Dataset Information

Spot identification and quality control in cell-based microarrays.

ABSTRACT: Cell-based microarrays are being increasingly used as a tool for combinatorial and high throughput screening of cellular microenvironments. Analysis of microarrays requires several steps, including microarray imaging, identification of cell spots, quality control, and data exploration. While high content image analysis, cell counting, and cell pattern recognition methods are established, there is a need for new postprocessing and quality control methods for cell-based microarrays used to investigate combinatorial microenvironments. Previously, microarrayed cell spot identification and quality control were performed manually, leading to excessive processing time and potentially resulting in human bias. This work introduces an automated approach to identify cell-based microarray spots and spot quality control. The approach was used to analyze the adhesion of murine cardiac side population cells on combinatorial arrays of extracellular matrix proteins. Microarrays were imaged by automated fluorescence microscopy and cells were identified using open-source image analysis software (CellProfiler). From these images, clusters of cells making up single cell spots were reliably identified by analyzing the distances between cells using a density-based clustering algorithm (OPTICS). Naïve Bayesian classifiers trained on manually scored training sets identified good and poor quality spots using spot size, number of cells per spot, and cell location as quality control criteria. Combined, the approach identified 78% of high quality spots and 87% of poor quality spots. Full factorial analysis of the resulting microarray data revealed that collagen IV exhibited the highest positive effect on cell attachment. This data processing approach allows for fast and unbiased analysis of cell-based microarray data.

SUBMITTER: Bauer M

PROVIDER: S-EPMC3495599 | biostudies-other | 2012 Aug

REPOSITORIES: biostudies-other

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Spot identification and quality control in cell-based microarrays.

Bauer Michael M Kim Keekyoung K Qiu Yiling Y Calpe Blaise B Khademhosseini Ali A Liao Ronglih R Wheeldon Ian I

ACS combinatorial science 20120801 8

Cell-based microarrays are being increasingly used as a tool for combinatorial and high throughput screening of cellular microenvironments. Analysis of microarrays requires several steps, including microarray imaging, identification of cell spots, quality control, and data exploration. While high content image analysis, cell counting, and cell pattern recognition methods are established, there is a need for new postprocessing and quality control methods for cell-based microarrays used to investi ...[more]

PMID: 22850537

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Project description:BackgroundCell-based microarrays were first described by Ziauddin and Sabatini in 2001 as a powerful new approach for performing high throughput screens of gene function. An important application of cell-based microarrays is in screening for proteins that modulate gene networks. To this end, cells are grown over the surface of arrays of RNAi or expression reagents. Cells growing in the immediate vicinity of the arrayed reagents are transfected and the arrays can then be scanned for cells showing localised changes in function. Here we describe the construction of a large-scale microarray using expression plasmids containing human genes, its use in screening for genes that induce apoptosis when over-expressed and the characterisation of a number of these genes by following the transcriptional response of cell cultures during their induction of apoptosis.ResultsHigh-density cell-based arrays were successfully fabricated using 1,959 un-tagged open reading frames (ORFs) taken from the Mammalian Gene Collection (MGC) in mammalian expression vectors. The arrays were then used to screen for genes inducing apoptosis in Human Embryonic Kidney (HEK293T) cells. Using this approach, 10 genes were clearly identified and confirmed to induce apoptosis. Some of these genes have previously been linked to apoptosis, others not. The mechanism of action of three of the 10 genes were then characterised further by following the transcriptional events associated with apoptosis induction using expression profiling microarrays. This data demonstrates a clear pro-apoptotic transcriptional response in cells undergoing apoptosis and also suggests the use of common apoptotic pathways regardless of the nature of the over-expressed protein triggering cell death.ConclusionThis study reports the design and use of the first truly large-scale cell-based microarrays for over-expression studies. Ten genes were confirmed to induce apoptosis, some of which were not previously known to possess this activity. Transcriptome analysis on three of the 10 genes demonstrated their use of similar pathways to invoke apoptosis.

Project description:BACKGROUND: Microarrays are a powerful tool for transcriptome analysis. Best results are obtained using high-quality RNA samples for preparation and hybridization. Issues with RNA integrity can lead to low data quality and failure of the microarray experiment. RESULTS: Microarray intensity data contains information to estimate the RNA quality of the sample. We here study the interplay of the characteristics of RNA surface hybridization with the effects of partly truncated transcripts on probe intensity. The 3'/5' intensity gradient, the basis of microarray RNA quality measures, is shown to depend on the degree of competitive binding of specific and of non-specific targets to a particular probe, on the degree of saturation of the probes with bound transcripts and on the distance of the probe from the 3'-end of the transcript. Increasing degrees of non-specific hybridization or of saturation reduce the 3'/5' intensity gradient and if not taken into account, this leads to biased results in common quality measures for GeneChip arrays such as affyslope or the control probe intensity ratio. We also found that short probe sets near the 3'-end of the transcripts are prone to non-specific hybridization presumable because of inaccurate positional assignment and the existence of transcript isoforms with variable 3' UTRs. Poor RNA quality is associated with a decreased amount of RNA material hybridized on the array paralleled by a decreased total signal level. Additionally, it causes a gene-specific loss of signal due to the positional bias of transcript abundance which requires an individual, gene-specific correction. We propose a new RNA quality measure that considers the hybridization mode. Graphical characteristics are introduced allowing assessment of RNA quality of each single array ('tongs plot' and 'degradation hook'). Furthermore, we suggest a method to correct for effects of RNA degradation on microarray intensities. CONCLUSIONS: The presented RNA degradation measure has best correlation with the independent RNA integrity measure RIN, and therefore presents itself as a valuable tool for quality control and even for the study of RNA degradation. When RNA degradation effects are detected in microarray experiments, a correction of the induced bias in probe intensities is advised.

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Spot identification and quality control in cell-based microarrays.

Publications

Spot identification and quality control in cell-based microarrays.

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