Glycosphingolipids with Very Long-Chain Fatty Acids Accumulate in Fibroblasts from Adrenoleukodystrophy Patients

Adrenoleukodystrophy (X-ALD) is an X-linked genetic disorder caused by mutation of the ATP-binding cassette subfamily D member 1 gene, which encodes the peroxisomal membrane protein, adrenoleukodystrophy protein (ALDP). ALDP is associated with the transport of very-long-chain fatty acids (VLCFAs; carbon chain length ≥ 24) into peroxisomes. Defective ALDP leads to the accumulation of saturated VLCFAs in plasma and tissues, which results in damage to myelin and the adrenal glands. Here, we profiled the glycosphingolipid (GSL) species in fibroblasts from X-ALD patients. Quantitative analysis was performed using liquid chromatography–electrospray ionization–tandem mass spectrometry with a chiral column in multiple reaction monitoring (MRM) mode. MRM transitions were designed to scan for precursor ions of long-chain bases to detect GSLs, neutral loss of hexose to detect hexosylceramide (HexCer), and precursor ions of phosphorylcholine to detect sphingomyelin (SM). Our results reveal that levels of C25 and C26-containing HexCer, Hex2Cer, NeuAc-Hex2Cer, NeuAc-HexNAc-Hex2Cer, Hex3Cer, HexNAc-Hex3Cer, and SM were elevated in fibroblasts from X-ALD patients. In conclusion, we precisely quantified SM and various GSLs in fibroblasts from X-ALD patients and determined structural information of the elevated VLCFA-containing GSLs.

Microbial-based magnetic nanoparticles production: a mini-review

The ever-increasing biomedical application of magnetic nanoparticles (MNPs) implies increasing demand in their scalable and high-throughput production, with finely tuned and well-controlled characteristics. One of the options to meet the demand is microbial production by nanoparticles-synthesizing bacteria. This approach has several benefits over the standard chemical synthesis methods, including improved homogeneity of synthesis, cost-effectiveness, safety and eco-friendliness. There are, however, specific challenges emanating from the nature of the approach that are to be accounted and resolved in each manufacturing instance. Most of the challenges can be resolved by proper selection of the producing organism and optimizing cell culture and nanoparticles extraction conditions. Other issues require development of proper continuous production equipment, medium usage optimization and precursor ions recycling. This mini-review focuses on the related topics in microbial synthesis of MNPs: producing organisms, culturing methods, nanoparticles characteristics tuning, nanoparticles yield and synthesis timeframe considerations, nanoparticles isolation as well as on the respective challenges and possible solutions.

Reproducible Untargeted Metabolomics Data Analysis Workflow for Exhaustive MS/MS Annotation

<p>Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the <i>post-hoc</i> validation of ions selected by new statistical methods impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for untargeted mass spectrometry identification of unknown compounds. By removing redundant peaks and performing pseudo-targeted MS/MS analysis on independent peaks, we can comprehensively cover unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We show that compared to DDA, PMDDA is more comprehensive and robust against samples' matrix effects. Further, more compounds were identified by database annotation using PMDDA compared with CAMERA and RAMClustR. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template. </p>

Reproducible Untargeted Metabolomics Data Analysis Workflow for Exhaustive MS/MS Annotation

<p>Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the <i>post-hoc</i> validation of ions selected by new statistical methods impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for untargeted mass spectrometry identification of unknown compounds. By removing redundant peaks and performing pseudo-targeted MS/MS analysis on independent peaks, we can comprehensively cover unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We show that compared to DDA, PMDDA is more comprehensive and robust against samples' matrix effects. Further, more compounds were identified by database annotation using PMDDA compared with CAMERA and RAMClustR. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template. </p>

Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC–MS Metabolomics

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC–MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC–MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.