Table of Contents

 guest
2025-05-04
Event Information
Speakers
   Michael MacCoss
   Brendan MacLean
   Matt Champion
   Emily Crawford
   Lincoln Harris
   Chris Hsu
   Jonathan Krieger
   Britt Lee
   Marcelino Varona Ortiz
   Philip Remes
   Mike Riffle

Event Information


REGISTRATION OPEN - 2025 Skyline User Group Meeting in Baltimore, June 1, 2025

Welcome

The Skyline Team is pleased to announce this year's big event, the Annual Skyline User Group Meeting, which will be held in Baltimore, MD on Sunday afternoon before ASMS. We love seeing you all in-person -- so be sure to register and attend! We would like to thank the event sponsors (see below) for their generosity and interest in collaborating with the Skyline project on exciting new targeted and quantitative mass spectrometry techniques.  


--Brendan


When: Sunday, June 1, 2025
         12:00 - 1:00 pm : Lunch served
         1:00 - 2:30 pm : Presentations
         2:30 - 3:00 pm : Snacks and break-out discussions
         3:00 - 4:30 pm : Presentations


Where: Hyatt Regency Baltimore Inner Harbor PISCES room, 15th Floor, 300 Light Street, Baltimore MD 21202 (map) - Just east of the Convention Center.


Register Now!

 


Scheduled 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 17 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 15 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. (More info...)

Britt Lee (Thermo Fisher Scientific): Lipidomics at Scale: Skyline-Powered Targeted Workflows on the Stellar Mass Spectrometer
The growing prevalence of metabolic disorders underscores the need for high-throughput lipidomics workflows that enable comprehensive, precise, and selective lipid quantification. Structurally related lipids with varying fatty acid compositions play distinct biological roles, making highly selective analysis essential for risk prediction, diagnostics, and translational research. Skyline provides a powerful and accessible platform for developing and optimizing targeted lipidomics methods, making it an ideal tool for nominal mass instruments like the Thermo Scientific Stellar MS. (More info...)

Matt Champion Ph.D. (University of Notre Dame): Skyline Workflows to Quantify N-terminal Acetylation Peptidoforms While Preserving Protein Abundance in Bottom-up Proteomics
N-terminal Acetylation (N𝛼TA) is the co-and post-translational addition of an acetyl group to the N-terminus of a protein by N-acetyl transferases (NATs). It is a ubiquitous, but under-studied modification with a variety of roles in protein stability, trafficking and recognition. In mycobacteria, N𝛼TA is associated with increased virulence and several virulence factors are differentially acetylated with PTM. (More info...)

Emily Crawford (University of North Carolina): Assessing Antidepressant Pharmaceuticals in the Environment through the Development of a Multidimensional Liquid Chromatography-Ion Mobility Spectrometry-Mass Spectrometry Library
Antidepressant (AD) medications are globally prescribed to alleviate depression symptoms by altering neurotransmitter reuptake in the brain. Up to 90% of an orally administered AD dose is excreted from the body as either the parent chemical or pharmacologically active metabolites which remain active even at low concentrations. These molecules are not currently removed by wastewater treatment plants (WWTPs), posing great environmental and ecological concerns. To understand exposure risks, we developed methods to quantify 26 parent ADs and 16 metabolites in surface water with liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS). (More info...)

Jonathan Krieger Ph.D. (Bruker): Improved targeted quantitation with diagonal-PASEF using Skyline
Combining Data-Independent Acquisition (DIA) with Trapped Ion Mobility Separation (TIMS) reduces sample complexity by providing an additional dimension of separation, thereby increasing overall system peak capacity. This scan mode, termed dia-PASEF, employs fixed quadrupole isolation windows for ion selection in both the m/z and mobility panes. To further enhance the analytical performance, diagonal-PASEF has been developed, which optimizes the acquisition of the precursor ion cloud by continuously tracking its diagonal shape. (More info...)

