Power tools apt software

sEVs were purified from control or syntenin knockdown NCI-H226 cells and miRNA array was performed by Macrogen Inc. (Seoul, Republic of Korea). The miRNA Microarray system with Affymetrix GeneChip^® 4.0 array containing 2578 human mature miRNA oligonucleotide probes was used according to the manufacturer’s recommended protocol (Santa Clara, CA, USA). Raw data were extracted automatically in Affymetrix data extraction protocol using the software provided by Affymetrix GeneChip^® Command Console^® Software (AGCC). The CEL files import, miRNA level RMA + DABG-All analysis and result export using Affymetrix^® Power Tools (APT) Software. Array data were filtered by probes annotated species. The comparative analysis between test sample and control sample was carried out using fold-change. For a significant DEmiRNA set, hierarchical cluster analysis was performed using complete linkage and Euclidean distance as a measure of similarity. All Statistical test and visualization of differentially expressed genes was conducted using R statistical language v.3.3.2 (https://www.r-project.org).

RNA samples were obtained from the medium treated with 5 or 20 μg/mL PCS NP for 24 h. The concentration of the sample was confirmed through quality control analysis and was confirmed to be between 0.17 and 0.26 ng/μL. To check the purity and quantity of RNA, a NanoDrop spectrophotometer was used to measure the absorbance at 260 nm and 280 nm. All raw data were extracted automatically in the Affymetrix data extraction protocol using the Affymetrix GeneChip^® Command Console^® Software (AGCC). The CEL files were imported, miRNA levels were normalized using the RMA algorithm, the detection above background p-values were calculated for all data, and the results were exported using the Affymetrix^® Power Tools (APT) Software.
Array data were filtered using species-specific annotated probes. The comparative analysis between the test and control samples was carried out using fold change. All statistical testing and visualization of differentially expressed genes were conducted using R statistical language 3.3.3 (https://www.r-project.org/).