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The February HUPOST is now available. See the latest info about the HUPO 2023 Congress, ECR and ETC activities, and more!
The January HUPOST is now available – it's a huge issue - chock full of news and information including farewell and welcome messages from HUPO Presidents, an ETC webinar, ECR Initiative recap of HUPO 2022, job opportunities and much, much more!
I am truly honoured and grateful to accept the role of HUPO President for the term 2023-2024. Thank you for the confidence placed in me and the entire HUPO Executive Committee (EC) team to continue to build our legacy. I also want to acknowledge the extraordinary leadership provided by Yu-Ju Chen and the previous HUPO Executive. Their work and dedication, particularly with the Strategic Plan, has moved the association into a new era. Together with the EC, I look forward to serving the HUPO membership and advancing a number of HUPO activities and initiatives.
HUPO boasts a number of distinctive strengths, namely the powerful passion and tireless work of its volunteers on various committees and initiatives with diversity and inclusion. These individuals together with HUPO’s management team, have realized a number of significant accomplishments over the past seven years including a 38% increase in financial assets, a 143% increase in membership, 122% increase in Industrial Advisory Board (IAB) members, highly successful virtual and in-person annual congresses, the digital HUPOST newsletter, implementation of the Proteomics Knowledge Resource (PKR), use of social media platforms, the establishment of the Early Career Researcher (ECR) Initiative and much more.
In looking to the future and how we can continue to build on our accomplishments, a Strategic Plan Working Group completed a thorough review of HUPO and focused on new aspirations that would create a better HUPO in the future. The most important aspirational goals were prioritized with the following major initiatives being developed to help attain these goals:
Every day, I am impressed by this extraordinary community that nurtures a culture of mutual care and international pride, and is dedicated to making life better for everyone who is touched by the work of thousands of individuals involved with proteomics. Each day, I see the brilliance, passion and commitment of so many and am inspired to continue strengthening and building this organization so it continues to flourish well into the future. HUPO has a remarkable story to tell, and I am eager to share it far and wide. I hope you will join me in continuing to propel HUPO and the proteomics community forward.
Jennifer Van Eyk
HUPO President 2023-2024
It was truly a great honor and an inspiring journey to serve as the HUPO President in the past two years. Looking back at the period of 2021-2022, the COVID-19 global pandemic continuously caused economic and social disruption, and seriously interrupted research projects, restricted travel and closed the borders of many countries, thus creating challenges for the congress organization and financial sustainability of HUPO.
“If you want to go fast, go alone. If you want to go far, go together." ~ African Proverb
When I started this unexpected journey two years ago, I was indeed full of worry if I was able to continue the previous great leadership in order to move forward and achieve transformative changes for the future of HUPO. I sincerely appreciate the great mentorship from past President Steve Pennington and past Vice President Susan Weintraub. At that time, I shared my firm belief that joint wisdom and collaborative efforts always inspire new ideas with success beyond the expectation of a single mind. It is truly admirable how passionate and committed many of the volunteers on HUPO committees and Council have been to the organization. The HUPO Executive Committee, all Committees, Council and HPP Executive Committee actively maintained the activities, expanded promotion worldwide and continued the research of the human proteome. For the benefit of members, HUPO continues to create new resources including the Proteomics Knowledge Resource (PKR), proteomics tutorial videos and stories from HUPO members. It is nice to see that HUPO is approaching a stable status with 1100+ members from 57 countries and 36 national societies.
Despite the pandemic challenges, somehow we learned to find a way to adapt to the environmental stress. Following the first HUPO Connect 2020 virtual congress, we had another successful virtual HUPO ReConnect 2021 with a record number of 1300 attendees. After two virtual meetings, we were finally able to meet each other and enjoy a great scientific program and fantastic social networking at the first in-person HUPO Congress in beautiful Cancun. A big thank you to Serio Encarnación-Guevara and Luis Manuel Teran who lead the Congress Organization Committee and the ICS team. Most importantly, it is the active participation of our members and great support from the industry sponsors who contributed to the congress's success with 900+ attendees.
