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Tissue Engineering

Tissue Engineering - Poster Session A

Poster L16 - Determining Transcription Factor Dynamics During Osteogenic Differentiation

Thursday, October 24, 2024
10:00 AM - 11:00 AM EST
Location: Exhibit Hall E, F & G

Introduction:  The ability of Mesenchymal Stem Cells (MSCs) to self-renew and differentiate into many cell types makes them a prime candidate for use in regenerative medicine, however the ability to direct stem cell fate for tissue-specific applications remains challenging. Stem cell fate is controlled by a complex network of Gene Regulatory Networks that respond to stimuli through dynamic changes in expressed proteins that can turn genes on or off, called transcription factors. Synthetic biology offers a method of controlling stem cell fate through reprogramming cells with genetic circuits that can mimic the dynamic effects of gene regulatory networks on command. Osteogenesis, the process in which MSCs differentiate into bone tissue, offers a potential opportunity to examine, decipher, and utilize the dynamic transcription networks behind differentiation. A major hurdle in understanding osteogenic differentiation is the dynamic nature of these transcription factor patterns, which consist of multiple genes interacting spatially and temporally during the process. Recent advancements in machine learning, a kind of Artificial Intelligence (AI) that can estimate statistical patterns based on input data, can be used to overcome this hurdle. By first building a library of expression data for key genes of interest across multiple time points during osteogenesis, machine learning can be used to analyze the data and form an understanding of how transcription factor expression is regulated in cells during differentiation. This can be used to build genetic tools that mimic these dynamic expression patterns to reprogram cells and gain control over osteogenic differentiation.

Materials and

Methods:  To begin compiling a data library, osteogenic differentiation was induced in Umbilical Cord (UC) derived-MSCs (ATCC, PCS-500-010) using StemPro™ Osteogenic Differentiation media (Gibco, A1007201). Samples were collected every 3 days after adding the osteogenic differentiation media, starting at day 3 and going until day 21, with a control day 0 (before osteogenic media conditions). Measurements of expressed genes were made using fluorophore-bound antibodies targeted for the proteins of interest. The stained cells were analyzed using flow cytometry, which measures the emission levels of multiple fluorophores for hundreds of individual cells per second. The transcription factors of interest were Runt-related transcription factor 2 (Runx2) and Osterix (SP7, Osx), which serve as regulatory ‘shepherds’ during differentiation, as well as factors related to Runx2 and SP7 expression, Foxc1, Msx2, Dlx5, and ATF4. Other proteins monitored were stem cell markers CD105, CD90, and CD44, and proteins expressed higher during osteogenesis such as Collagen type 1 alpha 1 (Col1α1), Osteocalcin, Osteopontin, and Alkaline phosphatase.

Results, Conclusions, and Discussions: 

Results:
Flow cytometry showed an increase in Col1α1 and Osteopontin, indicating that osteogenesis was successfully induced in the MSCs, this increase was accompanied with an observed increase in extracellular matrix (ECM) deposition and change in morphology. Flow data (Figure 1) showed dynamic change in transcription factor expression during the 21-day period, Runx2, Foxc1, and Msx2 increased most towards the first 6 days and plateaued afterward, while Osterix remained steady before increasing quickly over the last 9 days. ATF4 changed only a small amount, but showed both increases and decreases in expression, along with Dlx5, which appeared to oscillate in expression throughout differentiation.

Conclusion:
The results demonstrate that changes in transcription factor expression during differentiation are dynamic in nature, and regulated over time, rather than just over- or underexpressing genes at a single level. To tightly control osteogenic differentiation, a broad synthetic biology toolbox is required to design circuits tailored to match the expression patterns of key transcription factors.

Discussion:
Thus far, the data collected shows dynamic expression of transcription factors. To find relationships between transcription factors and osteogenic gene expression, the data collected will be used to train a machine learning algorithm that will be used to predict which of the transcription factors involved with osteogenesis can be used to build synthetic circuits in various expression patterns to drive differentiation. Once this is done, genetic circuits will be built that express transcription factors dynamically over time to during osteogenesis, that will improve outcomes of MSC differentiation down the osteogenic pathway.

Acknowledgements (Optional):