The signal-to-background ratio (SBR) is the key determinant of sensitivity, detectability, and linearity in optical imaging. As signal strength is often constrained by fundamental limits, background reduction becomes an important approach for improving SBR. We recently reported that a zwitterionic near-infrared (NIR) fluorophore, ZW800-1, exhibits low background. Here we show that this fluorophore provides much-improved SBR when targeted to cancer cells or proteins by conjugation with a cyclic RGD peptide, fibrinogen, or antibodies. ZW800-1 outperforms the commercially available NIR fluorophores IRDye800-CW and Cy5.5 in vitro for immunocytometry, histopathology and immunoblotting, and in vivo for image-guided surgery. In tumor model systems, tumor-to-background ratios of 17.2 are achieved after only 4 h post-injection, compared with 5.1 for IRDye800-CW and 2.7 for Cy5.5. Our results suggest that introducing zwitterionic properties into targeted fluorophores may be a general strategy for improving the SBR in diagnostic and therapeutic applications.

Local recurrence is a common cause of treatment failure for patients with solid tumors. Tumor-specific intraoperative fluorescence imaging may improve staging and debulking efforts in cytoreductive surgery and, thereby improve prognosis. Here, we report in vivo assembly of the second near-infrared window (NIR-II) emitting downconversion nanoparticles (DCNPs) modified with DNA and targeting peptides to improve the image-guided surgery for metastatic ovarian cancer. The NIR-II imaging quality with DCNPs is superior to that of clinically approved ICG with good photostability and deep tissue penetration (8 mm). Stable tumor retention period experienced 6 h by in vivo assembly of nanoprobes can be used for precise tumor resection. Superior tumor-to-normal tissue ratio is successfully achieved to facilitate the abdominal ovarian metastases surgical delineation. Metastases with ≤1 mm can be completely excised under NIR-II bioimaging guidance. This novel technology provides a general new basis for the future design of nanomaterials for medical applications.

Repair of large bone defects represents a major challenge for orthopedic surgeons. The newly formed microvessels inside grafts play a crucial role in successful bone tissue engineering. Previously, an active role for mesenchymal stem cell (MSC)-derived exosomes in blood vessel development and progression was suggested in the repair of multiple tissues. However, the reports on the application of MSC-derived exosomes in the repair of large bone defects are sparse. In this study, we encapsulated umbilical MSC-derived exosomes (uMSCEXOs) in hyaluronic acid hydrogel (HA-Gel) and combined them with customized nanohydroxyapatite/poly-ε-caprolactone (nHP) scaffolds to repair cranial defects in rats. Imaging and histological evaluation indicated that the uMSCEXOs/Gel/nHP composites markedly enhanced bone regeneration in vivo, and the uMSCEXOs might play a key role in this process. Moreover, the in vitro results demonstrated that uMSCEXOs promoted the proliferation, migration, and angiogenic differentiation of endothelial progenitor cells (EPCs) but did not significantly affect the osteogenic differentiation of BMSCs. Importantly, mechanistic studies revealed that exosomal miR-21 was the potential intercellular messenger that promoted angiogenesis by upregulating the NOTCH1/DLL4 pathway. In conclusion, our findings exhibit a promising exosome-based strategy in repairing large bone defects through enhanced angiogenesis, which potentially regulated by the miR-21/NOTCH1/DLL4 signaling axis.

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000-Genomes Project. The challenge participants developed algorithms to predict inter-individual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against a blinded experimental dataset. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r<0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r<0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.