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Flow Cytometry

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.

FlowView solution providing asudes AI algorithm

What is Flow Cytometry

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.

test tubes of blood samples being evaluated using flow cytometry to analyze its data

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.

test tubes of blood samples being evaluated using flow cytometry to analyze its data

01

What happens to collected 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.

computer screen displaying cell sorting platform

02

Clinical Challenge

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. 

illustration of a maze with the healthcare symbol in the middle

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. 

illustration of a maze with the healthcare symbol in the middle
illustration of complex lines intersection

Complexity of analysis

• multiple markers
• multiple lasers
• manual decision steps
• experienced based job
• human biased and errors

hourglass with sand flowing to the bottom

Time Consuming

• pre-processing
• gating
‎• manual identification of cell populations of interest
‎• operator dependent
‎‎• high dimensionality of data

magnifying glass enlarging a dataset to display cells

Large datasets

• manual analysis of homogeneous cell population
• subjective but critical to correct findings
• technological evolution FC devices

Asudes platform.

AI ALGORITHM

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 algorithm filters out all the normal cells, leading to a non-crowded representation of  the abnormal cells only. Consequently, interpretation requires less expertise, less time and is trained to detect early indications of e.g. relapse. 

Eclipse Algorithm

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 control cells of patient into control model

1. STEP

PCA model on the control cells + projection of cells from patients

healthy-like cells

2. STEP

Elimination of healthy-like cells in patients: “equal to normal” as principle

abnormal cells

3. STEP

PCA model on abnormal / disease specific cells

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