Table of Contents

guest
2021-09-19
Thanks!
Questions and Answers
Event Information
Speakers
   Michael MacCoss
   Brendan MacLean
   Josue Baeza
   Karine Bagramyan
   Aivett Bilbao
   Viktoria Dorfer
   Sebastien Gallien
   Todd Greco
   Kaylie Kirkwood
   Benjamin Orsburn
   Roman Sakson
   Tobias Schmidt

Thanks!


Thanks for helping make this year's uniquely challenging Skyline User Group Meeting very much a success!

With coronavirus travel restrictions still in place, we were forced, like ASMS itself, to take our annual face-to-face event online in webinar form. Held over two days -- May 27 and 28, 2020, Skyline users came out spectacularly with over 1,000 registrations (a 3-fold record) to hear 11 speakers on a diverse spectrum of mass spectrometry topics plus Skyline Principal Developer Brendan MacLean's annual update on the year past and future aims of the Skyline project. Despite the much less interactive format, 450 attendees (nearly a 2-fold record) engaged in lively Q&A sessions after each presentation.

Session recordings including the Q&A sessions are now available from the Speaker's page under each speaker's bio below.

Written responses to the questions posed in the Q&A sessions will be posted shortly.

We would like to thank our speakers who never wavered in their commitment to sharing their work. They volunteered their time to create and deliver informative presentations despite myriad timezones, practice sessions, and technical challenges. Thanks too for the ongoing support from instrument vendors who help fund our efforts and lastly, to our users who continually inspire us with the new and innovative applications for Skyline. That so many managed to join us in a time when we are all so confined meant a lot to us.

We look forward to seeing and conversing with you all at future ASMS conferences in person!

Thank you!

-- Brendan MacLean and Mike MacCoss Event Organizers


Speakers

Michael J. MacCoss, Ph.D. (University of Washington): Introduction and event host

Full Talks

Brendan MacLean (MacCoss Lab, University of Washington): Status of the Skyline Open-source Software Project 12 Years after its Inception
The Skyline project started just after ASMS 2008 as a 2-year effort to bring better SRM/MRM software tools to the NCI-CPTAC Verification Working Group that could support the variety of mass spectrometers in use in participating laboratories. Nearly 12 years later, the Skyline project is a thriving multi-omics community open-source collaboration supporting 6 mass spec instrument vendors integrated with a wide variety of external software, with thousands of users worldwide and many thousands of instances started each week. (More info...)

Josue Baeza, Ph.D., (Garcia Lab, University of Pennsylvania): Applications of Skyline for Method Development and Quantification of Histone Marks
Despite a growing interest in epigenetics, performing proteomics studies of histone tail marks remains highly specialized. Mass spectrometry of histone tail marks is difficult due to the variety of modifications, coeluting isoforms, and dynamic range. Here, we have designed a robust histone DIA method and a flexible Skyline-based analysis workflow to more accurately and precisely quantify histone marks. (More info...)

Viktoria Dorfer, Ph.D., (University of Applied Sciences - Upper Austria): MS Amanda goes West: Integrating a Search Engine into Skyline
Mass spectrometry has become the method of choice for analysing proteins, demanding reliable and state-of-the-art software. Skyline has emerged as one of the most popular of these tools, supporting the generation and use of spectrum libraries from various analysis pipelines, however requiring separate pipeline execution. We present a fully integrated workflow for peptide identification and quantification within Skyline that incorporates the MS Amanda search algorithm. (More info...)

Todd Greco, Ph.D., (Cristea Lab, Princeton University): Unbiased and Targeted Mass Spectrometry Provides Insight into Huntington’s Disease Pathogenesis
The causative agent of Huntington’s disease (HD) is the CAG repeat expansion of the huntingtin gene, producing a mutant protein with an expanded glutamine tract (mHTT). mHTT toxicity selectively impacts the brain and liver. mHTT-induced proteome and protein interaction alterations have been investigated in the brain, yet those proximal to disease progression remain poorly understood. We detected 219 differential protein candidates in mHTT liver using MS1-based LFQ, which were all targeted for validation by PRM using Skyline. (More info...)

Kaylie Kirkwood, Ph.D., (Baker Lab, North Carolina State University): Developing Multidimensional Small Molecule Spectral Libraries for Rapid Lipid Detection and Quantitation
Multidimensional lipidomics data provides valuable polarity, structural and mass information, but results in large and complex datasets which are extremely difficult to process. Skyline offers rapid and targeted processing of lipid data which ultimately allows for confident detection of diverse lipid species. We have developed sample-specific lipid spectral libraries which include hundreds of target lipids from multiple lipid categories for human plasma, brain total lipid extract, zebrafish, bronchoalveolar lavage fluid, flies, and lettuce. (More info...)

Roman Sakson,, (Heidelberg Molecular Biology Center): Unleashing Versatile Skyline Features for the Everyday Needs of a Proteomics Core Facility
Proteomics Core Facilities need to support a set of robust qualitative and quantitative workflows for a broad customer base. We routinely use Skyline as a versatile, vendor-independent platform that helps us to address two major issues, namely quality control and sharing information between MS experts and users, especially if they are not located in the same place. Customized reports and integrated tools, such as Protter for protein sequence visualization, are extremely helpful while discussing results with customers. (More info...)

Lightning Talks

Karine Bagramyan, Ph.D., (Kalkum Lab, Diabetes and Metabolism Research Institute): Using Skyline to Quantify Botulinum Neurotoxin Activity in Complex Biological Samples
This presentation will highlight Skyline’s utility for the design and optimization of our PRM and MRM assays. This brilliant software provided us with a solid bioinformatics pipeline for the entire project: From the generation of calibration curves using stable isotope-labeled synthetic peptide standards, to the quantification of attomolar concentrations of BoNT, resulting in a novel assay that has unmatched limits of detection and quantification. (More info...)

Aivett Bilbao, Ph.D., (PNNL): Metabolite Profiling for Synthetic Biology using Ion Mobility-Mass Spectrometry and Data-Independent Acquisition with Improved Targeted Data Extraction Software
Combining liquid chromatography, drift-tube ion mobility spectrometry (DTIMS)-mass spectrometry (MS) and data-independent acquisition (DIA) with improved targeted data extraction software, we developed a workflow to enable more effective synthetic biology research of hundreds Aspergillus pseudoterreus strains engineered for production of organic acids of industrial relevance. (More info...)

