Immunometabolic changes and potential biomarkers in CFS peripheral immune cells revealed by single-cell RNA sequencing, 2024, Sun et al

[Wyva]

• Yujing Sun,

• Zhenhua Zhang,

• Qincheng Qiao,

• Ying Zou,

• Lina Wang,

• Tixiao Wang,

• Bo Lou,

• Guosheng Li,

• Miao Xu,

• Yanxiang Wang,

• Zhenhong Zhang,

• Xinguo Hou,

• Li Chen &

• Ruxing Zhao

Abstract

The pathogenesis of Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remains unclear, though increasing evidence suggests inflammatory processes play key roles. In this study, single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) was used to decipher the immunometabolic profile in 4 ME/CFS patients and 4 heathy controls.

We analyzed changes in the composition of major PBMC subpopulations and observed an increased frequency of total T cells and a significant reduction in NKs, monocytes, cDCs and pDCs. Further investigation revealed even more complex changes in the proportions of cell subpopulations within each subpopulation. Gene expression patterns revealed upregulated transcription factors related to immune regulation, as well as genes associated with viral infections and neurodegenerative diseases.

CD4+ and CD8+ T cells in ME/CFS patients show different differentiation states and altered trajectories, indicating a possible suppression of differentiation. Memory B cells in ME/CFS patients are found early in the pseudotime, indicating a unique subtype specific to ME/CFS, with increased differentiation to plasma cells suggesting B cell overactivity. NK cells in ME/CFS patients exhibit reduced cytotoxicity and impaired responses, with reduced expression of perforin and CD107a upon stimulation. Pseudotime analysis showed abnormal development of adaptive immune cells and an enhanced cell-cell communication network converging on monocytes in particular.

Our analysis also identified the estrogen-related receptor alpha (ESRRA)-APP-CD74 signaling pathway as a potential biomarker for ME/CFS in peripheral blood. In addition, data from the GSE214284 database confirmed higher ESRRA expression in the monocyte cell types of male ME/CFS patients. These results suggest a link between immune and neurological symptoms. The results support a disease model of immune dysfunction ranging from autoimmunity to immunodeficiency and point to amyloidotic neurodegenerative signaling pathways in the pathogenesis of ME/CFS.

While the study provides important insights, limitations include the modest sample size and the evaluation of peripheral blood only. These findings highlight potential targets for diagnostic biomarkers and therapeutic interventions. Further research is needed to validate these biomarkers and explore their clinical applications in managing ME/CFS.

Open access: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-05710-w

 

[Wyva]

They used Fukuda, CCC and the IOM criteria.

 

[hotblack]

A very small sample size but a lot of information to take in!

Here’s an AI generated audio summary of the paper:
https://u.pcloud.link/publink/show?code=XZ6HWi0ZBMorHqJoUY7MyiDX6LMnPBiwpM17
So I don’t completely derail discussions of the papers please post any feedback on these audio summaries to this thread:
https://www.s4me.info/threads/enhan...h-technology-feedback-and-ideas-wanted.40207/

Text summary

This scientific paper investigates immune system changes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, also known as M.E./C.F.S., using a technique called single-cell RNA sequencing.

Let's break down some of the terms.

M.E./C.F.S. is a complex, debilitating condition with hallmark symptoms like persistent fatigue, post-exertional malaise, cognitive difficulties, and pain. Single-cell RNA sequencing, or scRNA-seq, is a powerful tool that allows scientists to study the gene expression of individual cells. This allows for a very detailed look at how different cell types are behaving and interacting.

The study involved four patients with M.E./C.F.S. and four healthy individuals as a control group. Researchers took blood samples and focused on the immune cells within the blood, specifically a type called Peripheral Blood Mononuclear Cells or P.B.M.C.s.

Key Findings:

They found several key differences in the immune cells of M.E./C.F.S. patients.

First, there was a significant increase in a type of immune cell called T cells, but a decrease in Natural Killer or N.K. cells and monocytes in the M.E./C.F.S. group.
Second, looking deeper into T cells, they found specific changes in the subsets of CD4+ and CD8+ T cells, suggesting a disruption in how these cells mature and function in M.E./C.F.S..
Third, B cells, another important immune cell type, also showed unusual activity. M.E./C.F.S. patients had more plasma cells, which are responsible for producing antibodies, suggesting an overactive response.
Fourth, N.K. cells, known for their ability to kill infected cells, were less functional in the M.E./C.F.S. group. This was evident from gene expression analysis and further confirmed by experiments showing reduced ability to kill target cells.

