Establishment of Optimal Housekeeping Genes for Urinary Extracellular Vesicle Biomarker Development: A Step Towards Non-Invasive Diagnostics

doi:10.21203/rs.3.rs-769529/v1

Background. Urinary extracellular vesicles (UEVs) hold RNAs and can find their application in multiplex biomarker development. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the most commonly used method for gene expression studies. However, there are no reported optimal housekeeping genes available to normalize qPCR data for the UEVs RNA pool.

Methods. UEVs were precipitated by Polyethylene glycol (P.E.G. Mn6000) from the urine of 40 human individuals and characterized for their molecular, biophysical, and biochemical purity and integrity. In addition, the expression of five commonly used housekeeping genes B2M, RPL13A, PPIA, HMBS, and GAPDH, were quantified by qPCR in the UEV RNA pool and analyzed through algorithms of NormFinder, geNorm, BestKeeper, and Delta Ct integrated into ReFinder.

Results: 12% PEG precipitation yielded round and cup-shaped UEVs. The size and distribution profile of UEVs were around 30 – 100nm through electron microscopy, NTA, and DLS. Acetylcholine esterase and Dipeptidyl peptidase-IV activity were used to arrive at their functional purity. B2M and RPL13A genes were identified as stable genes with a mean stability score of 1.5(geNorm) and below 1 (NormFinder) through ReFinder, which yield comprehensive ranking analysis

Conclusions: B2M and RPL13A are optimal reference genes and can be used for UEVs based gene expression studies.

Molecular Biology

Extracellular vesicles

housekeeping genes

normalization

Polyethylene glycol

Real-time PCR

Urinary biomarkers

Currently, the diagnosis of renal complications is based on insensitive and non-specific biomarkers. Additionally, renal biopsy is the gold standard for establishing a confirmatory pathology which is expensive, invasive, and often inconvenient [1]. Therefore, the development of new biomarker strategies avoiding the above complexities is the need of the hour. RNA profiling is one of the common biomarker strategies for ascertaining renal pathology. But, mRNA profiling from neat urine is challenging as free-floating RNA is often degraded by urinary ribonucleases[2], resulting in a biased and inconsistent interpretation. Recent studies have identified microvesicles as an essential source of RNA species, including mRNA, miRNA, and other small RNA. The extracellular vesicle (EV) membrane protects vesicular RNA from degradation by urinary ribonucleases[2]. In this context, UEVs are especially gaining importance as ideal diagnostic tools, forming the basis for liquid biopsies.

Urinary exosomes are an essential category of extracellular vesicles secreted by the kidney and urinary tract and are in size range of 30–150nm [3]. They maintain homeostasis and mediate intra and intercellular communication between cells by shuttling proteins, RNAs, lipids, and other biomolecules [4]. Their cargo represents the cells 'physiological state' in real-time and is thus regarded as ideal non-invasive diagnostics which are amenable to longitudinal sampling. Precipitation, size exclusion chromatography, filtration, and differential centrifugation are conventional UEVs isolation methods [5]. Although ultracentrifugation (UC) is the ideal laboratory level isolation method for isolating EVs, challenges include lower vesicular yields, variable workflows, and the need for a large amount of starting material forestalled UC to be adopted in a clinical setting. Therefore, a simple, cost-effective alternative method of UEV isolation is warranted. Exosome precipitation is the standard protocol adopted by many commercial exosome isolation kits, including Exo Quick (SBI), Total Exosome isolation (Invitrogen), etc. But handling large volumes of urine for longitudinal research studies through the above kits would be expensive. In the current study, we adapted the Polyethylene Glycol (PEG)-based isolation method, widely used in viral-particle isolation from various biofluids [6]. We are uniformly using the term "UEVs" throughout our description for describing the vesicles isolated from urine.