Under the supervision of HUPO Treasurer Peter Hoffmann and joint efforts from past congress organizers, industry sponsors, HUPO members as well as the support from regional/national societies, available budget was directed to support future HUPO activities. The HUPO EC approved utilizing the available budget to formalize a way of supporting its committees and initiatives through funding requests.
In 2021, we celebrated HUPO’s 20th anniversary. To move forward with a modern version to advance proteome knowledge and accelerate proteomics research in the next 10 years, one of the most important tasks we completed in 2022 was the launch of the HUPO Strategic Plan. I take this opportunity to thank Secretary-General Henning Hermjakob, facilitator Ian Wright and the working group with representatives from HUPO EC, Committees, and the three regions who provided the final recommendations for education/training, increasing HUPO visibility, creating resources, improving funding, and external networking. Translating proteomics into utility, such as in the clinical community, is not a trivial issue. What we can really do for the next generation research, is to bridge both HUPO and the clinical community and partner with policy. The HPP Grand Project, aiming to explore the function of every protein, is the new phase of the HUPO Proteome Project and it is a great linkage to outreach to the global biomedical community.
Despite many successes in proteomics technology and discovery, there remains much to be done to demonstrate the value of proteomics to create benefits in our lives, such as clinical care, prevention medicine, precision agriculture, and food safety. I continue to encourage all HUPO members and everyone who loves proteomics from different parts of the world, to work together and create successful proteomics stories in the future.
Finally, I want to express my most sincere appreciation to Henning Hermjakob who has been the major driver to promote HUPO affairs, Chelsea Prangnell and Michele O’Bright from ICS to maintain the operation of the HUPO office during my term. I am also delighted to welcome the new leadership of President Jennifer Van Eyk and the new HUPO Executive Committee. In the Chinese zodiac, Rabbit is the symbol for 2023, representing smartness, speed, momentum and longevity. With Jennifer’s signature warmth, energy and passion, and the launch of the HUPO Strategic Plan, I would like to express my best wishes and look forward to fruitful achievements and growth of the HUPO community in 2023 and beyond.
HUPO Past President, 2021-2022
The HUPO Education and Training Committee (ETC) presents: The Essence of Data Visualization: How to Create Effective Figures of Your Data
Date/Time: January 31, 2023 - 16:00-17:30 (Central European Time/Zurich)
Stimulated by a huge audience and feedback after the first ETC Auditorium on proteomics and academic writing, we are happy to announce our second speaker, Martin Krzywinski.
Martin Krzywinski works in bioinformatics, data visualization, science communication and the interface of science and art. He applies design, both data and artistic, to assist discovery, explanation and engagement with scientific data and concepts. Martin’s information graphics have appeared in the New York Times, Wired, Scientific American and covers of numerous books and scientific journals such as Nature and Genome Research. He is going to elaborate on how small changes to critical elements can turn a muddled figure into one that is clear and concise.
More about Martin: https://www.bcgsc.ca/martin-krzywinski-msc
Submit your figures and posters to Martin’ s dropline for redesign and discussion. The more you send in, the better. There are no bad figures, only figures waiting to be made better:
The December HUPOST is now available – See the latest Congress information, news from ECR and MOC, a Humans of HUPO profile, research article, and much, much more!
Written by Dr. Edward Lau and Dr. Rob Beynon
"A mouse has a new liver every few days, but the lifespan of a mouse liver cell is hundreds of days” - Dr. Rob Beynon
In 1965, Robert Schimke led a team that examined the response of rat liver tryptophan oxygenase (then called tryptophan pyrrolase, the first enzyme in the breakdown of the gluconeogenic amino acid tryptophan) to glucocorticoids and to feeding of the substrate, tryptophan [ref 1]. – both treatments led to elevated enzyme levels in the liver. They concluded that glucocorticoids induced synthesis of the enzyme, whereas dietary tryptophan prevented degradation - clear evidence of the importance of the two ‘opposing’ processes of synthesis and degradation controlling the intracellular abundance of an enzyme. This was, parenthetically, one of the first examples of a connection between the transcriptome (controlling synthesis) and the metabolome (controlling degradation).