Chris Hsu (University of Washington): Bridging the Gap in Mass Spectrometry Hardware Evaluation: Ion counting in Skyline for Cross-Platform Comparisons
The rapid advancement of mass spectrometry (MS) hardware has exposed a critical gap in performance evaluation. Traditional metrics, such as database search-derived peptide and protein identifications, remain rooted in qualitative assessments and don’t necessarily capture differences in instrument performance. New MS instruments continue to improve, generating data with improved sensitivity and speed, but analytical parameters, such as ion utilization efficiency, transmission, and quantitative precision, are rarely assessed. Even widely used methodologies like automatic gain control (AGC), which dynamically adjust ion populations to optimize detection, lack standardized frameworks to compare its effectiveness across platforms. This limits our ability to objectively evaluate hardware advancements. (More info...)

Lightning Talks

Philip Remes Ph.D. (Thermo Fisher Scientific): Improved Peak Picking in Skyline using Concepts from Adaptive RT
Targeted MS/MS methods validate the retention times (RT) and dissociation fragments of compounds, store these data in a library, and perform data analysis by integrating a RT range about the LC/MS features that most closely resemble the library data. Besides being exceedingly fast, this method has the advantage of registering a measurement value even when the compound is not present. (More info...)

Lincoln Harris (University of Washington): Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation
Missing values in data-independent (DIA) acquisition proteomics reduce statistical power and reproducibility and make it difficult to compare across runs. Traditionally, missing values have been handled in one of two ways. The first is peptide identity propagation (PIP), in which peptide identifications from “donor” runs are transferred to unassigned MS2 features in “acceptor” runs. Popular methods for PIP include MaxQuant match-between-runs (MBR) feature and TRIC. The second way missing values are handled is “plug-in” imputation, in which statistical or machine learning methods are used to estimate missing quantitations using only the observed quantitations. Popular plug-in methods include Perseus and k-nearest neighbors (kNN). (More info...)

Marcelino Varona Ortiz (Genentech): Traveling Wave Ion Mobility Spectrometry for the identification and quantitation of impurities in small molecule APIs
In this presentation, I will speak of our group's recent work using Traveling Wave Ion Mobility Spectrometry (TWIMS) coupled with High Resolution Mass Spectrometry (HRMS) to characterize impurities in small molecule drug candidates. In addition, we have implemented a workflow with Skyline to the enable quantitation of some of these impurities. The developed methods have enabled the significant reduction of analysis times when compared to traditional chromatographic methods. (More info...)

Mike Riffle (University of Washington): An automated workflow for de novo DIA searches using Nextflow, Cascadia, and Skyline
Typically, DIA proteomics searches require a priori knowledge of expected peptide sequences to identify peptides in a sample. This can represent a very real practical limitation in fields such as environmental metaproteomics or microbiome analyses where the expected proteomes are not known. (More info...)


Sponsors


Agilent Bruker
Shimadzu
Thermo Scientific Waters
LabKey Software


Speakers


Here are biographies and abstracts for this year's speakers -- each users of Skyline -- scheduled to speak at the Skyline User Group Meeting at ASMS 2025 in Baltimore, MD.

 

Michael MacCoss

Brendan MacLean

Matt Champion

Emily Crawford

Lincoln Harris

Chris Hsu

Jonathan Krieger

Britt Lee

Marcelino Varona Ortiz

Philip Remes

Mike Riffle

 



Michael MacCoss


Mike MacCoss   Michael MacCoss  Ph.D., 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. 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.


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. Since that time, he has worked as a Sr. Software Engineer within the MacCoss lab and led all aspects of design, development and support in creating the Skyline Targeted Mass Spectrometry Environment and its growing worldwide user community.

Status of the Skyline open-source software project 17 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 17 years later, the Skyline project is a thriving mass spectrometry community open-source collaboration supporting 6 mass spec instrument vendors, integrated with a wide variety of external software, with many thousands of users worldwide and over fourteen thousand instances started each week. Read More
In this presentation, the Skyline principal developer will present recent developments and a glimpse of the project's future. Topics covered will include:
  • Supporting new upstream processing pipelines
  • Supporting new instrument acquisition methods
  • Enhanced visualizations to better understand input data
  • Strong industry support from instrument vendors
  • Courses, workshops, webinars and other efforts to create instructional resources for the Skyline community


Matt Champion


Matt Champion   Matt Champion, Ph.D., earned his B.S. in Microbiology from The University of Iowa and his Ph.D. at Texas A&M University in Biochemistry. After that he worked at Applied Biosystems from 2003-2009 and worked as a research professor in the mass spectrometry and proteomics facility at Notre Dame. In 2019 he started his own research group which includes development of proteomics methods, processing and visualization for the analysis of proteomes in microbial systems and pathogens.

