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NIST launched the Flow Cytometry Standards Consortium to accelerate the adoption of quantitative flow cytometry in biomanufacturing of cell and gene therapies.
The consortium has two active working groups for
- instrument standardization via Equivalent Number of Reference Fluorophores (ERF) beads, and
- assay standardization to enable more comparable and quantitative assays commonly used for cell and gene therapy characterization and release.
NIST will establish a third working group focused on data analysis.
The purpose of this workshop is to learn about the state-of-the-art in flow cytometry data analysis tools and procedures, identify pressing gaps and challenges, as well as opportunities for collaborative solutions. The expected outcome of the meeting is a summary that defines the work of the consortium.
Click here to download the workshop agenda.
WORKSHOP Objectives
- Provide an overview of the NIST Flow Cytometry Standards Consortium’s work to date and future
- Have multiple companies outline their data analysis software’s unique capabilities
- Discuss other data analysis considerations
Slides
- NIST Consortium Working Groups and Interlaboratory Studies Overview and Goals, Lili Wang, Paul DeRose and John Elliott, NIST
- Company presentations
- Enabling Biomarker Data Science in the Cloud, Daniel Crichton, JPL
- Standardization and Interoperability Aspects of BD Cytometry Software, Josef Spidlen, BD Bioscience
- Making Machine Learning-assisted Data Analysis Accessible on the Cytobank Platform, Giulia Grazia, Beckman Coulter Life Sciences
- Utilizing FCS Express to Standardize Cytometry Analysis Protocols and Consolidation of Reporting Results, Sean Burke, De Novo Software
- Automation-Assisted Flow Cytometry Analysis with TASBE Flow Analytics, Jake Beal, Raytheon BBN
- The Roles of Uncertainty Quantification in Cytometry, Paul Patrone, ITL/NIST
- Advancement in Bioanalytical Techniques to Improve Cell Therapy Product Quantification - Heba Degheidy, CBER/FDA
- ISAC Perspectives on Cytometry Data Reporting and Transparent Analysis – Jonathan M. Irish, ISAC