Nick Riley

Potentially controversial #TeamMassSpec #glycotime statement: it's time to start using ETD-based methods over HCD for both N- & O-glycopeptides A new @riley-research.bsky.social preprint led by Dr Haley Schramm speeds up ETD scan acquisition to make it the MS/MS method of choice for glycoproteomics

Combining this expedient ETD rxn with ion pipelining improvements on the Orbitrap Apex leads to gains in MS/MS spectra and glycopeptide identifications (Note this is a collaboration with Thermo R&D and implementation is default on the Orbitrap Apex, so you can explore this yourself)

Potentially controversial #TeamMassSpec #glycotime statement: it's time to start using ETD-based methods over HCD for both N- & O-glycopeptides A new @riley-research.bsky.social preprint led by Dr Haley Schramm speeds up ETD scan acquisition to make it the MS/MS method of choice for glycoproteomics

The high utility of ETD for both N- and O-glycopeptides despite slower acquisition rates motivated us to reduce the time costs of ETD and improve sensitivity of ETD-based methods Our fix: alter ETD rxn kinetics and improve ion pipelining to significantly reduce scan overhead times needed for ETD

Potentially controversial #TeamMassSpec #glycotime statement: it's time to start using ETD-based methods over HCD for both N- & O-glycopeptides A new @riley-research.bsky.social preprint led by Dr Haley Schramm speeds up ETD scan acquisition to make it the MS/MS method of choice for glycoproteomics

Glycoproteoforms are the unit of biological information in the glycoproteome, but measuring intact glycoproteins to capture combinatorial modification states remains a huge #glycotime challenge. @timveth.bsky.social tackled this issue head on in the latest preprint from @riley-research.bsky.social

We then took a deeper dive into our EpCAM data to look at what integrating bottom-up glycopeptide and intact glycoproteoform measurements can get us, and @emmajays.bsky.social helped us use @glycoshape.org to model how a glycoproteoform with two occupied N-glycosites might look like.

Glycoproteoforms are the unit of biological information in the glycoproteome, but measuring intact glycoproteins to capture combinatorial modification states remains a huge #glycotime challenge. @timveth.bsky.social tackled this issue head on in the latest preprint from @riley-research.bsky.social

One challenge was that tools traditionally used for low Rp spectra were not satisfactorily deconvolving our high Rp spectra that had isotopic resolution. @timveth.bsky.social developed a new workflow handle hRp-DIA-PTCR data, including software packages called PTsliCR and IsoTrac (both @ our GitHub)

Glycoproteoforms are the unit of biological information in the glycoproteome, but measuring intact glycoproteins to capture combinatorial modification states remains a huge #glycotime challenge. @timveth.bsky.social tackled this issue head on in the latest preprint from @riley-research.bsky.social

Here is an example from EpCAM, where we can assign discrete glycoproteoform identities.

Glycoproteoforms are the unit of biological information in the glycoproteome, but measuring intact glycoproteins to capture combinatorial modification states remains a huge #glycotime challenge. @timveth.bsky.social tackled this issue head on in the latest preprint from @riley-research.bsky.social

It is very cool to see this piece from a former student of mine, @mnicolen.bsky.social, in Nature's Career Feature section: www.nature.com/articles/d41...

(Disclaimer: I was not involved with this article, but Nicole and I have had a lot of great conversations since she took my Quantitative Analysis course, so I am a bit biased!)

It is very cool to see this piece from a former student of mine, @mnicolen.bsky.social, in Nature's Career Feature section: www.nature.com/articles/d41...

Check out the newest entry in the ACS Div. of Analytical Chem. Education Committee's "Meet an Analytical Chemist" initiative. This quarter we highlight Dr. Christina Jones, Partnerships and Outreach Strategist at NIST: acsanalytical.org/2026/02/26/m...

The complexity of glycoproteomics data creates a bottleneck in data interpretation and communication. We (@timveth.bsky.social, @riley-research.bsky.social) built GlycoDiveR as a step toward bridging gaps between search-engine output and biological interpretation through #glycotime visualizations.

These figures are a sampling of GlycoDiveR's >25 customizable functions & publication-quality visualizations, each accessible with a single line of code. Its modular architecture can accommodate new analyses from us and others, and we plan to update it with new visualizations regularly.

The complexity of glycoproteomics data creates a bottleneck in data interpretation and communication. We (@timveth.bsky.social, @riley-research.bsky.social) built GlycoDiveR as a step toward bridging gaps between search-engine output and biological interpretation through #glycotime visualizations.

Importing data w/ GlycoDiveR automatically connects to the @glycosmos.bsky.social API to provide GlyTouCan accession numbers for glycopeptides, and it connects to the #uniprot API to pull in associated protein annotations to assess, for example, which BP, CC, or MF are associated with glycopeptides.

The complexity of glycoproteomics data creates a bottleneck in data interpretation and communication. We (@timveth.bsky.social, @riley-research.bsky.social) built GlycoDiveR as a step toward bridging gaps between search-engine output and biological interpretation through #glycotime visualizations.

Classic visualizations for differential expression, like volcano plots, are available, and subsets of glycopeptides can be further processed to look for trends, such as glycan categories more represented in up- or down-regulated species. Glycoprotein rank plots can also show abundance shifts.

The complexity of glycoproteomics data creates a bottleneck in data interpretation and communication. We (@timveth.bsky.social, @riley-research.bsky.social) built GlycoDiveR as a step toward bridging gaps between search-engine output and biological interpretation through #glycotime visualizations.

For glycoproteome-scale analyses, visualizing data completeness and filtering for desired numbers of quantitated species per condition are functions our group uses often. Look at where in the LC-MS/MS gradient glycopeptides eluted can help with data assessment and experiment iterations, too.

The complexity of glycoproteomics data creates a bottleneck in data interpretation and communication. We (@timveth.bsky.social, @riley-research.bsky.social) built GlycoDiveR as a step toward bridging gaps between search-engine output and biological interpretation through #glycotime visualizations.

GlycoDiveR is an open-source, modular R framework with >25 customizable glycoproteome- and glycosite-scale visualizations that can be generated with minimal programming expertise. We reanalyzed data from Kawahara et al. to demonstrate GlycoDiveR's features: www.mcponline.org/article/S153...