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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

Overlap of identified probesets with cancer to normal differential expression in the bulk, LCM, and linear amplified bulk datasets. The number of upregulated probesets (a) identified is consistent between datasets, with the closest agreement between bulk and linear amplified bulk samples. For down-regulated probesets (b), there remains a tight association between bulk and linear amplified bulk samples. In the LCM samples the number of observed down-regulated probesets is substantially higher than that observed in either the bulk or linear amplified bulk samples.
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Figure 4: Overlap of identified probesets with cancer to normal differential expression in the bulk, LCM, and linear amplified bulk datasets. The number of upregulated probesets (a) identified is consistent between datasets, with the closest agreement between bulk and linear amplified bulk samples. For down-regulated probesets (b), there remains a tight association between bulk and linear amplified bulk samples. In the LCM samples the number of observed down-regulated probesets is substantially higher than that observed in either the bulk or linear amplified bulk samples.

Mentions: To estimate the effect of the amplification process bias on the selection of differentially expressed probesets, the analysis was repeated using the two-sample average values for the bulk, LCM, and linear amplified samples. In the bulk data, 297 up-regulated probesets were identified, 238 in the linear amplified bulk data, and 217 in the LCM data. As illustrated in Figure 4a, 111 up-regulated probesets were found in all three datasets. There were substantially more probesets commonly identified between the bulk and linear amplified bulk samples, than between the bulk and LCM sample sets, or between the linear-amplified bulk and LCM sample sets. A total of 339 down-regulated probesets were identified in the bulk dataset, 278 in the linear amplified bulk dataset, and 565 in the LCM dataset. Of these, there were 184 probesets commonly identified as down-regulated in all three datasets (Figure 4b). A further elaboration of these observations is presented in Additional file 1.


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)

Overlap of identified probesets with cancer to normal differential expression in the bulk, LCM, and linear amplified bulk datasets. The number of upregulated probesets (a) identified is consistent between datasets, with the closest agreement between bulk and linear amplified bulk samples. For down-regulated probesets (b), there remains a tight association between bulk and linear amplified bulk samples. In the LCM samples the number of observed down-regulated probesets is substantially higher than that observed in either the bulk or linear amplified bulk samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Overlap of identified probesets with cancer to normal differential expression in the bulk, LCM, and linear amplified bulk datasets. The number of upregulated probesets (a) identified is consistent between datasets, with the closest agreement between bulk and linear amplified bulk samples. For down-regulated probesets (b), there remains a tight association between bulk and linear amplified bulk samples. In the LCM samples the number of observed down-regulated probesets is substantially higher than that observed in either the bulk or linear amplified bulk samples.
Mentions: To estimate the effect of the amplification process bias on the selection of differentially expressed probesets, the analysis was repeated using the two-sample average values for the bulk, LCM, and linear amplified samples. In the bulk data, 297 up-regulated probesets were identified, 238 in the linear amplified bulk data, and 217 in the LCM data. As illustrated in Figure 4a, 111 up-regulated probesets were found in all three datasets. There were substantially more probesets commonly identified between the bulk and linear amplified bulk samples, than between the bulk and LCM sample sets, or between the linear-amplified bulk and LCM sample sets. A total of 339 down-regulated probesets were identified in the bulk dataset, 278 in the linear amplified bulk dataset, and 565 in the LCM dataset. Of these, there were 184 probesets commonly identified as down-regulated in all three datasets (Figure 4b). A further elaboration of these observations is presented in Additional file 1.

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