A sensitive, noninvasive method to detect localized prostate cancer, particularly for early detection and repetitive study in patients undergoing active surveillance, remains an unmet need. Here, we propose a molecular photoacoustic (PA) imaging approach by targeting the prostate‐specific membrane antigen (PSMA), which is over‐expressed in the vast majority of prostate cancers. We performed spectroscopic PA imaging in an experimental model of prostate cancer, namely, in immunocompromised mice bearing PSMA+ (PC3 PIP) and PSMA− (PC3 flu) tumors through administration of the known PSMA‐targeted fluorescence agent, YC‐27. Differences in contrast between PSMA+ and isogenic control tumors were observed upon PA imaging, with PSMA+ tumors showing higher contrast in average of 66.07‐fold with 5 mice at the 24‐hour postinjection time points. These results were corroborated using standard near‐infrared fluorescence imaging with YC‐27, and the squared correlation between PA and fluorescence intensities was 0.89. Spectroscopic PA imaging is a new molecular imaging modality with sufficient sensitivity for targeting PSMA
The development of high-performance photoacoustic (PA) probes that can monitor disease biomarkers in deep tissue has the potential to replace invasive medical procedures such as a biopsy. However, such probes must be optimized for in vivo performance and exhibit an exceptional safety profile. In this study, we have developed PACu-1, a PA probe designed for biopsy-free assessment (BFA) of hepatic Cu via photoacoustic imaging. PACu-1 features a Cu(I)-responsive trigger appended to an aza-BODIPY dye platform that has been optimized for ratiometric sensing. Owing to its excellent performance, we were able to detect basal levels of Cu in healthy wild-type mice as well as elevated Cu in a Wilson’s disease model and in a liver metastasis model. To showcase the potential impact of PACu-1 for BFA, we conducted two blind studies in which we were able to successfully identify Wilson’s disease animals from healthy control mice in each instance.
more » « less- Award ID(s):
- 1752879
- PAR ID:
- 10307840
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 36
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- Article No. e2106943118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
in vivo , demonstrating the potential applications for other saturable targets relevant to cancer and other disorders. -
more » « less
Abstract Copper is essential in a host of biological processes, and disruption of its homeostasis is associated with diseases including neurodegeneration and metabolic disorders. Extracellular copper shifts in its speciation between healthy and disease states, and identifying molecular components involved in these perturbations could widen the panel of biomarkers for copper status. While there have been exciting advances in approaches for studying the extracellular proteome with mass spectrometry–based methods, the typical workflows disrupt metal–protein interactions due to the lability of these bonds either during sample preparation or in gas-phase environments. We sought to develop and apply a workflow to enrich for and identify protein populations with copper-binding propensities in extracellular fluids using an immobilized metal affinity chromatography (IMAC) resin. The strategy was optimized using human serum to allow for maximum quantity and diversity of protein enrichment. Protein populations could be differentiated based on protein load on the resin, likely on account of differences in abundance and affinity. The enrichment workflow was applied to plasma samples from patients with Wilson’s disease and protein IDs and differential abundancies relative to healthy subjects were compared to those yielded from a traditional proteomic workflow. While the IMAC workflow preserved differential abundance and protein ID information from the traditional workflow, it identified several additional proteins being differentially abundant including those involved in lipid metabolism, immune system, and antioxidant pathways. Our results suggest the potential for this IMAC workflow to identify new proteins as potential biomarkers in copper-associated disease states.
-
In retinal degenerative diseases, such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD), the photoreceptors become stressed and start to degenerate in the early stages of the disease. Retinal prosthetic devices have been developed to restore vision in patients by applying electrical stimulation to the surviving retinal cells. However, these devices provide limited visual perception as the therapeutic interventions are generally considered in the later stages of the disease when only inner retinal layer cells are left. A potential treatment option for retinal degenerative diseases in the early stages can be stimulating bipolar cells, which receive presynaptic signals from photoreceptors. In this work, we constructed computational models of healthy and degenerated (both ON and OFF-type) cone bipolar cells (CBCs) with realistic morphologies extracted from connectomes of the healthy and early-stage degenerated rabbit retina. We examined these cells’ membrane potential and axon terminal calcium current differences when subjected to electrical stimulation. In addition, we investigated how differently healthy and degenerated cells behave with respect to various stimulation parameters, including pulse durationand cells’ distance from the stimulating electrode. The results suggested that regardless of the position of the OFF CBCs in the retina model, there is not a significant difference between the membrane potential of healthy and degenerate cells when electrically stimulated. However, the healthy ON CBC axon terminal membrane potential rising time-constant is shorter (0.29 ± 0.03 ms) than the degenerated cells (0.8 ± 0.07 ms). Moreover, the ionic calcium channels at the axon terminals of the cells have a higher concentration and higher current in degenerated cells (32.24 ± 6.12 pA) than the healthy cells (13.64 ± 2.88 pA) independently of the cell’s position.more » « less
-
more » « less
Abstract A method for fabricating localized EC‐SERS probes based on nanopipettes and electrodeposition is described. Gold particles of fractal geometry with excellent SERS performance are produced, reliably and at low cost. By adapting the electrodeposition procedure, nanostructures of different sizes can be obtained, allowing the SERS platform to be tailored to many experimental configurations. In particular, by producing unique SERS platforms of dimensions comparable to the laser spot, quantitative comparison with electrochemical current is possible. By analyzing hundreds of samples, we thoroughly characterize the resulting geometry of the structures and their ability to enhance Raman signal, providing guidelines for the fabrication of optimized platforms. Control over the probes' surface potential also allows convenient modulation of surface‐analyte affinity and enable chemically unstable materials, such as Cu, to be reliably used. These are demonstrated by showing that Cu particles exposed to air can be easily re‐reduced, with no detriment in SERS performance.
-
more » « less
During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, which is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify Fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 600 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 along with a loss of 0.46 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.
