Characterization of Mandible and Femur Canine Mesenchymal Stem Cells: A Pilot Study

Mesenchymal stem cells (MSCs) are emerging donor grafts for bone regeneration in dentistry. MSCs are phenotypically and functionally skeletal site- specific based on extensive studies using human and rodent MSCs but there is paucity of information on canine MSCs (cMSCs) and their regenerative applications in veterinary dentistry. We hypothesized that cMSCs are functionally skeletal-site specific and that mandible cMSCs (M-cMSCs) are highly osteogenic relative to femur cMSCs (F-cMSCs). Trabecular bone samples were obtained from mandible and femur of 2 healthy beagle dogs (ages: 3 weeks, females). Primary M-cMSCs and F-cMSCs were established in culture. Using early passage cells, colony-forming units (CFU), cell proliferation and population doubling capacity were assessed. Using established induction culture conditions, in vitro osteogenesis, chondrogenesis, adipogenesis, and neurogenesis were also assessed. Western blotting and real time PCR were used to assess the following osteogenic markers: alkaline phosphatase (ALP), bone sialoprotein (BSP), osteocalcin (OCN) and osteopontin (OPN). Chondrogenesis was assessed using pellet culture method and histologic sections were stained with Alcian blue; adipogenically induced-cultures were stained with Oil Red O. Neural differentiation was evaluated using morphological analysis and immunostaining to nestin and βIII-tubulin antibodies. Furthermore, in vivo osteogenesis was assessed using the mouse model of in vivo bone regeneration. Transplants were harvested at 6, 8 and 12 weeks for histological analysis.The M-cMSCs demonstrated 1.5 to 2 fold increases in cell proliferation (p =0.006) and life span (five more passages of survival) relative to F-cMSCs. Similar pattern was displayed by M-cMSCs based on expression levels of BSP (14 days p=0.05), ALP (14 days p= 0.004) and OCN (14 days p= 0.03) but OPN levels were not significantly different. Adipogenesis based on number of stained lipid droplets per unit area in M-cMSCs was significant higher than F-cMSCs (p=0.007) and chondrogenic response was also significant higher in M-cMSCs compared with F-cMSCs (4 weeks p= 0.009). Canine MSCs induced substantial in vivo bone formation. The canine MSCs phenotypic and functional properties are site-dependent as the M-cMSCs were apparently more responsive to multi-lineage differentiation relative to F-cMSCs. While the sample size in this study is limited, our findings are still consistent with previous studies using human, mouse and rat MSCs for site-to-site comparative characterizations (Akintoye et al, 2006; Yoshimura et al, 2007; Aghaloo et al, 2010; Lee et al, 2011). Additionally, it is imperative to further confirm these in a larger sample size and in other dog breeds since dogs exhibit an extremely wide range of body physique. New information will advance our understanding of pre-clinical applications of orofacial MSCs as donor graft materials for oral bone regeneration

On a quasi-stationary approach to bayesian computation, with application to tall data

Markov Chain Monte Carlo (MCMC) techniques have traditionally been used in a Bayesian inference to simulate from an intractable distribution of parameters. However, the current age of Big data demands more scalable and robust algorithms for the inferences to be computationally feasible. Existing MCMC-based scalable methodologies often uses discretization within their construction and hence they are inexact. A newly proposed field of the Quasi-Stationary Monte Carlo (QSMC) methodology has paved the way for a scalable Bayesian inference in a Big data setting, at the same time, its exactness remains intact. Contrary to MCMC, a QSMC method constructs a Markov process whose quasi-stationary distribution is given by the target. A recently proposed QSMC method called the Scalable Langevin Exact (ScaLE) algorithm has been constructed by suitably combining the exact method of diffusion, the Sequential Monte Carlo methodology for quasi-stationarity and sub-sampling ideas to produce a sub-linear cost in a Big data setting. This thesis uses the mathematical foundations of the ScaLE methodology as a building block and carefully combines a recently proposed regenerative mechanism for quasistationarity to produce a new class of QSMC algorithm called the Regenerating ScaLE (ReScaLE). Further, it provides various empirical results towards the sublinear scalability of ReScaLE and illustrates its application to a real world big data problem where a traditional MCMC method is likely to suffer from a huge computational cost. This work takes further inroads into some current limitations faced by ReScaLE and proposes various algorithmic modifications for targeting quasistationarity. The empirical evidences suggests that these modifications reduce the computational cost and improve the speed of convergence

Machine learning models of cell differentiation processes with single-cell transcriptomic measurements

Dynamic biological phenomena such as the development of immunity due to vaccination or the division of a single zygote into the 37 trillion cells in an adult human are triggered and driven by bio-molecular interactions. The bio-molecular species involved in these interactions are categorised based on their molecular properties and physiological function. Typically, the abundance or characteristics of only a single category of molecular species are measured in experimental protocols, and the data generated is noisy, biased and incomplete. Due to the limitations of measurement technology, computational models cannot represent bio-molecular interactions in full mechanistic detail and have to be restricted to operational definitions of complex biological phenomena. Despite these constraints, computational models tailored to the idiosyncracies of data generated by various technologies enable the identification of bio-molecular species and interactions relevant to particular biological processes. A cell is composed of various bio-molecular species such as nucleic acids, proteins, metabolites etc. The entire bio-molecular composition of a cell is known as a cell-state. mRNA are polymeric bio-molecules whose sequence encodes information for the production of proteins. While proteins are ultimately responsible for the execution of cellular functions, mRNA can be measured much more comprehensively with single-cell RNA sequencing technology. mRNA sequences corresponding to different protein segments are called transcripts, and the relative abundance of the various transcripts indicates the functional properties of the cell. Therefore, the cell-state can be approximated as a vector of mRNA transcript abundance. The change of the cell-state over the course of a biological process is called differentiation. This thesis presents three models of cell differentiation and their application for different scRNAseq. experimental protocols and discovery goals. The first two models are based on the simulation of cell differentiation with Markov chains. The first model provides a generally applicable trajectory inference approach to model differentiation in any biological system with no topological constraints. The second model utilises simulations to model differentiation as a latent state-space process and is used to cluster cells based on transcriptional activity in order to identify transitional cell-states. The third model is based on ordinal logistic regression and is used to identify transcripts whose expression varies along a specified ordinal axis, even in data with other prominent sources of variation