Identification and validation of plasma biomarkers for diagnosis of breast cancer in South Asian women

Breast cancer is the most common malignancy among women globally. Development of a reliable plasma biomarker panel might serve as a non-invasive and cost-effective means for population-based screening of the disease. Transcriptomic profiling of breast tumour, paired normal and apparently normal tissues, followed by validation of the shortlisted genes using TaqMan® Low density arrays and Quantitative real-time PCR was performed in South Asian women. Fifteen candidate protein markers and 3 candidate epigenetic markers were validated first in primary breast tumours and then in plasma samples of cases [N = 202 invasive, 16 DCIS] and controls [N = 203 healthy, 37 benign] using antibody array and methylation specific PCR. Diagnostic efficiency of single and combined markers was assessed. Combination of 6 protein markers (Adipsin, Leptin, Syndecan-1, Basic fibroblast growth factor, Interleukin 17B and Dickopff-3) resulted in 65% sensitivity and 80% specificity in detecting breast cancer. Multivariate diagnostic analysis of methylation status of SOSTDC1, DACT2, WIF1 showed 100% sensitivity and up to 91% specificity in discriminating BC from benign and controls. Hence, combination of SOSTDC1, DACT2 and WIF1 was effective in differentiating breast cancer [non-invasive and invasive] from benign diseases of the breast and healthy individuals and could help as a complementary diagnostic tool for breast cancer.


Results
Study population. The clinicopathological details of the samples used for the microarray are given in Supplementary Table S1 and that used in antibody array are furnished in Supplementary Tables S2 and S3 respectively.
Identification of potential biomarkers using BC gene expression profiling. Transcriptomic profiling was performed on 41 T,18 PN and 6 AN tissue to obtain a BC gene expression signature (Supplementary Table S1). The raw data analysis using the Class comparison module in the BRB-Array Tools software v 3.7.0 (criteria used were P-value = 0.001 and twofold or higher difference) identified 57 genes overexpressed in T compared to either AN or PN, 146 genes with elevated expression in PN compared to AN or T and 173 genes with higher expression in AN compared to PN or T [indicative of downregulated genes in T] (Fig. 1a). Validation of 67 differentially expressed genes (short-listed from microarray analyses) using TaqMan ® Low density arrays (TLDA) resulted in 56 genes being identified as differentially expressed, resulting in more than 80% concordance with the microarray data. Fifteen genes were highly overexpressed in T compared to AN and PN; 9 genes were overexpressed in PN compared to AN and T; 32 genes were grossly downregulated in tumours compared to PN or AN samples (Fig. 1b, c). Further, we confirmed the differentially expressed genes from our series using an independent GEO Microarray Dataset [GSE 22820] which included 176 primary breast tumour samples and 10 normal breast tissues from reduction mammoplasties.

Diagnostic Evaluation of the protein markers in plasma of BC patients.
To analyse the diagnostic properties of the protein markers, the plasma samples were split into training (N = 371) and test (141) sets (Supplementary Table S4). The individual diagnostic performance of the 15 markers was determined using the receiver operating characteristic (ROC) analysis. The specificity was fixed at 70% and sensitivities were generated. The markers which had an AUC < 0.5 were CFD (35.7 ng/ml, AUC 0.494, 31%), LEP (45.7 ng/ml, AUC 0.455, 29%) and DKK3 (124.6 ng/ml, AUC 0.418, 30%). Markers with AUC > 0.5 were FGF2 (0.0068 ng/ml, Figure 1. Identification of candidate biomarkers for breast cancer by microarray analyses. (a) Unsupervised hierarchical clustering of primary breast tumour, paired normal (PN) and apparently normal (nor) tissues. Each column represents an individual sample and each row a gene. Upregulated genes are indicated in red and downregulated genes in green. (b) List of genes upregulated in tumour and paired normal tissues relative to apparently normal (> 2.5-fold differential expression, P < 0.0001) in gene expression profiling. (c) List of genes downregulated in tumour and paired normal tissues relative to apparently normal (> 2.5-fold differential expression, P < 0.0001).  Table S5). Overall, the combination of 6 proteins CFD, LEP, DKK3, IL17B, SDC1, FGF2 showed the best diagnostic potential with sensitivity and specificity of 65% and 79% in training set (AUC 0.661), and 70% and 85% in test set (AUC 0.678). respectively (Table 3). Although the sensitivity of multiple proteins improved the sensitivity of the model, it did not satisfy our objective, hence we evaluated additional markers.

