Rapid flow cytometric analysis of fibrin amyloid microclots in Long COVID

doi:10.21203/rs.3.rs-2731434/v1

Download PDF

Method Article

Rapid flow cytometric analysis of fibrin amyloid microclots in Long COVID

https://doi.org/10.21203/rs.3.rs-2731434/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 01 Sep, 2023

Read the published version in Heliyon →

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Long COVID has become a significant global health and economic burden, yet there are currently no established diagnostic tools to identify which patients might benefit from specific treatments. One of the major pathophysiological factors contributing to Long COVID is the presence of hypercoagulability; this results in insoluble amyloid microclots that are resistant to fibrinolysis. Our previous research using fluorescence microscopy has demonstrated a significant amyloid microclot load in Long COVID patients. However, this approach lacked statistical robustness, objectivity, and rapid throughput. In the current study, we have used imaging flow cytometry for the first time to show significantly increased concentration and size of these microclots. We identified notable variations in size and fluorescence between microclots in Long COVID and those of controls even using a 20x objective. By combining cell imaging and the high-event-rate nature of a conventional flow cytometer, imaging flow cytometry can eliminate erroneous results and increase accuracy in gating and analysis beyond what pure quantitative measurements from conventional flow cytometry can provide. Although imaging flow cytometry was used in our study, our results suggest that the signals indicating the presence of microclots should be easily detectable using a conventional flow cytometer. Flow cytometry is a more widely available technique which has been used in pathology laboratories for decades, rendering it a potentially more suitable and accessible method for detecting microclots in individuals suffering from both Long COVID and other conditions with similar pathology, such as myalgic encephalomyelitis.

Long COVID

amyloid microclots

imaging flow cytometry

fluorescence

The application of imaging flow cytometry to evaluate the presence, concentration, and size of fibrinaloid microclots has the potential to be a diagnostic tool for individuals suffering from Long COVID.

Long COVID has become a major global health and economic burden ^1–10. A major contributor to the exponential rise in numbers is arguably the absence of a clear suite of diagnostic tools to identify patients that may benefit from specific treatments. The causes of the persistent symptoms of Long COVID include immune dysregulation, autoantibodies, immune dysregulation, reactivation of latent viruses, viral persistence, organ damage, and hypercoagulability ^8,11,12.

The presence of clotting pathology in the form of fibrinaloid microclots, hyperactivated platelets and endothelial dysfunction represents a key target for both the diagnosis and the treatment of the condition ^1,8,11,12. The spike protein from the virus has a key role in causing the various pathologies; it can activate clotting factors ¹³, it is itself amyloidogenic ¹⁴, and it may have direct protein-protein interactions with the main plasma clotting protein, fibrinogen ^2,15. These plasma protein interactions, that can also be driven by bacterial cell wall components ^16,17, result in the formation of amyloid fibrin microclots (fibrinaloids) that are unusually resistant to fibrinolysis. Receptors on platelets and endothelial cells may also interact with viral inflammagens and circulating inflammatory molecules ^15,18−22. These interactions may lead to widespread platelet hyperactivation and endothelial damage. Pathological platelets may also form platelet complexes with various immune cells. Hence there are potentially several ways by which platelets, endothelial cells and plasma proteins may trigger an immune-thrombotic cascade ²³.

Previously, we provided fluorescence microscopic evidence that there is a significant insoluble fibrin amyloid microclot load along with platelet hyperactivation in both acute and Long COVID ^{1–6,15,21,24−27}. We also proposed a clotting and grading system for use in the absence of a general quantitative pathology laboratory method ²⁵. We also previously confirmed entrapped inflammatory molecules within these samples using proteomics analysis ^4,6.

