Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity

Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

Unveiling the metabolic and coagulation disruptions in SARS-CoV-2-associated acute macular neuroretinopathy: A case-control study

SARS-CoV-2 infection has been associated with the increased incidence of acute macular neuroretinopathy (AMN), an infrequent ocular disorder. However, the precise mechanisms underpinning AMN in the context of SARS-CoV-2 infection (AMN-SARS-CoV-2) remain elusive. In this case-control study, 14 patients diagnosed with AMN-SARS-CoV-2 between 2022/12 and 2023/3 were enrolled and compared with 14 SARS-CoV-2-infected individuals without AMN, who served as controls (SARS-CoV-2-no AMN). Metabolomic profiling using ultrahigh-performance liquid chromatography-online electrospray mass spectrometry revealed significant alterations in serum metabolites in AMN-SARS-CoV-2 patients. Coagulation abnormalities were observed in AMN-SARS-CoV-2 patients, and their relationship with metabolic disorders was studied. Finally, a predictive model for AMN-SARS-CoV-2 was established. Seventy-six upregulated and 42 downregulated metabolites were identified in AMN-SARS-CoV-2 cases. Notably, arginine metabolism within the urea cycle was significantly altered, evidenced by variations in ornithine, citrulline,  l-proline, and ADAM levels, correlating with abnormal coagulation markers like platelet crit, fibrinogen degradation product, and fibrinogen. Additionally, increased arginase 1 (AGR1) activity within the urea cycle and reduced nitric oxide synthase activity were observed in AMN-SARS-CoV-2. The integration of urea cycle metabolite levels with coagulation parameters yielded a robust discriminatory model for AMN-SARS-CoV-2, as evidenced by an area under the curve of 0.96. The findings of the present study enhance our comprehension of the underlying metabolic mechanisms associated with AMN-SARS-CoV-2 and offer potential diagnostic markers for this uncommon ocular disorder within the context of SARS-CoV-2 infection.

Extracellular vesicles are a late marker of inflammation, hypercoagulability and COVID-19 severity

Exacerbated inflammation and coagulation are a hallmark of COVID-19 severity. Extracellular vesicles (EVs) are intercellular transmitters involved in inflammatory conditions, which are capable of triggering prothrombotic mechanisms. Since the release of EVs is potentially associated with COVID-19-induced coagulopathy, the aim of this study was to evaluate changes in inflammation- and hypercoagulability-related EVs during the first month after symptom onset and to determine whether they are associated with disease severity. Blood samples of patients with mild or severe forms of the disease were collected on three occasions: in the second, third and fourth weeks after symptom onset for the quantification by flow cytometry of CD41A (platelet glycoprotein IIb/IIIa), CD162 (PSGL-1), CD31 (PECAM-1) and CD142 cells (tissue factor). Analysis of variance (ANOVA) with repeated measures, Kruskal-Wallis and correlation tests were used. Eighty-five patients were enrolled, 71% of whom had mild disease. Seventeen uninfected individuals served as controls. Compared to controls, both mild and severe COVID-19 were associated with higher EV-CD31+, EV-CD41+ and EV-CD142+ levels. All EV levels were higher in severe than in mild COVID-19 only after the third week from symptom onset, as opposed to C-reactive protein and D-dimer levels, which were higher in severe than in mild COVID-19 earlier during disease progression. EV levels were also associated with C-reactive protein and D-dimer levels only after the third week of symptoms. In conclusion, EVs expressing CD41A, CD31, TF, and CD162 appear as late markers of COVID-19 severity. This finding may contribute to the understanding of the pathogenesis of acute and possibly long COVID-19.

Effects of Recombinant SARS-CoV-2 Spike Protein Variants on Platelet Morphology and Activation

Platelets are central elements of hemostasis and also play a pivotal role in the pathogenesis of thrombosis in coronavirus disease 2019. This study was planned to investigate the effects of different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recombinant spike protein variants on platelet morphology and activation. Citrated whole blood collected from ostensibly healthy subjects was challenged with saline (control sample) and with 2 and 20 ng/mL final concentration of SARS-CoV-2 recombinant spike protein of Ancestral, Alpha, Delta, and Omicron variants. Platelet count was found to be decreased with all SARS-CoV-2 recombinant spike protein variants and concentrations tested, achieving the lowest values with 20 ng/mL Delta recombinant spike protein. The mean platelet volume increased in all samples irrespective of SARS-CoV-2 recombinant spike protein variants and concentrations tested, but especially using Delta and Alpha recombinant spike proteins. The values of both platelet function analyzer-200 collagen-adenosine diphosphate and collagen-epinephrine increased in all samples irrespective of SARS-CoV-2 recombinant spike protein variants and concentrations tested, and thus reflecting platelet exhaustion, and displaying again higher increases with Delta and Alpha recombinant spike proteins. Most samples where SARS-CoV-2 recombinant spike proteins were added were flagged as containing platelet clumps. Morphological analysis revealed the presence of a considerable number of activated platelets, platelet clumps, platelet-monocyte, and platelet-neutrophils aggregates, especially in samples spiked with Alpha and Delta recombinant spike proteins at 20 ng/mL. These results provide support to the evidence that SARS-CoV-2 is capable of activating platelets through its spike protein, though such effect varies depending on different spike protein variants.