Identification of Circulating DNA methylation markers. The top downregulated genes [WIF1,
DACT2, ID4, TP63, SOX10] in tumours in our transcriptomic profile were regulators of Wnt signalling, cell differentiation and morphogenesis and acted as inhibitors of tumour growth in BC [31][32][33][34] . Epigenetic silencing of various Wnt signalling antagonists promote abnormal cell proliferation in BC, since they have a tumour suppressive role 35 . Hence, we chose WIF1 and DACT2 since they showed the greatest downregulation in T relative to AN (Fig. 1c). Additionally, SOSTDC1 found to be downregulated in tumours in our gene expression study, had been confirmed to be downregulated in 98.2% (56/57) of breast tumour tissues and cell lines due to promoter hypermethylation in our earlier study, was also included 36 .   www.nature.com/scientificreports/ mRNA levels of all three genes were found to be decreased in BC cell lines with greater downregulation in triple negative MDAMB231, MDAMB468 cells and HER2 overexpressing SKBR3 cells when compared to others (Fig. 2a). We then did Methylation Specific PCR (MSP) analysis and found complete methylation of WIF1 and SOSTDC1, and partial methylation of DACT2 in all BC cell lines (Fig. 2b). Bisulfite Sequencing PCR (BSP) analysis of the putative promoter regions of WIF1 and DACT2 uncovered methylated CpG sites, concordant with the MSP analysis (Fig. 2c). The BSP sequencing of SOSTDC1 was not repeated since, its promoter region was already characterized with the promoter region having a 54 bp CpG island upstream of 5'UTR encompassing 4 CpG sites 38 . Further, the methylation status of these genes was validated in 10 primary breast carcinoma tissues (having > 70% tumour cells) and 10 paired normal tissues. In concordance with the cell lines, SOSTDC1 and WIF1 were completely methylated but, DACT2 was hemi-methylated in tumours. All three markers were unmethylated in the paired normal tissues and lymphocytes confirming that the methylated alleles of SOSTDC1, DACT2 and WIF1 originate from the breast tumour ( Fig. 3a-c).

WIF1, DACT2 and SOSTDC1 as potential circulating biomarkers in BC.
For the validation of SOSTDC1, DACT2 and WIF1 in plasma samples, cfDNA was isolated from patients (invasive BC = 202, DCIS = 16, benign = 37) and controls (n = 203) recruited for the case control study. Increased concentration of cfDNA was detected in plasma from patients with invasive BC and DCIS when compared to plasma from patients with benign breast diseases and healthy controls (P < 0.0001). cfDNA levels were associated with stage of the disease and were significantly elevated (P < 0.0001) in stage II ( Supplementary Fig. S3). This lack of significance could also be due to the smaller number of DCIS and stage 1 patients included in the study.
MSP detection of SOSTDC1, DACT2 and WIF1 was carried out with bisulphite modified cfDNA samples isolated from the cases and controls. The amplicons were visualised in 2% agarose gel. (Fig. 3d Fig. S5). The overall performance of the markers in single and in combination were concordant in the training and test sets. Hence, individually the markers indicated excellent discriminatory power and when combined, the 3 markers were able to detect BC with 100% efficiency.
To confirm the accuracy of the markers, an arbitrary cut-off of '1' was assigned and leave one out analysis was performed using the training set. In cases, if M/U (ratio of methylated allele and unmethylated allele) value is greater than 1, a score of '1' was assigned and if M/U value is lesser than 1, a score of '0' was assigned. In controls, if M/U value is greater than 1, a score of '-1' was assigned and if M/U value is lesser than 1, a score of '0' was assigned. The diagnostic parameters were calculated using MEDCALC ® tool. In the training set, individually all three markers showed > 90% sensitivity and specificity, with an accuracy of 94% for SOSTDC1 (91. 17  The median with range intensity was plotted and Kruskal Wallis statistic was used to test the differential methylation intensity. The full-length gels of above images are provided in Supplementary Fig. S4 (Table 5). Thus, combining multiple markers improved the overall sensitivity of the predicted diagnostic model.

