Detection of chromosomal rearrangements using fluorescence
in situ hybridization (FISH) in lymphomas is an important diagnostic measure for treating this aggressive tumour type. This article aims to describe the recent advancements in the technology.
by Dr Michael Liew, Leslie Rowe and Prof. Mohamed E. Salama
Background
Lymphomas are cancers that affect cells of the lymphatic or immune system, affect a wide range of age groups and are not gender specific. Differentiation of different lymphomas types is important for prognosis and treatment regimens. Lymphomas can be differentiated according to the chromosomal rearrangements they have undergone. For example, Burkitt’s lymphoma is characterized by a rearrangement in the MYC gene (8q24). Diffuse large B-cell lymphoma (DLBCL) is a very aggressive tumour that is characterized by multiple gene rearrangements involving MYC, IGH, BCL2 or BCL6. Mantle cell lymphomas are characterized by the presence of the CCND1 gene rearrangement. All of these rearrangements are routinely detected by fluorescence in situ hybridization (FISH) [1].
Digital FISH analysis
Microscope slides prepared for FISH analysis are still currently viewed by eye using an epifluorescence microscope. An emerging technology is digital FISH analysis. The microscope slides are prepared in the same way, but the fluorescent signals are captured and analysed digitally. The entire field of microscopy has benefited from whole slide imaging (WSI). This enables the capture, analysis, storage and sharing of whole slide pathology images [2]. However, WSI is still in its infancy as a diagnostic tool because there is a lack of evidence that it can be used as a primary diagnostic tool compared to viewing the slide directly with a traditional microscope. Similarly FISH analysis of a tissue slide is starting to become digitized and laboratories are validating its use diagnostically.
There are three main components to digital FISH analysis. The first component, which is critical for its accuracy, is the need for z-stacking of multiple digital images for FISH applications. This is in contrast to WSI for bright field microscopy, which only needs to capture a single digital image. The second component is the identification, or segmentation, of individually stained nuclei that have been stained with a fluorescent dye, such as DAPI. With a suspension of cells, having software that identifies isolated nuclei is relatively straightforward. However, the digital analysis of formalin-fixed paraffin-embedded (FFPE) tissue sections, which can be more clinically informative, can be more challenging. The final component of digital FISH analysis is the signal count, or signal classification. Digital FISH analysis provides a powerful way of documenting and analysing, what can be complex signal patterns.
Validation of digital FISH assays
Our laboratory has validated digital FISH assays for the detection of MYC rearrangements in FFPE tissue (Fig. 1) [3]. We demonstrated a good correlation between traditional and digital FISH analysis for MYC rearrangements using both locus specific identifier (LSI) break-apart and IGH-MYC fusion probes. Our findings document improved diagnostic accuracy with the implementation of digital FISH analysis. Segmented and classified digital images allow for permanent storage of analysed specimens and easy accessibility for further review and/or educational purposes. The digital platform is also conducive to laboratory workflow as it allows timely segmentation and classification of nuclei, remote access for review of cases, elimination of manual slide transportation, and accurate identification/assessment of tumour from digital tissue matching.
We are not the first to adopt digital FISH analysis; however, we are among the first efforts to validate the system for clinical use, and results are in good agreement with previous studies that looked at automated analysis of FISH results from FFPE B-cell lymphoma tissue [4, 5]. The automated analysis yielded 100% agreement with conventional FISH in both studies. In contrast though, the amount of time it took to analyse a sample was approximately twice as long as the previous study. This difference in time was due to the manual editing required when using the digital system particularly in the initial phases of validation, whereas the other system was able to rely more on the automated analysis. We are developing the current workflow and system to reduce this step.
Implementation of digital FISH
The primary cost of switching over to a digital FISH system from a manual system is the new hardware including computer, scanning system and slide loaders. The digital FISH system increases the cost of a stand-alone epifluorescence microscope significantly. Depending upon whether whole slide images are captured, or just a few fields of view, additional computer servers may be needed for data storage. The promise is that if the digital FISH analysis is completely automated, and the results are 100% accurate, it is faster than manual FISH analysis. From our experience though, additional development is needed to reach the 100% accuracy and much development is needed to minimize the time needs to be spent manually editing the results. However, using the digital FISH analysis versus current direct visualization of the FISH slide with manual recording of a scoresheet, will come with several benefits including automatic digitized records and the possibility of remote connectivity for pathologists; to mention a few.
