Project description:This SuperSeries is composed of the following subset Series:; GSE11440: Role of Caveolin 1, E-Cadherin, Enolase 2 and PKCa on resistance to methotrexate in human HT29 colon cancer cells; GSE16066: Networking of differentially expressed genes in CaCo2 human colon cancer cells resistant to methotrexate; GSE16070: Networking of differentially expressed genes in human MCF7 breast cancer cells resistant to methotrexate; GSE16080: Networking of differentially expressed genes in human MDA-MB-468 breast cancer cells resistant to methotrexate; GSE16082: Networking of differentially expressed genes in human MIA PaCa2 pancreatic cancer cells resistant to methotrexate; GSE16085: Networking of differentially expressed genes in human K562 erythtoblastic leukemia cells resistant to methotrexate; GSE16089: Networking of differentially expressed genes in human Saos-2 osteosarcoma cells resistant to methotrexate Experiment Overall Design: Refer to individual Series
Project description:BackgroundIt is essential to identify the chemical components for the quality control methods establishment of Chinese Classical Formula (CCF). However, CCF are complex mixture of several herbal medicines with huge number of different compounds and they are not equal to the combination of chemical components from each herb due to particular formula ratio and preparation techniques. Therefore, it is time-consuming to identify compounds in a CCF by analyzing the LC-MS/MS data one by one, especially for unknown components.MethodsAn ultra-high pressure liquid chromatography-linear ion trap-orbitrap high resolution mass spectrometry (UHPLC-LTQ-Orbitrap-MS/MS) approach was developed to comprehensively profile and characterize multi-components in CCF with Erdong decoction composed of eight herbal medicines as an example. Then the MS data of Erdong decoction was analyzed by MS/MS-based molecular networking and these compounds with similar structures were connected to each other into a cluster in the network map. Then the unknown compounds connected to known compounds in a cluster of the network map were identified due to their similar structures.ResultsBased on the clusters of the molecular networking, 113 compounds were rapidly tentative identification from Erdong decoction for the first time in the negative mode, which including steroidal saponins, triterpenoid saponins, flavonoid O-glycosides and flavonoid C-glycosides. In addition, 10 alkaloids were tentatively identified in the positive mode from Nelumbinis folium by comparison with literatures.ConclusionMS/MS-based molecular networking technique is very useful for the rapid identification of components in CCF. In Erdong decoction, this method was very suitable for the identification of major steroidal saponins, triterpenoid saponins, and flavonoid C-glycosides.
Project description:This study demonstrates how the latest UHPLC (ultra-high-performance liquid chromatography) technology can be combined with high-resolution accurate-mass (HRAM) mass spectrometry (MS) and long columns packed with fully porous particles to improve bottom-up proteomics analysis with nano-flow liquid chromatography mass-spectrometry (nanoLCMS) methods. The increased back pressures from the UHPLC system enabled the use of 75 µm I.D. x 75 cm columns packed with 2 µm particles at a typical 300 nL/min flow rate as well as elevated and reduced flow rates. The constant pressure pump operation at 1500 bar reduced sample loading and column washing/equilibration stages and overall overhead time that maximizes MS utilization time. The versatility of flow rate optimization to balance the sensitivity, throughput with sample loading amount, and capability of using longer gradients contribute to a greater number of peptide and protein identifications for single-shot bottom-up proteomics experiments. The routine proteome profiling and precise quantification of >7,000 proteins with single-shot nanoLC-MS analysis open possibilities for large-scale discovery studies with deep dive into the protein level alterations.
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.