Discussion
In the last few years several attempts have been made to develop a non-invasive assay for the early diagnosis of BC. In recent times, the use of multiple markers for BC detection is gaining interest because, the combination of functionally important genes and proteins involved in breast tumorigenesis, have exhibited better diagnostic accuracy than single candidate markers 30    In the present study, we have aimed at developing a minimally invasive, multi-marker BC diagnostic model with improved sensitivity and specificity in South Asian women. Transcriptomic profiling followed by qRT PCR validation identified 56 candidate markers for biomarker evaluation. Fifteen of the 56 genes were then analysed at the protein level using a multiplexed sandwich ELISA platform (Quantibody array), first in tissues and later in plasma. Eight markers (FGF2, IL17B, IP10, MIG, MIP1d, LOX1, OPN and SDC1) found to be upregulated in primary tumours were present in higher concentration in plasma of breast cancer patients. Similarly, 3 markers (CFD, LEP and DKK3) were downregulated both in tumour lysates and plasma from patients with cancer. Hence, the expression of 11 out 15 markers showed similar trend in tumour tissues and in plasma. IGF-1 induces Ras-Raf-MAPK and PI3K-AKT signalling components to promote tumour progression and elevated levels of serum IGF-1 was associated with poor prognosis [44][45][46] . Consistent with the existing literature, IGF-1 mRNA and protein levels were found to be higher in BC in our study population. The mitogenic growth factors FGF1 and FGF2, known to possess potent angiogenic properties 47,48 , were increased in patients with malignant breast tumours and decreased in patients with benign breast lesions indicating their possible role in malignant cell transformation. IL17B, is produced by induction of memory T lymphocytes and plays an important role in inflammatory responses by binding to the membrane receptor IL17RB. IL17B-IL17RB interaction triggers the activation of NF-kB signalling cascade leading to production of anti-apoptotic Bcl-2 49 . IL17B levels were significantly higher in our DCIS and invasive mammary carcinoma patients and diminished in benign and control groups. The protein SDC1 was also significantly increased in our BC cases, on par with previous findings which have demonstrated that high serum levels of SDC1 were associated with aggressive phenotype, poor prognosis and decreased response to chemotherapy [50][51][52] . In majority of studies, increased levels of CFD and LEP have been linked to obesity associated BC progression via enhanced TGFβ signalling and MMP modulation [53][54][55] . In contrast, our results indicate decreased levels of the adipokines, CFD and LEP in BC tissues as well as plasma when compared to controls but it is similar to a transcriptome profiling study in Arabian women that has reported the downregulation of leptin and other downstream leptin metabolism genes in BC 56 .
The dysregulation of WNT signalling cascade in tumorigenesis has been well documented and WNT antagonists sFRP3, DKK3 and WIF1 are frequently downregulated in BC contributing to constitutive activation of oncogenic growth factors 35 . Our data depicts decreased plasma levels of DKK3 in cases which correlate with the previous gene expression data but the median protein levels of WIF1 were higher in BC tissues and plasma, contrary to our microarray results. Inter-individual variability of WIF1 was observed to be high in our data, and WIF1 levels were below the detection limit in 30-40% of the plasma samples, hence the median and normal range was difficult to establish making it less reliable. Univariate ROC analysis of the markers showed limited sensitivity and specificity, so markers which had AUC above or below 0.5 were chosen for multivariate analysis. Combination of 6 proteins including CFD, IL17B, SDC1, DKK3, FGF2, LEP showed a sensitivity of 65%, 70% and specificity of 79% and 85% in training and test sets respectively. The multi-protein panel possessed higher discriminant performance than single markers but did not achieve the desired sensitivity and specificity.