Skyline Workflows to Quantify N-terminal Acetylation Peptidoforms While Preserving Protein Abundance in Bottom-up Proteomics

N-terminal Acetylation (N𝛼TA) is the co-and post-translational addition of an acetyl group to the N-terminus of a protein by N-acetyl transferases (NATs). It is a ubiquitous, but under-studied modification with a variety of roles in protein stability, trafficking and recognition. In mycobacteria, N𝛼TA is associated with increased virulence and several virulence factors are differentially acetylated with PTM. Read More
N𝛼TA like many PTM’s are studied using chemical enrichment techniques followed by bottom up proteomics (BUP) acquisition, and search. Enrichment techniques by definition discard the proteotypic peptides which are crucial for accurate and precise abundance determination. As a thought experiment; a 2-fold change in a PTM could arise from a 2-fold change in protein abundance or a 2-fold change in PTM occupancy with no change in protein expression. In response to this, we developed OnePotN𝛼TA is an efficient label-based technique to quantify N-terminal acetylation which involves adding deuterated acetyl groups to all free N-termini prior to proteolytic digestion directly in a silica filter trap. After bottom-up proteomic analysis and database search / quantification, the ratio between the abundance of light and heavy acetyl groups can be used to measure the fraction of acetylation in the original termini without enrichment; relying on the high-density acquisition rates of current-gen LC-MS proteomics instruments. This preserves protein quantification data within the same injection/analysis. However, In normal BUP workflows we run into two main issues: (1) Many injections contain missing data, decreasing our statistical power in the subsequent analysis and resulting in false 0 and 100% acetylation values; and (2) Gratuitous Match-Between-Run (MBR) algorithms, which incorrectly assign identify to peaks even in cases where they are genetically deleted from the proteome. Incorporating Skyline into the data analysis workflow has substantially improved our results in several ways. Using the isotopic modification menu, we can group light/heavy peptides and ensure that the retention times for these peptide pairs are constrained to the same value. Skyline also allows for the integration of light/heavy peaks even when peptides were not actually identified in the database search, which reduces the number of missing values and improves the precision of our results. Comparing a workflow using commercial database-MBR search results vs MSFragger and Skyline combined, we see an improvement in the median %RSD of calculated percent acetylation from 34% to 13%. We can also view the XICs and visually compare light/heavy N-terminal peptides to manually eliminate false positives and incorrect identifications. Using OnePotN𝛼TA and Skyline we have characterized a newly identified N-terminal acetyltransferase (NAT) in pathogenic mycobacteria and identified approximately 40 new substrates.




Emily Crawford


Emily Crawford   Emily Crawford completed her B.S. in Chemistry in 2023 at the Georgia Institute of Technology in Atlanta, GA. She is currently an analytical chemistry Ph.D. candidate at the University of North Carolina at Chapel Hill in Dr. Erin Baker’s laboratory. Here, her research focuses on optimizing separation and mass spectrometry techniques to assess the effects of pharmaceutical pollution on the environment.

Assessing Antidepressant Pharmaceuticals in the Environment through the Development of a Multidimensional Liquid Chromatography-Ion Mobility Spectrometry-Mass Spectrometry Library

Antidepressant (AD) medications are globally prescribed to alleviate depression symptoms by altering neurotransmitter reuptake in the brain. Up to 90% of an orally administered AD dose is excreted from the body as either the parent chemical or pharmacologically active metabolites which remain active even at low concentrations. These molecules are not currently removed by wastewater treatment plants (WWTPs), posing great environmental and ecological concerns. To understand exposure risks, we developed methods to quantify 26 parent ADs and 16 metabolites in surface water with liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS). Read More
This work provides the first multidimensional LC-IMS-MS reference library for ADs and their metabolites, enabling targeted suspect screening in Skyline (MacCoss Lab Software, v. 23.1) and enhancing confidence in identifications within complex samples. The library was used to evaluate AD pollution in North Carolina surface water and is publicly available to all Skyline users for broader AD analysis. Water samples were collected from five locations across central North Carolina: four directly downstream from WWTP discharge pipes, and a fifth from a recreational lake with no issued permits for wastewater discharge. Each sample, as well as calibration standards and method blanks, were spiked with a mixture of deuterated standards, concentrated using solid phase extraction (SPE), and analyzed using an Agilent 1290 UHPLC coupled with an Agilent 6560 IM-QTOF. Identifications were made on Skyline by comparing LC retention times, collision cross section values, fragmentation patterns, and m/z ratios to the multidimensional AD library. Each detection was quantified by comparing light-to-heavy signal ratios against the calibration curve. Out of the 42 pharmaceuticals present in the library, ten ADs and six metabolites were detected in the surface water samples at concentrations ranging from ng/L to µg/L. Since previous studies indicate behavioral disturbances and altered swimming abilities in both fish and amphipods exposed to ADs at ng/L levels, these results demonstrate the urgent need for improvements in WWTP practices.




