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

Klee EW, Erdogan S, Tillmans L, Kosari F, Sun Z, Wigle DA, Yang P, Aubry MC, Vasmatzis G - BMC Med Genomics (2009)

Bottom Line: 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.LCM-coupled microarray expression profiling was shown to uniquely identify a large number of differentially expressed probesets not otherwise found using bulk tissue sampling.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA. klee.eric@mayo.edu

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.

No MeSH data available.


Related in: MedlinePlus

The concordance of differentially expressed probeset rankings between (a) LCM cell-sampling and bulk tissue-sampling datasets at different levels of selection. For the top 10 and top 20 ranked probesets there is high concordance in upregulated probesets (~70%). Concordance levels for both upregulated and down-regulated probesets converge to ~58% for the top 100 ranked probesets. For the LCM, bulk, and linear amplified bulk samples (b), there is consistently strong concordance between bulk and linear amplified bulk rankings of up- and down-regulated probesets (~75%). Concordance values are significantly lower for upregulated probesets between LCM and linear amplified bulk (~58%) or between LCM and bulk samples (~53%). The lowest concordance exists for the ranking of down-regulated probesets between LCM and either bulk or linear amplified bulk samples (~45%).
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Figure 5: The concordance of differentially expressed probeset rankings between (a) LCM cell-sampling and bulk tissue-sampling datasets at different levels of selection. For the top 10 and top 20 ranked probesets there is high concordance in upregulated probesets (~70%). Concordance levels for both upregulated and down-regulated probesets converge to ~58% for the top 100 ranked probesets. For the LCM, bulk, and linear amplified bulk samples (b), there is consistently strong concordance between bulk and linear amplified bulk rankings of up- and down-regulated probesets (~75%). Concordance values are significantly lower for upregulated probesets between LCM and linear amplified bulk (~58%) or between LCM and bulk samples (~53%). The lowest concordance exists for the ranking of down-regulated probesets between LCM and either bulk or linear amplified bulk samples (~45%).

Mentions: The impact LCM cell-sampling has on the relative rankings of differential probesets was also examined, by calculating the concordance between differentially expressed probeset rankings in the bulk and LCM datasets. A sliding threshold was used to select for the top 10, to the top 100 differentially expressed probesets. Probesets previously identified as up- or down-regulated were ranked by the magnitude of expression value change. The ranking concordance values were plotted against the selection thresholds, with independent response curves generated for up-regulated and down-regulated probesets. As illustrated in Figure 5a, the up-regulated probesets identified from the average expression levels across all cases, showed diminishing levels of concordance as the threshold for selection was increased. This trend was not observed for the down-regulated probesets. Concordance values for both upregulated and down-regulated probesets converged to approximately 55%.


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

Klee EW, Erdogan S, Tillmans L, Kosari F, Sun Z, Wigle DA, Yang P, Aubry MC, Vasmatzis G - BMC Med Genomics (2009)

The concordance of differentially expressed probeset rankings between (a) LCM cell-sampling and bulk tissue-sampling datasets at different levels of selection. For the top 10 and top 20 ranked probesets there is high concordance in upregulated probesets (~70%). Concordance levels for both upregulated and down-regulated probesets converge to ~58% for the top 100 ranked probesets. For the LCM, bulk, and linear amplified bulk samples (b), there is consistently strong concordance between bulk and linear amplified bulk rankings of up- and down-regulated probesets (~75%). Concordance values are significantly lower for upregulated probesets between LCM and linear amplified bulk (~58%) or between LCM and bulk samples (~53%). The lowest concordance exists for the ranking of down-regulated probesets between LCM and either bulk or linear amplified bulk samples (~45%).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2667433&req=5

Figure 5: The concordance of differentially expressed probeset rankings between (a) LCM cell-sampling and bulk tissue-sampling datasets at different levels of selection. For the top 10 and top 20 ranked probesets there is high concordance in upregulated probesets (~70%). Concordance levels for both upregulated and down-regulated probesets converge to ~58% for the top 100 ranked probesets. For the LCM, bulk, and linear amplified bulk samples (b), there is consistently strong concordance between bulk and linear amplified bulk rankings of up- and down-regulated probesets (~75%). Concordance values are significantly lower for upregulated probesets between LCM and linear amplified bulk (~58%) or between LCM and bulk samples (~53%). The lowest concordance exists for the ranking of down-regulated probesets between LCM and either bulk or linear amplified bulk samples (~45%).
Mentions: The impact LCM cell-sampling has on the relative rankings of differential probesets was also examined, by calculating the concordance between differentially expressed probeset rankings in the bulk and LCM datasets. A sliding threshold was used to select for the top 10, to the top 100 differentially expressed probesets. Probesets previously identified as up- or down-regulated were ranked by the magnitude of expression value change. The ranking concordance values were plotted against the selection thresholds, with independent response curves generated for up-regulated and down-regulated probesets. As illustrated in Figure 5a, the up-regulated probesets identified from the average expression levels across all cases, showed diminishing levels of concordance as the threshold for selection was increased. This trend was not observed for the down-regulated probesets. Concordance values for both upregulated and down-regulated probesets converged to approximately 55%.

Bottom Line: 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.LCM-coupled microarray expression profiling was shown to uniquely identify a large number of differentially expressed probesets not otherwise found using bulk tissue sampling.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA. klee.eric@mayo.edu

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.

No MeSH data available.


Related in: MedlinePlus