Next, we assessed the methylation status of SOSTDC1, DACT2 and WIF1 in cfDNA samples isolated from reserved aliquots of plasma from cases and controls recruited. All three genes are known negative modulators of the canonical Wnt signalling cascade. The deficiency of SOSTDC1 correlated with greater tumour size and treatment with recombinant SOSTDC1 effectively blocked WNT signalling components which contribute to cell proliferation, indicating its antagonistic role against Wnt pathway 57 . In addition, our previous study reported that SOSTDC1 is downregulated in 98.1% of breast tumour tissues and it coincides with DNA methylation of CpG sites in promoter region of SOSTDC1 36 . Epigenetic inactivation of WIF1 contributes to constitutive activation of WNT signalling pathway in breast tumorigenesis 58 . The differential methylation of WIF1 was also able to predict the clinical efficacy of neoadjuvant chemotherapy (docetaxel, pirarubicin and cyclophosphamide) in sera of locally advanced BC patients 59 . DACT2 acts as a tumour suppressor by inhibiting canonical WNT signalling and is frequently silenced by promoter hypermethylation in BC. Overexpression of DACT2 inhibited the expression of β-catenin target genes associated with tumour growth and metastasis 60,61 . Although these 3 genes were known to be frequently methylated in breast tumour tissues, their methylation status in circulating DNA remained unknown. Therefore, SOSTDC1, DACT2 and WIF1 seemed candidate markers for the development of a potential methylation-based diagnostic model.
Our data showed reduced mRNA levels and methylation of the 3 genes in breast cancer cell lines and tumour tissues consistent with previous studies which have reported that promoter methylation mediated silencing of WIF1 and DACT2 was observed in 63.3% (95/150) 62 and 73% (107/147) 33 of primary BC tissues.
BSP analysis was then carried out to confirm the methylation status in a methylation-independent manner. Consequently, the putative promoter methylation of the 3 markers was analysed in cfDNA isolated from plasma of patients diagnosed with either non-invasive or invasive BC, benign breast diseases and healthy individuals. The markers of interest were found to be methylated in more than 90% of invasive and 75% of pre-invasive BC cases. They were either unmethylated or weakly methylated in more than 80% of the benign cases and 95% of healthy controls. The clinicopathological correlation revealed that rise in methylation intensity of the said genes were significantly associated with advanced tumour stage, high grade, nodal status, and metastases. These results signify the putative tumour suppressive role of the markers and possible role in progression and severity of BC.
The receptor status of breast tumours did not show any influence on the methylation levels except for DACT2 which displayed significant variation among tumours which were HER2 positive and negative. The HER2 receptor status of 50% of the patients had a score of 2 + and FISH confirmation was not done for all due to financial issues (Supplementary Table S7). Diagnostic evaluation of the single markers displayed a high sensitivity range of 92%-98% and specificity of 95% in the training set. In the test set, the sensitivity and specificity of the single markers ranged from 85 to 95% and 92-99% respectively. Combination of 2 or 3 biomarkers generated 100% sensitivity and 89-91% specificity in both the training and test datasets. Hence, the predicted diagnostic model was robust www.nature.com/scientificreports/ and reliable since it could discriminate BC (non-invasive and invasive) from plasma obtained from patients with benign breast disease and healthy controls with a better sensitivity than the previously proposed models. Despite, the promising results of various biomarker panels reported previously, the current study has some strengths. Although multiple markers show better diagnostic potential than single markers, it is imperative to establish an assay comprising of minimal number of markers with high diagnostic ability. In addition, the assay platform should also be cost-effective and easily adaptable in a clinical setting. In this study we were able to achieve 97-100% sensitivity and 89-91% specificity by combining 2 or 3 genes. We have used MSP, a PCR based semi-quantitative platform which is simple and cost-effective compared to sequencing and mass spectrometric approaches. Pre-analytical parameters such as use of plasma instead of serum [plasma is preferred for cfDNA assays since serum cfDNA has increased genomic DNA levels 63,64 and a three-step centrifugation step to avoid lymphocyte contamination was incorporated. Unlike many of the existing studies the specificity of our panel was also tested in benign breast abnormalities and DCIS and validated in an independent sample set. The differential methylation of the said epigenetic markers, between the benign, DCIS and invasive groups suggest that these markers shall be able to differentiate benign lesions from malignant. Although the epigenetic silencing of WIF1, DACT2 and SOSTDC1 in BC has been reported before, we are the first team to evaluate the combined diagnostic efficiency of these genes in circulating cfDNA from BC patients.
The shortcoming of our study is the limited number of DCIS and early-stage BC samples which can contribute to the lack of statistical significance in the differential methylation intensity between controls, benign and early-stage tumours (DCIS and stage I). Hence, validation of these markers in a larger number of DCIS and early-stage breast tumour patients is necessary before it is considered as a complementary tool for BC diagnosis.

