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Yasumoto, A.

Publications and source records attributed to Yasumoto, A..

2 recordsLinked to original sources

EBF1 limits the numbers of cochlear hair and supporting cells and forms the scala tympani and spiral limbus during the development of the inner ear

Early B-cell factor 1 (EBF1) is a basic helix-loop-helix transcription factor essential for the differentiation of various tissues. Our single-cell RNA sequencing data suggest that Ebf1 is expressed in the sensory epithelium of the inner ear. Here, we found that the Ebf1 gene and its protein are expressed in the prosensory domain of the inner ear, medial region of the cochlear floor, inner ear mesenchyme, and cochleo-vestibular ganglion. Ebf1 deletion results in incomplete formation of the spiral limbus and scala tympani, increased number of cells in the organ of Corti and Kollikers organ, and aberrant course of the spiral ganglion axons. The inner ear-specific deletion of Ebf1 revealed that scala tympani formation depended on Ebf1 expressed in the otic mesenchyme. Ebf1 deletion in the cochlear epithelia caused the proliferation of SOX2-positive cochlear cells at E13.5, indicating that EBF1 suppresses the proliferation of the prosensory domain and cells of Kollikers organ to facilitate the development of appropriate numbers of hair and supporting cells. Furthermore, mice with deletion of cochlear epithelium-specific Ebf1 showed poor postnatal hearing function. Our results suggest that Ebf1 is essential for normal auditory function in mammals.

developmental biology↗

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↗