Reverse transcription-quantitative polymerase chain reaction (qPCR) is the most common and standard technique used in gene-expression studies. QPCR's primary requisite is the normalization of the data with housekeeping genes or reference genes, which depends on endogenous expression of genes that are constitutively active, endogenously abundant, and stable across different physiological and pathological conditions. Unfortunately, optimal endogenous reference genes are not reported in the UEVs RNA population [7]. As the field of UEVs diagnostics is rapidly developing, and with the approval of a urinary exosome-based prostate cancer diagnostic [8], the need for identifying ideal housekeeping genes in UEVs has become eminent.

B2M, L13A, GAPDH, PPIA, and HMBS are used as reference genes because of their role in cellular process, cell structure integrity, and primary metabolism. NormFinder, geNorm, BestKeeper, and Delta Ct are the standard algorithms used in transcript normalization and reference gene evaluation. NormFinder identifies optimal normalization genes from a set of candidates [9]. geNorm measures the pairwise standard deviation from all genes in the study and eliminates the genes based on their stability [10]. BestKeeper generates a stability index based on their quantification cycle (C_q) values and amplification efficiencies, followed by a pairwise correlation to rank each candidate in the list[11]. Finally, the Delta Ct method assigns stability values based on C_q standard deviation differences [12]. RefFinder (https://www.heartcure.com.au/reffinder/); a web-based tool evaluates and screens reference genes from the experimental data sets, integrating the weighted average from the above four algorithms.

This study establishes an optimal UEVs isolation protocol and identifies the most stable housekeeping genes among the five most conventional reference genes used commonly among renal gene expression studies.

Urine collection and processing

Urine samples from forty healthy individuals (mean age 47.2±13.4 years) were collected in the protease inhibitor cocktail and processed for UEV isolation by modified salt precipitation method and represented in Fig 1. In addition, a complete Urine Examination was carried by Multistix 10 SG urinalysis strips in the CLINITEK Advantus instrument (both from Siemens, Munich, Germany), as shown in Table 1.

Urinary Extracellular Vesicle Characterisation

The isolated UEVs were subjected to biophysical and biochemical characterization to ascertain the quality of isolated vesicles.

Morphological characterization

Morphological characterization of the UEVs was performed in detail using a Transmission electron microscope (TEM, JEM-2100, JEOL Ltd. Tokyo, Japan), Nanoparticle Tracking Analysis (NTA, NanoSight LM10 instrument, NanoSight, Amesbury, UK), and Differential Light Scattering instrument (DLS, Nicomp Z3000, Entegris, MA, USA). For TEM, UEV samples were initially fixed with 1% glutaraldehyde (5 min) on the 400 mesh copper grids (FCF400-Cu, Electron Microscopy Sciences, Hatfield, PA), then washed twice with water and further stained with 2% uranyl acetate followed by air-drying at room temperature and capturing the images. For further confirmation of the size and relative concentrations of UEVs, they were subjected to laser light-scattering at 488 nm using NTA. The hydrodynamic diameter of a homogenous suspension of UEVs was measured using DLS.

Biochemical Characterisation

The total protein and lipid contents and the protein to lipid ratio of the UEVs were determined using Bicinchoninic (BCA) protein assay kit (G-Biosciences) and Phospho Vanillin Assay, respectively (35). Further, SDS-PAGE was used to resolve the UEV protein profile. 10μg of UEV was resolved on a 10% SDS PAGE (1.5 h at 120 V) followed by silver nitrate staining (2% AgNO3).

Enzyme assays

As additional criteria, we used acetylcholinesterase (AChE) assay to assess the presence of acetylcholinesterase (AChE), a specific marker for exosomes [13]. Dipeptidyl peptidase-IV (DPPIV) activity is measured through a DPPIV activity assay. [14].