Sebastien Gallien, Ph.D., (Thermo Fisher Scientific): Towards Turnkey Targeted Proteomics Solutions Using SureQuant Internal Standard Triggered Acquisition on Orbitrap Mass Spectrometers
An extension of HR-PRM, called SureQuant method, has recently been introduced to progress targeted proteomics. This method, implemented in the native instrument control software of Orbitrap instruments, uses spiked-in internal standards to dynamically control the acquisition process and maximize its productivity. This included new data processing functionalities implemented in Skyline, which is a key component of the optimized informatic pipeline supporting the workflow. (More info...)

Benjamin Orsburn, Ph.D., (Johns Hopkins Medical School): Skyline -- A Comprehensive Package for Cannabis Testing Labs
Recent changes in the laws regarding Cannabis in North America has created a profitable new market for agriculture and small batch production facilities. Due to the lack of federal oversight in the US, state and local municipalities are currently responsible for determining safety testing and product characterization requirements and these vary widely across the country. We demonstrate how the Skyline software can be utilized as a near solution for both the testing and quality control monitoring for Cannabis testing labs. (More info...)

Tobias Schmidt (Kuster Lab, Technical University Munich): Real-time Spectrum Prediction in Skyline via ProteomicsDB’s gRPC Interface to Prosit
Prosit is able to accurately predict fragment ion intensities and retention times of peptides by deep learning. However, deep-learning requires GPUs for predictions that are not yet readily available in many labs and thus limit its applicability. In order to circumvent this shortcoming, we made Prosit available via gRPC on ProteomicsDB, such that Skyline is able to directly request predictions in real-time and on-demand. (More info...)




Questions and Answers


Q: For the stoichiometry experiment, do you express the two full-length proteins in fusion as the heavy standards? How big are they?

Ans: No, our users fused the N-terminal parts from both proteins (around 250 amino acids from each), not the full-length. This resulted in a fusion construct of around 50 kDa molecular weight (the full FASTA is provided in the pdf). The full-length proteins were 50 and 75 kDa, respectively.

Q: Does SProCop consider the number points across peak for each of the fragments as well while testing the symmetry of co-alignment?

Ans: To my knowledge, SProCop is not considering the number of points across the peak, however, this is a metric that can be displayed in Skyline in a customized report, which we routinely do. SProCop also does not check for symmetry of fragment co-alignment, this we do outside of Skyline using Excel or R. Please, feel free to reach out to me if you would like to give it a try.

Q: How much state regulation is there?

Ans: Regulation in the cannabis industry varies from state to state and can be a challenge to keep up with. What is consistent, however, is sample tracking. From the time the lab picks up that sample to the time it is used by the instruments and the remainder destroyed, you and the state have to know exactly where that sample is. The states can also randomly inspect any lab approved to operate -- and may -- with little notice, add new pesticides or metals or residual solvents that the lab is required to monitor.

Q: Do you need Part 11 compliance (audit trail)?

Ans: Cannabis laws are evolving rapidly and it's tough to keep up. It's safe to assume that they will, at some point, require it. At this point, I don't know of a state that does, but I've only built labs in Maryland, California, and Pennsylvania.

Q: Is PNNL preprocessor tool freely avaliable?

Ans: Yes, you can download the free software from https://omics.pnl.gov/software/pnnl-preprocessor. Also, if you google "pnnl preprocessor" it will be the first hit in the list.

Q: Are there plans to incorporate the PNNL preprocessor into the skyline software?

Ans: We are currently executing the PNNL PreProcessor in an automated workflow but as a separate and initial step (it is very easy to use and have both GUI and command-line access). The output pre-processed IMS raw files are the ones used as input for Skyline and this is working well. At the moment there is no plan to have a closer integration in Skyline but it could be possible.

Q: Does Skyline have IMS CCS values?

Ans: Skyline has extensive support for using CCS and also for raw arrival times. In the case of Agilent raw IMS files, these must be previously CCS-calibrated based on the single-field method in a quick procedure using the Agilent IM-MS Browser.

Q: How many peptides per a protein were used in your study?

Ans: For the targeted PRM experiments, 2-3 peptides / proteins were selected.

Q: In two of the volcano plots, it seems a majority of the proteins showed increased expression although many of them are not statistically significant. Can you comment on it?

Ans: To quickly clarify, the volcano plots indicate changes in interaction abundance. That's correct. The expansion of polyQ causes mostly increases in interaction abundance. We usually observe that measurement of interaction abundances have greater variance than proteome abundance measurements. The scale of our experiments is an important consideration as the IP study requires more mouse tissue than proteome analysis. Therefore, we use t-tests to find the most reproducible differential interactions for downstream functional analysis.

Q: Are spectral libraries transferrable between labs or are they too system dependent?

Ans: They are often transferred between labs and there are also sources like NIST, theGPM.org, and PeptideAtlas which make them publicly available for peptides. There other sources for small molecules.

Q: Are RTs with the RT calculator predictive or only useful for known lipids?

Ans: They require an initial empirical measurement. Those measurements get stored as normalized retention times which can later be calibrated to any system with similar chromatography.

Q: Does skyline use metlin and other standard databases and how many MSMS spectra are empirical vs in silico? If Metlin is not currently included, could it be added by the user, and how?

Ans: Skyline small molecule library support currently runs primarily through the standard .msp format. If your library can be expressed in that format then chances are Skyline supports it or can easily be made to support it. We are finding small variants within the standard format, which we are fixing as they are reported. If you find that Skyline does not support what you need, please post a request to the Skyline support board and we will work with you to make sure it is supported.

Q: Will the lipid analysis and LipidCreator also work without ion mobility data but data acquired using a Thermo Q Exactive?

Ans: Yes, the Baker lab is adding empirical measured CCS values for lipids, but LipidCreator is a good starting point for a wide variety of systems without IMS.

Q: Are the SN1 and SN2 lipids separated using LC or IMS? If LC, how does that work? You showed retention time so I just wanted to clarify you were not referring to IMS.

Ans: The example was an LC separation, but this is specific to lysophospholipids. In some cases, they also separate in the IMS dimension. Lysophospholipid standards can be used to determine the order of the peaks. A good paper to reference is Kyle, J.E. et. al. Analyst 2016, 141.

Q: How much time do you need to analyze a lipidomic data set, you never saw before, in-depth?

Ans: This depends on many factors such as the complexity of the matrix and sample type, the instrument used, data analysis method, your familiarity with lipid data, etc. For me, building libraries initially took a long time as I was learning about lipids and getting to know the software. It takes a while to build a library from scratch with a new sample type, at least a month, but the time goes way down after the library has been built.

Q: You mentioned using standards and you also mentioned using LipidCreator for library generation. Do you use both? If so, how well do you find they agree?

Ans: We haven't directly compared LipidCreator libraries to standards, but they generally do agree with endogenous lipids. We do use LipidCreator libraries initially, but the libraries that we plan to publish are generated from our data within Skyline.

