Multicolour Flow Cytometry (MFC) is a technology for analyzing cell characteristic with high sensitivity: it can detect up to one divergent cell amongst one million normal cells.
Flow cytometry is a technology that plays a vital role in medical research, enabling in-depth cell analysis and immunophenotyping. Researchers utilize fluorescent markers to explore cell populations, immune system functions, and biomarker detection.
This technique offers valuable data on single-cell characteristics, advancing our understanding of immunology, hematology, and cancer research. Flow cytometry’s applications extend to clinical diagnostics, offering a powerful tool for analyzing and diagnosing immunological disorders.
With multicolor flow cytometry, researchers gain unprecedented insights into complex cellular interactions, driving transformative discoveries in the medical field.
The crowded data output is currently manually interpreted by the expert for diagnosis and monitoring. Translating the data into relevant information requires lots of expertise, time, and experience. Moreover, the process is restricted to the availability of clinical expert and presents risks of human biases and errors.
The fast growing advancements in MFC have led to the possibility of measuring dozens of characteristics per individual cell, providing large volumes of raw data. The crowded data output is currently manually interpreted by the expert for diagnosis and monitoring.
Translating the data into relevant information requires lots of expertise, time, and experience. Moreover, the process is restricted to the availability of clinical expert and presents risks of human biases and errors.
• multiple markers
• multiple lasers
• manual decision steps
• experienced based job
• human biased and errors
• manual identification of cell populations of interest
• operator dependent
• high dimensionality of data
• manual analysis of homogeneous cell population
• subjective but critical to correct findings
• technological evolution FC devices
To tackle this clinical challenge, researchers of the Radboud University and the University Medical Centre Utrecht developed an AI algorithm that can analyze the large volumes of raw data produced by the Flow Cytometry and represents the findings as a simplified 2D-image.
The novel method ECLIPSE (Elimination of Cells Lying in Patterns Similar to Endogeneity) identifies and characterize aberrant cells present in individuals out of homeostasis. ECLIPSE combines dimensionality reduction by Simultaneous Component Analysis with Kernel Density Estimates.
PCA model on the control cells + projection of cells from patients
Elimination of healthy-like cells in patients: “equal to normal” as principle
PCA model on abnormal / disease specific cells