Antibody Array

Exo-Check exosome antibody array (SBI, Systems Biosciences, USA) was performed according to the manufacturer's instructions. The array contains 8 antibodies for known exosome markers, including (CD63, CD81, ALIX, FLOT1, ICAM1, EpCam, AnXA5, and TSG101) and 4 controls, including two positive controls, blank and gm130 cis-Golgi marker, which monitors for any cellular contamination. Membrane array was developed on the chemiluminescence imaging system (ChemiDoc, BioRad, USA).

RNA isolation, quantification and Reverse Transcription

Total RNA was isolated using TRIzol^TM LS Reagent (Invitrogen, California, USA) following the manufacturer's instructions. The RNA content was determined using Qubit^TM RNA HS Assay Kit (Invitrogen). Additionally, the UEV RNA quality and quantity were analyzed in Agilent Bioanalyzer 2.1 instrument using RNA Pico kit (Agilent Technologies, California, USA). Further, Exosomal RNA was reverse transcribed with a High-capacity cDNA Reverse Transcription kit (Invitrogen) using exosomal RNA, and cDNA is prepared according to the manufacturer's instruction and stored appropriately for performing PCR.

Endogenous gene expression in Urinary EV

We selected five reference genes in the present study, namely GAPDH, B2M, RPL13A, PPIA, and HMBS, to be tested for normalization of quantitative real-time PCR in UEV samples from healthy individuals. All primer sequences were designed using NCBI primer designing software, and specificity was confirmed by primer BLAST at the NCBI database. The primer sequences were custom synthesized by (Bio serve, India), Table 2. cDNA from the above step was pre-amplified using Sapphire Amp fast PCR master mix (Takara Bio Inc. Shiga Prefecture, Japan). Pre-amplification with pooled primers was carried out for 20 cycles at 95˚C for 5 min, 95˚C for 1 min, 60˚C for 30 sec, 72˚C for 1 min, and 72˚C for 5 min and hold 4˚C. A primer efficiency test was conducted for the candidate genes by serial dilution of the pre-amplified product[15]. The pre-amplified transcript was then used as a template for PCR and RT PCR analysis. The pre-amplified cDNA was diluted 1:5 with nuclease-free water, and 1μl of the diluted product was used as a template for semi-quantitative as well as quantitative PCR. Briefly, a 10μl reaction was performed in Applied Biosystem 7500 Real-Time System using 5μl TB Green Premix Ex Taq (Takara Bio Inc.), Real-time PCR was conducted at 95˚C for 30 sec, followed by 35 cycles of 5s at 95˚C and 30s at 60˚C. All samples were evaluated in duplicates, with proper controls. In addition, PCR product purity was monitored from melting curve analysis and 2.0% agarose gel electrophoresis.

Normalization of housekeeping genes

RefFinder analyzed candidate gene expression stability. It determines a stable reference gene by the comprehensive ranking of each gene based on the combined expression and stability data from four statistical algorithms - geNorm, NormFinder, BestKeeper, and Delta Ct. These mathematical algorithms use threshold cycle (Ct) values to calculate expression stability of each candidate reference gene. Ranking of genes was done based on the stability score, where genes with the lowest stability score will be considered more stable.

Statistics

For the comparative analysis of UEV characteristics, a paired student's t-test was done. Statistical significance was tested using GraphPad software version 9.0, and P < 0.05 was considered statistically significant. All experimental data are shown as mean ± SEM unless otherwise mentioned. Gene stability analysis of housekeeping genes was performed in RefFinder. The stability value or SD data obtained using different algorithms were used for the ranking of the gene.

Urinary EV isolation from healthy subjects

UEVs were isolated using the PEG-based precipitation method described previously[5] with modifications. We also isolated UEVs using a commercially available kit, Thermo scientific (n=11), for comparative analysis. A summary of the experimental workflow is shown in Fig. 1. We had further determined the characteristic features of the isolated UEVs for their biochemical, biophysical, and molecular purity and integrity.

