Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology.
Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology.
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DOI：
10.1073/pnas.1719551115
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发表时间：
2018-06-19
影响因子：
11.1
通讯作者：
Bhargava R
中科院分区：
文献类型：
--
作者：
Mittal S;Yeh K;Leslie LS;Kenkel S;Kajdacsy-Balla A;Bhargava R
癌症改变组织中多种细胞类型的形态和生化特性。一般来说，上皮细胞的形态对于常规疾病诊断是实用的。在此，红外光谱成像对乳腺癌、上皮细胞和肿瘤相关微环境进行生化表征。不幸的是，传统的光谱分析速度很慢。因此，我们设计并制造了一款激光共焦显微镜，它具有高信噪比，可以实现可靠的诊断。该仪器将成像时间从几天缩短到几分钟，使该技术可用于研究和临床转化。最后，自动化的人类乳腺癌活检成像在约 1 小时内报告，为总肿瘤（上皮加微环境）特性的常规研究和快速、无标记诊断铺平了道路。基于上皮细胞空间模式的组织病理学是癌症临床诊断和研究的金标准；尽管已知组织微环境很重要，但由于现有工具的复杂和主观解释，组织微环境并不容易使用。在这里，我们利用新兴的高清傅里叶变换红外（HD FT-IR）光谱成像与机器学习算法相结合，展示了根据上皮细胞的分子特性进行准确的亚型分析。除了检测四种上皮亚型外，我们还同时描绘了表征乳腺肿瘤的三种基质亚型。虽然 FT-IR 成像数据能够实现具有丰富信息内容的全数字病理学，但信号平均和处理所需的长光谱扫描时间使得该技术对于常规研究或临床使用来说不切实际。因此，我们开发了一种共焦设计，其中折射红外光学器件旨在使用量子级联激光 (QCL) 源提供高清、快速空间扫描和离散光谱调谐。该仪器同时提供高分辨率（2μm 像素尺寸）和高信噪比（SNR）（>1,300），与现有成像光谱仪相比，获得分类结果的速度提高了约 50 倍。我们通过对一天内分析的 100 个乳腺组织组进行精确分析，展示了我们开发的仪器的光谱保真度和仪器间可操作性，从而大大加快了研究速度。患者病例中典型的临床乳腺活检可在约 1 小时内完成分析。这项研究为在可行的时间段内进行全面的肿瘤微环境分析铺平了道路，为实用的无标记分子组织病理学迈出了关键的一步。
Cancer alters both the morphological and the biochemical properties of multiple cell types in a tissue. Generally, the morphology of epithelial cells is practical for routine disease diagnoses. Here, infrared spectroscopic imaging biochemically characterizes breast cancer, both epithelial cells and the tumor-associated microenvironment. Unfortunately, conventional spectral analyses are slow. Hence, we designed and built a laser confocal microscope that demonstrates a high signal-to-noise ratio for confident diagnoses. The instrument cuts down imaging time from days to minutes, making the technology feasible for research and clinical translation. Finally, automated human breast cancer biopsy imaging is reported in ∼1 hour, paving the way for routine research into the total tumor (epithelial plus microenvironment) properties and rapid, label-free diagnoses. Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients’ caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.
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影响因子：
5.9
作者：
Huang, Monica L.;Hess, Kenneth;Fornage, Bruno D.
通讯作者：
Fornage, Bruno D.
影响因子：
4.2
作者：
Kroeger-Lui, N.;Gretz, N.;Petrich, W.
通讯作者：
Petrich, W.
影响因子：
35
作者：
Bai, Yanbo;Slivken, Steven;Razeghi, Manijeh
通讯作者：
Razeghi, Manijeh
DOI：
10.1073/pnas.1408129111
发表时间：
2014-10-21
影响因子：
11.1
作者：
Calligaris, David;Caragacianu, Diana;Agar, Nathalie Y. R.
通讯作者：
Agar, Nathalie Y. R.
影响因子：
50.3
作者：
Hanahan, Douglas;Coussens, Lisa M.
通讯作者：
Coussens, Lisa M.