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A Sensor Array Fabricated with Nanoscale Metal-Organic Frameworks for the Histopathological Examination of Colon Cancer Shuai Wu, Yiwei Han, Lin Wang, Jinlong Li, Zhaowei Sun, Meiling Zhang, Ping Liu, and Genxi Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b02381 • Publication Date (Web): 23 Jul 2019 Downloaded from pubs.acs.org on July 23, 2019

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Analytical Chemistry

A Sensor Array Fabricated with Nanoscale Metal-Organic Frameworks for the Histopathological Examination of Colon Cancer Shuai Wu,† Yiwei Han,† Lin Wang,† Jinlong Li,‡ Zhaowei Sun,† Meiling Zhang,§ Ping Liu,*,§ Genxi Li*,†,‖ †State

Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023,

P. R. China ‡Department

of Laboratory Medicine, the Second Affiliated Hospital of Southeast University, Nanjing 210003, P. R.

China §Department ‖Center

of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China

for Molecular Recognition and Biosensing, School of Life Sciences, Shanghai University, Shanghai 200444, P.

R. China ABSTRACT: There is still no good method for the diagnosis of colon cancer, so we have presented a nanoscale metal-organic frameworks (NMOFs)-based sensor array to effectively identify normal and pathological tissues in this work. Moreover, this method can enable a rapid and accurate histopathological examination of colon cancer with simple and easy operation. The method is designed by making use of the different interactions between the overall intracellular proteome signatures of colonic tissues and three structurally stable NMOFs featuring characteristic surface chemistry. We have demonstrated that this sensor array can exhibit excellent performance to detect the unknown specimens from low-dose tissue samples with clinically relevant specificity and accuracy. Collectively, the versatile detector array based on NMOFs offers a highly discerning and adaptive alternative to identify the colon cancer tissues, which possesses wide developing prospect in medical diagnosis.

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Nanoscale metal-organic frameworks (NMOFs), an emerging type of porous nanomaterials, have aroused tremendous interest in the field of biomedicine due to their synthetic tunability, multifunctionality, and biocompatibility.1-3 Particularly, their unique physical and chemical properties may promote them available in biosensing.4,5 However, current related research has not yet been extended to practical applications because of the harsh synthetic conditions for postmodification of NMOFs and the complexity of biological samples, which inspires us to design a programmable biosensing platform for clinical and translational science. In the meantime, benefited from the judicious choice of the metalcontaining units and organic linkers, a wide synthetic scope can be provided to generate a variety of NMOFs with different textural properties.6,7 In addition, NMOFs can feature appropriate surface areas for binding biomolecule exteriors.8-10 For instance, it has been demonstrated that labeled singlestranded DNA (ssDNA) can be effectively absorbed on the surface of NMOFs.11,12 So, the diversity of surface charges and aromatic groups of ligand may lead to different degrees of their interactions with biomolecules, allowing facile design of various receptors for pattern recognition that exploits exclusive target–receptor binding events for the sensor analysis of complex samples.13-15 Accordingly, the combination of various NMOFs should provide new insights to advance the sensing system for the disease diagnosis. Colon cancer is one of the most frequent malignancies and the fourth of most common causes of cancer-related deaths in the world.16 Although blood tests employing several cancer biomarkers such as CEA or CA199 have been used in clinical practice for the diagnosis of this cancer,17 they don’t have sufficient sensitivity and specificity to make a definite diagnosis, because these markers also exist in some other types of diseases.18 Histopathological examination is still the gold standard for the diagnosis of this cancer. It can provide more information to study the manifestations of cancer, including tumor initiation, differentiation, invasion, and metastasis,19 so it can guide the optimal treatment and monitor the prognosis information. Nevertheless, tissue biopsy is a process of analyzing complex sample, which involves the extraction of cells or tissues and analysis of pathologic slices to determine the presence and degree of cancer.20-22 Moreover, the process of paraffin embedding and sectioning of tissues requires trained laboratory operators and is quite time-consuming. Besides, medical results have to be depended on the individual judgment of pathologist in most cases, thus the subjectivity can easily produce inaccurate diagnosis outcomes. Therefore, it is urgent to develop reasonable methods for simple and rapid histopathological examination of colon cancer. There have been more than ten tumor marker proteins used in clinical tests to identify cancerous tissues of colon cancer, such as CDX2, CEA, CK20, CK7, PAX-8, MRP-1, and GSTπ, etc.23-25 Because of the presence of these proteins, abnormal and normal tissues should be distinguished, thus new method should be able to be proposed. So, we have designed and constructed a sensor array by taking advantage of the selective noncovalent interactions between these components and DNAadsorbed NMOFs, based on the fact that composition and configuration of the nanomaterials may greatly contribute to the surface chemical properties.26 Therefore, three structurally

