Deep Immunophenotyping in ME/CFS Using Spectral Flow Cytometry, 2025, Gibson, Brooks et al.

[SNT Gatchaman]

Deep Immunophenotyping in ME/CFS Using Spectral Flow Cytometry
Gibson, Anton; Chometon, Thaize Q.; Damani, Tanvi; Brooks, Anna E. S.

Immune dysfunction is reported to play a significant role in the etiology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). To gain an understanding of the underlying immune abnormalities associated with this complex condition, a comprehensive approach for characterizing immune cell subsets and their inferred functional states is essential.

We developed a high-dimensional flow cytometry method that enables detailed immunophenotyping of peripheral blood mononuclear cells (PBMCs) from ME/CFS patients. By simultaneously measuring over 40 markers on individual cells within one sample, this approach provides a comprehensive assessment of immune cell subsets, incorporating effector or functional states, to enable assessment of their potential roles in disease pathogenesis.

Link | PDF (SpringerLink) [Paywall]

 

[Hutan]

Nice to see this from the New Zealand ME/CFS researcher, Anna Brooks, as an indication of her research focus.

 

[DMissa]

Nice to see this from the New Zealand ME/CFS researcher, Anna Brooks, as an indication of her research focus.

We recently got a grant with Anna, very excited to benefit from her deep expertise and her passion for this field

 

[SNT Gatchaman]

While the chapters in this book are focused on methodologies and directed at researchers, there are passages that are worth posting so we understand the data when presented. Here for example how to look at "high dimensional flow cytometry" figures.

 

[SNT Gatchaman]

In the introductory section of this chapter, with some emphasis added for summary —

While clinical immunophenotyping is instrumental in diagnosing and monitoring various hematological disorders such as leukemia and lymphoma, the limited complexity offered is insufficient to diagnose or define immune dysfunction associated with ME/CFS and long COVID. In contrast, research-based immunophenotyping, enabled by advancements in systems immunology multi-omic technologies, allows for deeper characterization of the immune cell compartment, often including inferred effector function.

Full-spectrum high-dimensional flow cytometry is a powerful tool for in-depth immunophenotyping, capable of simultaneously measuring a wide range of markers on individual cells. This technology enables a comprehensive analysis of immune perturbations, while also reducing sample requirements. Panels targeting as many as 50 antigens can now be utilized to perform deep immunophenotyping to enable more information to be garnered from a single sample.

The 42-color flow cytometry panel presented provides comprehensive immunophenotyping to allow in-depth characterization of immune cell populations in ME/CFS. Various studies have highlighted the role of immune dysfunction in both ME/CFS and long COVID. Natural killer (NK) cell dysfunction, T cell exhaustion/dysfunction, changes in monocyte subset proportions, and metabolic dysfunction (in NK and T cells) have been observed in both ME/CFS and long COVID. However, few have utilized large panels to examine a broad spectrum of immune cell compartments and effector phenotypes within the same sample.

Our panel enables simultaneous analysis of the main cell subsets in peripheral blood mononuclear cells (PBMCs), including cell surface receptors linked to immunomodulatory, metabolic, or effector function associated with ME/CFS-related immune dysfunction. This approach facilitates a broader and more comprehensive characterization of immune cell subsets to help decipher their potential roles in disease pathogenesis. While some individuals with long COVID may meet the clinical diagnostic criteria for ME/CFS, it’s important to recognize that these conditions may also present as distinct entities with unique signatures of immune dysfunction. By conducting in-depth immunophenotyping studies on ME/CFS patients, researchers may be able to identify specific immune disturbances and define unique immune signatures that underly the immune dysfunction associated with ME/CFS-related immune dysfunction.

 

[SNT Gatchaman]

In the data analysis section (summary emphasis added) —

High-dimensional techniques such as opt-SNE (Optimized t-Distributed Stochastic Neighbor Embedding) and flowSOM (Self-Organizing Map) are valuable tools for dimensionality reduction and clustering of immune cell populations (Fig. 2), providing a global overview of immune cell perturbations. These techniques, which map high dimensional data into a lower-dimensional space while preserving underlying structure, are essential for visualizing and understanding complex immune cell populations.

While these techniques offer valuable insights, they can sometimes provide a simplified view of the data. To gain a more granular understanding of specific functional markers, manual gating in two-dimensional space can be applied in conjunction with these high-dimensional techniques to perform comparative analyses of data sets (Fig. 3). By combining these approaches, researchers can identify distinct immune cell subsets and their associated functional perturbations in ME/CFS patients compared to healthy controls.

The specific choice of high-dimensional analysis techniques can vary; however, the ultimate goal of a complex panel is to enable broad phenotypic analyses across immune cell subsets (see Note 18), while also allowing for more detailed exploration of functional markers. For example, Fig. 2 demonstrates that using a 42-color panel, at least 22 immune cell populations can be identified following dimensionality reduction (opt-SNE) and clustering (FlowSOM) computational tools (OMIQ, Dotmatics). Designation of immune cell populations is then assigned to each cluster by manual validation (i.e., based on marker expression) using traditional two-dimensional manual gating. These analyses enable subset proportions to be explored (Fig. 3), as each sample includes the same input cell number (in this case 200,000 CD45+ leukocytes). Each immune cell cluster can then be compared across all samples, as a percent of total cells to assess immune cell subset perturbations. Deeper analyses of each of the clusters can also be performed using two-dimensional gating to explore marker expression.

Fig. 2 Visualization of dimensionality reduction of high-dimensional spectral flow cytometry data was performed using OMIQ software. Manual gating was performed to select the leukocyte population from all samples to perform comparative analyses. CD45+ cells were selected by exclusion gating to remove debris, doublets, dye aggregates, and dead cells (a).

Data from 5 healthy controls and 5 ME/CFS donors were subsampled using the CD45+ gate and opt-SNE analyses were performed on equal proportions of leukocytes from each data file (200,000). 37 markers were included (excluded zombie, CD45, CD69, CD33, and CD36) and computational analyses were performed using the following parameters: Max Iterations = 1000, opt-SNE End = 5000, Perplexity = 70, Theta = 0.5, Components = 2, Random Seed = 5236, Verbosity = 25. Opt-SNE clusters were assigned immune cell population designations based on marker expression, identified by manual gating (b)

Fig. 3 Example data to demonstrate immune cell population proportion differences between ME/CFS patients and healthy controls can be explored by comparing abundance of immune cell populations, i.e., clusters identified by opt-SNE analyses, between sample groups (a). Intensity of marker expression (i.e., mean fluorescence intensity) can also be compared within the identified clusters/immune cell populations (b)