This example serves to illustrate that despite the high energetic cost of making and degrading proteins, the proteome is in a dynamic state of renewal. Even in the steady state, where the protein abundance is constant, new proteins are synthesised and at the same time, the pool is commensurately depleted through degradation. This process of protein turnover can account for a sizeable proportion of the energy budget. The rate at which any protein is replaced must have evolved through natural selection; some proteins are replaced with minute time scales, others are essentially static through the life of the individual. Moreover, the rate of protein replacement is not the same in different tissues, nor is it the same in different species – smaller mammals, with a higher basal metabolic rate replace their proteins at much higher rates than larger mammals. Indeed, the high rate of protein turnover in small mammals may be a way to elicit thermogenesis.
Although the phenomenon of protein turnover has been described since the pioneering work of Schoenheimer eight decades ago, our knowledge of how it is regulated in homeostasis and disease and how it contributes to the anatomy and physiology of the proteome has remained lagging. Early studies, mostly using radioisotopes, could only measure turnover of total protein, and the goal of measurement of individual protein turnover rates seems unattainable
In recent years this has changed. Advances in separation science and mass spectrometry delivered the ability to resolve proteins, peptides or amino acids labelled with stable isotopes.
We and others would argue that we need to understand the scope and scale of intracellular protein turnover at the level of the proteome – it is likely that the subtlety seen by Schimke and colleagues is manifest in many other biological systems. Many protein turnover studies have been conducted with mammalian cells in culture. With this experimental system, it is relatively straightforward to introduce label isotopologs of essential amino acids into the cell culture media to trace their incorporation into proteins (dynamic SILAC approaches).
Although many insights have been gained from such studies in vitro, it is increasingly evident that turnover of proteins in cell culture is very different from that in large, intact adult animals. In rapidly growing cells protein synthesis is driven by high rates of cell proliferation, with doubling times of a day or less. By contrast, in intact animals the doubling rate of cells is measured in hundreds of days (for example, the mouse hepatocyte). At such a low proliferation rate, protein abundance cannot be adjusted by cell number expansion (with commensurate dilution), and individual proteins can be expected to be replaced in time frames that sit within the lifespan of the cell, ranging from minutes to that lifespan. To avoid proteotoxic aggregates, damaged proteins will need to be removed by carefully regulated degradation, rather than simply diluted to daughter cells. These two differences between cells in culture and in tissues require us to adopt different approaches and analytical strategies.
To properly understand the dynamics of the proteome, whether in steady state or in flux states, we need to measure protein turnover rates in intact animals. But, the convenience of a simple, instantaneous medium change no longer exists, and whole animal labeling studies require a different approach, confounded by difficulties of isotope administration and reutilisation of labeled amino acids for new protein synthesis. Moreover, turnover can only feasibly be accessed through measurement of synthesis by tracking incorporation of stable isotope labels. It is almost obligatory to measure the rate of synthesis of a protein through isotope incorporation, administered over days, months or years.
Quantification of protein turnover in animals is further complicated by the slow precursor availability in the tissue of interest after the isotopic label is administered. For example, labeled proteins or amino acids supplied in the diet have to move through the digestive system, cross the intestinal mucosal barrier, pass through the hepatic system and be transported to peripheral tissues in the blood. Any delay in appearance of labeled amino acids in the precursor pool of a peripheral tissue would interfere with measurement of the true turnover rate. By contrast, heavy water ([2H]2O) crosses tissue barriers much more quickly. Thus, these two labelling methods could yield different apparent turnover rates for the same protein. But can the two approaches be brought to convergence? Intuitively, one would expect high turnover proteins to be most affected by slow precursor equilibration, and this is indeed the case.
