Table of Contents

guest
2024-05-30
Thanks!
Event Information
Speakers
   Michael MacCoss
   Brendan MacLean
   Nathan Basisty
   Joanna Bons
   James N. Dodds
   Lilian Heil
   Evan Hubbard
   Yishai Levin
   Alison Porter
   Florence Roux-Dalvai
   Axe Xie

Thanks!


Thanks to everyone who joined us in Minneapolis for the Skyline User Group Meeting before ASMS 2022. It was certainly a treat to share a beautiful space with passionate Skyline users presenting recent work enabled by the software -- in-person -- again!

Below are links to the speakers' pages with options to view their slides or watch their presentations. At the bottom of this page, you will also find some photos from the event.

Despite the uncertainty around travel and ambient concerns about attending large conferences this year, we were impressed by the turnout and enthusiasm of the audience! Thanks especially to the speakers for all the effort they gave to delivering great presentations. The presentations featured a range of Skyline-related topics, including everything from library-free-DIA pipeline comparisons, to enhancing newborn health screening, to improving thoroughbred horse blood doping detection. One presenter was even brave enough to share how Skyline saved his lab from inadvertently publishing erroneous data!

We hope these presentations long outlast the event itself, as previous years have.

Thank you! -- Brendan MacLean and Mike MacCoss Event Organizers


Confirmed 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 14 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 13 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...)

Nathan Basisty, Ph.D. (NIH): Accurate Calculation of Protein Half-Lives with the TurnoveR External Tool in Skyline
Loss of protein homeostasis is a hallmark of aging and age-related conditions, including neurodegeneration, sarcopenia, and type 2 diabetes. However, alterations in markers of proteostasis machinery do not necessarily reflect rates of protein turnover. Therefore, methods to measure the turnover rates of proteins directly, rather than surrogate measurements of translation and degradation machinery, are critically needed to accurately examine the stability of the proteome during aging and disease processes. (More info...)

James Dodds,   Ph.D., (North Carolina Statue University): <a=href="https:> Improving the Speed and Selectivity of Newborn Screening using Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) analyzed via Skyline.


Detection and diagnosis of congenital disorders is the principal aim of newborn screening (NBS) programs worldwide. Mass spectrometry (MS) has become the preferred primary testing method for high-throughput NBS sampling because of its speed and selectivity. However, the ever-increasing list of NBS biomarkers included in expanding panels creates unique analytical challenges for multiplexed MS assays due to isobaric/isomeric overlap and chimeric fragmentation spectra. (More info...)

Evan Hubbard (University of California - Riverside): Finding and Quantifying Amino Acid Isomers in Data-independent Acquisition Data to Achieve Isomer Proteomics
Some amino acids are capable of undergoing spontaneous chemical modifications to become a structural or enantiomeric isomer of the canonical residue. Confined to an individual amino acid and massless, these modifications are notoriously difficult to detect despite potentially affecting protein structure or inhibiting enzymatic digestion. Recently, we have shown that data-independent acquisition (DIA) is capable of detecting these modifications through retention time shifts of peptides containing these isomers.( More info...)

Yishai Levin, (Weizmann Institute of Science ): How Skyline Saved Us From Publishing Erroneous Data
Our story begins with a glycoproteomics project, with the aim of profiling glycopeptides from patient sera. We had two informatics tools at hand. One generates identifications, based on the MS/MS spectra, but not quantification (Byonic). The other, generates MS1 based, label free quantification from any list of peptide sequences (FlashLFQ). (More info...)

Florence Roux-Dalvai, (CHU de Québec - Université Laval, Québec, Canada ): <a=href="https:>Comparative analysis of library-based and library-free DIA strategies using Skyline software


Data independent acquisition (DIA) analysis has become a strategy of choice for the analysis of complex proteomes and a plethora of methods are now available in the literature. However, there is no consensus on the best acquisition parameters to use, whether a spectral library is needed, and which processing software is most efficient. In the most comprehensive comparative study of DIA pipelines ever published (Gotti et al. J.Proteome Res., 2021), we used a complex proteomic standard (E.Coli background + UPS1 Sigma) with 4 DIA acquisition methods on an Orbitrap Fusion instrument to benchmark 6 different processing tools. (More info...)



