in the bulk and LCM datasets also showed substantial differences. There were more than twice as many down-regulated probesets identified in the LCM data than identified in the bulk data. Controlling for the previously identified amplification bias did not have a substantial impact on the differences identified in the differentially expressed probesets found in the bulk and LCM samples.

Conclusion

LCM-coupled microarray expression profiling was shown to uniquely identify a large number of differentially expressed probesets not otherwise found using bulk tissue sampling. The information gain realized from the LCM sampling was limited to differential analysis, as the absolute expression values obtained for some probesets using this study's protocol were biased during the second round of amplification. Consequently, LCM may enable investigators to obtain additional information in microarray studies not easily found using bulk tissue samples, but it is of critical importance that potential amplification biases are controlled for.     

Improved microRNA quantification in total RNA from clinical samples

microRNAs are small regulatory RNAs that are currently emerging as new biomarkers for cancer and other diseases. In order for biomarkers to be useful in clinical settings, they should be accurately and reliably detected in clinical samples such as formalin fixed paraffin embedded (FFPE) sections and blood serum or plasma. These types of samples represent a challenge in terms of microRNA quantification. A newly developed method for microRNA qPCR using Locked Nucleic Acid (LNA?)-enhanced primers enables accurate and reproducible quantification of microRNAs in scarce clinical samples. Here we show that LNA?-based microRNA qPCR enables biomarker screening using very low amounts of total RNA from FFPE samples and the results are compared to microarray analysis data. We also present evidence that the addition of a small carrier RNA prior to total RNA extraction, improves microRNA quantification in blood plasma and laser capture microdissected (LCM) sections of FFPE samples.     

Optimized procedures for microarray analysis of histological specimens processed by laser capture microdissection

Analysis of cell-specific gene expression patterns using microarrays can reveal genes that are differentially expressed in diseased and normal tissue, as well as identify genes associated with specialized cellular functions. However, the cellular heterogeneity of the tissues precludes the resolution of expression profiles of specific cell types. While laser capture microdissection (LCM) can be used to obtain purified cell populations, the limited quantity of RNA isolated makes it necessary to perform an RNA amplification step prior to microarray analysis. The linearity and reproducibility of two RNA amplification protocols--the Baugh protocol (Baugh et al., 2001, Nucleic Acids Res 29:E29) and an in-house protocol have been assessed by conducting microarray analyses. Cy3-labeled total RNA from the colorectal cell line Colo-205 was compared to Cy5-labeled Colo-205 amplified RNA (aRNA) generated with each of the two protocols, using a human 10K cDNA array. The correlation of the gene intensities between amplified and total RNA measured in the two channels of each microarray was 0.72 and 0.61 for the Baugh protocol and the in-house protocol, respectively. The two protocols were further evaluated using aRNA obtained from normal colonic crypt cross-sections isolated via LCM. In both cases a microarray profile representative of colonic mucosa was obtained; statistically, the Baugh protocol was superior. Furthermore, a substantial overlap between highly expressed genes in the Colo-205 cells and colonic crypts underscores the reliability of the microarray analysis of LCM-derived material. Taken together, these results demonstrate that LCM-derived tissue from histological specimens can generate abundant amounts of high-quality aRNA for subsequent microarray analysis.     

Use of Formalin-Fixed Paraffin-Embedded Samples for Gene Expression Studies in Breast Cancer Patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.