Fluorescence in situ hybridization (FISH) is the gold standard for visualizing genomic DNA in fixed cells and tissues, but it is incompatible with live-cell imaging, and its combination with RNA imaging is challenging. Consequently, due to its capacity to bind double-stranded DNA (dsDNA) and design flexibility, the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (CRISPR-Cas9) technology has sparked enormous interest over the past decade. In this review, we describe various nucleic acid (NA)- and protein-based (amplified) signal generation methods that achieve imaging of repetitive and single-copy sequences, and even single-nucleotide variants (SNVs), next to highly multiplexed as well as dynamic imaging in live cells. With future progress in the field, the CRISPR-(d)Cas9-based technology promises to break through as a next-generation cell-imaging technique.

Sustainable production of chemicals and materials from renewable non-food biomass using biorefineries has become increasingly important in an effort toward the vision of 'net zero carbon' that has recently been pledged by countries around the world. Systems metabolic engineering has allowed the efficient development of microbial strains overproducing an increasing number of chemicals and materials, some of which have been translated to industrial-scale production. Fermentation is one of the key processes determining the overall economics of bioprocesses, but has recently been attracting less research attention. In this Review, we revisit and discuss factors affecting the competitiveness of bacterial fermentation in connection to strain development by systems metabolic engineering. Future perspectives for developing efficient fermentation processes are also discussed.

Enhancing biological nitrogen (N) fixation in cereal crops has been a long-sought objective. Recently, Yan et al. identified plant compounds that induce biofilm production of diazotrophic bacteria and then performed genetic engineering in order to improve nitrogen fixation in rice plants. These findings hold promise for sustainable agriculture.

Lithography bioprinting can fabricate constructs with high resolution for potential use in tissue engineering applications. Seminal work by Grigoryan and colleagues developed bioresins with precise control over the x, y, and z-planes during lithography bioprinting and applied this technique to fabricating physiologically biomimetic alveolar lung models.

Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Many synthetic biology applications rely on programming living cells using gene circuits - the assembly and wiring of genetic elements to control cellular behaviors. Extensive progress has been made in constructing gene circuits with diverse functions and applications. For many circuit functions, however, it remains challenging to ensure that the circuits operate in a predictable manner. Although the notion of predictability may appear intuitive, close inspection suggests that it is not always clear what constitutes predictability. We dissect this concept and how it can be confounded by the complexity of a circuit, the complexity of the context, and the interplay between the two. We discuss circuit engineering strategies, in both computation and experiment, that have been used to improve the predictability of gene circuits.

Emerging reprogenetic technologies may enable the enhancement of our offspring's genes. Beyond raising ethical questions, these biotechnologies may change some aspects of future morality. In the reproductive field, biotechnological innovations may transform moral views about reproductive choices regarding what we consider to be just or even of equal standing.

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.