Whilst fluorescence microscopy can identify microclots and platelet hyperactivation, it lacks statistical robustness, objectivity, and high throughput. Being a research tool, it is not readily accessible to patients or clinicians. However, flow cytometric methods are widely available, and since the 1980s have been capable of detecting micron-sized objects (Davey and Kell, 1996). Flow cytometry is a powerful diagnostic tool that has broad applications in various fields including immunology, molecular biology, bacteriology, virology, cancer biology, and infectious disease monitoring (McKinnon, 2018). Here we describe an imaging flow cytometric method that we have developed to measure the level of fibrinaloid microclots in platelet-poor plasma (PPP). Whilst we used an imaging flow cytometer, the results indicate that these signals will be readily detectable on a non-imaging instrument.

Ethical clearance

Ethical clearance for the study was obtained from the Health Research Ethics Committee (HREC) of Stellenbosch University (South Africa) (references: B21/03/001_COVID-19, project ID: 21911 (long COVID registry) and N19/03/043, project ID 9521 with yearly re-approval). Participants were either recruited via the Long COVID registry or identified from our clinical collaborator’s practice. The experimental objectives, risks, and details were explained to volunteers and informed consent was obtained prior to blood collection. Strict compliance to ethical guidelines and the principles of the Declaration of Helsinki, South African Guidelines for Good Clinical Practice, and Medical Research Council Ethical Guidelines for Research were kept for the duration of the study and for all research protocols.

Patient demographics

Participant identification for flow cytometry analysis

For flow cytometry analysis, we randomly selected blood samples from Long COVID and control patients; from these samples, we obtained platelet poor plasma (PPP). We included blood from 20 healthy individuals to serve as controls (7 males; 13 females; mean age 46 (±11) and 40 Long COVID patients (18 males; 22 females; mean age 48 (±14)). Healthy participants did not smoke, did not suffer from coagulopathies, and were not pregnant. The questionnaire collected information regarding their age, gender, pre-existing comorbid conditions, regular medication, information regarding the onset, diagnosis, and severity of their acute COVID-19 illness, and their self-reported long COVID symptoms.

Blood sample collection and sample preparation

Either a qualified phlebotomist or a medical practitioner drew blood samples. To obtain PPP, whole blood (WB) from sodium citrate tubes was centrifuged at 3000xg for 15 minutes at room temperature, and the supernatant PPP samples were collected in 1.5 mL Eppendorf tubes and stored at − 80°C. On the day of analysis, the stored PPP was brought to room temperature and exposed to thioflavin T (ThT), a fluorescent dye that binds to amyloid protein, at a final concentration of 0.03mM (obtained from Sigma-Aldrich, St. Louis, MO, USA). The exposure lasted 30 minutes, during which time the plasma was protected from light.

Flow Cytometry

For data acquisition, we used an Amnis® FlowSight® Imaging Flow Cytometer from Luminex. Imaging flow cytometry combines the advantages of microscopy's single-cell image acquisition with the high-event-rate capacity, quantification, and statistical reliability of traditional flow cytometry. For acquisition, a template was created where the 405nm and 488nm lasers were turned on and a gate was established to collect all ThT positive (ThT +) events by using a negative control (water and ThT), a second negative control (PPP without ThT), and a standard PPP control as reference (see Fig. 1, Fig. 2, and Fig. 3). To reduce background noise and file size, we only collected events within the ThT-positive gate for analysis. The brightfield images were viewed in Channel 1 and ThT-positive events were viewed in Channel 7 using the 405 laser. All samples were acquired using the same acquisition template for 5 minutes each. Given a sample flow rate of 2.58µL/min, the sample volume assessed during this time was 13 microlitres. Given that the number of microclots per mL ranged from 3000-60,000 (Table 2) per sample, this typically involved the detection of 40–800 objects within the microclot gate.

