Flow Clotometry: Measuring Amyloid Microclots in ME/CFS, Long COVID, and Healthy Samples with Imaging Flow Cytometry

doi:10.21203/rs.3.rs-4507472/v1

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Flow Clotometry: Measuring Amyloid Microclots in ME/CFS, Long COVID, and Healthy Samples with Imaging Flow Cytometry

https://doi.org/10.21203/rs.3.rs-4507472/v1

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) has received more attention since the characterization of Long COVID (LC), a condition somewhat similar in symptom presentation and, to some extent, pathophysiological mechanisms. A prominent feature of LC pathology is amyloid, fibrinolysis-resistant fibrin(ogen) fragments, termed microclots. Despite prior identification of microclots in ME/CFS, quantitative analysis has remained challenging due to the reliance on representative micrographs and software processing for estimations. Addressing this gap, the present study uses a cell-free imaging flow cytometry approach, optimized for the quantitative analysis of Thioflavin T-stained microclots, to precisely measure microclot concentration and size distribution across ME/CFS, LC, and healthy cohorts. We refer to our cell-free flow cytometry technique for detecting microclots as 'flow clotometry'. We demonstrate significant microclot prevalence in ME/CFS and LC, with LC patients exhibiting the highest concentration (18- and 3-fold greater than the healthy and ME/CFS groups, respectively). This finding underscores a common pathology across both conditions, emphasizing a dysregulated coagulation system. Moreover, relating to microclot size distribution, the ME/CFS group exhibited a significantly higher prevalence across all area ranges when compared to the controls, but demonstrated a significant difference for only a single area range when compared to the LC group. This suggests a partially overlapping microclot profile in ME/CFS relative to LC, despite the overall higher concentration in the latter. The present study paves the way for prospective clinical application that aims to efficiently detect, measure and treat microclots.

Health sciences/Medical research/Biomarkers/Diagnostic markers

Biological sciences/Biological techniques/Imaging/Fluorescence imaging

Expanded Key Points for Review Only

We have recently developed a method to detect microclots in plasma samples using cell-free flow cytometry analysis, which we have coined 'flow clotometry'.
Quantitative data presented in this paper corroborate that microclots are present in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) plasma samples at significant levels when compared to ‘healthy’ participants, and that Long COVID patients exhibit the highest concentrations of microclots, with an 18- and 3-fold change in their median compared to the healthy participants and ME/CFS group, respectively.
The size distribution of microclots in the ME/CFS group is significantly different from that of healthy participants in all area ranges, but were only significantly different in one area range when compared to the LC group. Hence, the size distribution of microclots in ME/CFS samples is broadly comparable to that of LC samples.
Importantly, microclots are present in an easily accessible and measurable fraction of blood, and hence the implementation of flow cytometry (clotometry) microclot assessment in the clinical setting is warranted. Such methods can provide a high throughput, and deliver quantitative information regarding both burden and size distribution of fibrinaloid microclots.

Main Key Points

We have developed a method to detect microclots in plasma samples using cell-free flow cytometry analysis, which has been coined 'flow clotometry'.
Quantitative data corroborate that microclots are present in myalgic encephalomyelitis/chronic fatigue samples at significant levels when compared to healthy participants

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating condition that predominantly presents with unresolved fatigue, post-exertional symptom exacerbation (PESE), cognitive dysfunction, and orthostatic intolerance, amongst other symptoms^1,2. The intersection of Long COVID (LC) and ME/CFS has sparked considerable research interest due to their striking clinical similarities ^3–5. A subset of individuals that have resolved SARS-CoV-2 infection exhibit post-infection symptoms aligning particularly with ME/CFS diagnostic criteria ^6,7, further amplifying the connection between the two conditions. Although ME/CFS can arise from various viral triggers beyond SARS-CoV-2^8,9, shared pathophysiological mechanisms potentially underpin the overlap of symptoms between LC and ME/CFS ^10–12.

One overlapping feature of the two conditions are amyloid, fibrinolysis-resistant fibrin(ogen) fragments, which we have termed fibrinaloid microclots ^13–16. Other groups have corroborated our findings with regards to microclots in acute COVID-19 and LC ^17,18, and have even uncovered their significance in sepsis ¹⁹. In addition to microclots, we also demonstrated hypercoagulability and platelet hyperactivation in roughly half of our ME/CFS cohort ¹³. The coagulation system has been somewhat understudied in ME/CFS, with few studies assessing coagulation after the mid-2000s ²⁰. As of recent years, however, other groups have also found abnormalities in the coagulation system in ME/CFS, including hyperactivated platelets ^21–24, dysfunctional platelet activity post-exertion ²⁵, and dysregulated proteins within the coagulation system ^23,26.

