Appropriate reference intervals are critical for interpretation of laboratory test results and accurate assessment of health and disease. However, pediatric reference intervals are severely lacking, leading to significant risk of misdiagnosis. CALIPER has addressed these gaps by establishing a robust reference interval database based on thousands of healthy children and adolescents.
by Victoria Higgins and Dr Khosrow Adeli
Introduction
The clinical laboratory provides objective data through laboratory testing of bodily fluids (e.g. serum, plasma) to aid in several aspects of medical decision making, including identifying risk factors and symptoms, diagnosing disease, and monitoring treatment. To correctly interpret laboratory test results, they are often compared to reference intervals (RIs), sometimes referred to as ‘normative’ or ‘expected’ values. RIs are commonly defined as the central 95% of the distribution of laboratory test results expected in a healthy, reference population [1]. Laboratory values that fall outside the appropriate RI may be interpreted as abnormal, possibly indicating the need for additional medical follow-up and/or treatment [2]. Given their critical importance to healthcare it would be expected that accurate RIs, appropriate for the patient population, are used in clinical practice. However, this is unfortunately far from the truth.
Importance of pediatric reference intervals
It can be challenging and costly for individual laboratories to develop RIs for their specific patient population, due to the necessity of recruiting a sufficiently large number of healthy individuals [i.e. The Clinical Laboratory Standards Institute (CLSI) recommends 120 individuals per partition] [1]. This is particularly true for pediatrics, a population in which unique RIs are of high importance. To interpret pediatric test results, laboratories often use RIs that were established on an adult reference population. The use of adult RIs to interpret pediatric test results can lead to erroneous and inaccurate interpretation. This is highlighted in Figure 1, which depicts the concentration of alkaline phosphatase (ALP) throughout pediatric, adult and geriatric age. It is evident that the pediatric population has vastly unique normative ALP values. Unique analyte concentrations in pediatrics is also true for sex hormones, growth hormones and several other analytes [3–5]. Therefore, children should not be viewed as small adults in the context of medical practice, but require separate RIs (i.e. partitions) for different age and/or sex groups, in addition to neonates and premature babies [5].
Closing the gaps in pediatric reference intervals
The current CLSI guidelines, which are mostly focused on adult RIs, acknowledge the special challenges of establishing age- and sex-specific pediatric RIs and recommend development of new initiatives to address the current gaps. The quality of a RI critically depends on the selected reference population. Therefore, the direct method of establishing RIs, which involves recruiting healthy individuals and applying exclusion criteria to select an appropriate reference population, is recommended over the indirect method, which involves using an already existing database (e.g. laboratory information system) to calculate RIs [1]. It is imperative for RI initiatives to focus on recruiting a sufficiently large and healthy reference population to accurately establish appropriate RIs for the pediatric population (i.e. using the direct method). Recognizing the critical need to establish pediatric RIs, several national initiatives have collected health information and blood samples from healthy pediatric populations. These initiatives include KiGGS in Germany [6], the Lifestyle of Our Kids (LOOK) study in Australia [7], CHILDx in the United States [8–10], The COPENHAGEN Puberty Study in the Nordic countries [11], and The Canadian Laboratory Initiative on Pediatric RIs (CALIPER) in Canada [5, 12].
The KiGGS initiative collected comprehensive, nationwide data on the health status of over 17 000 children and adolescents aged 0 to 17 years, across 167 locations in Germany [6]. This study has focused on laboratory parameters of general health indices, markers of nutritional status, immunization status, iron metabolism, thyroid, and indices of atopic sensitization. They have published age-dependent percentiles (3rd to 97th) in German, which may serve as a basis for RIs [13]. The LOOK study in Australia developed age-specific RIs for 37 chemistries, immunoassays, and derived parameters [7]. The CHILDx study was initiated in 2002 at ARUP (Associated Regional and University Pathologists) Laboratories and established RIs for 35 markers for children aged 6 months to 6 years and 58 markers for children aged 7–17 years [8–10]. The Nordic countries have also successfully established pediatric RIs for 21 biochemical properties using samples from healthy children and adolescents aged 5–19 years collected from schools from 2006–2008 in the Copenhagen area in Denmark as part of The COPENHAGEN Puberty Study [11]. However, arguably the most successful initiative has been the CALIPER project in Canada.