Q: Can you share where you purchased your lipids for the iRT?

Ans: Our current iRT calculators use endogenous lipid landmarks, but we are planning to validate and utilize the UltimateSPLASH ONE Lipidomix Mixture from Avanti Polar Lipids in the future.

Q: Is the library from Erin Baker's lab available for people to use?

Ans: They are not currently available but they will be made publically available on Panorama as soon as we publish.

Q: Do you think FAIMS could be used to enhance specificity?

Ans: Yes, I think FAIMS would be a great way to separate isobaric species and provide higher selectivity.

Q: How many points do you typically collect over the chromatographic peaks using your staggered windows?

Ans: The system I am using is a Dionex HPLC at 300 nL/min, in-house packed 2.4um ReproSil-Pur ~25 cm C18 column. Data were acquired on an HFX. Using this system, I typically see 9-12 data points across a peak.

Q: Knowing the (limited number of) histone peptide sequences, do you need to cover the full 400-1200 m/z in DIA or could you reduce the acquired mass range to a much narrower range, therefore reducing again the DIA isolation window size?

Ans: The smallest histone peptide we are interested in analyzing is 300.2156 m/z and the largest 1080.1068 m/z. If we were to use a narrower m/z range, then we would not have any information on these peptides. If an assay required a smaller subset of histone peptides, then we would definitely adjust the m/z range to only include the target peptides.

Q: Are there problems distinguishing trimethyl Lys and acetyl Lys?

Ans: It can be difficult to discriminate between acetyl and trimethylated peptides without any prior information. But we have not had too much difficulty since we use a spectral library that contains ms2 spectra for these peptides.

Q: How much manual integration is necessary for a typical run?

Ans: We performed manual validation of all the peptides with the use of heavy labeled synthetic standards. But with defined iRT values, manual integration should be minimal.

Q: Will SureQuant be available on the older Qe models (Plus, HF, HF-X) at some point? If not, why?

Ans: SureQuant will not be available on QE systems. The method has only been natively implemented on Orbitrap-based instruments operated with TNG software (Tribrid and Exploris families), as being built from the scan events and filters embedded in the Method Editor

Q: In the MS method do you suggest using the multiplexing mode (MSX) to capture both the light and heavy peptide for more accurate quantitation or do you capture/analyze them separately?

Ans: In the current implementation of the SureQuant method, the heavy and light peptides are measured separately, in distinct MS/MS spectra. A variant of this would be the multiplexing mode, which would require some adjustment in the functionalities of the MSMS acquisition for optimal performance. This variant might be explored in the future.

Q: Can one use custom/user-defined peptides for SureQuant?

Ans: Yes. While the deployment of SureQuant assays for commercial kits can be facilitated by the provision of preset methods embedded in the instrument control software (e.g., PQ500 kit from Biognsoys and SureQuant AKT/mTOR kit from Thermo Fisher Scientific), a user can develop his own assay for a custom panel of peptides. The development of such user-defined SureQuant assays is facilitated by the method templates provided.

Q: Have you thought about using the IT for the watch section? To make it through more precursors - to trigger the higher resolution SureQuant scans?

Ans: Yes. This is a variant that we are planning to explore in the future for Tribrid instruments.

Q: Can SureQuant be modified with iAPI 2.0?

Ans:I don't think there is something preventing a user from building his own sort of SureQuant method with iAPI on mass spectrometers enabling native SureQuant acquisition.

Q: Could any Skyline experts with experience of using QE HF machine provide us a model PRM (scheduled) method with detailed parameter setting?

Ans: Please post this request to the Skyline support board. https://skyline.ms/support.url (or Help > Support in Skyline)

Q: When will Skyline 20.2 be released?

Ans: Later this summer, but you can get new features early with Skyline-daily. Help us beta test! (https://skyline.ms/daily.url)

Q: It is nice to have a spectral library prediction tool. I am wondering whether Prosit has any instrument platform requirements?

Ans: We generally do not see a strong dependency with regard to a vendor. The spectrum prediction model was trained on data acquired on an Orbitrap Fusion Lumos but has proven effective on the SCIEX TripleTOF, as shown in our 2019 publication and new data which will be presented in the oral session WOD am 09:30 (by Brendan). Obviously, if fragmentation settings differ (e.g. rolling CE) or the precursor intensity is too low (e.g. TOFs, because of their high sensitivity, have the tendency to generate much more rather low signal-to-noise spectra, were the present intensities doe not necessarily reflect the predicted ones due to ion statistics) the prediction accuracy will be lower. In the MacCoss lab, we have found the Pan Human spectral library produced in the Aebersold lab using TripleTOF data works well for DIA on Thermo Q Exactive data collected in HCD mode.

Q: This is quite the ask: but will prediction software be extended from peptides to small molecules?

Ans: We are unaware of anyone doing that work. It is a little hard to imagine the kind of input used for peptides (an amino acid sequence) for small molecules. Certainly, the bare chemical formula is not enough, since molecules with the same atomic counts can have very different physical-chemical properties from fragmentation to CCS. Perhaps a full chemical structure specified in SMILES or similar could help, but this still seems like it may be a long way off. The reason for this is that in order to predict small molecules, we would likely need at least an order of magnitude more data for training such a system. Such training data is simply not available and from our point of view, won’t be in the foreseeable future (unfortunately). Happily, we can continue to rely on prior empirical measurements and building up libraries of these.

Q: How does this compare to experimental libraries? Is it better?

Ans: Highly similar for spectrum prediction. Although the current implementation lacks the best detection features of a true spectral library which guides the choice of peptides and precursor charge states. Better, in the sense that it allows prediction of a highly likely spectrum for any peptide sequence, which may require ordering a synthetic peptide to produce in an empirical library.

Q: What about non-tryptic peptides? Other proteases or even endogenous peptides?

Ans: The original Prosit paper https://doi.org/10.1038/s41592-019-0426-7 has shown that quality for non-tryptic paper is very close to tryptic peptides. We are in the process of releasing a new model trained including a large set of non-tryptic peptides (e.g. HLA class I and II peptides) and already see similar performance as the original model on all classes of peptides.

Q: How good are the fits of the fragmentation predictions to different mass analyzer results?

Ans: The spectrum prediction model was trained on data acquired on an Orbitrap Fusion Lumo but has proven effective on the SCIEX TripleTOF, as shown in our 2019 publication and new data which will be presented in the oral session WOD am 09:30 (by Brendan). Similarly, in the MacCoss lab, we have found the Pan Human spectral library produced in the Aebersold lab using TripleTOF data works well for DIA on Thermo Q Exactive data collected in HCD mode.