Lincoln Harris


Lincoln Harris   Lincoln Harris is a fifth year PhD student in the Genome Sciences department at the University of Washington. Prior to graduate school, he worked on data analysis and methods development for single-cell RNA-sequencing. In Genome Science, he made the transition to proteomics and focused on developing and evaluating methods for handling missing values in quantitative proteomics. He's a member of the Noble lab but work closely with the MacCoss lab on several projects.

Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation

Missing values in data-independent (DIA) acquisition proteomics reduce statistical power and reproducibility and make it difficult to compare across runs. Traditionally, missing values have been handled in one of two ways. The first is peptide identity propagation (PIP), in which peptide identifications from “donor” runs are transferred to unassigned MS2 features in “acceptor” runs. Popular methods for PIP include MaxQuant match-between-runs (MBR) feature and TRIC. The second way missing values are handled is “plug-in” imputation, in which statistical or machine learning methods are used to estimate missing quantitations using only the observed quantitations. Popular plug-in methods include Perseus and k-nearest neighbors (kNN). Read More
We have developed a hybrid method that imputes retention time (RT) boundaries in a spectral library. After boundary imputation, peptide quantitation is performed with Skyline by integrating the area under the chromatographic peaks (AUC) within the imputed RT bounds. We compare RT boundary imputation to existing PIP and plug-in methods. We evaluate quantitative accuracy on three DIA datasets: (i) a matrix-matched calibration curves dataset, (ii) a large-scale Alzheimer’s disease (AD) dataset, and (iii) a dataset derived from plasma extracellular vesicles. All three datasets were accessed via Panorama. We show that RT boundary impute increases the number of differentially quantifiable peptides, reduces peptide lower limit of quantification and produces more accurate quantitations than plug-in methods. We are currently working to integrate our RT boundary imputation method, called Nettle, into Skyline. This will provide Skyline users with an effective and principled method for reducing missingness in their DIA proteomics experiments. We stress that Nettle differs from traditional plug-in methods in that peptide quants are still derived from a measured quantity, that is, the AUC within an imputed RT window.




Chris Hsu


Chris Hsu   Chris Hsu completed his B.S. in Biochemistry in 2018 at the UW. Currently, he is a Genome Sciences Ph.D. candidate at the UW in Dr. Michael MacCoss’s lab. His research focuses on developing new methods to compare quantitative analysis of instruments and applying these new methods to answer questions about spatial proteomics. He uses Skyline for external tool development and extracting ion count information from peptides for his analysis.

Bridging the Gap in Mass Spectrometry Hardware Evaluation: Ion counting in Skyline for Cross-Platform Comparisons

The rapid advancement of mass spectrometry (MS) hardware has exposed a critical gap in performance evaluation. Traditional metrics, such as database search-derived peptide and protein identifications, remain rooted in qualitative assessments and don’t necessarily capture differences in instrument performance. New MS instruments continue to improve, generating data with improved sensitivity and speed, but analytical parameters, such as ion utilization efficiency, transmission, and quantitative precision, are rarely assessed. Even widely used methodologies like automatic gain control (AGC), which dynamically adjust ion populations to optimize detection, lack standardized frameworks to compare its effectiveness across platforms. This limits our ability to objectively evaluate hardware advancements. Read More
A fundamental challenge lies in the calibration of vendor-specific arbitrary intensity units into a universal metric. In an ideal world, we could inject a fixed amount of analyte to allow direct comparisons of the number of ions reaching the detector to compare between different MS instruments. To address this gap, we present a strategy that uses the relationship between the number of ions measured and the precision to derive a correction factor between the intensity reported by the vendor as a metric in ions/sec. We use this calibration factor to compare between different instrument platforms, and we show that Thermo Scientific linear ion traps report an intensity that is well calibrated to ions/sec, whereas orbitrap analyzers need to be divided by a 10-15x to obtain the same measurement. We integrated this ion-counting framework into Skyline by leveraging the signal intensity and injection time to measure the ion count for each peptide. We implement the calibration protocol in an app that will ultimately become a Skyline external tool. Furthermore, we implemented new metrics into the Skyline document grid that can be used to evaluate both targeted and untargeted proteomics measurements.




