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C260 Kawano Fig p23

Diagnosis and other aspects of uromodulin kidney disease

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Uromodulin kidney disease is a rare autosomal dominant kidney disease, characterized by hyperuricemia, gout and progressive kidney failure. Affected patients typically need renal replacement therapy in middle age. A considerable number of patients may reach end-stage kidney disease without a correct diagnosis, making improvements in diagnostic methods of vital importance.

by Dr Tamehito Onoe and Dr Mitsuhiro Kawano

Introduction
Uromodulin (UMOD), also known as Tamm–Horsfall protein, is the most abundant protein in healthy human urine. UMOD protein is a kidney-specific protein which is exclusively produced at the epithelial cells lining the thick ascending limb (TAL) of Henle’s loop.

The roles of urinary UMOD protein are assumed to be to protect against urinary tract infection, prevent urolithiasis formation and ensure water impermeability to create the countercurrent gradient. However, the accurate function and significance of UMOD protein are not yet fully elucidated [1].

Uromodulin kidney disease
Uromodulin kidney disease (UKD) is an inherited disease caused by UMOD gene mutations. So far, more than 100 mutations of the UMOD gene have been reported from all over the world [2]. Familial juvenile hyperuricemic nephropathy (FJHN), medullary cystic kidney disease type2 (MCKD2) and glomerulocystic kidney disease (GCKD), which are considered to be different diseases, have been proved to be caused by UMOD gene mutations [3]. Subsequently, because multiple names for one condition would be confusing and misleading, and also cysts are not pathognomonic for this disease, a new term, ‘Autosomal dominant tubulointerstitial kidney disease’ (ADTKD) was proposed in 2015 [4]. Mutations of renin (REN), hepatocyte nuclear factor 1β (HNF1β), and mucin-1 (MUC1) are also responsible for ADTKD besides UMOD. They all share common clinical characteristics, which are progressive kidney failure, tubulointerstitial nephritis and inheritance compatible with autosomal dominant trait with only trivial clinical differences. When UMOD mutation is identified in an ADTKD patient, the official diagnostic term is ADTKD-UMOD. However, UKD is also used to facilitate communication with patients, and so this term is used in the present article.

Patients with UKD have urinary concentration defect, hyperuricemia and gout from a young age. Their kidney function gradually deteriorates, and reaches end-stage kidney disease (ESKD) from 25 to 75 years of age. Their kidneys are usually of normal size or small and there are sometimes cysts, although the frequency of cysts does not differ from that of ‘non-cystic’ kidney diseases. Their urine tests usually show no or only very mild proteinuria or hematuria. The most prominent characteristic of UKD is a marked abundance of chronic kidney disease (CKD) patients in their pedigree, compatible with an autosomal dominant trait. Our group detected a novel A247P UMOD mutation in a UKD family (Fig. 1), many of whose members have hyperuricemia, CKD and ESKD, and are on hemodialysis (HD) therapy [5].

UKD is reported to be a rare disease, with a frequency of about 1.5 cases per million population. However, because hyperuricemia is a frequent complication in all CKD patients, when their family history is absent or unknown, it is difficult to suspect UKD, and so the frequency of UKD may be underestimated. This means that a certain proportion of UKD patients may reach ESKD without a correct diagnosis.

Diagnosis of uromodulin kidney disease
Clinically UKD should be suspected when a CKD patient has an abundant family history compatible with autosomal dominant trait, hyperuricemia, gout and bland urine findings. The final diagnosis of UKD is made by genetic test, which is, however, not commercially available, and only a limited number of laboratories are capable of performing it. So easier laboratory tests supportive of genetic tests would be helpful for the diagnosis of UKD and are awaited.

The renal histology of UKD patients shows nonspecific interstitial fibrosis, tubular atrophy and normal glomeruli. So it is difficult to make a diagnosis of UKD by ordinary histological methods. Moreover, not many UKD patients seem to undergo renal biopsy because their urine sediment shows no or only slight abnormalities and so clinicians may hesitate to undertake this invasive test. However, we believe that renal pathological examination is very informative not only for ruling out other kidney diseases but also for the diagnosis of UKD. UMOD proteins synthesized from mutated UMOD gene have protein folding disability and cannot escape from the endoplasmic reticulum (ER) of the epithelial cells. Immunostaining using anti-UMOD antibody in kidney sections of UKD patients shows massive UMOD accumulation in their epithelial cells (Fig. 2). Because of the question of whether there are any UKD patients among those who received kidney biopsy and were diagnosed as having nephrosclerosis or interstitial nephritis, we performed the following investigation.

