Harvard Catalyst Profiles

The primary objective of this project is to develop next-generation therapeutics for skeletal disease and injury through bone and cartilage reconstruction. Our goal is to establish guided bone ossification and cartilage formation by bio-print stem cell-laden hydrogels with well-controlled three-dimensional geometrical, mechanical, chemical, and biological properties. The biomaterials will be evaluated by ex vivo analyses and in vivo transplantation studies in mouse injury models for skeletal regeneration. The results obtained from these preclinical animal studies will be used to improve and upgrade the biomaterials for bone and cartilage tissue engineering. Students will use a 3D printer to print 3D objects from a computer-aided design (CAD), as well as learn how to modify an existing 3D printer to bio-print cell-laden hydrogels. They will also acquire skills in stem cell analyses. The project requires an interest and previous experience in the research area of skeletal biology, stem cell therapy, and 3D printers, especially fused deposition modeling (FDM) 3D printers, capable of setting up instruments, using Repetier and CAD software.

We are looking for motivated students interested in Data Science/Bioinformatics and participating in a team effort with the potential to publish papers in high-profile journals. Relevant courses or backgrounds in statistics, programming (R language, MetaCore), and basic biology are required for this position. The current study includes data mining of single-cell and spatial transcriptomics as well as epigenomics. These projects investigate a recent discovery of a skeletal stem cell population. By examining its transcriptomic and epigenomic diversity and relevant cellular heterogeneity, our goal is to identify a subpopulation with elevated expression of skeletal stem cell markers and genes linked to patients with congenital deformities, as well as to reveal novel regulatory processes of cell lineage development between the stem cell-enriched subpopulation and other subpopulations. The general strategy is to identify differentially expressed genes and differentially accessible genomic regions affected by the gene mutation in mouse models, followed by Gene Set Enrichment Analysis, Pathway Analysis, Upstream Regulator Assay, Cell-Cell Communication, etc. Using these data minings, we aim to identify candidates processing dynamic effects on phenotypic alteration in the mouse models and human diseases.