CALIPER project
The CALIPER project was initiated by The Paediatric Focus Group of the Canadian Society of Clinical Chemists (CSCC) and primarily based at The Hospital for Sick Children in Toronto (ON, Canada). CALIPER has recruited over 9 000 healthy children and adolescents from schools and community centres to participate at blood collection clinics by completing a health questionnaire, body measurements and donating a blood sample. Using this biobank of healthy pediatric samples, CALIPER has established age-, sex- and, for some biomarkers, Tanner Stage-specific pediatric RIs for over 100 biomarkers including, common biochemical markers, protein markers, lipids and enzymes [12], specialty endocrine markers [14], fertility hormones [15], cancer biomarkers [16], vitamins [17], metabolic disease biomarkers [18], testosterone indices [19] and specialized biochemical markers [20, 21]. All RIs were established in accordance with CLSI guidelines, including sample size requirements, outlier removal, statistical method for partitioning, as well as RI and confidence interval calculations [1].
The majority of RIs were established using Abbott ARCHITECT assays, initially limiting the direct applicability of the CALIPER database to all Canadian laboratories. CALIPER subsequently performed a series of transference and verification studies to expand the CALIPER database to additional assays commonly used in clinical laboratories, including Beckman, Ortho, Roche and Siemens [22–25]. Again, CALIPER performed these studies in accordance with CLSI guidelines and, in fact, often exceeded the sample size and statistical criteria requirements [1, 26]. The comprehensive CALIPER pediatric RI database is available online (www.caliperproject.ca), as well as through a mobile application (CALIPERApp) available on iTunes and Google Play. These tools allow the CALIPER database to be easily accessible to laboratory professionals, physicians, parents and patients.
Continued improvement in pediatric laboratory test interpretation
While significant improvements have been made in pediatric laboratory test interpretation over the past decade, several gaps remain. First, RI data for neonates (including premature babies) and infants (age 0 to <1 year) remains a challenge, owing to difficulties accessing a healthy neonate and infant population. However, the limited neonatal and infantile reference data CALIPER has collected highlights the profound differences in the newborn period, necessitating accurate RIs for this age group. For example, Figure 2 shows the dynamic concentration of creatinine throughout the pediatric age range, particularly the elevated and highly variable levels in the first two weeks of life. A large-scale, comprehensive study aimed at recruiting healthy neonates and infants is required to fill this gap. CALIPER is currently initiating a study with the aim of establishing a complete RI database for neonates and infants, which will greatly improve neonatal healthcare for premature babies, newborns, and infants from primary to complex, tertiary care pediatric centres.
Secondly, the effect of ethnicity on biomarker concentration remains to be comprehensively examined. The International Federation of Clinical Chemistry (IFCC) recommends that every country establishes RIs [27]; however, most nations adopt RIs from studies predominately performed in Western countries based on primarily Caucasian populations without considerations of ethnic differences. Although the majority of biomarkers do not differ between individuals of different ethnic backgrounds, a preliminary examination of the influence of ethnicity in pediatrics by CALIPER has shown that some biomarkers do significantly differ among ethnic groups, including immunoglobulin G (IgG), transferrin, ferritin, and follicle-stimulating hormone (FSH) [12, 14, 15]. Another study examined the influence of ethnicity in adults and found that serum creatine kinase (CK) activity is significantly higher for those of African ancestry. As elevated CK activity is an indicator of statin-induced myopathy, elevated CK activity in those of African ancestry could result in inappropriate discontinuation of statin therapy if ethnic-specific RIs are not used [28]. Another recent study used data from the National Health and Nutrition Examination Survey (NHANES) to develop racial/ethnic-specific RIs among Asians, Blacks, Hispanics, and Whites [29]. CALIPER has initiated a new study to robustly determine the effect of ethnicity on the concentration of routine serum biomarkers by examining and comparing reference values in the four major Canadian ethnic groups (i.e. Caucasian, South Asian, East Asian, and Black).