Future perspectives
There are several future considerations that arise from developing digital FISH imaging. WSI of a FISH slide is a possible application in digital FISH imaging. Similar to imaging an entire hematoxylin and eosin (H&E)-stained slide, it would mean that the entire section could be analysed, minimizing the possibility that a small area of tumour may be missed. However, magnification is an issue. FISH slides need to be analysed under higher magnification (at least 60×) to maintain the resolution between signals. Coupled with the acquisition of multiple z-stacked images, this would make the scanning time longer, and the image files extremely large, making data storage an issue. In addition, FDA concerns over digital pathology will affect future regulation of digital FISH analysis. This will mean that validated assays that are developed in the future will need to demonstrate that there is no loss of accuracy using a computer versus an epifluorescence microscope. Image formats, resolution and compression are important factors in accurate interpretation of digital FISH images. For the most accurate digital FISH data, images that do not lose data during compression (ie. TIF LZW or PNG) should be used. The drawback is that data storage becomes an issue, since files stay large, but is important to maintain image quality and accuracy.
Conclusion
Digital capture and analysis of FISH assays are a positive development for this important laboratory testing modality. The MYC FISH assays which we have converted to our digital imaging platform have provided numerous logistical and diagnostic advantages as indicated previously. In addition, individual signal patterns can be recorded and stored. These data alongside advances in computational power can potentially lead to correlation between signal pattern and unique tumour phenotypes, or overall tumour prognosis. There are still limitations to digital FISH analysis, in particular being able to reliably identify nuclei and hybridized signal. However, since the resolution of digital FISH images will only increase, and the algorithms used for detection of nuclei and signals will continually be refined, digital FISH analysis can only improve. As indicated, we feel that digital FISH analysis provides more efficient and accurate results and better patient care in comparison to traditional FISH methods. Efforts to convert other FFPE-based FISH assays to this digital platform are underway in our laboratory.
Acknowledgements
This work was funded by the Institute for Clinical and Experimental Pathology, ARUP Laboratories.
References
1. Martin-Subero JI, Gesk S, Harder L, Grote W, Siebert R. Interphase cytogenetics of hematological neoplasms under the perspective of the novel WHO classification. Anticancer Res. 2003; 23: 1139–1148.
2. Goacher E, Randell R, Williams B, Treanor D. The diagnostic concordance of whole slide imaging and light microscopy: a systematic review. Arch Pathol Lab Med. 2016; doi: http://dx.doi.org/10.5858/arpa.2016-0025-RA [Epub ahead of print].
3. Liew M, Rowe L, Clement PW, Miles RR, Salama ME. Validation of break-apart and fusion MYC probes using a digital fluorescence in situ hybridization capture and imaging system. J Pathol Inform. 2016; 7: 20.
4. Alpár D1, Hermesz J, Pótó L, László R, Kereskai L, Jáksó P, Pajor G, Pajor L, Kajtár B. Automated FISH analysis using dual-fusion and break-apart probes on paraffin-embedded tissue sections. Cytometry A. 2008; 73: 651–657.
5. Reichard KK, Hall BK, Corn A, Foucar MK, Hozier J. Automated analysis of fluorescence in situ hybridization on fixed, paraffin-embedded whole tissue sections in B-cell lymphoma. Mod Pathol. 2006; 19: 1027–1033.
