The use of nanovibration to discover specific and potent bioactive metabolites that stimulate osteogenic differentiation in mesenchymal stem cells

Abstract

Bioactive metabolites have wide-ranging biological activities and are a potential source of future research and therapeutic tools. Here, we use nanovibrational stimulation to induce osteogenic differentiation of mesenchymal stem cells, in the absence of off-target, nonosteogenic differentiation. We show that this differentiation method, which does not rely on the addition of exogenous growth factors to culture media, provides an artifact-free approach to identifying bioactive metabolites that specifically and potently induce osteogenesis. We first identify a highly specific metabolite, cholesterol sulfate, an endogenous steroid. Next, a screen of other small molecules with a similar steroid scaffold identified fludrocortisone acetate with both specific and highly potent osteogenic-inducing activity. Further, we implicate cytoskeletal contractility as a measure of osteogenic potency and cell stiffness as a measure of specificity. These findings demonstrate that physical principles can be used to identify bioactive metabolites and then enable optimization of metabolite potency can be optimized by examining structure-function relationships.

The role of mechanobiology in bone and cartilage model systems in characterizing initiation and progression of osteoarthritis

“…The type of force, magnitude, and frequency of extrinsically applied mechanical forces have been determined to drive differential responses in bone and cartilage cells through 3D in vitro studies, with even nanoscale displacements able to control MSC osteogenic differentiation. 246,247 These in vitro 3D mechanically stimulated assays are valuable tools as they allow precise control of loading type, magnitude, duration, and biochemical conditions, to understand thresholds for anabolic and catabolic cell responses and differences in the cell signaling. These can be taken into account in the development of treatment strategies.…”

NMR Metabolomics Assessment of Osteogenic Differentiation of Adipose-Tissue-Derived Mesenchymal Stem Cells

“…Interestingly, specific lipids ( e.g. , DHA and cholesterol sulfate) have been suggested to have osteoinductive properties. , Furthermore, it is known that MSCs isolated from distinct tissue sources often exhibit different proliferation, differentiation, and immunological properties , and therefore may be expected to exhibit different metabolic profiles. As previously mentioned, in the case of osteogenic differentiation, the majority of metabolomic studies have addressed MSCs from bone marrow, ,− ,− ,− adding to only a few publications using umbilical cord MSCs. ,, The use of adipose tissue in this context is increasingly interesting, including as a promising source of MSCs capable of osteogenic differentiation, because it is usually considered clinical waste and is typically available in large amounts from minimally invasive clinical procedures .…”

Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype

“… 38 Hodgkinson et al have recently used 3D OrbiSIMS images to observe metabolites in multiple mesenchymal stem cells (MSCs) using ToF and Orbitrap. 40 …”

“…In a tissue section of mouse brain, they identified lipid and amino acid fragments and were able to image a single cell in a tissue section . Hodgkinson et al have recently used 3D OrbiSIMS images to observe metabolites in multiple mesenchymal stem cells (MSCs) using ToF and Orbitrap …”

“…38 Hodgkinson et al have recently used 3D OrbiSIMS images to observe metabolites in multiple mesenchymal stem cells (MSCs) using ToF and Orbitrap. 40 Single-cell SIMS analysis represents an exciting method for single-cell metabolomic profiling to probe intercell variations. Here, we use 3D OrbiSIMS to investigate the metabolic profiles of human monocyte-derived M0, M1, and M2 macrophages at single-cell level, leading to identifying characteristic ions that are related to known biological processes for each subset.…”