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Abstract Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3‐bromopyruvate (3‐BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor‐associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody‐functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface‐enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC‐MS) analysis. The study reveals the efficacy of 3‐BP in treating pancreatic cancer and identifies drug treatment‐induced lipid species remodeling and associated pathways through bioinformatics analysis. 

Abstract Liquid interfaces facilitate the organization of nanometer‐scale biomaterials with plasmonic properties suitable for molecular diagnostics. Using hierarchical assemblage of 2D hafnium disulfide nanoplatelets and zero‐dimensional spherical gold nanoparticles, the design of a multifunctional material is reported. When the target analyte is present, the nanocomposites’ self‐assembling pattern changes, altering their plasmonic response. Using monkeypox virus (MPXV) as an example, the findings reveal that adding genomic DNA to the nanocomposite surface increases the agglomeration between gold nanoparticles and decreases the π‐stacking distance between hafnium disulfide nanoplatelets. Further, this self‐assembled nanomaterial is found to have minimal cross‐reactivity toward other pathogens and a limit of detection of 7.6 pg µL⁻¹(i.e., 3.57 × 10⁴copies µL⁻¹) toward MPXV. Overall, this study helped to gain a better understanding of the genomic organization of MPXV to chemically design and develop targeted nucleotides. The study has been validated by UV–vis spectroscopy, X‐ray diffraction, scanning transmission electron microscopy, surface‐enhanced Raman microscopy and electromagnetic simulation studies. To the best knowledge, this is the first study in literature reporting selective molecular detection of MPXV within a few minutes and without the use of any high‐end instrumental techniques like polymerase chain reactions. 

Abstract Stem cell‐based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non‐destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping‐based machine learning analysis is reported to distinguish differentiating human adipose‐derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single‐cell resolution. The label‐free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton‐based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix‐assisted laser desorption/ionization‐mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label‐free identification of stem cell differentiation with high spatial and chemical resolution.