One-Day Course:
Date to be announced; 8:30am – 5:00pm
Dr. Reihaneh Safavisohi, Seton Hall University, South Orange, NJ
COURSE DESCRIPTION
Analyzing limited samples like clinical biopsies, forensic traces, and low-abundance biomarkers presents a demanding challenge where traditional preparation often fails due to sample loss and background noise. While macroscale techniques effectively recover proteins from milligram quantities, these methods become crippling when only microgram amounts are available. This course provides advanced, practical strategies for proteomics researchers addressing low-input workflows. Participants will explore workflows tailored for micro-to-nanogram scales, emphasizing targeted enrichment and nanoparticle-based approaches. We focus on the bottom-up pipeline, addressing low-input samples, hydrophobic membrane proteins, and post-translational modifications. Attendees will learn to optimize recovery, improve clean-up, and maximize sequence coverage.
WHO SHOULD ATTEND
This course is designed for analytical chemists, proteomics scientists, and interdisciplinary life science researchers in academia and industry seeking to optimize micro-scale sample preparation workflows. It is particularly valuable for researchers handling restricted or precious materials, ranging from clinical biopsies and small-scale biological samples to forensic traces and environmental samples, where quantity is the limiting factor. Whether you are a student troubleshooting sample loss or an experienced investigator optimizing workflows for complex proteins and PTMs, the methodologies presented are broadly applicable. If your goal is to maximize data quality and sensitivity from microgram-level inputs in pharmaceutical, biotechnology, clinical, food chemistry, and forensic research settings, this course is for you.
TOPICS
- Challenges of Micro-scale Proteomics
a. Defining the “Doing More with Less” hurdle in modern research
b. Identifying sources of sample loss: surface adsorption and chemical interference
c. Transitioning from macroscale to micro-to-nanogram quantities
d. Understanding the limitations of conventional precipitation and preparation methods - Advanced Workflow Strategies
a. Optimizing recovery for limited biological samples
b. Implementing nanoparticle-based approaches for targeted enrichment
c. Minimizing contamination while enhancing clean-up efficiency
d. Single-pot and integrated preparation techniques (e.g. S-Trap, SP3, etc.) to minimize transfer loss - Applications Across Complex and Diverse Matrices
a. Extraction and analysis of hydrophobic and membrane proteins
b. Strategies for identifying post-translational modifications (PTMs)
c. Case studies: clinical biomarkers, forensic evidence, and trace-level analysis - Maximizing Method Performance
a. Improving sequence coverage and reproducibility
b. Diagnosing and troubleshooting low-visibility workflow failures - Open Discussion and Questions Encouraged Throughout the Course
ABOUT THE INSTRUCTORS
Dr. Reihaneh Safavisohi is an Assistant Professor in the Department of Chemistry and Biochemistry at Seton Hall University, where she leads the SAFAVI Lab. With nearly 13 years of experience as a bioanalytical chemist, her research integrates analytical chemistry, nanotechnology, and biochemistry to develop innovative platforms for the enrichment and identification of novel disease biomarkers. Her current research specifically focuses on the proteome profiling of extracellular vesicles for ovarian and colon cancer detection. Her research has made significant contributions to the field, garnering over 3,900 citations to date. Prior to joining Seton Hall University in 2023, Dr. Safavisohi completed postdoctoral fellowships at the University of Notre Dame’s Harper Cancer Research Institute and Michigan State University. She has extensive expertise in proteomics, mass spectrometry, and high-throughput data analysis. The SAFAVI Lab is dedicated to advancing micro-scale sample preparation and training the next generation of scientists in multidisciplinary research. Her work addresses the “doing more with less” challenge by optimizing analytical workflows for limited biological inputs.
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