Post-COVID-19 thrombotic sequelae: The potential role of persistent platelet hyperactivity

Postacute COVID-19 sequelae affect millions of individuals, and active research into the pathophysiological mechanisms and potential treatments is underway. The report by Nara and colleagues shows persistent platelet hyperactivity in the chronic phase of the infection, suggesting a possible role of platelets in post-COVID-19 complications and, consequently, a possible therapeutic target. Commentary on: Nara et al. Prolonged platelet hyperactivity after COVID-19 infection. Br J Haematol 2024;204:492-496.

Prolonged platelet hyperactivity after COVID-19 infection

Platelet hyperactivity often occurs in patients with coronavirus disease 2019 (COVID-19). However, it remains unclear how long platelet hyperactivity lasts after the acute phase, owing to a lack of follow-up studies. To elucidate the course of platelet hyperactivity, we serially measured platelet activity in patients with COVID-19 up to 40 days after hospital admission using an easily assessable haematology analyser that semi-quantitates platelet clumps on a scattergram. Our results showed that platelet hyperactivity persisted for at least 40 days even after acute inflammation subsided in most patients with COVID-19, regardless of disease severity. Persistent platelet hyperactivity may contribute to thromboembolic complications in post-COVID-19 patients.

Variation in presepsin and thrombomodulin levels for predicting COVID-19 mortality

Coronavirus disease (COVID-19) has caused extensive mortality globally; therefore, biomarkers predicting the severity and prognosis of COVID-19 are essential. This study aimed to evaluate the application of presepsin (P-SEP) and thrombomodulin (TM), which are biomarkers of sepsis and endothelial dysfunction, respectively, in the prognosis of COVID-19. Serum P-SEP and TM levels from COVID-19 patients (n = 183) were measured. Disease severity was classified as mild, moderate I, moderate II, or severe based on hemoglobin oxygen saturation and the history of intensive care unit transfer or use of ventilation at admission. Patients in the severe group were further divided into survivors and non-survivors. P-SEP and TM levels were significantly higher in the severe group than those in the mild group, even after adjusting for creatinine values. In addition, TM levels were significantly higher in non-survivors than in survivors. Changes in the P-SEP levels at two time points with an interval of 4.1 ± 2.2 days were significantly different between the survivors and non-survivors. In conclusion, TM and continuous P-SEP measurements may be useful for predicting mortality in patients with COVID-19. Moreover, our data indicate that P-SEP and TM values after creatinine adjustment could be independent predictive markers, apart from renal function.

Retrospective observational study of changes in serum cytokines and adiponectin with continuous plasma exchange with dialysis therapy for severe COVID-19

INTRODUCTION

Coronavirus disease 2019 (COVID-19) is associated with a marked increase in the inflammatory cytokines, IL-6 and IL-18. Blood purification therapy aimed at controlling cytokines is one treatment option; however, evidence of its effectiveness is needed. Plasma exchange with dialysis (PED) is a blood purification method involving selective plasma exchange with dialysate flowing through the outer hollow fiber of the plasma separator. In this retrospective study, we investigated the efficacy of continuous PED (cPED) over 48 h in five patients with severe COVID-19.

METHODS

We assessed changes in IL-6 and IL-18, as well as adiponectin (APN).

RESULTS

There were no significant differences in changes in IL-6 and IL-18, but there was a marked improvement in cases with abnormally high IL-6 and IL-18 levels at baseline. APN, which inhibits inflammatory cytokines, was significantly elevated post-cPED.

CONCLUSION

Our results suggest that cPED therapy is an effective treatment for COVID-19.

Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity

BACKGROUND

The clinical spectrum of acute SARS-CoV-2 infection ranges from an asymptomatic to life-threatening disease. Considering the broad spectrum of severity, reliable biomarkers are required for early risk stratification and prediction of clinical outcomes. Despite numerous efforts, no COVID-19-specific biomarker has been established to guide further diagnostic or even therapeutic approaches, most likely due to insufficient validation, methodical complexity, or economic factors. COVID-19-associated coagulopathy is a hallmark of the disease and is mainly attributed to dysregulated immunothrombosis. This process describes an intricate interplay of platelets, innate immune cells, the coagulation cascade, and the vascular endothelium leading to both micro- and macrothrombotic complications. In this context, increased levels of immunothrombotic components, including platelet and platelet-leukocyte aggregates, have been described and linked to COVID-19 severity.

METHODS

Here, we describe a label-free quantitative phase imaging approach, allowing the identification of cell-aggregates and their components at single-cell resolution within 30 min, which prospectively qualifies the method as point-of-care (POC) testing.

RESULTS

We find a significant association between the severity of COVID-19 and the amount of platelet and platelet-leukocyte aggregates. Additionally, we observe a linkage between severity, aggregate composition, and size distribution of platelets in aggregates.

CONCLUSIONS

This study presents a POC-compatible method for rapid quantitative analysis of blood cell aggregates in patients with COVID-19.

Breakthrough infections after COVID-19 vaccinations do not elicit platelet hyperactivation and are associated with high platelet-lymphocyte and low platelet-neutrophil aggregates

Background

Severe COVID-19 is associated with an excessive immunothrombotic response and thromboinflammatory complications. Vaccinations effectively reduce the risk of severe clinical outcomes in patients with COVID-19, but their impact on platelet activation and immunothrombosis during breakthrough infections is not known.

