SLAS Technology最新文献

Every year biotechnology labs generate a combined total of ∼5.5 million tons of plastic waste. As the global bioeconomy expands, biofoundries will inevitably increase plastic consumption in-step with synthetic biology scaling. Decontamination and reuse of single-use plastics could increase sustainability and reduce recurring costs of biological research. However, throughput and variable cleaning quality make manual decontamination impractical in most instances. Automating single-use plastic cleaning with liquid handling robots makes decontamination more practical by offering higher throughput and consistent cleaning quality. However, open-source, validated protocols using low-cost lab robotics for effective decontamination of plasticware—facilitating safe reuse—have not yet been developed. Here we introduce and validate TidyTron: a library of protocols for cleaning micropipette tips and microtiter plates that are contaminated with DNA, E. coli, and S. cerevisiae. We tested a variety of cleaning solutions, contact times, and agitation methods with the aim of minimizing time and cost, while maximizing cleaning stringency and sustainability. We tested and validated these cleaning procedures by comparing fresh (first-time usage) versus cleaned tips and plates for contamination with cells, DNA, or cleaning solutions. We assessed contamination by measuring colony forming units by plating, PCR efficiency and DNA concentration by qPCR, and event counts and debris by flow cytometry. Open source cleaning protocols are available at https://github.com/PlantSynBioLab/TidyTron and hosted on a graphical user interface at https://jbryantvt.github.io/TidyTron/.

Artificial cells, synthetic cells, or minimal cells are microengineered cell-like structures that mimic the biological functions of cells. Artificial cells are typically biological or polymeric membranes where biologically active components, including proteins, genes, and enzymes, are encapsulated. The goal of engineering artificial cells is to build a living cell with the least amount of parts and complexity. Artificial cells hold great potential for several applications, including membrane protein interactions, gene expression, biomaterials, and drug development. It is critical to generate robust, stable artificial cells using high throughput, easy-to-control, and flexible techniques. Recently, droplet-based microfluidic techniques have shown great potential for the synthesis of vesicles and artificial cells. Here, we summarized the recent advances in droplet-based microfluidic techniques for the fabrication of vesicles and artificial cells. We first reviewed the different types of droplet-based microfluidic devices, including flow-focusing, T-junction, and coflowing. Next, we discussed the formation of multi-compartmental vesicles and artificial cells based on droplet-based microfluidics. The applications of artificial cells for studying gene expression dynamics, artificial cell-cell communications, and mechanobiology are highlighted and discussed. Finally, the current challenges and future outlook of droplet-based microfluidic methods for engineering artificial cells are discussed. This review will provide insights into scientific research in synthetic biology, microfluidic devices, membrane interactions, and mechanobiology.

Cell-free expression (CFE) systems have been used extensively in systems and synthetic biology as a promising platform for manufacturing proteins and chemicals. Currently, the most widely used CFE system is in vitro protein transcription and translation platform. As the rapidly increased applications and uses, it is crucial to have a standard biophysical model for quantitative studies of gene circuits, which will provide a fundamental understanding of basic working mechanisms of CFE systems. Current modeling approaches mainly focus on the characterization of E. coli-based CFE systems, a computational model that can be utilized for both bacterial- and mammalian-based CFE has not been investigated. Here, we developed a simple ODE (ordinary differential equation)-based biophysical model to simulate transcription and translation dynamics for both bacterial- and mammalian- based CFE systems. The key parameters were estimated and adjusted based on experimental results. We next tested four gene circuits to characterize kinetic dynamics of transcription and translation in E. coli- and HeLa-based CFE systems. The real-time transcription and translation were monitored using Broccoli aptamer, double stranded locked nucleic acid (dsLNA) probe and fluorescent protein. We demonstrated the difference of kinetic dynamics for transcription and translation in both systems, which will provide valuable information for quantitative genomic and proteomic studies. This simple biophysical model and the experimental data for both E. coli- and HeLa-based CFE will be useful for researchers that are interested in genetic engineering and CFE bio-manufacturing.

The ability of cells to sense and respond to their physical environment plays a fundamental role in a broad spectrum of biological processes. As one of the most essential molecular force sensors and transducers found in cell membranes, mechanosensitive (MS) ion channels can convert mechanical inputs into biochemical or electrical signals to mediate a variety of sensations. The bottom-up construction of cell-sized compartments displaying cell-like organization, behaviors, and complexity, also known as synthetic cells, has gained popularity as an experimental platform to characterize biological functions in isolation. By reconstituting MS channels in the synthetic lipid bilayers, we envision using mechanosensitive synthetic cells for several medical applications. Here, we describe three different concepts for using ultrasound, shear stress, and compressive stress as mechanical stimuli to activate drug release from mechanosensitive synthetic cells for disease treatments.

Digital microfluidics (DMF) has found great applications in vitro diagnostics (IVD). Compared to the microfabrication-based DMF, printed circuit board (PCB)-based DMF is more economical and compatible with existing IVD instruments. Despite that, current PCB-based DMF is oftentimes limited by the available droplets that can be controlled simultaneously, compromising their throughput and applications as point-of-care tools. In this work, a platform that simultaneously controls multiple PCB-based DMF plates was constructed. The software and hardware were first developed, followed by the reliability tests. Colorimetric analysis of glucose was applied to the PCB-based DMF, demonstrating the capability of this platform. With the high throughput enabled by simultaneous operations of multiple plates, this PCB-based DMF can potentially allow point-of-care testing with low cost for resource-limited settings.

