Translating Pre-Clinical Research to Clinical Patient Care™

JMSACL Logo
Educational Grant Partners: Why no logos? CME.
Golden West Diagnostics


Brian Kelly

SCIEX


Short Courses (In-Person)

All times are PACIFIC TIME.

February 23 is the deadline to register if you wish to receive printed course material on-site.

The Short Courses for MSACL 2024 will be held only In-Person.

Short courses will be from 8-16 hrs.

Continuing Medical Education (CME) and CE credit will be offered.

You MUST be registered for the conference in order to attend a Short Course. Short Courses have an additional fee.

Short Course Pricing (per HOUR):

EarlyBird
Jan 17, 2024
Regular
Feb 23, 2024
After
Feb 23, 2024
Industry$41/h$47/h$57/h
Academic$33/h$36/h$39/h
Student$12/h$16/h$19/h

All courses are IN-PERSON only.

All short courses are being processed for Continuing Medical Education (CME) AMA PRA Category 1 Credit™. The amount of credit assigned to each course may be slightly less than the contact hours stated due to hourly breaks.

*Note : You can take more than one course if they don't overlap. Use segment times listed under the course to coordinate.

Course Offering

1. Clinical Proteomics 201 : Clinical Proteomics
March 18 Monday 08:00 - March 19 Tuesday 12:00 (12 contact hours)
Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

2. Clinical Proteomics 202 : MS-based Precision Diagnostics by Molecular Protein Analysis
March 18 Monday 08:00 - March 19 Tuesday 12:00 (12 contact hours)
Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

3. Data Science 101 : Breaking up with Excel: An Introduction to the R Statistical Programming Language
March 17 Sunday 14:30 - March 19 Tuesday 12:00 (16 contact hours)
Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

4. Data Science 201 : Flexing with R : Databases to Dashboards
March 18 Monday 14:00 - March 19 Tuesday 12:00 (8 contact hours)
Segment 1 : Monday 14:00 - 18:00 (4 h)
Segment 2 : Tuesday 08:00 - 12:00 (4 h)

5. Data Science 203 : Machine Learning : A Gentle Introduction
March 17 Sunday 14:30 - March 19 Tuesday 12:00 (16 contact hours)
Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

6. GlycoProteomics 101 : Clinical Glyco(proteo)mics by Mass Spectrometry
March 18 Monday 08:00 - March 19 Tuesday 12:00 (12 contact hours)
Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

7. LC-MSMS 101 : Getting Started with Quantitative LC-MSMS in the Diagnostic Laboratory
March 17 Sunday 14:30 - March 19 Tuesday 12:00 (16 contact hours)
Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

8. LC-MSMS 201 : Practical LC-MS/MS Method Development and Bioanalytical Method Validation for Clinical and Non-Clinical Samples
March 17 Sunday 14:30 - March 19 Tuesday 12:00 (16 contact hours)
Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

9. LC-MSMS 203 : Validation of Quantitative LC-MS/MS Assays for Clinical and Academic Use
March 18 Monday 08:00 - March 19 Tuesday 12:00 (12 contact hours)
Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

10. LC-MSMS 302 : Advanced LC-MS/MS Method Development, Method Troubleshooting and Instrument Operation Needed in Developing Successful Methods for Molecular identification and Quantitation in the Clinical Lab
March 17 Sunday 14:30 - March 19 Tuesday 12:00 (16 contact hours)
Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

11. Lipidomics 101 : Mass Spectrometry-based Lipidomics and Clinical Applications
March 18 Monday 14:00 - March 19 Tuesday 12:00 (8 contact hours)
Segment 1 : Monday 14:00 - 18:00 (4 h)
Segment 2 : Tuesday 08:00 - 12:00 (4 h)

12. Metabolomics 102 : Microsampling and Mass Spectrometry – Fit for Purpose in the Clinical Screening and Monitoring Space
March 17 Sunday 14:30 - March 18 Monday 12:00 (8 contact hours)
Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)

13. Metabolomics 203 : Practical Bioinformatics and Statistics in Metabolomics
March 18 Monday 14:00 - March 19 Tuesday 12:00 (8 contact hours)
Segment 1 : Monday 14:00 - 18:00 (4 h)
Segment 2 : Tuesday 08:00 - 12:00 (4 h)

14. Sample Preparation 201 : Sample Preparation and Alternative Matrices for LC-MS Assays
March 18 Monday 08:00 - March 19 Tuesday 12:00 (12 contact hours)
Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

Course Summaries

Short Course : Clinical Proteomics 201 : Clinical Proteomics
@ Steinbeck 3 (Conference Ctr > 2nd Floor)

Andy Hoofnagle, MD, PhD
University of Washington

Cory Bystrom, PhD
Ultragenyx

Christopher Shuford, PhD
Labcorp


Course Schedule

Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 12.00

---------------

Pre-requisites

None.

