Surescan

Manufactured by Agilent Technologies

The SureScan is a high-performance microarray scanner designed for accurate and reliable data acquisition. It features a dual-laser system and high-resolution optics to capture precise and reproducible signals from microarray slides.

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9 protocols using surescan

Microarray Analysis of BF-1 Mutants

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A custom microarray (Agilent Technologies) based on the genome sequence of BF-1 was used for microarray analysis. According to the manufacturer's protocol, the microarray images were scanned by a SureScan microarray scanner (Agilent Technologies) and analyzed by Feature Extraction 12.0 (Agilent Technologies). Microarray data were analyzed by GeneSpring 14 (Agilent Technologies). The array format and data were submitted to the Gene Expression Omnibus (GEO: GSE175843). Because bacterial transcriptomes are reproducible, no replications were performed; instead, we examined all three mutants to discover common gene expression profiles. The expression of genes shown in Figure 5 was verified by RT-qPCR.

Ishikawa E., Yamada T., Yamaji K., Serata M., Fujii D., Umesaki Y., Tsuji H., Nomoto K., Ito M., Okada N., Nagaoka M, & Gomi A. (2021). Critical roles of a housekeeping sortase of probiotic Bifidobacterium bifidum in bacterium–host cell crosstalk. iScience, 24(11), 103363.

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Quantifying Plasma Protein Levels

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Levels of proteins were quantified in a 50 µL aliquot of plasma using the SOMAscan 1.3K Proteomic Assay plasma/serum kit (SomaLogic, Boulder, CO). Procedures used for quality control filtering and analysis of differential protein abundances have been described previously⁴⁹. Briefly, microarrays were scanned with an Agilent SureScan instrument at 5 µm resolution and the Cy3 fluorescence readout was quantified. Raw fluorescence signal values from each SOMAmer were processed using standardization procedures recommended by the manufacturer (i.e., datasets were normalized to remove hybridization variation within a run followed by median normalization across all samples to remove other assay biases). The final .adat file was log₂-transformed, quantile-normalized and then filtered to remove non-human SOMAmers.

Delannoy-Bruno O., Desai C., Raman A.S., Chen R.Y., Hibberd M.C., Cheng J., Han N., Castillo J.J., Couture G., Lebrilla C.B., Barve R.A., Lombard V., Henrissat B., Leyn S.A., Rodionov D.A., Osterman A.L., Hayashi D.K., Meynier A., Vinoy S., Kirbach K., Wilmot T., Heath A.C., Klein S., Barratt M.J, & Gordon J.I. (2021). Evaluating microbiome-directed fibre snacks in gnotobiotic mice and humans. Nature, 595(7865), 91-95.

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Validating NODAL Copy Number Variants

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Droplet digital PCR (ddPCR) and/or array comparative genomic hybridization (aCGH) were performed for variant copy number validation and segregation analysis of two potential NODAL CNV deletions in two unrelated families. ddPCR experiments were performed using the QX200 AutoDG Droplet Digital PCR System according to the manufacturer’s protocols and previously described methods [22 (link)] (Additional file 2: Table S6 for primer information).
For aCGH, we used an Agilent custom-designed high-resolution array targeting Chr10q (AMADID: 086730). Microarray protocols, including DNA digestion, probe labeling, gender-matched hybridization, and post-washing, were performed as described previously with minor modifications [23 (link)]. Agilent SureScan and Feature Extraction software were utilized to achieve the image-to-digital transition, with further data analysis and visualization on the Agilent Genomic Workbench. Genomic coordinates were described in reference to GRCh37/hg19 assembly.

Dardas Z., Fatih J.M., Jolly A., Dawood M., Du H., Grochowski C.M., Jones E.G., Jhangiani S.N., Wehrens X.H., Liu P., Bi W., Boerwinkle E., Posey J.E., Muzny D.M., Gibbs R.A., Lupski J.R., Coban-Akdemir Z, & Morris S.A. (2024). NODAL variants are associated with a continuum of laterality defects from simple D-transposition of the great arteries to heterotaxy. Genome Medicine, 16, 53.

