Open Access Research article

Impact of sample acquisition and linear amplification on gene expression profiling of lung adenocarcinoma: laser capture micro-dissection cell-sampling versus bulk tissue-sampling

Eric W Klee1, Sibel Erdogan2, Lori Tillmans3, Farhad Kosari4, Zhifu Sun1, Dennis A Wigle5, Ping Yang1, Marie C Aubry3 and George Vasmatzis4*

Author Affiliations

1 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

2 Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA

3 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

4 Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA

5 Department of Surgery, Mayo Clinic, Rochester, MN, USA

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BMC Medical Genomics 2009, 2:13  doi:10.1186/1755-8794-2-13

Published: 9 March 2009

Abstract

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.