Transdermal photopolymerization of hydrogels for tissue engineering

Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 1999.Includes bibliographical references.by Jennifer Hartt Elisseeff.Ph.D

Microarray Embedding/Sectioning for Parallel Analysis of 3D Cell Spheroids.

Three-dimensional cell spheroid models can be used to predict the effect of drugs and therapeutics and to model tissue development and regeneration. The utility of these models is enhanced by high throughput 3D spheroid culture technologies allowing researchers to efficiently culture numerous spheroids under varied experimental conditions. Detailed analysis of high throughput spheroid culture is much less efficient and generally limited to narrow outputs, such as metabolic viability. We describe a microarray approach that makes traditional histological embedding/sectioning/staining feasible for large 3D cell spheroid sample sets. Detailed methodology to apply this technology is provided. Analysis of the technique validates the potential for efficient histological analysis of up to 96 spheroids in parallel. By integrating high throughput 3D spheroid culture technologies with advanced immunohistochemical techniques, this approach will allow researchers to efficiently probe expression of multiple biomarkers with spatial localization within 3D structures. Quantitative comparison of staining will have improved inter- and intra-experimental reproducibility as multiple samples are collectively processed, stained, and imaged on a single slide

Derivation of Chondrogenically-Committed Cells from Human Embryonic Cells for Cartilage Tissue Regeneration

Background: Heterogeneous and uncontrolled differentiation of human embryonic stem cells (hESCs) in embryoid bodies (EBs) limits the potential use of hESCs for cell-based therapies. More efficient strategies are needed for the commitment and differentiation of hESCs to produce a homogeneous population of specific cell types for tissue regeneration applications. Methodology/Principal Findings: We report here that significant chondrocytic commitment of feeder-free cultured human embryonic stem cells (FF-hESCs), as determined by gene expression and immunostaining analysis, was induced by coculture with primary chondrocytes. Furthermore, a dynamic expression profile of chondrocyte-specific genes was observed during monolayer expansion of the chondrogenically-committed cells. Chondrogenically-committed cells synergistically responded to transforming growth factor-b1 (TGF-b1) and b1-integrin activating antibody by increasing tissue mass in pellet culture. In addition, when encapsulated in hydrogels, these cells formed cartilage tissue both in vitro and in vivo. In contrast, the absence of chondrocyte co-culture did not result in an expandable cell population from FF-hESCs. Conclusions/Significance: The direct chondrocytic commitment of FF-hESCs can be induced by morphogenetic factor

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells’ (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo–derived senescence signature (SenSig) using a foreign body response–driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and “cartilage-like” fibroblasts as senescent and defined cell type–specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34–CSF1R–TGFβR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.</p