Array CGH was performed for all cases using the Agilent platform with a 180k genome-wide design. The cases were referred for genetic investigation due to a diagnosis of ID/ASD but did not use the DSM-5 guidelines systematically.
Agilent platform
Manufactured by Agilent Technologies
Sourced in United States
The Agilent platform is a comprehensive suite of analytical instruments and software designed for a wide range of laboratory applications. It provides reliable, high-performance solutions for tasks such as chromatography, spectroscopy, and mass spectrometry. The platform is engineered to offer accurate, repeatable results and streamlined workflow integration.
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Lab products found in correlation
Ampure spri beads, by Beckman Coulter (2 mentions)
Hiseq 4000 platform, by Illumina (2 mentions)
Feature extraction software, by Agilent Technologies (1 mentions)
Sureprint g3 human gene expression array, by Agilent Technologies (1 mentions)
Suretag complete dna labeling kit, by Agilent Technologies (1 mentions)
Agilent microarray scanner, by Agilent Technologies (1 mentions)
Genespring gx11, by Agilent Technologies (1 mentions)
Novaseq platform, by Illumina (1 mentions)
Agilent dna microarray scanner system, by Agilent Technologies (1 mentions)
Gene expression hybridization kit, by Agilent Technologies (1 mentions)
Low input quick amp labeling kit, by Agilent Technologies (1 mentions)
Rneasy mini kit, by Qiagen (1 mentions)
Trizol, by Thermo Fisher Scientific (1 mentions)
Pierce 660 nm protein assay, by Thermo Fisher Scientific (1 mentions)
Agilent whole human genome oligo microarray profiling, by Agilent Technologies (1 mentions)
25 protocols using agilent platform
1
Array CGH for ID/ASD Diagnosis
2
NCI-60 Cell Line Transcriptomic and Proteomic Profiling
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The NCI-60 dataset contained transcriptomic [31 (link)] and proteomic [32 (link)] tables for a collection of 60 cell lines from the National Cancer Institute (NCI-60). The NCI-60 panel included cell lines derived from various cancer types: colon (7 cell lines), renal (8), ovarian (6), breast (8), prostate (2), lung (9) and central nervous system (6), as well as leukemia (6) and melanoma (8). The gene expression profiles used here were generated using an Agilent platform [31 (link)] and downloaded from Cellminer [33 (link)]. Data were log2-transformed. To facilitate data interpretation and computation, the transcriptomic data were filtered to exclude probes that did not map to an official HUGO gene symbol and to retain only the probe with the highest average value when multiple probes mapped to the same gene, as previously described in [34 (link)]. Gene invariants across all 60 cell lines, corresponding to genes without any effect between cancer types, were also removed. Filtering produced a dataset of 1,433 genes. The NCI-60 proteome table was also downloaded from Cellminer [33 (link)]. Proteomic data were obtained using high-density reverse-phase lysate microarrays [32 (link)]. Data were log2-transformed and protein abundance levels were available for 162 proteins [32 (link)].
3
Agilent-based Classifier Performance
4
Whole-Genome Microarray Analysis of RNA
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High-quality RNA samples from WB and dLNs were subjected to a whole-genome microarray analysis on an Agilent platform (Agilent Technologies). The RNA was labeled with a Fluorescent Linear Amplification Kit according to manufacturer’s instructions. The quantity and labeling efficiency were verified before the samples were hybridized to whole-genome 8 × 60 k mouse expression arrays, which were scanned at 5 μm using an Agilent scanner. Image analysis was performed with Feature Extraction software (version 11.5.1.1, Agilent Technologies) to generate raw microarray data.
5
Transcriptome Sequencing of Lower-Grade Glioma
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In total, 188 patients with primary or recurrent LGG were enrolled in the training group and 103 patients in the validation group. Resected tumor samples were immediately placed in liquid nitrogen and only samples with more than 80% tumor cells, judged by HE staining of adjacent tissues, were selected for further sequencing. Transcriptome data of LGG samples were generated by the Agilent platform. Molecular testing was performed at the Molecular Pathology Testing Center of Beijing Neurosurgical Institute. All patients were followed up trimonthly by telephone or clinic for an average of 1813 days. 15 of 188 patients (7.98%) lost to follow‐up in the training group and 5 of 103 patients (4.85%) lost to follow‐up in the validation group. Clinical information of patients was summarized in Table S1 .