Optimization of the exosome isolation procedure

Initially, we had optimized the UEV isolation by altering PEG concentrations, i.e. (6%, 12%, 24%) for precipitating EVs. As a result, the total protein content and yield were maximum using 12% PEG compared to 6% or 24% (Supplementary Figure, Fig. S1). To establish the purity of the isolated EVs, we have compared their biochemical properties with those isolated using the standard commercially available kit as a reference. Quantitative analysis of the UEV protein and lipid content showed moderate differences in the yield, Fig. 2a-b. The ratio of protein to lipid content, a commonly used parameter to assess the quality of vesicles [16], was comparable (Fig. 2c). Also, acetylcholine esterase (AChE) enzyme activity was quantified in the UEVs to assess the purity of isolation as previously reported [13]. AChE enzyme activity of the PEG-isolated UEVs was slightly lower (1.7-fold) compared to the Kit –method (Fig. 2d). As the biochemical properties of the vesicle-derived using both methods were comparable, we chose to isolate UEVs using the PEG-based method.

Qualitative assessment of Urinary EVs

Following UEVs isolation, THP contaminant proteins were removed by treating the fraction with reducing agent DTT[17]. The SDS-PAGE analysis confirmed the decrease of THP protein in the DTT treated fraction compared to the untreated fraction (Fig. 3a. THP removal from the UEV fractions enhanced the yield by 1.5-fold compared to the unremoved fractions (Fig. 3b, n=3). Hence, 12% PEG-based isolation following DTT treatment was considered optimal for UEVs isolation. Following this procedure, UEVs were isolated from healthy individuals (n=12) and were characterized as described in the methods section. The exosomes' average total protein and lipid content was 646µg/ml and 1240µg/ml, respectively, as shown in Fig. 3c. A semi-quantitative exosome array was performed, Fig. 3d. Results showed intense positive staining for various proteins, including in the exosome array – Intercellular Adhesion Molecule 1 (ICAM), Tumor Susceptibility Gene 101 (TSG101) tetraspanins CD63 and CD81. A weak signal was observed for Flotillin 1 (FLOT1) and programmed cell death 6 interacting protein (ALIX) and Annexin A5 (ANXA5), while no signal was detected for Epithelial Cell Adhesion Molecule (EpCAM). cis-Golgi matrix protein (GM130) was employed as a cellular protein contamination spot and negative control (blank spot). The quality of UEVs was assessed from the ratio of protein to lipid content (P/L), which ranged from 0.3-1.6, Fig. 3e, indicating the integrity of extracellular vesicles. The average total UEV yield was 2.45 mg per 100 ml of urine. The average AChE activity was 550mU per mg of protein, confirming the purity, as shown in Fig. 3f. Furthermore, DDPIV activity in the isolate was quantified in Fig. 3g to verify that the activity was indeed in the micro vesicular components of urine[17]. These observations confirmed the quality of UEVs isolated from urine.

Morphology and size distribution of Urinary Exosomes

UEVs appeared round cup-shaped when visualized under the transmission electron microscope (Fig. 4a). Nanoparticle tracking (NTA) analysis revealed the size distribution profile of UEV with a mean particle size of 59 ± 22nm (Fig. 4b). The concentration of the UEV particles was 3.19 E8 particles per ml as quantified by NTA. Additionally, the size was determined from the intensity distribution calculated by DLS (Fig. 4c). Results of DLS measurements are shown as a number-weighted distribution curve. At least three independent aliquots were measured in triplicate. The average size of the UEVs was 78 ± 56 nm based on the number distribution. Thus, we verified that our isolated UEV fractions were in the exosomes reported size range and morphology [18].

Selection of optimal housekeeping genes

RNA Quality

UEV RNA was extracted and quantified using the Agilent Pico RNA detection kit. The average RNA yield was 137pg/µl, n=6. The quality of isolated RNA was assessed against a standard marker, a sharp 5S peak was identified in between 25-200 nucleotides in RNA from UEV fractions, which confirmed the presence of exosome RNA, as shown in Fig. 5a. There were no detectable 18S or 28S peaks. An overlay of urinary cell pellet RNA with the exosomal RNA shows degraded cellular RNA in the pellet RNA.