In a 3787-sample kidney biopsy database of Kanazawa University, patients meeting all of the following criteria were selected for UMOD immunostaining. (1) Renal insufficiency (serum creatinine >1.0 mg/dL) below 50 years of age; (2) hyperuricemia: serum uric acid higher than 7mg/dl or under treatment for hyperuricemia; (3) no or only very mild abnormalities in urinalysis; and (4) no other apparent renal disease present clinically or histopathologically. Finally, 15 patients were selected and abnormal UMOD accumulations were detected in three independent patients by UMOD immunostaining. A247P UMOD gene mutations were detected in the proband of the family in Figure 1 and the other independent patient, indicating that they may share the same ancestor. The other patient had no family history of CKD. These results show that there may be more UKD patients than expected before, and also indicate that when kidney biopsy shows only nonspecific interstitial fibrosis in patients with renal insufficiency, UMOD immunostaining may be considered to detect UKD with or without a family history of CKD, especially with hyperuricemia and bland urinary findings.

Most of the synthesized UMOD protein is carried to the apical membrane of epithelial cells and excreted in the urine. However, a low but considerable amount of UMOD protein goes to the basolateral membrane and is secreted into the serum [6]. Serum UMOD protein concentrations are reported to be 45–490 ng/mL, while urine UMOD protein concentrations are 1000–80 000 ng/mL. The functions and significance of serum UMOD proteins are unknown.

Some results of animal experiments indicate that UMOD protein has a renoprotective effect against various types of injury. A renal ischemia-reperfusion experiment in UMOD knockout mice showed significantly worse results than in wild-type animals [7]. It is well known that urinary UMOD concentrations in UKD patients are decreased. The authors recently reported that serum UMOD protein concentrations are also significantly decreased in UKD patients besides urinary UMOD (Fig. 3). Serum and urinary UMOD concentrations decline in parallel with the decrease of estimated glomerular filtration rate (eGFR) due to the diminishment of UMOD producing epithelial cells in CKD patients. In UKD patients, the serum and urine UMOD concentrations were significantly lower compared with CKD patients beyond their eGFRs. Decreased serum and urinary UMOD concentrations may be good clues to suspect and diagnose UKD; however, verification in more UKD patients with various mutations will be indispensable.

Conclusions
So far no treatment has been devised that slows the rate of renal functional deterioration of UKD. At present, management for UKD patients is not different from that for other CKD patients. Anti-hyperuricemia drugs or anti-hypertensive therapy is used when necessary and the appropriate renal replacement therapy or renal transplantation should be considered when ESKD is reached. To clarify the pathogenesis and achieve effective treatment for UKD, establishment of more efficient diagnostic methods for UKD is expected. UMOD immunostaining for renal sections and measurement of serum and urinary UMOD concentrations are considered to be good modalities for the diagnosis of UKD. It is expected that through these tests, more UKD patients will be diagnosed at earlier stages and will be able to benefit from starting appropriate therapy before ESKD.

Recently some particular SNPs of UMOD promoter areas have been proved to be associated with hypertension or renal insufficiency from the genome-wide association study [8]. UMOD will likely attract greater attention as a renal-prognostic marker for not only UKD patients but also the general population.

