Ratiometric Raman nanotags enable intraoperative detection of metastatic sentinel lymph node
Introduction
Sentinel lymph node (SLN) is defined as the first lymph node to receive lymphatic drainage from the primary tumor. In recent years, SLN imaging and biopsy has been advocated as an important technique to identify regional lymph node metastasis and guide subsequent surgical procedure for many cancers [[1], [2], [3]]. It can effectively minimize the surgical morbidity associated with traditional complete lymphadenectomy, such as lymphedema, lymphocele and neurovascular injury [4,5]. Histopathological assessment is now the routine technique for diagnosing nodal metastasis, including intraoperative frozen section and postoperative paraffin section. The former allows preliminary diagnosis of nodal metastasis in surgery, which determines whether the complete lymphadenectomy should be performed or not. But frozen section has a poor diagnostic value, the sensitivity was only 20.7 %–46 % and it typically takes dozens of minutes, which extends the entire operation [6,7]. Paraffin section is the current diagnostic gold standard of nodal metastasis, but it takes approximately one week to obtain the final result, which may delay subsequent therapy or lead to a second surgery. Therefore, it is highly desirable to develop a technique to accurately and rapidly predict nodal status in surgery thus speeding up the intraoperative decision making.
Previous studies have reported a series of tumor-specific targeted nanotags to distinguish metastatic node from normal [[8], [9], [10]]. Ideally, metastatic lymph node exhibits higher uptake of targeted nanotags than normal. This difference can help to identify the nodal metastasis. However, this single-tag strategy still remains great challenges for clinical application, since the quantification of nanotags mainly relies on the absolute optical signal readout (e.g., fluorescence intensity), but the uptake of nanotags varies greatly among lymph nodes due to many factors, such as the metastatic status of lymph node, the interstitial fluid pressure in the tumor, enlarged reactive hyperplasia lymph node, and others [11]. Furthermore, the nonspecific harvest of nanotags in lymph node by passive unidirectional lymphatic movement is considered as another hurdle to accurately quantify the targeted nanotags. In light of these problems, a dual-tag ratiometric strategy, with built-in self-calibration for signal correction, rather than absolute intensity-dependent signal readout may offer advantages for detecting metastatic SLN [12,13].
In recent years, surface-enhanced Raman spectroscopy (SERS)-based imaging has demonstrated great potential in the field of biomedical imaging and detection. SERS imaging offers favorable properties including ultrahigh sensitivity, an excellent spatial resolution of sub-micrometers, enormous multiplexing capability with narrow spectral bandwidth, and particularly high specificity with the ‘fingerprint’ spectrum of Raman molecules that can be well distinguished from the background signal of the living organisms [[14], [15], [16],49,50]. In fact, we have recently achieved high-contrast SLN imaging in mouse models using a new type of SERS nanotags, namely, gap-enhanced Raman tags (GERTs) [[17], [51]]. With the unique gold (Au)-based core-shell structure, the specific Raman reporters were embedded in sub-nanometer sized internal gaps, resulting in a remarkable Raman enhancement and ultrahigh photostability at the near-infrared (NIR) region with the off-resonance excitation condition owing to the electromagnetic enhancement and the electron transport effect [18]. The surface coated mesoporous silica layer confers on GERTs good biocompatibility and capability of conjugation with functional molecules or drugs [[19], [20], [52]]. A further optimized version of GERTs with a petal-like shell structure (P-GERTs) can realize rapid and high-resolution cell imaging within 6 s and a high-contrast (a signal-to-background ratio of 80) wide-area (3.2 × 2.8 cm2) SLN imaging within 52 s, with a Raman enhancement factor beyond 5 × 109 and a detection sensitivity down to the single-nanoparticle (NP) level [21].
