Simikyan, R. G. | bioRxiv Science

bioRxiv2024

Frem2 Knockout Mice Exhibit Fraser Syndrome Phenotypes and Neonatal Lethality Due to Bilateral Renal Agenesis

Fraser syndrome is a rare autosomal recessive disorder characterized by multiple congenital malformations, including cryptophthalmos, syndactyly, and renal agenesis, which can lead to severe complications beginning at the embryonic stage. Mutations in genes encoding extracellular matrix proteins such as FRAS1, FREM1, FREM2, and the associated trafficking protein GRIP1, are implicated in Fraser syndrome. These proteins are critical for maintaining epithelial integrity during embryogenesis, with deficiencies leading to tissue detachment and blistering phenotypes in mouse models. The FREM2 protein is a single-pass membrane protein of 3169 amino acids. While Frem2-deficient mouse models encoding missense variants found in patients, or a truncated FREM2 protein product were previously reported, it has not been studied in a constitutive knockout (KO) mouse model. Here, we developed constitutive Frem2-KO mice exhibiting neonatal lethality, mainly due to bilateral renal agenesis, along with blood-filled blisters, cryptophthalmos, and syndactyly. Only one mouse survived to adulthood exhibiting unilateral renal agenesis and Fraser syndrome-like phenotypes. These findings confirm FREM2s crucial role in the development of the kidneys, skin, and eyes and provide an animal model for further studies of FREM2-related developmental disorders.

developmental biology↗

bioRxiv2023

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90,000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

neuroscience↗