Additionally, a number of studies have demonstrated endothelial dysfunction in ME/CFS ^27–32, which, with the relationship between the endothelium and coagulation in mind ^33,34, might be a reflection of or be a causative factor for dysregulated clotting processes. These findings beg for further elucidation of the role of dysregulated coagulation and endothelial dysfunction in this disease population, and to determine to what extent the dysregulation of these systems contribute to pathology and symptom manifestation.

We have previously shown via fluorescence microscopy that ME/CFS plasma samples contain significant levels of microclots ¹³. While the utmost care was taken in our previous study to ensure representative micrographs were obtained for each sample, it is almost impossible to determine the exact concentration of sample constituents (in this case, microclots) by microscopy (qualitative) analysis unless one is able to analyse an entire sample ^15,17,35,36. We have recently developed a solution to this issue: cell-free flow cytometry analysis of microclots in plasma samples ³⁵, which, in actuality, is not true flow cytometry – hence the term flow clotometry. The assessment of plasma via this method offers robust and more reliable measurements of microclots – furthermore, we possess an imaging flow cytometer which allows the visualization of each event, thereby allowing both quantitative and qualitative analysis of microclots, and other constituents.

Here, we analyze ME/CFS plasma samples for microclots using cell-free imaging flow clotometry, and compare the findings to both a healthy participant group and LC group. The intent is both to show corroboration of previous findings obtained from microscopy analyses – showing that ME/CFS plasma samples do indeed contain significant levels of microclots – and to provide a more efficient, quantitative, and informative method for the detection and measurement of microclots. Furthermore, we compare in a fully quantitative manner the concentration of microclots in ME/CFS plasma samples against LC samples, and also discern differences in size distribution of microclots between the two disease groups. This study is important for both the advancement of our understanding of ME/CFS (and LC) and for prospective clinical applications that aim to efficiently detect, measure and treat microclots, and to discriminate the diseases that cause them to manifest.

Ethical Clearance

Ethical clearance was issued by the Health Research Ethics Committee (HREC) of Stellenbosch University (South Africa) (N19/03/043, project ID #9521). Ethical guidelines were strictly adhered to, as per the Declaration of Helsinki, South African Guidelines for Good Clinical Practice, and Medical Research Council Ethical Guidelines for Research.

Blood Collection and Demographics

Stored PPP samples from 16 healthy individuals (10 females; 6 males), 30 individuals with ME/CFS (22 females; 8 males), and 30 individuals with LC (17 females; 13 males) were used for this study. Healthy subjects were excluded if they smoked, were pregnant, on contraceptive medication, and suffered from coagulopathies and/or cardiovascular disease. The ME/CFS population was previously recruited from the ME/CFS Foundation of South Africa and blood collection was performed at PathCare. This cohort includes 25 samples that were analyzed in a previous study (Nunes et al., 2022). All ME/CFS participants in this study had not experienced a SARS-CoV-2 infection prior to the date of sample collection. LC individuals were recruited from our clinical collaborator's practice. Informed consent was obtained for the LC cohort and participants were also given a questionnaire to complete which pertains to age, gender, pre-existing comorbid conditions, regular medication, information regarding the onset, diagnosis, and severity of acute COVID-19 illness, vaccination status, and self-reported LC symptoms. LC patients were identified based on self-reported symptoms which developed after acute infection. To note, the LC samples used in this study have not been utilized in previous publications. Sodium citrate tubes were used for blood collection, and centrifuged at 3000×g for 15 min at room temperature. The PPP was collected, and stored at -80°C.

Sample Preparation for Imaging Flow Clotometry

After samples were left to thaw at room temperature, 47µL of PPP was incubated with 3µL of thioflavin T (ThT) (Sigma-Aldrich, St. Louis, MO, USA) (final exposure concentration of 0.03mM) for 30 minutes at room temperature in a dark room. The samples were then transferred to the Amnis® FlowSight® Imaging Flow Cytometer (Luminex). An acquisition template was used with the 405nm and 488nm lasers turned on and a gate established to collect all ThT positive (ThT+) events. Details regarding optimization of the flow clotometry setup are given in our previous publication ³⁵. To reduce background noise and file size, only events within the ThT + gate were collected for analysis. All samples were acquired using the same acquisition template for 5 minutes each. As the sample flow rate was set to 2.58µL/min, a total of 13µL of sample was assessed during acquisition.

Imaging Flow Clotometry Data Analysis

Raw imaging flow cytometer data were processed using IDEAS 6.2 as developed for the Amnis® FlowSight® Imaging Flow Cytometer. A ThT gate was implemented to eliminate background and cellular structures, and an intensity mask was used for data acquired and assessed in channel 7 ³⁵. For analysis, we included objects/mL and count within specified area ranges. Objects/mL is a representation of event counts which are then transformed into a concentration value. Count within specified area ranges determines the prevalence of microclots within given area ranges, which are as follows: 0-100µm², 100–400µm², 400–900µm², 900–1600µm², and + 1600µm². Because the number of objects analysed between groups will not be equal, the average count within specified area ranges will be converted into a percentage of the total objects analysed for that group. GraphPad Prism software (version 9.5.0) and R statistical software (version 4.3.2) were used for statistical analysis. The Shapiro-Wilk normality test was used to test for normality. All data/features were found to be non-normally distributed, and were assessed with pairwise comparisons between groups (control, Long COVID, and ME/CFS) using the Wilcoxon Rank-Sum Test, according to the following hypotheses:

Let,\({M}_{C}^{n}\), \({M}_{L}^{n}\), and \({M}_{M}^{n}\) be the population medians for the control, Long COVID, and ME/CFS groups, respectively, where \(n\) can represent either the concentration of microclots (objects/mL) or the count of microclots within specified area ranges. Then:

Null Hypotheses (H₀):

Alternative Hypotheses (H₁)*:

\({H}_{01}: {M}_{C}^{n}={M}_{L}^{n}\)

\({H}_{02}: {M}_{C}^{n}={M}_{M}^{n}\)

\({H}_{03}: {M}_{L}^{n}={M}_{M}^{n}\)

\({H}_{11}: {M}_{C}^{n}<{M}_{L}^{n}\)

\({H}_{12}: {M}_{C}^{n}<{M}_{M}^{n}\)

\({H}_{13}: {M}_{M}^{n}<{M}_{L}^{n}\)

*Statistical significance was recorded as * = p < 0.05; ** = p < 0.01; *** = p < 0.001.

A Random Forest classifier was deployed to identify key features for categorizing patient status among control, Long COVID, and ME/CFS groups, utilizing the ‘randomForest’ library from CRAN in R statistical software. This model outputs feature importance metrics, including mean decrease in Gini and accuracy. The former quantifies a feature's contribution to decision tree purity within the model, with higher values signifying greater importance for node splitting. Conversely, mean decrease in accuracy indicates the extent to which a feature's absence degrades model performance.

The three cohorts do not differ in terms of age as conducted by a one-way ANOVA (healthy participants = 54,31 ± 10,54; ME/CFS = 45,97 ± 14,76; LC = 52,70 ± 17,37). See Table 1 for demographics.

The results from the present flow clotometry analysis of ThT-stained PPP samples are contained within Table 2 and are graphically presented in Fig. 1 and Fig. 2. Substantial differences in microclot concentration are observed across the three groups studied. The ME/CFS group exhibits a significant higher median value for objects/mL (25150) than did the healthy participants (4521). As expected ³⁵, we find evidence that the LC cohort exhibits an objects/mL median value (82754) that is significantly higher than those of both the healthy participants (***) and ME/CFS (***) cohort, representing an 18-fold and 3-fold change, respectively.

Another informative parameter assessed is the count within defined area ranges (Table 2 and Fig. 2): this provides information about the prevalence of microclots within given area ranges. The area ranges are as follows: 0-100µm², 100–400µm², 400–900µm², 900–1600µm², and + 1600µm². The comparative panels (A-E) from Fig. 2 underscore that LC patients exhibit significantly higher counts than controls (in all area ranges except for the last) and ME/CFS patients (in the first three area ranges). When compared to the healthy participants group, the ME/CFS cohort presented with a significantly higher median count within all area ranges. The data are also presented as a percentage of the total count for each group, in order to enable comparison of distribution across groups that have different total counts. With regards to the last area range (1600 + µm²), some samples, especially within the two disease groups, do indeed contain a number of microclots that register within this notably large area range. These large microclots, despite being present at a low prevalence compared to microclots within the other area ranges, will contribute to pathology and are thus still important to measure and target. Individualized assessment of prevalence within this range is required.