Materials and methods
Patients and samples. The study was approved by the Institute Ethical committee, Cancer Institute (WIA). The study population primarily consisted of Indian women. The patient samples were collected after informed consent and all methods were performed in accordance with relevant guidelines and regulations set by the committee. Our Institute's Tumour bank provided 6 apparent normal (AN) samples; 41 breast tumours (T) with at least 70% tumour cells in the sample provided as confirmed by frozen section and 18 paired normal (PN) samples that were included in the microarray study and for qRT-PCR validation of the microarray data. For the validation of the markers identified, the protein levels were initially estimated in BC tissue lysates comprising of 6 AN, 23 PN and 46 T samples (Supplementary Table S1). The frequency of women opting for reduction mammoplasty or for prophylactic mastectomy is very rare in India, hence it is difficult to procure normal breast tissue samples. Our work hence used samples well away from the benign lesions and confirmed to be morphologically normal, from women with benign breast disease.
Further, an age-matched case-control study (age distribution with 5-year intervals) was performed with 202 BC patients and 203 healthy controls. The inclusion and exclusion criteria are provided in (Supplementary  Table S6). Additionally, 16 patients with ductal carcinoma in-situ (DCIS) and 37 patients with benign breast disease were included in the study. The samples were randomly split into training set and test set for validation ( Supplementary Fig. S1).
Sample preparation. The tissue lysates were prepared according to Rajkumar et al. 65 . The concentration of proteins was estimated in 100ul of lysate (1 mg/ml). Post clinical examination, 10 ml blood sample was collected in ethylene diamine tetra acetic acid (EDTA) coated tubes from each individual. The cells and plasma were separated from whole blood by centrifugation at 2500 rpm for 20 min at 37℃ and stored in aliquots in LoBind tubes [Eppendorf, India] at − 80 °C. The plasma samples were thawed and centrifuged at 13,000 rpm and 100 µl (1:1) of diluted cell-free plasma was added to each well of the custom designed Quantibody array slide (Raybiotech, Inc. USA).
All the cell lines were purchased from National Centre for Cell Science (Pune, India).
Nucleic acid extraction. Genomic DNA was isolated from cell lines, tissues samples (breast tumour, paired normal and apparent normal) by QIAamp ® DNA Mini Kit (Qiagen, Hilden) according to manufacturer's instructions. Cell free DNA was isolated from 1 ml of plasma using QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden). DNA and RNA quantification were done using NanoDrop ND1000 (NanoDrop Technologies, USA) spectrophotometer. Cell free DNA was quantitated using Qubit™ dsDNA HS Assay Kit (Invitrogen, USA).
The RNA was extracted from the tissue samples using the RNeasy RNA extraction kit (Qiagen, Hilden; Cat no: 74106) as per the manufacturer's instructions. The quality of the RNA used for microarray analysis was checked using the Bioanalyzer and samples with RNA Integrity Number (RIN) of 7 or more were included in the study. RNA was quantitated using NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, USA).

Microarray based gene expression profiling.
The microarray experiments were done as described previously 65 . Briefly, 1 µg of total RNA from the tumour/PN/AN sample and universal RNA (Stratagene; Cat no: 740000-41) were reverse transcribed using Array script at 42 °C for 2 h to obtain cDNA using the Amino Allyl MessageAmp II aRNA amplification kit (Ambion, Austin, Tx; Cat no: AM1797). The cDNA was amplified, labelled, hybridized and slides scanned as described earlier. All the raw data files have been submitted to GEO (accession number GSE139038). Quantitative real time PCR. Validation of the gene expression was done using the TLDA quantitative real time PCR (Applied Biosystems, Foster City, CA). Triplicate cDNA template samples were amplified and analysed on the ABI Prism 7900HT sequence detection system (Applied Biosystems, Foster City, CA). The protocol for validation was adapted from literature 65 . The raw data from the Prism 7900HT sequence detection system was imported into Microsoft Excel for statistical analysis of the data. Among the endogenous reference genes included on the array (18S ribosomal gene; UBC; GAPDH), UBC and GAPDH were chosen after visualizing the global Ct value distribution, for data normalization. The TLDA assays were run at Lab India Instruments Pvt Ltd laboratories at Gurgaon, New Delhi. The AN tissue samples were used as calibrators and the relative quantitation values were calculated for all the genes and the samples. Geometric mean was calculated for each of the 67 genes (excluding the 3 endogenous controls). The relative quantitation values for all the samples and genes were imported into BRB Array Tools and Class Comparison analysis was done comparing the different clinicopathological parameters with the gene expression values. Fifteen genes which were secreted or potentially secreted were short-listed, based on our data as well as corroborated with GEO Microarray Dataset (176 primary breast tumors and 10 normal breast samples [from reduction mammoplasties] [GSE22820]). An additional requirement was that the protein could be assayed in the multiplexed sandwich-ELISA platform.

