Trumpet is an operating system for simple and robust cell-free biocomputing

Split T7 switch-based orthogonal logic operation of fluorogenic RNA aptamer for small molecules detection

Recent advances in fluorescent biosensors have stimulated the development of molecular detection. We herein developed a new orthogonal logic operation of fluorescent biosensor with cell-free to accomplish the detection of atrazine (ATZ) and tetrachlorobiphenyls (PCB77). The transcriptional process to generate fluorescent RNA aptamers (Mango) was controlled by molecules-probe bindings, which regulate split T7 promoter transcription switches ON or OFF. Leveraging the rapid in vitro T7 transcription process and high signal-to-background ratio of the Mango-TO1-Biotin complex, this biosensor demonstrates remarkable sensitivity in detecting ATZ and PCB77, with detection limit of 1.56 pM and 10.2 pM. Moreover, the orthogonality of four logic gates (AND, NOR, INHIBT, NIMPLY) were utilized the ATZ and PCB77 as input to construct, which could be activated by utilizing the target probe-driven association. The output of the fluorescence signal was controlled by split/intact fluorescent RNA aptamer (Mango) to achieve flexible and sensitive orthogonal operations. Significantly, the development of two-input logic gates has enabled the modular detection of various small molecules, offering a promising approach to intelligent multi-input analysis. Predictably, with the advantages of sensitivity, flexibility and easy-to-operate, this orthogonal logic gate platform holds immense potential in small molecules detection.

In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing, and metabolic engineering. Yet, the analysis of the activity of such in vitro networks is very often restricted by time- and cost-intensive methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that is compatible with the complex conditions of in vitro metabolism, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a 27-component in vitro metabolic system that converts CO2 into glycolate. As proof of concept, we constructed a novel glycolate sensor module that is based on the transcriptional repressor GlcR from Paracoccus denitrificans and established an IVT biosensing workflow that allows us to quantify glycolate from CETCH samples in the micromolar to millimolar range. We investigate the influence of 13 (shared) cofactors between the two in vitro systems to show that Mg2+, adenosine triphosphate , and other phosphorylated metabolites are critical for robust signal output. Our optimized IVT biosensor correlates well with liquid chromatography-mass spectrometry-based glycolate quantification of CETCH samples, with one or multiple components varying (linear correlation 0.94-0.98), but notably at ∼10-fold lowered cost and ∼10 times faster turnover time. Our results demonstrate the potential and challenges of IVT-based systems to quantify and prototype the activity of complex reaction cascades and in vitro metabolic networks.

PACRAT: pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription

The SARS-CoV-2 pandemic underscored the need for early, rapid, and widespread pathogen detection tests that are readily accessible. Many existing rapid isothermal detection methods use the recombinase polymerase amplification (RPA), which exhibits polymerase chain reaction (PCR)-like sensitivity, specificity, and even higher speed. However, coupling RPA to other enzymatic reactions has proven difficult. For the first time, we demonstrate that with tuning of buffer conditions and optimization of reagent concentrations, RPA can be cascaded into an in vitro transcription reaction, enabling detection using fluorescent aptamers in a one-pot reaction. We show that this reaction, which we term PACRAT (pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription) can be used to detect SARS-CoV-2 RNA with single-copy detection limits, Escherichia coli with single-cell detection limits, and 10-min detection times. Further demonstrating the utility of our one-pot, cascaded amplification system, we show PACRAT can be used for multiplexed detection of the pathogens SARS-CoV-2 and E. coli, along with multiplexed detection of two variants of SARS-CoV-2.

DNA as a universal chemical substrate for computing and data storage

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.