MSc-derived Osteocyte mechanosome

Description

The aim of this current study was to assess the effect of pathological mechanical load on the osteocyte signature. This will help identify mechanical mechanisms that cause pain or alter bone tissue structure in vitro and provide new mechanistic insight into disease progression. Y201 mesenchymal stem cells (MSCs) were differentiated in 3D collagen gels in silicone plates. Gels were loaded using a BOSE ElectroForce® 3200 loading instrument (TE Instruments, UK) to stretch the plate causing cyclic compression in all wells (pathophysiological load 4300με induced by 0.7mm displacement, 10Hz, 3000 cycles). Control gels in the silicone plate were placed into the loading device but received no load. RNA was harvested from gels 1 hour after load. RNA sequencing was carried out on n=4 control and n=5 loaded samples and differentially expressed genes identified using an DEseq2 analysis on normalised count data. The resultant p-values were corrected for multiple testing and false discovery issues using the FDR method. Mechanical loading of the osteocyte model regulated 7564 genes (Padj p<0.05, 3026 down, 4538 up). 93% of the osteocyte transcriptome signature was expressed in the model with 38% of these genes mechanically regulated. Mechanically loaded osteocytes regulated 26% of gene ontology pathways linked to OA pain, 40% reflecting bone remodelling and 27% representing inflammation.

Steps to reproduce

Y201 mesenchymal stem cells (MSCs) were differentiated in 3D collagen gels in silicone plates. Gels were loaded using a BOSE ElectroForce® 3200 loading instrument (TE Instruments, UK) to stretch the plate causing cyclic compression in all wells (pathophysiological load 4300με induced by 0.7mm displacement, 10Hz, 3000 cycles). Control gels in the silicone plate were placed into the loading device but received no load. RNA was harvested from gels 1 hour after load using an RNeasy kit (Promega). An RNA sequencing library was prepared for the mechanically loaded (n=5) and control (n=4) samples, using the New England Biolabs Ultra II directional RNA library prep kit. cDNA was synthesized using this RNA which, after undergoing fragmentation, had adaptors ligated to the ends. The MiSeq Nano system (Illumina) was used to complete a sequencing library quality control after which sequencing was performed using the NovaSeq 6000 system (Illumina) running a 2 x 100bp paired-end reads run on a NovaSeq S1 flow cell. Trimming to remove adapter sequencer and poor-quality ends of reads was performed by Trim Galore using default parameters in paired-end mode. Trimmed paired-end reads were aligned to the GRCh38 no_alt_plus_hs38d1 analysis set reference using STAR (v2.5.1b), an ultrafast universal RNAseq aligner, following the 2-pass method. QC metrics were generated using FastQC (v0.11.2), and summary statistics were generated using Samtools (v0.1.19) flagstat. Raw counts were calculated for all samples for both (i) exons and (ii) genes using Subread featureCounts Version 1.5.1. Counts were generated for paired end read fragments summarized at exon level and then aggregated at transcript level. Differentially expressed genes were identified using an DEseq2 analysis on normalised count data. The resultant p-values were corrected for multiple testing and false discovery issues using the FDR method.

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