Overview

The main goal of this course is to provide an interactive forum in which attendees will be introduced to critical aspects of clinical protein measurements.

The topics of this course will be templated on the framework of CLIS guidance document, C64: Quantitative Measurement of Proteins and Peptides by Mass Spectrometry.

The motivation for using mass spectrometry to quantify proteins in clinical research and in clinical care will be discussed as part of this interactive workshop. Technical topics uniquely affecting quantitative protein and peptides measurements by mass spectrometry will be a point of emphasis. Case studies from assay inception through validation will be presented and participants will work interactively to critique various aspects of clinical proteomic measurements.

Topics Covered

  1. Protein vs Peptide Measurands
  2. Workflows
  3. Sample Preparation (Digestion & Enrichment)
  4. Internal standards
  5. Calibration
  6. Validation
  7. Quality control

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Describe the holistic process of delivering a clinically relevant mass spectrometry based protein/peptide assay from inception to validation.
  2. Recognize the factors in assay development that are unique to proteins and peptides in comparison to traditional small molecule assays.
  3. Use guidance documents in conjunction with rigorous experimental design to support fit-for-purpose method development strategies.
2307
Short Course : Clinical Proteomics 202 : MS-based Precision Diagnostics by Molecular Protein Analysis
@ Ironwood 1 (Portola Hotel > 3rd Floor)

Renee Ruhaak, PhD
LUMC

Mirjam Kruijt, MSc
LUMC

Esther Reijnders, MSc.
LUMC


Course Schedule

Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 12.00

---------------

Pre-requisites

A background in quantitative proteomics is helpful but not required. You will need to know the principles of LC and QQQ analysis through multiple reaction monitoring.

Overview

Download Full Syllabus Here

Did you know proteins may exist in hundreds of molecular proteoforms? And that each specific proteoform may have different functionality, potentially leading to a pathophysiological clinical phenotype ? How could we measure such proteoforms using mass spectrometry? And how could measurement of proteoforms aid in precision diagnostics?

In this course, we will explain what proteoforms are, and why they may be relevant to measure in a medical laboratory. We will use real-lab examples of proteoforms known to affect the patients’ health status and guide you through the potential methods on identifying and characterizing proteoforms with multiple-reaction-monitoring MS. We will start the course with the rationale on when and how to develop new diagnostic tests. We will explain the diversity in proteoforms, with a focus on proteoforms caused by mutations, but we will also touch upon PTM-induced proteoforms. Lastly, we will discuss several quality related aspects of these tests. In the end, our aim is to provide the knowledge necessary to apply proteoform analysis by MS in your own (clinical) laboratory.

The course will consist of theoretical background, examples of applications and interactive sessions. A background in quantitative proteomics is helpful but not required. You will need to know the principles of LC and QQQ analysis through multiple reaction monitoring. At the end of the course, you will know why molecular protein analysis could be beneficial and how you can apply it in your laboratory.

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Discuss what proteoforms are and why they may be relevant to quantify.
  2. Discuss how the analysis of proteoforms will contribute to precision diagnostics and how clinical care pathways may be altered based on molecular protein measurements.
  3. Discriminate proteoforms using multiple-reaction-monitoring mass spectrometry.
  4. Evaluate molecular MS data and provide answers for laboratory specialists
  5. Ensure performance and quality of proteoform-based tests.
2310
Short Course : Data Science 101 : Breaking up with Excel: An Introduction to the R Statistical Programming Language
@ Colton 1/2 (Conference Ctr > 2nd Floor)

Dustin Bunch, PhD, DABCC
Nationwide Children's Hospital

Nicholas Spies, MD
Washington University in St. Louis / Barnes-Jewish Hospital


Course Schedule

Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 16.00

---------------

Pre-requisites

There will be some handouts and software installation before the meeting but no pre-req classes.

Overview

Does Excel lag on you when you open a file bigger than 1000 rows? Has it ever changed your data to a date against your will? Are you ready to jump right past Tableau and into the world of Data Science using a real programming language?

Well, your wait is over because at MSACL we again will be offering a course for complete programming newbies that will help you get going analyzing real data related to LC-MS/MS assay development, validation, implementation and publication.

The only background expected is the ability to use a spreadsheet program. The skills that you will acquire will allow you to take advantage of the many tools already available in the R language and thereafter, when you see that your spreadsheet program does not have the capabilities to do what you need, you will no longer have to burst into tears.

The course will be run over two days and time will be evenly split between didactic sessions and hands-on problem solving with real data sets. Drs Bunch & Spies will adopt a “no student left behind policy”. Students will be given ample time to solve mini problems taken from real-life laboratory work and focused on common laboratory tasks. All attendees will need to bring a laptop with the R language installed RStudio interface installed. Students may use Windows, Mac OSX or Linux environments. Both R and RStudio are free and open-source. No cash required.