In a recent study [ref 2], we compared two common methods of measuring protein turnover in animals either using heavy labeled amino acids in diet or provision of [2H]2O in drinking water. The two strategies differ in precursor availability and metabolism, as well as the mass spectral features of peptides following label incorporation. Our question was very simple: what were the optimal data analysis strategies and when applied, do the two methods yield comparable turnover rate results?
We set up mouse labelling studies in which the only significant variable was the labeling protocol. Two groups of adult mice of identical strain, sex, age, maintained with identical husbandry, were labeled either with [13C6]lysine or heavy water ([2H]2O) over about a month. Animals were sampled at different times over this period, and the proteomes of the heart, liver, kidney, and skeletal muscle were analyzed by mass spectrometry. These tissues differ in their median turnover rate, allowing our analysis to extend over a broader range (liver and kidney are higher than heart, and in turn all three are higher than skeletal muscle). To ensure consistency in data processing, one of us (EL; https://ed-lau.github.io/riana) wrote Riana, new Python software that quantitates peptide labelling, recovers isotope abundance as a function of labelling time and fits these data to recover the first order rate of replacement, equivalent to steady state half-life.
Because heavy water is known to rapidly equilibrate across tissues and compartments, it would give data closest to the ground truth at least where label utilization is concerned. This experimental design therefore allows us to use water data as a reference for optimizing the analysis of lysine labelling data.
As anticipated, with [13C6]lysine labeling there was a delay in precursor equilibration in all tissues. Simple exponential models of protein turnover are compromised by this delay, and the rate of turnover of high turnover proteins is underestimated. This can be corrected by using a suitable two-compartment kinetic model that also models the delay in the precursor pool, the protein turnover rate constant (kd) and the precursor availability rate constant (kd).
Surprisingly, finding suitable kp values to adjust for labeling delay was not straightforward. Although the ratio of [13C6]lysine vs. [12C6]lysine can be measured in a tissue by LC-MS, an empirical sampling of tissue lysine isotope enrichment over time led to an apparent underestimation of the true precursor pool. Moreover, the best strategy for finding kp is dependent on the tissue being examined. Global parameter optimization method can effectively find the best kp value that explains the data sets in slow turnover tissues but is less effective in high turnover tissues. Therefore although the complications of slow precursor equilibration can be overcome with the proper strategy, careful considerations must be given based on the tissue and animal under study.
Such complications make a compelling case for heavy water as a turnover label in intact animals. Heavy water is inexpensive, virtually all peptides demonstrate isotopic incorporation, and the speed with which water equilibrates in the body mitigates complications due to precursor pool delay and reutilisation. That being said, heavy water is not without drawbacks. The pathways of heavy water labeling of different essential and non-essential amino acids is incompletely understood, and measurement of isotope incorporation in precursor spectra is more prone to errors and isobaric contamination in the mass spectra.
Accurate measurement of whole animal, proteome-wide protein turnover is still difficult, and there are several largely unresolved issues.
● What are the specific considerations in data analysis strategy when using different labeling protocols?
● Do existing analytical workflows and software packages give comparable turnover rates and profiles when analyzing a common data set?
● Each protein yields multiple peptides, and the rate of labeling of each peptide yields a measure of turnover. How are these data aggregated - is it better to combine all peptide data and fit once curve, or fit each peptide data individually? If the latter, are there objective criteria that can be applied to eliminate outlier turnover values?
● What are the optimal practices for error estimation in turnover measurements, and statistics in comparing turnover across conditions (e.g., homeostasis vs. disease)?
● Is it possible to establish a set of 'gold standard' turnover rates in different tissues and in different species?
We’d like to propose that those who are interested in this challenge bring together their knowledge and expertise in a community effort to resolve some of these questions. Should anyone be interested they are invited to contact us for further information.