Importantly, this study found that a protein called APP, Amyloid Beta Precursor Protein, was significantly increased in monocytes of M.E./C.F.S. patients. This protein is known to be involved in Alzheimer's disease, and its increase in M.E./C.F.S. is a novel and potentially important finding.

Furthermore, they found that a gene called ESRRA, Estrogen Related Receptor Alpha, was a key regulator of APP in M.E./C.F.S. patients. This suggests a potential link between immune dysfunction and the neurological symptoms seen in the disease.

Methods:

As mentioned earlier, the core method was single-cell RNA sequencing. This was complemented by additional techniques to confirm the findings, including flow cytometry to analyze immune cell function and immunohistochemistry to visualize protein expression in tissues.

Limitations:

A primary limitation of the study is its small sample size of only four M.E./C.F.S. patients. This limits the ability to generalize findings to all patients with the condition.
Secondly, the study only looked at peripheral blood. Examining other tissues, such as the brain or spinal cord, might provide further insights into the disease.

Despite these limitations, the study offers valuable clues into the complex immune dysregulation in M.E./C.F.S. The identification of potential biomarkers like APP, CD74, and ESRRA opens up new avenues for diagnosis and possibly targeted treatments. However, further research with larger cohorts and a broader range of analyses is necessary to validate these findings and to fully understand their implications for managing M.E./C.F.S..

 

[forestglip]

They used Fukuda, CCC and the IOM criteria.

Just want to clarify - they had to meet all three criteria.

 

[SNT Gatchaman]

Some selected quotes from methods (intermixed with discussion where relevant) —

The study participants were recruited from the Jinan Biolnoo Institute of Immunometabolism. Enrolled ME/CFS participants were required to meet the research criteria of the International Chronic Fatigue Syndrome Study Group, the Canadian consensus criteria, and the diagnostic tool issued by the Institute of Medicine in 2015.

Subjects under 18 years of age, those with progressive malignancies, acute progressive illnesses within a 2-week period, clinical or laboratory evidence of current infections, established or suspected mental or neurological disorders and autoimmunity, and those in other circumstances deemed unsuitable for study participation by the investigator were excluded.

our investigation was conducted prior to the participants’ exposure to COVID-19, which transpired to have the potential to influence their immune profiles and induce symptoms similar to those of chronic fatigue syndrome, or “long-Covid.”

It is important to acknowledge that the current viral infections have been excluded from the participants through NGS sequencing and serological screening.

Finally, 4 patients (ME/CFS) vs. 4 age-paired heathy volunteers (HC) were enrolled for scRNAseq analysis. Lately, 10 vs. 10 subjects were enrolled for further validation of the primary findings of the scRNAseq analysis. Moreover, to strengthen our findings, we conducted external validation using publicly available scRNAseq datasets from an independent cohort (GSE214284, n = 58).

They state age-paired. In supplementary 2, the HCs are 2 female, 2 male. Supplementary 1 is missing the demographics, but figure 1A indicates ME/CFS are 1 female, 3 males. We don't know height/weight, hopefully matched.

HC1 175cm 65kg 32y male
HC2 172cm 65kg 37y male
HC3 168cm 51kg 39y female
HC4 158cm 50kg 55y female

In the context of ME/CFS, pseudo-time analysis helps to map out how cells progress through different states of differentiation and how these transitions might be disrupted in disease conditions. By modeling the trajectory of cell states over pseudo-time, we gain insights into the dynamic changes in gene expression associated with cell maturation or dysfunction. This approach allows us to identify key regulatory genes and pathways that are altered during the progression of ME/CFS, providing a deeper understanding of the disease mechanism and potential targets for therapeutic intervention.

In A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples (2023, Nature Communications) —

When cells in a sample come from a continuous biological process, computationally placing the cells along a pseudotemporal trajectory based on their progressively changing transcriptomes is a powerful approach to reconstructing the dynamic gene expression programs of the underlying biological process. This approach, also known as pseudotime analysis, is now widely used to study cell differentiation, immune responses, disease development, and many other biological systems with temporal dynamics.