Antimicrobial-resistant (AMR) bacterial pathogens are a continually growing threat as our methods for combating these infections continue to be overcome by the evolution of resistance mechanisms. Recent therapeutic methods have not staved off the concern of AMR infections, so continued research focuses on new ways of identifying small molecules to treat AMR pathogens. While chemical modification of existing antibiotics is possible, there has been rapid development of resistance by pathogens that were initially susceptible to these compounds. Synthetic biology is becoming a key strategy in trying to predict and induce novel, natural antibiotics. Advances in cloning and mutagenesis techniques applied through a synthetic biology lens can help characterize the native regulation of antibiotic biosynthetic gene clusters (BGCs) to identify potential modifications leading to more potent antibiotic activity. Additionally, many cryptic antibiotic BGCs are derived from non-ribosomal peptide synthase (NRPS) and polyketide synthase (PKS) biosynthetic pathways; complex, clustered genetic sequences that give rise to amino acid-derived natural products. Synthetic biology can be applied to modify and metabolically engineer these enzyme-based systems to promote rapid and sustainable production of natural products and their variants. This review will focus on recent advances related to synthetic biology as applied to genetic pathway characterization and identification of antibiotics from naturally occurring BGCs. Specifically, we will summarize recent efforts to characterize BGCs via general genomic mutagenesis, endogenous gene expression, and heterologous gene expression.

Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the “Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the “Liebowitz Social Anxiety Scale questionnaire” and “The fear of speaking in public” are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.

Laboratory capacities are often limited by time-consuming manual repetitive procedures rather than analysis time itself. While modern instruments are typically equipped with an autosampler, sample preparation often follows manual procedures including many labor-intensive, monotonous tasks. Particularly, for a high number of samples, well plates, and low microliter pipetting, manual preparation is error-prone often requiring repeated experiments. Sampling and sample preparation can account for greater analytical variability than instrument analysis.

Repetitive tasks such as liquid handling benefit strongly from technological advances and led to the increasing applications of various automated liquid handlers (ALHs). In this review, we discuss the considerations for ALHs in the microliter range and highlight advantages and challenges when transforming from manual to automated workflows. We strongly focused on differences in liquid handling and outlined advantages due to sensor-controlled pipetting. ALHs can substantially improve costs-effectiveness and laboratory capacity. This is a consequence of increased efficiency, and throughput of laboratories while simultaneously raising data quality. Additionally, ALHs can improve safety, documentation of data, and sustainability. While automation requires careful consideration and resource demanding implementation, we believe it offers numerous advantages and can help to transform modern laboratories.

High-throughput experimentation (HTE) has become more widely utilized in drug discovery for rapid reaction optimization and generation of large synthetic compound arrays. While this has accelerated medicinal chemistry design, make, test (DMT) iterations, the bottleneck of purification persists, consuming time and resources. Herein we describe a general parallel purification approach based on solid phase extraction (SPE) that provides a more efficient and sustainable workflow producing compound libraries with significantly upgraded purity. This robust, user-friendly workflow is fully automated and integrated with HTE library synthesis, as demonstrated by its application to a diverse parallel library compound array generated via amide-bond coupling in HTE microscale format.

Objective

Our goal was to find metabolism-related lncRNAs that were associated with osteoporosis (OP) and construct a model for predicting OP progression using these lncRNAs.

Methods

The GEO database was employed to obtain gene expression profiles. The WGCNA technique and differential expression analysis were used to identify hypoxia-related lncRNAs. A Lasso regression model was applied to select 25 hypoxia-related genes, from which a classification model was created. Its robust classification performance was confirmed with an area under the ROC curve close to 1, as verified on the validation set. Concurrently, we constructed a ceRNA network based on these genes to unveil potential regulatory processes. Biologically active compounds of STZYD were identified using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database. BATMAN was used to identify its targets, and we obtained OP-related genes from Malacards and DisGeNET, followed by identifying intersection genes with metabolism-related genes. A pharmacological network was then constructed based on the intersecting genes. The pharmacological network was further integrated with the ceRNA network, resulting in the creation of a comprehensive network that encompasses herb-active components, pathways, lncRNAs, miRNAs, and targets. Expression levels of hypoxia-related lncRNAs in mononuclear cells isolated from peripheral blood of OP and normal patients were subsequently validated using quantitative real-time PCR (qRT-PCR). Protein levels of RUNX2 were determined through a western blot assay.

Results

CBFB, GLO1, NFKB2 and PIK3CA were identified as central therapeutic targets, and ADD3-AS1, DTX2P1-UPK3BP1-PMS2P11, TTTY1B, ZNNT1 and LINC00623 were identified as core lncRNAs.

Conclusions

Our work uncovers a possible therapeutic mechanism for STZYD, providing a potential therapeutic target for OP. In addition, a prediction model of metabolism-related lncRNAs of OP progression was constructed to provide a reference for the diagnosis of OP patients.