Objectives

To investigate how preemptive vaccinations modify the platelet-immune crosstalk during COVID-19 infections.

Methods

Cross-sectional flow cytometry study of the phenotype and interactions of platelets circulating in vaccinated (n = 21) and unvaccinated patients with COVID-19, either admitted to the intensive care unit (ICU, n = 36) or not (non-ICU, n = 38), in comparison to matched SARS-CoV-2-negative patients (n = 48), was performed.

Results

In the circulation of unvaccinated non-ICU patients with COVID-19, we detected hyperactive and hyperresponsive platelets and platelet aggregates with adaptive and innate immune cells. In unvaccinated ICU patients with COVID-19, most of whom had severe acute respiratory distress syndrome, platelets had high P-selectin and phosphatidylserine exposure but low capacity to activate integrin αIIbβ3, dysfunctional mitochondria, and reduced surface glycoproteins. In addition, in the circulation of ICU patients, we detected microthrombi and platelet aggregates with innate, but not with adaptive, immune cells. In vaccinated patients with COVID-19, who had no acute respiratory distress syndrome, platelets had surface receptor levels comparable to those in controls and did not form microthrombi or platelet-granulocyte aggregates but aggregated avidly with adaptive immune cells.

Conclusion

Our study provides evidence that vaccinated patients with COVID-19 are not associated with platelet hyperactivation and are characterized by platelet-leukocyte aggregates that foster immune protection but not excessive immunothrombosis. These findings advocate for the importance of vaccination in preventing severe COVID-19.

Unravelling the Mystery inside Cells by Using Single-Molecule Fluorescence Imaging

Live-cell imaging is a powerful technique to study the dynamics and mechanics of various biological molecules like proteins, organelles, DNA, and RNA. With the rapid evolution of optical microscopy, our understanding of how these molecules are implicated in the cells' most critical physiological roles deepens. In this review, we focus on how spatiotemporal nanoscale live-cell imaging at the single molecule level allows for profound contributions towards new discoveries in life science. This review will start by summarizing how single-molecule tracking has been used to analyze membrane dynamics, receptor-ligand interactions, protein-protein interactions, inner- and extra-cellular transport, gene expression/transcription, and whole organelle tracking. We then move on to how current authors are trying to improve single-molecule tracking and overcome current limitations by offering new ways of labeling proteins of interest, multi-channel/color detection, improvements in time-lapse imaging, and new methods and programs to analyze the colocalization and movement of targets. We later discuss how single-molecule tracking can be a beneficial tool used for medical diagnosis. Finally, we wrap up with the limitations and future perspectives of single-molecule tracking and total internal reflection microscopy.

Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms

Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.

Impact of P-selectin-PSGL-1 Axis on Platelet-Endothelium-Leukocyte Interactions in Fatal COVID-19

In critically ill patients infected with SARS-CoV-2, early leukocyte recruitment to the respiratory system was found to be orchestrated by leukocyte trafficking molecules accompanied by massive secretion of proinflammatory cytokines and hypercoagulability. Our study aimed to explore the interplay between leukocyte activation and pulmonary endothelium in different disease stages of fatal COVID-19. Our study comprised 10 COVID-19 postmortem lung specimens and 20 control lung samples (5 acute respiratory distress syndrome, 2 viral pneumonia, 3 bacterial pneumonia, and 10 normal), which were stained for antigens representing the different steps of leukocyte migration: E-selectin, P-selectin, PSGL-1, ICAM1, VCAM1, and CD11b. Image analysis software QuPath was used for quantification of positive leukocytes (PSGL-1 and CD11b) and endothelium (E-selectin, P-selectin, ICAM1, VCAM1). Expression of IL-6 and IL-1β was quantified by RT-qPCR. Expression of P-selectin and PSGL-1 was strongly increased in the COVID-19 cohort compared with all control groups (COVID-19:Controls, 17:23, P < .0001; COVID-19:Controls, 2:75, P < .0001, respectively). Importantly, P-selectin was found in endothelial cells and associated with aggregates of activated platelets adherent to the endothelial surface in COVID-19 cases. In addition, PSGL-1 staining disclosed positive perivascular leukocyte cuffs, reflecting capillaritis. Moreover, CD11b showed a strongly increased positivity in COVID-19 compared with all controls (COVID-19:Controls, 2:89; P = .0002), indicating a proinflammatory immune microenvironment. Of note, CD11b exhibited distinct staining patterns at different stages of COVID-19 disease. Only in cases with very short disease course, high levels of IL-1β and IL-6 mRNA were observed in lung tissue. The striking upregulation of PSGL-1 and P-selectin reflects the activation of this receptor-ligand pair in COVID-19, increasing the efficiency of initial leukocyte recruitment, thus promoting tissue damage and immunothrombosis. Our results show that endothelial activation and unbalanced leukocyte migration play a central role in COVID-19 involving the P-selectin-PSGL-1 axis.