Custom designed protein antibody array (quantibody array). Quantibody array [multiplexed sand-
wich ELISA on a glass slide] was used [Ray Biotech, Inc, USA] to study the protein expression in tissue lysates and subsequently in plasma. The assay was done as per the manufacturer's instructions with modifications 65 .
Quantibody array data normalization. The data was analysed using the H15S90 Genxbio Q-Analyzer v8 16.4, an array specific, Microsoft Excel based program, supplied with the custom arrays. 2 positive controls for signal normalization, a negative control for background subtraction and an internal control to minimize inter-slide variation were used. A user defined reference array was used, to which the signals of other arrays were normalized. Median + Median absolute deviation was calculated for each protein and the cut-offs which yielded a sensitivity and specificity of more than 75% was chosen.
cDNA conversion and semiquantitative RT-PCR. 1 µg of RNA was used to synthesize first strand cDNA (QuantiTect Reverse Transcription Kit, Qiagen, Hilden). The reaction mixture was diluted 50% (v/v) with water and 2 µl of cDNA was used for 25 µl PCR reaction (Eurogentec Takyon™ Low ROX SYBR ® 2X Mastermix Blue). RT-PCR was amplified for 35 cycles and GAPDH was used as an internal control for normalization. The products were visualized in 2% agarose gel.
Bisulfite conversion, methylation specific PCR (MSP) and bisulfite sequencing PCR (BSP) analysis. Bisulfite conversion was performed using the EZ DNA Methylation-Gold Kit (Zymo Research, USA), according to manufacturer's instructions. The amount of input DNA was adjusted to be uniform for each sample. For MSP and BSP analysis of Dapper 2 homolog (DACT2), genomic sequence 1000 bp upstream of transcription start site (TSS) and 200 bp downstream of TSS containing the putative promoter region was retrieved from Database of transcriptional start sites (http:// dbtss. hgc. jp/). MethPrimer 2.0 (http:// www. uroge ne. org/ methp rimer2/) was used for CpG island prediction and generation of primer sets. For Sclerostin domain containing 1 (SOSTDC1) 36 and Wnt Inhibitory Factor 1 (WIF1) 62 , pre-designed primer sets (spanning promoter CpG islands) from previous studies were used (Supplementary Table S8). HotStar Taq ® Master Mix (Qiagen, Hilden, Germany) was used for MSP and the conditions were set according to manufacturer's instructions. The amplicons were resolved in 2% agarose gel.
BSP products of WIF1 and DACT2 were amplified from bisulfite modified MDAMB231 genomic DNA using HotStar Taq ® with gene-specific primers (Supplementary Table S8) and cloned using TOPO ® TA Cloning ® Kit for Sequencing (Invitrogen, Massachusetts USA). The clones were screened using PCR and restriction enzyme digestion. The clones were sequenced using a Big Dye Terminator Cycle Sequencing kit (Applied Biosystems, Foster city, CA, USA) as per the manufacturer's instructions on ABI 310 genetic analyser. Densitometric analysis was used to quantify (Image Lab 6.0.1.) the intensity of the DNA bands relative to 100 bp DNA ladder (Promega, Madison USA). Ratio of methylated and unmethylated band intensity was calculated for each sample in cases and controls. Median + Median absolute deviation was calculated for each gene and used as cut-offs.
Equipment and settings. The Quantibody array slides were scanned at 5 μm resolution, ratio (635/532) and a PMT of 580 using Molecular Devices GenePix Pro 4100 A scanner. At these scanner settings the signals from the highest standard concentration did not reach saturation.
The MSP amplicon resolved agarose gels were docked using Biorad ChemiDoc MP system. Image Lab version 4.1 software (Bio Rad CA, USA, https:// www. bio-rad. com/ en-in/ produ ct/ image-lab-softw are) 66 was used for gel imaging with acquisition settings such as single channel, SyBR Safe mode and gray scale image colour.