Biology subjects

Sun, C.-W.

Publications and source records attributed to Sun, C.-W..

3 recordsLinked to original sources

bioRxiv2020

Methylation of Dual Specificity Phosphatase 4 Controls Cell Differentiation

A collection of signaling and epigenetic events needs to be orchestrated for normal development of hematopoietic lineages. While mitogen-activated protein (MAP) kinases (MAPKs) and multiple epigenetic modulators have been implicated in the megakaryocytic (Mk) cell differentiation, the underlying molecular mechanisms of signaling-epigenetic crosstalk remain unclear. MAPKs are in general inactivated by dual specificity phosphatases (DUSPs), whose activities are tightly regulated by various posttranslational modifications. Using knockdown screening and single-cell transcriptional analysis, we determined that DUSP4 is the phosphatase that inactivates p38 MAPK in hematopoietic cells and serves as a key regulator to promote Mk differentiation. With the nextgeneration Bioorthogonal Profiling of Protein Methylation technology for live cells, we identified DUSP4 as a PRMT1 substrate. Mechanistically, PRMT1-mediated Arg351 methylation of DUSP4 triggers its ubiquitinylation by HUWE1 (an E3 ligase) and then degradation, which results in p38 MAPK activation and inhibition of Mk differentiation in vitro and in vivo. Interestingly, the mechanistic axis of the DUSP4 degradation and p38 activation is also associated with a transcriptional signature of immune activation and thus argues immunological roles of Mk cells. Collectively, these results demonstrate a critical role of PRMT1-mediated posttranslational modification of DUSP4 in regulation of Mk differentiation and maturation. In the context of thrombocytopenia observed in myelodysplastic syndromes (MDS), we demonstrated that high levels of p38 MAPK and PRMT1 are associated with low platelet counts and adverse prognosis, while pharmacological inhibition of p38 MAPK or PRMT1 stimulates megakaryopoiesis in MDS samples. These findings provide novel mechanistic insights into the role of the PRMT1-DUSP4-p38 axis on Mk differentiation and present a targeting strategy for treatment of thrombocytopenia associated with myeloid malignancies such as MDS.

cell biology↗

bioRxiv2020

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. For example, at high flow speed (i.e., high throughput), the integration time of the image sensor becomes short, resulting in reduced sensitivity or pixel resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually "freezing" the motion of flowing cells on the image sensor to effectively achieve 1,000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells/s without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

cell biology↗

bioRxiv2019

Classification of platelet aggregates by agonist type

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

cell biology↗