Q: The Kuster lab is known for using DMSO in mobile phases, which drastically effects precursor ion (charge state) distribution. In your last slide, you showed optimal precursor prediction...does this include for DMSO?

Ans: The data for precursor ion (charge state) distribution isn’t solely based on data from the Kusterlab but is based on all the data available in ProteomicsDB. As ProteomicsDB is hosting experiments from many different laboratories we do not expect Prosit to be overfitting to DMSO. However, certainly, an important question to keep and mind which requires a more detailed analysis.

Q: For the "consistency" of identification/scoring of peptides, would you generate 2 libraries: one experimental and one from Prosit separately?

Ans: There are certain advantages in mixing libraries, but the answer, unfortunately, depends on the software used. Generally speaking, one has to be careful when mixing libraries especially for DIA data analysis as correct FDR estimation becomes more complex. You can check Brian Searle’s (2020) work on so-called hybrid workflows.

Q: Can Prosit get feedback from Skyline users with regards to the quality of match to further train the prediction algorithm?

Ans: We haven’t implemented any kind of feedback between Skyline and Prosit. As of right now, we can’t easily judge how much “feedback” we would need to train the algorithm for individual users and it would increase the burden on our GPUs to host-specific models for every user.

Q: How good is Prosit at predicting the spectra of peptides containing PTM's such as phosphorylation and acetylation?

Ans: Please watch Mathias Wilhelm’s talk MOD am 10:10 showing the benefits of predicted spectra for the localization of phosphorylation. This can help especially in the case of adjacent phosphorylation events which are often a problem for classical database approaches.

Q: What justifies the claim that Amanda is "better" than any other search engine? Why should we abandon our previous favorite?

Ans: There is not going to be the perfect search engine -- being better than all the others on all data sets. There will always be search engines better on a certain type of data and another one better on different kinds of data. In our experiments we saw that MS Amanda works very well on HCD and EThcD data sets, and also got feedback from users, that they very much like it to analyze phosphorylated data. Just give it a try.

Q: How does Amanda do false discovery?

Ans: The implementation integrated into Skyline will use Percolator for FDR estimation and q-value assignment.

Q: Does MS Amanda work with raw files from all vendors? Does work with MSE data from Waters instruments?

Ans: The integration with Skyline is using ProteoWizard to access raw data files of all types. That said, it is unlikely to work directly for Waters MSE raw data files. For this, you would need to export a deconvoluted spectrum file like an MGF, which we understand is possible with Waters software. Skyline can still extract chromatograms from the raw MSE data, but the spectra searched by MS Amanda must be deconvoluted to look like DDA spectra. This is the same principle described in the presentation for DIA with DIA-Umpire for deconvolution.

Q: Is there a maximum number of modifications that MSAmanda can consider? Can it consider neutral losses from precursor and fragment ions (that are not phosphorylation or sulfation)?

Ans: MS Amanda does not limit the number of modifications that can be considered, however, every additional modification does increase the search space and therefore also the search time. Although, MS Amanda is multi-threaded and takes advantage of all available cores. It definitely considers neutral losses on both, precursor and fragment ions, as soon as they are specified in the unimod database. You can even define your own.

Q: When will the ‘import DDA search’ be available in Skyline-daily?

Ans: We hope by the end of June, but feel more confident in saying by the end of this summer. The work started in September 2019 and remains somewhat exploratory, but we are determined to see it in use by Skyline users soon.

Q: How does the search time compare to Mascot and Sequest in ProteomeDiscoverer? Is there a maximum on the number of MS/MS submitted?

Ans: No, there is no maximum number of spectra that can be processed. In the settings, the number of spectra processed in parallel can be specified and MS Amanda will further distribute these spectra to the available cores to speed up the analysis. Users can therefore easily take influence on the speed if enough computing power is available. Compared to the other search engines in PD, I have not really checked but since MS Amanda 2.0, I would say, it is comparable, although I have to admit that the Mascot search in PD is really super fast. The standalone version is definitely faster than the PD version as the overhead of communication with PD is omitted.

Q: Have you tested MS Amanda+Percolator in Skyline for samples other than cell isolates? I.e. Bacterial mixtures, etc...

Ans: Testing is still very much in "proof of concept" mode and we expect to rely heavily on user testing once we can release it in Skyline-daily.

Q: For DIA, would you consider the integration of Prosit?

Ans: Yes. This is happening more and more in the field. You should review Brendan's ASMS talk WOD am 09:30 which contains other references.

Q: Is there any way to evaluate TOMAHAQ data in Skyline?

Ans: We added support for the extraction of ions from MS3 in the past year. Provided that you are willing to acquire your MS3 in targeted mode (PRM), you could do this in Skyline by using the Transition Settings>Filtering>Special ions to target isobaric fragments, but if you acquire MS3 only through DDA, then you will not acquire enough on any given molecule to form a chromatogram peak, which is a requirement for using Skyline.

Q: Do you have data from QTOF using UNIFI software?

Ans: We have been working on that support with Waters.

Q: Which webinar/tutorials would you recommend we suggest to coworkers who are brand new to Skyline?

Ans: I would start on the Tutorials page (https://skyline.ms/tutorials.url) in the Introductory section. These tutorials use SRM data, which is small and fast for instruction, but they each introductory concepts which will be useful for any Skyline user. Then move from there to the other sections and tutorials of greatest interest. Both the tutorials and webinars contain cross-links to each other to help you find the written tutorials and webinar presentations that present similar material. More broadly, there are even entirely recorded courses you can find through the "Join Us" section on the Skyline main page.

Q:Does Skyline Support Waters, Synapt-G2, IMS data?

Ans: Absolutely.

Q: Is the MAM workflow to monitor relative abundances of peptide modifications?

Ans: Yes, but also other quality metrics, such as glycosylation, which is monitored by grouping a set of glycopeptides and monitoring their relative abundance to each other from sample to sample over time. A paper by Richard Rodgers is a good place to start reading (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623056/). We are in discussions with Richard Rogers on getting funding and guidance from his MAM Consortium on continued development in this area.

Q: Are there any plans to have a small molecule library similar to Prosit, or a connection to the Fiehn lib or MONA database?

Ans: Prosit-like fragmentation prediction for small molecules is an extremely challenging problem, as discussed above. However, we are extremely interested in supporting as many existing small molecule library efforts as we possibly can, just as we have for proteomics over the past decade to the point were we now support over 20 sources of peptide library data. Currently, our support runs through the .msp format, which many small molecule sources seem to provide. If you find we do not support what you need, you should post a request to the Skyline support board and let us work with you to support it. Your feedback and example data may enable future research for you and others.

Q: For TMT, can define it as a special ion.

