Photoacoustic physio-chemical analysis (PAPCA) is a recently developed technology capable of simultaneously quantifying the content of molecular components and the corresponding microarchitectures in biological tissue. We have successfully quantified the diagnostic information in livers with PAPCA. In this study, we implemented PAPCA to the diagnosis of prostate cancers. 4 human prostates were scanned ex vivo. The PA signals from normal and cancerous regions in the prostates were acquired by an interstitial needle PA probe. A total of 14 interstitial measurements, including 6 within the normal regions and 8 in the cancerous regions, were acquired. The observed changes in molecular components, including lipid, collagen and hemoglobin were consistent with the findings by other research groups. The changes were quantified by PA spectral analysis (PASA) at wavelengths where strong optical absorption of the relevant molecular components was found. Statistically significant differences among the PASA parameters were observed (p=0.025 at significance of 0.05). A support vector machine model for differentiating the normal and cancerous tissue was established. With the limited number of samples, an 85% diagnostic accuracy was found. The diagnostic information in the PCPCA can be further enriched by targeted optical contrast agents visualizing the microarchitecture in PCa tissues. F3 PAA-PEG nanoparticles was employed to stain the PCa cells in a transgenic mouse model, in which the microarchitectures of normal and cancerous prostate tissues are comparable to that in human. Statistically significant differences were observed between the contrast-enhanced normal and cancerous regions (p=0.038 at a significance of 0.05).
Ion selective optical nanosensors allow accurate ion measurements in biological systems, without the physical limitations and invasiveness of ion selective electrodes. Optically based nanosensors (Photonic Explorers for Bioanalysis with Biologically Localized Embedding, PEBBLEs), have been optimized for fluorescence microscopy imaging, and have been applied for imaging various biochemical analytes. In here, we report the first example of a potassium selective nanosensor optimized for photoacoustic (PA) imaging. Notably, PA imaging overcomes the severe light penetration depth problem faced by fluorescence imaging in vivo. The new potassium selective nanosensor shows excellent response in the biological range, from 0 to 200 mM, as confirmed by both UV-Vis Spectroscopy and PA Spectroscopy. Furthermore, the K<sup>+</sup> PEBBLE showed a 2 orders of magnitude, or higher, selectivity to K<sup>+</sup> , relative to any other biological cations, such as Li<sup>+</sup>, Na<sup>+</sup>, Ca<sup>2+</sup>, and Mg<sup>2+</sup>.
The Methylene Blue loaded Polyacrylamide Nanoparticles (MB-PAA NPs) are used for oxygen sensing and Photodynamic therapy (PDT), a promising therapeutic modality employed for various tumors, with distinct advantages of delivery of biomedical agents and protection from other bio-molecules overcoming inherent limitations of molecular dyes. Lifetime-resolved photoacoustic spectroscopy using quenched-phosphorescence method is applied with MB-PAA NPs so as to sense oxygen, while the same light source is used for PDT. The dye is excited by absorbing 650 nm wavelength light from a pump laser to reach triplet state. The probe laser at 810 nm wavelength is used to excite the first triplet state at certain delayed time to measure the dye lifetime which indicates oxygen concentration. The 9L cells (106 cells/ml) incubated with MB-PAA NP solution are used for monitoring oxygen level change during PDT in situ test. The oxygen level and PDT efficacy are confirmed with a commercial oximeter, and fluorescence microscope imaging and flow cytometry results. This technique with the MB-PAA NPs allowed us to demonstrate a potential non-invasive theragnostic operation, by monitoring oxygen depletion during PDT in situ, without the addition of secondary probes. Here, we demonstrate this theragnostic operation, in vitro, performing PDT while monitoring oxygen depletion. We also show the correlation between O2 depletion and cell death.