Delighted that @teerap16's paper on MAP-MS is out! https://t.co/tGpEOarO6u It changes how precursor ions accumulate in Orbitraps to achieve 2× dynamic range without consequences, improving DDA and DIA. It's a free instrument upgrade requiring no special software for Lumos on up!

Our new @Nature study reveals a proteotoxic shock as a key mechanism of T-cell exhaustion. TexPSR (proteotoxic stress response in T-cell exhaustion) is driven by increased protein synthesis + buildup of misfolded proteins, leading to T-cell dysfunction. 👉 https://t.co/154ORc2MnD

It was a lovely chance to share a bit about this work and journey in the „Researcher Spotlight“ interview. https://t.co/PzQ5MwBlHd

As with all science, this work would never have been possible without collaboration. Thanks to everyone who contributed to this work! @ArianaShannon @zilu_ye @erwinschoof @Earthnut89 Special thanks also to our friends and colleagues at Thermo for double checking our work.

It’s fitting that the Lumos was released 10 years ago this month. All Orbitrap instruments from the last decade benefit from this (we used an Exploris 480). Setup is easy with our extensive supplementary data and example method files are on our website:

MAP-MS produces precursor quantification with half the coefficient of variation versus "normal" 1e6 MS1s and 1/3rd compared to "common" 3e6 MS1s. This is huge for people doing DDA-based quantification!

What is the end result? A small (~10%) increase in detections using both DDA and DIA for free on existing hardware (no upgrades or software required). The REAL improvement is much, much more consistent MS1 quantification with match-between-runs.

Tangent: people ask how different MAP-MS is from BoxCar, but it’s more like MSX since it hides ion accumulation inside MS2s “consequence-free”. BoxCar takes multiple long transients to improve dynamic range, which eats up MS2 time. BoxCar produces better MS1s at significant cost!

MAP-MS provides a roadmap for making better use of the ion beam. To accelerate open science, our raw data ( https://t.co/6vMtb0Fbew) is already public. We've also uploaded the Exploris 480 .meth method files so you can start experimenting immediately!

Ok, but does this screw up intensities? It turns out: no. The variable fill logic already adjusts peak intensity as ions/second based on the actual ion injection time. The resulting MAP-MS MS1 appears like “normal” tSIM MS1s to all downstream tools. No programming needed!

Instead of multiplexing different windows together, MAP-MS multiplexes the entire precursor range. At first this seems strange, but the benefit is to variably fill each range for a different length of time, allowing the instrument to spend more time on "empty" m/z regions.

In 2013, the MacCoss lab showed a way to combine multiple groups of ions in a single spectrum to improve multiplexing with DIA to narrow isolation windows ( https://t.co/3IQKELLHsg), which morphed into the popular staggering technique. MAP-MS does the same, but for precursors.

In a hybrid Q-Orbitrap, parallelization still occurs: while one ion batch is measured, a new batch is accumulated. The Orbi's small capacity means MS1s accumulate in 1-2 ms, wasting the rest of the ion beam. MAP-MS accumulates pieces of the MS1 mass range in a single spectrum.

Orbitraps are slow with limited ion capacity. As such, parallel processing has long been part of how Orbitraps work. Since the Fusion Tribrid, while the Orbitrap was busy measuring precursors, the ion trap collects serial MS2s (ie the classic Senko paper: https://t.co/daFetmAiOi)

People have asked about how MAP-MS works so here's a tweetorial on @teerap16's new preprint: https://t.co/s3palUNQbE. MAP-MS takes advantage of parallelization in a trapping MS, collecting Orbitrap MS1s with 2x the dynamic range by differentially amplifying parts of the ion beam.

New research in @NatureComms on rapid assay development for low input targeted #proteomics using a versatile linear ion trap. This method promises to accelerate #immuno_oncology research with #targeted_proteomics using minimal sample input. @ArianaShannon @briansearle @Zihai

So proud of @V_Akshintala well deserved! We are going to do great things for the diagnosis and management of #pancreatitis @briansearle .. be careful of the triple effect 😎

@briansearle w/ @OSUWexMed presenting his work on urine proteomics as identifiers of chronic #pancreatitis biomarkers. His work, in collaboration w/ @MaisamHaijaMD and the PCC won the Best Abstract in #Pediatric #Pancreas #Research at #APA2024! @CincyKidsGastro

A wonderful lecture by ⁦@briansearle⁩ on #proteomic #applications in #childhood #pancreatitis, next phase #collaboration with ⁦@SanthiVegeMD⁩ will be great. #TeamworkMakesTheDreamWork ⁦@TMFoundationHQ⁩

Come join the OSU proteomics community! We're hiring a senior staff scientist at @OhioStatePIIO run by @Zihai to direct a mass spec proteomics facility to monitor immune proteins in cancer. A great opportunity for independent MS researchers! DM for details