Event Information


Welcome

The Skyline Team is pleased to announce we are bringing back our live event, the Eleventh Annual Skyline User Group Meeting, which will be held in Minneapolis, MN on Sunday afternoon before ASMS. After two years online, we are thrilled at the prospect of 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 5, 2022
         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: Lumber Exchange 10 South 5th Street Minneapolis, MN 55402 (map)

  • From the Convention Center, head north towards South 12th Street
  • Turn left on South 12th Street, walk one block
  • Turn right onto South Marquette Ave
  • Walk seven blocks and turn left onto South 5th Street
  • Lumber Exchange (10 South 5th Street) is on the right in 1.5 blocks
  • Using the 5th Street entrance, take the elevator to the 3rd floor

There is also a bus option with a stop at 2nd Ave South and Convention Center. Board the #10 and ride for five stops. Get off at Nicollet Mall & South 5th Street. Walk a half a block up to 10 South 5th Street.


[registration closed]

We have closed down our registration process but you can still attend! Please just come to Lumber Exchange (10 South 5th Street) for "day of" registration.

 


Confirmed 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 13 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 13 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...)

Nathan Basisty, Ph.D. (NIH): Accurate Calculation of Protein Half-Lives with the TurnoveR External Tool in Skyline
Loss of protein homeostasis is a hallmark of aging and age-related conditions, including neurodegeneration, sarcopenia, and type 2 diabetes. However, alterations in markers of proteostasis machinery do not necessarily reflect rates of protein turnover. Therefore, methods to measure the turnover rates of proteins directly, rather than surrogate measurements of translation and degradation machinery, are critically needed to accurately examine the stability of the proteome during aging and disease processes. (More info...)

James Dodds,   Ph.D., (North Carolina Statue University): <a=href="https:> Improving the Speed and Selectivity of Newborn Screening using Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) analyzed via Skyline.


Detection and diagnosis of congenital disorders is the principal aim of newborn screening (NBS) programs worldwide. Mass spectrometry (MS) has become the preferred primary testing method for high-throughput NBS sampling because of its speed and selectivity. However, the ever-increasing list of NBS biomarkers included in expanding panels creates unique analytical challenges for multiplexed MS assays due to isobaric/isomeric overlap and chimeric fragmentation spectra. (More info...)

Evan Hubbard (University of California - Riverside): Finding and Quantifying Amino Acid Isomers in Data-independent Acquisition Data to Achieve Isomer Proteomics
Some amino acids are capable of undergoing spontaneous chemical modifications to become a structural or enantiomeric isomer of the canonical residue. Confined to an individual amino acid and massless, these modifications are notoriously difficult to detect despite potentially affecting protein structure or inhibiting enzymatic digestion. Recently, we have shown that data-independent acquisition (DIA) is capable of detecting these modifications through retention time shifts of peptides containing these isomers.( More info...)

Yishai Levin, (Weizmann Institute of Science ): How Skyline Saved Us From Publishing Erroneous Data
Our story begins with a glycoproteomics project, with the aim of profiling glycopeptides from patient sera. We had two informatics tools at hand. One generates identifications, based on the MS/MS spectra, but not quantification (Byonic). The other, generates MS1 based, label free quantification from any list of peptide sequences (FlashLFQ). (More info...)

Florence Roux-Dalvai, (CHU de Québec - Université Laval, Québec, Canada ): <a=href="https:>Comparative analysis of library-based and library-free DIA strategies using Skyline software


Data independent acquisition (DIA) analysis has become a strategy of choice for the analysis of complex proteomes and a plethora of methods are now available in the literature. However, there is no consensus on the best acquisition parameters to use, whether a spectral library is needed, and which processing software is most efficient. In the most comprehensive comparative study of DIA pipelines ever published (Gotti et al. J.Proteome Res., 2021), we used a complex proteomic standard (E.Coli background + UPS1 Sigma) with 4 DIA acquisition methods on an Orbitrap Fusion instrument to benchmark 6 different processing tools. (More info...)



