Whole genome dasl assay

Manufactured by Illumina

Sourced in Canada

The Whole Genome DASL assay is a gene expression profiling tool developed by Illumina. It utilizes the DASL (cDNA-mediated Annealing, Selection, extension, and Ligation) technology to measure the expression levels of thousands of genes simultaneously from small or degraded RNA samples.

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Lab products found in correlation

Exon array, by Tecan (2 mentions) Wt ovation ffpe process, by Illumina (2 mentions) Ncounter system, by Illumina (2 mentions) Whole genome dasl array, by Tecan (2 mentions) Ffpe affymetrix exon assay, by Tecan (2 mentions) Rnaqueous kit, by Thermo Fisher Scientific (2 mentions) Rna 6000 nano chip, by Agilent Technologies (2 mentions) Titanium taq dna polymerase, by Takara Bio (1 mentions) Hiscan, by Illumina (1 mentions) Ncounter digital analyzer system, by NanoString (1 mentions) Ncounter assay, by NanoString (1 mentions) Humanhap300 duo genotyping beadchips, by Illumina (1 mentions) Rneasy kit, by Qiagen (1 mentions) Recoverall total nucleic acid isolation kit, by Thermo Fisher Scientific (1 mentions) Trizol, by Thermo Fisher Scientific (1 mentions) Nd 1000 spectrophotometer, by Thermo Fisher Scientific (1 mentions) High pure rna paraffin kit, by Roche (1 mentions) Human genome u219 array, by Thermo Fisher Scientific (1 mentions) Nsolver 3, by NanoString (1 mentions)

12 protocols using whole genome dasl assay

Illumina Whole Genome-DASL Expression Analysis

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The Illumina Whole Genome-DASL assay was performed using 200 ng of RNA following the manufacturer's instructions. Briefly, RNA was reverse transcribed to cDNA using biotinylated primers, followed by immobilization to streptavidin-conjugated paramagnetic particles. Biotinylated cDNAs were then simultaneously annealed to a set of assay-specific oligonucleotides. Extension and ligation of the annealed oligonucleotides generated PCR templates that were amplified using Titanium Taq DNA Polymerase (Clontech). Labeled PCR products were washed and denatured to yield single-stranded fluorescent molecules, which were hybridized to the HumanHT12 v4.0 Whole Genome Gene Expression BeadChips for 16 h at 58°C. The Illumina HiScan was used to scan the arrays.

Borys A.M., Seweryn M., Gołąbek T., Bełch Ł., Klimkowska A., Totoń-Żurańska J., Machlowska J., Chłosta P., Okoń K, & Wołkow P.P. (2019). Patterns of gene expression characterize T1 and T3 clear cell renal cell carcinoma subtypes. PLoS ONE, 14(5), e0216793.

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Evaluation of RNA profiling platforms

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Example 4

This study was initiated to determine which technology was best suited to dealing with a range of good quality and degraded RNA samples. The platforms that were evaluated were the Affymetrix Exon Array with the NuGEN WT-Ovation FFPE process carried out at a laboratory services vendor (50 ng input), the Illumina whole genome DASL assay carried out at Expression Analysis (using 100 ng input RNA), and the Nanostring nCounter system carried out at Nanostring (100 ng and 200 ng input RNA). RNA samples were a selection of banked FNAs, ex vivo FNAs and prospective FNAs all collected in TRIzol. The samples had a range of RIN values (1-8) from highly degraded to intact samples and two sample subtypes (NHP/PTC).

In summary, although Nanostring was the only platform that did not require amplification of RNA, it was slightly less robust than Exon/FFPE and DASL at low RIN ranges, possibly due to the placement of primers at approximately 200 bp. The Exon/FFPE and DASL systems are comparable across the range of sample characteristics tested. While the Illumina Whole-Genome DASL array performed as well as the NuGEN FFPE/Affymetrix exon assay the decision was made to proceed with the NuGEN/Affymetrix system because of FDA readiness considerations of the various microarray platforms.