Reference 1: Schimke et al. 1965 PMID 14253432
Reference 2: Hammond et al. 2022 PMID 35636728
The goal of the “Stylish Academic Writing” professional development webinar series is to help students and trainees improve their scientific writing skills. The inaugural webinar was presented by Professor John Yates III from the Scripps Research Institute. Professor Yates is Editor-in-chief at the Journal of Proteome Research (JPR). This webinar covered different aspects of scientific writing, including Professor Yates’ personal approach to writing, how to make a perfect figure, and even how to manage disputes over authorship. In China alone, the webinar attracted nearly 3,000 online viewers even though it was aired close to midnight.
The title of Professor Yates’ presentation was "Write, Write, Write." Throughout the 45-minute presentation, Professor Yates shared several important tips including:
• Develop a daily writing habit and try to publish often
• Write manuscripts in small sections and paragraphs a little at a time
• Don’t blindly accept changes. Instead, improve your writing by reviewing the stylistic and grammatical reasons behind each edit
• Focus on the discussion section, which shows how your results advance the current understanding in the field
• When choosing a journal to submit to, aim high, but choose appropriately
• Seriously consider reviewer criticism to improve the quality of your paper
Professor Yates noted that writing is like working out in the gym. The more you do it, the stronger you get. As such, trainees should try to write often and publish everything they accomplish, no matter how small. Professor Yates discussed three pillars of academic writing: mechanics, style, and content.
With regards to mechanics and style, Professor Yates suggested that trainees read and pay attention to the writing of others. Proper grammar is essential to getting your message across and incorrect grammar can detract from your scientific content and can cause challenges to the review process. He recommends taking classes in English technical writing, using a copyeditor or software such as Grammarly, and to write frequently. He recommended “The Elements of Style” by Strunk and White as an excellent writing handbook.
Professor Yates discussed scientific content with respect to publishing in JPR. JPR prioritizes exciting, groundbreaking science, where studies are welcome from a wide swath of proteomic and metabolomic research. Publishable research requires proper statistical design for quantitative experiments. For this, he suggested reviewing a review article in JPR by Oberg and Vitek from 2009 (DOI: 10.1021/pr8010099). Studies should include enough samples and sufficient controls to properly power the experiment. Validation of biological experiments (including computational modeling) should be performed using experimental methods on independent patient or biological samples.
This session included an extended Q&A session with the audience, guided by Drs. Justyna Fert-Bober (Cedars Sinai, USA), Tiannan Guo (Westlake University, China) and Brian Searle (Ohio State University, USA). A full video recording of the session including the Q&A session is available on the HUPO Proteomics Knowledge Source website (https://pkr.hupo.org/).
Yun-En Chung1 and Mathieu Lavallée-Adam1
1Department of Biochemistry, Microbiology and Immunology and Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
Mass spectrometry-based proteomics data analysis has never been more exciting. The combination of computational hardware improvements and a wide diversity of instruments and experimental techniques has created a gigantic playground for computational researchers and software developers. In recent years, one attraction at this playground has gained a lot of attention from both academia and industry: real-time analysis of proteomics data.
In a traditional mass spectrometry-based proteomics experiment, tens of thousands of mass spectra are collected for a biological sample. After the conclusion of the experiment these mass spectra are then inputted into software packages for peptide and protein identification and quantification. Hence, since biological information is inferred solely through post-hoc analysis, the mass spectrometry experiment is mostly running blind and does not adapt in real-time based on the biological data that is being acquired.
Improvements in computational hardware and the recent availability of Application Programming Interfaces (APIs) enabling mass spectrometry data analysis during proteomics experiments have paved the way to the design of a new family of algorithms and software packages performing the real-time analysis of mass spectrometry data. Tools such as the Thermo Fisher Scientific Instrument API (IAPI)1 and the Bruker Parallel database Search Engine in Real-time (PaSER)2 are enabling the design of analyses that accelerate data processing, help diagnose problems with instrumentation and enhance the characterization capabilities of mass spectrometry.
