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Cited by 230Molecular Biology Techniques and ApplicationsGene expression and cancer classificationSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.

Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies

Open access
Trabzuni, Daniah, Ryten, Mina, Walker, Robert, Smith, Colin, Imran, Sabaena, Ramasamy, Adaikalavan, Weale, Michael E., Hardy, John·2011·Journal of Neurochemistry
DOI 10.1111/j.1471-4159.2011.07432.xPMC PMC3664422
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Abstract

We are building an open-access database of regional human brain expression designed to allow the genome-wide assessment of genetic variability on expression. Array and RNA sequencing technologies make assessment of genome-wide expression possible. Human brain tissue is a challenging source for this work because it can only be obtained several and variable hours post-mortem and after varying agonal states. These variables alter RNA integrity in a complex manner. In this report, we assess the effect of post-mortem delay, agonal state and age on gene expression, and the utility of pH and RNA integrity number as predictors of gene expression as measured on 1266 Affymetrix Exon Arrays. We assessed the accuracy of the array data using QuantiGene, as an independent non-PCR-based method. These quality control parameters will allow database users to assess data accuracy. We report that within the parameters of this study post-mortem delay, agonal state and age have little impact on array quality, array data are robust to variable RNA integrity, and brain pH has only a small effect on array performance. QuantiGene gave very similar expression profiles as array data. This study is the first step in our initiative to make human, regional brain expression freely available.

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Integrity signals

Registry

Crossref · 10.1111/j.1471-4159.2011.07432.x

Cites

35 of 35 references verified0 citation errors · 0 not checkable

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Figures

Fig. 1 Description of the data used in the analysis.
Fig. 1 Description of the data used in the analysis.CC BY · PubMed Central
Fig. 2 (a) Scatter plot for total RNA samples with linear regression line of RIN numbers for pH. Plot shows the effect of pH on RIN number for RNA samples isolated from 13 region of control brain tissue. Test p-values is test p-value = 1.0 × 10−4, r-value = 0.145. Including the low pH values of < 5.9. (b) Scatter plot for total RNA samples with linear regression line of RIN numbers for pH. Plot shows the effect of pH on RIN number for RNA samples isolated from 13 region of control brain tissue. No significant correlation was obtained with r-value = 0.0016 and p-value = 0.82. Samples with low pH value of < 5.9 were excluded.
Fig. 2 (a) Scatter plot for total RNA samples with linear regression line of RIN numbers for pH. Plot shows the effect of pH on RIN number for RNA samples isolated from 13 region of control brain tissue. Test p-values is test p-value = 1.0 × 10−4, r-value = 0.145. Including the low pH values of < 5.9. (b) Scatter plot for total RNA samples with linear regression line of RIN numbers for pH. Plot shows the effect of pH on RIN number for RNA samples isolated from 13 region of control brain tissue. No significant correlation was obtained with r-value = 0.0016 and p-value = 0.82. Samples with low pH value of < 5.9 were excluded.CC BY · PubMed Central
Fig. 3 Bar chart to show variation in %P by brain region (CRBL, WHMT).This graph shows the different performance of samples from specific brain regions on the array. These results are highly significant p-values (1.3 × 10−24 ). The heights of the bars represent the mean. The error bars represent the SEM.
Fig. 3 Bar chart to show variation in %P by brain region (CRBL, WHMT).This graph shows the different performance of samples from specific brain regions on the array. These results are highly significant p-values (1.3 × 10−24 ). The heights of the bars represent the mean. The error bars represent the SEM.CC BY · PubMed Central
Fig. 4 Scatter plot for total RNA samples with linear regression line of Present call (%P) for pH. Scatter plot shows that pH significantly explains 12.0% of the variation in %P (p = 2.3 × 10−9, r = 0.353), including samples with low pH values. Low pH values are driving the regression analysis.
Fig. 4 Scatter plot for total RNA samples with linear regression line of Present call (%P) for pH. Scatter plot shows that pH significantly explains 12.0% of the variation in %P (p = 2.3 × 10−9, r = 0.353), including samples with low pH values. Low pH values are driving the regression analysis.CC BY · PubMed Central
Fig. 5 QuantiGene validation of microarray expression data. (a) SCN8A expression level between different regions. The graph shows high expression in OCTX compare with other regions. It is clear that this gene in mostly not expressed in WHMT region (expression level close to zero) also it presenting large error bar. (b) MAPT, showing higher expression in OCTX compare with other brain regions. These results confirm the array data with significant p-values of < 0.01. The expression level is presenting the mean and the error bars is SEM.
Fig. 5 QuantiGene validation of microarray expression data. (a) SCN8A expression level between different regions. The graph shows high expression in OCTX compare with other regions. It is clear that this gene in mostly not expressed in WHMT region (expression level close to zero) also it presenting large error bar. (b) MAPT, showing higher expression in OCTX compare with other brain regions. These results confirm the array data with significant p-values of < 0.01. The expression level is presenting the mean and the error bars is SEM.CC BY · PubMed Central
Fig. 6 QuantiGene validation of microarray expression data for LRRK2 expression level in different regions. The graph shows higher expression in OCTX compare with other regions. Wilcoxon-signed rank test was performed and these results confirm the difference between regions in array data is significant. The expression level is presenting the median and the stars indicating the significant difference in expression with p-values of < 0.01. In this case, as LRRK2 expression level was very low, it was unreliable to use SEM and Wilcoxon’s test was performed using the median values.
Fig. 6 QuantiGene validation of microarray expression data for LRRK2 expression level in different regions. The graph shows higher expression in OCTX compare with other regions. Wilcoxon-signed rank test was performed and these results confirm the difference between regions in array data is significant. The expression level is presenting the median and the stars indicating the significant difference in expression with p-values of < 0.01. In this case, as LRRK2 expression level was very low, it was unreliable to use SEM and Wilcoxon’s test was performed using the median values.CC BY · PubMed Central

