Immune Monitoring Laboratory at Dartmouth-Hitchcock Medical Center

News

CMS - 1.6.8 - Pouebo

Yeti

27-color ZE5 flow cytometer (Bio-Rad)

formerly known as the Yeti (Propel Labs)

Yeti

The ZE5 flow cytometer is a 27-color flow cytometer.  For more information on the capabilities of the ZE5, click here.  Intuitive EVO software provides automated fluorescence compensation, a fluorochrome selector panel, and a runlist design wizard. Use this software on your computer to pre-configure the instrument settings, staining panels, and samples.  Integrated training modules, and the ability to analyze files while acquiring saves time and streamlines your workflow.  EVO software is PC-specific and is obtainable at no charge from DartLab.

ZE5 optical bench

Each graph depicts a single laser and shows the band width for each detector.  Be aware of inter-laser spectral overlap in detectors with similar band widths.

Filter sets for Yeti lasers

 

We have designed this template to download and enter fluors when designing a staining panel:

Yeti staining panel template

UV Laser Fluors

BUV395

Alexa fluor 350 (AF350), DAPI, Hoechst-Blue, Indo-hi

BUV496, Indo-lo

Do not use BUV563.

BUV661

BUV737, BUV 805, Hoechst-red

 

Yeti UV laser filtersViolet laser fluors

BV421, cascade blue

BV450, pacific blue

BV510, AMCyan, pacific orange, cascade yellow

BV605

BV650

BV711

BV786

Filters for 405 nm laserBlue laser fluors

Brilliant Blue 515, FITC, AF488, eGFP, eYFP

PE, PE-Dazzle, PE-CF594, PE-Texas red (more useful off yellow laser)

PerCP-Cy5.5 but no not use PerCP (this is an excellent dump PMT)

PE-Cy7 (more useful off yellow laser)

Yeti blue laser fluorsYellow laser fluors

PE

dTomato, DsRed

PE-Dazzle, PE-CF594, PE-Texas red

mPlum

PE-Cy5, PE-AF647

PE-Cy5.5

PE-Cy7

Yeti yellow laser fluorsYeti red laser fluors

APC, AF647, Cy5

APC-R700, AF680, AF700, DRAQ5, Cy5.5

APC-Fire750, APC-Cy7, APC-H7, AF750

AF790

Yeti red laser fluors

Data Analysis

'The high-throughput nature of flow cytometry, combined with the increasing capacity to measure more cell parameters at once, is generating massive and high-dimensional datasets on a routine daily basis. These data can no longer be adequately analysed using the classical, mostly manual, analysis techniques and therefore require the development of novel computational techniques, as well as their adoption by the broad community. Computational flow cytometry is a new discipline that straddles the fields of immunology and computational biology and provides a set of tools to analyse, visualize and interpret large amounts of cell data in a more automated and unbiased way.' Saeys et al., 2016.

To assist with high dimensional flow cytometry data analysis, DartLab has a Cytobank Premium license available for use (ask DartLab).

References

Start here with this excellent slide deck by Jonathan Irish.

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data (Diggins et al., 2015)

Characterizing cell subsets using marker enrichment modeling (Diggins et al., 2017)

Computational flow cytometry: helping to make sense of high-dimensional immunology data (Saeys et al., 2016)

Here's a viSNE tutorial from Cytobank.  Use this for analysis.