Spoiler: See also

A comparison of single-cell trajectory inference methods (2019, Nature Biotechnology)

 

[SNT Gatchaman]

Selected quotes from results (GeneCards links added) —

Alterations in peripheral immune cell composition in ME/ CFS patients

we focused exclusively on the primary immune cell types - T cells, NK cells, B cells, monocytes, conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs)— - to exclusion of platelets, erythrocytes and hematopoietic stem/progenitor cells. A remarkable increase in the proportion of T cells was observed in the ME/CFS group, accompanied by a relative decrease in the proportion of NK and monocyte cells.

Characterization of CD4+ and CD8+ T cell subsets

In the ME/CFS group, CD4 + naïve cells showed a significant increase (2.18-fold, p = 0.035), while CD4 + TEM cells showed a significant decrease (2.23-fold, p = 0.045) in total CD4 + T cells. However, no significant changes were observed in the relative proportions of CD4 + TCM and CD4 + CTL to total CD4 + T cells between the two groups.

TCM is T central memory
TEM is T effector memory
CTL is cytotoxic T lymphocytes

The CD4+ T cells in ME/CFS were in the mid to late pseudotime, while the HC cells were in the early pseudotime, which is similar to naïve CD4+ T cells. A unique branch of the CD4+ TCM cluster, which was absent in HCs, suggests that the differentiation capacity of CD4+ TEM is increased in individuals with ME/CFS.

Based on the differences in trajection patterns, CD4+ TCM cells from ME/CFS exhibited a more diverse differentiation status compared to HC

In the ME/CFS group, the highly activated TFs [transcription factors] were mainly enriched in the regulation of primary metabolic processes. However, we identified a unique group of highly activated TFs that become disorganized in ME/CFS, including KLF2, JUND, ZBTB7A, USF2, RELA, and KDM5A. Importantly, the ME/CFS regulons of IRF1, KLF2, FOS, ZBTB7A, FLT1 and RELA show significant enrichment in cell differentiation, IL-18 and IL-17 signaling pathways.

KEGG analysis revealed significant upregulation in cellular senescence and neurodegenerative signaling pathways, including amyotrophic lateral sclerosis (ALS), prion disease, Parkinson’s disease (PD) and Huntington’s disease (HD).

A significant increase (2.27-fold, p = 0.028) in CD8 + naïve cells was observed among total CD8 + T cells in ME/CFS, with no significant changes in CD8 + TCM cells, CD8 + TEM cells and γδ T cells.

The analysis of pseudotime patterns revealed dynamic changes over time in CD8+ T cell states. A more distinct trajectory branch was observed in CD8+ naïve cells of the ME/CFS group, which was absent in the HCs. This indicates a potential suppression of differentiation from CD8+ naïve to CD8+ TCM-like cells in ME/CFS. Furthermore, a less distinct trajectory branch was noted in CD8+ TEM cells of the ME/CFS group, contrasting with a more pronounced branch in the Con group. This suggests a potential suppression of differentiation from CD8+ TCM to CD8+ TEM cells in ME/CFS.

In healthy controls (HCs), the highly activated TFs were mainly involved in regulating integrated stress response signaling and cytokine signaling in the immune system. Notably, we observed higher activation of TFs in ME/CFS, including ZBTB7A, JUND, ESRRA, SP3, and KDM5A, among others. Importantly, the highly activated TFs in ME/CFS, such as PSMD8, NFKBIA, PSMD7, NFKB1, REST, TAF7, etc. and MYC, showed significant enrichment in processes related to cell proliferation and differentiation, cellular senescence, infectious diseases, and neurodegenerative diseases like AD and HD, shedding light on potential molecular mechanisms underlying the observed differences.

Diferentially expressed genes (DEGs) such as CCR7, CD28, LCK, and SMAD3 play important roles in immune cell activation, signaling, viral processes, and cytokine synthesis. DEGs such as EZR, CSK, and ACTB, as well as ITGB7, encode proteins that contribute to cytoskeleton dynamics, cell adhesion and motility.

 

[SNT Gatchaman]

Selected quotes from results cont'd —

Comprehensive insight into B cell dynamics in ME/CFS

B cells were divided into discrete s sets based on the location and expression of traditional subtype markers. Transcriptome analysis allowed us to successfully identify naive B cells, memory B cells, and plasma cells. A trend towards higher plasma cell proportions (of total B cells) was noted, but statistical significance was not achieved

Memory B cells from the ME/CFS group were positioned in early pseudotime, whereas those from the HCs were positioned in mid-to-late pseudotime (Fig. 4G). Notably, a distinct branch appeared in the trajectory map for both the memory B cluster and the plasma B cluster within the ME/CFS group, but not in the HCs. These findings indicate that this plasma cell cluster may represent a distinct subset of circulating B lymphocytes specific to ME/CFS.

