The development of liquid biopsy as a minimally invasive technique for tumor profiling has created a need for efficient biomarker extraction systems from body fluids. The analysis of circulating cell-free DNA (cfDNA) is especially promising, but the low amounts and high fragmentation of cfDNA found in plasma pose challenges to its isolation. While the potential of aqueous two-phase systems (ATPS) for the extraction and purification of various biomolecules has already been successfully established, there is limited literature on the applicability of these findings to short cfDNA-like fragments. This study presents the partitioning behavior of a 160 bp DNA fragment in polyethylene glycol (PEG)/salt ATPS at pH 7.4. The effect of PEG molecular weight, tie-line length, neutral salt additives, and phase volume ratio is evaluated to maximize DNA recovery. Selected ATPS containing a synthetic plasma solution spiked with human serum albumin and immunoglobulin G are tested to determine the separation of DNA fragments from the main plasma protein fraction. By adding 1.5% (w/w) NaCl to a 17.7% (w/w) PEG 400/17.3% (w/w) phosphate ATPS, 88% DNA recovery was achieved in the salt-rich bottom phase while over 99% of the protein was removed.

In the late 19th century, progress in dye chemistry led to advances in industrial organic chemistry in Germany. Over the next few decades, this revealed dyes not just as color agents but as promising lead compounds for drug development. Collaborations between dye chemists and medical researchers were crucial in turning these unexpected discoveries into structured medicinal chemistry efforts. The outcomes included major drug classes like sulfa antibiotics, antifungal azoles, and others, resulting in a legacy where dyes served not only as biological stains but as crucial tools for understanding complex natural products and drug interactions. Today, the impact of dye molecules persists in clinical therapies, molecular probing, pharmacokinetic tracing, and high-throughput screening. This review underscores the historical contributions shaping contemporary pharmaceutical sciences, highlighting the role of dyes as indispensable tools propelling drug discovery across generations.

Molecularly imprinted polymers (MIPs) show significant promise as effective alternatives to antibodies in disease diagnosis and therapy. However, the challenging process of screening extensive libraries of monomer combinations and synthesis conditions to identify formulations with enhanced selectivity and affinity presents a notable time constraint. The need for expedient methods becomes clear in accelerating the strategic development of MIPs tailored for precise molecular recognition purposes. In this study, an innovative high-throughput screening methodology designed to identify the optimal MIP formulation for targeting tumors is presented. Employing a microtiter plate format, over 100 polymer syntheses are conducted, incorporating diverse combinations of functional monomers. Evaluation of binding performance utilizes fluorescence-based assays, focusing on an epitope of the epidermal growth factor receptor (EGFR). Through this meticulously structured screening process, synthesis conditions that produced MIP nanoparticles exhibiting substantial specificity for EGFR targeting (KD = 10-12 m) are identified. These \"bionic antibodies\" demonstrate selective recognition of cancer cells in whole blood samples, even at concentrations as low as 5 cells mL-1. Further validation through fluorescent imaging confirms the tumor-specific localization of the MIPs in vivo. This highly efficient screening approach facilitates the strategic synthesis of imprinted polymers functioning as precision bioprobes.

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision-language models (VLMs) with chemical analysis techniques. A cutting-edge VLM is unveiled, utilizing the expansive UMDFood-90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro-AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high-throughput solution for nutritional analysis.

Biosensors are valuable tools in accelerating the test phase of the design-build-test-learn cycle of cell factory development, as well as in bioprocess monitoring and control. G protein-coupled receptor (GPCR)-based biosensors enable cells to sense a wide array of molecules and environmental conditions in a specific manner. Due to the extracellular nature of their sensing, GPCR-based biosensors require compartmentalization of distinct genotypes when screening production levels of a strain library to ensure that detected levels originate exclusively from the strain under assessment. Here, we explore the integration of production and sensing modalities into a single Saccharomyces cerevisiae strain and compartmentalization using three different methods: (1) cultivation in microtiter plates, (2) spatial separation on agar plates, and (3) encapsulation in water-in-oil-in-water double emulsion droplets, combined with analysis and sorting via a fluorescence-activated cell sorting machine. Employing tryptamine and serotonin as proof-of-concept target molecules, we optimize biosensing conditions and demonstrate the ability of the autocrine screening method to enrich for high producers, showing the enrichment of a serotonin-producing strain over a nonproducing strain. These findings illustrate a workflow that can be adapted to screening for a wide range of complex chemistry at high throughput using commercially available microfluidic systems.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs (\"dark matter\") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.