Monitoring circulating platelet activity to predict cancer-associated thrombosis

A characteristic clinical complication in cancer patients is the frequent incidence of thrombotic events. Numerous studies have shown hyperactive/activated platelets to be a critical earlier trigger for cancer-associated thrombus formation. However, there currently is no viable approach to monitor specific changes in tumor-associated platelet activity. Here, we describe a chromatograph-like microfluidic device that is highly sensitive to the activity status of peripheral circulating platelets in both tumor-bearing mice and clinical cancer patients. Our results show a strongly positive correlation between platelet activation status and tumor progression. Six-month follow-up data from advanced cancer patients reveal positive links between platelet activity level and thrombus occurrence rate, with a high predictive capacity of thrombotic events (AUC = 0.842). Our findings suggest that circulating platelet activity status determined by this microfluidic device exhibits sensitive, predictive potential for thrombotic events in cancer patients for directing well-timed antithrombosis treatment.

Unlocking the secrets of the invisible world: incredible deep optical imaging through in-silico clearing

In-silico clearing enables deep optical imaging of biological samples by correcting image blur caused by scattering and aberration. This breakthrough method offers researchers unprecedented insights into three-dimensional biological systems, with enormous potential for advancing biology and medicine to better understand living organisms and human health.

The landscape of abnormal pathway activation confers COVID-19 patients' molecular sequelae earlier than clinical phenotype

Rationale: The 2019 coronavirus disease (COVID-19) pandemic poses a significant threat to human health. After SARS-CoV-2 infection, major clinical concerns are organ damage and possible sequelae. Methods: In this study, we analyzed serum multi-omics data based on population-level, including healthy cohort, non-COVID-19 and COVID-19 covered different severity cohorts. We applied the pseudo-SpatioTemporal Consistency Alignment (pST-CA) strategy to correct for individualized disease course differences, and developed pseudo-deterioration timeline model and pseudo-recovery timeline model based on the "severe index" and "course index". Further, we comprehensively analyzed and discussed the dynamic damage signaling in COVID-19 deterioration and/or recovery, as well as the potential risk of sequelae. Results: The deterioration and course models based on the pST-CA strategy can effectively map the activation of blood molecular signals on cellular, pathway, functional and disease phenotypes in COVID-19 deterioration and throughout the disease course. The models revealed the neurological, cardiovascular, and hepatic toxicity present in SARS-CoV-2. The abundance of differentially expressed proteins and the activity of upstream regulators were comprehensively analyzed and evaluated to predict possible target drugs for SARS-CoV-2. On molecular docking simulation analysis, it was further demonstrated that blocking CEACAM1 is a potential therapeutic target for SARS-CoV-2. Conclusions: Clinically, the risk of organ failure and death in COVID-19 patients rises with increasing number of infections. Individualized sequelae prediction for patients and assessment of individualized intervenable targets and available drugs in combination with the upstream regulator analysis results are of great clinical value.

Investigation of liquid biopsy analytes in peripheral blood of individuals after SARS-CoV-2 infection

BACKGROUND

Post-acute COVID-19 syndrome (PACS) is linked to severe organ damage. The identification and stratification of at-risk SARS-CoV-2 infected individuals is vital to providing appropriate care. This exploratory study looks for a potential liquid biopsy signal for PACS using both manual and machine learning approaches.

METHODS

Using a high definition single cell assay (HDSCA) workflow for liquid biopsy, we analysed 100 Post-COVID patients and 19 pre-pandemic normal donor (ND) controls. Within our patient cohort, 73 had received at least 1 dose of vaccination prior to SARS-CoV-2 infection. We stratified the COVID patients into 25 asymptomatic, 22 symptomatic COVID-19 but not suspected for PACS and 53 PACS suspected. All COVID-19 patients investigated in this study were diagnosed between April 2020 and January 2022 with a median 243 days (range 16-669) from diagnosis to their blood draw. We did a histopathological examination of rare events in the peripheral blood and used a machine learning model to evaluate predictors of PACS.

FINDINGS

The manual classification found rare cellular and acellular events consistent with features of endothelial cells and platelet structures in the PACS-suspected cohort. The three categories encompassing the hypothesised events were observed at a significantly higher incidence in the PACS-suspected cohort compared to the ND (p-value < 0.05). The machine learning classifier performed well when separating the NDs from Post-COVID with an accuracy of 90.1%, but poorly when separating the patients suspected and not suspected of PACS with an accuracy of 58.7%.

INTERPRETATION

Both the manual and the machine learning model found differences in the Post-COVID cohort and the NDs, suggesting the existence of a liquid biopsy signal after active SARS-CoV-2 infection. More research is needed to stratify PACS and its subsyndromes.

FUNDING

This work was funded in whole or in part by Fulgent Genetics, Kathy and Richard Leventhal and Vassiliadis Research Fund. This work was also supported by the National Cancer InstituteU54CA260591.

Platelet activation and coronavirus disease 2019 mortality: Insights from coagulopathy, antiplatelet therapy and inflammation

BACKGROUND

Coronavirus disease 2019 (COVID-19) is associated with an inflammatory cytokine burst and a prothrombotic coagulopathy. Platelets may contribute to microthrombosis, and constitute a therapeutic target in COVID-19 therapy.

AIM

To assess if platelet activation influences mortality in COVID-19.