Students should be prepared for learning what computer programming is really like. This may involve some personal frustration, but it will be worth it.

Obtaining the Software

!!! DOWNLOAD PROGRAM PACKAGES PRIOR TO ARRIVAL ONSITE !!! THERE WILL NOT BE OPEN INTERNET WIFI IN THE CONFERENCE CENTER.

!!! POWER : Make sure your computer is charged to hold power for 4-8 hrs, as power outlets may not be available.

Instructions for installing the R language are here: http://cran.r-project.org/

Instructions for installing RStudio are here: http://www.rstudio.com/

Topics Covered

  1. Brief overview of RStudio, R variables: vectors (numerical, character, logical), matrices, data frames and lists and classes: numeric, character, list and changing between them
  2. Importing data from CVS and Excel
  3. Dealing with non-numeric instrument data & manipulating and cleansing your data
  4. Exporting data in Excel-like format or to share
  5. Basics of tidyverse: dplyr, filter, mutate, join
  6. Regression: ordinary least squares, Passing Bablok, Deming, weighted regression
  7. Non-linear regressions
  8. Looping: Doing things repeatedly
  9. group_by and summarize
  10. Making highly customized figures with base plot or ggplot
  11. Putting it all together projects:
    -- Preparing method comparison regression and Bland Altman plots
    -- Preparing mass spectrometry data for upload to LIS

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Manage and analyze data in the R programming language using RStudio.
  2. Identify resources to continue learning the R programming language.
  3. Develop computational scripts in the R programming Language.
  4. Use both base R and tidyverse tools for data cleansing and data manipulation.
  5. Develop an algorithmic approach to common laboratory data processing needs.
  6. Prepare publication quality figures using ggplot.
2285
Short Course : Data Science 201 : Flexing with R : Databases to Dashboards
@ Colton 3 (Conference Ctr > 2nd Floor)

Shannon Haymond, PhD
Northwestern University Feinberg School of Medicine

Patrick Mathias, MD, PhD
University of Washington


Course Schedule

Segment 1 : Monday 14:00 - 18:00 (4 h)
Segment 2 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 8.00

---------------

Pre-requisites

An introductory R course (including MSACL Data Science 101) and/or experience using R for data analysis.

Overview

Do you have data files that you would like to accumulate over time into an organized, accessible format and then visualize different aspects of the combined data in an interactive, web-based dashboard?

If so, this course is for you!

Reproducibility is an important principle for making data analysis trustworthy and reliable. Automation enables users to scale their data analysis steps. The R programming language is one of many tools that can help users automate data analysis workflows while adopting best practices in reproducibility, but there are several packages to choose from when developing these skills.

In this short course we will introduce a combination of workflows, packages, and tools that help learners set up data analysis projects, develop pipelines for extracting and storing data, and then develop interactive visualizations to gain understanding from the data.

First, we will orient learners to reproducible document formats such as R Markdown and Quarto, emphasizing how data analyses can be communicated effectively.

Next, we will do a crash course on relational databases such as SQLite, which can be powerful tools for storing and accessing data at scale.

We will then tie together concepts in iteration and automation to develop the basics needed to set up a data ingestion pipeline.

In the last portion of the course, we align these concepts with interactive visualization tools to develop an automated dashboard.

This short course will be interactive, with frequent short exercises to reinforce new concepts. Familiarity with the R programming language, either from an introductory course or self-learning, is required to participate in the exercises.

Finally, concepts in this short course overlap material taught in previous intermediate R courses at MSACL, but here we will focus putting together the tools to develop reproducible, automated dashboards for visualization of laboratory data and provide updates to include some of the latest developments in the R ecosystem.

Obtaining the Software

!!! DOWNLOAD PROGRAM PACKAGES PRIOR TO ARRIVAL ONSITE !!! THERE WILL NOT BE OPEN INTERNET WIFI IN THE CONFERENCE CENTER.

!!! POWER : Make sure your computer is charged to hold power for 4-8 hrs, as power outlets may not be available.

