Thermo Fisher Scientific Orbitrap Exploris Mass Spectrometers, Proteome Discoverer 2.5 Software, Xcalibur 4.4 Software
Thermo Fisher Scientific has released the Thermo Scientific Orbitrap Exploris 240 and 120 mass spectrometers. The Exploris 240 is designed for researchers working in proteomics, metabolomics, biopharmaceutical characterization, and small-molecule analysis. It promises discovery and identification with increased accuracy and offers operational simplicity and streamlined time-to-result. The system delivers mass accuracy, sensitivity, and resolving power across a wide dynamic range. It offers positive/negative-mode switching for comprehensive sample coverage and fast scan speeds.
The Exploris 120 offers internal calibration for consistent data quality and decision making. It features fast scanning modes and rapid polarity switching for comprehensive sample coverage and increased productivity.
In addition, the company launched the Thermo Scientific Proteome Discoverer 2.5 software for higher-confidence peptide identification, more accurate quantification, and higher-throughput data analysis of proteomics data. It uses deep learning to more accurately predict fragmentation mass spectra, facilitated by a Prosit-derived neural network licensed from bioinformatics firm and collaborator MSAID. It also provides more confident results in applications that require large search spaces, such as human leukocyte antigen (HLA) or metaproteomic analyses. In addition, the new release includes an enrichment service that helps provide biological meaning for differential analyses and support of targeted workflows. It also has tools to help deploy Thermo Scientific SureQuant Targeted Mass Spectrometry Assay Kits and custom SureQuant IS-triggered acquisition assays in new proteomics labs.
The company also launched the Thermo Scientific Xcalibur 4.4 software, which brings the AcquireX intelligent data acquisition workflow to Thermo Scientific Orbitrap Exploris mass spec platform users. It enables the fully automated collection of high-quality MS/MS data on components of interest in a sample and reduces manual input and the need for repeat runs.