Oreffo, R. O. | Search bioRxiv

bioRxiv2023

Human bone tissue-derived ECM hydrogels: Controlling physicochemical, biochemical, and biological properties through processing parameters

Decellularized tissues offer significant potential as biological materials for tissue regeneration due to their ability to preserve the complex compositions and architecture of the native extracellular matrix (ECM). While the use of decellularized ECM hydrogels from bovine and porcine bone tissues has been extensively studied, the evaluation and derivation of decellularized matrices from human bone tissue remain largely unexplored. The objective of this study was to investigate how the physiochemical and biological properties of ECM hydrogels derived from human bone ECM could be controlled by manipulating bone powder size (45-250 m, 250-1000 m, and 1000-2000 m) and ECM composition through modulation of digestion time (3, 5, and 7 days). The current studies demonstrate that a reduction in material bone powder size and an increase in ECM digestion time resulted in enhanced protein concentrations in the ECM hydrogels, accompanied by the presence of a diverse array of proteins. Furthermore, these adjustments in the physicochemical properties generated improved gelation strength of the hydrogels. The evaluation of human bone marrow-derived stromal cells (HBMSCs) cultured on ECM hydrogels derived from 45-250 m bone powder, treated for 7 days, demonstrated enhanced osteogenic differentiation compared to hydrogels derived from both larger bone powders and collagen gels. In conclusion, this study highlights the significant promise of human bone ECM hydrogels as biologically active materials for bone regeneration. The ability to manipulate digestion time and bone powder size enables the generation of hydrogels with enhanced release of ECM proteins and appropriate gelation and rheological properties, offering new opportunities for application in bone tissue engineering.

bioengineering↗

bioRxiv2019

Mapping biology from mouse to man using transfer learning

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research this transfer process is not currently rigorously quantified. Here, we show that transfer learning - the branch of machine learning that concerns passing information from one domain to another - can be used to efficiently map biology from mouse to man, using the bone marrow (BM) as a representative example of a complex tissue. We first trained an artificial neural network (ANN) to accurately recognize various different cell types in mouse BM using data obtained from single-cell RNA-sequencing (scRNA-Seq) experiments. We found that this ANN, trained exclusively on mouse data, was able to identify individual human cells obtained from comparable scRNA-Seq experiments of human BM with 83% overall accuracy. However, while some human cell types were easily identified, others were not, indicating important differences in biology. To obtain a more accurate map of the human BM we then retrained the mouse ANN using scRNA-Seq data from a limited sample of human BM cells. Typically, less than 10 human cells of a given type were needed to accurately learn its representation in the updated model. In some cases, human cell identities could be inferred directly from the mouse ANN without retraining, via a process of biologically-guided zero-shot learning. These results show how machine learning can be used to reconstruct complex biology from limited data and have broad implications for biomedical research.

systems biology↗