A simple random forest model was developed to ascertain the relative importance of various features – objects/mL and count within specific area ranges – in discriminating between healthy participants, ME/CFS, and LC patients. In Fig. 3, which showcases a variable importance plot, the feature for the count range 100–400µm² emerges as the most significant predictor, closely followed by the 400–900µm² count range – both of which are critical in patient classification. This finding suggests that mid-range object counts hold substantial predictive power, although lower count ranges are also influential in the model's classification decisions. In contrast, the lower importance attributed to higher count ranges in determining model accuracy and Gini impurity suggests these counts are less discriminative for the model’s predictive tasks. Notably, the overall objects/mL feature displays strong predictive capability as well. These findings corroborate the findings from Figs. 1 and 2. Figure 4 contains illustrative micrographs of events (microclots) obtained by the flow cytometer.

Table 1

Sample demographics and considerations.
Previously stored platelet poor plasma
Mean age in years [and standard deviation (SD)] of healthy participants, ME/CFS and Long COVID patients
Healthy Participants (n = 16)	54.31 ± 10.54
ME/CFS (n = 30)	45.97 ± 14.76
Long COVID (n = 30)	52.70 ± 17.37
Gender of controls and Long COVID patients
Healthy Participants (n = 16)	10 females; 6 males
ME/CFS (n = 30)	22 females; 8 males
Long COVID (n = 30)	17 females; 13 males
Vaccination status of controls and Long COVID patients before blood collection
Percentage of healthy participants vaccinated (n = 16)	56% (9/16)vaccinated
Percentage of ME/CFS patients vaccinated (n = 30)	83% (25/30) vaccinated
Percentage of Long COVID patients vaccinated (n = 30)	90% (27/30) vaccinated
Co-morbidities of ME/CFS and Long COVID patients
Co-morbidity	% in 30 ME/CFS patients	% In 30 Long COVID patients
Hypertension	13%	30%
Hypercholesterolemia	13%	17%
Type 1 Diabetes Mellitus	0%	3%
Type 2 Diabetes Mellitus	0%	3%
Rosacea	3%	0%
Cardiovascular disease	10%	0%
Psoriasis	17%	0%
Cancer	0%	3%
Rheumatoid Arthritis	10%	7%
Periodontal Disease	10%	10%
Dementia-Like Symptoms	0%	3%
Orthostatic Intolerance	17%	Not available
Mast Cell Activation Syndrome	3%	Not available
Fibromyalgia	17%	Not available

Table 2

Results from flow clotometry analysis of ThT-stained platelet poor plasma (PPP) samples. Objects/mL is a transformation of count values into a concentration value. The count within defined area ranges parameter refers to the number (count) of microclots that fall within a specific area range, namely: 0-100µm², 100–400µm², 400–900µm², 900–1600µm² and 1600µm². This data has also been converted to a percentage of the total, in order to enable comparison between groups whose total counts differ. Significance was determined at p < 0.05. Values that are in bold print represent significance. The data are expressed as median [IQR].
Parameter	Healthy participants	ME/CFS	LC	P-value (Healthy participants vs ME/CFS)	P-value (Healthy participants vs LC)	P-value (ME/CFS vs LC)
Objects/mL (count)	4521 [78958]	25150 [107203]	82754 [620187]	< 0.0001***	< 0.0001***	0.0003***
Count within defined area ranges
0-100µm² (count; percentage)	8 [105]; 23.9%	36.5 [1069]; 21.1%	105.5 [2358]; 16.6%	0.01*	< 0.0001***	0.0361*
100–400µm² (count; percentage)	19.5 [286]; 35%	132 [710]; 40.4%	604 [6598]; 67.4%	< 0.0001***	< 0.0001***	< 0.0001***
400–900µm² (count; percentage)	10.5 [574]; 30.8%	93 [374]; 26.8%	150.5 [1284]; 14%	0.0002***	< 0.0001***	0.0234*
900–1600µm² (count; percentage)	3.5 [146]; 8.9%	19 [158]; 9.9%	15.5 [113]; 1.8%	0.0014**	0.0014**	0.5500
1600 + µm² (count; percentage)	0 [15]; 1.4%	2 [19]; 1.7%	1 [9]; 0.2%	0.0160*	0.1490	0.9384

We have previously shown the significant presence of microclots in ME/CFS plasma samples using fluorescence microscopy coupled to ImageJ analysis of the respective micrographs ¹³. In the aforementioned study fluorescent microscopy was used to visualize these anomalous clot structures, from which representative micrographs of each sample were obtained for subsequent analysis. The total percentage of area signal in the micrographs – which was termed ‘% area amyloid’ – was used to infer, crudely, the concentration of microclots in both the healthy participants and ME/CFS group. This is obviously not an objectively quantitative analysis, and is open to error and some subjectivity. To improve on this technique and, specifically, to provide a fully quantitative mode of analysis for detecting microclots in plasma samples, we have developed and optimized a cell-free flow clotometry analysis where ThT-stained microclots are registered and measured as events ³⁵. Here, we compared the concentrations and sizes of microclots between an ME/CFS and LC cohort, and sought to corroborate and improve on the findings of our previous ME/CFS study ¹³.