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stable NMOFs (Cu-MOFs, Fe-MOFs, and Zr-MOFs) are selected as versatile scaffolds to adsorb two fluorescentlylabeled ssDNA as well as nanoquenchers for signal transduction. As shown in Scheme 1, six recognition elements (NP1-NP6) are firstly formed to fabricate the sensor array. These ssDNA reporters are adsorbed on NMOFs through π-π stacking and quenched in different degrees through the long-range energy transfer.27 Then the protein analytes are added. Since there are lots of hydrophobic and hydrophilic patches on the surface of proteins, they will interact with the organic moieties or accessible coordination sites on the surface of NMOFs through intermolecular forces such as electrostatic interaction, coordination interaction, π-π stacking, and hydrogen bonding attraction.28-30 Moreover, the subsequent adsorption event is much more strong than the binding of ssDNA, which will liberate the DNA reporters from the surface of NMOFs through binding competition,31 generating the changes of fluorescence intensity, and these gathered signals from the binding pairs can give a map of a discriminative fingerprint by the method of linear discriminant analysis (LDA).32 Consequently, an effective identification of the tumor tissues of colon cancer can be achieved. Moreover, the inspection time can be greatly reduced and the efficiency of histopathological examination can be extremely improved. EXPERIMENTAL SECTION Materials and Reagents. Iron(III) chloride hexahydrate (FeCl3·6H2O), benzoic acid (BDC), ethanol, dimethylformamide (DMF), bovine serum albumin (BSA), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), and proteinase K were ordered from Sigma-Aldrich Chemical Co., Ltd. (Shanghai, China). Zirconium chloride (ZrCl4) and 1,3,5-benzenetricarboxylic acid (H3BTC) were purchased from Alfa Aesar Co., Ltd. (Shanghai, China). Tetrakis(4-carboxyphenyl) porphyrin (TCPP) was obtained from J&K Scientific Co., Ltd. (Shanghai, China). Polyvinyl pyrrolidone (PVP, MW40000) was obtained from Energy Chemical Co., Ltd. (Shanghai, China). Copper(ii) nitrate hydrate (Cu(NO3)2·3H2O) and dichloroacetic acid were provided by Aladdin Biotechnology Co., Ltd. (Shanghai, China). One Step Animal Tissue Active Protein Extraction Kit and Modified BCA Protein Assay Kit were purchased from Sangon Biotechnology Co., Ltd (Shanghai, China). All other chemicals used were of analytical grade without any further purification. Deionized water was obtained from a Millipore water purification system (Milli-Q, ≥18.2 MΩ) for the experiments. All DNA sequences were synthesized and HPLC purified from Sangon Biotechnology Co., Ltd (Shanghai, China). 5-Carboxytetramethylrhodamine (5-TAMRA) was labeled at the 3’ end, the sequences of oligonucleotides were as follows: DNA 1: AAAAAAAAAAAAAAAAAA DNA 2: CCCCCCCCCCCCCCCCCCCC Apparatus. Powder X-ray diffraction (PXRD) data was recorded by a D8 Advance diffractometer (Bruker, Germany). The transmission electron microscope (TEM) images of MOFs were performed using a Tecnai G2 F20 S-TWIN (FEI, USA). Fluorescence intensity was measured with F-7000 spectrometer (Hitachi, Japan) and M200 Pro Multimode Plate Reader (Tecan,

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Analytical Chemistry Scheme 1. The Schematic Illustration of Sensing Mechanism. (A) Three Types of NMOFs Employed in the Sensor Array. (B) Six Recognition Elements (NP1-NP6) Designed by Employing the Nano-Assembly between the NMOFs and Two Fluorescence-Labeled Single-Stranded DNA. The Shades of the Color Represent the Intensity of the Fluorescence.