Data analysis

All flow cytometry data analysis was performed using the IDEAS 6.2 software developed for the Amnis® FlowSight® Imaging Flow Cytometer. For analysis, only ThT positive events were collected during acquisition. A microclot gate was also established to eliminate background and cellular structures, such as red blood cells, white blood cells, and fibrinogen strands (Fig. 4). For analysis, we included the following microclot parameters: objects/mL, mean area, and microclots in area range. GraphPad Prism software (version 9.5.0) was used to perform statistical analysis on all data. To determine whether data were normally distributed, the Shapiro-Wilk normality test was applied. Parametric data were analysed using unpaired, one-tailed t-tests with Welch’s correction to test for statistical significance. For non-parametric data, an unpaired, one-tailed Mann-Whitney test was utilized to test for statistical significance. Statistical significance was taken to be at p < 0.05 (and was also marked with asterisks with * = p < 0.05; ** = p < 0.01). Data are represented as either mean ± standard deviation (parametric data) or as median ± [Q1-Q3] (non-parametric data).

Table 1 shows the sample demographics of the 60 samples included in this cohort, including co-morbidity distributions of the 40 Long COVID samples. As part of the flow cytometry data analysis and acquisition, a template was created where the 405nm and 488nm lasers were turned on and a gate was established to collect all ThT-positive (ThT +) events by using a negative control (water and ThT), a second negative control (PPP without ThT), and a standard PPP control sample as references (see Fig. 1, Fig. 2, and Fig. 3). A microclot gate was also established to eliminate background and cellular structures, such as red blood cells, white blood cells, and fibrinogen strands (see Fig. 4).

Table 1

Sample demographics and considerations.
Previously stored platelet poor plasma
Mean age [and standard deviation (SD)] of controls and Long COVID patients
Controls (n = 20)	47 (± 11)
Long COVID (n = 40)	48 (±14)
Gender of controls and Long COVID patients
Controls (n = 20)	7 males; 13 females
Long COVID (n = 40)	18 males; 22 females
Co-morbidities of Long COVID patients (n = 40)
Co-morbidity	% In 40 Long COVID patients
Hypertension	28%
Hyperlipidaemia	25%
Type 2 Diabetes Mellitus	8%
Rosacea	3%
Thrombosis (previous blood clots)	5%
Cardiovascular disease	8%
Psoriasis	3%
COPD	3%
Cancer	8%
Gout	5%
Hypothyroidism	8%

As shown in Fig. 5, two masks were created for analysis; the brightfield mask for channel 1, and an intensity mask for channel 7. The intensity mask distinguished intensity values above a specific threshold in channel 7, as well as a feature that evaluated the intensity of channel 7 within the mask to eliminate weakly-stained events and background noise. We focused on the data from the intensity mask in this analysis, as it accurately reflected the fluorescence intensity resulting from ThT binding to microclots. Therefore, this mask provides a true representation of amyloid microclots. A statistical report was generated that included the following parameters for the microclot gate for the intensity mask: objects/mL, mean area, and count within area ranges. This template was applied consistently to all the samples.

Table 2 shows the parameters of interest that were captured, which included: microclot objects/per mL, microclot mean area, and microclot count within four area ranges. The first two parameters are displayed in Fig. 6, whereas the last parameter is displayed in Fig. 7. For the last-mentioned, we assessed the proportion of microclots falling into four area categories: 100–400µm², 400–900µm², 900–1600µm, and 1600 + µm². Analyzing the number of microclots in these area categories provides a more comprehensive understanding of the distribution of microclot sizes, compared to just looking at the mean area. Figure 8 and Fig. 9 compare the analysis graphs of controls and Long COVID patients generated through the IDEAS software, while Fig. 10 compares the micrographs using the imaging flow cytometer.

Table 2

Microclot parameters (using intensity mask) of 20 controls and 40 Long COVID patients. The non-parametric data are expressed as Median[Q1-Q3]. The count within area range parameter refers to the number of microclots that fall within a specific area range namely: 100–400µm², 400–900µm², 900–1600µm² and 1600 + µm².
Microclot Parameter	Control Median[Q1-Q3]	Control Min-Max	Long COVID Median[Q1-Q3]	Long COVID Min-Max	P-value
Objects/mL	7225[3320–24831]	1071–64525	21396[11973–60745]	1078-1363020	*p < 0.05
Mean area	488[421–555]	372–712	584[444–799]	223–1122	*p < 0.05
Count within area range(µm²)
100–400	53[24–148]	6-346	100[31–198]	0-3663	ns
400–900	34[19–98]	4-416	95[45–226]	4-9104	*p < 0.05
900–1600	8[3–22]	0–75	22[5–95]	1-5470	*p < 0.05
1600+	2[0–3]	0–27	4[1–28]	0-1770	**p < 0.01

