Unleashing the power of DIA combined with short gradients using the Evosep One 

May 27 2021 I 4:00 PM CET I 10:00 AM EdT  I  7:00 am PDT

In the ever lasting effort to boost the number of protein identified in MS-based proteomic experiments, it has become clear that Data Independent Analysis (DIA), combined with short gradients, has become a winning combination.

During this webinar Florian Meier (Friedrich Shiller University, Jena) and Vadim Demichev (Crick Institute and University of Cambridge) will discuss their use of Evosep One, using novel DIA methodologies.

SPEAKERS

A practical guide to high-throughput data-independent acquisition

Talk by Florian Meier, Junior professor at Friedrich Schiller University Jena
Data-independent acquisition (DIA) is an increasingly attractive strategy for high-throughput mass spectrometry-based proteomics. This presentation will give an introduction to state-of-the-art acquisition methods such as very fast Orbitrap mass analysis and diaPASEF.
We highlight key method parameters and illustrate their optimization for short gradients. Finally, we will discuss the integration of ion mobility spectrometry to improve ion sampling efficiency, increase sensitivity and reduce spectral complexity, leading to accurate label-free quantification of 6,000 proteins at a throughput of 60 samples per day.

Beyond 5000 proteins in 4.8 minutes from low sample amounts

Talk by Vadim Demichev , Project research scientist at the Francis Crick Institute and the university of cambridge 

There is an increasing need for fast methods in proteomics. We have previously introduced DIA-NN, a neural network-based software suite capable of processing complex DIA proteomic data acquired with gradients as fast as 60-seconds. We now show that DIA-NN quantifies over 5000 proteins from just 200ng of a HeLa sample analysed with the 200 samples/day method on an Evosep One coupled to a timsTOF Pro.
This workflow enables high-throughput proteomics with data quality and sample amount requirements comparable to long-gradient nanoLC methods

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