Switzerland). The processing of pathological tissue was conducted using ASP300S biological tissue dehydrator (Leica, Germany), HistoCore Arcadia H/C tissue embedding machine (Leica, Germany), AUTOSTAINER XL/LEICA CV5030 pathological section dyeing and sealing machine (Leica, Germany), and the BX41 microscope (Olympus, Japan). Preparation of Nanoscale Cu-MOFs. A methanol aqueous solution (50 mL) containing Cu(NO3)2·3H2O (0.9 g) and PVP (0.4 g) was under stirring at room temperature. Next, H3BTC (0.43 g) was dissolved in another 50 mL methanol to form ligand stock solution. Then, the above solution was added dropwise to the metal precursor solution by syringe. Blue colloidal suspension could be observed during the process of dropping, about 10 minutes. After that, stop stirring and let stand to age the colloidal nanoparticles for 24 h in the dark conditions. The resulting blue solid was collected by centrifugation for 10 minutes at 10000 rpm and washed with fresh methanol for several times. Finally, the precipitates were dried at 60 °C under reduced pressure overnight for later use. Preparation of Nanoscale Fe-MOFs. FeCl3·6H2O (1.350 g) and BDC (0.412 g) in 30 mL of DMF were ultrasonically dissolved in a 50 mL Teflon-liner and heated at 110 °C for 20 h. The resulting orange solid was harvested by centrifugation for 10 minutes at 10500 rpm and washed with fresh ethanol for several times. Finally, the precipitates were dried at 120 °C for 6 h under reduced pressure. Preparation of Nanoscale Zr-MOFs. ZrCl4 (37.5 mg) and TCPP (6.5 mg) were dissolved in 16.25 mL of DMF with ultrasound for about 3 minutes. Then, dichloroacetic acid (0.25 mL) was added into the solution. The mixture was loaded into a Teflon-liner and heated at 130 °C for 18 h. The resulting dark

purple solid was collected by centrifugation for 10 minutes at 10500 rpm and washed with fresh DMF and ethanol for several times. Finally, the precipitates were dried at 130 °C under reduced pressure overnight for later use. Cell culture. NCM-460 and Colo 205 were cultured in RPMI 1640 medium (Gibco, Invitrogen) supplemented with 10% FBS in a 5% CO2 humidified incubator (Thermo 3111). HCT-116 was grown in McCoy’s 5A medium (Gibco, Invitrogen), other culture conditions are the same as above. Cells were collected by centrifugation for 5 minutes at 1000 rpm and washed with PBS buffer for twice. The resulting cells were resuspended in the PBS solution to form a cell suspension. The numbers were counted with an automated cell counter (Invitrogen Countess) for later use. Processing of Colonic Tissues. The tissues of patients were obtained from the Second Affiliated Hospital of Southeast University. Informed consents were obtained in all cases, and the research was approved by the scientific ethical committee of Nanjing University and Southeast University. Histopathological section: The obtained tissues were dehydrated and embedded in paraffin, and then sectioned followed by H&E staining. The observation was proceeded using a microscope, while recording images. The whole process was completed with clinical medical equipment. Preparation of the lysates: Firstly, fresh tissues were washed for twice using cold PBS buffer. Then precooled extraction reagent (One Step Animal Tissue Active Protein Extraction Kit) was added into the collected tissues for ultrasonication (30 seconds each time, 3 to 4 times, each interval of 1 min). After that, whole tissue lysates were centrifuged for 10 minutes at 12000 rpm (4 °C), and the supernatants were transferred into a



