Flow cytometry is a powerful tool for research and clinical diagnosis that makes cell population analysis incredibly easy. Even with the potential it has, flow cytometry single cell data analysis might be difficult because of its complicated structure.
With the help of this method, we can look into marker expression at the level of individual cells, which provides important information on cellular properties. In addition, Automated Flow Cytometry (MFC) allows thorough examination of various cell populations by determining multiple markers on cells at the same time.
Recognizing the complexities in flow cytometry data
Flow cytometry data is filled with information but difficult to sort through, similar to a maze or a gold mine. This high-dimensional set of data offers significant analytical challenges and covers multiple parameters at the single-cell level:
- Biological variability: an additional level of complexity may be added if each sample shows unique behavior.
- Problems with manual gating: Traditional analysis techniques rely on manual gating, which introduces subjectivity and variability across operators. This may affect the reliability and consistency of the results.
Our solution for better single cell data analysis
To solve these problems, FlowView Diagnostics has developed AI algorithms that make the laborious task of single cell data analysis simpler:
- Automated analysis: These methods reduce the likelihood of human error while accelerating the analysis by automating the complex process.
- Accuracy and consistency: Artificial intelligence generates results that are both accurate and consistent, in contrast to human analysis, which is prone to variability.
- Handling complex data: Artificial Intelligence is very good at finding links and patterns in high-dimensional data that even experienced analysts might overlook.
- Better decision support: AI helps the process of making well-informed decisions, which is essential in both clinical and scientific settings.
Presented at ESCCA 2023, a recent study from the University Medical Center Utrecht demonstrates the advantages of combining AI and flow cytometry:
Study focus
- Objective: To determine whether rapid, automated flow cytometry when combined with sophisticated multidimensional analysis techniques might effectively distinguish between bacterial and viral infections in patients seen in emergency departments.
- Methods: By using the dynamic nature of neutrophils and monocytes during infections as biomarkers, researchers used innovative software to build a “innate cells as an inflammometer” for monitoring inflammation levels.
Creative Results
- Quick diagnosis: Rapid on-site diagnosis was made possible by automated point-of-care flow cytometry, which successfully distinguished between bacterial, viral, and non-infections.
- Prospects for the future: In order to establish a seamless and fully automated diagnosis process, the team is ready to move forward with the direct integration of these intelligent algorithms with flow cytometry data.
Asudes platform for single cell data analysis
The Asudes platform is a framework for analysis of Flow Cytometry (FC) data. This type of data are defined by its numerical and multidimensional structure which makes interpretation extremely difficult. Plus, as a result of recent technology developments, FC panels are becoming more complicated which is letting operators and researchers to measure multiple variables per cell at once.
Asudes is designed to meet the demands of medical users by integrating supervised learning methods with unsupervised tools for detailed visualization.
Supervised & unsupervised learning
We can identify and classify abnormal cells through supervised learning, and we can visualize these variations using unsupervised approaches. With the use of this dual method, we can distinguish between responder and control models automatically. This gives users the ability to tell the difference between the traits that underlie each.
Eclipse algorithm
First, our algorithm applies customized differentiation of cells adaptations relevant to research indications. We examine the distributions of variable combinations that account for the greatest variation and their resulting densities after simultaneous component analysis using the ECLIPSE filter.
This quick method has two benefits:
↳ First, it improves awareness of data variation by identifying cells that differ significantly from the control population.
↳ Second, it offers an understanding of the accumulation of data density across multiple variables.
By identifying cells that have a marker expression that is also present in control subjects, this technique maximizes the insights gained from the dataset by identifying population shifts between responders and controls.
After the first stage is finished, we improve the visual interpretability of the ECLIPSE algorithm’s outputs by integrating Principal Component Analysis (PCA) techniques into our workflow.
Our platform allows for the independent visualization of responder and control features. It delivers a distinctive model into user-friendly 2-dimensional plots with several individual traces for each case. We provide insights into each variable’s relative contribution to each component by examining the loadings on each one. High loadings on one component indicate shared variance and also that the variable is highly related to it.
Analysts can identify significant patterns among the various components and evaluate the relative importance of each one.
What about our future?
The use of AI into flow cytometry is not just a step forward, but a revolution in itself. Using technologies from FlowView Diagnostics, researchers and doctors can deal with the complexity of flow cytometry data with previously unknown accuracy and efficiency. Not only does artificial intelligence (AI) greatly benefit flow cytometry analysis, but it also signals a new age in this field.
In summary
The use of AI for better flow cytometry analysis is an important development in research and diagnosis. As we move forward with bringing these most recent advancements into practice, there seems to be a limitless amount of potential to improve patient outcomes and research efficiency.
The Asudes platform combines unsupervised visualization tools with AI-driven supervised learning to create standardized protocols and give medical professionals the confidence to interpret multidimensional flow cytometry data. It is improving our ability to analyze complex medical measurements. This gives experts the clarity they need to understand such data more quickly and in accordance with established protocols.
COO of FlowView Diagnostics, using her deep knowledge of Six Sigma and experience in Healthcare IT.