The specifics of microclots and associated complications have been published before ^{11,14,37–39}, and hence these points will not be discussed here. Previously, our microscopy analysis and subsequent ImageJ assessment of micrographs led to the inference that microclots are present in our ME/CFS cohort at a level 10x that of the healthy participants group. In the present study, cell-free flow clotometry analysis of ME/CFS PPP samples incubated with ThT reflect the same direction of significance but a slightly lower fold change in the concentration of microclots: in terms of objects/mL, the present experiment revealed that the plasma of the ME/CFS cohort contains over 5x the number of microclots than those of the healthy participants (Table 2 & Fig. 1A) (***). The present flow clotometry analysis corroborates quantitatively that ME/CFS individuals contain significantly higher levels of microclots in their circulation than do healthy participants, albeit less than inferred from our previous microscopy analysis ¹³. With that being said, our previous study assessed area (% area amyloid) across micrographs and therefore cannot be reliably compared to the present quantitative technique where microclot concentration (count) was measured.

Another important parameter assessed in this study is the prevalence of microclots within defined area ranges (Fig. 2). In the present study, the ME/CFS group measured with a higher count within all area ranges when compared to healthy participants, of which the greatest differences were recorded in the 100–400µm² (***) and 400–900µm² (***) ranges. Ultimately, we provide evidence that both the concentration and prevalence of large microclots in ME/CFS PPP samples are significantly greater when compared to a healthy participant cohort. When assessing the percentages of distribution across area ranges, it is apparent that the ME/CFS group has an overall greater prevalence of larger microclots than does the LC group – although, a similar profile is observed in the control group. This particular analysis can be made more robust if the same number of total objects are compared across groups, from which size distribution can be assessed with statistical confidence.

From our experience, it is normal for healthy participants to possess a low-level of microclots which will typically be localised to the smallest area ranges, 0-100µm² and 100–400 µm². Indeed, it is expected that a higher prevalence of microclots within this range may be harmful, but the significance of pathology is hypothesized to relate to a higher prevalence of microclots within larger area ranges, i.e. it is the burden of microclots large in size (registered within area ranges from 100–400µm² and higher) that is responsible for pathology and symptom manifestation. Related to this idea, it has been shown independently that larger microclots (as well as a greater prevalence) are potentially effective predictors of clinical outcomes, including disseminated intravascular coagulation (DIC) and mortality ¹⁹. Their larger size can clearly have a greater impact on the integrity and function of microcirculation, and may also bind and/or entrap more inflammatory molecules ^14,37, causing vascular inflammation and damage, and potentially occluding capillaries. This is likely the basis for pathology induced by these microclots: both their concentration in plasma/circulation as well as their size. Another important facet to these microclots are the proteins or molecules that they ‘mop up’ and/or entrap: we have shown previously that microclots from specific disease states contain different associated proteins ³⁷; we are currently finalizing a more comprehensive study of this nature.

Because ME/CFS shares many similarities with LC, and because LC has been a focal point of study in the context of microclots, we wanted to compare microclot analyses on ME/CFS samples with those in LC samples. Because we know that the burden of microclots is greatest in LC patients, and because we have already published on this topic ³⁵, the LC cohort in this present study served as a positive control, where we expected the concentration and size of microclots to be highest.

Indeed, the LC cohort exhibits a significantly higher concentration of microclots than do both the healthy participants and ME/CFS cohorts. Between the ME/CFS and LC cohorts, the LC group exhibits a 3x higher concentration of microclots. Further study is required to explain the biochemistry of these microclots (both intra- and inter-disease assessment), which can then be linked to specific pathological states and mechanisms.

Healthy participant samples do indeed possess some microclots (especially nowadays since the arrival of SARS-CoV-2 and the vaccines based on the sequence of its spike protein). What is important, however, is the load in diseased states, particularly LC and ME/CFS, as well as the size of these microclots. Healthy participant samples contain low levels of microclots and possess relatively few that are of a large size. As shown in this study and the previous one conducted on solely LC samples ³⁵, in ME/CFS and LC, microclots in PPP samples are present at significant levels when compared to healthy participants and the prevalence of large microclots is also greater.