US10731223B2. Algorithms for disease diagnostics (2020-08-04). Veracyte, Inc. [US]. Inventors: Giulia C. Kennedy [US], Darya I. Chudova [US], Eric T. Wang [US], Jonathan I. Wilde [US].

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Gene Expression Profiling of Liver Biopsies

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The 186-gene signature was implemented in the digital transcript counting (nCounter) assay (NanoString). Expression profiling was performed with 100ng to 400ng total RNA by using nCounter Digital Analyzer system (NanoString) according to manufacturer's instruction. For the analysis of the training cohort, the first generation of reagent plate (“white” Prep Plate) was used. For the validation cohort, newer version (“green” New Prep Plate) with improved sensitivity for signal detection was used. Poor quality profiles were detected based on maximum signal intensity from positive control probes <8,000U for the older reagent plate, and median signal intensity >100U for the newer reagent plate according to manufacturer’s recommendation. Raw transcript count data were log-transformed and scaled by geometric mean of control probe data by using NanoString normalizer module implemented in GenePattern genomic analysis toolkit (www.broadinstitute.org/genepattern). Genome-wide expression profiling for paired biopsies and explanted liver was performed by using whole-genome DASL assay (Illumina) according to manufacturer's instruction. Scanned data were extracted by Genome Studio software ver.3 (Illumina), and normalized by cubic spline algorithm implemented in GenePattern Illumina Normalizer module as previously described.^{18 (link)}

King L.Y., Canasto-Chibuque C., Johnson K.B., Yip S., Chen X., Kojima K., Deshmukh M., Venkatesh A., Tan P.S., Sun X., Villanueva A., Sangiovanni A., Nair V., Mahajan M., Kobayashi M., Kumada H., Iavarone M., Colombo M., Fiel M.I., Friedman S.L., Llovet J.M., Chung R.T, & Hoshida Y. (2014). A genomic and clinical prognostic index for hepatitis C-related early-stage cirrhosis that predicts clinical deterioration. Gut, 64(8), 1296-1302.

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Genetic Regulation of MTNR1A Expression

Cited 2 times

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To examine association of rs12506228 with melatonin receptor 1A gene (MTNR1A) expression, we used publicly available eGWAS Mayo data through the National Institute of Aging Genetics Data Storage Site (NIAGADS).^{34 (link)} The data set included gene expression profiles of cerebellum and temporal cortex, and genome-wide single nucleotide polymorphism (SNP) genotyping data for patients with neuropathologically verified Alzheimer’s disease (AD) pathology and non-AD individuals, many of whom had other brain pathologies (55% with progressive supranuclear palsy, 13% with Lewy body disease, 12% with corticobasal degeneration).To avoid the effect of widespread neurodegeneration related to AD, only the non-AD subjects (n = 177, cerebellum) were included in this study. Gene expression levels were measured with the Whole Genome DASL assay (Illumina, Inc., San Diego, CA). Probes with detection in less than 75% of the samples were excluded. A probe for MTNR1A was excluded in the temporal lobe samples and therefore only results for cerebellum were reported. The SNP genotyping was based on Illumina’s HumanHap300-Duo Genotyping BeadChips. Available statistics from the eGWAS Mayo data were based on linear regression analysis of PLINK using an additive model and covariates of age at death, gender, PCR plate, and RNA integrity number (RINmean).^{2 (link)}

Sulkava S., Ollila H.M., Alasaari J., Puttonen S., Härmä M., Viitasalo K., Lahtinen A., Lindström J., Toivola A., Sulkava R., Kivimäki M., Vahtera J., Partonen T., Silander K., Porkka-Heiskanen T, & Paunio T. (2017). Common Genetic Variation Near Melatonin Receptor 1A Gene Linked to Job-Related Exhaustion in Shift Workers. Sleep, 40(1), zsw011.