Primer Efficiency

Primers for B2M, RPL13, PP1A, HMBS, GAPDH were designed using NCBI primer designing software. The serial dilution curve method was used to confirm the primer efficiency. Efficiency Testing gave efficiencies from 100 -112% with a correlation coefficient (R²) from 0.92 to 0.99 (Table 3), indicating that all the primers worked efficiently within the acceptable ranges. The Limit of Detection (LOD) was 0.0001 for all primers except for HMBS, which was 0.1. The experiment was done in duplicates (n=2).

Stability Test for candidate reference genes

RNA was extracted from UEVs isolated from forty healthy urine samples, and the expression levels of five selected reference genes were quantified using qPCR as described above. The expression levels of all genes were normally distributed, as shown in Fig. 5b, with an average Ct value of 10.26 ± 1.33. To identify the most stably expressed housekeeping genes, we have normalized the gene expression levels using the online software RefFinder. Each gene's stability score was determined using different tools included in the software, such as geNorm, NormFinder, BestKeeper, and the comparative delta Ct method to minimize the error associated with a single software-based evaluation. As different statistical algorithms were employed to evaluate the stability score, the ranking of genes slightly varied in different tools (details listed in Table 4). Nevertheless, B2M and RPL13 A were identified to be consistently stable with a lower mean stability score below 1.5 (geNorm) and below 1 (NormFinder), indicating a reduced variation of the expressed genes shown in Fig. 5c. Furthermore, the recommended ranking values for the average gene expression data (n=40) showed a similar trend with B2M and RPL13A in top-ranking positions (Fig. 5d). Therefore, the above two genes may be used as the most stable housekeeping genes for accurate gene expression analysis, particularly in urine samples.

UEVs constitute a significant subset of extracellular vesicles derived from the kidney and urogenital tract [19]. They hold a concentrated source of biomarkers (RNA, protein, and lipid), protected from urinary ribonucleases and proteases degradation[2]. Easy access and non-invasiveness have made urine an ideal source for biomarker analysis. This study established a clinically adaptable protocol for UEVs isolation, characterization and defined a normalization strategy for UEVs based gene expression studies.

Several techniques, including ultracentrifugation, ultra-filtration, size exclusion chromatography, and precipitation, are currently used to isolate UEVs [18]. In this study, owing to the lower cost and easy adaptability of PEG-based precipitation of UEVs in clinical laboratories, we optimized the procedure with modifications to isolate EVs from urine [5]. Furthermore, comparing biochemical properties like total protein, lipid, P/L ratio, and acetylcholine esterase between vesicular preparations obtained by PEG-based precipitation and commercial kit showed similar yield and quality. Therefore, our results emphasize that PEG-based UEVs precipitation can be used in clinical labs for microvesicle-based downstream applications.

It is known that the presence of contaminant protein Tamm-horse fall protein (THP) in the urine leads to reduced UEVs yields. THP is a protein that is abundantly found in urine under physiological conditions. It is known to oligomerize into long polymers and compromises the vesicular yield at lower temperatures by forming a mesh that entraps the vesicles [17]. A previous study emphasized employing a DTT treatment step to release THP-entrapped exosomes[20]. Similarly, we observed a decrease of THP protein density and an increase in the micro vesicular yield after treating the fraction with dithiothreitol (DTT), suggesting a dissociation of THP mesh entrapping UEVs, as reported earlier [5].