METHODS

We explored two cohorts of patients with COVID-19. Cohort A included 208 ambulatory and hospitalized patients with varying clinical severities and non-COVID patients as controls, in whom plasma concentrations of the soluble platelet activation biomarkers CD40 ligand (sCD40L) and P-selectin (sP-sel) were quantified within the first 48hours following hospitalization. Cohort B was a multicentre cohort of 2878 patients initially admitted to a medical ward. In both cohorts, the primary outcome was in-hospital mortality.

RESULTS

In cohort A, median circulating concentrations of sCD40L and sP-sel were only increased in the 89 critical patients compared with non-COVID controls: sP-sel 40,059 (interquartile range 26,876-54,678)pg/mL; sCD40L 1914 (interquartile range 1410-2367)pg/mL (P<0.001 for both). A strong association existed between sP-sel concentration and in-hospital mortality (Kaplan-Meier log-rank P=0.004). However, in a Cox model considering biomarkers of immunothrombosis, sP-sel was no longer associated with mortality, in contrast to coagulopathy evaluated with D-dimer concentration (hazard ratio 4.86, 95% confidence interval 1.64-12.50). Moreover, in cohort B, a Cox model adjusted for co-morbidities suggested that prehospitalization antiplatelet agents had no significant impact on in-hospital mortality (hazard ratio 1.05, 95% CI 0.80-1.37; P=0.73).

CONCLUSIONS

Although we observed an association between excessive biomarkers of platelet activation and in-hospital mortality, our findings rather suggest that coagulopathy is more central in driving disease progression, which may explain why prehospitalization antiplatelet drugs were not a protective factor against mortality in our multicentre cohort.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.

Long-term effects of Pfizer-BioNTech COVID-19 vaccinations on platelets

There is a global concern about the safety of COVID-19 vaccines associated with platelet function. However, their long-term effects on overall platelet activity remain poorly understood. Here we address this problem by image-based single-cell profiling and temporal monitoring of circulating platelet aggregates in the blood of healthy human subjects, before and after they received multiple Pfizer-BioNTech (BNT162b2) vaccine doses over a time span of nearly 1 year. Results show no significant or persisting platelet aggregation trends following the vaccine doses, indicating that any effects of vaccinations on platelet turnover, platelet activation, platelet aggregation, and platelet-leukocyte interaction was insignificant.

Studying the efficacy of antiplatelet drugs on atherosclerosis by optofluidic imaging on a chip

Vascular stenosis caused by atherosclerosis instigates activation and aggregation of platelets, eventually resulting in thrombus formation. Although antiplatelet drugs are commonly used to inhibit platelet activation and aggregation, they unfortunately cannot prevent recurrent thrombotic events in patients with atherosclerosis. This is partially due to the limited understanding of the efficacy of antiplatelet drugs in the complex hemodynamic environment of vascular stenosis. Conventional methods for evaluating the efficacy of antiplatelet drugs under stenosis either fail to simulate the hemodynamic environment of vascular stenosis characterized by high shear stress and recirculatory flow or lack spatial resolution in their analytical techniques to statistically identify and characterize platelet aggregates. Here we propose and experimentally demonstrate a method comprising an in vitro 3D stenosis microfluidic chip and an optical time-stretch quantitative phase imaging system for studying the efficacy of antiplatelet drugs under stenosis. Our method simulates the atherogenic flow environment of vascular stenosis while enabling high-resolution and statistical analysis of platelet aggregates. Using our method, we distinguished the efficacy of three antiplatelet drugs, acetylsalicylic acid (ASA), cangrelor, and eptifibatide, for inhibiting platelet aggregation induced by stenosis. Specifically, ASA failed to inhibit stenosis-induced platelet aggregation, while eptifibatide and cangrelor showed high and moderate efficacy, respectively. Furthermore, we demonstrated that the drugs tested also differed in their efficacy for inhibiting platelet aggregation synergistically induced by stenosis and agonists (e.g., adenosine diphosphate, and collagen). Taken together, our method is an effective tool for investigating the efficacy of antiplatelet drugs under vascular stenosis, which could assist the development of optimal pharmacologic strategies for patients with atherosclerosis.

Alterations in platelet proteome signature and impaired platelet integrin αIIbβ3 activation in patients with COVID-19

BACKGROUND

Patients with COVID-19 are at increased risk of thrombosis, which is associated with altered platelet function and coagulopathy, contributing to excess mortality.

OBJECTIVES

To characterize the mechanism of altered platelet function in COVID-19 patients.

METHODS

The platelet proteome, platelet functional responses, and platelet-neutrophil aggregates were compared between patients hospitalized with COVID-19 and healthy control subjects using tandem mass tag proteomic analysis, Western blotting, and flow cytometry.

RESULTS

COVID-19 patients showed a different profile of platelet protein expression (858 altered of the 5773 quantified). Levels of COVID-19 plasma markers were enhanced in the platelets of COVID-19 patients. Gene ontology pathway analysis demonstrated that the levels of granule secretory proteins were raised, whereas those of platelet activation proteins, such as the thrombopoietin receptor and protein kinase Cα, were lowered. Basally, platelets of COVID-19 patients showed enhanced phosphatidylserine exposure, with unaltered integrin α_IIbβ₃ activation and P-selectin expression. Agonist-stimulated integrin α_IIbβ₃ activation and phosphatidylserine exposure, but not P-selectin expression, were decreased in COVID-19 patients. COVID-19 patients had high levels of platelet-neutrophil aggregates, even under basal conditions, compared to controls. This association was disrupted by blocking P-selectin, demonstrating that platelet P-selectin is critical for the interaction.