In contrast to T cells, mature effector B cells (plasma cells producing antibodies) appeared to be more abundant in ME/CFS.

NK cell dynamics and functionality in ME/CFS

In addition to T cells and B cells, natural killer cells (NK cells) are the third main class of lymphocytes. They are involved in the regulation of the immune system, the prevention of viral infections, and the treatment of tumors. Additionally, they are involved in the development of autoimmune diseases and hypersensitivity. We were able to effectively identify classical NK cells (NK-CD56dim), NK-CD56bright, and included NKT cells in this subpopulation analysis.

we observed a discernible decrease in the proportion of CD56bright NK cells (relative to total NK cells) in the ME/CFS group. Interestingly, we observed a statistically significant and noteworthy increase in the frequency of NKT cells (3.15-fold, p = 0.017).

In contrast to HCs, which occupied a mid-to-late pseudotime trajectory, NKT and NK-CD56bright cells from the ME/CFS patients were positioned along the early pseudotime trajectory […]] suggests that the NK-CD56 bright and NKT clusters exhibited suppressed differentiation in ME/CFS.

The KEGG enrichment analysis of upregulated genes in the ME/CFS group revealed associations with disease pathways related to infectious diseases (HIV, EBV infection, COVID-19, etc.), Amyloidotic neurodegenerative diseases including ALS and PD, and autoimmunity. Conversely, downregulated genes were involved in IL-17 signaling and NK cell-mediated cytotoxicity.

Afterward, we implemented flow cytometry to evaluate the functional status of NK cells in ME/CFS. Granzyme B expression was at the lower limit of the range in NK cells and unaffected in CTLs (ΔGranzyme B NK = 77.09%, Normal ≥ 77%; ΔGranzyme B CTL = 19.86%, Normal ≥ 6%). The CD107a surface expression of NK cells from ME/CFS was significantly impaired (ΔCD107a = 11.05%, Normal ≥ 10%) in comparison to CTLs (ΔCD107a = 11.05%, Normal ≥ 10%) upon PBMC stimulation with PMA/Ionomycin. Furthermore, the expression of perforin was significantly diminished in NK cells (ΔPerforin = 49.58%, Normal ≥ 81%). Furthermore, NK cells from ME/CFS patients exhibited significantly lower cytotoxicity against target cells (CD4 + T cells) compared to normal (13.48%, Normal ≥ 15.11%), which provides evidence that NK functions were impaired in ME/CFS patients.

 

[SNT Gatchaman]

Results cont'd, with added GeneCards links —

Upregulated genes enriched in phagocytosis related function in monocytes

In the trajectory map, the CD14 monocyte cells in the ME/CFS group exhibited a reduced number of branches. This finding suggests that the differentiation diversity of CD14 monocyte cells in the ME/CFS group has decreased.

One notable observation was the increased activity of transcription factors (TFs) in the ME/CFS group, which included pivotal factors such as ZBTB7A, KLF13, RXRA, IRF2, and SPI1, among others.

The ME/CFS highly activated TFs, which include STAT3, IRF7, MYC, CEBPD, POU2F2, ESRRA, CEBPB, CEBPD, and SPI1, exhibited a notable enrichment in cellular processes such as the regulation of monocyte differentiation, EBV infection, prion disease pathway, overlap between signal transduction pathways contributing to LMNA laminopathies, nuclear receptors, and pathways like IL-17, IL-2, and IL-5 signaling. Conversely, regulons in HCs were primarily associated with the genes EGR1, EGR2, FOSB, EGR3, and DDIT3, indicating an enrichment in processes that were exclusively associated with the response to endogenous stimulus.

Collectively, these DEGs and regulatory patterns demonstrated substantial shifts in ME/CFS based on monocyte cell behavior, such as migration, phagocytic activities, and immune response modulation, underscoring their importance in the innate immune system.

Cell-cell communications in ME/CFS indicates that APP is core signaling of the observed interactions between monocytes and other cells

In comparison to the Con group, the ME/CFS group exhibited a significant upregulation of ligand-receptor pairs, including APP-CD74, HLA-DRB5-CD4, HLA-DMA-CD4, HLA-DMB-CD4, HLA-DPA1-CD4, HLA-DPB1-CD4, HLA-DQA1-CD4, HLA-DQA2-CD4, HLA-DQB1CD4, HLA-DRA-CD4, HLA-DRB1-CD4, IL16-CD4, and RETN-CAP1.