new EP tube for protein quantification and following sensing experiments. Array-Based Sensing Studies. For target sensing study, two fluorescently-labeled DNA strands (DNA 1 and 2) were incubated with three kinds of NMOFs separately and diluted with HEPES (10 mM, 100 mM NaCl, pH 7.2) buffer to perform mixture solutions where the final concentrations of DNA elements, Cu-MOFs, Fe-MOFs, and Zr-MOFs were 50 nM, 1 μg/mL, 10 μg/mL, and 20 μg/mL, respectively. The mixtures (200 μL in each well) were transfered into a black Microlon 96well plate (Corining Inc., America). After incubation for 30 minutes, fluorescence intensity (I0) at 570 nm was measured at an excitation wavelength of 540 nm on a multimode plate reader. When the targets (proteins, cell lines or lysates of colonic tissues) were introduced and incubated for 30 minutes (cell lines for 1 h), the reaction system of each well was given a different response (I) at 570 nm. The difference between the two values (Δ I = I-I0) was used as the response signal for array sensing analysis. Repeat six times for each set of target experiment. Finally, the raw data matrix was handled using linear discriminant analysis (LDA) in Xlstat (version 2015) and IBM SPSS Statistics 24. RESULTS AND DISCUSSION In this work, we have selected three kinds of MOFs containing different valence states of metal ions and numbers of carboxyl groups of organic ligands to effectively construct diverse receptors. Nanoscale materials are obtained by controlling the process of rapid nucleation or the coordination modulation with regulators. The powder X-ray diffraction (PXRD) has been conducted to confirm the purity and crystallinity of the as-synthesized nanoparticles. As illustrated in Figure 1 (a, c and e), each NMOF has its own crystal characteristic peak, which is consistent with those previous reports.33-35 Transmission electron microscopy (TEM) images have displayed the morphology structure and size of different NMOFs. Cu-NMOFs are grown into spherical nanostructures

Figure 1. Characterizations of PXRD patterns and TEM images of three NMOFs, Cu-MOFs (a and b), Fe-MOFs (c and d), and ZrMOFs (e and f).

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Figure 2. Fluorescence titrations at 570 nm of fluorescently labeled DNA 1 towards Fe-MOFs, that is NP3 recognition element. The intensity of fluorescence is normalized and plotted against the mass concentration of Fe-MOFs. The concentration of report DNA was 50 nM. The inset shows the photograph of fluorescence intensity changes with the addition of concentration gradient Fe-MOFs under ultraviolet light.

with an average diameter of 200 nm (Figure 1b). But Fe-MOFs are typical octahedral nanostructures with a particle diameter of 220−270 nm (Figure 1d). Moreover, tetravalent zirconium ions reacting with TCPP have yielded rod-shaped Zr-MOF nanoparticles with mean sizes of about 460 nm in length and a width of approximately 110 nm (Figure 1f). Due to the differences in valence and ligand composition, the three structurally NMOFs will perform selective binding with DNA molecules. Next, fluorescence titration has been conducted by incubating the labeled-DNA (50 nM) with different NMOFs. As shown in Figure 2, the fluorescence signal is significantly weakened with the addition of MOF nanoparticles. With the concentration increases, the quenching effect enhances and the trend of the curve fitted by least square method gradually reaches a stable level at 20 mg/L. For the same type of Fe-MOF, the NP4 complexation performs a stronger quenching effect than NP3 (Figure S1), which means that cytosine-rich sequences have a greater affinity than adenine-rich sequences. The same experimental phenomenon is also found in the two other kinds of NMOFs. Nevertheless, with regard to the same singlestranded DNA, Cu-MOFs are the most effective quencher, followed by Fe-MOFs and Zr-MOFs. Hence, 6 probes have different affinity and quenching effect with each other, facilitating them to be artificial receptors in sensor array for targets analysis. We have then conducted experiments to evaluate the discriminating ability against standard protein samples. Five different proteins, bovine serum albumin (BSA), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), and proteinase K, are employed as targets, some of which have similar molecular weights or similar isoelectric points (Table S1). It is observed that recoveries can be achieved in most recognition events, implying that the DNA reporters on the


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Analytical Chemistry surface of NMOFs can be detected by binding-induced competitive displacement of the proteins (Figure 3A and Table S2). By contrast, negligible change of the fluorescence can be detected when removing the NMOFs from the system (Figure S2), further confirming the disassembly of NMOFs/DNA caused by proteins. The heat map that makes the results more intuitionistic is shown in Figure 3B. By LDA analysis, five apparent regions are located in different quadrants, revealing a highly classification accuracy (Figure 3C). Moreover, 20 unknown protein samples randomly composed of the above five proteins are identified with 95% accuracy by calculation of their Mahalonobis distance (Table S3). More details are provided in the Supporting Information.