One of the early modern applications of real-time mass spectrometry data analysis is the on-the-fly quality assessment of mass spectrometry experiments. Instrument performance drop or malfunction are often only identified after post-hoc data analysis. Such a late discovery results in a waste of time and resources that are used to acquire unusable or subpar data. The QC-ART approach3 has been developed to evaluate instrument performance in near real-time and allow for immediate intervention. QC-ART ensures consistent high-quality data collection and the rapid detection of instrumentation problems.
Since the early beginnings of mass spectrometry-based proteomics, data acquisition remained an extremely active research topic. Still today, new acquisition techniques are being developed to supplement the current families of approaches including data-dependent acquisition4, data-independent acquisition5 and targeted methods6,7. Traditionally, an instrument would apply the same acquisition strategy (precursor ion selection algorithm, scan window size, …) for the entirety of an experiment. This standard acquisition method works reasonably well in common proteomics use cases. However, since the instrument does not consider the biological relevance of the data it is acquiring, a significant proportion of this data does not translate into meaningful biological discoveries.
An excellent example of this is how real-time database search for peptide identification can support the selection of peptides for quantification with isobaric labeling. It was previously shown that MS3 spectra lead to more accurate quantification using tandem mass tag reporter ions than MS2 spectra8. However, acquiring MS3 spectra is resource intensive. It is therefore important to acquire MS3 spectra for data that is biologically relevant. Orbiter was therefore developed to identify peptides in real-time from MS2 spectra with a database search method9. Orbiter then only acquires MS3 spectra from MS2 spectra that yield a confident peptide identification and therefore optimizes resource usage for protein quantification.
Other groups developed software packages to identify peptides in real-time10,11, while McQueen et al. presented a pseudo real-time approach that paused the experiment to adjust future data acquisition based on such peptide identifications. Inspired by these methods, our team proposed that the real-time identification of peptides and proteins can be used to guide mass spectrometry data acquisition in order to optimize resource usage and maximize protein identifications. Indeed, our computational approach, named MealTime-MS12, uses real-time database search to identify peptides and supervised learning to assess the confidence of protein identifications. MealTime-MS then uses confident protein identifications to generate an exclusion list preventing the acquisition of tandem mass spectra for peptide ions that are expected to belong to proteins that were already identified in the mass spectrometry run. MealTime-MS showed that up to 33% of the mass spectra collected in traditional experiments could be safely ignored with minimal losses of proteins identified compared to standard experiments and that these mass spectra could be repurposed for the identification of additional proteins.
Alternatively, real-time analysis of mass spectrometry data has demonstrated its utility in targeted proteomics. In a typical targeted proteomics experiment, specific elution time windows need to be determined for targeted peptides. Due to run-to-run technical variation, the size of these scheduled windows must be kept relatively large to ensure the instrument encounters these peptides, thereby limiting the number of possible targets. MaxQuant.Live presented a solution via real-time recognition of precursor ions13. The algorithm uses the retention time, mass-to-charge ratio, and intensity of the precursor ions encountered to predict and therefore select those that should be targeted for quantification. This approach enabled the targeting of over 25,000 peptides in a single mass spectrometry run.
Real-time analysis of mass spectrometry-based proteomics data has also demonstrated its clinical applications. Devices such as the MasSpec Pen demonstrated how a small handheld device can be used to rapidly detect features including lipids, metabolites and proteins in human tissue. Such features can be used as biomarkers to diagnose in real-time whether tissues are cancerous or healthy.
After reading about these applications, we would like you to join the conversation on Twitter by answering our poll question here and letting us know where the future of real-time proteomics data analysis sits:
In which area do you think real-time analysis of mass spectrometry-based proteomics data will have the greatest impact in the future:
Figure 1. Graphical representation of the traditional mass spectrometry-based proteomics pipeline, where acquired data is analyzed after the completion of the experiment and of a pipeline integrating real-time data analysis to adjust mass spectrometry data acquisition during the experiment.
Computer Icon created by Freepik - Flaticon: https://www.flaticon.com/free-icons/course.
