Developing a blood cell-based diagnostic test for ME/CFS using peripheral blood mononuclear cells, 2023, Xu, Morten et al

Preprint.
Paper now published, see this post

Abstract

A blood-based diagnostic test for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and multiple sclerosis (MS) would be of great value in both conditions, facilitating more accurate and earlier diagnosis, helping with current treatment delivery, and supporting the development of new therapeutics.

Here we use Raman micro-spectroscopy to examine differences between the spectral profiles of blood cells of ME/CFS, MS and healthy controls. We were able to discriminate the three groups using ensemble classification models with high levels of accuracy (91%) with the additional ability to distinguish mild, moderate, and severe ME/CFS patients from each other (84%). To our knowledge, this is the first research using Raman micro-spectroscopy to discriminate specific subgroups of ME/CFS patients on the basis of their symptom severity. Specific Raman peaks linked with the different disease types with the potential in further investigations to provide insights into biological changes associated with the different conditions.

https://www.medrxiv.org/content/10.1101/2023.03.18.23286575v1

 

“[Blood] Samples were provided by the UK ME/CFS Biobank (UKMEB)”

 

It seems that there was also a failed replication attempt included in this paper. The authors write (my bolding):

"We first used a simple approach to examining mitochondrial oxidative phosphorylation in frozen PBMCs from 41 out of the 98 subjects. A previous report by Tomas et al. has shown a difference in whole-cell mitochondrial respiration, consistent with a deficiency in cellular energetics associated with 135 mitochondrial dysfunction or substrate flux feeding into the TCA cycle and mitochondrial respiratory chain (15). However, this assay was difficult to reproduce; Missailidis et al. failed to reproduce this finding in PBMCs but did find differences in immortalised lymphocytes (24). In our study, cell viability following thawing was between 70–85% with a noticeable drop in viability following 24 hr in culture. Mitochondrial respiration was measured in 5-mM glucose media with rates measured over 1–2 hr. No difference was observed in rates of mitochondrial respiration between ME/CFS patients, MS patients and healthy controls (Figure S1A). When ME/CFS patients were divided into severe, moderate and mild patients, no difference was observed (Figure S1B). This demonstrated that mitochondrial function assessment of PBMCs using an oxygen consumption assay on cryopreserved frozen samples failed to discriminate disease cohorts and will be challenging to be developed as a diagnostic approach."​

 

Among the things they found with the Raman spectroscopy were increase in tryptophan and tyrosine, elevated glycerol levels, reduced cholesterol and cholesteryl esters, and reduced glycogen levels.

But it seems that most of the differences applied to ME/CFS and MS patients compared to healthy controls and that there were few clear differences between the MS and ME/CFS groups.

 

Xu et al: “This demonstrated that mitochondrial function assessment of PBMCs using an oxygen consumption assay on cryopreserved frozen samples failed to discriminate disease cohorts and will be challenging to be developed as a diagnostic approach”


The study they were trying to replicate is Thomas et al: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231136

Thomas et al: “Blood samples were prepared and PBMCs isolated as described previously in Tomas et al. (6)”

Thomas et at (6) (my bold): “Blood samples were processed using the Histopaque® method. Briefly, the whole blood sample was centrifuged at 700 x g for 10 minutes and plasma removed. Blood was made up to its original volume with sterile PBS (Sigma Aldrich D8537). Density gradients were prepared with Histopaque® 1.077 (Sigma Aldrich 10771) and Histopaque® 1.119 (Sigma Aldrich 11191). Blood was slowly layered on top of the Histopaque® gradient and the tube spun at 700 x g for 30 minutes with the break off. Plasma layer was aspirated off and the PBMC layer collected. PBMCs were washed with fresh PBS and either used for experiments immediately or frozen at -80°C after being combined with freezing medium (40% FBS (Sigma Aldrich F0804), 10% DMSO (Sigma Aldrich D8418) and 50% RPMI-1640 (Sigma Aldrich R7388). To revive frozen samples, vials were rapidly defrosted in a water bath at 37°C and added to 10ml of fresh RPMI-1640. Cells were centrifuged at 700 x g for 10 minutes, the supernatant removed and cells resuspended in fresh RPMI-1640. Cell viability was then determined using the trypan blue method (see below). PBMC experiments were conducted using RPMI-1640 medium supplemented with 10% FBS and 1% penicillin-streptomycin (Sigma Aldrich P4333). Blood samples were processed within 4 hours of blood collection.”


Xu et al say they couldn’t replicate the results using cryopreserved frozen samples but I don’t know if this is more likely to be because of the biobank’s cryopreservation freezing process or because the Thomas et al result isn’t reliable.

I’m also not sure if Thomas et al looked for any differences between their frozen and fresh samples.

I’ve not read both papers in full so I may have missed discussion of these issues.

 

Figure 2 in the paper you quoted shows the difference between fresh and frozen. There is a big difference.
https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0186802.g002

For bioenergetics it's important to process samples promptly and all samples should be prepared the same way. I think I read somewhere that the UK ME/CFS Biobank can take up to 14 hours to process which is not ideal for many studies where metabolite half life is important.

 

First, awesome paper and glad to see the Morten lab getting their ME/CFS work into the literature. Very nice to see other people looking to cell-based biomarker potential.

