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High-Dimensional Flow Cytometry for Improved Data Analysis


Manual gating can lead to errors during flow cytometry data analysis and interpretation1. High-dimensional flow cytometry offers the possibility for improved visualisation and analysis.
 

What challenges do you face when analysing flow cytometry data?
 

Time: Two-dimensional gating in dotplots to spot any differences in the sample sets can be time-consuming with conventional data analysis1.


Subjectivity:
Manual gating of cells can also lead to subjectivity through individual user bias1.


Missing populations:
Additionally, populations may be hidden in complex datasets that get lost by two-dimensional gating1.


Compensation errors
2: Compensation is necessary when using panels with a large number of fluorochromes. But it is difficult to perform manually. If done incorrectly, it can lead to misinterpretation of the data.


Spreading:
Another source of error in data analysis, this phenomenon can lead to negative values3.


More on this topic: How to Improve Your Flow Cytometry Panel Design with More Accurate Spread Measurements: Webinar


High-dimensional flow cytometry may be the solution for you


What if you could:

  • Optimise existing panels with bright dyes to minimise compensation and data spreading?
  • Simultaneously detect up to 50 parameters?
  • Get a better understanding of the complex functions of the immune system with a 27-colour phenotyping panel?
  • Choose from 26 different laser wavelengths to configure the flow cytometer to your specific research needs?


More on this topic: Improved Compensation for Cytometric Data Using the FlowJo™ Software AutoSpill Algorithm


A comprehensive high-dimensional flow cytometry solution


The BD high-parameter solution for high-dimensional flow cytometry is all set to give unprecedented power to your multicolor flow cytometry experiments. The BD solution includes the BD FACSymphony™ Flow Cytometer, BD Horizon™ Brilliant Dyes and the FlowJo™ V10.5 Data Analysis Software.


It features more colours and improved dyes, allowing you to design broad-lineage and deep phenotyping panels as well as optimise existing panels.


This solution may help you get better insights into cellular compositions with unknown targets, analyse multiple expression patterns on defined cell subsets or screen new cell lineages.


Check out the results we obtained with our 27-color phenotyping panel

References

  1. Chester C, Maecker HT. Algorithmic Tools for Mining High-Dimensional Cytometry Data. J Immunol. 2015;195(3):773-779. doi:10.4049/jimmunol.1500633
  2. Drescher H, Weiskirchen S, Weiskirchen R. Flow Cytometry: A Blessing and a Curse. Biomedicines. 2021;9(11). doi:10.3390/biomedicines9111613
  3. Folcarelli R, van Staveren S, Tinnevelt G, et al. Transformation of multicolour flow cytometry data with OTflow prevents misleading multivariate analysis results and incorrect immunological conclusions. Cytometry A. 2022;101(1):72-85. doi:10.1002/cyto.a.24491

   

   

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