References
1. Lhotta K, Piret SE, Kramar R, Thakker RV, Sunder-Plassmann G, Kotanko P. Epidemiology of uromodulin-associated kidney disease – results from a nation-wide survey. Nephron Extra 2012; 2: 147–158.
2. Scolari F, Izzi C, Ghiggeri GM. Uromodulin: from monogenic to multifactorial diseases. Nephrol Dial Transplant. 2015; 30: 1250–1256.
3. Hart TC, Gorry MC, Hart PS, Woodard AS, Shihabi Z, Sandhu J, Shirts B, Xu L, Zhu H, Barmada MM, Bleyer AJ. Mutations of the UMOD gene are responsible for medullary cystic kidney disease 2 and familial juvenile hyperuricaemic nephropathy. J Med Genet. 2002; 39: 882–892.
4. Eckardt KU, Alper SL, Antignac C, Bleyer AJ, Chauveau D, Dahan K, Deltas C, Hosking A, Kmoch S, Rampoldi L, Wiesener M, Wolf MT, Devuyst O. Autosomal dominant tubulointerstitial kidney disease: diagnosis, classification, and management-A KDIGO consensus report. Kidney Int. 2015; 88(4): 676–683.
5. Onoe T, Yamada K, Mizushima I, Ito K, Kawakami T, Daimon S, Muramoto H, Konoshita T, Yamagishi M, Kawano M. Hints to the diagnosis of uromodulin kidney disease. Clin Kidney J. 2016; 9: 69–75.
6. Bachmann S, Koeppen-Hagemann I, Kriz W. Ultrastructural localization of Tamm-Horsfall glycoprotein (THP) in rat kidney as revealed by protein A-gold immunocytochemistry. Histochemistry 1985; 83: 531–538.
7. El-Achkar TM, Wu XR, Rauchman M, McCracken R, Kiefer S, Dagher PC. Tamm-Horsfall protein protects the kidney from ischemic injury by decreasing inflammation and altering TLR4 expression. Am J Physiol Renal Physiol. 2008; 295: F534–544.
8. Trudu M, Janas S, Lanzani C, Debaix H, Schaeffer C, Ikehata M, Citterio L, Demaretz S, Trevisani F, Ristagno G, Glaudemans B, Laghmani K, Dell’Antonio G, Loffing J, Rastaldi MP, Manunta P, Devuyst O, Rampoldi L. Common noncoding UMOD gene variants induce salt-sensitive hypertension and kidney damage by increasing uromodulin expression. Nat Med. 2013; 19: 1655–1660.

The authors
Tamehito Onoe MD, PhD and Mitsuhiro Kawano* MD, PhD
Division of Rheumatology,
Department of Internal Medicine,
Kanazawa University Hospital,
Kanazawa, 920-8641,
Japan

*Corresponding author
E-mail: sk33166@gmail.com

C257 Euroimmun Fig1

Novel nephrological markers: anti-PLA2R, anti-THSD7A and uromodulin

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Autoantibody diagnostics have in recent years transformed the diagnosis of the rare kidney disease primary membranous nephropathy (MN). The identification of the target antigens M-type phospholipase A2 receptor (PLA2R) and thrombospondin type 1-domain-containing 7A (THSD7A) paved the way for the development of specific immunological assays to detect the corresponding antibodies. Determination of both anti-PLA2R and anti-THSD7A antibodies allows serological diagnosis in 75% to 80% of cases of primary MN. Anti-PLA2R tests are, moreover, an indispensable tool for patient monitoring. A further new biomarker, uromodulin, acts as an indicator of impaired renal function, especially in chronic kidney disease, supplementing established markers such as creatine and cystatin C.

Membranous nephropathy
Membranous nephropathy is an organ-specific autoimmune disease and a major cause of nephrotic syndrome in adults. The disease is characterized by formation of immune complexes in the glomerular basement membrane, resulting in complement-mediated proteinuria and progressive loss of kidney function. 70-80% of cases are of the primary or idiopathic form. The remaining 20-30% of cases are secondary, arising from underlying causes such as malignancy, infection, drug intoxication or another autoimmune disease such as systemic lupus erythematosus. Diagnostic differentiation of primary and secondary forms is crucial due to different treatment regimes. Primary MN is treated with immunosuppressants, while therapy for the secondary form is targeted at the underlying disease. Treatment decisions for primary MN are further complicated by the extreme variability in clinical outcome. Patients can experience spontaneous remission or persistent proteinuria without renal failure, or progress to end-stage renal disease.

Anti-PLA2R antibodies
Autoantibodies against PLA2R are a highly specific marker for primary MN. They occur in around 70% to 75% of patients at time of diagnosis, while they are only very rarely found in patients with secondary MN or in healthy individuals. Their titer, moreover reflects the disease activity and severity. The target antigen, which was identified in 2009, is a type 1 transmembrane glycoprotein which is expressed on the surface of podocytes.

Following the discovery of the target antigen, standardized assays for the determination of anti-PLA2R antibodies in a routine setting were rapidly developed. The recombinant-cell indirect immunofluorescence test (RC-IIFT, Figure 1) utilizes transfected cells expressing full-length PLA2R on the cell surface as the antigenic substrate. The RC-IIFT is a reliable screening test for qualitative detection of anti-PLA2R autoantibodies. Using this assay, anti-PLA2R antibodies were detected with maximum specificity (100%) and a sensitivity of 77% in a cohort of 275 biopsy-proven primary MN patients. In the Anti-PLA2R ELISA, purified recombinant receptor is used as a solid-phase coating of microtitre plates. This assay provides accurate quantification of autoantibody concentrations and is particularly useful for disease monitoring. In a large cohort of clinically well characterized patients, the assay revealed very high sensitivity with respect to the RC-IIFT (96.5%) at a set specificity of 99.9%. The quantitative results of ELISA and RC-IIFT show a good correlation.

Anti-PLA2R is now an established parameter for diagnosing primary MN, differentiating it from secondary MN, assessing the disease status and monitoring responses to therapy [1, 2]. The antibody titre reflects the immunological as opposed to the clinical disease activity, and a change in the antibody titer, either spontaneous or treatment-induced, precedes the corresponding change in proteinuria by weeks or months (Figure 2) [3]. Thus, anti-PLA2R measurements provide a much earlier indicator than proteinuria of patient improvement or deterioration, helping to guide therapy decisions. Complete remission is always preceded by complete antibody depletion.

Anti-PLA2R titres also allow predictions regarding clinical outcome. High antibody titres are associated with a lower chance of spontaneous remission, a longer therapy period to achieve remission, and progression to kidney failure (Table 1) [4]. A low anti-PLA2R antibody titre at baseline, on the other hand, is the most pronounced independent predictor of spontaneous remission [5]. Patients with low anti-PLA2R titres are less likely to require immunosuppressive therapy than those with high titres. Overall, anti-PLA2R assessment is recommended every two months before starting immunosuppressive therapy to avoid unnecessary treatment in patients entering remission, and every month for the first six months of immunosuppression [2].

Anti-PLA2R analysis is also useful for predicting primary MN recurrence after kidney transplantation. Up to 40% of patients relapse after transplantation, and anti-PLA2R positivity is associated with a higher risk of recurrence. In a recent study, pre-transplant anti-PLA2R determination demonstrated a positive predictive value of 100% and a negative predictive value of 91% for a diagnosis of recurrent MN [6]. Further, if anti-PLA2R antibodies are persistently found during the first six months after transplantation, the risk of relapse is particularly high. Antibody determination may therefore be helpful for assessing the necessary and intensity of immunosuppressive therapy following transplantation.

Anti-THSD7A antibodies
Autoantibodies against THSD7A have been recently identified as a further marker in primary MN [7]. Similarly to PLA2R, THSD7A is an N-glycosylated, high-molecular-mass protein expressed on the podocyte membrane. Antibodies against THSD7A occur in around 2.5% to 5% of patients with idiopathic MN. Significantly, they are found predominantly in patients who are negative for anti-PLA2R, suggesting a distinct disease subgroup. Nevertheless, some rare cases with dual positivity for anti-PLA2R and anti-THSD7A have recently been described [8]. No reactivity to THSD7A has been observed in healthy controls or patients with other proteinuric or renal autoimmune diseases.

Anti-THSD7A serves as an additional, complementary marker in primary MN, reducing the diagnostic gap of anti-PLA2R analysis. Moreover, like anti-PLA2R, anti-THSD7A antibody levels also appear to be associated with disease activity. Further studies are currently underway to investigate this link.

Circulating anti-THSD7A antibodies can be determined by RC-IIFT using transfected cells expressing recombinant antigen (Figure 3). Combined testing for anti-PLA2R and anti-THSD7A provides a comprehensive screening for primary MN.

Uromodulin
Uromodulin, also known as Tamm-Horsfall protein, is a glycoprotein which is synthesized exclusively in the kidneys in the ascending limb of the loop of Henle, and subsequently secreted. When renal function is impaired, the uromodulin concentration in the serum or plasma decreases [9]. The concentration exhibits a linear correlation to the estimated glomerular filtration rate (eGFR) (Figure 4). Thus, uromodulin shows inverse kinetics to conventional markers like creatine and cystatin C, which increase with declining kidney function. Moreover, uromodulin concentrations change already in the early stages of chronic kidney disease, when there are few symptoms. Thus, uromodulin measurements enable detection of renal insufficiency in the creatine-blind area in the initial stages of kidney disease. Measurement of uromodulin is also suitable for monitoring kidney vitality during therapy and as a predictive marker after kidney transplantation.