Importantly, we corroborate the inferences from our previous study where we assessed microclot loads in ME/CFS PPP samples using microscopy analysis, where we determined that microclots (assessed by area) are present at a level 10x that of healthy participants. In the present study, a value of 5x that of healthy participants was obtained, as inferred by the parameter, objects/mL. Hence, dysregulation of the coagulation system and persistence of amyloid, fibrinolysis-resistant fibrin(ogen) fragments (microclots) are features of ME/CFS, as it is in LC. Additionally, we have shown that there are minor differences in terms of the size distribution between ME/CFS and LC microclots, as well as there being significant differences in concentration of microclots between these two disease groups.

Furthermore, there is great clinical potential regarding the implementation of this cell-free imaging flow clotometry technique – flow clotometry – in both the research and clinical sector. Flow clotometry analysis of microclots provides throughput that is unattainable with fluorescent microscopy techniques, and will provide important information regarding both the concentration and size distribution of microclots in patient blood samples.

STUDY LIMITATIONS AND FUTURE DIRECTIONS

We recognize the necessity for more comprehensive studies to elucidate the concentrations and size distributions of microclots across a broad spectrum of diseases and comorbidities. Such investigations are crucial for deepening our understanding of the specific roles and impacts of microclots in different disease states. Although our current methodology enables clear differentiation between microclot characteristics in control groups versus those in ME/CFS and Long COVID, the challenge of distinguishing between ME/CFS and Long COVID remains. Addressing this will be vital for further refining our diagnostic approaches and enhancing the specificity of microclot-based assessments in these closely related conditions.

In addition, the difference in data between fresh and stored samples still requires additional investigation, and holds relevance to the clinical implementation of this cell-free flow clotometry technique. In this study, we used stored plasma samples and not freshly obtained blood samples. We are currently conducting such an experiment where we are investigating whether the microclot concentrations and sizes change as a result of freezing (and thawing).

An experiment of this nature will also benefit from an expansion in sample size across all groups. A sample size of 30 was used to represent both the ME/CFS and LC group, and was due to the fact that we possess stored samples from only 30 ME/CFS patients. Future studies should acknowledge these limitations and focus on discerning differences in microclot parameters between various disease groups (that is, conditions beyond ME/CFS and LC), between freezing and thawing cycles, and between study groups of a larger sample size. Lastly, with regards to the size distribution (count within specified area ranges), groups with the same number of total count should be compared to enable robust assessment.

ACKNOWLEDGEMENTS

EP thanks Polybio Research Foundation, Balvi Research Foundation, the South African Medical Research Council (SIR grant) and the National Research Foundation of South Africa (grant 142142). DBK thanks the Balvi Foundation (grant 18) and the Novo Nordisk Foundation for funding (grant NNF20CC0035580). The content and findings reported and illustrated are the sole deduction, view and responsibility of the researchers and do not reflect the official position and sentiments of the funders.

AUTHORSHIP CONTRIBUTIONS

J.M.N.: Conducting of the research, writing of the paper; J.H.P.: statistical analysis; D.B.K.: editing of the manuscript; co-corresponding author; E.P.: study leader, funding, editing of the manuscript corresponding author.

DISCLOSURE OF CONFLICTS OF INTEREST

E.P. holds a patent for the detection of microclots in Long COVID and is the founding director of Biocode Technologies. The rest of the authors have no competing interests to declare