The authors
Michael Liew1 PhD, Leslie Rowe1 MS, Mohamed E. Salama1, 2 MD
1ARUP Institute for Clinical and
Experimental Pathology, Salt Lake City, UT, USA
2Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA
*Corresponding author
E-mail: liewm@aruplab.com
Type 1 diabetes is a multigenic disease in which the pancreatic β-cells are destroyed by an autoimmune process. At time of diagnosis, only poorly functional β-cell mass exists. Prediction of type 1 diabetes progression and prognosis using genetic markers may improve treatment strategies and increase the patient´s life quality.
by Dr Caroline A. Brorsson and Dr Joachim Størling
Type 1 diabetes
Type 1 diabetes (T1D) is a chronic disease that results from an autoimmune destruction of the insulin-producing pancreatic β-cells in the islets of Langerhans. Worldwide, T1D is affecting an increasing number of people and the strongest increase in incidence is observed among young children. The disease is complex and caused by an interplay between genetic and environmental risk factors. Genes of the human leukocyte antigen (HLA) locus are the most prominent risk-conferring genes, but dozens (>50) of other risk loci have now been established to influence the risk of T1D [1, 2]. The exact mechanisms, however, by which HLA and other associated loci affect T1D risk, islet autoimmunity and the time course of β-cell destruction, remain elusive. T1D is preceded by a pre-clinical phase characterized by the appearance of autoantibodies directed against islet antigens. Several studies have confirmed the strong predictive effect of islet autoantibodies for the risk of developing T1D in genetically susceptible individuals. Still the rate of progression from autoantibody positivity to clinical onset is highly variable between individuals, and likely influenced by a combination of genetic and environmental risk factors [3].
As any immune-modulating interventions are possible only after the first signs of autoimmunity have occurred, there is a high risk that most of the β-cells have already been destroyed. Therefore, there is a need for more precise, and preferably earlier, methods for predicting disease risk and progression in order to preserve the residual β-cell mass and to choose optimal treatment regimens. Genetic markers can be measured before the onset of autoimmunity and offers an opportunity for early screening. Also, after the onset of T1D, preservation of β-cell function, as assessed by higher C-peptide levels, has been associated with decreased risk of diabetes complications including acute hypoglycemic events and long-term microvascular complications [4–6].
Prediction of T1D progression in high-risk individuals
Children with a high risk of diabetes, as characterized by either by carrying high-risk HLA genotypes or by having first-degree relatives with T1D, have been followed from birth until autoantibody development and diabetes onset in several cohort studies. A few of these studies have investigated the predictive effect of non-HLA genetic variants for islet autoimmunity and progression to T1D. Steck et al. studied the largest such prospective cohort from the U.S. population (the Diabetes Autoimmunity Study of the Young; DAISY) consisting of 861 first-degree relatives and 882 high-risk children from the general population [7]. They found that the risk alleles for PTPN22 and UBASH3A predicted both islet autoimmunity and diabetes, whereas PTPN2 predicted islet autoimmunity alone and INS predicted diabetes alone.
Studying a similar population of 1650 children of type 1 diabetic parents in the German BABYDIAB cohort, Winkler and co-workers showed that the cumulative sum of risk alleles of 12 T1D risk variants (a so-called genetic risk score; GRS) could stratify the risk of developing islet autoantibodies and diabetes, and progression from islet autoimmunity to diabetes [8]. In a subsequent study, Bonifacio et al. studied the rate of progression from the development of islet autoantibodies to diabetes in the same cohort of high-risk children [9]. They found that the genetic risk score of 12 genes could only marginally predict the risk of islet autoimmunity, but could significantly modify the risk of progressing from autoantibody positivity to diabetes. The most predictive power had a genetic risk score constructed from the five risk variants in INS, IFIH1, IL18RAP, CD25 and IL2, which could identify 80% of islet autoantibody-positive children who progressed to diabetes within 6 years and discriminate high risk (63% within 6 years) and low risk (11% within 6 years) antibody-positive children.
Achenbach and colleagues used the same cohort to investigate whether the 12 genetic variants could discriminate between slow and rapid progression to T1D in multiple autoantibody-positive children (3). Among the 1650 children, 23 developed multiple autoantibodies and progressed to diabetes within 3 years, while 24 developed multiple autoantibodies but did not progress to diabetes during more than 10 years of follow-up. The slow and rapid progressors were similar in regards to HLA risk genotypes, development of autoantibodies to insulin (IAA), glutamic acid decarboxylase (GADA) and zinc transporter 8 (ZnT8A), and progression to multiple autoantibodies. However, autoantibodies to insulinoma-associated antigen-2 (IA-2A) developed significantly later in children who progressed slowly. The GRS could clearly discriminate between the two groups of progressors. Best discriminatory power had a GRS including seven of the 12 risk variants (for the genes IL2, CD25, INS, IL18RA, IL10, IFIH1, and PTPN22). Interestingly, the risk score did particularly well in discriminating between children that carried high-risk HLA genotypes.