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Age-Dependent Liver Transcriptomics in Cirrhosis

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Fractions of liver biopsy specimens obtained by transjugular route, and primarily processed for clinical pathology, were stored in diethyl pyrocarbonate (DEPC) solution for mRNA isolation using the RNeasy kit (Qiagen). 250 ng of highly pure and preserved RNA were deeply analyzed using the Illumina Whole Genome-DASL assay, which quantifies approximately 24,000 transcripts. The microarray data were deposited and stored in GEO (GSE77627).
Illumina were processed using lumi package quantile normalisation [27 (link)]. Coefficient for age, derived from a linear model using probe set expression versus age and gender adjusted [28 (link)], was employed as a metric score to evaluate the influence of age in the gene expression from cirrhotic liver tissue. We performed pre-ranked gene set enrichment analysis (GSEA) using the canonical pathways MSigDB collection signatures [29 (link)].
Ethics Committee of the Barcelona Hospital Clinic approved the experimental protocol (HCB/2011/6814). Experimental groups were defined considering patients’ age: young (n=7, mean age 42 ± 5 years old, range 33-48), old (n=7, mean age 62 ± 4, range 58-70).

Maeso-Díaz R., Ortega-Ribera M., Lafoz E., Lozano J.J., Baiges A., Francés R., Albillos A., Peralta C., García-Pagán J.C., Bosch J., Cogger V.C, & Gracia-Sancho J. (2019). Aging Influences Hepatic Microvascular Biology and Liver Fibrosis in Advanced Chronic Liver Disease. Aging and Disease, 10(4), 684-698.

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Nucleic Acid Isolation and Profiling

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RecoverAll Total Nucleic Acid Isolation Kit (Ambion, Life Technologies, Carlsbad, CA, USA) was used to extract total RNA from FFPE tissues according to the manufacturer's procedure. Total RNA from normal T-lymphocytes subset was extracted with Trizol according to the manufacturer's instructions (Invitrogen, Life Technologies, Carlsbad, CA, USA). RNA was quantified using ND-1000 spectrophotometer running software version 3.0.1 (NanoDrop Technologies Inc., Rockland, DE, USA).
After RNA extraction of samples, microRNA profiling was carried out by using the TaqMan Array Human MicroRNA Card A v.2.0 (Life Technologies, Carlsbad, CA USA). We used the Illumina Whole Genome DASL assay for gene profile generation from FFPE samples, as described previously^{7 (link), 35 (link), 36 (link)} (details are provided in Supplementary File).

Laginestra M.A., Piccaluga P.P., Fuligni F., Rossi M., Agostinelli C., Righi S., Sapienza M.R., Motta G., Gazzola A., Mannu C., Sabattini E., Bacci F., Tabanelli V., Sacchetti C.A., Barrese T.Z., Etebari M., Melle F., Clò A., Gibellini D., Tripodo C., Inghirami G., Croce C.M, & Pileri S.A. (2014). Pathogenetic and diagnostic significance of microRNA deregulation in peripheral T-cell lymphoma not otherwise specified. Blood Cancer Journal, 4(11), 259-.

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Microarray Analysis of Formalin-Fixed Paraffin-Embedded Samples

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Microarray analysis was described previously [3 (link)]. In brief, RNA was extracted from formalin-fixed, paraffin-embedded sections with removal of nontumor elements. RNA was extracted using the High Pure RNA Paraffin kit (Roche Diagnostic, Mannheim, Germany), and then, the whole-genome DASL assay was performed following the manufacturer’s instructions (Illumina, San Diego, CA). With the exclusion of inadequate samples, a total of 300 patient samples were evaluable (Supplementary Table 1), and the expression data were deposited in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44001).

Choi C.H., Chung J.Y., Park H.S., Jun M., Lee Y.Y., Kim B.G, & Hewitt S.M. (2015). Pancreatic adenocarcinoma up-regulated factor expression is associated with disease-specific survival in cervical cancer patients. Human pathology, 46(6), 884-893.

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Cerebellar and Temporal Cortex mRNA Expression GWAS

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These data were generated and previously reported in a brain expression GWAS (eGWAS) by Zou and colleagues^{23 (link)}, where the detailed mRNA extraction and quality assessment using RNAqueous kit (Ambion, Grand Island, NY) and RNA 6000 Nano Chip (Agilent, Santa Clara, CA), Whole Genome DASL assay (Illumina, San Diego, CA), and data quality control methods can be found. This eGWAS cohort included 374 cerebellar and 399 temporal cortex mRNA samples from the Mayo Clinic Florida Brain Bank. Neuropathologic diagnoses for the cohort include Alzheimer’s disease, PSP, CBD, Lewy body disease, frontotemporal lobar degeneration, and other diagnoses (Supplementary Table 7). The Bonferroni P-value threshold for SNP/transcript association significance was P < 1.56 × 10⁻³ (four SNPs and eight probes).

