Chemical & Biomedical Imaging最新文献

Over the last decades, various tissue-clearing techniques have broken the ground for the optical imaging of whole organs and whole-organisms, providing complete representative data sets of three-dimensional biological structures. Along with advancements in this field, the development of fluorescent markers for staining vessels and capillaries has offered insights into the complexity of vascular networks and their impact on disease progression. Here we describe the use of a modified water-soluble chitosan linked to cyanine dyes in combination with ethyl cinnamate-based optical tissue clearing for the 3D visualization of tissue vasculature in depth. The water-soluble fluorescent Chitosans have proven to be an optimal candidate for labeling both vessels and capillaries ex vivo thanks to their increased water solubility, high photostability, and optical properties in the near-infrared window. In addition, the nontoxicity of these markers broadens their applicability to in vitro and in vivo biological applications. Despite the availability of other fluorescent molecules for vascular staining, the present study, for the first time, demonstrates the potential of fluorescent chitosans to depict vessels at the capillary level and opens avenues for advancing the diagnostic field by reducing the complexity and costs of the currently available procedures.

The development of peptide-based, radiometal-labeled PET imaging agents has seen an increase in attention due to the favorable properties the peptide backbone exhibits. These include high selectivity and affinity to proteins and cells directly linked to various types of cancers. In addition, rapid clearance from circulation and low toxicity allow for unique approaches to engineering a viable peptide-based imaging agent. Utilizing peptides as the backbone allows for various modifications to improve metabolic stability, target cell affinity, and image quality and imaging capabilities and reduce toxicity. Select radiolabeled peptides have already been FDA approved, with many more in late-stage trials. This review summarizes the current state of the radiometal-labeled PET peptide imaging field as well as explores methods used by researchers to modify peptides, concluding with a look at the future of peptide-based therapy and diagnostics.

The development of peptide-based, radiometal-labeled PET imaging agents has seen an increase in attention due to the favorable properties the peptide backbone exhibits. These include high selectivity and affinity to proteins and cells directly linked to various types of cancers. In addition, rapid clearance from circulation and low toxicity allow for unique approaches to engineering a viable peptide-based imaging agent. Utilizing peptides as the backbone allows for various modifications to improve metabolic stability, target cell affinity, and image quality and imaging capabilities and reduce toxicity. Select radiolabeled peptides have already been FDA approved, with many more in late-stage trials. This review summarizes the current state of the radiometal-labeled PET peptide imaging field as well as explores methods used by researchers to modify peptides, concluding with a look at the future of peptide-based therapy and diagnostics.

Milk-derived exosomes are widely used for diagnosis, delivery, imaging, and theranostic applications. Near-Infrared (NIR) based fluorescence bioimaging is an attractive and safer technique that is used for clinical applications. However, almost all NIR imaging agents tend to have poor photostability, short half-life, nonspecific protein binding, and concentration-dependent aggregation(s). Therefore, there is an unmet clinical need to develop newer and safer modalities to package and deliver NIR imaging agents. Bovine milk exosomes are natural, biocompatible, safe, and efficient nanocarriers that facilitate the delivery of micro- and macromolecules. Herein, we developed an exosome-based NIR dye loaded nanoimaging formulation that offers improved solubility and photostability of NIR dye. Following the acetic acid based extracellular vesicle (EV) treatment method, we extracted the bovine milk exosomes from a variety of pasteurized grade milk. The EVs were screened for their physicochemical properties such as particle size and concentration and zeta potential. The stability of these exosomes was also determined under different conditions, including storage temperatures, pH, and salt concentrations. Next, indocyanine green, a model NIR dye was loaded into these exosomes (Exo-Glow) via a sonication method and further assessed for their improved fluorescence intensity and photostability using an IVIS imaging system. Initial screening suggested that size of the selected bovine milk exosomes was ∼100-135 nm with an average particle concentration of 5.8 × 10² particles/mL. Exo-Glow further demonstrated higher fluorescence intensity in cancer cells and tissues when compared to free dye. These results showed that Exo-Glow has the potential to serve as a safer NIR imaging tool for cancer cells/tissues.

Milk-derived exosomes are widely used for diagnosis, delivery, imaging, and theranostic applications. Near-Infrared (NIR) based fluorescence bioimaging is an attractive and safer technique that is used for clinical applications. However, almost all NIR imaging agents tend to have poor photostability, short half-life, nonspecific protein binding, and concentration-dependent aggregation(s). Therefore, there is an unmet clinical need to develop newer and safer modalities to package and deliver NIR imaging agents. Bovine milk exosomes are natural, biocompatible, safe, and efficient nanocarriers that facilitate the delivery of micro- and macromolecules. Herein, we developed an exosome-based NIR dye loaded nanoimaging formulation that offers improved solubility and photostability of NIR dye. Following the acetic acid based extracellular vesicle (EV) treatment method, we extracted the bovine milk exosomes from a variety of pasteurized grade milk. The EVs were screened for their physicochemical properties such as particle size and concentration and zeta potential. The stability of these exosomes was also determined under different conditions, including storage temperatures, pH, and salt concentrations. Next, indocyanine green, a model NIR dye was loaded into these exosomes (Exo-Glow) via a sonication method and further assessed for their improved fluorescence intensity and photostability using an IVIS imaging system. Initial screening suggested that size of the selected bovine milk exosomes was ∼100–135 nm with an average particle concentration of 5.8 × 10² particles/mL. Exo-Glow further demonstrated higher fluorescence intensity in cancer cells and tissues when compared to free dye. These results showed that Exo-Glow has the potential to serve as a safer NIR imaging tool for cancer cells/tissues.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Correlation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second-order cross-correlation function XC ₂ has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously. Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.

Correlation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second-order cross-correlation function XC₂ has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously. Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.