This matter is a bit complicated. It's kind of a replication but also not. There are similarities and differences in both method and outcome across all 3 studies (Tomas, mine (Missailidis) and Xu/Morten) I'll break it down by the important points in sequence needed to understand what's going on here.

1) Tomas et al found reduced basal OCR in PBMCs. Their paper says the PBMCs were "washed with fresh PBS and either used for experiments immediately or frozen at -80°C after being combined with freezing medium". This means that their results show a combination of frozen and fresh PBMCs. Their frozen ME/CFS PBMCs have the lowest oxygen consumption.

2) I found reduced basal OCR in PBMCs and unchanged basal OCR (but other abnormalities) in lymphoblastoid cell lines (LCLs) created by immortalising PBMCs with EBV. I also found that PBMCs from pwME died faster than healthy control PBMCs post-thaw. PBMCs without stimulation or immortalization will die after being isolated from the blood since their metabolism and proliferation is inactivated. It just happens faster for those from pwME from what I have seen/reported, and maybe damage from the freezing process interacts with the cells being diseased to make this more observable. You also can't really measure untreated PBMCs reliably with seahorse respirometry. The oxygen consumption rate is so low that it's effectively barely at the threshold of detection. So much so that you often get erroneous readings below 0 because the normal error/variation in the reading is probably greater than the actual signal itself in those cases (ie the machine is not sensitive enough to always handle the negligible amount of respiration done by effectively sleeping/dying cells). Not only have I seen this myself but if you look at the Tomas paper they have OCR values below 0 as well so it's happened for them too. So this makes sense, especially given that their post-thaw ME/CFS PBMCs consumed the least oxygen.

3) Xu/Morten et al found no difference in basal OCR in post-thaw PBMCs using a different instrument to measure OCR. It may be that their ME/CFS PBMCs do not die faster like mine did, or were not incubated long enough that dead cells would accumulate (24-48 hr mark was where the difference became most apparent, and my PBMCs were also incubated for a day prior to assay post-thaw as with the Tomas paper). Maybe the sensitivity of the different instrument used is related, it's different to the instrument used by Tomas et al and by myself. Don't know which of these possibilities it is. But IMO this doesn't matter so much since, again, PBMCs are metabolically quiescent anyway. I wouldn't expect to see much or any difference if the cells weren't dying faster in one group. The analogy is like trying to hear which of two speakers has its volume set higher while both speakers are both powered off.

So basically I think that everything makes total sense taken together as a whole across the 3 studies. PBMCs are useful for a lot of things but I don't think they are for oxygen consumption measurements. Xu/Morten and friends have done amazing work here and their other, much more important results using raman spectroscopy in this paper are extremely valuable.

 

Thanks for your insights @DMissa

Your comments imply that you’re an ME/CFS researcher. Can I ask who you are? (When I click on your S4ME profile I’m getting an error message.)

 

I think Daniel Missailidis. We actually have a tag for him, here are those threads. Also a bio here. I'm happy he is here, I wish there were more such researchers on the forum.

 

DMissa is Daniel Missailidis, as confirmed by Daniel himself here, https://www.s4me.info/threads/“a-ce...-cfs-21-missailidis-thesis.22017/#post-407547. His Researchgate profile can be seen here, https://www.researchgate.net/profile/Daniel-Missailidis

Papers where he is an author and where we have discussion threads,
Cell-Based Blood Biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Missailidis et al Feb 2020
An Isolated Complex V Inefficiency and Dysregulated Mitochondrial Function in Immortalized Lymphocytes from ME/CFS Patients Missailidis et al. 2019
Pathological Mechanisms Underlying Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, 2019, Fisher et al.

[Cross posted with Wyva]

 

Yes, thank you!

 

Thanks for the breakdown @Hutan ! The problem with supervised machine learning of any kind - where you first tell the algorithm what the groups are (ME, MS, Controls) and then tell it to separate them as best as possible - is that if you have a large enough amount of 'features' for each sample you will always find some way to be able to separate the groups no matter what. This is because there will be some noise in the data that just so happens to divide the data up by chance.

We've seen this a lot in metabolomics data where for each sample (a patient) they have thousands of features (measurements of thousands of different metabolites). They then go on to train some machine learning classifier and get good results able to distinguish the patients and controls. The usefulness of their classifier can only be demonstrated if they then apply it to a completely independent set of test data and see if they get good separation. If they don't do this step then it's basically useless.

 

I've modelled this in R here using data with no signal and only random noise.

In this paper they have about 1000 features (readings for 1000 different wavelengths) over 1000s of cells - so its dimensionality is high. I've generated a dataset for 1000 'samples' each with 1000 'features.' The dataset is populated with random decimal values between 0 and 1, so there is no pattern only noise. I've then assigned each of the samples randomly to a group number 0, 1 or 2 to emulate groups of (controls, ME or MS).

Here is a scatterplot of features 1 and 2, each dot corresponds to a sample. You can see there's no pattern:


I've then split up the data into two parts, 70% of the samples will be used to train an LDA model to predict the groups from the data, and the remaining 30% will be used to test it. When you plot the first two LDs from the training data you can see it separates the groups amazingly - based off of absolutely no real signal at all. I was surprised at just how strongly this resembles the plot in the paper:


Then when you go on to use the trained model to predict the groupings on the test data you can see it can't do it at all:


here's the R code if anyone wants to retry: