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Epithelial-mesenchymal transition (EMT) is a complex process that plays a critical role in tumor progression. In this study, we present an EMT sensing panel for the classification of cancer cells at different EMT stages. This sensing panel consists of three types of fluorescent probes based on boronic acid-functionalized carbon-nitride nanosheet (BCN) derivatives. The selective response toward different EMT-associated biomarkers, namely, EpCAM, N-cadherin, and sialic acid (SA), was achieved by conjugating the corresponding antibodies to each BCN derivative, whereas the rare-earth-doping ensures simultaneous sensing of the three biomarkers with fluorescent emission of the three probes at different wavelengths. Sensitive sensing of the three biomarkers was achieved at the protein level with LODs reaching 1.35 ng mL–1for EpCAM, 1.62 ng mL–1for N-cadherin, and 1.54 ng mL–1for SA. The selective response of these biomarkers on the cell surface also facilitated sensitive detection of MCF-7 cells and MDA-MB-231 cells with LODs of 2 cells/mL and 2 cells/mL, respectively. Based on the simultaneous sensing of the three biomarkers on cancer cells that underwent different extents of EMT, precise discrimination and classification of cells at various EMT stages were also achieved with an accuracy of 93.3%. This EMT sensing panel provided a versatile tool for monitoring the EMT evolution process and has the potential to be used for the evaluation of the EMT-targeting therapy and metastasis prediction.

Near-infrared (NIR) luminescent lanthanide materials hold great promise for bioanalysis, as they have anti-interference properties. The approach of efficient luminescence is sensitization through a reasonable chromophore to overcome the obstacle of the aqueous phase. The involvement of the surfactant motif is an innovative strategy to arrange the amphiphilic groups to be regularly distributed near the polymer to form a closed sensitized space. Herein, a lanthanide polymer (TCPP-PEI70K-FITC@Yb/SDBS) is designed in which the meso-tetra(4-carboxyphenyl)porphine (TCPP) ligand serves as both a sensitizer and photocatalytic switch. The surfactant sodium dodecyl benzenesulfonate (SDBS) wraps the photosensitive polymers to form a hydrophobic layer, which augments the light-harvesting ability and expedites its photocatalysis. TCPP-PEI70K-FITC@Yb/SDBS is subsequently applied as an amplified photocatalysis toolbox for universally regulating the generation of reactive oxygen species (ROS). Boosting 3,3′,5,5′-tetramethylbenzidine (TMB) oxidation to produce blue products, a dual-mode biosensor is fabricated for improving the diagnosis of programmed death ligand-1-positive (PDL1) cancer exosomes. Exosomes were captured by Fe3O4modified by the PDL1 aptamer, enabling replacement of alkaline phosphatase (ALP)-labeled multiple hybridized chains; then, the isolated ALP triggered a hydrolysis reaction to block the generation of oxTMB. Detection sensitivity improves by 1 order of magnitude through SDBS modulation, down to 104particles/mL. The sensor performed well clinically in distinguishing cancer patients from healthy individuals, expanding physiological applications of near-infrared lanthanide luminescence.

Conventional methods for detecting single nucleotide polymorphisms (SNPs) in clinical practice often require substantial time, labor, and specialized equipment, limiting their widespread application. To address this limitation, we refined our previous SNP detection method, IMAS-RPA [introducing an extra mismatched base adjacent to the single-base mutant site by recombinase polymerase amplification (RPA)], resulting in an updated version termed IMAS-RPAv2. We began by introducing a suboptimal protospacer adjacent motif (PAM) sequence, GTTG, into the double-stranded DNA (dsDNA) products using either RPA or reverse transcription RPA. This modification decreased the efficiency with which CRISPR RNA (crRNA) recognizes the PAM and locally unwinds the dsDNA to form an R loop. After a delay, the R loop forms. However, due to the intentional incorporation of a mismatched base on the crRNA relative to the wild-type double-stranded DNA (WT-dsDNA), a continuous two-base mismatch is established between the crRNA and WT-dsDNA. Consequently, WT-dsDNA does not activate CRISPR/Cas12a’s cleavage activity within a short time, while variant-type dsDNA continues to activate CRISPR/Cas12a and produce a robust fluorescence signal. This improvement significantly enhances the SNP discrimination sensitivity, allowing for detection at the single-copy level. Results were observed using both a conventional microplate reader and a specially designed portable device created through 3D printing. This device allows a direct fluorescence observation without the need for additional equipment. Consequently, the entire detection process becomes independent of large-scale equipment. This greatly expands its range of applications and offers promising prospects for clinical use.

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 105CFU/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.