Lastly, as clinical laboratories adopt their RIs from numerous different sources, including textbooks, manufacturer product inserts, expert opinions, or published literature, RIs in clinical practice may vary substantially between laboratories. A national survey performed in Australia by the Australian Association of Clinical Biochemists (AACB) Harmonisation Group highlights the extensive variation in adult RIs used in clinical practice, which greatly compromises the consistency and reliability of laboratory test result interpretation and patient care [30]. A recent Canadian RI study (manuscript submitted; Adeli K, et al. 2017) by the CSCC Harmonized RI (hRI) Working Group, further highlights the considerable variation in RIs across laboratories with a greater variation observed in pediatric RIs in current clinical use, even between clinical laboratories using the same instrument. These surveys highlight the critical need for harmonized RIs in clinical practice. Initiatives in the Nordic countries [31], UK [32], Australia [33] and Japan [34] have already established harmonized RIs for a number of laboratory tests primarily for adults, but also for pediatrics. The CSCC hRI Working Group is now also working towards Canada-wide RI harmonization.
Conclusion
Children cannot be viewed as small adults and indeed require pediatric-specific RIs appropriately partitioned by age and sex for accurate laboratory test result interpretation. Several national initiatives have begun to address these critical gaps over the past decade by establishing age-, sex- and Tanner Stage-specific RIs for several major analytical platforms. The CALIPER initiative in Canada has arguably been the most comprehensive study to date, with clinical laboratories in several countries globally implementing the CALIPER database into clinical practice. Despite the significant strides recently achieved, further research is warranted in several areas including the establishment of RIs specific to the neonatal and infantile period, ethnic-specific RI for a subset of laboratory markers, and RI harmonization. Collectively, the comprehensive reference database published by CALIPER and the emerging data from ongoing studies directly address the evidence gap in pediatric RIs and contribute to evidence-based interpretation of laboratory test results and enhanced diagnostic accuracy of laboratory biomarkers in current clinical practice.
References
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10. Johnson-Davis KL, Moore SJ, Owen WE, Cutler JM, Frank EL. A rapid HPLC method used to establish pediatric RIs for vitamins A and E. Clin Chim Acta 2009; 405(1–2): 35–38.
11. Hilsted L, Rustad P, Aksglæde L, Sørensen K, Juul A. Recommended Nordic paediatric RIs for 21 common biochemical properties. Scand J Clin Lab Invest 2013; 73(1): 1–9.
12. Colantonio DA, Kyriakopoulou L, Chan MK, Daly CH, Brinc D, Venner AA, Pasic MD, Armbruster D, Adeli K. Closing the gaps in pediatric laboratory RIs: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children. Clin Chem 2012; 58(5): 854–868.
13. Dortschy R, Schaffarth Rosario A, Scheidt-Nave C, Thierfelder W, Thamm M, Gutsche J. Bevölkerungsbezogene Verteilungswerte ausgewählter Laborparameter aus der Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland (KiGGS). Beiträge zur Gesundheitsberichterstattung des Bundes. Berlin: Robert Koch-Institut; 2009.
14. Bailey D, Colantonio D, Kyriakopoulou L, Cohen AH, Chan MK, Armbruster D, Adeli K. Marked biological variance in endocrine and biochemical markers in childhood: establishment of pediatric RIs using healthy community children from the CALIPER cohort. Clin Chem 2013; 59(9): 1393–1405.
15. Konforte D, Shea JL, Kyriakopoulou L, Colantonio D, Cohen AH, Shaw J, Bailey D, Chan MK, Armbruster D, Adeli K. Complex biological pattern of fertility hormones in children and adolescents: a study of healthy children from the CALIPER cohort and establishment of pediatric RIs. Clin Chem 2013; 59(8): 1215–1227.
16. Bevilacqua V, Chan MK, Chen Y, Armbruster D, Schodin B, Adeli K. Pediatric population reference value distributions for cancer biomarkers and covariate-stratified RIs in the CALIPER cohort. Clin Chem 2014; 60(12): 1532–1542.
17. Raizman JE, Cohen AH, Teodoro-Morrison T, Wan B, Khun-Chen M, Wilkenson C, Bevilaqua V, Adeli K. Pediatric reference value distributions for vitamins A and E in the CALIPER cohort and establishment of age-stratified RIs. Clin Biochem 2014; 47(9): 812–815.
