High Quality Multiparametric Flow Cytometry: Seeing the Full Picture Through Full Spectrum Cytometry
Speaker: Julie Hill, Technical Application Specialist
Introduction: Full Spectrum Cytometry differs from conventional cytometry in the way that light emitted from the excitation of a particular fluorochrome is collected. Rather than collect this light at only the peak emission wavelength, full spectrum cytometry captures this light across a range of wavelengths from UV to far red (420-830 nm). This strategy makes it possible to approach multicolor flow cytometry in a more flexible way when it comes to fluorochrome choices and enables high dimension (> 20 color) high resolution flow assays with fewer lasers. The aim of this work was to optimize a 24-color panel for immunophenotyping in human whole blood.
Methods: An assay aimed at identifying the main circulating cell subsets in whole blood was designed and included 24 different markers. 53 commercially available fluorochromes were characterized in terms of spectrum signature, brightness and spread using an Aurora full spectrum cytometer equipped with 3 lasers (405, 488 and 635 nm). 24 fluorochromes that could be used in combination on the 3 laser Aurora were selected. Following the principles of panel design, a theoretical panel was created. Whole blood from normal donors was used to evaluate the panel performance, and both bead and cell controls were tested to identify optimal controls for best panel performance.
Results: Detailed analysis of the single stained controls revealed that beads were not always optimal controls as the spectral characteristics of these controls sometimes differed from cell controls. Moreover, the analysis of the initial multicolor panel showed good resolution for the great majority of the markers but needed further optimization to better resolve 3 out of the 24 markers. A second panel was designed and showed optimal resolution of all the markers in the panel. Manual and automated analysis of the data showed that all populations of interest were clearly identified.
Conclusion(s): Developing a highly multiparametric panel using a full spectrum flow cytometer proved to be a straightforward process that resulted in high resolution data. The possibility to fully assess the spectrum of each dye not only guided fluorochrome selection but was also key for successful full spectrum cytometry panel design.
Big data in single cell: explore the full potential of your high dimensional data with machine learning
Speaker: Qianjun Zhang, Sr. Application Scientist
Due to the advancement of technology in the cytometry field, nowadays, we are able to measure many parameters simultaneously on a single cell level. The dimensionality of datasets has increased from traditional 4-5 color low parameter to 10-20 color and even more with many of the advanced instruments on the market. The sample size is also getting increasingly larger in terms of the number of events and number of samples collected. Many machine learning algorithmic tools are developed for dimensionality reduction and clustering to handle this increase in dimensionality and data complexity.
In this workshop, we will present Cytobank, a cloud-based cytometry data analysis platform that enables biologists to perform machine learning algorithms like viSNE, SPADE, FlowSOM, and CITRUS at your fingertips without programming. You will explore the power of those algorithms to make discovering the next generation of biomarkers and rare cell populations easier than ever.
In this session, you will learn how each algorithm works and have a walkthrough of how to set up and interpret a viSNE analysis with real datasets and use cases to answer your biological questions. We will also touch base on data management with easy collaboration and sharing among colleagues with Cytobank.
5 Steps to Easier Panel Building
Speaker: Natalie Oxford, R&D Scientist
Abstract: Panel building usually requires many steps for optimal experimental design. In this session, learn the 5 steps for building immune cell panels and how to incorporate functional antibodies, dyes, and viability reagents. We will present sample data to show simple optimization techniques for better results.