Ans: Yes. Agreed. That will work for PRM, but it won't help for MS1 filtering from DDA data, which is how most people acquire data on TMT labeled samples.




Event Information


Welcome

The Skyline Team is pleased to announce the Nineth Annual Skyline User Group Meeting before ASMS. We are moving online with ASMS itself, due to COVID-19. We will be holding this event as two webinars the week before ASMS. There is just too much exciting work going on, by you the research community using Skyline, not to share this year.

Thanks to the event sponsors (see below) for their many years of collaborating with the Skyline project on exciting new quantitative mass spec technologies and data processing methods.

We will miss seeing so many of you in-person, getting as much of your feedback as we usually do, and sending you off in a stylish Skyline T- or sweatshirt. Next year! Stay healthy and productive.

Brendan MacLean
Principal Developer


When: Wednesday, May 27 2020 from 9:00 - 10:30 am (Pacific Time)
Thursday, May 28 2020 from 9:00 - 10:30 am (Pacific Time)
Where: webinar (details to be sent to registered attendees)

[registration closed]

 


Speakers

Michael J. MacCoss, Ph.D. (University of Washington): Introduction and event host

Brendan MacLean (MacCoss Lab, University of Washington): Status of the Skyline Open-source Software Project 12 Years after its Inception
The Skyline project started just after ASMS 2008 as a 2-year effort to bring better SRM/MRM software tools to the NCI-CPTAC Verification Working Group that could support the variety of mass spectrometers in use in participating laboratories. Nearly 12 years later, the Skyline project is a thriving multi-omics community open-source collaboration supporting 6 mass spec instrument vendors integrated with a wide variety of external software, with thousands of users worldwide and many thousands of instances started each week. (More info...)

Josue Baeza, Ph.D., (Garcia Lab, University of Pennsylvania): Applications of Skyline for Method Development and Quantification of Histone Marks
Despite a growing interest in epigenetics, performing proteomics studies of histone tail marks remains highly specialized. Mass spectrometry of histone tail marks is difficult due to the variety of modifications, coeluting isoforms, and dynamic range. Here, we have designed a robust histone DIA method and a flexible Skyline-based analysis workflow to more accurately and precisely quantify histone marks. (More info...)

Viktoria Dorfer, Ph.D., (University of Applied Sciences - Upper Austria): MS Amanda goes West: Integrating a Search Engine into Skyline
Mass spectrometry has become the method of choice for analysing proteins, demanding reliable and state-of-the-art software. Skyline has emerged as one of the most popular of these tools, supporting the generation and use of spectrum libraries from various analysis pipelines, however requiring separate pipeline execution. We present a fully integrated workflow for peptide identification and quantification within Skyline that incorporates the MS Amanda search algorithm. (More info...)

Todd Greco, Ph.D., (Cristea Lab, Princeton University): Unbiased and Targeted Mass Spectrometry Provides Insight into Huntington’s Disease Pathogenesis
The causative agent of Huntington’s disease (HD) is the CAG repeat expansion of the huntingtin gene, producing a mutant protein with an expanded glutamine tract (mHTT). mHTT toxicity selectively impacts the brain and liver. mHTT-induced proteome and protein interaction alterations have been investigated in the brain, yet those proximal to disease progression remain poorly understood. We detected 219 differential protein candidates in mHTT liver using MS1-based LFQ, which were all targeted for validation by PRM using Skyline. (More info...)

Kaylie Kirkwood, Ph.D., (Baker Lab, North Carolina State University): Developing Multidimensional Small Molecule Spectral Libraries for Rapid Lipid Detection and Quantitation
Multidimensional lipidomics data provides valuable polarity, structural and mass information, but results in large and complex datasets which are extremely difficult to process. Skyline offers rapid and targeted processing of lipid data which ultimately allows for confident detection of diverse lipid species. We have developed sample-specific lipid spectral libraries which include hundreds of target lipids from multiple lipid categories for human plasma, brain total lipid extract, zebrafish, bronchoalveolar lavage fluid, flies, and lettuce. (More info...)

Roman Sakson,, (Heidelberg Molecular Biology Center): Unleashing Versatile Skyline Features for the Everyday Needs of a Proteomics Core Facility
Proteomics Core Facilities need to support a set of robust qualitative and quantitative workflows for a broad customer base. We routinely use Skyline as a versatile, vendor-independent platform that helps us to address two major issues, namely quality control and sharing information between MS experts and users, especially if they are not located in the same place. Customized reports and integrated tools, such as Protter for protein sequence visualization, are extremely helpful while discussing results with customers. (More info...)

Lightning Talks

Karine Bagramyan, Ph.D., (Kalkum Lab, Diabetes and Metabolism Research Institute): Using Skyline to Quantify Botulinum Neurotoxin Activity in Complex Biological Samples
This presentation will highlight Skyline’s utility for the design and optimization of our PRM and MRM assays. This brilliant software provided us with a solid bioinformatics pipeline for the entire project: From the generation of calibration curves using stable isotope-labeled synthetic peptide standards, to the quantification of attomolar concentrations of BoNT, resulting in a novel assay that has unmatched limits of detection and quantification. (More info...)

Aivett Bilbao, Ph.D., (PNNL): Metabolite Profiling for Synthetic Biology using Ion Mobility-Mass Spectrometry and Data-Independent Acquisition with Improved Targeted Data Extraction Software
Combining liquid chromatography, drift-tube ion mobility spectrometry (DTIMS)-mass spectrometry (MS) and data-independent acquisition (DIA) with improved targeted data extraction software, we developed a workflow to enable more effective synthetic biology research of hundreds Aspergillus pseudoterreus strains engineered for production of organic acids of industrial relevance. (More info...)

Sebastien Gallien, Ph.D., (Thermo Fisher Scientific): Towards Turnkey Targeted Proteomics Solutions Using SureQuant Internal Standard Triggered Acquisition on Orbitrap Mass Spectrometers
An extension of HR-PRM, called SureQuant method, has recently been introduced to progress targeted proteomics. This method, implemented in the native instrument control software of Orbitrap instruments, uses spiked-in internal standards to dynamically control the acquisition process and maximize its productivity. This included new data processing functionalities implemented in Skyline, which is a key component of the optimized informatic pipeline supporting the workflow. (More info...)

Benjamin Orsburn, Ph.D., (Johns Hopkins Medical School): Skyline -- A Comprehensive Package for Cannabis Testing Labs
Recent changes in the laws regarding Cannabis in North America has created a profitable new market for agriculture and small batch production facilities. Due to the lack of federal oversight in the US, state and local municipalities are currently responsible for determining safety testing and product characterization requirements and these vary widely across the country. We demonstrate how the Skyline software can be utilized as a near solution for both the testing and quality control monitoring for Cannabis testing labs. (More info...)

