A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. However, the underlying process of COVID-19-associated microvascular thrombosis remains elusive due to the lack of tools to statistically examine platelet aggregation (i.e., the initiation of microthrombus formation) in detail. Here we report the landscape of circulating platelet aggregates in COVID-19 obtained by massive single-cell image-based profiling and temporal monitoring of the blood of COVID-19 patients (n = 110). Surprisingly, our analysis of the big image data shows the anomalous presence of excessive platelet aggregates in nearly 90% of all COVID-19 patients. Furthermore, results indicate strong links between the concentration of platelet aggregates and the severity, mortality, respiratory condition, and vascular endothelial dysfunction level of COVID-19 patients.
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.
Cell sorting is the workhorse of biological research and medicine. [1] Cell sorters are commonly used to sort heterogeneous populations of cells based on their intrinsic features and have numerous applications in microbiology, immunology, virology, stem cell biology, and metabolic engineering. [2][3][4][5][6] While several schemes have been developed for sorting cells based on features such as size, [7] morphology, [8][9][10] viability, [11,12] and surface antigens, [13] those able to sort at high throughput for intracellular chemical content are limited to fluorescence-activated and Raman-activated cell sorting (FACS and RACS, respectively). [1,14] In both methods, sorting is accomplished by a stepwise automatic process: 1) measurement of chemical content with single-cell resolution, 2) analysis of the measurement data to identify cells of interest, and 3) mechanical separation of target cells from the original sample. [1] FACS machines commonly identify molecular targets by signals from attached exogenous fluorophores or coexpressed transgenic fluorescent proteins. This indirect detection scheme is often referred to as labeled detection. [1] Although fluorescent labeling is essential for microbiological research, the use of labels has inherrent drawbacks. These include reduced cellular vitality due to the cytotoxicity of labels themselves, nonspecific binding of labels, a lack of labels or transfection methods for many molecular targets and species, incompatibility with human-targeted cell therapies, and increased experimental complexity imposed by labeling protocols. [14] On the other hand, RACS machines directly identify target molecules in a label-free manner via Raman spectroscopy, which measures the inelastic scattering of incident photons by characteristic molecular vibrations. [14] The chemical specificity of this optical measurement contrasts with non-chemically-specific label-free methods such as impedance-based sorting for cell viability and image-based sorting for morphology or deformability. [10][11][12] To date, FACS technology is well developed, commercialized, and broadly adopted, while RACS is nascent and comprised of a small number of lab-based demonstrations. [14][15][16][17][18][19][20][21][22][23][24][25][26] A major hurdle in the broad deployment of RACS for biomedical applications is the weak signal produced by the Raman scattering process. This necessitates comparatively Cell sorting is the workhorse of biological research and medicine. Cell sorters are commonly used to sort heterogeneous cell populations based on their intrinsic features. Raman-activated cell sorting (RACS) has recently received considerable interest by virtue of its ability to discriminate cells by their intracellular chemical content, in a label-free manner. However, the broad deployment of RACS beyond lab-based demonstrations is hindered by a fundamental trade-off between throughput and measurement bandwidth (i.e., cellular information content). Here this trade-off is overcome and broadband RACS in the fingerprint reg...
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