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  1. 10.1016/0003-2697(87)90021-2 · DOI

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Metadata

Authors
Trabzuni, D., Ryten, M., Walker, R., Smith, C., Imran, S., Ramasamy, A., Weale, M. E., & Hardy, J.
Affiliations
  • UCL Queen Square Institute of Neurology (GB)
  • University of Edinburgh (GB)
  • Guy's Hospital (GB)
Corresponding author
Hardy, John
ORCID
Venue
Journal of Neurochemistry
Publisher
Wiley
Published
2011-09-20
Volume
119
Issue
2
Pages
275-282
ISSN
  • 0022-3042 (ISSN-L)
  • 1471-4159
DOI
10.1111/j.1471-4159.2011.07432.x
PMC
PMC3664422
OpenAlex
W2066399252
Cited by
229
References
36
Citations by year
2026: 5 · 2025: 7 · 2024: 4 · 2023: 10 · 2022: 10 · 2021: 22 · 2020: 13 · 2019: 24 · 2018: 23 · 2017: 25 · 2016: 24 · 2015: 20 · 2014: 16 · 2013: 14 · 2012: 9
FWCI
5.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Neurochemistry (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, other-oa)
  • UCL Discovery (University College London) (submittedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
Funders
  • Wellcome Trust (089698)
  • King Faisal Specialist Hospital and Research Centre
  • Medical Research Council (MC_G1000735)
  • Army Research Office
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
postmortem delay, agonal state, brain age, RNA integrity number, brain pH
MeSH terms
Adolescent, Adult, Aged, Aging, Brain Chemistry, Cause of Death, Exons, Female, United Kingdom, Humans, Hydrogen-Ion Concentration, Male, Middle Aged, Quality Control, RNA, Sex Characteristics, Tissue Banks, United States, Reproducibility of Results, DNA, Complementary, Reverse Transcriptase Polymerase Chain Reaction, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic, Microarray Analysis, Young Adult

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