Tobias Schmidt (Kuster Lab, Technical University Munich): Real-time Spectrum Prediction in Skyline via ProteomicsDB’s gRPC Interface to Prosit
Prosit is able to accurately predict fragment ion intensities and retention times of peptides by deep learning. However, deep-learning requires GPUs for predictions that are not yet readily available in many labs and thus limit its applicability. In order to circumvent this shortcoming, we made Prosit available via gRPC on ProteomicsDB, such that Skyline is able to directly request predictions in real-time and on-demand. (More info...)


Sponsors


AgilentBruker
SCIEXShimadzu
Thermo ScientificWaters
Cambridge Isotope LaboratoriesSISCAPA Assay Technology
  LabKey Software



Speakers


Eleven speakers with interesting and different areas of expertise in Skyline use and development spoke at the Skyline User Group Meeting online in conjunction with ASMS 2020.

Video recordings of their talks and PDFs of their slides have posted on each speaker's page below:

Day 1: Wednesday, May 27

Day 2: Wednesday, May 28




Michael MacCoss


Mike MacCoss Michael MacCoss Mike became interested in biomedical applications of mass spectrometry while working in Dr. Patrick Griffin’s protein mass spectrometry lab at Merck Research Laboratories. He obtained a Ph.D. with Professor Dwight Matthews and pursued a postdoc with Professor John R. Yates III. In 2004 he started the MacCoss lab at the University of Washington and it became obvious that while mass spectrometry data could be collected quickly and robustly, the lack of computational tools for the visualization and analysis of these data was a stumbling block. In 2008 he hired Brendan MacLean with the goal of developing professional quality software tools for quantitative proteomics. Read More
Mike has worked closely with the Skyline development team and our outstanding group of laboratory scientists and collaborators to ensure that our software uses analytical approaches that have been thoroughly vetted by the mass spectrometry community.


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Brendan MacLean


Brendan MacLean Brendan MacLean worked at Microsoft for 8 years in the 1990s where he was a lead developer and development manager for the Visual C++/Developer Studio Project.  Since leaving Microsoft, Brendan has been the Vice President of Engineering for Westside Corporation, Director of Engineering for BEA Systems, Inc., Sr. Software Engineer at the Fred Hutchinson Cancer Research Center, and a founding partner of LabKey Software.  In this last position he was one of the key programmers responsible for the Computational Proteomics Analysis System (CPAS), made significant contributions to the development of X!Tandem and the Trans Proteomic Pipeline, and created the LabKey Enterprise Pipeline. For the last 10 and a half years he has worked as a Sr. Software Engineer within the MacCoss lab and lead all aspects of design, development and support in creating the Skyline Targeted Proteomics Environment and its growing worldwide user community.

Status of the Skyline open-source software project 12 years after its inception

The Skyline project started just after ASMS 2008 as a 2-year effort to bring better SRM/MRM software tools to the NCI-CPTAC Verification Working Group that could support the variety of mass spectrometers in use in participating laboratories. Nearly 12 years later, the Skyline project is a thriving proteomics community open-source collaboration supporting 6 mass spec instrument vendors, integrated with a wide variety of external software, with thousands of users worldwide and many thousands of instances started each week. Read More
In this presentation, the Skyline principal developer will present recent developments and a roadmap for the project's future. Topics covered will include:
  • New! Prosit integration
  • New! timsTOF PASEF support for DDA, DIA, and PRM
  • New! Crosslinked peptide support
  • Growth in the Skyline software ecosystem for targeted MS (Skyline, Panorama, and External Tools)
  • Strong industry support from instrument vendors
  • Efforts to create instructional resources for the Skyline community


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Josue Baeza


Josue Baeza Josue Baeza is a postdoctoral fellow in the laboratory of Dr. Benjamin Garcia at the University of Pennsylvania. He obtained his Ph.D. at the University of Wisconsin-Madison under the mentorship of Dr. John Denu. During his Ph.D., he developed a chemical labeling strategy to quantify lysine acetylation stoichiometry and applied this method to determine rates of non-enzymatic acetylation (measured as second-order rate constants) as well as histone acetylation turnover rates. As a postdoctoral fellow in the Garcia Lab, Josue is interested in understanding mechanisms regulating protein turnover including how changes in protein turnover influence the epigenetic landscape as well as developing methods to quantify protein turnover in vivo.

Applications of Skyline for Method Development and Quantification of Histone Marks

Despite a growing interest in epigenetics, performing proteomics studies of histone tail marks remains highly specialized. Mass spectrometry of histone tail marks is difficult due to the variety of modifications, coeluting isoforms, and dynamic range. Conventionally, these challenges have been met with database searching shotgun DDA, which detects histone marks but doesn’t provide accurate quantification; or optimizing high-level PRM methods, which accurately quantifies but cannot detect novel histone marks. Here, we have designed a robust histone DIA method and a flexible Skyline-based analysis workflow to more accurately and precisely quantify histone marks. Read More
Our DIA-MS Skyline-based workflow for quantifying histone tail modifications takes advantage of Skyline’s latest features, including staggered DIA isolation window demultiplexing to process raw data, the “Quantitative” fragment ion demarcation for site-localizing isobaric histone marks, and a retention time calculator that uses co-enriched peptides as iRT anchors. Our workflow and an accompanying tutorial are available on Panorama Public.


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Karine Bagramyan


Karine Bagramyan Karine Bagramyan , Ph.D., is a staff scientist in the laboratory of Professor Markus Kalkum at the Department of Molecular Imaging and Therapy of Diabetes and Metabolism Research Institute of City of Hope in Duarte, CA. Her research involves the development of mass spectrometry-based targeted proteomics methods, and their application for botulinum neurotoxin detection, evaluation, and quantitation. She began using Skyline in 2019 to analyze LC-MS/MS (MRM and PRM) data dealing with a large number of samples for targeted quantitative analysis.

Using Skyline to Quantify Botulinum Neurotoxin Activity in Complex Biological Samples

Botulinum neurotoxins (BoNTs) are the most potent toxins known, with the lethal dose for mice (MLD50) of ~33 amol. Due to BoNT’s extraordinary toxicity, BoNT detection assays have to be highly sensitive and capable of detecting toxin concentrations equal or below one MLD50 in biological matrices. We have applied high-resolution PRM and MRM LC/MS techniques to quantify BoNTs in human serum. Our methodology is based on the detection of BoNT’s proteolytic activity and does not require BoNT-specific antibodies. This presentation will highlight Skyline’s utility for the design and optimization of our PRM and MRM assays. Read More
This brilliant software provided us with a solid bioinformatics pipeline for the entire project: From the generation of calibration curves using stable isotope-labeled synthetic peptide standards, to the quantification of attomolar concentrations of BoNT, resulting in a novel assay that has unmatched limits of detection and quantification.


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Aivett Bilbao


Aivett Bilbao Aivett Bilbao Ph.D., conducts research on computational tools for mass spectrometry-based omics, working directly with experimental biologists and chemists in interdisciplinary teams. She has acquired extensive experience developing software for mass spectrometry using multiple programming languages and technologies. Projects include proteomics and metabolomics molecular characterization entailing both algorithm design and software implementation for data from different instruments (time-of-flight and Fourier transform-based mass analyzers) and analytical separation techniques (liquid chromatography, ion mobility, solid-phase extraction, and gas chromatography). She obtained her Ph.D. from the University of Geneva in Switzerland with a special interest in data independent acquisition (DIA). Her first degree is in computer engineering from Universidad de Oriente in Venezuela (Cum Laude) and her M.Sc. studies were focused on machine learning algorithms and statistical methods at Telecom SudParis in France.

Metabolite Profiling for Synthetic Biology using Ion Mobility-Mass Spectrometry and Data-Independent Acquisition with Improved Targeted Data Extraction Software

Combining liquid chromatography, drift-tube ion mobility spectrometry (DTIMS)-mass spectrometry (MS) and data-independent acquisition (DIA) with improved targeted data extraction software, we developed a workflow to enable more effective synthetic biology research of hundreds Aspergillus pseudoterreus strains engineered for production of organic acids of industrial relevance. Data were acquired in two platforms: triple-quadrupole (QQQ) MS in MRM mode and DTIMS-QTOF MS in DIA mode. Read More
A library was created from >50 standards (transitions, retention times, and analyte ion collision-cross sections). Datasets from both platforms were processed using the command-line tool SkylineRunner.exe and customized R scripts to automatically extract chromatograms, generate Skyline projects and optimize peak-integration boundaries across replicates, greatly facilitating the comparison across instruments and multiple conditions. Preliminary results show that 90% of standards were detected at a 50 µM concentration by both platforms. The DTIMS detection increased the number of transitions, enhancing confident identification and accurate quantification in complex matrixes.


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Viktoria Dorfer


Viktoria Dorfer Viktoria Dorfer  studied Bioinformatics at the University of Applied Sciences Upper Austria and received her Ph.D. in informatics from the Johannes Kepler University Linz. Her research interests focus on computational proteomics, especially on peptide identification, which was also the topic of her Ph.D. thesis, entitled “Identification of Peptides and Proteins in High-resolution Tandem Mass Spectrometry Data”. Part of this thesis was the development of the MS Amanda peptide identification algorithm. At present, Vikoria is working as Professor for Bioinformatics at the University of Applied Sciences Upper Austria and is supervising two Ph.D. students and one master's student in the field of computational proteomics.

MS Amanda goes West: Integrating a Search Engine into Skyline

Mass spectrometry has become the method of choice for analysing proteins, demanding reliable and state-of-the-art software. Skyline has emerged as one of the most popular of these tools, supporting the generation and use of spectrum libraries from various analysis pipelines, however requiring separate pipeline execution. We present a fully integrated workflow for peptide identification and quantification within Skyline that incorporates the MS Amanda search algorithm. Read More
MS Amanda is a freely-available peptide spectrum matching algorithm, optimized for the analysis of high-resolution MS2 data. We have integrated MS Amanda into Skyline providing access to all available components in both tools. This gives researchers immediate access to a complete peptide identification and quantification pipeline inside Skyline starting directly from raw data. Finally, we expect it to apply DDA library-free DIA analysis, by running the MS Amanda search pipeline on spectra extracted from more complex and often chimeric DIA spectra using the DIA-Umpire algorithm.


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Sebastien Gallien


Sebastien Gallien Sebastien Gallien Ph.D., works as a Scientist at Thermo Fisher Scientific. He received his Ph.D. in analytical chemistry from the University of Strasbourg, France, at the Bioorganic Mass Spectrometry Laboratory in 2009. In 2010, he joined the Luxembourg Clinical Proteomics Center to work with Prof. Bruno Domon as a post-doctoral fellow and then a staff scientist. His research was focused on technology and methodology developments for targeted quantitative proteomics with emphasis on clinical applications. In 2016, he joined the Precision Medicine Science Center at Thermo, where he focuses developments to improve proteomics analytical workflows, with a focus on targeted analyses that lead to clinically actionable targeted protein panels.

Towards Turnkey Targeted Proteomics Solutions Using SureQuant Internal Standard Triggered Acquisition on Orbitrap Mass Spectrometers

An extension of HR-PRM, called SureQuant method, has recently been introduced to progress targeted proteomics. This method, implemented in the native instrument control software of Orbitrap instruments, uses spiked-in internal standards to dynamically control the acquisition process and maximize its productivity. Its ability to deliver high-density, ultra-sensitive measurements in large-scale experiments has benefited to a variety of applications (including signaling pathway monitoring, global plasma profiling, or host cell protein monitoring during biopharmaceuticals development. Read More
In addition to these analytical benefits, the technique exhibits an enhanced usability and robustness, owing to its independence from time-scheduling acquisition, and therefore high potential for automation. In order to progress further towards a turnkey solution, several informatic developments have been conducted to optimize the preparation of SureQuant method and the processing of generated data. This included new data processing functionalities implemented in Skyline, which is a key component of the optimized informatic pipeline supporting the workflow.


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Todd Greco


Todd Greco Todd Greco  Ph.D. As a senior scientist in the laboratory of Ileana Cristea at Princeton University, his research focuses on understanding the contribution of proteome dynamics to pathological states, including virus infection and neurodegenerative diseases. A primary goal is to accelerate the identification of disease-relevant proteins by combining multiple proteomic perspectives from a single model system. Using quantitative mass spectrometry-based proteomics, paired with molecular and optical approaches, Todd gains insight into the temporal dynamics of protein abundance, localization, protein complexes, and post-translational modifications. Moreover, he employs IDIRT-based AP-MS workflows to monitor protein interaction abundance and stability, which are suitable for global and targeted MS studies. In the proteomics community, Todd regularly teach Ph.D. candidates and facility staff about MS experimental design and software, including Skyline and Proteome Discoverer.

