Dataset Information

Tracking the binding of multi-functional fluorescent tags for Alzheimer's disease using quantitative multiphoton microscopy.

ABSTRACT: A recent theranostic approach to address Alzheimer's disease (AD) utilizes multifunctional targets that both tag and negate the toxicity of AD biomarkers. These compounds, which emit fluorescence with both an activation and a spectral shift in the presence of Aβ, were previously characterized with traditional fluorescence imaging for binary characterization. However, these multifunctional compounds have broad and dynamic emission spectra that are dependent on factors such as the local environment, presence of Aβ deposits, etc. Since quantitative multiphoton microscopy is sensitive to the binding dynamics of molecules, we characterized the performance of two such compounds, LS-4 and ZY-12-OMe, using Simultaneous Label-free Autofluorescence Multi-harmonic (SLAM) microscopy and Fast Optical Coherence, Autofluorescence Lifetime imaging and Second harmonic generation (FOCALS) microscopy. This study shows that the combination of quantitative multiphoton imaging with multifunctional tags for AD offers new insights into the interaction of these tags with AD biomarkers and the theranostic mechanisms.

SUBMITTER: Iyer RR

PROVIDER: S-EPMC9728943 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tracking the binding of multi-functional fluorescent tags for Alzheimer's disease using quantitative multiphoton microscopy.

Iyer Rishyashring R RR Renteria Carlos A CA Yang Lingxiao L Sorrells Janet E JE Park Jaena J Sun Liang L Yu Zhengxin Z Huang Yiran Y Marjanovic Marina M Mirica Liviu M LM Boppart Stephen A SA

Journal of biophotonics 20220624 9

A recent theranostic approach to address Alzheimer's disease (AD) utilizes multifunctional targets that both tag and negate the toxicity of AD biomarkers. These compounds, which emit fluorescence with both an activation and a spectral shift in the presence of Aβ, were previously characterized with traditional fluorescence imaging for binary characterization. However, these multifunctional compounds have broad and dynamic emission spectra that are dependent on factors such as the local environmen ...[more]

PMID: 35686672

Similar Datasets

Project description:BackgroundDuodenal gastrinomas (DGASTs) are neuroendocrine tumors that develop in the submucosa of the duodenum and produce the hormone gastrin. Surgical resection of DGASTs is complicated by the small size of these tumors and the tendency for them to develop diffusely in the duodenum. Endoscopic mucosal resection of DGASTs is an increasingly popular method for treating this disease due to its low complication rate but suffers from poor rates of pathologically negative margins. Multiphoton microscopy can capture high-resolution images of biological tissue with contrast generated from endogenous fluorescence (autofluorescence [AF]) through two-photon excited fluorescence (2PEF). Second harmonic generation is another popular method of generating image contrast with multiphoton microscopy (MPM) and is a light-scattering phenomenon that occurs predominantly from structures such as collagen in biological samples. Some molecules that contribute to AF change in abundance from processes related to the cancer disease process (e.g., metabolic changes, oxidative stress, and angiogenesis).Study design/materials and methodsMPM was used to image 12 separate patient samples of formalin-fixed and paraffin-embedded duodenal gastrinoma slides with a second-harmonic generation (SHG) channel and four 2PEF channels. The excitation and emission profiles of each 2PEF channel were tuned to capture signal dominated by distinct fluorophores with well-characterized fluorescent spectra and known connections to the physiologic changes that arise in cancerous tissue.ResultsWe found that there was a significant difference in the relative abundance of signal generated in the 2PEF channels for regions of DGASTs in comparison to the neighboring tissues of the duodenum. Data generated from texture feature extraction of the MPM images were used in linear discriminant analysis models to create classifiers for tumor versus all other tissue types before and after principal component analysis (PCA). PCA improved the classifier accuracy and reduced the number of features required to achieve maximum accuracy. The linear discriminant classifier after PCA distinguished between tumor and other tissue types with an accuracy of 90.6%-93.8%.ConclusionsThese results suggest that multiphoton microscopy 2PEF and SHG imaging is a promising label-free method for discriminating between DGASTs and normal duodenal tissue which has implications for future applications of in vivo assessment of resection margins with endoscopic MPM.

Project description:Rationale: Cerebral cavernous malformation (CCM) is prone to recurring microhemorrhage, which can lead to drug-resistant epilepsy. Surgical resection is the first choice to control seizures for CCM-associated epilepsy. At present, removal of resection-related tissue only depends on cautious visual identification of CCM lesions and perilesional yellowish hemosiderin rim by the neurosurgeon. Inspired by the resection requirements, we proposed quantitative multiphoton microscopy (qMPM) for a histopathology-level diagnostic paradigm to assist clinicians in precisely complete resection. Methods: A total of 35 sections specimens collected from 12 patients with the CCM-related epilepsy were included in this study. First, qMPM utilized a label-free multi-channel selective detection to image the histopathological features based on the spectral characteristics of CCM tissues. Then, qMPM developed three customized algorithms to provide quantitative information, a vascular patterns classifier based on linear support vector machine, visualization of microhemorrhage regions based on hemosiderin-related parameters, and the CCM-oriented virtual staining generative adversarial network (CCM-stainGAN) was constructed to generate two types of virtual staining. Results: Focused on CCM lesion and perilesional regions, qMPM imaged malformed vascular patterns and micron-scale hemosiderin-related products. Four vascular patterns were automatically identified by the classifier with an area under the receiver operating characteristic curve of 0.97. Moreover, qMPM mapped different degrees of hemorrhage regions onto fresh tissue while providing three quantitative hemosiderin-related indicators. Besides, qMPM realized virtual staining by the CCM-stainGAN with 98.8% diagnostic accuracy of CCM histopathological features in blind analysis. Finally, we provided pathologists and surgeons with the qMPM-based CCM histopathological diagnostic guidelines for a more definitive intraoperative or postoperative diagnosis. Conclusions: qMPM can provide decision-making supports for histopathological diagnosis, and resection guidance of CCM from the perspectives of high-resolution precision detection and automated quantitative assessment. Our work will promote the development of MPM diagnostic instruments and enable more optical diagnostic applications for epilepsy.

Dataset Information

Tracking the binding of multi-functional fluorescent tags for Alzheimer's disease using quantitative multiphoton microscopy.

Publications

Tracking the binding of multi-functional fluorescent tags for Alzheimer's disease using quantitative multiphoton microscopy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets