Spectronaut 14 is here and comes with several exciting new features and improvements.
Thanks to deep-learning augmented peptide identification, you can now find more precursors and proteins in your samples than ever before. We have further broadened our support for the latest MS data acquisition technologies, and provide new ways to quickly and thoroughly analyze quantitative proteomics data.
«We found Spectronaut to be the best match for our DIA projects. It provides the quantitative reliability we aim for, and the speed and scalability our workflows require.»
Prof. Dr. Jesper Velgaard Olsen,
CPR University of Copenhagen
To read our complete Spectronaut 14 release notes, click here.
Benefit from the simplicity and power of DIA analysis without additional measurements for library generation.
Key Parameters of the
Benchmark Datasets
In total 13 datasets:
Sample types break down:
On average 28 DDA measurements for library
On average 29 DIA measurements
In the past, data-independent acquisition (DIA) analysis was dependent on spectral libraries generated from separate data-dependent acquisition (DDA) runs, requiring additional instrument run time and sample fractionation. Spectronaut’s industry-leading library-free DIA analysis (directDIA™) offers a fast, cost-saving alternative that only requires single-shot DIA data and a FASTA file as inputs.
First introduced in 2017, directDIA has been significantly improved in Spectronaut 14 with deep learning enhancements [ASMS 2020, MP125]. The resulting workflow identifies 25% more proteins than it did in Spectronaut 13, pushing protein coverage depth to the level of more resource-intensive project-specific DDA library-based analysis. With directDIA, you can get the same great results from your DIA data through a simplified workflow.
The average identification numbers and instrument time with directDIA 2.0 are relative to a more classic DIA analysis workflow using project-specific libraries based on DDA measurements. directDIA 2.0 achieves 98% of the protein identification performance of the more traditional workflow with DDA libraries while also identifying more precursors and requiring only half of the instrument time on average. This shows that no additional library-specific runs need to be acquired for quantitative DIA analysis.
Take full advantage of the latest technologies in ion mobility based acquisition. Spectronaut 14 supports timsTOF Pro™ (Bruker), FAIMS Pro™ (Thermo Scientific), and Synapt G2-Si HDMSE (Waters).
Ion mobility has commercially emerged as an additional separation dimension for mass spectrometry data and is even more established in the proteomics field.
Spectronaut 14 fully supports Bruker’s dia-PASEF as well as Thermo FAIMS technologies, offering a convenient way to use these new acquisition modes and make the best use of them to get better proteome coverage than ever before.
dia-PASEF Support:
Spectronaut 14 adds full support for dia-PASEF analysis. You can analyze dia-PASEF runs either using a DDA (PASEF) based spectral library or directDIA (Figure). Pulsar, our database search engine, can generate an ion-mobility-enhanced library from PASEF or dia-PASEF runs. For the targeted analysis of dia-PASEF data, Spectronaut introduces a novel high-precision ion mobility workflow [ASMS 2020, MP-115] which is similar to our workflow for retention time analysis [Bruderer, 2016]. Additionally, the time it takes to create PASEF based spectral libraries has been almost cut in half.
Three species were mixed in different ratios, in two samples using human as the background [Kaspar-Schoenefeld, 2019]. Samples were measured using timsTOF Pro in dia-PASEF mode on a 100 min gradient. The measurements were analyzed using directDIA (i.e. without using any library-specific runs). In total, 7’434 protein groups were identified on average. Label-free quantification reflects the expected ratios of the species.
FAIMS Support:
We significantly improved our support for FAIMS Pro™ devices with ion mobility filtering [ASMS 2020, MP131]. DIA methods with FAIMS usually come in two main flavors: Single compensation voltage (CV) methods and multi CV methods. Both of these methods are now supported. Pulsar, our database search engine, can create a compatible library both from FAIMS DIA and DDA runs.
DIA data for triplicate runs of HeLa cell samples were recorded on a Thermo Orbitrap Exploris 480 using a 120-minute gradient, with and without FAIMS Pro™ (three CVs). Data analysis was done using project-specific hybrid libraries [Muntel, 2019]. On average, we saw a 14% increase in modified peptide identifications and a 34% increase in protein group identifications per file when using FAIMS Pro™.
Use Spectronaut’s unique advanced SNE combine functionality to save time and memory resources to process huge experiments. It is now possible to batch large datasets from thousands of samples for simultaneous processing across multiple workstations or to start analysis while additional data is still being acquired.
Since the emergence of DIA as the method of choice for high-throughput quantitative proteomics, the perception of what constitutes a large experiment has shifted with each new generation of instruments and analysis software. Today, large experiments exceed 1’000 raw files with even larger studies already on the horizon. This poses an immense challenge to memory consumption and overall runtime of processing pipelines that analyze this data in an experiment-wide context rather than on a run-by-run basis.
Spectronaut 14 comes with a new batch processing workflow that allows the analysis of data in parallel on multiple computers. Importantly, partial processing of large datasets can be started before all of the data has been acquired. The results can then be combined into a single, controlled experiment while keeping system requirements to a minimum [ASMS 2020, TP 248].
Spectronaut 14 is our latest DIA proteomics software and comes with many new features and improved performance. Get in touch with us for a trial license and for help on upgrading your proteomics software to Spectronaut 14.
Let us help you do great proteomics!
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