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The bone is a mechanosensitive organ that is also a common metastatic site for prostate cancer. However, the mechanism by which the tumor interacts with the bone microenvironment to further promote disease progression remains to be fully understood. This is largely due to a lack of physiological yet user-friendly models that limit our ability to perform in-depth mechanistic studies. Here, we report a tunable bioreactor which facilitates the 3D culture of the osteocyte cell line, MLO-Y4, in a hydroxyapatite/tricalcium phosphate (HA/TCP) scaffold under constant fluidic shear stress and tunable hydrostatic pressure within physiological parameters. Increasing hydrostatic pressure was sufficient to induce a change in the expression of several bone remodeling genes such as Dmp1, Rankl, and Runx2. Furthermore, increased hydrostatic pressure induced the osteocytes to promote the differentiation of the murine macrophage cell line RAW264.7 toward osteoclast-like cells. These results demonstrate that the bioreactor recapitulates the mechanotransduction response of osteocytes to pressure including the measurement of their functional ability in a 3D environment. In conclusion, the bioreactor would be useful for exploring the mechanisms of osteocytes in bone health and disease. 

Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analysed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.