Dataset Information

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction.

ABSTRACT: Since their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages (e.g., cells in either osteogenesis or adipogenesis) is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconstruct the MSC metabolic network to understand the activity of various metabolic pathways and compare their usage under different conditions and use these models to perform experimental design. We present three new genome-scale metabolic models (GEMs) each representing a different MSC lineage (proliferation, osteogenesis, and adipogenesis) that are biologically feasible and have distinctive cell lineage characteristics that can be used to explore metabolic function and increase our understanding of these phenotypes. We present the most distinctive differences between these lineages when it comes to enriched metabolic subsystems and propose a possible osteogenic enhancer. Taken together, we hope these mechanistic models will aid in the understanding and therapeutic potential of MSCs.

SUBMITTER: Sigmarsdottir TB

PROVIDER: S-EPMC8212021 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction.

Sigmarsdottir Thora Bjorg TB McGarrity Sarah S Yurkovich James T JT Rolfsson Óttar Ó Sigurjónsson Ólafur Eysteinn ÓE

Frontiers in cell and developmental biology 20210604

Since their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages (e.g., cells in either osteogenesis or adipogenesis) is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconst ...[more]

PMID: 34150750

Similar Datasets

Project description:Human mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes. These cells have been extensively employed in the field of cell-based therapies and regenerative medicine due to their inherent attributes of self-renewal and multipotency. Traditional approaches for assessing hMSCs differentiation capacity have relied heavily on labor-intensive techniques, such as RT-PCR, immunostaining, and Western blot, to identify specific biomarkers. However, these methods are not only time-consuming and economically demanding, but also require the fixation of cells, resulting in the loss of temporal data. Consequently, there is an emerging need for a more efficient and precise approach to predict hMSCs differentiation in live cells, particularly for osteogenic and adipogenic differentiation. In response to this need, we developed innovative approaches that combine live-cell imaging with cutting-edge deep learning techniques, specifically employing a convolutional neural network (CNN) to meticulously classify osteogenic and adipogenic differentiation. Specifically, four notable pre-trained CNN models, VGG 19, Inception V3, ResNet 18, and ResNet 50, were developed and tested for identifying adipogenic and osteogenic differentiated cells based on cell morphology changes. We rigorously evaluated the performance of these four models concerning binary and multi-class classification of differentiated cells at various time intervals, focusing on pivotal metrics such as accuracy, the area under the receiver operating characteristic curve (AUC), sensitivity, precision, and F1-score. Among these four different models, ResNet 50 has proven to be the most effective choice with the highest accuracy (0.9572 for binary, 0.9474 for multi-class) and AUC (0.9958 for binary, 0.9836 for multi-class) in both multi-class and binary classification tasks. Although VGG 19 matched the accuracy of ResNet 50 in both tasks, ResNet 50 consistently outperformed it in terms of AUC, underscoring its superior effectiveness in identifying differentiated cells. Overall, our study demonstrated the capability to use a CNN approach to predict stem cell fate based on morphology changes, which will potentially provide insights for the application of cell-based therapy and advance our understanding of regenerative medicine.

Project description:Bone-marrow mesenchymal stem cells (BMSC) are considered the gold standard for use in tissue regeneration among mesenchymal stem cells (MSC). The abundance and ease of harvest make the adipose-derived stem cells (ASC) an attractive alternative to BMSC. The aim of the present study was to compare the transcriptome of ASC and BMSC, respectively isolated from subcutaneous adipose tissue and femur of 3 adult pigs, during in vitro osteogenic and adipogenic differentiation for up to four weeks. At 0, 2, 7, and 21 days of differentiation RNA was extracted for microarray analysis. A False Discovery Rate ?0.05 for overall interactions effect and P<0.001 between comparisons were used to determine differentially expressed genes (DEG). Ingenuity Pathway Analysis and DAVID performed the functional analysis of the DEG. Functional analysis of highest expressed genes in MSC and genes more expressed in MSC vs. fully differentiated tissues indicated low immunity and high angiogenic capacity. Only 64 genes were differentially expressed between ASC and BMSC before differentiation. The functional analysis uncovered a potential larger angiogenic, osteogenic, migration, and neurogenic capacity in BMSC and myogenic capacity in ASC. Less than 200 DEG were uncovered between ASC and BMSC during differentiation. Functional analysis also revealed an overall greater lipid metabolism in ASC, while BMSC had a greater cell growth and proliferation. The time course transcriptomic comparison between differentiation types uncovered <500 DEG necessary to determine cell fate. The functional analysis indicated that osteogenesis had a larger cell proliferation and cytoskeleton organization with a crucial role of G-proteins. Adipogenesis was driven by PPAR signaling and had greater angiogenesis, lipid metabolism, migration, and tumorigenesis capacity. Overall the data indicated that the transcriptome of the two MSC is relatively similar across the conditions studied. In addition, functional analysis data might indicate differences in therapeutic application.

Dataset Information

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction.

Publications

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets