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Watson, E.

Publications and source records attributed to Watson, E..

2 recordsLinked to original sources

A specialized bone marrow microenvironment for fetal haematopoiesis

Local signals provided by cells in specialized niche microenvironments regulate stem cell behaviour in many different organs and species. In adult mammalian bone marrow (BM), leptin receptor-positive (LepR+) reticular cells express secreted factors that control the function of haematopoietic stem and progenitor cells (HSPCs). During fetal development, the developing skeletal system is colonized by c-Kit+ haematopoietic cells de novo after a transient phase of liver haematopoiesis. The cellular and molecular mechanisms regulating de novo haematopoietic cell colonization and expansion remain largely unknown. Here, we report that fetal and adult BM exhibit fundamental differences both in terms of cellular composition and molecular interactions by single cell RNA sequencing (scRNA-seq) analysis. While LepR+ reticular cells are almost completely absent in fetal femur, arterial endothelial cells (AECs) are a source of signals controlling the initial HSPC expansion during BM development. Long-term haematopoietic stem cells (HSCs) and other c-Kit+ HSPCs are reduced when Wnt ligand secretion by AECs is genetically blocked. We identify Wnt2 as an AEC-derived signal that directly activates {beta}-catenin dependent proliferation of fetal HSPCs. Treatment of HSPCs ex vivo with Wnt2 promotes their proliferation and improves engraftment in vivo after transplantation. Our work reveals a fundamental switch in the cellular organization and molecular regulation of BM niches in the embryonic and adult organism.

developmental biology

Convolutional neural networks for automated CMR image segmentation in rats with myocardial infarcts

In translational cardiovascular research, delineation of left ventricle (LV) in magnetic resonance images is a crucial step in assessing hearts function. Performed manually, this task is time-consuming and prone to inter- and intra-reader variability. Here we report first AI-based tool for segmentation of rat cardiovascular MRI. The method is an ensemble of fully convolutional networks and can quantify clinically relevant measures: end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) automatically. Overall, our method reaches Dice score of 0.93 on the independent test set. The mean absolute difference of segmented volumes between automated and manual segmentation is 22.5L for EDV, 13.6L for ESV, and for EF 2.9%. Our work demonstrates the value of AI in development of tools that will significantly reduce time spent on repetitive work and result in increased efficiency of reporting data to project teams.

bioengineering