Progenesis qi software

Pre-processing and analysis of the UPLC-MS data were performed as described previously (Yi et al., 2020 (link)). In brief, the data were collected in both positive and negative mode. All raw data were then loaded into Progenesis QI software (Waters, Milford, MA, United States) and SIMCA-P14.0 software (Umetrics AB, Umea, Vasterbotten, Sweden) for further analysis.
In order to conduct multivariate statistical analysis, Majorbio Cloud Platform (https://cloud.majorbio.com) and SIMCA-P 14.1(Umetrics, Sweden) was carried out for the principal components analysis (PCA) and orthogonal partial least-squares discriminate (OPLS-DA) analysis. Variable importance in the projection (VIP) > 1 and p < 0.05 were selected as candidate metabolites. Besides, significantly altered metabolite data were imported into MetaboAnalyst 5.0 to investigate the neuroprotective mechanisms of BSTSF treatment on AD. The impact value threshold calculated from pathway topology analysis was set to 0.10, and a raw p value <0.05 was regarded as significant.

Progenesis QI software (Waters Corporation, Milford, USA) was used to preprocess the raw LC/MS data. Principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis (PLS-DA) were then conducted on this processed data, using the R software package ropls (v1.6.2). Metabolites with significant differences were selected based on their variable importance in the projection (VIP) derived from the OPLS-DA model and the p-value from a Student’s t-test. Metabolites with VIP > 1.5 and p < 0.05 were considered to be statistically significant. Metabolite enrichment and pathway analysis based on the KEGG database were used to determine the differential pathways between the groups.

The UPLC-Q-TOF/MS data acquisition from MasslCIx software (version 4.1, Waters Corporation, Milford, MA, USA) was imported to Progenesis QI software (version 2.0, Waters Corporation) for data handling, involving peak detection, alignment and normalization. The compound information, scanned by UPLC-Q-TOF/MS, was compared with the database by UNIFI software. The analysis conditions were set as follows: mass error ≤ 3 mDa, chromatographic peak extraction time 0–70 min, positive ion mode adduct ions +H, +Na.

About 10⁶ cells were cultured in 100 mm culture dish, and infected with EV71 at an indicated MOI for 1 h. An equal volume of MEM/2% FCS was added. Sixteen hours later, the cells were harvested in 80% methanol/0.1% formic acid solution as described previously [7 (link)]. The extract was dried under nitrogen gas and dissolved in 0.1% formic acid. The sample was analyzed using liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) as described elsewhere [7 (link)]. Spectral data were analyzed with Progenesis QI software (Waters Corp., Milford, MA, USA) for peak peaking, alignment, and normalization. The features were identified through Metabolite Link (METLIN) and Human Metabolome (HMDB) database search, and/or through comparison to the chromatographic and spectral data of standard compounds.

The acquired LC–MS raw data were analyzed by the progenesis QI software (Waters Corporation, Milford, CT, USA) to identify the metabolites. The raw data were converted to .abf format, followed by processing on the MD-DIAL software through LUG database (Untargeted database of GC–MS rom Lumingbio).
Principle component analysis (PCA) and (orthogonal) partial least-squares-discriminant analysis (O) PLS-DA were carried out to visualize the metabolic alterations among experimental groups after mean centering (Ctr) and Pareto variance (Par) scaling, respectively. Variable importance in the projection (VIP) ranks the overall contribution of each variable to the OPLS-DA model, and those variables with VIP > 1 are considered relevant for group discrimination. In this study, the default seven-round cross-validation was applied with one/seventh of the samples being excluded from the mathematical model in each round.
The differential metabolites were selected based on the combination of a statistically significant threshold of VIP values obtained from the OPLS-DA model and p values from a two-tailed Student’s t-test on the normalized peak areas, where metabolites with VIP values larger than 1.0 and p values less than 0.05 were considered as differential metabolites.