Previously, we have used fluorescence microscopy and a semi-quantitative grading system to evaluate the presence of significant microclotting in Long COVID compared to healthy individuals ^2–4,6,26. This method provided good insight into the morphology and size of the microclots but lacked statistical robustness, objectiveness, and throughput. Therefore, we decided to use an imaging flow cytometer that allowed us to combine cell imaging as well as the high-event-rate nature of a conventional flow cytometer. Flow cytometry is a widely used analytical approach for counting, analyzing, and segregating cells that are suspended in a fluid stream ²⁸. Due to its quantitative and multiparametric characteristics, as well as the analytical capacity to process up to 50,000 cells per second, flow cytometry is widely recognized as the gold standard technique for counting and characterizing cells in complex samples ²⁸. At its most fundamental level, flow cytometry entails the sequential measurement of individual cells or microscopic particles as they as they move through an optical probe volume at a high-speed rate ²⁸.

In contrast to the pure quantitative measurements provided by conventional flow cytometry, analyzing cell images can also help eliminate erroneous results, such as distinguishing between cells and debris ^28–30. This approach can lead to more accurate gating and results ^28,29. Furthermore, conventional flow cytometers are incapable of providing spatially resolved information, which is frequently vital for quantifying intricate cellular phenotypes ^28,30,31. Imaging flow cytometry also allows individual cells to be captured in multiple fluorescence channels, as well as brightfield (transmitted light) and darkfield (scattered light) channels ^30,31.

In the current study, we chose to focus on four parameters that we considered best to characterize the severity of microclot presence. We therefore included objects/mL, mean area, and count in area range as representative parameters for this analysis. We chose not to focus solely on microclot per mL, as that might give the false impression of similar counts in healthy individuals and patients. We have previously suggested that clotting pathology is mainly caused by larger microclots that trap inflammatory molecules. Therefore, in addition to microclot concentration, the mean area of the microclots as well as the area distribution (number of microclots within area range) is important to consider.

As illustrated in Table 2 and Fig. 6, our flow cytometry analysis confirmed that microclot objects/mL (*p < 0.05) and microclot mean area (*p < 0.05) were significantly increased in Long COVID compared to controls. Furthermore, Table 2 and Fig. 7 indicate that there are significant differences in the microclot area distribution between controls and Long COVID. Within the 400–900µm² (*p < 0.05), 900–1600µm² (*p < 0.05), and 1600 + µm² (**p < 0.01) area ranges, the number of microclots was significantly higher in Long COVID than in controls. This suggests that the majority of microclots in controls are smaller in size. Hence in Long COVID, not only is the number and concentration of microclots significantly higher, but their size is significantly larger when compared to controls.

The graphs depicted in Fig. 8 and Fig. 9 provide additional evidence of discernible variations in density and distribution between the control group and individuals with Long COVID. Specifically, the analysis graphs demonstrate that Long COVID patients have a greater number of events occurring within the microclot gate and a higher incidence of events falling to the right side of the graph, which indicates larger sized microclots. Examining the analysis graphs, we also observed a significant amount of background events in Long COVID patients in comparison to controls. These events were initially included in the ThT + gate as they may have had low fluorescence intensity but were excluded from the microclot gate as they did not represent true amyloid microclots. By looking at the brightfield images, we determined that some of this background activity could be attributed to red and white blood cells; however, most of the background activity appears to be caused by cellular debris from damaged endothelial cells and fibrinogen strands. These findings align with our previous publications, which suggest the presence of endothelial damage in Long COVID patients ^3,4,7,26. In Fig. 10, micrographs of microclots in controls and Long COVID patients are compared using the imaging flow cytometer. The results demonstrate that even at a 20x objective, there are notable variations in size and fluorescence between Long COVID and controls.