Speakers


Back in-person, there are ten speakers -- each users of Skyline -- scheduled to speak at the Skyline User Group Meeting at ASMS 2022 in Minneapolis, MN.

Nathan Basisty, Ph.D.

Joanna Bons, Ph.D.

James Dodds

Lilian Heil

Evan Hubbard

Yishai Levin

Brendan MacLean

Alison Porter

Florence Roux-Dalvai

Yixuan (Axe) Xie




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. 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. Since that time, 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 Mass Spectrometry Environment and its growing worldwide user community.

Status of the Skyline open-source software project 14 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 14 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 ten 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
  • Growth in the Skyline software ecosystem for targeted MS (Skyline, Skyline Batch, AutoQC, Panorama, and External Tools)
  • Strong industry support from instrument vendors
  • Courses, workshops, webinars and other efforts to create instructional resources for the Skyline community


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Nathan Basisty


Nathan Basisty   Nathan Basisty Ph.D., is currently a tenure track Investigator at the NIA (NIH) and head of the Translational Geroproteomics Unit (TGU). He received his Ph.D., in Pathology and B.S. in Biochemistry from the University of Washington, where he investigated the role of protein turnover in mammalian aging and longevity using novel combinations of metabolic labeling, LC-MS/MS, and software tools. In 2015, he joined The Buck Institute for Research on Aging, where he did his postdoctoral fellowship in the labs of Dr. Birgit Schilling and Dr. Judith Campisi. There he developed novel and specialized proteomic approaches to understand aging processes and age-related diseases, including the application of data-independent acquisition (DIA) or SWATH workflows to identify and quantify PTMs and secretomes. Dr. Basisty has been recognized for his work investigating the role of protein turnover in mammalian aging and longevity using a combination of metabolic labeling, LC-MS/MS, and software tools.

Accurate Calculation of Protein Half-Lives with the TurnoveR External Tool in Skyline

Loss of protein homeostasis is a hallmark of aging and age-related conditions, including neurodegeneration, sarcopenia, and type 2 diabetes. However, alterations in markers of proteostasis machinery do not necessarily reflect rates of protein turnover. Therefore, methods to measure the turnover rates of proteins directly, rather than surrogate measurements of translation and degradation machinery, are critically needed to accurately examine the stability of the proteome during aging and disease processes. Conducting a protein turnover study in multicellular organisms in vivo remains very computationally complex and difficult for most scientists. The development of versatile computational tools on widely accessible, open-source platforms is needed to make this approach more user-friendly. Read More
In this presentation Dr Nathan Basisty introduces a new computational tool – TurnoveR – for the accurate calculation of protein turnover rates from mass spectrometry analysis of metabolic labeling experiments in the Skyline software platform. Using data generated from metabolic labeling of mice with heavy leucine in independent experiments, we demonstrate how this tool enables the calculation of protein turnover rates seamlessly within a Skyline workspace using raw data acquired on multiple mass spectrometric platforms and derive new biological insights into age-related diseases.


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Joanna Bons


Joanna Bons   Joanna Bons ,Ph.D., is a postdoctoral fellow in the laboratory of Dr. Birgit Schilling at the Buck Institute for Research on Aging. After an engineer degree in Biotechnology, she joined the team of Dr. Christine Carapito at the BioOrganic Mass Spectrometry Laboratory in Strasbourg, France, where she specialized in quantitative mass spectrometry-based proteomics method development (SRM, PRM, DIA) for proteome quantification and characterization. She received her Ph.D. in Analytical Chemistry in 2019, and then joined Dr. Birgit Schilling’s laboratory. She focuses on developing and optimizing innovative DIA and targeted strategies, using CID/HCD and electron activated dissociation (EAD), for deciphering proteome and PTM remodeling in various collaborative projects.