CONCLUSIONS

Overall, our data suggest the presence of 2 platelet populations in patients with COVID-19: one of circulating platelets with an altered proteome and reduced functional responses and another of P-selectin-expressing neutrophil-associated platelets. Platelet-driven thromboinflammation may therefore be one of the key factors enhancing the risk of thrombosis in COVID-19 patients.

Histology and cytokine levels in hepatic injury accompanying a case of non-severe COVID-19

The pathogenesis of liver dysfunction that complicates coronavirus disease 2019 (COVID-19) remains unclear, especially in mild to moderate severity cases. In this case, a novel coronavirus infection was detected by polymerase chain reaction (PCR) in a 76-year-old woman hospitalized after presenting with fever. No other abnormal physical findings were observed, and oxygen administration was not required. Chest computed tomography (CT) showed a ground-glass-like and an infiltrative shadow in the right lung, and moderate COVID-19 was diagnosed. Initially, the fever resolved, and PCR turned negative; however, the fever reappeared on hospitalization day 14, and CT showed pneumonia exacerbation accompanied by new onset of fatty liver. Biochemical testing revealed marked liver dysfunction, accompanied by elevated serum interleukin (IL)-6, IL-10, and tumor necrosis factor-α levels. Physical findings and all laboratory parameters improved after conservative treatment, and she was discharged on day 22. A liver biopsy performed 44 days post-discharge showed T-cell-dominant inflammatory cell infiltration, mainly in the portal region. Some hepatocytes showed fatty degeneration.We report a case of moderate COVID-19 in which histological hepatitis persisted after a substantial period had passed since the initial infection had cleared and associated transaminase elevations had resolved, with a comparison of serum cytokine dynamics.

Color-scalable flow cytometry with Raman tags

Flow cytometry is an indispensable tool in biology and medicine for counting and analyzing cells in large heterogeneous populations. It identifies multiple characteristics of every single cell, typically via fluorescent probes that specifically bind to target molecules on the cell surface or within the cell. However, flow cytometry has a critical limitation: the color barrier. The number of chemical traits that can be simultaneously resolved is typically limited to several due to the spectral overlap between fluorescence signals from different fluorescent probes. Here, we present color-scalable flow cytometry based on coherent Raman flow cytometry with Raman tags to break the color barrier. This is made possible by combining a broadband Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS) flow cytometer, resonance-enhanced cyanine-based Raman tags, and Raman-active dots (Rdots). Specifically, we synthesized 20 cyanine-based Raman tags whose Raman spectra are linearly independent in the fingerprint region (400 to 1,600 cm^-1). For highly sensitive detection, we produced Rdots composed of 12 different Raman tags in polymer nanoparticles whose detection limit was as low as 12 nM for a short FT-CARS signal integration time of 420 µs. We performed multiplex flow cytometry of MCF-7 breast cancer cells stained by 12 different Rdots with a high classification accuracy of 98%. Moreover, we demonstrated a large-scale time-course analysis of endocytosis via the multiplex Raman flow cytometer. Our method can theoretically achieve flow cytometry of live cells with >140 colors based on a single excitation laser and a single detector without increasing instrument size, cost, or complexity.

Active regression model for clinical grading of COVID-19

Background

In the therapeutic process of COVID-19, the majority of indicators that physicians have for assisting treatment have come from clinical tests represented by proteins, metabolites, and immune levels in patients' blood. Therefore, this study constructs an individualized treatment model based on deep learning methods, aiming to realize timely intervention based on clinical test indicator data of COVID-19 patients and provide an important theoretical basis for optimizing medical resource allocation.

Methods

This study collected clinical data from a total of 1,799 individuals, including 560 controls for non-respiratory infectious diseases (Negative), 681 controls for other respiratory virus infections (Other), and 558 coronavirus infections (Positive) for COVID-19. We first used the Student T-test to screen for statistically significant differences (Pvalue<0.05); we then used the Adaptive-Lasso method stepwise regression to screen the characteristic variables and filter the features with low importance; we then used analysis of covariance to calculate the correlation between variables and filter the highly correlated features; and finally, we analyzed the feature contribution and screened the best combination of features.

Results

Feature engineering reduced the feature set to 13 feature combinations. The correlation coefficient between the projected results of the artificial intelligence-based individualized diagnostic model and the fitted curve of the actual values in the test group was 0.9449 which could be applied to the clinical prognosis of COVID-19. In addition, the depletion of platelets in patients with COVID-19 is an important factor affecting their severe deterioration. With the progression of COVID-19, there is a slight decrease in the total number of platelets in the patient's body, particularly as the volume of larger platelets sharply decreases. The importance of plateletCV (count*mean platelet volume) in evaluating the severity of COVID-19 patients is higher than the count of platelets and mean platelet volume.

Conclusion

In general, we found that for patients with COVID-19, the increase in mean platelet volume was a predictor for SARS-Cov-2. The rapid decrease of platelet volume and the decrease of total platelet volume are dangerous signals for the aggravation of SARS-Cov-2 infection. The analysis and modeling results of this study provide a new perspective for individualized accurate diagnosis and treatment of clinical COVID-19 patients.

