Discovery
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Matthew Kunicki, Christoph Eberle, PhD
AI’s Next Leap: Universal Comprehensive Discovery using Flow Cytometry
Combining standardized, high dimensional single cell data with immune state context enables better translational understanding of diverse human immunity for research and clinical applications.
In our last article, we discussed the data generation and interpretation problem underlying AI adoption in flow cytometry. Before we dive into what universal comprehensive discovery means, it is important to first understand why solving this problem in biomedical research matters. Consider cell and gene therapy (CGT): pharmacovigilance is widely acknowledged as an essential component for transparency across providers, regulators, and patients to maintain public trust. Predictive safety modeling across research, manufacturing, clinical trials, and post-approval has driven emerging multi-omic, AI-driven, and surveillance strategies to dynamically refine therapeutic safety and efficacy, accounting for immune-related adverse events and pharmacodynamics.
Prominent Immune Responses in Therapeutic and Optimal Care Research | |
Immune Response | Description |
When transplanted tissue is rejected by the recipient's immune system, which destroys the transplanted tissue | |
A state triggered by sustained and overwhelming inflammatory stress, causing progressive loss of function | |
The complex, phased process of donor stem cells rebuilding the recipient's innate and adaptive immune system | |
The ability to recognize, remember, and rapidly respond to previously encountered pathogens | |
A form of cancer treatment that empowers the patient's immune system to fight disease | |
The process of adjusting the body's immune response to a desired level, to fight disease or manage overactivity | |
A systemic inflammatory response caused by rapid, massive release of cytokines into the blood | |
A condition where the immune system mistakenly attacks the body's own healthy cells, tissues, and organs | |
An exaggerated, hypersensitive immune response to harmless environmental substances, called allergens | |
The natural, protective response to injury, infection, or irritation, designed to remove harmful stimuli and initiate healing | |
While targeted biomarker-driven approaches have evolved from AI-driven multi-omic strategies, algorithmic inference does not necessarily represent actual occurrences or real-world evidence. Helping AI bridge the gap between research and real-world implementation, though, is no simple feat. The diversity of genes among patient populations that drive immune responses is well illustrated by research on inborn errors of immunity. The sheer number and complexity of genes, post-transcriptional and translational modifications, environmental triggers, age, diet, and lifestyle factors influencing function continue to support the modern theory that immunity is in active equilibrium.
Why solving this problem matters lies in the invaluable single-cell abundance measurements that flow cytometry contextualizes within the broader immune state. Deep single-cell profiling has offered viable solutions for discovery, mechanism of action, stratification, and more. However, as long as the immune response is driven by the active equilibrium of immune cell populations that make up our immune state, we are missing this key evidence to help contextualize predictive single-cell characteristics within the diversity of human immunity. Flow cytometry has played a long-lasting role in the targeted measurement of clinically relevant cell types for biomedical research and clinical application, and AI-adoption in flow cytometry is enabling its capacity as a universal comprehensive discovery tool.
Universal
A common detriment of multi-parametric cytometry is the curse of dimensionality. Identifying immune biomarkers from high-dimensional datasets does not always yield well-understood, clinically defined cellular phenotypes, slowing translation. AI adoption in flow cytometry needs stable, shared identifiers for cell types to not only map across siloed and targeted research areas, but also bridge disease areas, leveraging cell ontology for multi-omic strategies.
Comprehensive
The diversity of human immunity is highly dynamic and cannot be predicted solely by genetics. Besides immunity’s active equilibrium, targeted cell populations have emerged as most relevant to specific research and disease areas, isolating flow cytometry datasets from the broader context of immune state. There are still large gains to be made in monitoring as many key parent-cell populations across systemic immunity, where the context of cell abundance and immune state can be combined with a functional molecule repertoire.
Discovery
With the advent of laboratory-developed tests (LDTs), research discoveries have more quickly translated into clinical applications, helping support the industry’s migration to AI-native biology, such as with pharmacovigilance. Broad comprehensive screening by flow cytometry can elucidate context-of-use (COU) for LDTs targeting immune-related physiology from blood and other tissues in suspension.
Therefore, universal, comprehensive discovery using flow cytometry is not just an important opportunity for therapeutic and optimal care research but also a well-understood challenge for AI adoption in the context of diverse human immunity. Knowing not just the molecular mechanisms of a single cell but also the abundance required for a desirable immune response poses a critical question best suited to flow cytometry. Scaling, generalizing, standardizing, and monitoring the quality of flow cytometry data for AI-driven immunophenotyping is actively being pursued worldwide. Creative and innovative solutions in data generation and interpretation have evolved to position flow cytometry once again as the staple of translation for years to come.
As Elliot Joslin coined decades ago, leading immunologists and geneticists understand
“genetics loads the gun, but environment pulls the trigger.”
What is better than one’s own immune system, built to know the difference between self and not-self, to deeply understand how the environment impacts one’s personal health?
(Leave a comment if you know)