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In praise of arrays.

Ying L, Sarwal M - Pediatr. Nephrol. (2008)

Bottom Line: Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago.Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced.Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.

ABSTRACT
Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago. Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced. Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms. To aid the understanding of this powerful tool, its versatility, and how it is dramatically changing the molecular approach to biomedical and clinical research, this teaching review describes the technology and its applications, as well as the limitations and evolution of microarrays, in the field of organ transplantation. Finally, it calls upon the attention of the transplant community to integrate into multidisciplinary teams, to take advantage of this technology and its expanding applications in unraveling the complex injury circuits that currently limit transplant survival.

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Related in: MedlinePlus

Correlation of acute rejection gene expression in biopsy vs blood. Significant genes for graft rejection are identified in blood and biopsy tissue (Sarwal et al., unpublished data) with low false discovery rates (q scores < 1% by significance analysis of microarrays (SAM) analysis (http://www-stat.stanford.edu/∼tibs/SAM/)). The logarithmic fold expression values are shown on the X and Y axes. Only 26% of the significant genes overlap in the two tissue sources. These overlapping genes show much higher fold expression in tissue than in blood
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Fig2: Correlation of acute rejection gene expression in biopsy vs blood. Significant genes for graft rejection are identified in blood and biopsy tissue (Sarwal et al., unpublished data) with low false discovery rates (q scores < 1% by significance analysis of microarrays (SAM) analysis (http://www-stat.stanford.edu/∼tibs/SAM/)). The logarithmic fold expression values are shown on the X and Y axes. Only 26% of the significant genes overlap in the two tissue sources. These overlapping genes show much higher fold expression in tissue than in blood

Mentions: The interrogation of minimally invasive or non-invasive biomarkers of graft injury has been more challenging than the direct interrogation of the transcriptional changes in the injured graft. Until recently, most gene-expression profile studies were performed on biopsy specimens. Transcriptional changes in peripheral blood during graft rejection demonstrate significantly disparate gene-expression changes from those of the inflamed graft, suggesting that the local response to inflammation and injury in the rejecting organ is highly localized. Additionally, the intensity (fold-change) and quantity (number of significant genes in rejection) of the rejection response in peripheral blood is much smaller than the corresponding response in the organ, even when biopsy and blood samples from the same patient are examined simultaneously [44] (Fig. 2). A recently published study [45, 46] using both microarray and reverse transcriptase-polymerase chain reaction (RT-PCR) to discriminate rejection from non-rejection in peripheral blood samples from heart transplant patients gave a reasonable correlation only with severe and high-grade tissue rejection. Further, this was only from samples taken later than 6 months after transplantation, even though the risk of rejection is highest during the first 6 months after transplantation [47]. When biopsy predictor sets were used on blood samples, these microarray data from blood did not give significant predictions [47]. Despite these limitations of peripheral blood sampling, efforts to examine this sample source for clinical monitoring continue to hold promise. The answer for increasing the sensitivity and specificity of biomarker detection in peripheral blood may lie in the more careful attention to improved methods of sample collection, storage, and processing [44].Fig. 2


In praise of arrays.

Ying L, Sarwal M - Pediatr. Nephrol. (2008)

Correlation of acute rejection gene expression in biopsy vs blood. Significant genes for graft rejection are identified in blood and biopsy tissue (Sarwal et al., unpublished data) with low false discovery rates (q scores < 1% by significance analysis of microarrays (SAM) analysis (http://www-stat.stanford.edu/∼tibs/SAM/)). The logarithmic fold expression values are shown on the X and Y axes. Only 26% of the significant genes overlap in the two tissue sources. These overlapping genes show much higher fold expression in tissue than in blood
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2719727&req=5

Fig2: Correlation of acute rejection gene expression in biopsy vs blood. Significant genes for graft rejection are identified in blood and biopsy tissue (Sarwal et al., unpublished data) with low false discovery rates (q scores < 1% by significance analysis of microarrays (SAM) analysis (http://www-stat.stanford.edu/∼tibs/SAM/)). The logarithmic fold expression values are shown on the X and Y axes. Only 26% of the significant genes overlap in the two tissue sources. These overlapping genes show much higher fold expression in tissue than in blood
Mentions: The interrogation of minimally invasive or non-invasive biomarkers of graft injury has been more challenging than the direct interrogation of the transcriptional changes in the injured graft. Until recently, most gene-expression profile studies were performed on biopsy specimens. Transcriptional changes in peripheral blood during graft rejection demonstrate significantly disparate gene-expression changes from those of the inflamed graft, suggesting that the local response to inflammation and injury in the rejecting organ is highly localized. Additionally, the intensity (fold-change) and quantity (number of significant genes in rejection) of the rejection response in peripheral blood is much smaller than the corresponding response in the organ, even when biopsy and blood samples from the same patient are examined simultaneously [44] (Fig. 2). A recently published study [45, 46] using both microarray and reverse transcriptase-polymerase chain reaction (RT-PCR) to discriminate rejection from non-rejection in peripheral blood samples from heart transplant patients gave a reasonable correlation only with severe and high-grade tissue rejection. Further, this was only from samples taken later than 6 months after transplantation, even though the risk of rejection is highest during the first 6 months after transplantation [47]. When biopsy predictor sets were used on blood samples, these microarray data from blood did not give significant predictions [47]. Despite these limitations of peripheral blood sampling, efforts to examine this sample source for clinical monitoring continue to hold promise. The answer for increasing the sensitivity and specificity of biomarker detection in peripheral blood may lie in the more careful attention to improved methods of sample collection, storage, and processing [44].Fig. 2

Bottom Line: Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago.Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced.Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.

ABSTRACT
Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago. Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced. Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms. To aid the understanding of this powerful tool, its versatility, and how it is dramatically changing the molecular approach to biomedical and clinical research, this teaching review describes the technology and its applications, as well as the limitations and evolution of microarrays, in the field of organ transplantation. Finally, it calls upon the attention of the transplant community to integrate into multidisciplinary teams, to take advantage of this technology and its expanding applications in unraveling the complex injury circuits that currently limit transplant survival.

Show MeSH
Related in: MedlinePlus