Intelligent sort-timing prediction for image-activated cell sorting

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.

The trajectory of COVID-19 cardiopulmonary disease: insights from an autopsy study of community-based, pre-hospital deaths

Background

Post mortem examination of lung and heart tissue has been vital to developing an understanding of COVID-19 pathophysiology; however studies to date have almost uniformly used tissue obtained from hospital-based deaths where individuals have been exposed to major medical and pharmacological interventions.

Methods

In this study we investigated patterns of lung and heart injury from 46 community-based, pre-hospital COVID-19-attributable deaths who underwent autopsy.

Results

The cohort comprised 22 females and 24 males, median age 64 years (range 19-91) at time of death with illness duration range 0-23 days. Comorbidities associated with poor outcomes in COVID-19 included obesity (body mass index >30 kg·m-2) in 19 out of 46 cases (41.3%). Diffuse alveolar damage in its early exudative phase was the most common pattern of lung injury; however significant heterogeneity was identified with bronchopneumonia, pulmonary oedema consistent with acute cardiac failure, pulmonary thromboembolism and microthrombosis also identified and often in overlapping patterns. Review of clinical records and next of kin accounts suggested a combination of unexpectedly low symptom burden, rapidly progressive disease and psychosocial factors may have contributed to a failure of hospital presentation prior to death.

Conclusions

Identifying such advanced acute lung injury in community-based deaths is extremely unusual and raises the question why some with severe COVID-19 pneumonitis were not hospitalised. Multiple factors including low symptom burden, rapidly progressive disease trajectories and psychosocial factors provide possible explanations.

Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort

Background

Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method combining imaging flow cytometry (IFC) and artificial intelligence for the direct label-free and operator-independent quantification of schistocytes in whole blood.

Methods

We used 135,045 IFC images from blood acquisition among 14 patients to extract 188 features with IDEAS® software and 128 features from a convolutional neural network (CNN) with Keras framework in order to train a support vector machine (SVM) blood elements’ classifier used for schistocytes quantification.

Finding

Keras features showed better accuracy (94.03%, CI: 93.75-94.31%) than ideas features (91.54%, CI: 91.21-91.87%) in recognising whole-blood elements, and together they showed the best accuracy (95.64%, CI: 95.39-95.88%). We obtained an excellent correlation (0.93, CI: 0.90-0.96) between three haematologists and our method on a cohort of 102 patient samples. All patients with schistocytosis (>1% schistocytes) were detected with excellent specificity (91.3%, CI: 82.0-96.7%) and sensitivity (100%, CI: 89.4-100.0%). We confirmed these results with a similar specificity (91.1%, CI: 78.8-97.5%) and sensitivity (100%, CI: 88.1-100.0%) on a validation cohort (n=74) analysed in an independent healthcare centre. Simultaneous analysis of 16 samples in both study centres showed a very good correlation between the 2 imaging flow cytometers (Y=1.001x).

Interpretation

We demonstrate that IFC can represent a reliable tool for operator-independent schistocyte quantification with no pre-analytical processing which is of most importance in emergency situations such as TMA.

Funding

None.

Association between antidepressant use and ED or hospital visits in outpatients with SARS-CoV-2

Antidepressants have previously been associated with better outcomes in patients hospitalized with COVID-19, but their effect on clinical deterioration among ambulatory patients has not been fully explored. The objective of this study was to assess whether antidepressant exposure was associated with reduced emergency department (ED) or hospital visits among ambulatory patients with SARS-CoV-2 infection. This retrospective cohort study included adult patients (N = 25 034) with a positive SARS-CoV-2 test performed in a non-hospital setting. Logistic regression analyses tested associations between home use of antidepressant medications and a composite outcome of ED visitation or hospital admission within 30 days. Secondary exposures included individual antidepressants and antidepressants with functional inhibition of acid sphingomyelinase (FIASMA) activity. Patients with antidepressant exposure were less likely to experience the primary composite outcome compared to patients without antidepressant exposure (adjusted odds ratio [aOR] 0.89, 95% CI 0.79-0.99, p = 0.04). This association was only observed with daily doses of at least 20 mg fluoxetine-equivalent (aOR 0.87, 95% CI 0.77-0.99, p = 0.04), but not with daily doses lower than 20 mg fluoxetine-equivalent (aOR 0.94, 95% CI 0.80-1.11, p = 0.48). In exploratory secondary analyses, the outcome incidence was also reduced with exposure to selective serotonin reuptake inhibitors (aOR 0.87, 95% CI 0.75-0.99, p = 0.04), bupropion (aOR 0.70, 95% CI 0.55-0.90, p = 0.005), and FIASMA antidepressant drugs (aOR 0.87, 95% CI 0.77-0.99, p = 0.03). Antidepressant exposure was associated with a reduced incidence of emergency department visitation or hospital admission among SARS-CoV-2 positive patients, in a dose-dependent manner. These data support the FIASMA model of antidepressants' effects against COVID-19.

