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

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

 

They used Fukuda, CCC and the IOM criteria.

 

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

 

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

 

Thanks again @SNT Gatchaman for the excellent highlights!!

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

 

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.

 

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

 

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

 

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.

 

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.

 

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.

 

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."