Search for: All records

A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications. 

Protein adsorption to solid carbohydrate interfaces is critical to many biological processes, particularly in biomass deconstruction. To engineer more-efficient enzymes for biomass deconstruction into sugars, it is necessary to characterize the complex protein–carbohydrate interfacial interactions. A carbohydrate-binding module (CBM) is often associated with microbial surface-tethered cellulosomes or secreted cellulase enzymes to enhance substrate accessibility. However, it is not well known how CBMs recognize, bind, and dissociate from polysaccharides to facilitate efficient cellulolytic activity, due to the lack of mechanistic understanding and a suitable toolkit to study CBM–substrate interactions. Our work outlines a general approach to study the unbinding behavior of CBMs from polysaccharide surfaces using a highly multiplexed single-molecule force spectroscopy assay. Here, we apply acoustic force spectroscopy (AFS) to probe a Clostridium thermocellum cellulosomal scaffoldin protein (CBM3a) and measure its dissociation from nanocellulose surfaces at physiologically relevant, low force loading rates. An automated microfluidic setup and method for uniform deposition of insoluble polysaccharides on the AFS chip surfaces are demonstrated. The rupture forces of wild-type CBM3a, and its Y67A mutant, unbinding from nanocellulose surfaces suggests distinct multimodal CBM binding conformations, with structural mechanisms further explored using molecular dynamics simulations. Applying classical dynamic force spectroscopy theory, the single-molecule unbinding rate at zero force is extrapolated and found to agree with bulk equilibrium unbinding rates estimated independently using quartz crystal microbalance with dissipation monitoring. However, our results also highlight critical limitations of applying classical theory to explain the highly multivalent binding interactions for cellulose–CBM bond rupture forces exceeding 15 pN. 

The detection of nucleic acids and their mutation derivatives is vital for biomedical science and applications. Although many nucleic acid biosensors have been developed, they often require pretreatment processes, such as target amplification and tagging probes to nucleic acids. Moreover, current biosensors typically cannot detect sequence-specific mutations in the targeted nucleic acids. To address the above problems, herein, we developed an electrochemical nanobiosensing system using a phenomenon comprising metal ion intercalation into the targeted mismatched double-stranded nucleic acids and a homogeneous Au nanoporous electrode array (Au NPEA) to obtain (i) sensitive detection of viral RNA without conventional tagging and amplifying processes, (ii) determination of viral mutation occurrence in a simple detection manner, and (iii) multiplexed detection of several RNA targets simultaneously. As a proof-of-concept demonstration, a SARS-CoV-2 viral RNA and its mutation derivative were used in this study. Our developed nanobiosensor exhibited highly sensitive detection of SARS-CoV-2 RNA (∼1 fM detection limit) without tagging and amplifying steps. In addition, a single point mutation of SARS-CoV-2 RNA was detected in a one-step analysis. Furthermore, multiplexed detection of several SARS-CoV-2 RNAs was successfully demonstrated using a single chip with four combinatorial NPEAs generated by a 3D printing technique. Collectively, our developed nanobiosensor provides a promising platform technology capable of detecting various nucleic acids and their mutation derivatives in highly sensitive, simple, and time-effective manners for point-of-care biosensing.