Acute Pulmonary Embolism in COVID-19: A Potential Connection between Venous Congestion and Thrombus Distribution

Background: Vascular abnormalities, including venous congestion (VC) and pulmonary embolism (PE), have been recognized as frequent COVID-19 imaging patterns and proposed as severity markers. However, the underlying pathophysiological mechanisms remain unclear. In this study, we aimed to characterize the relationship between VC, PE distribution, and alveolar opacities (AO). Methods: This multicenter observational registry (clinicaltrials.gov identifier NCT04824313) included 268 patients diagnosed with SARS-CoV-2 infection and subjected to contrast-enhanced CT between March and June 2020. Acute PE was diagnosed in 61 (22.8%) patients, including 17 females (27.9%), at a mean age of 61.7 ± 14.2 years. Demographic, laboratory, and outcome data were retrieved. We analyzed CT images at the segmental level regarding VC (qualitatively and quantitatively [diameter]), AO (semi-quantitatively as absent, <50%, or >50% involvement), clot location, and distribution related to VC and AO. Segments with vs. without PE were compared. Results: Out of 411 emboli, 82 (20%) were lobar or more proximal and 329 (80%) were segmental or subsegmental. Venous diameters were significantly higher in segments with AO (p = 0.031), unlike arteries (p = 0.138). At the segmental level, 77% of emboli were associated with VC. Overall, PE occurred in 28.2% of segments with AO vs. 21.8% without (p = 0.047). In the absence of VC, however, AO did not affect PE rates (p = 0.94). Conclusions: Vascular changes predominantly affected veins, and most PEs were located in segments with VC. In the absence of VC, AOs were not associated with the PE rate. VC might result from increased flow supported by the hypothesis of pulmonary arteriovenous anastomosis dysregulation as a relevant contributing factor.

Coagulopathy and Fibrinolytic Pathophysiology in COVID-19 and SARS-CoV-2 Vaccination

Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is frequently complicated by thrombosis. In some cases of severe COVID-19, fibrinolysis may be markedly enhanced within a few days, resulting in fatal bleeding. In the treatment of COVID-19, attention should be paid to both coagulation activation and fibrinolytic activation. Various thromboses are known to occur after vaccination with SARS-CoV-2 vaccines. Vaccine-induced immune thrombotic thrombocytopenia (VITT) can occur after adenovirus-vectored vaccination, and is characterized by the detection of anti-platelet factor 4 antibodies by enzyme-linked immunosorbent assay and thrombosis in unusual locations such as cerebral venous sinuses and visceral veins. Treatment comprises high-dose immunoglobulin, argatroban, and fondaparinux. Some VITT cases show marked decreases in fibrinogen and platelets and marked increases in D-dimer, suggesting the presence of enhanced-fibrinolytic-type disseminated intravascular coagulation with a high risk of bleeding. In the treatment of VITT, evaluation of both coagulation activation and fibrinolytic activation is important, adjusting treatments accordingly to improve outcomes.

A guide to molecular and functional investigations of platelets to bridge basic and clinical sciences

Platelets have been shown to be associated with pathophysiological process beyond thrombosis, demonstrating critical additional roles in homeostatic processes, such as immune regulation, and vascular remodeling. Platelets themselves can have multiple functional states and can communicate and regulate other cells including immune cells and vascular smooth muscle cells, to serve such diverse functions. Although traditional platelet functional assays are informative and reliable, they are limited in their ability to unravel platelet phenotypic heterogeneity and interactions. Developments in methods such as electron microscopy, flow cytometry, mass spectrometry, and 'omics' studies, have led to new insights. In this Review, we focus on advances in platelet biology and function, with an emphasis on current and promising methodologies. We also discuss technical and biological challenges in platelet investigations. Using coronavirus disease 2019 (COVID-19) as an example, we further describe the translational relevance of these approaches and the possible 'bench-to-bedside' utility in patient diagnosis and care.

A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications

Post-acute sequelae of COVID (PASC), usually referred to as 'Long COVID' (a phenotype of COVID-19), is a relatively frequent consequence of SARS-CoV-2 infection, in which symptoms such as breathlessness, fatigue, 'brain fog', tissue damage, inflammation, and coagulopathies (dysfunctions of the blood coagulation system) persist long after the initial infection. It bears similarities to other post-viral syndromes, and to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Many regulatory health bodies still do not recognize this syndrome as a separate disease entity, and refer to it under the broad terminology of 'COVID', although its demographics are quite different from those of acute COVID-19. A few years ago, we discovered that fibrinogen in blood can clot into an anomalous 'amyloid' form of fibrin that (like other β-rich amyloids and prions) is relatively resistant to proteolysis (fibrinolysis). The result, as is strongly manifested in platelet-poor plasma (PPP) of individuals with Long COVID, is extensive fibrin amyloid microclots that can persist, can entrap other proteins, and that may lead to the production of various autoantibodies. These microclots are more-or-less easily measured in PPP with the stain thioflavin T and a simple fluorescence microscope. Although the symptoms of Long COVID are multifarious, we here argue that the ability of these fibrin amyloid microclots (fibrinaloids) to block up capillaries, and thus to limit the passage of red blood cells and hence O2 exchange, can actually underpin the majority of these symptoms. Consistent with this, in a preliminary report, it has been shown that suitable and closely monitored 'triple' anticoagulant therapy that leads to the removal of the microclots also removes the other symptoms. Fibrin amyloid microclots represent a novel and potentially important target for both the understanding and treatment of Long COVID and related disorders.