ZenoTOF 7600 Acquisitions with Electron Activated Dissociation and Novel Skyline Features for Quantification of Protein Post-translational Modifications

Protein post-translational modifications (PTMs) are key players involved in many cellular processes and signaling. Proteomic analysis of PTMs however can be challenging, due to the presence of labile modifications, the low stoichiometry of PTMs, the presence of multiple PTMs per peptide, and modified isomeric peptides. Collision induced dissociation (CID) has been commonly used for PTM analysis, but the relatively high fragmentation energy applying CID fragmentation can lead to side chain losses and incomplete sequence coverage for some modified peptides. An alternative and milder fragmentation mechanism, electron activated dissociation (EAD), implemented in the novel ZenoTOF 7600 Q-TOF system (SCIEX), was very recently introduced, and generates z+1-ion and c-ion series. We assessed the performances of EAD fragmentation to preserve PTM groups, and improve labile PTM characterization, site localization and quantification, and compared with CID fragmentation. Read More
Specifically, a series of synthetic post-translationally modified peptides, featuring phosphorylated, succinylated, malonylated and acetylated mono- and doubly-modified isomeric peptides, were analyzed using targeted high-resolution multiple reaction monitoring (MRM-HR) assays, with or without Zeno trap activation. EAD tunable kinetic energy values were ramped from 0 eV to 11 eV, and optimized for each analyte, conferring efficient EAD product ion fragmentation, namely z+1-ion and c-ion series. Acquired EAD MRM-HR data was analyzed using Skyline to extract peak areas for c, z+1 and b and y fragment ion series, as well as their counterparts with neutral loss. Indeed, taking advantage of the novel z+1 fragment ion support implemented into Skyline, we were able to benefit from the user-friendly Skyline features for EAD data visualization and quantification.Finally, this novel MS workflow, combining EAD MRM-HR and Skyline data processing, to a cohort of Sirt5(-/-) and WT human K562 cells enriched for malonyl peptides in order to gain insights into the regulation of Sirtuin 5, a NAD+-dependent lysine deacylase, enzymatically removing malonyl groups.


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James N. Dodds


James N. Dodds   James N. Dodds Ph.D. is a postdoc in Erin Baker's lab at NC State in Raleigh, NC. The Baker lab utilizes ion mobility spectrometry for a variety of applications including lipidomics, environmental sampling, and more recently newborn screening method development as described in this abstract. We almost exclusively utilize Skyline for all of our data processing.

Improving the Speed and Selectivity of Newborn Screening using Ion Mobility Spectrometry – Mass Spectrometry (IMS-MS) analyzed via Skyline.

Detection and diagnosis of congenital disorders is the principal aim of newborn screening (NBS) programs worldwide. Mass spectrometry (MS) has become the preferred primary testing method for high-throughput NBS sampling because of its speed and selectivity. However, the ever-increasing list of NBS biomarkers included in expanding panels creates unique analytical challenges for multiplexed MS assays due to isobaric/isomeric overlap and chimeric fragmentation spectra. Here, we explore the utility of ion mobility spectrometry (IMS) to enhance the accuracy of MS assays for primary (tier 1) screening. Read More
Furthermore, to address the need for increased speed in NBS analyses we utilized an automated solid-phase extraction (SPE) system for ~10 second sampling of simulated NBS samples prior to IMS-MS. The preliminary data presented in this abstract highlight the unique capabilities of IMS for rapid diagnostic screening with streamlined data processing via Skyline. Following the validation of these methods in future experiments, automated data processing should be achievable through AutoQC, however, this work focuses on the utility of Skyline to provide automated IMS-MS data processing and feature integration for our preliminary study.


