Facile Synthesis of Advanced Photodynamic Molecular Beacon Architectures

Nucleic acid photodynamic molecular beacons (PMBs) are a class of activatable photosensitizers that increase singlet oxygen generation upon binding a specific target sequence. Normally, PMBs are functionalized with multiple solution-phase labeling and purification steps. Here, we make use of a flexible solid-phase approach for completely automated synthesis of PMBs. This enabled the creation of a new type of molecular beacon that uses a linear superquencher architecture. The 3′ terminus was labeled with a photosensitizer by generating pyropheophorbide-labeled solid-phase support. The 5′ terminus was labeled with up to three consecutive additions of a dark quencher phosphoramidite. These photosensitizing and quenching moieties were stable in the harsh DNA synthesis environment and their hydrophobicity facilitated PMB purification by HPLC. Linear superquenchers exhibited highly efficient quenching. This fully automated synthesis method simplifies not only the synthesis and purification of PMBs, but also the creation of new activatable photosensitizer designs

Stimuli-Responsive Photoacoustic Nanoswitch for <i>in Vivo</i> Sensing Applications

Photoacoustic imaging provides high-resolution images at depths beyond the optical diffusion limit. To broaden its utility, there is need for molecular sensors capable of detecting environmental stimuli through alterations in photoacoustic signal. Photosynthetic organisms have evolved ingenious strategies to optimize light absorption through nanoscale ordered dye aggregation. Here, we use this concept to synthesize a stimuli-responsive nanoswitch with a large optical absorbance and sensing capabilities. Ordered dye aggregation between light-harvesting porphyrins was achieved through intercalation within thermoresponsive nanovesicles. This causes an absorbance red-shift of 74 nm and a 2.7-fold increase in absorptivity of the Q_<i>y</i>-band, with concomitant changes in its photoacoustic spectrum. This spectral feature can be reversibly switched by exceeding a temperature threshold. Using this thermochromic property, we noninvasively determined a localized temperature change <i>in vivo</i>, relevant for monitoring thermal therapies of solid tumors. Similar strategies may be applied alongside photoacoustic imaging, to detect other stimuli such as pH and enzymatic activity

Porphyrin Shell Microbubbles with Intrinsic Ultrasound and Photoacoustic Properties

Porphyrin–phospholipid conjugates were used to create photonic microbubbles (MBs) having a porphyrin shell (“porshe”), and their acoustic and photoacoustic properties were investigated. The inclusion of porphyrin–lipid in the MB shell increased the yield, improved the serum stability, and generated a narrow volumetric size distribution with a peak size of 2.7 ± 0.2 μm. Using an acoustic model, we calculated the porshe stiffness to be 3–5 times greater than that of commercial lipid MBs. Porshe MBs were found to be intrinsically suitable for both ultrasound and photoacoustic imaging with a resonance frequency of 9–10 MHz. The distinctive properties of porshe MBs make them potentially advantageous for a broad range of biomedical imaging and therapeutic applications

Heatmap of the association between imaging features and disease recurrence.

Imaging features extracted from A) free breathing (FB) and B) average intensity projection (AIP) images were evaluated for their association with distant metastasis (DM) and locoregional recurrence (LRR). Features are grouped according to conventional (conv.) features, common features and unique features. “Common” features are radiomic features that had been selected from both FB and AIP images. “Unique” features are the radiomic features that were selected that are different between FB and AIP. The difference between the median values for each event status (event vs. no event) is plotted with the corresponding p-value indicated (Wilcoxon rank-sum test, FDR corrected p-values). The time point considered for DM and LRR was the median time of event (10 and 9 months for DM and LRR, respectively). *p-value < 0.05.</p

Supplementary Video from Multimodal Image-Guided Surgical and Photodynamic Interventions in Head and Neck Cancer: From Primary Tumor to Metastatic Drainage

PET imaging of HNC with PLP</p

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

<div><p>Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638–0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643–0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601–0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.</p></div

Performance of each multivariate model in predicting distant metastasis.

<p>Concordance indices are reported for the FB and AIP conventional and radiomic models, and a combined FB+AIP radiomics model, comparing the performance of each of model and image type. Cross validation was performed (80% training, 20% validation) to generate 100 models for each model type. Comb. Indicates the combined FB and AIP radiomics model. *p-value < 0.05; “ns” indicates not significant (p-value > 0.05).</p

Imaging features selected for analysis.

<p>Imaging features selected for analysis.</p

Supporting Information from Multimodal Image-Guided Surgical and Photodynamic Interventions in Head and Neck Cancer: From Primary Tumor to Metastatic Drainage

Figure S1. The PET/CT imaging of rabbit at 24 h post-intravenous injection of 64Cu-PLP. Figure S2. Representative H&E, pan-Cytokeratin staining and fluorescence microscopic imaging of the tumor (a) and metastatic lymph node (b) after 24 h intravenous injection of 64Cu- PLP Figure S3. The non-toxicity of PLP-PDT to healthy tissues. Figure S4. The temperature change of tumors during laser irradiation. Figure S5. The tumor size change of laser control and PLP control was monitored by CT imaging after treatment. Figure S6. Representative CT images of the regional lymph node post treatment.</p

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT - Fig 1

<p>A) Examples of free breathing (FB) and average intensity projection (AIP) images, demonstrating the observable differences in tumor phenotype between each image type. AIP images were reconstructed from 4D computed tomography (CT) scans. B) Schematic representation of the radiomics workflow for FB and AIP images. I. CT images of the patient are acquired and the tumor is segmented. II. Imaging features (radiomic and conventional features) are extracted from the tumor volume. III. Radiomic features undergo a feature dimension reduction process to generate a low-dimensional feature set based on feature stability and variance. IV. Imaging features are then analyzed with clinical outcomes to evaluate their prognostic power. FB and AIP radiomics features are compared.</p