For example, we have recently demonstrated SWATH-MS experiments that use 5-minute 800μL/min high flow chromatography and can maintain high quantification accuracy over thousands of samples ( Demichev et al., 2020 Messner et al., 2020b). These applications include, among others, exploratory drug screens and clinical studies with high participant numbers. These developments now enable applications where throughput, consistent quantification and low batch effects are essential. These approaches benefit from higher peak capacities, increased column lifetime, improved chromatographic stability and higher throughput, but at the expense of a higher sample dilution ( Bian et al., 2020 Messner et al., 2020b Vowinckel et al., 2018). Further, significant progress in the analysis of DIA data has allowed it to move to faster gradients and higher flow rates. For example, a recent study demonstrated quantification of more than 10000 proteins in human cell lysates ( Muntel et al., 2019). DIA methods have become attractive in classic proteomics that relies on conventional nano-flow chromatography, where they increase depth in ‘single-shot’ experiments.
#Fragger gas software
Hence, the combination of FragPipe and DIA-NN provides a simple-to-use software platform for dia-PASEF data analysis, yielding significant gains in high-sensitivity proteomics.ĭata-independent acquisition (DIA) methods in proteomics ( Gillet et al., 06/2012 Venable et al., 2004) have been actively developed in the past few years, leading to increased depth, higher data consistency and accurate quantification ( Ludwig et al., 2018). In complex samples, featuring a mix of human and yeast lysates, the workflow detects over 11700 proteins in single runs acquired with a 100-minute nanoflow gradient, while demonstrating quantitative precision.
For example, we quantify over 5200 proteins from 10ng of HeLa peptides separated with a 95-minute nanoflow gradient, and over 5000 proteins from 200ng using a 4.8-minute separation with an Evosep One system. Using spectral libraries generated with the MSFragger-based FragPipe computational platform, the DIA-NN analysis of dia-PASEF raw data increases the proteomic depth by up to 69% compared to the originally published dia-PASEF workflow. Here, we demonstrate neural network-based processing of the ion mobility data, which we implement in the DIA-NN software suite. The dia-PASEF technology exploits ion mobility separation for high-sensitivity analysis of complex proteomes.
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