Flow cytometry has been utilized in pathology laboratories for a considerable amount of time, making it a more appropriate and feasible means of identifying microclots in people with Long COVID and other conditions with clotting pathologies. Here we have demonstrated significant differences in the concentration, mean area, and count in area range between Long COVID and controls. These parameters have the potential to guide clinical decisions about diagnosis and treatment. The imaging cytometer measurements used both size and fluorescence. Thus, since forward scatter depends mainly on the size of scattering objects (as well as their refractive index), we may anticipate the ability of non-imaging flow cytometers to prove effective in making similar disseminations for the purposes of high-throughput diagnostics.

Acknowledgements

We wish to thank all the participants who donated blood for this study.

Funding

DBK thanks the Novo Nordisk Foundation (grant NNF20CC0035580) for financial support. EP acknowledges the South African Medical Research Council and the NRF (grant number: 142142) for consumables. We thank KERNLS (https://kernls.com/), Balvi Research Foundation and Polybio Research Foundation for funding of the Luminex Flow Cytometer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Consent for publication

All authors approved submission of the paper.

Competing interests

EP is a director of Biocode Technologies; ST is the COO at Biocode Technologies. The other authors have no competing interests to declare.

Authors' contributions

ST: sample analysis and writing of paper; GJL: clinical advice; MAK: clinical advice and editing of paper; DBK: editing of paper, technical advice on methods; EP: study leader, funding, writing and editing of paper.