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Agilent Microarray Methylation Analysis

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Microarray image was scanned using Agilent SureScan and raw β values were exported using Agilent’s Feature Extraction Software (V.10.7). Raw methylation data were preprocessed using Agilent Genomic Workbench v6.5 BATMAN algorithm (Bayesian Tool for Methylation Analysis) and normalized to control probes present on the array. Outlier features on the arrays were flagged by the same software package. Values were log₂-transformed and logged data were used for principal component analysis (PCA) and for statistical analysis. The Welch T-test was used for identification of differentially methylated genes. An individual probe was considered differentially methylated if its p value was <0.05 (not corrected for multiple testing) and if the effect size (Cohen’s d) of t-tests was ≥0.25 (range 0.25–0.8). The resulting p values for each gene were then adjusted for multiple testing using the Benjamini-Hochberg method with a false discovery rate threshold of 10%.

Diaz M., Garde E., Lopez-Bermejo A., de Zegher F, & Ibañez L. (2020). Differential DNA methylation profile in infants born small-for-gestational-age: association with markers of adiposity and insulin resistance from birth to age 24 months. BMJ Open Diabetes Research & Care, 8(1), e001402.

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aCGH-based CNV Confirmation Protocol

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An aCGH method was used to complement the NGS CNV flagging algorithm. All specimens flagged as having CNVs by NGS were reflexed to aCGH for CNV confirmation. Custom array probes for the 34-genes in the panel were designed using Agilent SureDesign custom design tool. Approximately 57,000 probes were designed, giving on average 26 probes per exon with less density for introns and promoters. Patient specimens and gender-matched reference specimens were labeled with Cy5 and Cy3, respectively, using Agilent SureTag Complete DNA Labeling Kit followed by column purification and volume reduction. The labeled DNA was then combined and hybridized onto a custom microarray slide (Agilent, Mississauga, ON). After hybridization, the slides were washed to remove non-specific binding and then immediately scanned on the Agilent SureScan or C scanner; data were extracted using Agilent CytoGenomics Software. The results were manually reviewed by licensed personnel and a licensed director for report generation.

Rosenthal S.H., Sun W., Zhang K., Liu Y., Nguyen Q., Gerasimova A., Nery C., Cheng L., Castonguay C., Hiller E., Li J., Elzinga C., Wolfson D., Smolgovsky A., Chen R., Buller-Burckle A., Catanese J., Grupe A., Lacbawan F, & Owen R. (2020). Development and Validation of a 34-Gene Inherited Cancer Predisposition Panel Using Next-Generation Sequencing. BioMed Research International, 2020, 3289023.

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Validation of Pathogenic CNV Architecture

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Validation and characterization of the genomic architecture of each predicted potential pathogenic CNV was experimentally investigated in probands by high-resolution array-based comparative genomic hybridization (aCGH). A custom 8 × 60K Agilent high-resolution oligonucleotide microarray (AMADID 086718) spanning 17.344 Mb targeting 59 genes mapping within predicted CNVs and their flanking regions with an average probe spacing of 245 bp was designed using (https://earray.chem.agilent.com/suredesign/). Microarray protocols, including DNA digestion, probe labeling, gender-matched hybridization, and post-washing, were performed as described previously with minor modifications (18 (link)). Agilent SureScan and Feature Extraction software were utilized to achieve the image-to-digital transition, with further data analysis and visualization on the Agilent Genomic Workbench. Genomic coordinates were described in reference to GRCh37/hg19 assembly.

Du H., Dardas Z., Jolly A., Grochowski C.M., Jhangiani S.N., Li H., Muzny D., Fatih J.M., Yesil G., Elçioglu N.H., Gezdirici A., Marafi D., Pehlivan D., Calame D.G., Carvalho C.M., Posey J.E., Gambin T., Coban-Akdemir Z, & Lupski J.R. (2023). HMZDupFinder: a robust computational approach for detecting intragenic homozygous duplications from exome sequencing data. Nucleic Acids Research, 52(4), e18.