Prediction of T1D prognosis in new-onset patients
The Hvidoere Study Group for Childhood Diabetes (HSG) has collected a cohort of 275 newly diagnosed children with the purpose of identifying factors that control changes in β-cell function and glycemic control over time. All children underwent a standardized mixed-meal test at 1, 6 and 12 months after the diagnosis of T1D, to assess the stimulated C-peptide response at these time-points. Mortensen et al. (10) were able to demonstrate several factors that predict lower β-cell function at 12 months after diagnosis, including younger age and ketoacidosis at diagnosis, and stimulated C-peptide levels, post-meal blood glucose levels, and IAA and GADA autoantibodies at 1 month.
Only a few studies have investigated the genetic effect on prognosis after disease onset in T1D, including the cohort collected by the HSG. Candidate gene studies of single genetic variants have shown that the INS and PTPN22 risk variants are associated with residual β-cell function, glycemic control, autoantibody titres and proinsulin in new-onset T1D [11–13]. Furthermore, in one of the first studies that used a combination of cell biology experiments and clinical observations to study the impact of a T1D risk gene, we investigated the function of CTSH. That study showed that the risk variant of CTSH was associated with β-cell function and insulin dose in the children one year after diagnosis [14]. Interestingly, it was observed that within the β-cells, CTSH is a protective gene that inhibitsβ-cell death induced by pro-inflammatory cytokines – believed to contribute to β-cell killing in T1D – thus providing a mechanistic explanation for how genetic variation in CTSH affects T1D risk. In a separate cohort studying children diagnosed with T1D before the age of 11 years, we demonstrated that the risk variant in ERBB3 was associated with better β-cell function and lower HbA1c levels, and thereby a better glycemic control, after controlling for the effects of sex, age at diagnosis and duration of diabetes [15]. In that study, we also found that ERBB3 is regulating β-cell death in response to pro-inflammatory cytokines providing a possible mechanistic link.
In our most recent study on the HSG cohort, we investigated the impact of an increasing GRS on β-cell function and glycemic control during the first year after diabetes onset (16). The GRS was constructed from 11 T1D risk genes that we found to be expressed in human pancreatic islets, and whose expression changed upon stimulation with cytokines. We chose to focus strictly on the islet-expressed risk genes because we hypothesized that these would be the best predictors of islet (β-cell) function. We found that for each additional risk variant, i.e. for each unit increase in the GRS, a decreased β-cell function and a worsened glycemic control from 6 to 12 months after onset were observed, after controlling for the effect of age at diagnosis, sex and HLA risk groups. Further, we found that several of the genes used in the GRS interacted in a network suggesting that they may cooperate to regulate important processes within the β-cells. The results from these reviewed studies are summarized in Table 1.
Benefits for patients
Use of genetics in prediction models could lead to earlier prediction useful for immune-modulatory interventions to preserve residual β-cell mass and will be beneficial both in the pre-clinical phase and after diagnosis. Better stratification for fast and slow progressors both from autoantibody positivity to diabetes and disease progression after diagnosis would be a major achievement in diabetes care. Being able to foresee which genetically-predisposed individuals progress to T1D and these patients’ remaining β-cell function at time of diagnosis and first year(s) to come would have a tremendous impact on the individual patient’s health burden and quality of life, due to lowering the risk for hypo- and hyperglycemia and long-term complications.
Conclusion
In summary, profiling of selected genetic variants may hold promise to better predict T1D progression in risk individuals and residual β-cell function in new-onset type 1 diabetics. Such knowledge may in the future be exploited to offer personalized medicine to optimize treatment regimens to increase patient care and reduce severe long-term complications.
References
1. Barrett JC, Clayton DG, Concannon P, et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet. 2009; 41: 703–707.
2. Onengut-Gumuscu S, Chen WM, Burren O, et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat Genet. 2015; 47: 381–386.