Instructions for installing the R language are here: http://cran.r-project.org/

Instructions for installing R Studio are here: http://www.rstudio.com/

Topics Covered

  1. Reproducible workflows using computational notebooks
  2. Organizing data in relational databases
  3. Reading files and iterating
  4. Tools to automate routine tasks
  5. Flexing with some sweet dashboards

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Utilize best practices for reproducible data analyses.
  2. Configure a database within R and load data into it.
  3. Automate tasks such as file reading.
  4. Create web-based dashboards.
  5. Implement packages available in R to organize data into relational databases, automate routine tasks, and create web-based dashboards.
  6. Apply learned skills to organize data analysis projects reproducibly using tools such as Quarto, dashboards, and databases.
2287
Short Course : Data Science 203 : Machine Learning : A Gentle Introduction
@ Bonsai (Portola Hotel > Ground Floor)

Randall Julian, PhD
Indigo BioAutomation

Stephen Master, MD, PhD, FADLM
Children's Hospital of Philadelphia


Course Schedule

Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 16.00

---------------

Pre-requisites

Data Science 101 or 201 (or equivalent experience)

Overview

Machine learning techniques have been highly successful in driving the growth of companies like Amazon, Google, Netflix, and other companies that rely on identifying patterns in big data. More importantly, these algorithms are beginning to revolutionize clinical diagnosis and mass spectrometry, from FDA-approved retinal image analysis to robust detection of mass spec chromatographic peaks.

But ... what exactly is machine learning? How does it work? How can you apply it to your own data?

In this course, we will help you sort through the hype and provide an introduction to machine learning, including an overview of common approaches, known pitfalls, and other important concepts.

We will include practical instruction on applying machine learning algorithms using the R statistical language, so familiarity with R at the level of the material taught in Data Science 101 and/or 201 is desirable.

Topics Covered

  1. What is machine learning?
  2. Basic practices
  3. Exploring your data
  4. Preparing your data for ML algorithms
  5. Features: Selection and Engineering
  6. Decision trees
  7. Model evaluation
  8. Solutions to overfitting: Ensembles
  9. Random Forests
  10. Explaining complex models
  11. Gradient Boosting with XGBoost

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Explain principles of machine learning
  2. Describe machine learning processes
  3. Perform classification using multiple machine learning models
  4. Evaluate and test the performance of machine learning models
2294
Short Course : GlycoProteomics 101 : Clinical Glyco(proteo)mics by Mass Spectrometry
@ Cottonwood 2 (Portola Hotel > 3rd Floor)

Tamas Pongracz, PhD
Leiden University Medical Center

Guinevere Lageveen-Kammeijer, PhD
University of Groningen


Course Schedule

Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 12.00

---------------

Pre-requisites

Knowledge on basic mass spectrometry and spectral interpretation.

Overview

Did you ever encounter glycans, but you -kind of- neglected them as they seemed too complicated to characterize? Or did you just perform a glycan release to make the analysis of your protein a lot easier? You have no idea how to interpret your data when a glycan is present? Fear no more! We are here to provide you with the basics in the field of mass spectrometric glycomics and glycoproteomics.

The course will start with a historical overview on glycan research (i.e. how did glycans work their way up to being acknowledged as important study objects) and we will guide you through the maze of different nomenclatures. Moreover, although glycans are well known for their complexity, we will reveal to you the “rules of glycan structures” based on known biosynthetic pathways. This will be followed by an in-depth discussion on glyco(proteo)mic mass spectrometric technologies and workflows. In addition, different sample preparation steps and data analysis approaches will be covered. We will close-up with a session about glycomic biomarker discovery.

The course will run over two days and time will be split between lectures and workshops (e.g. how do you recognize a glycan in a mass spectrum and how do you assign it). While not everything can be covered within these two days we will ensure that you will know your “glyco-basics” in the end. Moreover, participants are encouraged to submit any specific glyco-questions they have prior to the course and we will try to discuss them during the course.

Objectives:

At the conclusion of this short course, the participant will be able to:

  1. Discuss glycan nomenclature and biosynthesis
  2. Select an appropriate analytical method for a specific glycomics research question.
  3. Interpret mass spectrometry data using the biological background of a sample and the biosynthetic restrictions of the system.
  4. Define the identity of released glycan and glycopeptide molecules using MS1 and MS2 data.
  5. Select the appropriate software tools to aid glyco(proteo)mics MS data processing knowing the used analytical platforms.
2315
Short Course : LC-MSMS 101 : Getting Started with Quantitative LC-MSMS in the Diagnostic Laboratory
@ De Anza 2 (Portola Hotel > Ground Floor)

Grace van der Gugten, B.Sc. Chemistry
Alberta Precision Laboratories

Deborah French, PhD, DABCC (CC, TC)
UCSF

Jacqueline Hubbard, PhD, DABCC
Three Rivers Diagnostics

Lorin Bachmann, PhD, DABCC
VCU Health System


Course Schedule

Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 16.00

---------------

Pre-requisites

Interested in a detailed, practical introduction to clinical quantitative LCMS

Overview

Is your laboratory under pressure to purchase an LC-tandem MS or is the ROI you wrote last year haunting you now? This short course is designed for attendees implementing quantitative LC-tandem MS for patient testing who have laboratory medicine experience but no mass spectrometry training - CLS bench analysts, supervisors, R&D scientists, and laboratory directors. Theoretical concepts necessary for a robust implementation of clinical mass spectrometry will be presented – but the emphasis is on practical recommendations for:

