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: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: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.