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Lilian Heil


Lilian Heil   Lilian Heil, Lilian is a Ph.D. student in Genome Sciences interested in developing methods for the acquisition and analysis of quantitative proteomics data. She joined the department in the fall of 2019 after graduating from the University of North Carolina in the winter of 2018 with a B.S. in Chemistry. Before graduate school, she worked in the Hicks Lab at UNC for 2 years as an undergraduate trainee and for several months as a full time research assistant using mass spectrometry to identify and characterize novel bioactive peptides. There, she helped to discover and sequence a novel antimicrobial peptide from Amaranthus tricolor and developed a passion for mass spectrometry.

Automating Transition Refinement for Unit Resolution PRM

One benefit of parallel reaction monitoring compared to selected reaction monitoring is the ability to refine transitions after data acquisition. Transition refinement is a critical piece of quantitative analysis, particularly in unit resolution data where interferences are common. While it is possible to manually select the best set of transitions for each peptide, the process can be highly inefficient for large-scale assays. Here, we demonstrate two transition refinement methods in Skyline: one uses calibration curves to select the set of transitions that yields the most sensitivity, and the second identifies the set of transitions with strong correlation.


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Evan Hubbard


Evan Hubbard   Evan Hubbard currently works as a graduate student in the Julian Lab at UC Riverside, using DIA data and gas-phase radical chemistry to find new ways of detecting amino acid isomers.

Finding and Quantifying Amino Acid Isomers in Data-independent Acquisition Data to Achieve Isomer Proteomics

Some amino acids are capable of undergoing spontaneous chemical modifications to become a structural or enantiomeric isomer of the canonical residue. Confined to an individual amino acid and massless, these modifications are notoriously difficult to detect despite potentially affecting protein structure or inhibiting enzymatic digestion. Recently, we have shown that data-independent acquisition (DIA) is capable of detecting these modifications through retention time shifts of peptides containing these isomers. Such shifts produce multiple peaks in the chromatogram of a single peptide. Read More
In DIA data from AD and control brains, a semi-automated isomer search was able to find instances of chromatograms containing multiple peptide peaks. This facilitated whole-proteome isomer detection. For discovered isomers, quantification occurred through a process of manual adjustment of integration windows for individual isomer peaks. Using this method, aspartic acid isomerization within the tau peptide TDHGAEIVYK was found to be substantially increased in diseased brains. This pathological isomerization, along with proteomic analysis of autophagy markers, has significant implications for an autophagic flux theory of Alzheimer’s disease.


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Yishai Levin


Yishai Levin   Yishai Levin Ph.D., received his PhD at the University of Cambridge, UK. He then joined the Weizmann Institute to setup a proteomics core facility, which later became part of the Israel National Center for Personalised Medicine. Today his work focuses on clinical proteomics in immunology and Alzheimer’s disease. In his spare time he likes off-road cycling and off-road driving.

How Skyline Saved Us From Publishing Erroneous Data

Our story begins with a glycoproteomics project, with the aim of profiling glycopeptides from patient sera. We had two informatics tools at hand. One generates identifications, based on the MS/MS spectra, but not quantification (Byonic). The other, generates MS1 based, label free quantification from any list of peptide sequences (FlashLFQ). So we formatted the output from Byonic and analysed it using FlashLFQ to generate the quantitative data. After performing some Read More
basic statistics, we ended up with a list of significant glycopeptides, and started to write the manuscript. One thing kept bothering me about the process. We wrote an entire manuscript based on an automated quantitative output from a combination of two software tools, with no visualisation relating to how the peptide intensities were generated. So I insisted we look at some of the significant peptides in Skyline, to make sure the quantification was correct. We chose 9 glycopeptides and generated the MS1 based quantification in Skyline. We found that there was no correlation between the intensity values generated by Skyline and the output from FlashLFQ. This was very puzzling and after lengthy investigation, which I will discuss, we found the issue. Once we fixed it, the correlation was >0.9 and we were confident out data is solid and worthy of publication.


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Alison Porter


Alison Porter   Alison Porter began her college career at Big Sandy Community and Technical College in August of 2015, earning an Associate of Arts degree in May of 2017. She transferred to Morehead State University in August of 2017, and earned her Bachelor of Science in Veterinary Science in May of 2020. She accepted a position in University of Kentucky’s Master of Science in Veterinary Science program and will earn that degree in May of 2022. Through the master’s program, she worked as a graduate research assistant. This position exposed her to research in equine analytical chemistry which aided in furthering the research on the Equine Biological Passport. This abstract topic is one that comes directly from her thesis work over the last two years. She identified and validated four protein biomarkers that changed in response to bisphosphonate administration in horses, work which has not previously been done. She has been submerged in research using a mass spectrometer and has learned how to analyze data using programs such as Skyline. In the fall, she is planning on continuing her education and research efforts at the University of Kentucky College of Medicine.

Identifying and Validating Bisphosphonate Protein Biomarkers in Equine Sera Using Mass Spectrometry Methods

Bisphosphonates are a class of drugs used in humans and animals to treat resorptive diseases of the bone by inhibiting bone resorption. In equine, these anti-resorptive drugs are FDA-approved to treat clinical signs of navicular disease. Currently, there are two non-nitrogen containing bisphosphonate drugs approved for use in equine, tiludronate and clodronate. Bisphosphonates are heterogeneously distributed and tend to accumulate in areas of high bone turnover. These drugs do not undergo phase 1 or 2 metabolism, which prevents them from being readily detected using normal metabolite based drug testing techniques. Alternatively, we selected to monitor protein biomarkers, which demonstrated changes in response to bisphosphonate administration. This study developed a targeted acquisition method to validate four protein biomarkers that were identified using discovery analyses in response to bisphosphonate administration. Through untargeted LC-MS/MS methods, four key protein biomarkers—actin, carbonic anhydrase, fibrinogen, and fibronectin—were identified that exhibited changes across a time course and were found to have a role in bone remodeling. Following identification, a targeted parallel reaction monitoring (PRM) method was developed to validate these protein biomarkers. Read More
Samples were analyzed using an Orbitrap Exploris 480 mass spectrometer coupled with an UltiMate 3000 RSLCnano. In all serum samples, albumin was depleted using an organic solvent fractionation (isopropanol, trichloroacetic acid) protocol. Depleted samples were reduced, alkylated, and enzymatically digested using trypsin, followed by de-salting. Data were analyzed using Skyline (version 21) software, and were compared to pre-administration at day 0, as well as non-related equine sera that were used as a control. Using Skyline, optimal proteotypic peptides and transitions were selected for each protein biomarker using a data-driven approach. Initially, 103 candidate peptides were selected for evaluation. In Skyline, the 103 candidate peptides were refined down to 23 unique peptides. The refinement process was based on retention time, number of detectable transitions, and fragment ion ppm tolerance (±10 ppm). The 23 unique peptides were also chosen based on their peak area and proteotypic properties. Peptides with poor peak shape, low area counts, or that were non-distinguishable from the background matrix were removed. This iterative refinement process from initial peptide identification (103 candidate peptides) to optimized peptides (23 unique peptides) was all performed in Skyline software. Semi-quantitative analyses were performed using the top three most abundant peptides for each protein. These analyses led to determination of the average relative abundance of each time point, as well as percent difference of each time point from day 0 abundance. Each protein biomarker exhibited the same patterns that were established in the discovery phase with more intensity. Actin and carbonic anhydrase were both downregulated on day 18 and upregulated on days 7, 21, 28, 42, and 49 depending on the horse. Compared with day 0, these patterns of upregulation and downregulation remained true. Fibrinogen exhibited downregulation on day 18 and upregulation on day 7. Compared with day 0, fibrinogen exhibited downregulation on days 1, 3, 18, 34, 42, 49, 57, and 62. Fibronectin was initially identified as a protein that had an opposite abundance pattern to the other three proteins. This was also seen in the targeted phase. Fibronectin exhibited downregulation on day 7 and upregulation on day 18. When compared to day 0 abundance, it appeared that each time point was upregulated. However, even though day 7 was upregulated in comparison with day 0, it was downregulated when compared to day 18, keeping its initial established pattern. These patterns, while obvious in the discovery phase, were much clearer in the targeted phase. Skyline software was able to depict these patterns, as well as aid in refining the initial 103 peptides down to 23 unique peptides for all four proteins. These four protein biomarkers have relevance to bone remodeling and bisphosphonate administration. This study has developed a targeted LC-MS/MS method for detection of peptide biomarkers in equine sera, a technique which has not been previously established.