D-amino acid oxidase (DAAO)-catalyzed selective oxidative deamination is a very promising process for synthesizing l-amino acids including l-phosphinothricin (l-PPT, a high-efficiency and broad-spectrum herbicide). However, the wild-type DAAO\'s low activity toward unnatural substrates like d-phosphinothricin (d-PPT) hampers its application. Herein, a DAAO from Caenorhabditis elegans (CeDAAO) was screened and engineered to improve the catalytic potential on d-PPT. First, we designed a novel growth selection system, taking into account the intricate relationship between the growth of Escherichia coli (E. coli) and the catalytic mechanism of DAAO. The developed system was used for high-throughput screening of gene libraries, resulting in the discovery of a variant (M6) with significantly increased catalytic activity against d-PPT. The variant displays different catalytic properties on substrates with varying hydrophobicity and hydrophilicity. Analysis using Alphafold2 modeling and molecular dynamic simulations showed that the reason for the enhanced activity was the substrate-binding pocket with enlarged size and suitable charge distribution. Further QM/MM calculations revealed that the crucial factor for enhancing activity lies in reducing the initial energy barrier of the reductive half reaction. Finally, a comprehensive binding-model index to predict the enhanced activity of DAAO toward d-PPT, and an enzymatic deracemization approach was developed, enabling the efficient synthesis of l-PPT with remarkable efficiency.

Peroxisomal compartmentalization has emerged as a highly promising strategy for reconstituting intricate metabolic pathways. In recent years, significant progress has been made in the peroxisomes through harnessing precursor pools, circumventing metabolic crosstalk, and minimizing the cytotoxicity of exogenous pathways. However, it is important to note that in methylotrophic yeasts (e.g. Pichia pastoris), the abundance and protein composition of peroxisomes are highly variable, particularly when peroxisome proliferation is induced by specific carbon sources. The intricate subcellular localization of native proteins, the variability of peroxisomal metabolic pathways, and the lack of systematic characterization of peroxisome targeting signals have limited the applications of peroxisomal compartmentalization in P. pastoris. Accordingly, this study established a high-throughput screening method based on β-carotene biosynthetic pathway to evaluate the targeting efficiency of PTS1s (Peroxisome Targeting Signal Type 1) in P. pastoris. First, 25 putative endogenous PTS1s were characterized and 3 PTS1s with high targeting efficiency were identified. Then, directed evolution of PTS1s was performed by constructing two PTS1 mutant libraries, and a total of 51 PTS1s (29 classical and 22 noncanonical PTS1s) with presumably higher peroxisomal targeting efficiency were identified, part of which were further characterized via confocal microscope. Finally, the newly identified PTS1s were employed for peroxisomal compartmentalization of the geraniol biosynthetic pathway, resulting in more than 30% increase in the titer of monoterpene compared with when the pathway was localized to the cytosol. The present study expands the synthetic biology toolkit and lays a solid foundation for peroxisomal compartmentalization in P. pastoris.

Hydrogel-based 3D cell cultures can recapitulate (patho)physiological phenomena ex vivo. However, due to their complex multifactorial regulation, adapting these tissue and disease models for high-throughput screening workflows remains challenging. In this study, a new precision culture scaling (PCS-X) methodology combines statistical techniques (design of experiment and multiple linear regression) with automated, parallelized experiments and analyses to customize hydrogel-based vasculogenesis cultures using human umbilical vein endothelial cells and retinal microvascular endothelial cells. Variations of cell density, growth factor supplementation, and media composition are systematically explored to induce vasculogenesis in endothelial mono- and cocultures with mesenchymal stromal cells or retinal microvascular pericytes in 384-well plate formats. The developed cultures are shown to respond to vasculogenesis inhibitors in a compound- and dose-dependent manner, demonstrating the scope and power of PCS-X in creating parallelized tissue and disease models for drug discovery and individualized therapies.

A 3D bioprinted neurovascular unit (NVU) model is developed to study glioblastoma (GBM) tumor growth in a brain-like microenvironment. The NVU model includes human primary astrocytes, pericytes and brain microvascular endothelial cells, and patient-derived glioblastoma cells (JHH-520) are used for this study. Fluorescence reporters are used with confocal high content imaging to quantitate real-time microvascular network formation and tumor growth. Extensive validation of the NVU-GBM model includes immunostaining for brain relevant cellular markers and extracellular matrix components; single cell RNA sequencing (scRNAseq) to establish physiologically relevant transcriptomics changes; and secretion of NVU and GBM-relevant cytokines. The scRNAseq reveals changes in gene expression and cytokines secretion associated with wound healing/angiogenesis, including the appearance of an endothelial mesenchymal transition cell population. The NVU-GBM model is used to test 18 chemotherapeutics and anti-cancer drugs to assess the pharmacological relevance of the model and robustness for high throughput screening.