Figure 4. Array-based sensing of cell lines and tissue samples. (A) Fluorescence response (ΔI, I-I0) patterns of the sensor array (NP1NP6) against various cell lines: NCM 460, Colo 205 and HCT 116 with constant cell number (~1000 cells). (B) Canonical score plot for the first two factors of fluorescence response patterns obtained from LDA analysis against three types of cell lines, with 95% confidence ellipses. (C) Fluorescence response (ΔI, I-I0) patterns of the sensor array (NP1-NP6) against various tissue samples: healthy, cancerous and paracancerous ones with 150 ng. (D) Canonical score plot for the first two factors of fluorescence response patterns obtained from LDA analysis against three types of samples, with 95% confidence ellipses.

Figure 3. Array-based sensing of protein analytes at 5 μM. (A) fluorescence response (ΔI, I-I0) patterns of the sensor array (NP1NP6) against various proteins: bovine serum albumin (BSA), hemoglobin (Hb), lysozyme (Lyso), horseradish peroxidase (HRP), and Proteinase K. Error bars represent standard deviations of six parallel measurements. (B) Heat map derived from fluorescence response pattern for five proteins. (C) Canonical score plot for the first two factors of fluorescence response patterns obtained from LDA analysis against five proteins, with 95% confidence ellipses.

Colonic cells cultured in vitro are then introduced to the sensor array, as to further understand and validate the promise of our approach. Three distinct human colon cell types including two kinds of colon cancer cells (Colo 205 and HCT 116) and a normal one (NCM 460) are chosen as the analytes. Under the same conditions, the probes are incubated with different cell types for 1 hour, approximately 1000 cells in each well. The experiment of each group of cell has been repeated six times, generating a 6*3*6 matrix. As shown in Figure 4A, fluorescence response patterns have been obtained (Table S4). The collected raw data of fluorescence signal change are analyzed by LDA and two canonical factors (69.21% and 30.79% of the variation) are obtained. The two factors are respectively served as abscissa and ordinate to map a two-dimensional pot, in which each point represents the fluorescence-change fingerprint for a single cell line against the sensing system (Figure 4B). As illustrated in the graph, three apparent regions are located in different quadrants, without overlap between the 95% confidence ellipses. Thus the cancerous colon cells can be easily distinguished from normal ones on account of the commonalities and differences in cell membrane composition, such as proteins, amphipathic phospholipids, and carbohydrates.36,37 Clinical tissue samples collected from a local hospital are then used to challenge the sensing system. The pathological tissues are characterized by the cessation of cellular metabolism, abnormal function, and morphological changes, which is



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Figure 5. (A) The predicted medical results using the sensor array compared to the gold standard for histopathological sections. (B) The distribution of points representing 42 individuals in canonical score plot based on the training dataset. (C) The performance of LDA using two groups (cancerous versus normal individuals). The histogram is marked with normal distributions fitted to the full data. (D) ROC analysis of the two-group diagnostic study, and the AUC value is 0.90.

related to the abnormal expression of multiple functional proteins.38 So, three types of tissue samples (healthy, cancerous, and paracancerous samples) have been collected from healthy and cancerous individuals, of which are the most common adenocarcinomas. Informed consents were obtained in all cases, and the research was approved by the local scientific ethical committee. In consideration of the heterogeneity of the samples,39 each cancer type has been firstly sampled from 6 individuals respectively, which is identified by pathology slice examination. Then, the tissue lysates are prepared by extracting the total protein, and quantified by BCA Protein Assay Kit. At the same concentration of protein (150 ng), the samples are added in the test solutions containing NMOFs/DNA-based probe in a standard microplate for 30 minutes. Each group is made of 5 replicates and the average of the fluorescence responses is taken to generate the ﬂuorescent pattern (Figure 4C and Table S5). The differences in fluorescence intensity fromeach probe are calculated to form a training dataset by the LDA model. As shown in Figure 4D, cancerous tissue samples are classified in the right area of canonical score plot, while the other two are on the left without overlapping parts, demonstrating underlying differences on protein expression. For sake of further research, unknown samples have been applied to test the array-based sensor. Based on the results of the gold standard for histopathological sections, 14 samples of each type are selected as analytes, of which the cancer group contains individuals with different degrees of differentiation (Figure S3). Under the same process, the response emissions of 42 samples are recorded for each as before. Using the established data model, the discriminative type is predicted by the Mahalanobis distance of each data in the training dataset (Table S6). The results have been concluded in Figure 5A, then this classification matrix can identify between the three individual types with 69% accuracy, with the most misclassification between healthy and paracancerous ones