In monocyte cells, APP signaling is the predominant pathway, as evidenced by its substantial outgoing and incoming interaction strengths in comparison to other signaling pathways. This is a significant finding. The APP signaling was also one of the distinctive features of ME/CFS, but it did not occur in HCs. Furthermore, it appears that the Amyloid-beta precursor protein (APP)/ CD74 pathway is primarily involved in the interactions between monocytes and other cells in ME/CFS. CD74 is a membrane protein that functions as an MHC class II chaperone, thereby facilitating the antigen presentation process. CD74’s function may be linked to neuroinflammatory mechanisms or aberrant antigen presentation in the context of conditions such as autoimmune disorders or neurodegenerative diseases. These data collectively suggest that monocytes may play a critical role in the expansion and malfunction of lymphocytes (T and B cells), which would contribute to the unexpected yet critical discoveries regarding amyloidotic neurodegenerative signaling in immune cells that extend beyond immune disturbance.

Following this, we conducted immunohistochemistry (IHC) on APP, Aβ (Amyloid-beta), and Tau, and discovered a significant increase in APP protein accumulation, which is an unprecedented discovery in ME/CFS.

The significant correlation between ESRRA and APP expression is indicated by the modifications to higher regulons that are unique to ME/CFS. This suggests that the production of Amyloid β peptides in ME/CFS may be influenced by ESRRA’s influence on APP gene expression. Estrogen-related receptor-α (ESRRA) is a nuclear receptor of transcription factor that binds to estrogen and estrogen-related responsive elements. It was recently identified as a molecular immune-metabolic antitumor target. ESRRA encodes this receptor. ESRRA has been demonstrated to be one of the most highly enriched DEGs in the aforementioned cell types.

Furthermore, our observations were further validated by the ESRRA expression in the monocyte cell types of male patients from the GSE214284 database, which yielded the same results.

 

[SNT Gatchaman]

Summary quotes from discussion —

In ME/CFS, we observed unusually elevated numbers and percentages of naïve T cells (both CD4 + and CD8+ ), absolute numbers of central memory T cells, and decreased percentages of CD4 + effector memory T cells. This may indicate that T cells in ME/CFS exhibit a non-antigen-activated early expansion, but they are relatively insensitive to pathogens for the development of effector T cells. The other main components of the adaptive immune system, the B cells, also underwent similar changes, resulting in an increase in the production of plasma cells that produce antibodies.

we were taken aback by the discovery of numerous cellular pathways that are implicated in certain well-known diseases. Our attention was drawn to two classes of diseases within the “Human Disease” KEGG category. One is the virus infection-related pathways, which include non-specific viral processes and specific virus-specific pathways (such as HSV, HPV, CMV, EBV, or COVID-19). It is important to acknowledge that the current viral infections have been excluded from the participants through NGS sequencing and serological screening. Consequently, the clinical manifestations of ME/CFS are more likely to be consequential than etiological or pathogenic, as the activation of these diseaserelated pathways. The second unexpected category of diseases consisted of amyloid neurodegenerative disorders, such as AD, ALS, PD, HD, or Prion diseases.

Finally, the gene expression patterns substantially increased the signal pathways involved in cell senescence and exhaustion, particularly in T cells. The state of cellular senescence is characterized by the irreversible arrest of the cell cycle and is linked to chronic inflammatory conditions and aging.

both CD4+ and CD8+ T cells from ME/CFS patients exhibit reduced glycolysis at rest, with CD8+ T cells also exhibiting diminished glycolysis following activation. This implies that the impaired immune function of ME/CFS patients may be attributed to a fundamental alteration in energy metabolism in T cells.

We found a unique branch in both naïve CD4+ and CD8+ T cells and CD4 + TCM cells. […] we also identified a unique group of expanded plasma (mature antibody-producing B) cell population that was originated directly from memory B cells.

Monocytes have the ability to cross the blood-brain barrier, particularly when activated. Once in the central nervous system (CNS), they can differentiate into macrophages and contribute to neuroinflammation. In ME/CFS, neuroinflammation has been proposed as a significant contributing factor to cognitive dysfunction, pain, and sleep disturbances. Activated monocytes and the cytokines they release can interact with microglia, the resident immune cells of the brain, promoting a neuroinflammatory response that may underlie some of the neurological symptoms of ME/ CFS.