Astin, R., Banerjee, A., Baker, M.R., Dani, M., Ford, E., Hull, J.H., Lim, P.B., McNarry, M., Morten, K., O'Sullivan, O., et al. (2023). Long COVID: mechanisms, risk factors and recovery. Exp Physiol 108, 12-27. 10.1113/ep090802.
Kell, D.B., Laubscher, G.J., and Pretorius, E. (2022). A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications. Biochem J 479, 537-559. 10.1042/bcj20220016.
Kell, D.B., and Pretorius, E. (2022). 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. Biochem J 479, 1653-1708. 10.1042/bcj20220154.
Kruger, A., Vlok, M., Turner, S., Venter, C., Laubscher, G.J., Kell, D.B., and Pretorius, E. (2022). 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. 10.1186/s12933-022-01623-4.
Pretorius, E., Venter, C., Laubscher, G.J., Lourens, P.J., Steenkamp, J., and Kell, D.B. (2020). Prevalence of readily detected amyloid blood clots in 'unclotted' Type 2 Diabetes Mellitus and COVID-19 plasma: a preliminary report. Cardiovasc Diabetol 19, 193. 10.1186/s12933-020-01165-7.
Pretorius, E., Vlok, M., Venter, C., Bezuidenhout, J.A., Laubscher, G.J., Steenkamp, J., and Kell, D.B. (2021). 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. 10.1186/s12933-021-01359-7.
Turner, S., Naidoo, C.A., Usher, T.J., Kruger, A., Venter, C., Laubscher, G.J., Khan, M.A., Kell, D.B., and Pretorius, E. (2022). Increased levels of inflammatory molecules in blood of Long COVID patients point to thrombotic endotheliitis. medRxiv, 2022.2010.2013.22281055. 10.1101/2022.10.13.22281055.
Davis, H.E., McCorkell, L., Vogel, J.M., and Topol, E.J. (2023). Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol, 1-14. 10.1038/s41579-022-00846-2.
Taquet, M., Dercon, Q., Luciano, S., Geddes, J.R., Husain, M., and Harrison, P.J. (2021). Incidence, co-occurrence, and evolution of long-COVID features: A 6-month retrospective cohort study of 273,618 survivors of COVID-19. PLoS Med 18, e1003773. 10.1371/journal.pmed.1003773.
Perlis, R.H., Santillana, M., Ognyanova, K., Safarpour, A., Lunz Trujillo, K., Simonson, M.D., Green, J., Quintana, A., Druckman, J., Baum, M.A., and Lazer, D. (2022). Prevalence and Correlates of Long COVID Symptoms Among US Adults. JAMA Netw Open 5, e2238804. 10.1001/jamanetworkopen.2022.38804.
GAO (2022). Science, Technology Assessment, and Analytics: SCIENCE & TECH SPOTLIGHT: LONG COVID https://www.gao.gov/assets/gao-22-105666.pdf
Proal, A.D., and VanElzakker, M.B. (2021). Long COVID or Post-acute Sequelae of COVID-19 (PASC): An Overview of Biological Factors That May Contribute to Persistent Symptoms. Front Microbiol 12, 698169. 10.3389/fmicb.2021.698169.
Kastenhuber, E.R., Mercadante, M., Nilsson-Payant, B., Johnson, J.L., Jaimes, J.A., Muecksch, F., Weisblum, Y., Bram, Y., Chandar, V., Whittaker, G.R., et al. (2022). Coagulation factors directly cleave SARS-CoV-2 spike and enhance viral entry. Elife 11. 10.7554/eLife.77444.
Nyström, S., and Hammarström, P. (2022). Amyloidogenesis of SARS-CoV-2 Spike Protein. J Am Chem Soc 144, 8945-8950. 10.1021/jacs.2c03925.
Grobbelaar, L.M., Venter, C., Vlok, M., Ngoepe, M., Laubscher, G.J., Lourens, P.J., Steenkamp, J., Kell, D.B., and Pretorius, E. (2021). SARS-CoV-2 spike protein S1 induces fibrin(ogen) resistant to fibrinolysis: implications for microclot formation in COVID-19. Biosci Rep 41. 10.1042/bsr20210611.
Pretorius, E., Bester, J., and Kell, D.B. (2016). A Bacterial Component to Alzheimer's-Type Dementia Seen via a Systems Biology Approach that Links Iron Dysregulation and Inflammagen Shedding to Disease. J Alzheimers Dis 53, 1237-1256. 10.3233/jad-160318.
Pretorius, E., Page, M.J., Hendricks, L., Nkosi, N.B., Benson, S.R., and Kell, D.B. (2018). Both lipopolysaccharide and lipoteichoic acids potently induce anomalous fibrin amyloid formation: assessment with novel Amytracker™ stains. J R Soc Interface 15. 10.1098/rsif.2017.0941.
Althaus, K., Marini, I., Zlamal, J., Pelzl, L., Singh, A., Häberle, H., Mehrländer, M., Hammer, S., Schulze, H., Bitzer, M., et al. (2021). Antibody-induced procoagulant platelets in severe COVID-19 infection. Blood 137, 1061-1071. 10.1182/blood.2020008762.
