Differential near-infrared imaging of heterocysts using single-walled carbon nanotubes.

The internalization of near-infrared (NIR) optical nanoprobes in photosynthetic microbes can be exploited for applications ranging from energy conversion to biomolecule delivery. However, the intrinsic, species-dependent properties of microbial cell walls, including their surface charge density, composition, thickness, and elasticity, can severely impact nanoprobe uptake and affect the cellular response. An examination of the interaction of the optical nanoprobe in various species and its impact on cell viability is, therefore, imperative for the development of new imaging technologies. Herein, we extend the technology recently developed for internalizing fluorescent single-walled carbon nanotubes (SWCNTs) in prokaryotes, specifically unicellular Synechocystis sp. PCC 6803, to a filamentous cyanobacterial strain, Nostoc punctiforme. Using a combination of NIR fluorescence, scanning electron microscopy (SEM), and Raman spectroscopy, we investigate uptake in vegetative cells as well as differentiated heterocysts. We demonstrate a strong dependence of long-term cell integrity, activity, and viability on SWCNT surface functionalization. We further show differential uptake of SWCNTs across a single filament, with positively charged functionalized SWCNTs preferentially localizing within the heterocysts of the filament. This cell dependency of the nanoparticle internalization motivates the use of SWCNTs as a NIR stain for monitoring cell differentiation.

Carbon nanotube uptake in cyanobacteria for near-infrared imaging and enhanced bioelectricity generation in living photovoltaics.

The distinctive properties of single-walled carbon nanotubes (SWCNTs) have inspired the development of many novel applications in the field of cell nanobiotechnology. However, studies thus far have not explored the effect of SWCNT functionalization on transport across the cell walls of prokaryotes. We explore the uptake of SWCNTs in Gram-negative cyanobacteria and demonstrate a passive length-dependent and selective internalization of SWCNTs decorated with positively charged biomolecules. We show that lysozyme-coated SWCNTs spontaneously penetrate the cell walls of a unicellular strain and a multicellular strain. A custom-built spinning-disc confocal microscope was used to image the distinct near-infrared SWCNT fluorescence within the autofluorescent cells, revealing a highly inhomogeneous distribution of SWCNTs. Real-time near-infrared monitoring of cell growth and division reveal that the SWCNTs are inherited by daughter cells. Moreover, these nanobionic living cells retained photosynthetic activity and showed an improved photo-exoelectrogenicity when incorporated into bioelectrochemical devices.

Synthetic Biology: A Solution for Tackling Nanomaterial Challenges.

Bioengineers have mastered practical techniques for tuning a biomaterial's properties with only limited information on the relationship between the material's structure and function. These techniques have been quintessential to engineering proteins, which are most often riddled with ill-defined structure-function relationships. In this Perspective, we review bioengineering approaches aimed at overcoming the elusive protein structure-function relation. We extend these principles to engineering synthetic nanomaterials, specifically applying the underlying theory to optical sensors based on single-stranded DNA-wrapped single-walled carbon nanotubes (ssDNA-SWCNTs). Bioengineering techniques such as directed evolution, computational design, and noncanonical synthesis are reviewed in the broader context of nanomaterials engineering. We further provide an order-of-magnitude analysis of empirical approaches that rely on random or guided searches for designing new nanomaterials. The underlying concepts presented in these approaches can be further extended to a broad range of engineering fields confronted with empirical design strategies, including catalysis, metal-organic frameworks (MOFs), pharmaceutical dosing, and optimization algorithms.