Finally, we have described a specific signaling pathway that is particularly appealing in cell-to-cell communication.

Initially, we observed heightened communication networks between monocyte clusters and other cell populations, which confirmed that the monocyte was the central cell type involved in the cellular interactions in ME/CFS. CD74 is also known as the invariant chain of the MHC class II complex, playing a critical role in antigen presentation and immune response modulation [45]. APP-CD74 was the most specific and predominant contributor to exaggerated cell communication in monocytes among the various ligand-receptor pairings.

Its interaction with APP in monocytes could be a mechanism driving chronic immune activation in ME/CFS

Furthermore, subsequent regulon analyses revealed an orphan nuclear receptor, [ESRRA], as the primary regulator of APP transcription in monocyte cells, providing evidence for its potential involvement in the pathogenesis of ME/CFS. […] These data, when combined, revealed a previously unknown elevated ESRRA-APP-CD74 signaling in ME/CFS.

 

[Turtle]

Thanks again @SNT Gatchaman for the excellent highlights!!

Could activated monocytes, infections/(over)excertion, being prevented from getting to the brain by lying flat?

 

[Turtle]

The poor astronauts on ISS can't lie flat, can they?

I remember seing an astronaut on ISS, some time ago, getting into his sleeping bag in a head down position; others were upright.
I wonder if his brainfog was influenced by that, even in zero-gravity.

 

[SNT Gatchaman]

In comparison to the Con group, the ME/CFS group exhibited a significant upregulation of ligand-receptor pairs, including APP-CD74, [multiple HLAs], and RETN-CAP1.

RETN is resistin. CAP1 is its receptor: Cyclase Associated Actin Cytoskeleton Regulatory Protein 1. See eg Adenylyl Cyclase-Associated Protein 1 Is a Receptor for Human Resistin and Mediates Inflammatory Actions of Human Monocytes (2014, Cell Metabolism)

Resistin has been mentioned in ME/CFS studies in the past. From Cytokine signature associated with disease severity in chronic fatigue syndrome patients (2017, PNAS) —

resistin was significantly lower in the mild (P = 0.0370) and severe (P = 0.0208) groups

Though normal in moderate severity.

It is unclear at this time why resistin had this unusual behavior in our study, increasing with mild to moderate disease severity but decreasing with moderate to severe disease.

A second apparent paradox is harder to explain: The two cytokines that did distinguish cases from controls, TGF-β and resistin, did not exhibit a linear relationship with disease severity. It may be that TGF-β and resistin contribute to ME/CFS pathogenesis independent of disease severity.

Resistin was also said to be decreased in CSF in Cytokine network analysis of cerebrospinal fluid in myalgic encephalomyelitis/chronic fatigue syndrome (2015, Nature Molecular Psychiatry)

I don't think we have threads for either of those latter two papers, though they were probably discussed on PR back in 2015/2017.

If other immune cells were over-expressing CAP1 (even if only in mild and severe, but not moderate) would that explain the low serum levels even if the (monocyte) expression/secretion of resistin itself was normal?

 

[Simon M]

Does anyone know how this compares with the much larger and probably more rigourous study by Andrew Gelman(?) in the Maureen Hansen group? I seem to remember that one came out with a surprising finding it it was all about monocytes. Sorry, I’ve got a migraine, no chance of link

 

[SNT Gatchaman]

Is it Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation (2024, Cell Reports Medicine)?

 

[Simon M]

Is it Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation (2024, Cell Reports Medicine)?

I think so. The research results were first described two or three years ago, But it’s single cell RNA sequencing from the right authors and involving a cohort before and after a maximal single exercise test

 

[chillier]

Does anyone know how this compares with the much larger and probably more rigourous study by Andrew Gelman(?) in the Maureen Hansen group? I seem to remember that one came out with a surprising finding it it was all about monocytes. Sorry, I’ve got a migraine, no chance of link

The Grimson paper is much better powered (n~30 per group I believe compared to n~4 here). The two papers disagree in the abundances of immune cells types. This threads paper reports significantly decreased monocyte and NK cell abundances and increased T cell abundance. Grimson et al report significantly decreased CD8 T cell abundance.

In this thread's paper it's a bit difficult to tell if they see differentially expressed genes which significantly differ between patients and controls - let alone survive multiple test correction. I think they probably don't because they would explicitly report them if they had, but I've only briefly skimmed it and may have missed it.

