Automated Quantification of Human Osteoclasts Using Object Detection

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results

Automated Quantification of Human Osteoclasts Using Object Detection

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results

Diet-induced obesity reduces bone marrow T and B cells and promotes tumor progression in a transplantable Vk*MYC model of multiple myeloma

Abstract Obesity is associated with an increased risk of developing multiple myeloma (MM). The molecular mechanisms causing this association is complex and incompletely understood. Whether obesity affects bone marrow immune cell composition in multiple myeloma is not characterized. Here, we examined the effect of diet-induced obesity on bone marrow immune cell composition and tumor growth in a Vk*MYC (Vk12653) transplant model of multiple myeloma. We find that diet-induced obesity promoted tumor growth in the bone marrow and spleen and reduced the relative number of T and B cells in the bone marrow. Our results suggest that obesity may reduce MM immune surveillance and thus may contribute to increased risk of developing MM

Intracellular IL-32 regulates mitochondrial metabolism, proliferation, and differentiation of malignant plasma cells

Interleukin-32 (IL-32) is a nonclassical cytokine expressed in cancers, inflammatory diseases, and infections. Its expression is regulated by two different oxygen sensing systems; HIF1α and cysteamine dioxygenase (ADO), indicating that IL-32 may be involved in the response to hypoxia. We here demonstrate that endogenously expressed, intracellular IL-32 interacts with components of the mitochondrial respiratory chain and promotes oxidative phosphorylation. Knocking out IL-32 in three myeloma cell lines reduced cell survival and proliferation in vitro and in vivo. High-throughput transcriptomic and MS-metabolomic profiling of IL-32 KO cells revealed that cells depleted of IL-32 had perturbations in metabolic pathways, with accumulation of lipids, pyruvate precursors, and citrate. IL-32 was expressed in a subgroup of myeloma patients with inferior survival, and primary myeloma cells expressing IL-32 had a gene signature associated with immaturity, proliferation, and oxidative phosphorylation. In conclusion, we demonstrate a previously unrecognized role of IL-32 in the regulation of plasma cell metabolism

Intracellular IL-32 regulates mitochondrial metabolism, proliferation, and differentiation of malignant plasma cells

Interleukin-32 (IL-32) is a nonclassical cytokine expressed in cancers, inflammatory diseases, and infections. Its expression is regulated by two different oxygen sensing systems; HIF1α and cysteamine dioxygenase (ADO), indicating that IL-32 may be involved in the response to hypoxia. We here demonstrate that endogenously expressed, intracellular IL-32 interacts with components of the mitochondrial respiratory chain and promotes oxidative phosphorylation. Knocking out IL-32 in three myeloma cell lines reduced cell survival and proliferation in vitro and in vivo. High-throughput transcriptomic and MS-metabolomic profiling of IL-32 KO cells revealed that cells depleted of IL-32 had perturbations in metabolic pathways, with accumulation of lipids, pyruvate precursors, and citrate. IL-32 was expressed in a subgroup of myeloma patients with inferior survival, and primary myeloma cells expressing IL-32 had a gene signature associated with immaturity, proliferation, and oxidative phosphorylation. In conclusion, we demonstrate a previously unrecognized role of IL-32 in the regulation of plasma cell metabolism