Yun-En Chung is an undergraduate student in Translational and Molecular Medicine and researcher in Dr. Mathieu Lavallée-Adam’s lab at the University of Ottawa. His research focuses on the development of software packages to guide mass spectrometry experiments in real-time to improve data acquisition efficiency. His publication on the real-time identification of proteins in mass spectrometry data was recognized as the best paper from a Master’s or Undergraduate student at the Ottawa Institute of Systems Biology in 2020. He also received several awards for his presentations, including a 2nd place for his oral presentation at the Undergraduate Research Opportunities Program Seminar day at the University of Ottawa and an honorable mention for his poster at the American Society for Mass Spectrometry annual conference in 2022. Yun-En’s research is funded by awards from the Natural Sciences and Engineering Research Council of Canada and Mitacs.
Mathieu Lavallée-Adam is an Associate Professor at the University of Ottawa in the Department of Biochemistry, Microbiology and Immunology and is affiliated to the Ottawa Institute of Systems Biology. He obtained a B.Sc. in Computer Science and a Ph.D. in Computer Science, Bioinformatics option, from McGill University and performed his postdoctoral research at The Scripps Research Institute. His research focuses on the development of statistical and machine learning algorithms for the analysis of mass spectrometry-based proteomics data and protein-protein interaction networks. Dr. Lavallée-Adam is a recipient of the John Charles Polanyi Prize in Chemistry, rewarding the impact of his bioinformatics algorithms on the mass spectrometry community and was named Early Career Researcher of the Year by the Ottawa Institute for Systems Biology in 2021. He is also Co-Chair of the HUPO Early Career Researcher Initiative and a member of the HUPO Executive Committee, in which he develops training activities and advocates for junior investigators in proteomics and organize events highlighting their research on the international stage.
1. Scientific, T. F. Thermo Fisher Scientific IAPI GitHub. (2022). Available at: https://github.com/thermofisherlsms/iapi.
2. Bruker. PaSER 2022. (2022).
3. Stanfill, B. A., Nakayasu, E. S., Bramer, L. M., Thompson, A. M., Ansong, C. K., Clauss, T. R., Gritsenko, M. A., Monroe, M. E., Moore, R. J., Orton, D. J., Piehowski, P. D., Schepmoes, A. A., Smith, R. D., Webb-Robertson, B.-J. M., Metz, T. O. & TEDDY Study Group. Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data. Mol. Cell. Proteomics 17, 1824–1836 (2018).
4. Liu, H., Sadygov, R. G. & Yates, J. R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–201 (2004).
5. Venable, J. D., Dong, M.-Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).
6. Kuhn, E., Wu, J., Karl, J., Liao, H., Zolg, W. & Guild, B. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 4, 1175–86 (2004).
7. Anderson, L. & Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–88 (2006).
8. Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–40 (2011).
9. Schweppe, D. K., Eng, J. K., Yu, Q., Bailey, D., Rad, R., Navarrete-Perea, J., Huttlin, E. L., Erickson, B. K., Paulo, J. A. & Gygi, S. P. Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics. J. Proteome Res. 19, 2026–2034 (2020).
10. Bailey, D. J., Rose, C. M., McAlister, G. C., Brumbaugh, J., Yu, P., Wenger, C. D., Westphall, M. S., Thomson, J. A. & Coon, J. J. Instant spectral assignment for advanced decision tree-driven mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 109, 8411–6 (2012).
11. Graumann, J., Scheltema, R. A., Zhang, Y., Cox, J. & Mann, M. A framework for intelligent data acquisition and real-time database searching for shotgun proteomics. Mol. Cell. Proteomics 11, M111.013185 (2012).
12. Pelletier, A. R., Chung, Y.-E., Ning, Z., Wong, N., Figeys, D. & Lavallée-Adam, M. MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion. J. Am. Soc. Mass Spectrom. 31, 1459–1472 (2020).
13. Wichmann, C., Meier, F., Winter, S. V., Brunner, A.-D., Cox, J. & Mann, M. MaxQuant.Live enables global targeting of more than 25,000 peptides. bioRxiv 443838 (2018). doi:10.1101/443838
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