| Literature DB >> 31297148 |
Anh H Nguyen1,2, Paul Marsh2, Lauren Schmiess-Heine2, Peter J Burke2,3,4, Abraham Lee3,5, Juhyun Lee6, Hung Cao2,3,7.
Abstract
The purpose of this review is to assess the state-of-the-art fabrication methods, advances in genome editing, and the use of machine learning to shape the prospective growth in cardiac tissue engineering. Those interdisciplinary emerging innovations would move forward basic research in this field and their clinical applications. The long-entrenched challenges in this field could be addressed by novel 3-dimensional (3D) scaffold substrates for cardiomyocyte (CM) growth and maturation. Stem cell-based therapy through genome editing techniques can repair gene mutation, control better maturation of CMs or even reveal its molecular clock. Finally, machine learning and precision control for improvements of the construct fabrication process and optimization in tissue-specific clonal selections with an outlook of cardiac tissue engineering are also presented.Entities:
Keywords: 3D scaffolds; CRISPR/Cas9 systems; Cardiac tissue engineering; Machine learning
Year: 2019 PMID: 31297148 PMCID: PMC6599291 DOI: 10.1186/s13036-019-0185-0
Source DB: PubMed Journal: J Biol Eng ISSN: 1754-1611 Impact factor: 4.355
Fig. 1(1) Introduction of LQTS genes in independent healthy hPSC lines using CRISPR/Cas9. (2) Generation of disease-cardiomyocyte hiPSCs. (3) Isogenic sets of hPSC-CMs were differentiated from the edited hiPSCs lines. (4) Molecular analysis and phenotyping of hPSC-CMs (upper) molecular pathogenesis, (middle) drug screening, and (bottom) physiological functions
Fig. 2Biomaterials are based on self-assembled monolayers from bacteriophage display for 3D scaffolds formation. (Top), RGD peptide is displayed and fused to the solvent-exposed terminal of each copy of major coat protein (pVIII) through genetic engineering. The side wall of filamentous phage by RGD-coding gene into gene VIII to generate RGD-phage. (Bottom) The 3D scaffold of RGD-phage nanofibers (negatively charged) self-assemblies with polycationic biomaterials and integrated into a 3D printed bio-ceramic scaffold [156], which electrically stabilizes the phage nanofiber inside the scaffold. The resulted scaffold is seeded with hiPSCs and the implanted into cardiac defect. The presence of RGD-phage in the scaffold induced the formation of cardiomyocytes [157]
Fig. 3Fabrication and characterization of PLCL/FDM. a Illustration represents the fabrication process of PLCL/FDM. b Random and aligned orientations of PLCL fibers. Scale bar of SEM images is 10 μm. c Fibrillary ECM components in FDM were stained against FN and collagen type I. The direction of PLCL fiber alignment is shown by double headed arrows. Scale bar is 50 μm. d ATR-FTIR spectra of FDM with C=O at 1753 cm− 1 from PLCL and amide group at 1645 cm− 1 from FDM. e AFM images for surface topographical features of PLCL and PLCL/FDM; color scale shows their surface roughness and difference in height. f Quantitative comparison of root mean square (RMS) roughness calculated from AFM images. Statistical significance (***p < 0.001). The reproduced image is permitted from [45]
Fig. 4Machine learning for drug screening on human iPSCs-derived engineered cardiac tissue. a Waveform pattern parameters are determined based on concentration of cardioactive compounds compared to the binary support vector machine (SVM). The collected data points would be in line with those of vehicle as if the compound does not modulate the contractile behavior of human ventricular cardiac tissue strips (hvCTSs). If data of cardio active effects are more distinguishable, it shows in a higher SVM accuracy which is possible to separate two compound groups. The degree of cardio activity of a given concentration for target compound is shown in a singular quantitative index with the binary SVM approach. b Library of compounds is built on a model for prediction of mechanistic action of screened compounds. Data from the library group allow the machine learning defines boundaries of various drug families. Finally, the developed model can be applied for the unknown compounds on tissue engineering. The image is reproduced with permission from [41]