Table of Contents

guest
2021-09-19
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

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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