Jonathan Krieger


Jonathan Krieger   Jonathan Krieger is the Head of Research at Bruker ProteoScape, where he has been contributing significantly since November 2021. His expertise spans various aspects of computational proteomics. Prior to his current role, Jonathan served as the Applications Manager and Core Facility Manager at Bioinformatics Solutions Inc., the creators of PEAKS software. He also gained valuable experience at the SPARC BioCentre, the core Mass Spectrometry facility at SickKids hospital. Based in Toronto, Canada, Jonathan's research at Bruker encompasses software development, data science, and computational proteomics.

Improved targeted quantitation with diagonal-PASEF using Skyline

Combining Data-Independent Acquisition (DIA) with Trapped Ion Mobility Separation (TIMS) reduces sample complexity by providing an additional dimension of separation, thereby increasing overall system peak capacity. This scan mode, termed dia-PASEF, employs fixed quadrupole isolation windows for ion selection in both the m/z and mobility panes. To further enhance the analytical performance, diagonal-PASEF has been developed, which optimizes the acquisition of the precursor ion cloud by continuously tracking its diagonal shape. Read More
Although several data processing software tools currently support this innovative scan mode, none specifically focus on targeted data extraction. We present the implementation of diagonal-PASEF data processing in Skyline, demonstrating exceptional performance for targeted quantitation. The integration of diagonal-PASEF data acquisition with Skyline data processing shows significant promise for targeted quantitation due to its straightforward method setup and remarkably short cycle times.




Britt Lee


Britt Lee   Britt Lee My experiences from my graduate work, postdoc and industry are all focused in the small molecule and multiomics space, specializing in metabolomics, lipidomics, and proteomics. Now at Thermo Fisher Scientific, I focus on developing applications that advance omics research using cutting-edge mass spectrometry technologies. My work aims to bridge the gap between emerging analytical tools and practical solutions for high-throughput, high-precision biomolecular analysis.

Lipidomics at Scale: Skyline-Powered Targeted Workflows on the Stellar Mass Spectrometer

The growing prevalence of metabolic disorders underscores the need for high-throughput lipidomics workflows that enable comprehensive, precise, and selective lipid quantification. Structurally related lipids with varying fatty acid compositions play distinct biological roles, making highly selective analysis essential for risk prediction, diagnostics, and translational research. Skyline provides a powerful and accessible platform for developing and optimizing targeted lipidomics methods, making it an ideal tool for nominal mass instruments like the Thermo Scientific Stellar MS. Read More
By integrating LipidCreator for targeted lipid library generation and PRM Conductor for streamlined parallel reaction monitoring (PRM) method setup, Skyline simplifies method development while ensuring efficient and reproducible data acquisition. Its intuitive data analysis tools further refine lipid quantification, allowing users to confidently analyze complex lipid panels with minimal manual intervention. To demonstrate this, we applied a Skyline-driven workflow to quantify over 1000 lipids in NIST 1950 plasma within a 12-minute acquisition on the Stellar MS. The integration of LipidCreator enabled the construction of an enhanced targeted list, incorporating multiple transitions per lipid species. This resulted in an average of 3.5 transitions per analyte, increasing lipid species diversity by 50% while improving peak definition and scan density by at least 20%. This work highlights Skyline’s role in advancing lipidomics, providing researchers with a flexible, scalable, and user-friendly solution for high-confidence lipid panel development. By leveraging automated method generation, streamlined PRM acquisition, and detailed data analysis, Skyline enables more efficient lipidomics research, enhancing quantitative reliability, specificity, and throughput for emerging mass spectrometry technologies.