Unbiased and Targeted Mass Spectrometry Provides Insight into Huntington’s Disease Pathogenesis

The causative agent of Huntington’s disease (HD) is the CAG repeat expansion of the huntingtin gene, producing a mutant protein with an expanded glutamine tract (mHTT). mHTT toxicity selectively impacts the brain and liver. mHTT-induced proteome and protein interaction alterations have been investigated in the brain, yet those proximal to disease progression remain poorly understood. Read More
Moreover, the molecular signatures of mHTT toxicity in the liver are unknown. In HD mouse models from pre-symptomatic and post-symptomatic stages, we explored polyQ- and age-dependent relative stabilities of mHTT protein interactions using AP-MS and 13C-labeled tissues. mHTT caused increased protein interaction stabilities, while specific components of phosphorylation signaling networks were impacted. Additionally, we detected 219 differential protein candidates in mHTT liver using MS1-based LFQ, which were all targeted for validation by PRM using Skyline. This provided large scale validation of HTT disease- and tissue-specific altered protein abundances.


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Kaylie Kirkwood


Kaylie Kirkwood Kaylie Kirkwood  is a first-year graduate student at North Carolina State University working under the advisement of Dr. Erin Baker. Thus far, her research has focused on the development of lipid libraries in Skyline and the application of these libraries to various clinical and environmental applications ranging from elucidating lipid markers associated with smoke inhalation injury to evaluating lipid dysregulation in plants following exposure to perfluoroalkyl substances. Skyline was the first software Kaylie learned to use as an undergraduate researcher for small molecule detection including cyanotoxins, amino acids, and metabolomics profiling of amyotrophic lateral sclerosis under the advisement of Dr. David Muddiman. Its adaptability and interactive developers have allowed her to continue utilizing it in her current research.

Developing Multidimensional Small Molecule Spectral Libraries for Rapid Lipid Detection and Quantitation

Multidimensional lipidomics data provides valuable polarity, structural and mass information, but results in large and complex datasets which are extremely difficult to process. Skyline offers rapid and targeted processing of lipid data which ultimately allows for confident detection of diverse lipid species. We have developed sample-specific lipid spectral libraries which include hundreds of target lipids from multiple lipid categories for human plasma, brain total lipid extract, zebrafish, bronchoalveolar lavage fluid, flies and lettuce. Read More
Each target lipid was populated with a manually extracted m/z value, normalized retention time, ion mobility collision cross section (CCS) and known fragmentation pattern. Recently created aspects of the Skyline small molecule interface were then utilized in our lipid evaluations including CCS filtering, iRT calculator linear and Lowess regressions, neutral loss assessment and spectral library capabilities. We plan to make these lipid spectral libraries publicly available through Panorama after completion and validation of our initial analyses.


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Benjamin Orsburn


Benjamin Orsburn Benjamin Orsburn Ph.D., received his Ph.D. from Virginia Tech and did postdoctoral work at Johns Hopkins and the NIH before spending the majority of his career as a proteomics scientists for Thermo. In 2019 he received a contract to design and oversee the construction and validation of two cannabis testing labs, in MD and CA, respectively. This spring he joined the faculty at the Johns Hopkins Medical School.

Skyline - A Comprehensive Package for Cannabis Testing labs

Recent changes in the laws regarding Cannabis in North America has created a profitable new market for agriculture and small batch production facilities. Due to the lack of federal oversight in the US, state and local municipalities are currently responsible for determining safety testing and product characterization requirements and these vary wildly across the country. These factors have combined to create an entirely new market for small mass spectrometry labs across the country. Read More
Varying lists of pesticides, heavy metals, residual solvents from extraction processes and active components such as cannabinoids must be accurately monitored to protect consumers. Other endogenous compounds such as terpenes are now being monitored to meet consumer preferences. We demonstrate how the Skyline software can be utilized as a near solution for both the testing and quality control monitoring for Cannabis testing labs.


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Roman Sakson


Roman Sakson Roman Sakson received his MSc degree in Molecular Biotechnology from Heidelberg University (Germany). During his master thesis project at the Core Facility for MS and Proteomics (CFMP) at Heidelberg Molecular Biology Center, Roman started working with Skyline and established an MRM assay for the relative quantification of the human Hsp90 and its cochaperome in hepatocytes. Since 2017, he continued at the CFMP as a Ph.D. candidate under supervision of Dr. Thomas Ruppert and Prof. Matthias Mayer. The main focus of Roman’s work is on relative and absolute quantification of enzymes involved in protein glycosylation using spike-in SILAC in combination with data-dependent and targeted approaches. Together with colleagues, Roman established a recurring hands-on Skyline course for CFMP users and for the members of his graduate school.

Unleashing Versatile Skyline Features for the Everyday Needs of a Proteomics Core Facility

Proteomics Core Facilities need to support a set of robust qualitative and quantitative workflows for a broad customer base. We routinely use Skyline as a versatile, vendor-independent platform that helps us to address two major issues, namely quality control and sharing information between MS experts and users, especially if they are not located in the same place. Customized reports and integrated tools, such as Protter for protein sequence visualization, are extremely helpful while discussing results with customers. Read More
Furthermore, AutoQC in Panorama makes it easy for us to supervise instrumentation performance, even remotely. I will present one study where partially contradicting DDA-based ID results were evaluated in Skyline via MS1 filtering with spectral libraries built from MaxQuant and Proteome Discoverer output files. In a second example, shared customized report templates allow for efficient and remote data analysis by users for MRM studies monitoring hundreds of peptides over hundreds of LC-MRM injections.


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Tobias Schmidt


Tobias Schmidt Tobias Schmidt is a fourth-year Ph.D. candidate at the chair of Proteomics and Bioanalytics of Prof. Kuster in Freising, Technical University Munich, Germany. Before coming to the Technical University of Munich for his MSc in molecular biotechnology he spent three years at the Karlsruhe Institute of Technology studying mathematics. His research interest is in in-memory databases (ProteomicsDB), combining modern machine learning with high quality synthetic and real-world data as well as porting legacy (academic) systems to new technologies. His doctoral thesis work explores among other things the usage of his prediction tools for data analysis pipelines requiring high-quality spectral libraries.

Real-time Spectrum Prediction in Skyline via ProteomicsDB’s gRPC Interface to Prosit

Prosit is able to accurately predict fragment ion intensities and retention times of peptides by deep learning. However, deep-learning requires GPUs for predictions that are not yet readily available in many labs and thus limit its applicability. In order to circumvent this shortcoming, we made Prosit available via gRPC on ProteomicsDB, such that Skyline is able to directly request predictions in real-time and on-demand. Read More
The service went online in December and within two months served over 330 users, covering 17 countries and had an uptime of 100%. Prediction of 5000 spectra takes on average 300 milliseconds. We envisage that Prosit will not be limited to fragment ion intensities and retention times, but will also serve other relevant peptide properties to Skyline. As a proof of principle, we started to develop a new model that predicts the expected charge state distribution of a peptide (R=0.7).


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