Infinicyt™ Flow Cytometry Data Analysis Software
APS: Automatic n-dimensional separation of clusters
File Merge: Analysis of multiple parameters simultaneously
Reference Image: Compare new samples with previous events
Compass: Identification of similarities and differences between samples
Maturation Tool: Advanced evaluation of maturation patterns
Batch Analysis: Automated gating and analysis strategies
Automatic Population Separator (APS)
The APS feature, based on principal component analysis, generates automatic n-dimensional separation of sample clusters for further characterization.
APS analyzes all parameter combinations to display the best separation of cell clusters. It also provides information on which of these parameters are the most important for optimal separation, helping indicate if new analysis panels should be developed or improved.
Figure 1: APS diagram displays all events separated by PCA and allows for the correct identification of different populations.
File merge allows the user to link information from different FCS files into one single file for a more complete analysis and a parameter band dot plot can be used to evaluate all parameters at the same time.
Infinicyt™ Flow Cytometry Data Analysis Software also provides a level of quality control that confirms the selected population is equally represented in all of the files for the common parameters.
Figure 2: A parameter dot plot displays the complete results of an experiment composed of multiple single tubes.
Reference images can be displayed as dot plots or density lines with corresponding statistical data and allows the user to compare previously stored events with the current sample being analyzed.
This tool is useful for comparing normal or abnormal marker expression, maturation patterns, sample results vs control/reference results and evaluating minimal residual disease (MRD) in research laboratories. For MRD evaluations, several images can be saved at different time points for monitoring purposes.
Figure 3: A reference image of an MRD sample at day 0 (red dots) compared with follow up (blue dots).
Infinicyt™ Flow Cytometry Data Analysis Software also features the Compass function which aids users in identifying similarities or differences between sample populations.
Several files of reference group results can be merged into a database and used to compare against a new case study file. Databases can also be shared with other users for multi-lab comparisons as long as the same reagent panels and SOPs are used.
The Compass pointer was designed to provide an instant and intuitive observation of results where a user can quickly verify whether a new case study compares or contrasts with reference files stored in the database.
Figure 4: Compass diagram with relevant comparatives for group assigning.
The Maturation tool introduces a brand new approach for studying maturation patterns and offers an easy solution for assessing the evolution of multiple markers in a single population.
It is especially useful in the study of maturation defects such as myelodysplastic syndromes (MDS). Since these syndromes comprise a heterogeneous group of clonal hematological disorders that can affect one or more bone marrow myeloid cell lineages, a tool for correctly identifying the stage and cell lineage involved is beneficial. When using a maturation database, the user can quantitatively evaluate multiple markers and how they affect the maturation blockage.
Calculate Data Module
The Calculate Data module incorporates powerful algorithms that give a precise and reliable calculation of marker expression. Information from different aliquots is integrated into the same file allowing for the parameters to be compared. It is used for cells with the same immunophenotype.
Multiple actions can be configured at the same time for high-throughput analysis of files: apply analysis strategies, export statistics, export reports, export files with a different configuration, apply compensation and compare files with a selected database using Compass.
Diagrams & Representations
Infinicyt™ Flow Cytometry Data Analysis Software offers many types of high definition diagrams that allow the user to fully customize data presentation. By choosing diagram configurations, the user can select exactly which population is to be displayed at any point of analysis.
- Multidimensional 3D Diagrams
- Box Plots
- Parameter Band Plots
Resources & References
ALPCO’s guest speaker, Roberto Juanes Juanes, from Cytognos S.L. discusses features of the Infinicyt™ program and illustrates how the software provides new capabilities for easier, faster and more sensitive analysis of data generated by any flow cytometry instrument.
Further information can also be viewed at www.infinicyt.com.
1. Sánchez-Abarca LI et al. Uptake and delivery of antigens by mesenchymal stromal cells. Cytotherapy. June 2013.
2. Pedreira CE et al. Overview of clinical flow cytometry data analysis: recent advances and future challenges. Trends in Biotechnology. June 2013.
3. O’Donnell E.A. et al. Multiparameter flow cytometry: advances in high resolution analysis. Immune Network. April 2013.
4. Sandes AF et al. Association of MDS with CD5+, CD23+ monoclonal B-cell lymphocytosis. Clinics (Sao Paulo). Dec 2012.
5. van Dongen JJ et al. EuroFlow: Resetting leukemia and lymphoma immunophenotyping. Basis for companion diagnostics and personalized medicine. Leukemia. Sept 2012.
6. van Dongen JJ et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia. Sept 2012.
7. Dekking EH et al. Flow cytometric immunobead assay for fast and easy detection of PML-RARA fusion proteins for the diagnosis of acute promyelocytic leukemia. Leukemia. Sept 2012.
8. Kalina T et al. EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols. Leukemia. Sept 2012.
9. Matarraz S et al. The proliferation index of specific bone marrow cell compartments from MDS is associated with the diagnostic and patient outcome. PLoS One. Aug 2012.
10. Subirá D et al. Role of flow cytometry immunophenotyping in the diagnosis of leptomeningealcarcinomatosis. NeuroOncol. Jan 2012.
11. Lugli E. et al. Data analysis in flow cytometry: the future just started. Cytometry A. July 2010.