Uromodulin can be measured in the serum or plasma by ELISA based on microplates coated with anti-uromodulin antibodies. The patient uromodulin concentrations are established using a simple cut-off-based interpretation, with a normal value being above 100 ng/ml. External factors such as body weight, nutrition or muscle mass do not need to be factored into the results by additional calculations, as is the case with classic markers. Further, since the uromodulin concentration is measured in serum or plasma, the laborious and error-prone collection of 24-hour urine is not required. This makes it a fast, easy and sensitive supplementary test for the early identification of nephropathies and loss in renal function.

Perspectives
Anti-PLA2R and anti-THSD7A assays are now a mainstay for the diagnosis of primary MN. Due to the high specificity, anti-PLA2R detection may even enable biopsy to be postponed or omitted in elderly patients, persons with poor clinical condition, or patients with life-threatening complications of nephrotic syndrome such as lung emboli. Nevertheless, a proportion of primary MN patients (around 20%) shows negative results for both anti-PLA2R and anti-THSD7A antibodies. This may reflect the disease activity at time of blood sampling (e.g. spontaneous remission) or a misclassification of patients who actually have secondary MN. It is also supposed that some primary MN patients react to other, as yet unidentified antigens. Anti-PLA2R measurements are also playing an increasingly central role in therapy decisions and prognosis, as the relationship between the anti-PLA2R titre and clinical outcome becomes better understood. Current research is directed at further elucidating the complex pathogenesis of primary MN and applying this knowledge to improve therapeutic care.

References
1. Mastroianni-Kirsztajn et al. Frontiers in Immunol. 2015: 6:221
2. Ronco et al. Lancet 2016: 385 (9981): 1983-92
3. Beck et al. Kidney Int. 2010: 77: 765-70
4. Hofstra et al. J. Am. Soc. Nephrol. 2012: 23(10): 1735-43
5. Timmermans et al. Am. J. Nephrol. 2015: 42(1): 70-7
6. Gupta et al. Clin. Transplant. 2016: 30: 461-9
7. Tomas et al. N. Engl. J. Med. 2014: 371(24): 2277-87
8. Larsen et al. Modern Pathol. 2016: 29: 421-6
9. Steubl et al. Medicine 2016: 95(10): e3011

The author
Jacqueline Gosink, PhD
EUROIMMUN AG
Seekamp 31,
23560 Luebeck, Germany
E-mail:j.gosink@euroimmun.de

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C274 Liew Figure 1

Chromosomal rearrangement detection in lymphomas: digital FISH analysis

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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

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p22 04

Using genetic risk factors for predicting type 1 diabetes progression and prognosis

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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

p20 03

Zinc oxide nanorod-based acute kidney injury biomarker detection technology and potential clinical implications

, /in /by

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].

References
1. Feldmann MJ. Many cytokines are very useful therapeutic targets in disease. Clin Invest. 2008; 118: 3533–3536.
2. Fichorova RN, Richardson-Harman N, Alfano M, et al.  Biological and technical variables affecting immunoassay recovery of cytokines from human serum and simulated vaginal fluid: a multicenter study. Anal Chem. 2008; 80: 4741–4751.
3. Borish LC, Steinke JWJ. Cytokines and chemokines. Allergy Clin Immunol. 2003; 111: S460–S475.
4. Nathan C, Sporn M. Cytokines in context. J Cell Biol. 1991; 113: 981–986.
5. Adalsteinsson V, Parajuli O, Kepics S, et al. Ultrasensitive detection of cytokines enabled by nanoscale ZnO arrays. Anal Chem. 2008; 80: 6594–6601.
6. Dorfman A, Kumar N, Hahm J. Nanoscale ZnO-enhanced fluorescence detection of protein interactions. Adv. Mater. 2006; 18: 2685–2690.
7. Singh M, Song S, Hahm J. Unique temporal and spatial biomolecular emission profile on individual zinc oxide nanorods. Nanoscale 2014; 6: 308–315.
8. Singh M, Jiang R, Coia H, et al. Insight into factors affecting the presence, degree, and temporal stability of fluorescence intensification on ZnO nanorod ends. Nanoscale 2015; 7: 1424–1436.
9. Singh M, Alabanza A, Gonzalez LE, et al. Ultratrace level determination and quantitative analysis of kidney injury biomarkers in patient samples attained by zinc oxide nanorods. Nanoscale 2016; 8: 4613–4622.

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

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