3. Achenbach P, Hummel M, Thumer L, et al. Characteristics of rapid vs slow progression to type 1 diabetes in multiple islet autoantibody-positive children. Diabetologia 2013; 56: 1615–1622.
4. Steffes MW, Sibley S, Jackson M, Thomas W. Beta-cell function and the development of diabetes-related complications in the diabetes control and complications trial. Diabetes Care 2003; 26: 832–836.
5. Panero F, Novelli G, Zucco C, et al. Fasting plasma C-peptide and micro- and macrovascular complications in a large clinic-based cohort of type 1 diabetic patients. Diabetes Care 2009; 32: 301–305.
6. Pinckney A, Rigby MR, Keyes-Elstein L, et al. Correlation among hypoglycemia, glycemic variability, and C-Peptide preservation after alefacept therapy in patients with type 1 diabetes mellitus: analysis of data from the immune tolerance network T1DAL trial. Clin Ther. 2016; doi: 10.1016/j.clinthera.2016.04.032. [Epub ahead of print].
7. Steck AK, Wong R, Wagner B, et al. Effects of non-HLA gene polymorphisms on development of islet autoimmunity and type 1 diabetes in a population with high-risk HLA-DR,DQ genotypes. Diabetes 2012; 61: 753–758.
8. Winkler C, Krumsiek J, Lempainen J, et al. Ziegler AG. A strategy for combining minor genetic susceptibility genes to improve prediction of disease in type 1 diabetes. Genes Immun. 2012; 13: 549–555.
9. Bonifacio E, Krumsiek J, Winkler C, et al. Ziegler AG. A strategy to find gene combinations that identify children who progress rapidly to type 1 diabetes after islet autoantibody seroconversion. Acta Diabetol. 2014; 51: 403–411.
10. Mortensen HB, Swift PG, Holl RW, et al. Multinational study in children and adolescents with newly diagnosed type 1 diabetes: association of age, ketoacidosis, HLA status, and autoantibodies on residual beta-cell function and glycemic control 12 months after diagnosis. Pediatr Diabetes 2010; 11: 218–226.
11. Nielsen LB, Mortensen HB, Chiarelli F, et al. Impact of IDDM2 on disease pathogenesis and progression in children with newly diagnosed type 1 diabetes: reduced insulin antibody titres and preserved beta cell function. Diabetologia 2006; 49: 71–74.
12. Nielsen LB, Porksen S, Andersen ML, et al. The PTPN22 C1858T gene variant is associated with proinsulin in new-onset type 1 diabetes. BMC Med Genet. 2011; 12: 41.
13. Petrone A, Spoletini M, Zampetti S, et al. The PTPN22 1858T gene variant in type 1 diabetes is associated with reduced residual beta-cell function and worse metabolic control. Diabetes Care 2008; 31: 1214–1218.
14. Floyel T, Brorsson C, Nielsen LB, et al. CTSH regulates beta-cell function and disease progression in newly diagnosed type 1 diabetes patients. Proc Natl Acad Sci. U S A 2014; 111: 10305–10310.
15. Kaur S, Mirza AH, Brorsson CA, et al. The genetic and regulatory architecture of ERBB3-type 1 diabetes susceptibility locus. Mol Cell Endocrinol. 2016; 419: 83–91.
16. Brorsson CA, Nielsen LB, Andersen ML, et al. Hvidoere Study Group On Childhood Diabetes. Genetic risk score modelling for disease progression in new-onset type 1 diabetes patients: increased genetic load of islet-expressed and cytokine-regulated candidate genes predicts poorer glycemic control. J Diabetes Res. 2016; 2016: 9570424.
