Biomolecular imaging of colorectal tumor lesions using a FITC-labeled scFv-Cκ fragment antibody

Molecular engineering of a spheroid-penetrating phage nanovector for photodynamic treatment of colon cancer cells

Photodynamic therapy (PDT) represents an emerging strategy to treat various malignancies, including colorectal cancer (CC), the third most common cancer type. This work presents an engineered M13 phage retargeted towards CC cells through pentavalent display of a disulfide-constrained peptide nonamer. The M13CC nanovector was conjugated with the photosensitizer Rose Bengal (RB), and the photodynamic anticancer effects of the resulting M13CC-RB bioconjugate were investigated on CC cells. We show that upon irradiation M13CC-RB is able to impair CC cell viability, and that this effect depends on i) photosensitizer concentration and ii) targeting efficiency towards CC cell lines, proving the specificity of the vector compared to unmodified M13 phage. We also demonstrate that M13CC-RB enhances generation and intracellular accumulation of reactive oxygen species (ROS) triggering CC cell death. To further investigate the anticancer potential of M13CC-RB, we performed PDT experiments on 3D CC spheroids, proving, for the first time, the ability of engineered M13 phage conjugates to deeply penetrate multicellular spheroids. Moreover, significant photodynamic effects, including spheroid disruption and cytotoxicity, were readily triggered at picomolar concentrations of the phage vector. Taken together, our results promote engineered M13 phages as promising nanovector platform for targeted photosensitization, paving the way to novel adjuvant approaches to fight CC malignancies.

Feasibility of moxifloxacin and proflavine dual fluorescence imaging for detecting gastrointestinal neoplastic lesions: A prospective study

OBJECTIVES

High-contrast and high-resolution imaging techniques would enable real-time sensitive detection of the gastrointestinal lesions. This study aimed to investigate the feasibility of novel dual fluorescence imaging using moxifloxacin and proflavine in the detection of neoplastic lesions of the human gastrointestinal tract.

METHODS

Patients with the colonic and gastric neoplastic lesions were prospectively enrolled. The lesions were biopsied with forceps or endoscopically resected. Dual fluorescence imaging was performed by using custom axially swept wide-field fluorescence microscopy after topical moxifloxacin and proflavine instillation. Imaging results were compared with both confocal imaging with cell labeling and conventional histological examination.

RESULTS

Ten colonic samples (one normal mucosa, nine adenomas) from eight patients and six gastric samples (one normal mucosa, five adenomas) from four patients were evaluated. Dual fluorescence imaging visualized detail cellular structures. Regular glandular structures with polarized cell arrangement were observed in normal mucosa. Goblet cells were preserved in normal colonic mucosa. Irregular glandular structures with scanty cytoplasm and dispersed elongated nuclei were observed in adenomas. Goblet cells were scarce or lost in the colonic lesions. Similarity analysis between moxifloxacin and proflavine imaging showed relatively high correlation values in adenoma compared with those in normal mucosa. Dual fluorescence imaging showed good detection accuracies of 82.3% and 86.0% in the colonic and the gastric lesions, respectively.

CONCLUSIONS

High-contrast and high-resolution dual fluorescence imaging was feasible for obtaining detail histopathological information in the gastrointestinal neoplastic lesions. Further studies are needed to develop dual fluorescence imaging as an in vivo real-time visual diagnostic method.

Machine learning modeling for solubility prediction of recombinant antibody fragment in four different E. coli strains

The solubility of proteins is usually a necessity for their functioning. Recently an emergence of machine learning approaches as trained alternatives to statistical models has been evidenced for empirical modeling and optimization. Here, soluble production of anti-EpCAM extracellular domain (EpEx) single chain variable fragment (scFv) antibody was modeled and optimized as a function of four literature based numerical factors (post-induction temperature, post-induction time, cell density of induction time, and inducer concentration) and one categorical variable using artificial neural network (ANN) and response surface methodology (RSM). Models were established by the CCD experimental data derived from 232 separate experiments. The concentration of soluble scFv reached 112.4 mg/L at the optimum condition and strain (induction at cell density 0.6 with 0.4 mM IPTG for 24 h at 23 °C in Origami). The predicted value obtained by ANN for the response (106.1 mg/L) was closer to the experimental result than that obtained by RSM (97.9 mg/L), which again confirmed a higher accuracy of ANN model. To the author's knowledge this is the first report on comparison of ANN and RSM in statistical optimization of fermentation conditions of E.coli for the soluble production of recombinant scFv.