Myeloma-modified adipocytes exhibit metabolic dysfunction and a senescence-associated secretory phenotype (SASP)

Bone marrow adipocytes (BMAd) have recently been implicated in accelerating bone metastatic cancers such as acute myelogenous leukemia and breast cancer. Importantly, bone marrow adipose tissue (BMAT) expands with aging and obesity - two key risk factors in multiple myeloma disease prevalence - suggesting that BMAd may influence and be influenced by myeloma cells in the marrow. Here we provide evidence that reciprocal interactions and cross-regulation of myeloma cells and BMAd play a role in multiple myeloma pathogenesis and treatment response. Bone marrow biopsies from MM patients revealed significant loss of BMAT with myeloma cell infiltration of the marrow, whereas BMAT was restored after treatment for multiple myeloma. Myeloma cells reduced BMAT in different pre-clinical murine models of multiple myeloma and in vitro using myeloma cell-adipocyte co-cultures. In addition, multiple myeloma cells altered adipocyte gene expression and cytokine secretory profiles, which were also associated with bioenergetic changes and induction of a senescent-like phenotype. In vivo, senescence markers were also increased in the bone marrow of tumor-burdened mice. BMAd, in turn, provided resistance to dexamethasone-induced cell cycle arrest and apoptosis, illuminating a new possible driver of myeloma cell evolution in a drug resistant clone. Our findings reveal that bi-directional interactions between BMAd and myeloma cells have significant implications for the pathogenesis and treatment of multiple myeloma. Targeting senescence in the bone marrow adipocyte or other bone marrow cells may represent a novel therapeutic approach for treatment of multiple myeloma

Deep learning is combined with massive-scale citizen science to improve large-scale image classification

Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.QC 20181001</p