The authors
Caroline A Brorsson*1 PhD and Joachim Størling2 PhD
1Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
2Copenhagen Diabetes Research Center, Pediatric Department E, University Hospital Herlev, Herlev, Denmark
*Corresponding author
E-mail: caroline@cbs.dtu.dk
We developed an ultrasensitive bioassay using micropatterned zinc oxide nanorods (ZnO NRs) for the multiplexed detection and quantification of trace levels of cytokines implicated in acute kidney injury (AKI) directly from patient samples. The remarkable limits of detection of the novel ZnO NR-based assay are compared directly with conventional methods.
by Manpreet Singh, Anginelle Alabanza, Lorelis E. Gonzalez, Weiwei Wang, Prof. W. Brian Reeves, and Prof. Jong-in Hahm
Introduction
Cytokines and chemokines are important immunoregulatory molecules produced by many cells such as neutrophils, monocytes, macrophages and T-cells that can serve as biomarkers of inflammatory diseases to predict and track disease pathogenesis [1–4]. Various cytokines and chemokines like interleukins (ILs) and tumour necrosis factors (TNFs) can serve as valuable clinical biomarkers of acute kidney injury (AKI), a rapidly acquired disorder associated with high morbidity and mortality that is commonly seen in hospitalized patients. As elevated levels of cytokines may reveal the activation of signalling pathways leading to inflammation and disease progression, methods enabling prompt and sensitive detection and quantification of multiple cytokines/chemokines simultaneously in a clinical setting are highly sought. Although conventional techniques such as enzyme-linked immunosorbent assays (ELISAs) are widely available and reliable, their applications may not be suitable for the rapid, multiplexed detection of weakly expressed cytokines due to their detection limits (DLs) of typically greater than tens of pg/mL, long assay times of several hours, and extensive serial workflows for detecting multiple protein analytes.
As the typical levels of important AKI-implicated cytokines and chemokines in healthy populations can often be well below the customary DLs of standard cytokine assays, which are generally around tens of pg/mL, there is great clinical interest in reducing the lower limits of detection down to the fg/mL range. In particular, the increasing need for early diagnosis and treatment in AKI and other cytokine-implicated diseases has driven the development of innovative detection schemes capable of reaching even lower DLs than have conventionally been offered. In this context, we have shown that zinc oxide nanorods (ZnO NRs) permit enhanced detection of fluorescence signals emitted by fluorophore-coupled biomolecules in the forms of custom-prepared oligonucleotide constructs and highly purified single-composition proteins in simple media [5–8]. In our most recent work, which is highlighted here, we have developed and validated an ultrasensitive fluorescence-based bioassay using micropatterned ZnO NRs as a novel optical platform for the multiplexed detection and quantification of two urinary biomarkers of AKI, tumour necrosis factor-α (TNF-α) and interleukin-8 (IL-8), in samples of patients at risk for and diagnosed with AKI [9].
In addition to the biomedical relevance of TNF-α and IL-8 in the pathophysiology of AKI, the biomarkers are ideal model cytokines and chemokines for this study due to the differences in their typical concentration levels found in human urine. The baseline expression of IL-8 in healthy populations generally ranges from tens of pg/mL to ng/mL and can be ascertained using conventional approaches, whereas TNF-α levels are typically below the DLs of traditional cytokine detection platforms. Accordingly, we performed ELISA- and ZnO NR-based assays on the same set of patient samples to first examine whether the highly expressed IL-8 levels agree between both detection methods and further demonstrated the detection capability of the ZnO NRs to reveal the ultralow protein levels of TNF-α that cannot otherwise be ascertained via ELISA.
Results and discussion
Overall approach of ZnO NRs-based fluorescence assay
Using a micropatterned array of densely grown, vertically oriented ZnO NRs synthesized using a facile, low-cost, chemical vapour-phase method, we employed a sandwich assay scheme for the multiplexed detection of both AKI-relevant biomarkers. The sequential assay steps included incubation of the ZnO NR platform with primary TNF-α and IL-8 antibodies, bovine serum albumin for surface blocking, standards for both proteins for generating calibration curves or patient urine samples for determining biomarker levels in subject individuals, and fluorophore-conjugated secondary antibodies. In Figure 1(A), representative emission data from the multiplexed assay are qualitatively presented for Alexa 488-labelled TNF-α (left) and Alexa 546-labelled IL-8 (right). As a direct comparison, the panels in Figure 1(B) display scanning electron microscope (SEM) images of the ZnO NR array platform to show the morphology and dimensions of the individual square patches of ZnO NRs. The highly crystalline ZnO NRs do not exhibit any background fluorescence, as evidenced in the inset (F) of image 1(B), and, hence, all optical signals detected for protein quantification are derived only from the surface-adsorbed fluorophore-tagged biomolecules.
