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对代谢和形态学同质性进行快速、高分辨率、无损评估,可独特地识别高级别宫颈癌前病变。

Rapid, high-resolution, non-destructive assessments of metabolic and morphological homogeneity uniquely identify high-grade cervical precancerous lesions.

作者信息

Polleys Christopher M, Singh Pramesh, Thieu Hong-Thao, Genega Elizabeth M, Jahanseir Narges, Zuckerman Andrea L, Díaz Francisca Rius, Patra Abani, Beheshti Afshin, Georgakoudi Irene

机构信息

Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA.

Data Intensive Studies Center, Tufts University, Medford, MA 02155, USA.

出版信息

bioRxiv. 2024 May 14:2024.05.10.593564. doi: 10.1101/2024.05.10.593564.

DOI:10.1101/2024.05.10.593564
PMID:38798665
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11118292/
Abstract

PURPOSE

Two-photon microscopy (2PM) is an emerging clinical imaging modality with the potential to non-invasively assess tissue metabolism and morphology in high-resolution. This study aimed to assess the translational potential of 2PM for improved detection of high-grade cervical precancerous lesions.

EXPERIMENTAL DESIGN

2P images attributed to reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and oxidized flavoproteins (FP) were acquired from the full epithelial thickness of freshly excised human cervical tissue biopsies (N = 62). Fifteen biopsies harbored high-grade squamous intraepithelial lesions (HSILs), 14 biopsies harbored low-grade SILs (LSILs), and 33 biopsies were benign. Quadratic discriminant analysis (QDA) leveraged morphological and metabolic functional metrics extracted from these images to predict the presence of HSILs. We performed gene set enrichment analysis (GSEA) using datasets available on the Gene Expression Omnibus (GEO) to validate the presence of metabolic reprogramming in HSILs.

RESULTS

Integrating metabolic and morphological 2P-derived metrics from finely sampled, full-thickness epithelia achieved a high 90.8 ± 6.1% sensitivity and 72.3 ± 11.3% specificity of HSIL detection. Notably, sensitivity (91.4 ± 12.0%) and specificity (77.5 ± 12.6%) were maintained when utilizing metrics from only two images at 12- and 72-μm from the tissue surface. Upregulation of glycolysis, fatty acid metabolism, and oxidative phosphorylation in HSIL tissues validated the metabolic reprogramming captured by 2P biomarkers.

CONCLUSION

Label-free 2P images from as few as two epithelial depths enable rapid and robust HSIL detection through the quantitative characterization of metabolic and morphological reprogramming, underscoring the potential of this tool for clinical evaluation of cervical precancers.

摘要

目的

双光子显微镜(2PM)是一种新兴的临床成像方式,有潜力以高分辨率非侵入性地评估组织代谢和形态。本研究旨在评估2PM在改进高级别宫颈癌前病变检测方面的转化潜力。

实验设计

从新鲜切除的人宫颈组织活检标本(N = 62)的全上皮厚度获取归因于还原型烟酰胺腺嘌呤二核苷酸(磷酸)(NAD(P)H)和氧化型黄素蛋白(FP)的2P图像。15份活检标本含有高级别鳞状上皮内病变(HSIL),14份活检标本含有低级别鳞状上皮内病变(LSIL),33份活检标本为良性。二次判别分析(QDA)利用从这些图像中提取的形态和代谢功能指标来预测HSIL的存在。我们使用基因表达综合数据库(GEO)上可用的数据集进行基因集富集分析(GSEA),以验证HSIL中代谢重编程的存在。

结果

整合来自精细采样的全层上皮的代谢和形态2P衍生指标,HSIL检测的灵敏度高达90.8±6.1%,特异性为72.3±11.3%。值得注意的是,当仅使用距组织表面12μm和72μm处的两张图像的指标时,灵敏度(91.4±12.0%)和特异性(77.5±12.6%)得以保持。HSIL组织中糖酵解、脂肪酸代谢和氧化磷酸化的上调验证了2P生物标志物捕获的代谢重编程。

结论

仅从两个上皮深度获取的无标记2P图像,通过对代谢和形态重编程的定量表征,能够快速且可靠地检测HSIL,突出了该工具在宫颈上皮内瘤变临床评估中的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/2f8987543885/nihpp-2024.05.10.593564v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/00b1d99f3afc/nihpp-2024.05.10.593564v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/aa607f41ddce/nihpp-2024.05.10.593564v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/3ac851c4162f/nihpp-2024.05.10.593564v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/9402d302456c/nihpp-2024.05.10.593564v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/2f8987543885/nihpp-2024.05.10.593564v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/00b1d99f3afc/nihpp-2024.05.10.593564v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/aa607f41ddce/nihpp-2024.05.10.593564v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/3ac851c4162f/nihpp-2024.05.10.593564v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/9402d302456c/nihpp-2024.05.10.593564v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ac/11118292/2f8987543885/nihpp-2024.05.10.593564v1-f0005.jpg

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