In this study, we explore the possibility of utilizing a dual-GERTs ratiometric strategy to detect metastatic SLN in cervical cancer. Folate receptor (FR), which has high affinity to folic acid (FA), is highly expressed on various cancers and has therefore become an ideal target for cancer specific imaging and therapeutics [[22], [23], [24]]. We utilize GERTs with an embedded-in Raman reporter 1 functionalized by FA as the targeted tags (FA-GERTs) and GERTs without functionalization but with an embedded-in Raman reporter 2 as the non-targeted tags (Nt-GERTs) (Scheme 1). We hypothesize that the non-specific uptake of FA-GERTs is equal to that of Nt-GERTs in normal SLN, but the specific uptake of FA-GERTs would increase in metastatic SLN due to the specific binding of FA-GERTs to tumor cells. Therefore, the ratio of FA-GERTs/Nt-GERTs in the SLN is a potent predictor for discriminating metastatic SLNs from their normal counterpart. After performing SLN imaging with a Raman imaging system, the obtained Raman spectra are demultiplexed using the classical least square (CLS) method, and then are utilized for the analysis of the abundances of each pure component (FA-GERTs, Nt-GERTs and lymph node tissue) and for the reconstruction of three-dimensional (3D) images of a SLN. The ratio of FA-GERTs/Nt-GERTs is then calculated and used to predict the status of the SLN (Scheme 1). Our data demonstrate that using Raman dual-nanotag ratiometric strategy can facilitate the diagnosis of metastatic SLN with a high accuracy of 87.5 %. This approach, independent of absolute optical signal intensity, effectively realizes the intraoperative visualization of metastatic SLN and shows great advantages over single-nanotag imaging method. We believe that it will be promising for clinical application in cancer therapeutics.
Section snippets
Materials and methods
All materials were used as received without any further purification. Chloroauric chloride (HAuCl4·H2O), ascorbic acid (AA), sodium hydroxide (NaOH), N, N-dimethylformamide (DMF), succinic anhydride and methanol were purchased from Sinopharm Chemical Reagent Co. Ltd. (China). Cetyltrimethylammonium chloride (CTAC), tetraethyl orthosilicate (TEOS), 3-aminopropyl triethoxysilane (APTES), N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC), N-Hydroxysuccinimide (NHS) were purchased
Synthesis and characterization of GERTs
Fig. 1a shows the schematic representation of two types of GERTs used in this work, composed of plasmonic Au core-shell NPs with a sub-nanometer thick junction in between containing the embedded Raman reporter molecules and an external mesoporous silica layer [31]. The GERTs with Raman reporters of 4-nitrobenzenethiol (4-NBT) were conjugated with folic acid (FA-GERTs for brevity) for targeting the cervical cancer cells (Fig. S1). Those embedded with 1,4-benzenedithiol (1,4-BDT) were used
Discussion
The main challenge of SLN biopsy lies in the inability to provide rapid and accurate diagnosis of metastatic nodes in surgery. Study data highlight the limited accuracy of intraoperative histological assessment, such as frozen sections and imprint cytology [45]. Although paraffin section can enhance the detection of nodal metastasis, it is relatively complex and time consuming. Previous studies have demonstrated that using tumor-specific targeting optical imaging nanotags can detect metastatic
Author Contributions
Zhouzhou Bao: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original draft, Funding acquisition. Binge Deng: Resources, Methodology, Investigation. Yuqing Zhang: Methodology, Investigation. Xiaowei Li: Methodology, Formal analysis. Ziyang Tan: Software, Data curation. Zhuowei Gu: Resources, Funding acquisition. Bobo Gu: Funding acquisition. Zhifeng Shao: Writing – review & editing. Wen Di: Supervision, Funding acquisition. Jian Ye: Conceptualization, Supervision,
Data availability
All data relevant to the study are included in the article or uploaded as supplementary information. Complementary data that support the findings of this study are available from the corresponding authors upon reasonable request.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors would like to thank Cong Xu (Everest Medicines Limited) for assistance with animal model establishment. This work was supported by the National Key Research and Development Program (863) of China [grant number 2016YFC1302900], the National Natural Sciences Foundation of China [grant numbers 81772770, 81871401, 61905057], the Science and Technology Commission of Shanghai Municipality [grant numbers 18441904800, 19441905300, 21ZR1438600], the Shanghai Municipal Commission of Health
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