  1. LC-MS/MS system purchasing, site preparation and installation
  2. Choosing internal standards, solvents, and water, making reagents and calibrators
  3. Selecting and optimizing LC parameters
  4. Selecting and optimizing MS/MS parameters
  5. Selecting and optimizing sample preparation
  6. Adjusting sample preparation, LC and MSMS parameters to achieve the desired assay performance
  7. Establishing data analysis & review criteria
  8. Pre-validation stress testing and method validation
  9. Maintaining quality in production
  10. Preventative maintenance and troubleshooting

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Describe the components of a triple quadrupole mass spectrometer and describe how they work.
  2. Evaluate sample preparation options for LC-MS/MS and explore matrix effect validation experiments.
  3. Explain the importance of developing an LC gradient method that is compatible with their analyte(s) of interest.
  4. Outline MS parameters that need optimization, including source and compound specific parameters.
  5. List quantitation and review criteria options for LC-MS/MS data.
  6. Formulate a validation plan and describe how to execute those experiments for an LC-MS/MS assay.
  7. Appraise equipment options and justify the purchase cost.
2270
Short Course : LC-MSMS 201 : Practical LC-MS/MS Method Development and Bioanalytical Method Validation for Clinical and Non-Clinical Samples
@ Ironwood 2 (Portola Hotel > 3rd Floor)

Perry Wang, PhD
LC-MS Technical Expert


Course Schedule

Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 16.00

---------------

Pre-requisites

LC-MSMS 101

Overview

Both LC-MS method development and bioanalytical method validation play a crucial role for successfully conducting regulatory studies including nonclinical, biopharmaceutics, and clinical pharmacology studies. The objective of this course is to offer practical training for practitioners, physicians, laboratory scientists, industrial scientists, and health care professionals in the clinical laboratory. It focuses on practical LC-MS method development and bioanalytical method validation for clinical and non-clinical samples. It takes participants step-by-step through the concepts and techniques to develop and validate bioanalytical methods. After this course, participants will be able to independently develop and validate their own LC-MS methods and apply the validated methods for their routine clinical and non-clinical studies.

Objectives:

At the conclusion of this short course, the participant will be able to:

  1. Describe the role of high-performance liquid chromatography (HPLC)
  2. Discuss the principle of HPLC
  3. Apply the resolution equation for chromatographic separation
  4. Develop and optimize HPLC methods
  5. Discuss HPLC troubleshooting cases
  6. Illustrate the principles of mass spectrometry (MS)
  7. Describe Atmospheric pressure ionization (API) in mass spectrometry
  8. Distinguish common ionization modes for MS: ESI, APCI, APPI and MALDI
  9. Develop MS methods
  10. Discuss preparation of clinical samples for LC-MS analysis
  11. Apply the FDA’s Bioanalytical Method Validation Guidance
  12. Describe how clinical sample collection, handling, and storage affect the reliability of the data
  13. Prepare for the challenges of assaying clinical samples by LC-MS
  14. Apply validated methods for clinical and pre-clinical studies
2273
Short Course : LC-MSMS 203 : Validation of Quantitative LC-MS/MS Assays for Clinical and Academic Use
@ De Anza 3 (Portola Hotel > Ground Floor)

Claire Knezevic, PhD
Johns Hopkins University

Joshua Hayden, PhD, DABCC, FACB
Norton Healthcare


Course Schedule

Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 12.00

---------------

Pre-requisites

Individuals with previous mass spectrometry experience (clinical or academic) or those who have taken the LC-MSMS 101 and/or 201 course and are looking to expand their knowledge.

Overview

This course is intended for those with previous mass spectrometry experience who are looking to expand their knowledge and skills with regards to assay validation for both clinical and academic purposes. The course will heavily focus on quantitative small molecule assays.

The course will provide a short overview of development followed by an in-depth discussion of how to validate liquid chromatography tandem mass spectrometry assays. The course will conclude with a discussion of the measures and metrics to use for monitoring assay performance once testing is live.

Throughout each section, applicable and practical guides for validation experiments and acceptance criteria will be provided, as well as processes for ensuring assay performance post-go-live. For each step of assay development, we will highlight experiments to perform along the way to identify issues pre-validation. Validation studies will include an overview of the studies necessary for both clinical and academic purposes. The clinical validation requirements for CLIA, CAP, NY State, and FDA regulated environments will be presented. The academic validation requirements for submitting such assays (or studies using them) to high-impact, peer-reviewed journals (Clinical Chemistry, Molecular & Cellular Proteomics, Journal of Clinical Endocrinology and Metabolism, etc) will be presented. Post-go live monitoring will include discussion of essential performance metrics, performing staff competency, minimizing manual data entry and how to facilitate interfacing with LIS, and finally a discussion of post-go-live issues.