Reproducibility and calibration
In Figure 1(C), exemplar fluorescence intensity plots show the different amounts of TNF-α and IL-8 simultaneously detected from selected patient urine samples as obtained by averaging the optical signal from about 550 NR square patches on different areas of the same ZnO NR detection platform. The reproducibility of the fluorescence signal on the ZnO NRs-based platform is shown in Figure 1(D) in which the same patient sample was assayed five times on the same ZnO NR platform for intra-assay variability (**) as well as on three different ZnO NR plates for inter-assay variations (*) that may arise from assay or array-to-array differences. This scheme was conducted for two patient samples, and the coefficients of variation for the intra-assay (16.5% for TNF-α and 2.5% for IL-8) and inter-assay (12% for TNF-α and 2.8% for IL-8) results were found to be below the generally accepted value of 10–20%.
In order to quantitatively compare the levels of TNF-α and IL-8 obtained via the ELISA- and ZnO NRs-based assays, calibration curves were generated using standard solutions of each cytokine. The DLs of the ELISA-based method, defined as 2 standard deviations above the mean of 20 zero concentration replicates, were determined as 5.5 and 7.5 pg/mL for TNF-α and IL-8, respectively. On the other hand, the DLs of the ZnO NR platform, assessed using the upper boundary of blank samples with a 95% accuracy goal, were found to be 4.2 and 5.5 fg/mL for TNF-α and IL-8, respectively. The unparalleled sensitivity down to the several fg/mL range enabled by the ZnO NR platform can reveal the levels of weakly expressed, disease-implicated cytokines such as TNF-α to promote early clinical diagnostics.
IL-8 testing and statistical analysis
Following calibration, the same patient samples were assayed on both the ELISA and ZnO NR platforms for quantitative comparison between both assays. When comparing the highly expressed IL-8 levels in the urine of 38 patients that ranged between several tens of pg/mL to a few ng/mL, the ZnO NRs-based assay had strong statistical agreement with the ELISA-based results allowing for direct validation of the novel bioassay. In Figure 2(A), a correlative plot displays the IL-8 readings from the same patients determined by the ELISA and ZnO NR assays on the x and y axes, respectively. The linear fit of the data points, shown in the dashed red line, lies very close to the superimposed line of y = x, shown in black, indicating excellent agreement between the two assay methods. In Figure 2(B), a histogram distribution chart reporting the differences in IL-8 readings between the two methods shows the majority of IL-8 readings from both assays fell within the range of ±2.5 pg/mL of each other. The IL-8 levels were further evaluated using the Bland-Altman analysis in Figure 2(C & D) in which the differences between the ELISA and ZnO NR readings for each patient were plotted against the mean concentration values. As shown, the data analysed over a large range of concentrations centred near the black lines, which represent the case of equivalent IL-8 readings obtained from the two different assays. The results of these comparative analyses validate the ZnO NR platform as a reliable technique to accurately quantify urinary biomarker proteins directly from patient samples.
TNF-α testing
To substantiate the applicability of the ZnO NR platforms in ultrasensitive cytokine detection using the weakly expressed biomarker of TNF-α, the protein levels for 46 patients were determined using both assay platforms. As seen in Figure 3(A), many of the patient samples exhibited values too close or below the DL of the ELISA assay (5.5 pg/mL) and are marked accordingly as grey blocks in the ELISA row. By contrast, the TNF-α values of all the patients were successfully quantified on the ZnO NR platform, well below several tens of pg/mL and into the low fg/mLrange. For the ZnO NR row in Figure 3(A), those samples that could not be measured via ELISA are shown with a magnifier sign, and their TNF-α concentrations, as determined by the ZnO NRs-based assay, are then shown in Figure 3(B & C) on two different scales for clarity. As demonstrated, the optical signal enhancement provided by the ZnO NR array platform enables the ultrasensitive detection of trace levels of proteins directly from patient samples.