Fard, M.B., Fard, S.B., Ramazi, S., Atashi, A., and Eslamifar, Z. (2021). Thrombosis in COVID-19 infection: Role of platelet activation-mediated immunity. Thromb J 19, 59. 10.1186/s12959-021-00311-9.
Fogarty, H., Townsend, L., Morrin, H., Ahmad, A., Comerford, C., Karampini, E., Englert, H., Byrne, M., Bergin, C., and O’Sullivan, J.M. (2021). Persistent endotheliopathy in the pathogenesis of long COVID syndrome. Journal of Thrombosis and Haemostasis 19, 2546-2553.
Grobler, C., Maphumulo, S.C., Grobbelaar, L.M., Bredenkamp, J.C., Laubscher, G.J., Lourens, P.J., Steenkamp, J., Kell, D.B., and Pretorius, E. (2020). Covid-19: The Rollercoaster of Fibrin(Ogen), D-Dimer, Von Willebrand Factor, P-Selectin and Their Interactions with Endothelial Cells, Platelets and Erythrocytes. Int J Mol Sci 21. 10.3390/ijms21145168.
Li, T., Yang, Y., Li, Y., Wang, Z., Ma, F., Luo, R., Xu, X., Zhou, G., Wang, J., Niu, J., et al. (2021). Platelets mediate inflammatory monocyte activation by SARS-CoV-2 Spike protein. The Journal of Clinical Investigation. 10.1172/JCI150101.
Bonaventura, A., Vecchié, A., Dagna, L., Martinod, K., Dixon, D.L., Van Tassell, B.W., Dentali, F., Montecucco, F., Massberg, S., Levi, M., and Abbate, A. (2021). Endothelial dysfunction and immunothrombosis as key pathogenic mechanisms in COVID-19. Nature Reviews Immunology 21, 319-329. 10.1038/s41577-021-00536-9.
Grobbelaar, L.M., Kruger, A., Venter, C., Burger, E.M., Laubscher, G.J., Maponga, T.G., Kotze, M.J., Kwaan, H.C., Miller, J.B., Fulkerson, D., et al. (2022). Relative Hypercoagulopathy of the SARS-CoV-2 Beta and Delta Variants when Compared to the Less Severe Omicron Variants Is Related to TEG Parameters, the Extent of Fibrin Amyloid Microclots, and the Severity of Clinical Illness. Semin Thromb Hemost 48, 858-868. 10.1055/s-0042-1756306.
Laubscher, G.J., Lourens, P.J., Venter, C., Kell, D.B., and Pretorius, E. (2021). TEG®, Microclot and Platelet Mapping for Guiding Early Management of Severe COVID-19 Coagulopathy. Journal of Clinical Medicine 10. 10.3390/jcm10225381.
Pretorius, E., Venter, C., Laubscher, G.J., Kotze, M.J., Oladejo, S.O., Watson, L.R., Rajaratnam, K., Watson, B.W., and Kell, D.B. (2022). Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/Post-Acute Sequelae of COVID-19 (PASC). Cardiovascular Diabetology 21, 148. 10.1186/s12933-022-01579-5.
Venter, C., Bezuidenhout, J.A., Laubscher, G.J., Lourens, P.J., Steenkamp, J., Kell, D.B., and Pretorius, E. (2020). Erythrocyte, Platelet, Serum Ferritin, and P-Selectin Pathophysiology Implicated in Severe Hypercoagulation and Vascular Complications in COVID-19. Int J Mol Sci 21. 10.3390/ijms21218234.
Stavrakis, S., Holzner, G., Choo, J., and deMello, A. (2019). High-throughput microfluidic imaging flow cytometry. Curr Opin Biotechnol 55, 36-43. 10.1016/j.copbio.2018.08.002.
Rees, P., Summers, H.D., Filby, A., Carpenter, A.E., and Doan, M. (2022). Imaging flow cytometry. Nature Reviews Methods Primers 2, 86. 10.1038/s43586-022-00167-x.
Hennig, H., Rees, P., Blasi, T., Kamentsky, L., Hung, J., Dao, D., Carpenter, A.E., and Filby, A. (2017). An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods 112, 201-210. 10.1016/j.ymeth.2016.08.018.
Doan, M., Vorobjev, I., Rees, P., Filby, A., Wolkenhauer, O., Goldfeld, A.E., Lieberman, J., Barteneva, N., Carpenter, A.E., and Hennig, H. (2018). Diagnostic Potential of Imaging Flow Cytometry. Trends Biotechnol 36, 649-652. 10.1016/j.tibtech.2017.12.008.

Download PDF

Journal Publication

published 01 Sep, 2023

Read the published version in Heliyon →

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Rapid flow cytometric analysis of fibrin amyloid microclots in Long COVID

Status:

Journal Publication

Version 1

Abstract

Figures

SENTENCE SUMMARY

Introduction

Materials And Methods

Ethical clearance

Patient demographics

Participant identification for flow cytometry analysis

Blood sample collection and sample preparation

Flow Cytometry

Data analysis

Results

Discussion

Declarations

References

Status:

Journal Publication

Version 1