Topic Covered

This short course will include 12 approximately 1 hour modules with 15 min for exercises and Q&A at the end of each module.

  1. Optimizing signal/tuning
  2. Chromatography
  3. Internal standard
  4. Reportable range
  5. Calibration and calibrators
  6. Matrix effect studies
  7. Stability studies
  8. Precision studies
  9. Accuracy and correlation studies
  10. Going live
  11. Performance metrics for post-go-live monitoring
  12. Discussion of post-go-live issues

Objectives:

At the conclusion of this short course, the participant will be able to:

  1. Design a validation plan for a target assay.
  2. Define performance characteristics for the intended use of the assay.
  3. Identify and address potential pitfalls in the developed assay.
2277
Short Course : LC-MSMS 302 : Advanced LC-MS/MS Method Development, Method Troubleshooting and Instrument Operation Needed in Developing Successful Methods for Molecular identification and Quantitation in the Clinical Lab
@ Steinbeck 2 (Conference Ctr > 2nd Floor)

Robert Voyksner, PhD
LCMS Limited


Course Schedule

Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)
Segment 3 : Monday 14:00 - 18:00 (4 h)
Segment 4 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 16.00

---------------

Pre-requisites

Working knowledge analytical chemistry, including experience with LC separations and/or mass spectrometry. Attending level 100 or 200 LC/MS courses at MSACL would be beneficial. This is a course for those who want to increase their understanding of LC-MS/MS, who want to learn how to develop a successful quantitative and qualitative LC-MS/MS assay and a deeper understanding the technique to achieve better sensitivity, specificity or throughput in their laboratory.

Overview

This course is designed for the scientist who uses LC-MS/MS in the clinical lab, who wants a deeper understanding in steps towards developing successful methods, optimizing methods, trouble shoot methods and solving problems employing LC-MS/MS.

The course covers important aspects in understanding and optimization ionization with electrospray on multiple instrument platforms including triple quadrupole, time-of-flight, quadrupole time of flight and orbit trap mass analyzers.

The course will discuss sample preparation, modes of chromatography and MS/MS considerations with respect to method development and optimization for the analysis of “real-world” samples by LC-MS/MS, to achieve the best sensitivity, specificity, and sample throughput.

This course focuses on method development method troubleshooting and application for the analysis of both small and large molecules that are clinically relevant. All examples are taken from real world analyses, performed by Dr. Voyksner at LCMS Limited. The concepts presented in the course are reinforced through numerous problem sets the attendees will work on throughout the 16 hour course.

The last part of the course is an open forum where each attendee is invited to share a current LC-MS/MS issue they face. As a class we work through potential solutions and experiments to be performed to find a solution to the student problem, applying the concepts taught in the class and Dr. Voyksner’s 40 plus years of experience in LC-MS/MS. From past classes this has been the attendee’s favorite part of the class.

Topics Covered

  1. Understanding API ionization processes for electrospray, APCI and APPI, what affects the ionization process and how to maximize the ionization for compounds of interest.
  2. Understanding the effects of LC columns (dimensions and particles size), flow rate, and mobile phases have upon the separation and LC/MS analysis.
  3. Determining the type of ions that can form by electrospray and APCI, how to interpret the MS and MS/MS spectra and approaches on how to perform qualitative analysis in LC-MS/MS and high-resolution MS/MS.
  4. Understanding important issues that affect quantitative analytical results and how to optimize the method to achieve the best performance, reduce matrix suppression, reduce background and generate the best accuracy and precision.
  5. Exploring what new techniques are available (e.g. direct analysis MS, chip method and MS instrumentation) that can improve the results one can obtain.
  6. Discuss aspects of method development and method trouble shooting from example problems of real world problem in quantitative LC-MS/MS.
  7. Open forum discussing attendees’ specific problems they face in method development or analysis using LC-MS/MS.

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Improve sensitivity and specificity for LC-MSMS analysis.
  2. Develop methods to analyze the target compounds.
  3. Select correct electrospray or APCI conditions to analyze the target compound.
  4. Reduce matrix suppression.
  5. Troubleshoot a method to improve accuracy, precision, sensitivity and specificity.
  6. Reduce background in LC-MSMS analysis.
2281
Short Course : Lipidomics 101 : Mass Spectrometry-based Lipidomics and Clinical Applications
@ Redwood 1 (Portola Hotel > 3rd Floor)

Anne K. Bendt, PhD
Singapore Lipidomics Incubator (SLING), National University of Singapore

Amaury Cazenave Gassiot, PhD
Singapore Lipidomics Incubator (SLING) and Department of Biochemistry, National University of Singapore

Michael Chen, M.D., C.M.
University of British Columbia


Course Schedule

Segment 1 : Monday 14:00 - 18:00 (4 h)
Segment 2 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 8.00

---------------

Pre-requisites

LC-MS/MS, clinical translation, lipidomic applications, method harmonization AND an interest in lab medicine and clinical lipidology.

Overview

This one-day course is meant to (1) create awareness for the importance and therefore potential value of lipid testing beyond cholesterol and triglycerides for future clinical applications. We will (2) then outline currently available technologies and their respective opportunities and challenges, and (3) discuss candidate molecules in the context of current case studies.

Topics Covered

  1. Looking beyond cholesterol and TAG:
    - Potential of blood-based lipid testing
    - Gain an understanding of the universe of lipids, how they are intricately linked to biology and their implications in health and diseases (e.g., inherited genetic disorders, cardiovascular disease, clinical nutrition, etc.)
    - Identify physiologically relevant candidate lipids for adoption by the clinical community, for future studies towards establishing clinical utility
  2. Current lipidomics R&D workflows:
    - Path of translation from R&D laboratory-style methods towards robust and quantitative assays with appropriate turnaround times
    - Pre-analytics (sampling requirements, plasma vs serum, storage, etc.)
    - Analytics (i.e., batches, internal standards, lipid extractions, direct infusion vs LC-MS and LC-MS/MS, quality assurance)
    - Post-analytics (raw data processing, lipid annotations, quality control, quantification)
    - Ongoing harmonization efforts
  3. Case studies of markers that have advanced to clinical settings
  4. Outreach and Engagement between the analytical scientist specialized in mass spectrometry of lipids, the clinician researcher and laboratory medicine as the end user are key to the development of impactful/ useful lipidomics in clinical applications

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Discuss the lipid universe beyond cholesterol and triglycerides,
  2. Explain what lipid molecular species are.
  3. Describe the process of biomarker validation and implementation in clinical labs and how the analysis of lipid metabolites will contribute to precision diagnostics.
  4. Describe how to measure lipid metabolites using multiple-reaction-monitoring mass spectrometry.
  5. Evaluate the performance and quality of lipid metabolite-based tests.
  6. Review molecular MS data and provide answers for laboratory specialists.
2312
Short Course : Metabolomics 102 : Microsampling and Mass Spectrometry – Fit for Purpose in the Clinical Screening and Monitoring Space
@ Cottonwood 1 (Portola Hotel > 3rd Floor)

Donald Chace, PhD, MSFS, FACB
Capitainer

Tim Garrett, PhD
University of Florida College of Medicine


Course Schedule

Segment 1 : Sunday 14:30 - 18:30 (4 h)
Segment 2 : Monday 08:00 - 12:00 (4 h)

Total Contact Hours: 8.00

---------------

Pre-requisites

None.

Overview

The classic dried blood spot (Guthrie Spot, NBS spot) has been used routinely for 60 years in inborn errors of metabolism (rare disease screening of newborns) space in addition to health monitoring of the detected disorders. Mass spectrometry advanced this space 30 years ago to take advantage of its multianalyte profiles (the early days of metabolomics) to detect rare diseases. It was the introduction of multiple biomarkers in clinical assessment. As mass spec evolved in this space, new workflows and pre-analytical methods, sample preparation chemistry was altered by taking advantage of the dried microsample format and extraction chemistry. DBS offered a replacement to liquid microsamples, and the risks and costs associated with infectious disease exposure, cost of shipping using the cold chain, storage and most recently patient centered sampling where remote, or home sampling is made possible.

Most chemistry workflows are still dominated by liquid blood or plasma and immunoassay platforms, they are not necessarily suitable for microsample collection as demonstrated in the choice for newborn screening (200-300 µL) versus 1-10 mL for a venous blood draw. Furthermore, a dried microsample offers better improved stability for some molecules due to degradation of active enzyme, light or heat. Beyond newborn screening standard, the pace of adoption of dried blood versus liquid plasma is slowed because of the lack of bridging studies. Therefore, an understanding of DBS versus liquid is critical in designing these experiments. This course will describe the advantages of filter paper for mass spec workflows in areas of sample cleanup, extraction, manipulation as well as examples of successful analysis. We will provide examples of existing methods in use in clinical analysis and will expand upon last year’s MSACL course.

As important are its advantages, we will discuss limitations from the lack of precision of classic Guthrie cards because of volume uncertainties to the problems of some mass spectrometry analysis of molecules like proteins. Finally, we will correlate these issues with the ever-expanding area of metabolomics, lipidomic and more important how a DBS can be integrated with other technique like molecular and immunoassays to provide a better clinical result from which the clinician can make earlier accurate diagnosis. Ultimate DBS can improve health care services as well as access with remote collection.

