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X-WR-CALNAME;VALUE=TEXT:Automated Deep Profiling of Human Immune Cells using Mass Cytometry
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SUMMARY:Automated Deep Profiling of Human Immune Cells using Mass Cytometry
DESCRIPTION:<p>	Snacks will be provided.  Please RSVP to <a href="mailto:nicole_paul@dfci.harvard.edu">nicole_paul@dfci.harvard.edu</a> OR <a href="mailto:zeeshan.farooq@fluidigm.com">zeeshan.farooq@fluidigm.com</a></p><p>	 </p><p>	<span style="color:#68a7bb;"><strong>Speakers:</strong></span></p><p>	<em><strong>Bruce Bagwell, MD, PhD </strong></em><strong>–</strong><strong> President, Verity Software House</strong></p><p>	<strong>Greg Stelzer, PhD – Director-Mass Cytometry Market Development, Fluidigm</strong></p><p>	<strong>Abstract</strong><strong>: </strong><span><span style="line-height:106%"><span style="color:black">The human hematopoietic cell population is a complex and dynamic system composed of morphologically and functionally diverse cell types. Mass cytometry combines the single cell analysis capability with the selectivity of mass spectrometry. Using antibodies conjugated to heavy metal isotopes, it enables quantitative analysis of high dimensional data on single cell level without confounding fluorescence overlap. With a significant increase in the number of simultaneous parameters measured, there is a need for computational tools to easily process and automate analysis of high-dimensional single-cell mass cytometry data.</span></span></span></p><p>	<span><span style="line-height:106%"><span style="color:black">In this seminar, we will introduce the <strong>Maxpar Human Immune Monitoring Panel Kit</strong>, a <strong>29-marker panel</strong> designed to characterize immune cell types human peripheral blood. We will describe a <strong>bioanalytical system developed to characterize major immune cell subsets</strong> with data acquired on a<strong> Helios Mass Cytometer (CyTOF)</strong> and paired with an <strong>automated analysis software</strong> developed by <strong>Verity</strong>. Utilizing a unique combination of probability state modeling with Cen-se’ mapping, a t-SNE based algorithm optimized for mass cytometry data, a comprehensive visualization of high parameter data is generated. The automated analysis tool first cleans the file by automatically identifying and removing cell doublets, debris and dead cells. Then it identifies major myeloid and lymphoid lineage cell types, including cells at different stages of maturation. </span></span></span></p><p>	 </p><p>	<span style="color:#68a7bb;"><strong><span style="line-height: 106%;">Sponsored by:</span></strong></span></p><p style="margin-bottom:.0001pt">	<span style="line-height:normal"><span><span><span style="color:black">LMA CyTOF Core<br>Dana-Farber Cancer Institute</span></span></span></span><br><span style="line-height:normal"><span><span><span style="color:black">450 Brookline Avenue</span></span></span></span><br><span style="line-height:normal"><span><span><span style="color:black">Boston, MA 02215</span></span></span></span><br><span style="line-height:normal"><span><span><span style="color:black">617-632-6561</span></span></span></span><br><span style="line-height:normal"><span><span>L</span></span><span><span><span style="color:black">MA-cytof.dana-farber.org</span></span></span></span></p><p>	 </p>
LOCATION:Dana-Farber Cancer Institute, Smith Building, Room 308/309, Jimmy Fund Way, Boston, MA
STATUS:CONFIRMED
DTSTART:20180223T163000Z
DTEND:20180223T180000Z
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