The sequencing data, clinical, and follow‐up information of primary and recurrent LGG patients were uploaded to the CGGA portal (http://cgga.org.cn/ ). All datasets used and/or analyzed in this study are available from the corresponding author on reasonable request.
The sequencing data, clinical, and follow‐up information of primary and recurrent LGG patients were uploaded to the CGGA portal (
6
Agilent-based Classifier Performance
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We repeated the above procedure using core consensus samples profiled on the Agilent platform (n = 416). Performance metrics were improved relative to the Agilent metrics from the global classifier. However, overall performance was below the Affymetrix/RNAseq classifier (Supplementary Fig. 3e,f ). Given the smaller number of samples available to train this model, the lower performance is not unexpected.
7
Chromosomal Analysis of Patients
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Reverse Heat Giemsa (RHG) banded karyotype was performed on metaphase chromosome preparations obtained from peripheral blood lymphocytes of both patients and parents according to standard protocol (450–550 band level). A minimum of 20 R-banded metaphase chromosomes were analyzed using Cytovision® Karyotyping software version 4.0. Karyotypes were classified according to the International System of Human Cytogenetic Nomenclature (ISCN 2020) [11 ]. Fluorescent in situ Hybridization (FISH) was carried out on metaphase chromosomes of the patients according to the standard protocol, using commercial probes. Array Comparative genomic hybridization (aCGH) 4 × 44 K micro-arrays was performed using the Agilent platform according to the manufacturer’s instructions (Feature Extraction 9.1, CGH Analytics 4.5, Santa Clara, California, United States). An abnormal ratio greater than + 0.58 or lower than − 0.75 was considered as an alteration. An in silico analysis of the unbalanced regions was executed using UCSC Genome Browser (https://genome.ucsc.edu/ ), the Database of Chromosome Imbalance and Phenotype in Humans using Ensemble Resources (DECIPHER: https://decipher.sanger.ac.uk/ ), the Database of Genomic Variants (DGV: http://dgv.tcag.ca/dgv/app/home ) and the Online Mendelian Inheritance in Man database (OMIM: https://omim.org/ ).
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Array CGH for ID/ASD Diagnosis
9
Gene Expression and Epigenetic Analysis
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Expression studies were performed using the Agilent platform following manufacturer's instructions. Briefly, 100 ng RNA extracted with TriZOL from MOs, MACs and OCs hybridized to a SurePrint G3 Human Gene Expression Array (Agilent Technologies, CA, USA). Probe intensity normalization and downstream analysis were obtained using statistical analysis language R in combination with Bioconductor repository functions (http://bioconductor.org ). Data normalization was followed by probe identity filtering, under strong statistical confidence thresholds (P < 0.01; FDR < 0.05; FC < 0.5 and FC > 2 for downregulated and upregulated respectively). Finally, comparison of expression and DNA methylation and hydroxymethylation data were performed by applying custom R scripting. To test for the association of gene expression and 5mC/5hmC we calculated the Pearson correlation coefficient using outlier corrected data. In detail, we fit a linear model and removed data points with Cooks distance measures greater than twice the median Cooks distance.
10
Array-CGH Analysis of DNA Copy Number Alterations
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DNA copy number alterations (CNAs) were evaluated by array-CGH analysis using the Agilent platform (Agilent Tech., Santa Clara, CA, USA) as previously described [93 (link)]. Briefly, equal amounts of the isolated tumor and reference genomic DNA (a pool obtained from multiple female individuals with no cancer) (100–300 ng) were digested and labeled using a SureTag Complete DNA Labeling Kit (Agilent Tech.) and hybridized in the arrays for 40 h. Only cases that showed satisfactory incorporation of more than 1.00 pico/mol labeling were selected for hybridization. The array data were extracted using Feature Extraction (FE) software v10.10, and the Agilent Cytogenomic v. 7.0 software (Agilent Tech.) was used to analyze the data using the aberration detection method-ADM2, a threshold of 6.0, and defined aberration filters. Copy number gains and losses were considered when present in at least 3 consecutive probes with values of mean absolute log2 ratio (intensity of the Cy5 dye (reference DNA)/intensity of the Cy3 dye (test DNA) value of ≥0.25 and ≤−0.25, respectively) as per our previous analysis [20 (link)]. UCSC Genome Browser (GRCh37/hg19) and miRbase 22.1 databases were used to determine the genes and miRNAs present in each selected cytoband affected by CNA, respectively.
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