Lim, E.-J. & Son, C.-G. Review of case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Journal of Translational Medicine 18, 289 (2020). https://doi.org/10.1186/s12967-020-02455-0
Vaes, A. W. et al. Symptom-based clusters in people with ME/CFS: an illustration of clinical variety in a cross-sectional cohort. Journal of Translational Medicine 21, 112 (2023). https://doi.org/10.1186/s12967-023-03946-6
Wong, T. L. & Weitzer, D. J. Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)—A Systemic Review and Comparison of Clinical Presentation and Symptomatology. Medicina 57, 418 (2021).
Komaroff, A. L. & Lipkin, W. I. ME/CFS and Long COVID share similar symptoms and biological abnormalities: road map to the literature. Frontiers in Medicine 10 (2023). https://doi.org/10.3389/fmed.2023.1187163
Sukocheva, O. A. et al. Analysis of post COVID-19 condition and its overlap with myalgic encephalomyelitis/chronic fatigue syndrome. Journal of Advanced Research 40, 179–196 (2022). https://doi.org/https://doi.org/10.1016/j.jare.2021.11.013
Petracek, L. S. et al. Adolescent and Young Adult ME/CFS After Confirmed or Probable COVID-19. Frontiers in Medicine 8 (2021). https://doi.org/10.3389/fmed.2021.668944
Kedor, C. et al. Chronic COVID-19 Syndrome and Chronic Fatigue Syndrome (ME/CFS) following the first pandemic wave in Germany – a first analysis of a prospective observational study. medRxiv, 2021.2002.2006.21249256 (2021). https://doi.org/10.1101/2021.02.06.21249256
Rasa, S. et al. Chronic viral infections in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Journal of Translational Medicine 16, 268 (2018). https://doi.org/10.1186/s12967-018-1644-y
Proal, A. & Marshall, T. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in the Era of the Human Microbiome: Persistent Pathogens Drive Chronic Symptoms by Interfering With Host Metabolism, Gene Expression, and Immunity. Frontiers in Pediatrics 6 (2018). https://doi.org/10.3389/fped.2018.00373
Tate, W. et al. Molecular Mechanisms of Neuroinflammation in ME/CFS and Long COVID to Sustain Disease and Promote Relapses. Frontiers in Neurology 13 (2022). https://doi.org/10.3389/fneur.2022.877772
Kell, D. B. & Pretorius, E. The potential role of ischaemia–reperfusion injury in chronic, relapsing diseases such as rheumatoid arthritis, Long COVID, and ME/CFS: evidence, mechanisms, and therapeutic implications. Biochemical Journal 479, 1653–1708 (2022). https://doi.org/10.1042/bcj20220154
Annesley, S. J., Missailidis, D., Heng, B., Josev, E. K. & Armstrong, C. W. Unravelling shared mechanisms: insights from recent ME/CFS research to illuminate long COVID pathologies. Trends in Molecular Medicine (2024). https://doi.org/10.1016/j.molmed.2024.02.003
Nunes, J. M., Kruger, A., Proal, A., Kell, D. B. & Pretorius, E. The Occurrence of Hyperactivated Platelets and Fibrinaloid Microclots in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Pharmaceuticals (Basel) 15 (2022). https://doi.org/10.3390/ph15080931
Pretorius, E. et al. Persistent clotting protein pathology in Long COVID/Post-Acute Sequelae of COVID-19 (PASC) is accompanied by increased levels of antiplasmin. Cardiovasc Diabetol 20, 172 (2021). https://doi.org/10.1186/s12933-021-01359-7
Pretorius, E. et al. Prevalence of readily detected amyloid blood clots in ‘unclotted’ Type 2 Diabetes Mellitus and COVID-19 plasma: a preliminary report. Cardiovascular Diabetology 19, 193 (2020). https://doi.org/10.1186/s12933-020-01165-7
Grobbelaar, Lize M. et al. SARS-CoV-2 spike protein S1 induces fibrin(ogen) resistant to fibrinolysis: implications for microclot formation in COVID-19. Bioscience Reports 41, BSR20210611 (2021). https://doi.org/10.1042/BSR20210611
Dalton, C. F. et al. Increased fibrinaloid microclot counts in platelet-poor plasma are associated with Long COVID. medRxiv, 2024.2004.2004.24305318 (2024). https://doi.org/10.1101/2024.04.04.24305318
Bergaglio, T., Synhaivska, O. & Nirmalraj, P. N. 3D Holo-tomographic Mapping of COVID-19 Microclots in Blood to Assess Disease Severity. Chemical & Biomedical Imaging 2, 194–204 (2024). https://doi.org/10.1021/cbmi.3c00126
Schofield, J. et al. Amyloid-Fibrinogen Aggregates ("Microclots") Predict Risks of Disseminated Intravascular Coagulation and Mortality. Blood Advances (2024). https://doi.org/10.1182/bloodadvances.2023012473
Kennedy, G., Norris, G., Spence, V., McLaren, M. & Belch, J. J. Is chronic fatigue syndrome associated with platelet activation? Blood Coagulation & Fibrinolysis 17, 89–92 (2006). https://doi.org/10.1097/01.mbc.0000214705.80997.73