We have used protein/lipid ratio as a quality control measure to distinguish soluble proteins and protein aggregates in the EVs preparations [16]. Acetyl-CoA choline esterase (AChE) was shown to be concentrated in exosomes and can thus be used to measure the quality of exosome preparations. [18]. Dipeptidyl peptidase-IV (DPP IV) is another pan membrane-associated peptidase widely expressed in tissues and reported in the kidney cortex and the brush border and microvillus fraction[14]. Both AChE and DPP IV are used to measure the bioactivity, purity, and functional purity of EV preparations [21]. The protein determination of the exosome antibody array confirmed the purity of our vesicular preparation [22]. Tetraspanins CD63 and CD81 and TSG101 and ALIX are pan-exosome markers present on urinary exosomes [23]. Biophysical characterization involving particle size concentration, distribution, and morphology was arrived at by DLS, NTA, and EM in our EV preparations[24]. Transmission Electron Microscopy was used to characterize the morphological structure of the UEV fraction isolated. The size profile by DLS and NTA was in accord with the performance of EVs. Thus, the above three methods estimate size, shape, morphology, and agree with the published literature[25].

The need for ensuring quality assurance measures for qPCR is well acknowledged. MIQE guidelines provide an operational framework for qPCR-based gene expression studies [26]. The above guidelines enable an efficient design of qPCR experiments, ascertain technical quality of gene expression studies, and reliable replication of experiments. For accurate interpretation of qPCR results, data normalization is a prerequisite step [12]. Currently, there is a deficit of reliable housekeeping genes for conducting UEVs gene expression studies. Lack of ideal housekeeping genes leads to interpretation bias and reproducibility errors.

Candidate reference genes analyzed from the UEV RNA pool showed a narrow Cq range among all experimental series ranging from 5 to 15. According to geNorm, the candidate genes with the lowest stability values are B2M (1.1) and RP13A (1.1) in the study. Based on NormFinder analysis, B2M (0.5) and RP13A (1.0) are the top stable housekeeping gene candidates. Low average expression stability values indicate genes with stable expression by NormFinder [27]. Therefore, B2M (1.7) and RP13A (1.5) can be considered stable gene candidates. BestKeeper calculates the Crossing Points (CP), standard deviation (SD), and coefficient of variation (CV) for each gene [28]. Genes with SD values <1 are considered stable and are categorized as reference genes. RefFinder integrated the above four programs' outcomes and derived the most stable gene profile[29]. RefFinder analysis reduced the individual bias of each software and yielded a cumulative score. Absolute ranking values of B2M (1.2) and RP13A (1.6) defined them as universal housekeeping genes in UEVs gene expression studies. Following the recent guidelines, two or more genes have to be used to arrive at a normalized score[30].

Taken together, we have developed an easily adoptable lab protocol for the isolation and characterization of UEVs. We established a quality control profile to characterize the urinary EVs preparations at the biochemical, biophysical, and molecular levels. To our knowledge, this is the first study reporting stable housekeeping genes in UEVs. The optimal reference genes derived from this study could provide meaningful choices for target gene expression and functional studies based on UEVs, thereby strengthening UEVs applications, including diagnostics and biomarker studies.

Ethics Statement:

The study was initiated after approval by Institutional Ethics Committee – Biomedical Research (IEC-BMR), Apollo Hospitals IEC Application no 114 with a study protocol no. CMBRC/2014/005.

Acknowledgments: This work was supported by a research grant from Apollo Hospitals Educational and Research Foundation (AHERF). We thank our AHERF President, Dr. N.K.Ganguly, Vice president, Ishita Shively, Clinical director, Dr. Jayanthi Swaminathan for active organizational support.

Funding: This study is supported by Apollo Hospital Educational and Research Foundation (AHERF). The Confederation of Indian Industry supported student fellowship (CII), DST-SERB (Department of Science and Technology-Science and Engineering Research Board), Government of India (SERB/PM Fellow/CII-FICCI/Meeting/2018), and Industry partner AHERF, Chennai under the scheme of Prime Minister's Fellowship for Doctoral Research, GOI.

Conflict of interest: There are no conflicts of interest to declare.