18. Teodoro-Morrison T, Kyriakopoulou L, Chen YK, Raizman JE, Bevilacqua V, Chan MK, Wan B, Yazdanpanah M, Schulze A, Adeli K. Dynamic biological changes in metabolic disease biomarkers in childhood and adolescence: a CALIPER study of healthy community children. Clin Biochem 2015; 48(13–14): 828–836.
19. Raizman JE, Quinn F, Armbruster DA, Adeli K. Pediatric RIs for calculated free testosterone, bioavailable testosterone and free androgen index in the CALIPER cohort. Clin Chem Lab Med 2015; 53(10): e239–243.
20. Kelly J, Raizman JE, Bevilacqua V, Chan MK, Chen Y, Quinn F, Shodin B, Armbruster D, Adeli K. Complex reference value distributions and partitioned RIs across the pediatric age range for 14 specialized biochemical markers in the CALIPER cohort of healthy community children and adolescents. Clin Chim Acta 2015; 450: 196–202.
21. Karbasy K, Lin DCC, Stoianov A, Chan MK, Bevilacqua V, Chen Y, Adeli K. Pediatric reference value distributions and covariate-stratified RIs for 29 endocrine and special chemistry biomarkers on the Beckman Coulter Immunoassay Systems: a CALIPER study of healthy community children. Clin Chem Lab Med 2016; 54(4): 643–657.
22. Estey MP, Cohen AH, Colantonio DA, Chan MK, Marvasti TB, Randell E, Delvin E, Cousineau J, Grey V, et al. CLSI-based transference of the CALIPER database of pediatric RIs from Abbott to Beckman, Ortho, Roche and Siemens Clinical Chemistry Assays: direct validation using reference samples from the CALIPER cohort. Clin Biochem 2013; 46(13–14): 1197–1219.
23. Higgins V, Chan MK, Nieuwesteeg M, Hoffman BR, Bromberg IL, Gornall D, Randell E, Adeli K. Transference of CALIPER pediatric RIs to biochemical assays on the Roche cobas 6000 and the Roche Modular P. Clin Biochem 2016; 49(1–2): 139–149.
24. Araújo PAT, Thomas D, Sadeghieh T, Bevilacqua V, Chan MK, Chen Y, Randell E, Adeli K. CLSI-based transference of the CALIPER database of pediatric RIs to Beckman Coulter DxC biochemical assays. Clin Biochem 2015; 48(13–14): 870–880.
25. Abou El Hassan M, Stoianov A, Araújo PAT, Sadeghieh T, Chan MK, Chen Y, Randell E, Nieuwesteeg M, Adeli K. CLSI-based transference of CALIPER pediatric RIs to Beckman Coulter AU biochemical assays. Clin Biochem 2015; 48(16–17): 1151–1159.
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The authors
Victoria Higgins PhD candidate; Khosrow Adeli* PhD, FCACB, DABCC, FACB
CALIPER program, Pediatric Laboratory Medicine, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
*Corresponding author
E-mail: khosrow.adeli@sickkids.ca
Urinalysis may provide evidence of significant renal disease in asymptomatic patients. The microscopic urinalysis is vital to making diagnoses in many asymptomatic cases, including urinary tract infection, urinary tract tumors, occult glomerulonephritis, and interstitial nephritis.
Presence or absence of different particles in urine sediment is crucial for clinical decision making. Urine sediment cells or particles provide important information for the diagnosis of renal or urinary diseases [1]. The patented UriSed Technology was developed to reduce the shortcomings of manual microscopy through automation. [2]. The UriSed analysers provide a reliable and reproducible solution since 2007 [3]. The new generation instrument based on the improved UriSed Technology, UriSed 3 was introduced in the market in 2015. UriSed 3 is an automated urine microscopy analyser with a revolutionary particle visualization utilizing both bright-field and phase-contrast microscopy. In the present study, we evaluated the analytical performance of UriSed 3 Automated Urine Microscopy Analyser (Manufactured by 77 Elektronika Kft., Budapest) and compared the results to those from manual microscopy using standardized KOVA counting chambers.