In Grimson's paper they see significant differences in a handful of monocyte genes, in particular CCL4 and CXCR4. These don't appear to survive multiple test correction. However, they replicate these two using bulk RNA-seq on monocytes in a completely separate cohort in the same paper. These molecules are chemokines involved in cell migration so the authors argue possibly patient monocytes are characterized by dysregulated tissue migration.

 

[chillier]

This thread's paper also relies heavily on Gene Ontology (GO) term enrichment analysis, which Grimson et al also do. Gene Ontology terms are short phrases that describe a gene's function, cellular location and so on. For example a cytoplasmic gene involved in cell signalling may have 'cytoplasm,' and 'cell signalling' as go terms.

GO term enrichment then looks across all the gene's expression levels between the two groups and checks if any terms are overrepresented in the patient or control group. In a similar way Grimson et al describe 'regulation of paletelet activation' as an enriched GO term in ME/CFS only after exercise. This kind of analysis is ok as a post hoc in my opinion but is quite sketchy because it relies on the GO annotations themselves being accurate - which in many cases they probably aren't. It's also quite vague and hard to interpret.

 

[Hutan]

This thread's paper also relies heavily on Gene Ontology (GO) term enrichment analysis

Thanks for explaining that, chillier.

 

[DMissa]

This kind of analysis is ok as a post hoc in my opinion but is quite sketchy because it relies on the GO annotations themselves being accurate - which in many cases they probably aren't. It's also quite vague and hard to interpret.

I agree that for interpreting function a deeper dive into specific genes needs to be done. Getting a GO hit for "cytoplasm" for example means absolutely nothing. It also doesn't deal with direction or effect. You could have a GO term for a metabolic pathway come up as significantly upregulated because a gene product technically "involved" in the pathway annotation is up but the gene product actually inhibits flux through that particular pathway. In this case the GO analysis would be misleading.

GO analysis is a good way to identify areas to look at in detail. I wouldn't really use it for more than that.

Also, not only can the annotations be incorrect or vague but they also change. GO analysis on the same datasets years apart will show different outcomes.

 

[jnmaciuch]

I'm quite late to this discussion but very happy to see it as I wrapped up a very similar single cell LC analysis recently.

Some two cents:
I'd be very very very skeptical of comparisons of immune cell frequencies based on single cell data. Even moreso considering how tiny the sample size is. Some of these subsets that they're comparing are extremely small. The main issue is that there's going to be a lot of cell drop-out in single-cell sequencing, meaning that only a fraction of the cells that you put in are actually giving you data. In such a small sample size, it's entirely possible that the differences can be explained by differences in which cells ended up getting sequenced more by chance.

At this level, I'd only accept findings on cell frequencies if there was also corresponding data from flow cytometry, which there doesn't appear to be.

I agree with previous comments on Gene Ontology. Though in a study with such a small sample size, I would expect that very very few genes are going to pass p-value adjustment anyways. Using gene ontology is pretty much the only way you're going to see any worthwhile differences from data such as this.

It seems like they were relying more heavily on the Biological Processes subset of pathways, which generally tend to be more descriptive. Most of their top hits are pathways with a huge amount of genes in them that basically come up in every single study I've ever done on any type of immune cell. All of the many genes in those pathways do very different things. Without seeing the leading edge genes (i.e. the differential genes in the analysis that are driving the pathway hit), you really can't characterize what it means that "positive regulation of cell activation" is significant.

The monocyte findings are interesting, CCL4 also came up in the Hanson lab study. Across all three studies, I think we're seeing some decent evidence that there seems to be upregulated signaling in monocytes associated with migration into tissues. Monocyte-derived macrophages will often have some differences in gene expression from tissue-resident macrophages, though you'd need data from the actual tissue to see if that chemokine signature actually is associated with increased migration and whether that population skew seems to have any effects in the tissue.

I also take pseudotime results from Monocle with a Heavy grain of salt. My personal experience with Monocle is it is possible to end up with completely different stories just by changing a couple arbitrary parameters. Hopefully the authors were strict about making sure their findings were robust to those sorts of changes, but it's not really possible to tell that from the text. This is probably harsh, but to me, the pseudotime analyses in anything other than the monocytes and maybe the T cells is quite uninterpretable.

So from all the single-cell analyses that I've seen so far in ME/CFS, I think the most we can take away is "something might be happening with the monocytes."