Author contributions: Conceptualizations, Funding acquisition, Supervision, MVS, methodology, data curation, ADS, SBD, RK, software, ADS, RK, RS, formal analysis, ADS and RS, investigation, SSM and SM, resources, MVS, ADS, SP, writing and original draft preparation, ADS, writing and review and editing, RS, MVS.

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Table 1 – Summary of urine analysis of healthy individuals

Samples (40)

Leukocyte

Nitrite

Urobilinogen (mg/dl)

Protein (mg/dl)

pH

Blood

Specific Gravity

Ketone

(mg/dl)

Bilirubin

Glucose

(mg/dl)

Colour

Clarity

Observed

Negative

0.2

Negative

5.5-7.5

Negative

1-1.03

Negative

Yellow

Clear

Reference range

Negative

0.2-3.2

Negative

5-8.5

Negative

1-1.03

Negative

NA

Table 2 – Primer pair sequences of the candidate reference genes used in this study.

Gene	Forward Primer (5'-3')	Reverse Primer (5'-3')
B2M (Beta-2-Microglobulin)	CCTGCCGTGTGAACCATGTGA	GCTGCTTACATGTCTCGATCCCA
RPL13 (Ribosomal Protein L13a)	TCCTTTCCGCTCGGCTGTTT	GGGCCTTACGTCTGCGGAT
PPIA (Peptidylprolyl Isomerase A)	CCGCCGAGGAAAACCGTGTA	TGCAAACAGCTCAAAGGAGACG
HMBS (Hydroxymethylbilane Synthase)	CAGCCTACTTTCCAAGCGGA	CCTGTGGTGGACATAGCAATGA
GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase)	CCACTAGGCGCTCACTGTTCT	GACCAAATCCGTTGACTCCGAC

Table 3 – Primer Efficiency of candidate housekeeping genes

Gene Name	Primer Efficiency (%)	Slope (m)	R²	LOD (Ratio)	Gene Function*
B2M	100.0	-3.3231	0.995	0.0001	Major component of MHC-Class I
RPL13	101.5	-3.2858	0.997	0.0001	Structural component of the ribosome
PPIA	103.6	-3.2378	0.999	0.0001	Protein folding
HMBS	101.1	-3.2955	0.982	0.1	Heme synthesis and porphyrin metabolism
GAPDH	111.4	-3.0755	0.983	0.0001	Oxidoreductase in glycolysis and gluconeogenesis

R² - correlation coefficient, LOD – Limit of Detection. Data is representative of n=2 experiments carried in duplicates.

* https://www.genecards.org/cgi-bin/carddisp.pl?gene=

Table 4 – Gene stability analysis of housekeeping genes

Gene Name	Accession Number	geNorm		NormFinder		Delta Ct		BestKeeper				Comprehensive Ranking
Gene Name	Accession Number	Stability value	Rank	Stability value	Rank	Avg of Std Dev	Rank	Std dev [+/- CP]	Rank	[r]	P-value	Geomean of ranking values	Rank
B2M	NM_004048.3	1.1	1	0.5	1	1.7	1	1.7	2	0.94		1.2	1
RPL13A	NM_000977.3	1.1	1	1.0	3	1.9	2	1.5	1	0.90	0.001	1.6	2
PPIA	NM_021130.5	1.3	3	1.3	4	2.0	4	1.8	3	0.94		3.5	4
HMBS	NM_000190.4	1.4	4	0.9	2	1.9	3	2.2	5	0.94	0.001	3.3	3
GAPDH	NM_001282587.2	2.2	5	3.3	5	3.4	5	2.2	4	0.43	0.006	4.7	5

[r] Pearson correlation coefficient, SD- standard deviation

SupplementaryFIle.docx

Establishment of Optimal Housekeeping Genes for Urinary Extracellular Vesicle Biomarker Development: A Step Towards Non-Invasive Diagnostics

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Declarations

References

Tables

Supplementary Files

Status:

Version 1