Kouri N., Ross O.A., Dombroski B., Younkin C.S., Serie D.J., Soto-Ortolaza A., Baker M., Finch N.C., Yoon H., Kim J., Fujioka S., McLean C.A., Ghetti B., Spina S., Cantwell L.B., Farlow M.R., Grafman J., Huey E.D., Ryung Han M., Beecher S., Geller E.T., Kretzschmar H.A., Roeber S., Gearing M., Juncos J.L., Vonsattel J.P., Van Deerlin V.M., Grossman M., Hurtig H.I., Gross R.G., Arnold S.E., Trojanowski J.Q., Lee V.M., Wenning G.K., White C.L., Höglinger G.U., Müller U., Devlin B., Golbe L.I., Crook J., Parisi J.E., Boeve B.F., Josephs K.A., Wszolek Z.K., Uitti R.J., Graff-Radford N.R., Litvan I., Younkin S.G., Wang L.S., Ertekin-Taner N., Rademakers R., Hakonarsen H., Schellenberg G.D, & Dickson D.W. (2015). Genome-wide association study of corticobasal degeneration identifies risk variants shared with progressive supranuclear palsy. Nature Communications, 6, 7247.

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RNA Expression Profiling from Blood Samples

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Gene expression profiling from peripheral blood samples collected using PAXgene tubes for RNA analysis was performed on the Affymetrix Human Genome U219 Array (www.affymetrix.com, Santa Clara, CA) for ADNI and on the Illumina Whole-Genome DASL assay (www.illumina.com, San Diego, CA) for AddNeuroMed and MCSA. All probe sets were mapped and annotated with reference to the human genome (hg19). Raw microarray expression values were pre-processed followed by standard quality control (QC) procedures on samples and probe sets.[26 (link)] Briefly, raw expression values were pre-processed using the robust multi-chip average normalization method.[27 (link)] We checked discrepancies between the reported sex and sex determined from sex-specific gene expression data including XIST and USP9Y.[28 (link)] We also evaluated whether SNP genotypes were matched with genotypes predicted from gene expression data.[29 (link)] After QC, the RNA expression profiles contained 21,150 probes in ADNI.

Nho K., Nudelman K., Allen M., Hodges A., Kim S., Risacher S.L., Apostolova L., Lin K., Lunnon K., Wang X., Burgess J.D., Ertekin-Taner N., Petersen R.C., Wang L., Qi Z., He A., Neuhaus I., Patel V., Foroud T., Faber K.M., Lovestone S., Simmons A., Weiner M.W, & Saykin A.J. (2020). Genome-wide transcriptome analysis identifies novel dysregulated genes implicated in Alzheimer’s pathology. Alzheimer's & dementia : the journal of the Alzheimer's Association, 16(9), 1213-1223.

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Cerebellar and Temporal Cortex mRNA Expression GWAS

Cited 1 time

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These data were generated and previously reported in a brain expression GWAS (eGWAS) by Zou et al.25 (link), where the detailed mRNA extraction and quality assessment using RNAqueous kit (Ambion, Grand Island, NY) and RNA 6,000 Nano Chip (Agilent, Santa Clara, CA), Whole Genome DASL assay (Illumina, San Diego, CA), and data quality control methods can be found. This eGWAS cohort included 374 cerebellar and 399 temporal cortex mRNA samples from the Mayo Clinic Florida Brain Bank. Neuropathologic diagnoses for the cohort include Alzheimer's disease, PSP, CBD, Lewy body disease, frontotemporal lobar degeneration, and other diagnoses (Supplementary Table 7). The Bonferroni P value threshold for SNP/transcript association significance was P<1.56 × 10⁻³ (four SNPs and eight probes).

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