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Describe the best fit for DBS utilization in clinical mass spectrometry and bioanalytical research including “omics” applications.
  2. Discuss bridge strategies for adopting existing MS methods that utilize venous blood, plasma or other liquids to the dried microsample format.
  3. Compare the advances in the quantitative micro sampling space and discuss issues with volume and solid matrix additives.
  4. Interpretate approaches for multiplexed analysis and multi-omics.
2302
Short Course : Metabolomics 203 : Practical Bioinformatics and Statistics in Metabolomics
@ Cottonwood 1 (Portola Hotel > 3rd Floor)

Tim Garrett, PhD
University of Florida College of Medicine


Course Schedule

Segment 1 : Monday 14:00 - 18:00 (4 h)
Segment 2 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 8.00

---------------

Pre-requisites

Basic understanding of the field of metabolomics and LC-MS.

Overview

Metabolomics refers to the comprehensive measurement of small molecules in biofluids by either mass spectrometry (MS) or nuclear magnetic resonance (NMR) with the aim of covering multiple KEGG pathways, exposome products, and chemical reactions to provide new insights into disease etiologies. MS based metabolomics generally requires the use of liquid chromatography to separate metabolites based on polarity and high-resolution MS to accurately measure the mass-to-charge (m/z). The combination of retention time and m/z accuracy provides a reliable method to identify metabolites, which is critical for making disease marker discoveries. Understanding how data is generated is key to understand how to process data. This short course will instruct attendees on bioinformatics components to data processing in metabolomics with hands on instruction using an open source software package. This short course will also discuss basic principles of statistical analysis with hands experiences provided.

Topics Covered

  1. Introduction to metabolomics science
  2. Experimental design for success in metabolomics
  3. Measuring quality in Metabolomics
  4. Data processing in metabolomics using MZmine 2.53 (open source and platform independent)
    -- This is an older version of MZmine, but useful to first use in a data processing work flow. Works with Mac and Windows.
    -- A laptop with MZmine 2.53 preloaded is not a requirement, but you can follow along with the instruction using your own laptop if available
    -- Data to work with will be provided
  5. Statistical analysis using Metaboanalyst, online statistical analysis package for metabolomics
    -- Step by step tutorial
    -- Data will be provided for students to go through the steps on their own followed by a discussion and additional walkthroughs
  6. Open session at the end for discussion and additional help to students in data processing and statistical analysis

Objectives

At the conclusion of this short course, the participant will be able to:

  1. Describe experimental design in metabolomics.
  2. Manipulate data from LC-HRMS metabolomics analysis including software to process data (bioinformatics).
  3. Describe statistical analysis in relation to metabolomics data.
  4. Perform metabolomic data processing using MZmine 2.53
  5. Perform statistical analysis using Metaboanalyst.
2304
Short Course : Sample Preparation 201 : Sample Preparation and Alternative Matrices for LC-MS Assays
@ Redwood 2 (Portola Hotel > 3rd Floor)

William Clarke, PhD, MBA, DABCC
Johns Hopkins University School of Medicine

Mark Marzinke, PhD, DABCC, FAACC
Johns Hopkins University School of Medicine


Course Schedule

Segment 1 : Monday 08:00 - 12:00 (4 h)
Segment 2 : Monday 14:00 - 18:00 (4 h)
Segment 3 : Tuesday 08:00 - 12:00 (4 h)

Total Contact Hours: 12.00

---------------

Pre-requisites

Individuals with previous mass spectrometry experience looking to expand their knowledge.

Summary:

This course will encompass various sample preparation approaches used for LC-MS assays. The course will highlight not only the importance of sample processing in the clinical laboratory environment, but also illustrate the “fit for purpose” application of processing techniques in clinical mass spectrometry. This course will also discuss the theory behind different specimen preparation methods, strengths and weaknesses of each approach, as well as opportunities for automation. The first section of the course will serve as a primer of the role of upfront sample management, utilizing examples in blood and urine specimen sources. There will also be an introduction to the application of LC-MS approaches in alternative matrices. The second section of the course will elaborate on the foundations established in the first half, and expand into newer technologies and automated alternatives for sample processing. Topics will be covered through lecture, Q&A, Case Studies, and small group exercises.

Topics covered include

  • Pain points in clinical LC-MS
  • Overview of specimen processing in laboratory medicine
  • Off-line and On-line sample processing
  • Analysis of blood and urine
  • Alternate body fluid specimens (e.g. CSF, breast milk, tissue, etc.)
  • Dried specimens as matrices
  • Automation of sample processing
Learning Objectives

After attending this short course, participants will be able to:

  1. Describe various pain points and challenges in clinical LC-MS;
  2. Discuss the impact of various specimen preparation approaches on LC-MS assay performance;
  3. Implement a fit-for-purpose approach to selection of a specimen preparation approach in their laboratory practice;
  4. Describe alternative specimen types and their potential utility in clinical practice or research.
2298