Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to \u3c70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT–PCR (qRT–PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified

Laser Microdissection of Narrow Sheath Mutant Maize Uncovers Novel Gene Expression in the Shoot Apical Meristem

Microarrays enable comparative analyses of gene expression on a genomic scale, however these experiments frequently identify an abundance of differentially expressed genes such that it may be difficult to identify discrete functional networks that are hidden within large microarray datasets. Microarray analyses in which mutant organisms are compared to nonmutant siblings can be especially problematic when the gene of interest is expressed in relatively few cells. Here, we describe the use of laser microdissection microarray to perform transcriptional profiling of the maize shoot apical meristem (SAM), a ~100-μm pillar of organogenic cells that is required for leaf initiation. Microarray analyses compared differential gene expression within the SAM and incipient leaf primordium of nonmutant and narrow sheath mutant plants, which harbored mutations in the duplicate genes narrow sheath1 (ns1) and narrow sheath2 (ns2). Expressed in eight to ten cells within the SAM, ns1 and ns2 encode paralogous WUSCHEL1-like homeobox (WOX) transcription factors required for recruitment of leaf initials that give rise to a large lateral domain within maize leaves. The data illustrate the utility of laser microdissection-microarray analyses to identify a relatively small number of genes that are differentially expressed within the SAM. Moreover, these analyses reveal potentially conserved WOX gene functions and implicate specific hormonal and signaling pathways during early events in maize leaf development

Isolation, Characterization, and Pericycle-Specific Transcriptome Analyses of the Novel Maize Lateral and Seminal Root Initiation Mutant rum1

The monogenic recessive maize (Zea mays) mutant rootless with undetectable meristems 1 (rum1) is deficient in the initiation of the embryonic seminal roots and the postembryonic lateral roots at the primary root. Lateral root initiation at the shoot-borne roots and development of the aerial parts of the mutant rum1 are not affected. The mutant rum1 displays severely reduced auxin transport in the primary root and a delayed gravitropic response. Exogenously applied auxin does not induce lateral roots in the primary root of rum1. Lateral roots are initiated in a specific cell type, the pericycle. Cell-type-specific transcriptome profiling of the primary root pericycle 64 h after germination, thus before lateral root initiation, via a combination of laser capture microdissection and subsequent microarray analyses of 12k maize microarray chips revealed 90 genes preferentially expressed in the wild-type pericycle and 73 genes preferentially expressed in the rum1 pericycle (fold change >2; P-value <0.01; estimated false discovery rate of 13.8%). Among the 51 annotated genes predominately expressed in the wild-type pericycle, 19 genes are involved in signal transduction, transcription, and the cell cycle. This analysis defines an array of genes that is active before lateral root initiation and will contribute to the identification of checkpoints involved in lateral root formation downstream of rum1

Isolation, Characterization, and Pericycle-Specific Transcriptome Analyses of the Novel Maize Lateral and Seminal Root Initiation Mutant rum1

The monogenic recessive maize (Zea mays) mutant rootless with undetectable meristems 1 (rum1) is deficient in the initiation of the embryonic seminal roots and the postembryonic lateral roots at the primary root. Lateral root initiation at the shoot-borne roots and development of the aerial parts of the mutant rum1 are not affected. The mutant rum1 displays severely reduced auxin transport in the primary root and a delayed gravitropic response. Exogenously applied auxin does not induce lateral roots in the primary root of rum1. Lateral roots are initiated in a specific cell type, the pericycle. Cell-type-specific transcriptome profiling of the primary root pericycle 64 h after germination, thus before lateral root initiation, via a combination of laser capture microdissection and subsequent microarray analyses of 12k maize microarray chips revealed 90 genes preferentially expressed in the wild-type pericycle and 73 genes preferentially expressed in the rum1 pericycle (fold change .2; P-value ,0.01; estimated false discovery rate of 13.8%). Among the 51 annotated genes predominately expressed in the wild-type pericycle, 19 genes are involved in signal transduction, transcription, and the cell cycle. This analysis defines an array of genes that is active before lateral root initiation and will contribute to the identification of checkpoints involved in lateral root formation downstream of rum1.This article is published as Woll, Katrin, Lisa A. Borsuk, Harald Stransky, Dan Nettleton, Patrick S. Schnable, and Frank Hochholdinger. "Isolation, characterization, and pericycle-specific transcriptome analyses of the novel maize lateral and seminal root initiation mutant rum1." Plant Physiology 139, no. 3 (2005): 1255-1267. doi: 10.1104/pp.105.067330. Posted with permission.</p

Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to This article is published as Skibbe, David S., Xiujuan Wang, Xuefeng Zhao, Lisa A. Borsuk, Dan Nettleton, and Patrick S. Schnable. "Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes." Bioinformatics 22, no. 15 (2006): 1863-1870. doi: 10.1093/bioinformatics/btl270.</p

Report from the STRAND Working Group on the 2019 STR sequence nomenclature meeting

This report summarizes topics discussed at the STR sequence nomenclature meeting hosted by the STRAND Working Group in April 2019. Invited attendees for this meeting included researchers known-to-us to be developing STR sequence-based nomenclature schemata, scientific representatives from vendors developing STR sequence bioinformatic methods, DNA intelligence database curators, and academic experts in STR genomics. The goal of this meeting was to provide a forum for individuals developing nomenclature schemata to present and discuss their ideas, encouraging mutual awareness, identification of differences in approaches, opposing aspects, and opportunities for parallelization while some approaches are still under development