Project description:In this study, an enhanced structure-guided molecular networking (E-SGMN) method was used, which is specifically tailored for the Orbitrap Astral mass spectrometer.
Reverse phase liquid chromatography (RPLC) analysis. ACQUITY BEH C8 column (100 mm×2.1 mm, 1.7 ?m, Waters, Milford, MA, USA) was used for the LC system in positive (ESI+) ionization modes, respectively. The temperature was set to 50°C, and the gradient elution flow rate was set at 0.35 mL/min. The mobile phase consisted of water (A) and acetonitrile with 0.1% formic acid (B) in ESI+ ionization mode. The initial gradient started with 5% B for 1 min, followed by a linear increase to 100% B within 23 min, and then maintained for an additional 4 min. Finally, the gradient was reduced to 5% B for system equilibration.
Astral MS analysis. Mass spectrometry was performed on Orbitrap Astral (Thermo Fisher Scientific, Rockford, IL, USA) in full scan MS/ddMS2 mode. For the full scan properties of the Orbitrap detector, the MS1 scan range was 85-1250 m/z. Orbitrap resolution was 120,000. RF lens was set as 50%. Full-scan orbitrap MS1 was performed in parallel with fast and sensitive DDA MS2 (top 30) on the Astral mass analyzer, with an MS/MS acquisition rate of 150 Hz. The isolation window was 1 m/z. Normalized collision energy type was used in our work, and the HCD collision energy was set as 15%, 30%, 45% and 80%, respectively. The MS2 scan range was 50-1250 m/z. The normalized AGC target and injection time were 10% and 5 ms, respectively. The spray voltages were 3.5 kV and 3.0 kV in positive and negative ionization modes, respectively. The aux gas heater temperature and capillary temperature were 350°C and 320°C, respectively. The sheath gas and aux gas were 45 and 10 (in arbitrary units), respectively.
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RybB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RybB in a rne131 ΔrybB strain. RybB (without MS2) was used as control
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RyhB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RyhB in a rne131 ?ryhB strain. RyhB (without MS2) was used as control
Project description:MetDNA3 Astral datasets were generated using the high-resolution Thermo Fisher Orbitrap Astral mass spectrometry platform and annotated with MetDNA3, which integrates a Knowledge- and Data-driven Two-layer Networking strategy for accurate metabolite annotation in untargeted metabolomics. This dataset collection covers a variety of sample types, including: NIST plasma, NIST urine, Mouse liver. These datasets provide a valuable resource for the development, benchmarking, and validation of metabolomics annotation algorithms across diverse biological matrices.
Project description:MetDNA3 Exploris 480 datasets were generated using the high-resolution Thermo Fisher Exploris 480 mass spectrometry platform and annotated with MetDNA3, which integrates a Knowledge- and Data-driven Two-layer Networking strategy for accurate metabolite annotation in untargeted metabolomics. This dataset collection covers a variety of sample types, including: NIST plasma, NIST urine, BV2 cell, Mouse brain, Mouse liver. These datasets provide a valuable resource for the development, benchmarking, and validation of metabolomics annotation algorithms across diverse biological matrices.
Project description:This is a dataset used for the orchestration of molecular networking which led the discovery of polyacetylated 18-norspirostanol saponins from Trillium tschonoskii.
Project description:These included influents and effluents collected in January 2019 at 16 WWTPs located in 16 Chinese provinces, influents and effluents collected from August 2019 to October 2019, at 15 WWTPs in 15 Chinese provinces, effluents collected at 2 WWTPs in the Wujing river in August 2020. The full process samples at a certain WWTP in Nanjing, Jiangsu were collected twice, in March and June 2023, respectively. The names of positive and negative files are one-to-one correspondence. Unless otherwise noted, those containing nxx or n-xx are in negative ion mode. Both pxx and p-xx are in positive ion mode. In represents water inflow and out represents water outflow. If there are only in and digits, it is positive ion mode.
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>