Chemical Nose Strategy with Metabolic Labeling and "Antibiotic-Responsive Spectrum" Enables Accurate and Rapid Pathogen Identification.

The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-labeling-assisted chemical nose strategy for phenotyping classification and antibiotic resistance identification of pathogens based on the "antibiotic-responsive spectrum" of different pathogens. d-Amino acids with click handles were metabolically incorporated into the cell wall of pathogens for further clicking with dibenzocyclooctyne-functionalized upconversion nanoparticles (DBCO-UCNPs) in the presence/absence of six types of antibiotics, which generates seven-channel sensing responses. With the assistance of machine learning algorithms, eight types of pathogens, including three types of antibiotic-resistant bacteria, can be well classified and discriminated in terms of microbial taxonomies, Gram phenotypes, and antibiotic resistance. The present metabolic-labeling-assisted strategy exhibits good anti-interference capability and improved discrimination ability rooted in the unique sensing mechanism. Sensitive identification of pathogens with 100% accuracy from artificial urinary tract infection samples at a concentration as low as 105 CFU/mL was achieved. Pathogens outside of the training set can also be discriminated well. This clearly demonstrated the potential of the present strategy in the identification of unknown pathogens in clinical samples.

M13 phage: a versatile building block for a highly specific analysis platform.

Viruses are changing the biosensing and biomedicine landscape due to their multivalency, orthogonal reactivities, and responsiveness to genetic modifications. As the most extensively studied phage model for constructing a phage display library, M13 phage has received much research attention as building blocks or viral scaffolds for various applications including isolation/separation, sensing/probing, and in vivo imaging. Through genetic engineering and chemical modification, M13 phages can be functionalized into a multifunctional analysis platform with various functional regions conducting their functionality without mutual disturbance. Its unique filamentous morphology and flexibility also promoted the analytical performance in terms of target affinity and signal amplification. In this review, we mainly focused on the application of M13 phage in the analytical field and the benefit it brings. We also introduced several genetic engineering and chemical modification approaches for endowing M13 with various functionalities, and summarized some representative applications using M13 phages to construct isolation sorbents, biosensors, cell imaging probes, and immunoassays. Finally, current issues and challenges remaining in this field were discussed and future perspectives were also proposed.

Efficient Pathogen Capture and Sensing Promoted by Dynamic Deformable Nanointerfaces.

The M13 bacteriophage (M13 phage) has emerged as an attractive bionanomaterial due to its chemistry/gene modifiable feature and unique structures. Herein, a dynamic deformable nanointerface is fabricated taking advantage of the unique feature of the M13 phage for ultrasensitive detection of pathogens. PIII proteins at the tip of the M13 phage are genetically modified to display 6His peptide for site-specific anchoring onto Ni-NTA microbeads, whereas pVIII proteins along the side of the M13 phage are orderly arranged with thousands of aptamers and their complementary strands (c-apt). The flexible M13 nanofibers with rich recognition sites act as octopus tentacles, resulting in a 19-fold improvement in the capture affinity toward the target. The competitive binding of the target pathogen releases c-apts and initiates rolling circle amplification (RCA). The sway motion of M13 nanofibers accelerates the diffusion of c-apts, thus promoting RCA efficiency. Benefiting from the strengthened capture ability toward the target and the accelerated RCA process, three-orders of magnitude improvement in the sensitivity is achieved, with a detection limit of 8 cfu mL-1 for Staphylococcus aureus. The promoted capture ability and assay performance highlights the essential role of the deformable feature of the engineered interface. This may provide inspiration for the construction of more efficient reaction interfaces.

Metabolism-triggered sensor array aided by machine learning for rapid identification of pathogens.

Chemical-nose strategy has achieved certain success in the discrimination and identification of pathogens. However, this strategy usually relies on non-specific interactions, which are prone to be significantly disturbed by the change of environment thus limiting its practical usefulness. Herein, we present a novel chemical-nose sensing approach leveraging the difference in the dynamic metabolic variation during peptidoglycan metabolism among different species for rapid pathogen discrimination. Pathogens were first tethered with clickable handles through metabolic labeling at two different acidities (pH = 5 and 7) for 20 and 60 min, respectively, followed by click reaction with fluorescence up-conversion nanoparticles to generate a four-dimensional signal output. This discriminative multi-dimensional signal allowed eight types of model bacteria to be successfully classified within the training set into strains, genera, and Gram phenotypes. As the difference in signals of the four sensing channels reflects the difference in the amount/activity of enzymes involved in metabolic labeling, this strategy has good anti-interference capability, which enables precise pathogen identification within 2 h with 100% accuracy in spiked urinary samples and allows classification of unknown species out of the training set into the right phenotype. The robustness of this approach holds significant promise for its widespread application in pathogen identification and surveillance.

Harnessing virus flexibility to selectively capture and profile rare circulating target cells for precise cancer subtyping.

The effective isolation of rare target cells, such as circulating tumor cells, from whole blood is still challenging due to the lack of a capturing surface with strong target-binding affinity and non-target-cell resistance. Here we present a solution leveraging the flexibility of bacterial virus (phage) nanofibers with their sidewalls displaying target circulating tumor cell-specific aptamers and their ends tethered to magnetic beads. Such flexible phages, with low stiffness and Young's modulus, can twist and adapt to recognize the cell receptors, energetically enhancing target cell capturing and entropically discouraging non-target cells (white blood cells) adsorption. The magnetic beads with flexible phages can isolate and count target cells with significant increase in cell affinity and reduction in non-target cell absorption compared to magnetic beads having rigid phages. This differentiates breast cancer patients and healthy donors, with impressive area under the curve (0.991) at the optimal detection threshold (>4 target cells mL-1). Immunostaining of captured circulating tumor cells precisely determines breast cancer subtypes with a diagnostic accuracy of 91.07%. Our study reveals the power of viral mechanical attributes in designing surfaces with superior target binding and non-target anti-fouling.