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Gene Expression Microarray Analysis

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Global transcript abundance was analyzed using biological triplicate measurements. RNA was purified using the RNeasy Mini Kit from Qiagen and RNA integrity was determined using the 2100 Bioanalyzer system (Agilent Technologies). Transcript abundance was measured using one-color 8 × 60K Mouse Gene Expression Microarrays (Agilent Technologies, G4852B) as per the manufacturer’s instructions. Briefly, 100 ng of total RNA was labeled using the Low Input Quick Amp Labeling Kit (Agilent Technologies, 5190-2305). Labeled samples were hybridized overnight and then washed and scanned using the high-sensitivity protocol (AgilentG3_HiSen_GX_1color) on a SureScan microarray scanner (Agilent Technologies), and probe intensities were obtained by taking the gProcessedSignal from the output of Agilent feature extraction software using default settings.

Weinert B.T., Narita T., Satpathy S., Srinivasan B., Hansen B.K., Schölz C., Hamilton W.B., Zucconi B.E., Wang W.W., Liu W.R., Brickman J.M., Kesicki E.A., Lai A., Bromberg K.D., Cole P.A, & Choudhary C. (2018). Time-Resolved Analysis Reveals Rapid Dynamics and Broad Scope of the CBP/p300 Acetylome. Cell, 174(1), 231-244.e12.

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Microarray Expression Analysis Protocol

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Eighteen microliters of enriched RNA was mixed with 4.5 μL of 10× Blocking Agent (Agilent Technologies) and 22.5 μL of Hi-RPM Hybridization Buffer (Agilent Technologies). The samples were incubated for 5 min in a heat block set at 104 °C. Then, the samples were immersed in ice water and incubated for 5 min. The samples were applied to an 8× 60 K Agilent microarray gasket slide (Agilent Technologies). The gasket slide and CGH custom array 8× 60 K (Agilent Technologies) were assembled with SureHyb. In a hybridization oven (Robbins Scientific), hybridization was performed for 20 h at a temperature of 55.5 °C at 20 rpm. After hybridization, the microarray slide was washed for 5 min with Gene Expression Wash Buffer 1 (Agilent Technologies) in a glass container at room temperature. Next, the microarray slide was transferred to a glass container containing Gene Expression Wash Buffer 2 (Agilent Technologies), which was immersed in a thermostat bath at 37 °C and washed for 5 min. Finally, we used SureScan (Agilent Technologies) to obtain fluorescence image data on the microarray. The captured images of the microarray slide were converted to numeric fluorescence intensities of each spot by Feature Extraction (Agilent Technologies) and GeneSpringGX (Agilent Technologies).

Komatsu K.R., Taya T., Matsumoto S., Miyashita E., Kashida S, & Saito H. (2020). RNA structure-wide discovery of functional interactions with multiplexed RNA motif library. Nature Communications, 11, 6275.

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Microarray Hybridization and Analysis

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Eighteen microlitres of the bound RNA structures were mixed with 4.5 μL of 10× Blocking Agent (Agilent Technologies) and 22.5 μL of Hi-RPM Hybridization Buffer (Agilent Technologies). The samples were incubated for 5 min in a heat block set at 104 °C, then rapidly cooled and incubated for 5 min in ice water. The samples were applied to an 8 × 60K Agilent microarray gasket slide (Agilent Technologies). The prepared gasket slide and CGH custom array 8 × 60K (Agilent Technologies) were assembled with SureHyb. Hybridization was performed for 20 h at a temperature of 55.5 °C at 20 rpm. The microarray slide was washed for 5 min with Gene Expression Wash Buffer 1 (Agilent Technologies) in a glass container at room temperature following hybridization. The microarray slide was moved to a glass container containing Gene Expression Wash Buffer 2 (Agilent Technologies), which was immersed in a thermostatic bath at 37 °C. The washing step was performed for 5 min. Fluorescence scanning was performed on the microarray, and fluorescence image data were acquired using SureScan (Agilent Technologies). The acquired images were converted to numeric fluorescence intensities for each spot by Feature Extraction (Agilent Technologies) and GeneSpringGX (Agilent Technologies).

Nagasawa R., Onizuka K., Komatsu K.R., Miyashita E., Murase H., Ojima K., Ishikawa S., Ozawa M., Saito H, & Nagatsugi F. (2024). Large-scale analysis of small molecule-RNA interactions using multiplexed RNA structure libraries. Communications chemistry, 7(1).

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