Marcelino Varona Ortiz


Marcelino Varona Ortiz   Marcelino Varona Ortiz I received my B.S in Biology and Chemistry from Concordia University Nebraska and PhD in Analytical Chemistry from Iowa State University. Currently I am a Principal Scientist in the Synthetic Molecule Analytical Chemistry department at Genentech. I am a CMC analytical lead who is responsible for the control strategy and characterization of synthetic molecules within our portfolio. Over the last couple of years I have been using High Resolution Ion Mobility Mass Spectrometry as a tool to enable the characterization of a variety of small molecule compounds.

Traveling Wave Ion Mobility Spectrometry for the identification and quantitation of impurities in small molecule APIs

In this presentation, I will speak of our group's recent work using Traveling Wave Ion Mobility Spectrometry (TWIMS) coupled with High Resolution Mass Spectrometry (HRMS) to characterize impurities in small molecule drug candidates. In addition, we have implemented a workflow with Skyline to the enable quantitation of some of these impurities. The developed methods have enabled the significant reduction of analysis times when compared to traditional chromatographic methods.



Philip Remes


Philip Remes   Philip Remes Ph.D., is a senior research scientist in the R&D department of Thermo Fisher Scientific in San Jose, California. He received his Ph.D. in Gary Glish’s lab at the University of Chapel Hill, North Carolina, where he worked on quadrupole ion trap and mass filter instrumentation. At Thermo, he worked with Jae Schwartz on instrument control software, ion trap analyzers, detectors and simulations for the LTQ Velos platform. Later he did similar work as a part of Mike Senko’s Tribrid development team on the Fusion, Lumos, and Eclipse projects. In recent years, Philip has continued working on ion trap technology as the technical lead of the Stellar project, branching out to learn about applications and get first-hand experience in how they can benefit from improvements in hardware, instrument control, signal processing, and informatics.

Improved Peak Picking in Skyline using Concepts from Adaptive RT

Targeted MS/MS methods validate the retention times (RT) and dissociation fragments of compounds, store these data in a library, and perform data analysis by integrating a RT range about the LC/MS features that most closely resemble the library data. Besides being exceedingly fast, this method has the advantage of registering a measurement value even when the compound is not present. Read More
However, integrating the proper time range is a challenging task, especially for dilution curve studies, and especially as the number of targets-per-assay increases into the >1000 range with the introduction the Thermo Scientific Stellar MS platform. Here, we evaluate a new peak-picking algorithm based on the Adaptive RT technique (Remes 2020). This algorithm is built into a Skyline external tool, and here we describe results for dilution curves for various sample types, as well as samples from a large (>1000 samples) study, with the goal of making large-scale analyte abundance measurements more reliable.




Mike Riffle


Mike Riffle   Mike Riffle has many years of experience in bioinformatics, data science, and statistics; and has worked with several leading academic proteomics laboratories in the US. He is a proponent of open and reproducible science, and has produced several open source tools for proteomics data visualization, including Proxl (protein cross-linking) and, more recently, Limelight (generalized proteomics data visualization and sharing). Michael has a Bachelors in Molecular and Cellular Biology from the University of Washington and his Masters in Computer Science from the University of Illinois Urbana-Champaign.

An automated workflow for de novo DIA searches using Nextflow, Cascadia, and Skyline

Typically, DIA proteomics searches require a priori knowledge of expected peptide sequences to identify peptides in a sample. This can represent a very real practical limitation in fields such as environmental metaproteomics or microbiome analyses where the expected proteomes are not known. Read More
Researchers may attempt to overcome this limitation by using large, non-specific databases that may (or may not) include relevant sequences (e.g., Uniprot) or endure the expense of generating a metagenome in order to generate a metaproteomic database. Cascadia is a deep learning model using a transformer architecture and was recently published as a means of performing a de novo DIA search that requires no a priori knowledge of expected peptides. Here we present a computational workflow written in Nextflow that fully automates all steps of the Cascadia de novo workflow, including raw file conversion, analysis by Cascadia, generation of a Skyline document, and optional upload to PanoramaWeb. The workflow greatly simplifies the installation and running of the Cascadia workflow, requiring only the installation of Nextflow, Docker, and Skyline (for visualization).