(Figure 5B). The distance of paracancerous issues towards focus of colon cancer may have an impact on the above results. Clinically, the detection of pathological tissue is the core of diagnosis. Thus, we have classified health and paracancerous issues into one class as “normal” group, with LDA giving an improved accuracy (83.3%). Above all, these studies indicate that our approach can rapidly and effectively distinguish cancerous issues from normal ones based on their lysate compositions. To further determine the sensitivity and specificity of this new method, receiver operator characteristic (ROC) curve analysis40 has been conducted through counting the number of individual by the difference in LDA score. As illustrated in Figure 5C, the LDA dataset has been transferred into a histogram of statistics to distinguish cancerous issues from normal group, where the distribution curve is partially crossed. Then the sensitivity and specificity is calculated by setting different thresholds, a two-dimensional ROC curve has been created with (1-specificity, sensitivity) as data point (Figure 5D). The Area Under Curve (AUC) is found to be 0.90. The high value demonstrates the excellent performance of the classification model, namely the reliable diagnosibility of this sensor array. Under the premise of the same importance of specificity and sensitivity, the optimal value of cut-off is calculated to be -0.05 on the basis of Youden index,41 giving a result of sensitivity as 86% and specificity as 93% of this assay. As a matter of fact, the pathological report of colon cancer should not only indicate the occurrence of cancer, but also give details of tumor grades, providing reasonable guidelines for subsequent clinical treatment. In general, well-differentiated tumors are less malignant and have a better prognosis, and vice versa. However, their high genetic similarities are expected to make difficulties for detection assays. Herein, we have prepared adenocarcinoma of colon as model targets and endeavored to analyze the degrees of tumor differentiation using the sensor


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Analytical Chemistry array. As shown in Figure S4A, although the fluorescence response of three grades (well-differentiated, moderatelydifferentiated, and poorly-differentiated) are similar, the results by LDA analysis satisfyingly reveal that the three tumor grades can be distinguish effectively (Figure S4B). It is a step closer to practical application. Naturally, it requires a larger sample population to assess its clinical applicability as the following step. All in all, the developed NMOFs/DNA-based sensor array performs very well on distinguishing healthy and tumor tissues, providing an alternative tool for tumor classification in cancer diagnosis. CONCLUSIONS In conclusion, we have demonstrated that the assemblies of NMOFs with DNA elements can be utilized to fabricate a ﬂuorescent sensor array capable of analyzing tumorous tissues of colon cancer. The size and morphology of the NMOFs surface can provide the selectivity of interactions with proteins, and the intrinsic fluorescence quenching properties can facilitate the signal transduction of the binding event. Using the sensor array, we can rapidly (~1 h) identify various analytes from pure standard samples to complex clinical specimens with high sensitivity and accuracy by LDA analysis. The short-time detection hugely improves the diagnostic efficiency. Remarkably, full differentiation can be achieved in the analysis of different tissue samples of colon cancer at a level as low as 150 ng, minimizing biopsy size. What’s more, the method has achieved a clinical preliminary diagnosis of unknown colon cancer tissue, which presents a complementary strategy to traditional methods for histopathological examination. To sum up, the sensor array based on NMOFs is believed to hold a promising future for medical diagnosis.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Basic properties of the proteins, data matrix of fluorescence response patterns against various proteins, cell lines, and tissue samples, histological examination images of issue specimens (PDF)

AUTHOR INFORMATION Corresponding Author *E-mail address: [email protected], Fax: +86 25 83592510.

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT This work is supported by the National Natural Science Foundation of China (Grant Nos. 81772593, 21235003).

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