Jahanbani, F. et al. Phenotypic characteristics of peripheral immune cells of Myalgic encephalomyelitis/chronic fatigue syndrome via transmission electron microscopy: A pilot study. PLOS ONE 17, e0272703 (2022). https://doi.org/10.1371/journal.pone.0272703
Bonilla, H. et al. Comparative Analysis of Extracellular Vesicles in Patients with Severe and Mild Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Frontiers in Immunology 13 (2022). https://doi.org/10.3389/fimmu.2022.841910
Giloteaux, L., Glass, K. A., Germain, A., Zhang, S. & Hanson, M. R. Dysregulation of extracellular vesicle protein cargo in female ME/CFS cases and sedentary controls in response to maximal exercise. bioRxiv (2023). https://doi.org/10.1101/2023.08.28.555033
Nacul, L. et al. Evidence of Clinical Pathology Abnormalities in People with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) from an Analytic Cross-Sectional Study. Diagnostics 9, 41 (2019).
Ahmed, F. et al. Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation. bioRxiv, 2022.2010.2013.512091 (2022). https://doi.org/10.1101/2022.10.13.512091
Sweetman, E. et al. A SWATH-MS analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome peripheral blood mononuclear cell proteomes reveals mitochondrial dysfunction. Journal of Translational Medicine 18, 365 (2020). https://doi.org/10.1186/s12967-020-02533-3
Sandvik, M. K. et al. Endothelial dysfunction in ME/CFS patients. PLOS ONE 18, e0280942 (2023). https://doi.org/10.1371/journal.pone.0280942
Haffke, M. et al. Endothelial dysfunction and altered endothelial biomarkers in patients with post-COVID-19 syndrome and chronic fatigue syndrome (ME/CFS). Journal of Translational Medicine 20, 138 (2022). https://doi.org/10.1186/s12967-022-03346-2
Blauensteiner, J. et al. Altered endothelial dysfunction-related miRs in plasma from ME/CFS patients. Scientific Reports 11, 10604 (2021). https://doi.org/10.1038/s41598-021-89834-9
Scherbakov, N. et al. Peripheral endothelial dysfunction in myalgic encephalomyelitis/chronic fatigue syndrome. ESC Heart Failure 7, 1064–1071 (2020). https://doi.org/https://doi.org/10.1002/ehf2.12633
Bertinat, R. et al. Decreased NO production in endothelial cells exposed to plasma from ME/CFS patients. Vascular Pharmacology 143, 106953 (2022). https://doi.org/https://doi.org/10.1016/j.vph.2022.106953
Sørland, K. et al. Reduced Endothelial Function in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome–Results From Open-Label Cyclophosphamide Intervention Study. Frontiers in Medicine 8 (2021). https://doi.org/10.3389/fmed.2021.642710
Neubauer, K. & Zieger, B. Endothelial cells and coagulation. Cell and Tissue Research 387, 391–398 (2022). https://doi.org/10.1007/s00441-021-03471-2
Norooznezhad, A. H. & Mansouri, K. Endothelial cell dysfunction, coagulation, and angiogenesis in coronavirus disease 2019 (COVID-19). Microvascular Research 137, 104188 (2021). https://doi.org/https://doi.org/10.1016/j.mvr.2021.104188
Turner, S., Laubscher, G. J., Khan, M. A., Kell, D. B. & Pretorius, E. Accelerating discovery: A novel flow cytometric method for detecting fibrin(ogen) amyloid microclots using long COVID as a model. Heliyon 9 (2023). https://doi.org/10.1016/j.heliyon.2023.e19605
Grixti, J. M., Theron, C. W., Salcedo-Sora, J. E., Pretorius, E. & Kell, D. B. Automated microscopic measurement of fibrinaloid microclots and their degradation by nattokinase, the main natto protease. bioRxiv, 2024.2004.2006.588397 (2024). https://doi.org/10.1101/2024.04.06.588397
Kruger, A. et al. Proteomics of fibrin amyloid microclots in long COVID/post-acute sequelae of COVID-19 (PASC) shows many entrapped pro-inflammatory molecules that may also contribute to a failed fibrinolytic system. Cardiovasc Diabetol 21, 190 (2022). https://doi.org/10.1186/s12933-022-01623-4
Kell, D. B., Laubscher, G. J. & Pretorius, E. A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications. Biochem J 479, 537–559 (2022). https://doi.org/10.1042/bcj20220016
Kell, D. B., Khan, M. A., Kane, B., Lip, G. Y. H. & Pretorius, E. Possible Role of Fibrinaloid Microclots in Postural Orthostatic Tachycardia Syndrome (POTS): Focus on Long COVID. Journal of Personalized Medicine 14, 170 (2024).

Yes there is potential Competing Interest. E.P. holds a patent for the detection of microclots in Long COVID and is the founding director of Biocode Technologies. The rest of the authors have no competing interests to declare

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Flow Clotometry: Measuring Amyloid Microclots in ME/CFS, Long COVID, and Healthy Samples with Imaging Flow Cytometry

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Sample Preparation for Imaging Flow Clotometry

Imaging Flow Clotometry Data Analysis

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