Head over to https://t.co/5GZHgjcbtL to try out the newest version of @TheCoxLab and @quiztoph's excellent software platform for targeted and prioritized data acquisition! It was a pleasure collaborating with you guys, as well, @quiztoph!

We always aimed to link single-cell protein measurements to cellular functions and regulatory mechanisms. This project was a major step for us in this directions, especially with the direct measurements proteolysis products: https://t.co/uccyi8wgTT Data: https://t.co/BIIiCyDKIH

Did you think that data completeness of DIA could be improved?

I'm very excited to share the work we've done @slavovLab in collaboration with @DemichevLab! Increasing the throughput of sensitive proteomics by plexDIA https://t.co/CSTKeZ5L9P

Yes, stay tuned ... more is coming, ... and it cannot be soon enough.

.@_lukekhoury and I highlight the identification of a signaling interactome and outline how MS proteomics can be combined with deep learning towards comprehensive identification of regulatory protein networks https://t.co/ZVaWC6YaVd

Interested in evaluating your own single-cell proteomics data sets with a user friendly and well documented pipeline? Check out the scp R package and accompanying preprint from @c_vanderaa and @lgatt0: https://t.co/vkomYEtn3O #SingleCell #Bioinformatics #proteomics

Led by @harrisonspecht and just in time for #scp2019, our next-generation version of single cell proteomics: SCoPE2 is now out on bioRxiv! In it we apply SCoPE2 to the study of macrophage differentiation.

Thanks for coming to my twitter poster! Development of click-amplifying FISH (clampFISH) probes to exponentially amplify RNA fluorescent signal in situ with single molecule resolution. All questions and comments welcome! @arjunrajlab #RSCPoster #RSCChemBio

Here's a preview of the DO-MS dashboard in action:

...whereas cross-cell-type peptide abundances are not. This type of plot can serve as a useful metric for determining the accuracy of quantitation, as well as for determining potential sample prep issues, such as poor label quenching (for which figure a. can also be useful).

Finally, the matrix of pairwise correlations for each TMT channel's peptide intensities (shown in figure b.), illustrates that measurements for the same cell type (Jurkat cells or U937 cells, as indicated by the plot labels) are highly correlated...

...while the relative reporter ion intensities of the single-cell channels should be approximately 50-fold lower than the carrier channels' reporter ions.

Plot a. in the above panel allows for the constitution of such a SCoPE-style set to be assessed, as one would expect the reporter ion intensities of the carrier channels (denoted by the labels "50 J cells" and "50 U cells") to be roughly equivalent...

SCoPE-style proteomics workflows use TMT to label and combine carrier channels (made up of 50 to 100 cells) with single cells for LC-MS/MS analysis. The carriers insulate the single cells from losses, while also generating enough fragment ions for enhanced peptide identification

Also, for anyone interested in adopting the SCoPE-MS workflow, DO-MS has a tab of diagnostic plots specifically made for this use case, a few of which can be seen on the attached figure panel.

Once the presence of contamination is identified with DO-MS, there are many excellent tools available for determining the identity of the contaminant (such as Matthew Rardin's library for Skyline: https://t.co/OJsxnFsUCM)

The above figure panel compares two replicate samples, illustrating the impact of co-eluting contaminant ions (shown in figure a.) on peptide identifications (figures b. and c.).

Low-input samples are more substantially impacted by the presence of co-eluting non-peptidic ions, and having a means to identify the impact of these contaminant ions can be quite helpful when evaluating/comparing SCoPE-style ( https://t.co/CyYYXTXjCh) experiments.

DO-MS also allowed us to quickly identify the presence of contaminants in our samples.