Advantages of the ZnO NR-based approach and future outlook
Within the realm of biodetection, the ZnO NR-based approach can provide many direct advantages including facile platform fabrication, desirable optical properties, biocompatibility, and promising multiplexed/high-throughput integration capacities. The ZnO NR arrays are easily fabricated using a gas-phase method through well-established synthesis procedures and can be used directly after growth without any post-synthetic modifications. Further, the highly crystalline NRs exhibit many desirable optical properties including no intrinsic fluorescence (i.e. absence of autofluorescence) as well as enhancement of the optical intensity and photostability of nearby signal emitters. Since the ZnO NRs do not display any photoluminescence in the visible and near-infrared range, they do not interfere with the spectroscopic profiles of fluorophores commonly used in biology and biomedical detection. At the same time, the reduced dimensions and high shape anisotropy of the ZnO NRs enable optical enhancement and prolonged stability of the signal from fluorophore-tagged biomolecules adsorbed on their surface allowing for the ultrasensitive detection of trace levels of bioconstituents.
In addition to the demonstrated sensitivity permitted by the ZnO NR-based platform, the cytokine bioassay also has the direct advantages of rapid analysis, minimal volume requirements, and reusability. The multiplexed detection was achieved with 90 min of total assay time and only 60 μL of total bioreagent/sample volume using commonly employed fluorescence microscopy instrumentation. Further, the highly biocompatible ZnO NRs platform was found to withstand at least 25 repeated assays in complex biological and chemical reaction environments that include urine samples.
As modern automation strategies in high-throughput screening have seen great advancements in the sophistication of robotic sample delivery strategies and the detection of many analytes simultaneously via multichannel optical sensors, the ZnO NR-based platform may be able to provide much-sought detection sensitivity when integrated into these breakthrough technologies. In the microarray, each square patch of densely grown ZnO NRs with a typical dimension of 3~50 μm in side length can be configured to serve as a discrete detection element for different patient samples when coupled with appropriate sample delivery and multiplexed optical sensing/readout platforms. The demonstrated detection capabilities combined with this integration potential suggests that the ZnO NR-based approach serves as more than just an alternative or tandem detection platform to existing methods, but rather provides an advanced approach which allows the much needed, ultrasensitive detection of biomarker proteins in samples that exhibit concentration levels much lower than those which standard techniques can ascertain.
Conclusion
We successfully demonstrated a ZnO NRs-based fluorescence bioassay for the rapid, ultrasensitive, quantitative and multiplexed detection of AKI-related biomarkers in patient urine samples. We first statistically validated the ZnO NR-based approach against a conventional ELISA-based method by comparing the measurements of highly expressed levels of IL-8 that were above the DLs of ELISA. We further revealed the full detection capabilities of the ZnO NRs platform by quantifying ultratrace amounts of a weakly expressed cytokine, TNF-α, whose levels in urine are often below the DLs of conventional cytokine assays. The unparalleled detection sensitivity and other discussed advantages of the ZnO NR-based bioassay can be readily extended to advance other optical-sensing applications in biological research and clinical diagnostics.
Acknowledgement
This article is a summary of the work first presented in Singh M, Alabanza A, Gonzalez LE, Wang W, Reeves WB, Hahm J. Ultratrace level determination and quantitative analysis of kidney injury biomarkers in patient samples attained by zinc oxide nanorods. Nanoscale 2016; 8: 4613–4622 [9].
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The authors
Manpreet Singh1 BS, Anginelle Alabanza1 BS, Lorelis E. Gonzalez1 BS, Weiwei Wang2 BS, W. Brian Reeves3 MD, and Jong-in Hahm*1 PhD
1Department of Chemistry, Georgetown University, Washington, DC 20057, USA
2Division of Nephrology, The Penn State College of Medicine, Milton S. Hershey Medical Center, Hershey, Pennsylvania 17033, USA
3Department of Medicine, University of Texas Health Sciences Center at San Antonio, San Antonio, TX 78229, USA
*Corresponding author
E-mail: jh583@georgetown.edu
November 2025
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