Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer detection. However, the complexity and heterogeneity of exosomes require improved methods to achieve the desired sensitivity and accuracy. Herein, we report our study on developing a breast cancer liquid biopsy system, including a fluorescence sensor array and deep learning (DL) tool AggMapNet. In particular, we used a 12-unit sensor array composed of conjugated polyelectrolytes, fluorophore-labeled peptides, and monosaccharides or glycans to collect fluorescence signals from cells and exosomes. Linear discriminant analysis (LDA) processed the fluorescence spectral data of cells and cellderived exosomes, demonstrating successful discrimination between normal and different cancerous cells and 100% accurate classification of different BC cells. For heterogeneous plasma-derived exosome analysis, CNN-based DL tool AggMapNet was applied to transform the unordered fluorescence spectra into feature maps (Fmaps), which gave a straightforward visual demonstration of the difference between healthy donors and BC patients with 100% prediction accuracy. Our work indicates that our fluorescent sensor array and DL model can be used as a promising noninvasive method for BC diagnosis.
Direct
arylation polymerization (DARP) is a novel approach to obtain conjugated
polymers (CPs) through the straightforward C–H activation of
monomer building blocks. In this work, a convenient DARP method with
high efficiency and excellent regioselectivity is developed to synthesize
a series of donor–acceptor (D–A)-type CPs composed of
electron-acceptor moiety fluorenones (FOs) and various electron-donor
moieties. CPs with different band gaps are obtained in good yields
and display large Stokes shifts up to 295 nm. Two ionic CPs, PFOP-NEt3(+)
and PFOP-COO(−), were prepared in a polar solvent system to
improve the water solubility and biocompatibility using the proposed
DARP method. Detailed photophysical studies of these two CPs suggest
that both solvation and hydrogen bonds play important roles in determining
the polymers’ spectroscopic properties. Further studies of
the cationic polymer PFOP-NEt3(+) in cell imaging demonstrate its
potential application in labeling cell membranes and lysosomes given
its low cytotoxicity, excellent photostability, and specific subcellular
localization.
Metal coordination-driven composite systems have excellent stability and pH-responsive ability, making them suitable for specific drug delivery in physiological conditions. In this study, an anionic conjugated polymer PPEIDA with a poly(p-phenylene ethynylene) backbone and iminodiacetic acid (IDA) side chains is used as a drug carrier to construct a class of pH-responsive nanoparticles, PPEIDA−Cu−DOX conjugated polymer nanoparticles (CPNs), by taking advantage of the metal coordination interaction of Cu 2+ with PPEIDA and the drug doxorubicin (DOX). PPEIDA−Cu−DOX CPNs have high drug loading and encapsulation efficiency (EE), calculated to be 54.30 ± 1.10 and 95.80 ± 0.84%, respectively. Due to the good spectral overlap, Forster resonance energy transfer (FRET) takes place between PPEIDA and the drug DOX, which enables the observation of the loading and the release of DOX from CPNs in an acidic environment by monitoring fluorescence emission changes. Therefore, PPEIDA−Cu−DOX CPNs can also be used in real-time cell imaging to monitor drug release in addition to delivering DOX targeting tumor cells. Compared with free DOX, PPEIDA−Cu−DOX CPNs show a similar inhibition to tumor cells and lower toxicity to normal cells. Our results demonstrate the feasibility and potential of constructing pH-responsive CPNs via metal−ligand coordination interactions for cancer treatment.
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