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Florence Roux-Dalvai


Florence Roux-Dalvai   Florence Roux-Dalvai is senior scientist at the CHU de Québec - Université Laval (Québec, Canada) in the proteomics and computational biology laboratory of Pr. Arnaud Droit. She received her M.Sc. in Structural Biochemistry at the University of Strasbourg (France) and was introduced to mass spectrometry for protein analysis under the supervision of Dr. Jérôme Garin at the CEA (Grenoble, France). She then worked at the Novartis Friedrich Miescher Institute (Basel, Switzerland) studying ErbB receptor signaling pathways in breast cancer using proteomics before to join the CNRS (Toulouse, France) where she developed new strategies for quantitative analysis of deep proteomes on Orbitrap instruments. Since 2014, at Université Laval, she coordinates development projects for the large-scale analysis of clinical samples and for the detection of microorganisms in biological fluids by combining the latest analysis strategies in proteomics (Data Independent Acquisition) and in artificial intelligence (Machine Learning).

Comparative analysis of library-based and library-free DIA strategies using Skyline software

Data independent acquisition (DIA) analysis has become a strategy of choice for the analysis of complex proteomes and a plethora of methods are now available in the literature. However, there is no consensus on the best acquisition parameters to use, whether a spectral library is needed, and which processing software is most efficient. In the most comprehensive comparative study of DIA pipelines ever published (Gotti et al. J.Proteome Res., 2021), we used a complex proteomic standard (E.Coli background + UPS1 Sigma) with 4 DIA acquisition methods on an Orbitrap Fusion instrument to benchmark 6 different processing tools. Read More
For each of them, we reported the number of protein and peptide identifications, linearity and reproducibility of quantification, and sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations.We extended our work using Skyline with a newly implemented library-free functionality. In this option, DIA-Umpire is used to generate a pseudo-MGF file that can be searched with MS Amanda or MSFragger database search engines, all these steps being fully integrated in Skyline. Thus, our complex proteomic standard was used to compare library-based and library-free DIA analysis in Skyline. Finally, we applied the Skyline library-free DIA pipeline to a large-scale study of cerebrospinal fluid with the objective to define new biomarkers that could improve the diagnosis and prognosis of Alzheimer’s disease.


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Axe Xie


Axe Xie   Yixuan (Axe) Xie, Ph.D., is a postdoctoral research associate in the laboratory of Dr. Benjamin Garcia at Washington University School of Medicine in St. Louis, where he focused on developing MS-based methods to characterize protein and RNA modifications and understand their biological roles. He obtained his Ph.D. at the University of California, Davis, under the mentorship of Dr. Carlito Lebrilla. During his Ph.D., he established bioorthogonal tools to investigate the glycan-mediated interactions on the cell surface, as well as glycoproteomic and glycomic methods to monitor the cell glycosylation state during significant biological events.

Development of data-independent acquisition (DIA-MS) methods for Glycan and RNA modification analysis

The data-dependent acquisition (DDA) methods have been utilized to characterize biomolecules (such as proteins, RNAs, and glycocalyx). However, the information about low-abundant molecules is inconsistent and underrepresented. The limitations of DDA methods provide an opportunity for a recent MS technique, data-independent acquisition (DIA), to be used. DIA allows for the detection of both high and low abundant species. We demonstrate that DIA is a viable approach for mass spectrometry-based characterizations of RNA modifications and glycans, while Skyline can be a useful tool for analyzing the data yielded from these analyses.


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