Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review.
Yufeng WenHuaxiao YangYi HongPublished in: ACS biomaterials science & engineering (2024)
Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.
Keyphrases
- single cell
- rna seq
- tissue engineering
- gene expression
- bone regeneration
- high throughput
- machine learning
- left ventricular
- healthcare
- dna methylation
- high glucose
- transcription factor
- heart failure
- mental health
- public health
- atrial fibrillation
- artificial intelligence
- pain management
- angiotensin ii
- cross sectional
- cell therapy
- optical coherence tomography
- climate change
- deep learning
- combination therapy
- health information
- liquid chromatography
- human health