UriSed 3 provides quantitative Red Blood Cell (RBC) and White Blood Cell (WBC) results, and semi-quantitative results for all other particle types: WBC Clumps (WBCc), Squamous Epithelial Cells (EPI), Non-squamous Epithelial Cells (renal tubular and urothelium cells) (NEC), Crystals (CRY): Calcium oxalate dihydrate (CaOxd), Calcium oxalate monohydrate (CaOxm), Uric acid (URI), and Triple-phosphate crystals (TRI), Hyaline casts (HYA), Pathological casts (PAT), Bacteria (cocci-like and rod-like) (BACc, BACr), Yeasts (YEA), Spermatozoa (SPRM) and Mucus (MUC) [4].
Phase-contrast microscopy by UriSed 3
Phase-contrast microscopy is an optical microscopy technique that converts phase shifts in light passing through a transparent specimen to brightness changes in the image. Phase shifts themselves are invisible, but become visible when shown as brightness variations. In particular, for urinary sediment examination, phase-contrast supplies an optimal identification of particles with a low refractive index (e.g., hyaline casts and RBC devoid of their hemoglobin content, the so-called “ghost RBC”) and of cellular morphological details) [9, 10]. Therefore the use of phase-contrast microscopy is encouraged also by international guidelines on urinalysis [5, 6].
The measurement technique of the UriSed 3 instrument is a combination of a bright-field microscope and a phase-contrast microscope in one optical system. Preparation of the UriSed 3 analyser for measurement takes only a few minutes. It needs distilled water for washing its pipette, and patented disposable plastic cuvettes for sample investigation. The instrument throughput is up to 120 samples per hour. The whole measurement process is completely automatic: 200 µl of urine sample is dispensed into the cuvette, then spinning the cuvette for a few seconds gently deposits formed elements into a monolayer at the bottom of the cuvette. The built-in digital camera takes and saves both a bright-field and a phase-contrast microscopic image from the same view-field at 15 different positions of the sediment layer. Information from both whole view-field images are evaluated by a neural network based image processing software.
Material and methods
Analysis of 311 samples was performed to evaluate UriSed 3 analytical performance compared to the manual microscopy urine examination method. Both measurements were carried out with the same anonymous urine samples. Fresh, native urine samples were used, that were typically held for no more than 4 hours before being analysed, as recommended in the relevant guidelines [5,6] to prevent change in the morphology of the particles. Samples were mixed until homogeneous and then split and run on each measuring procedure as close to the same time as possible. The standardized microscopic urinalysis of native samples (Level 3) was followed by using a KOVA counting chamber. The particle concentration for all particle types was evenly distributed in the evaluated urine samples. Carry-over, precision, diagnostic tests such as sensitivity, specificity, diagnostic accuracy, concordance and one category concordance were investigated according to well-established protocols [7].
Results
No carry-over was detected in any of the samples. UriSed 3 has better precision than microscopy at all of the tested RBC and WBC concentrations. The majority of all coefficients of variation obtained for within series imprecision (CV) using UriSed 3 was 7-16% versus 5,5-67% in case of manual microscopy [8]. Good correlation can be found between UriSed 3 and manual counting chamber for formed elements. The Pearson correlation of quantitative parameters are 0.91 (RBC), 0.93 (WBC). The clinical evaluation of UriSed 3 was based on McNemar test and concordance study. The results are shown in the table above.
Conclusion
UriSed 3 instruments utilize phase-contrast and bright-field microscopy to combine original and innovative technologies whose aim is the progressive improvement of automated urinary sediment examination and the progressive approach to the gold standard manual microscopy method. The automated measurement process of UriSed 3 is reproducible and operator-independent. Those sediment particles that are mostly transparent become visible with phase-contrast microscopy by UriSed 3, which is a spectacular advantage and leads to specific improvement in recognition at several particle types.
References
1. Fogazzi GB, The Urinary Sediment an Integrated View Third Edition. Milano: Elsevier, 2010.
2. Barta Z, Kránicz T, Bayer G. UriSed Technology – A Standardised Automatic Method of Urine Sediment Analysis. European Infectious Disease 2011;5:139–42.
3. Zaman Z, Fogazzi GB, Garigali G, Croci MD, Bayer G, Kránicz T. Urine sediment analysis: analytical and diagnostic performances of sediMAX – a new automated microscopy image-based urine sediment analyser. Clin Chim Acta 2010; 411: 147-154.
4. Fogazzi GB, Garigali G. The Urinary Sediment by UriSed Technology. A New Approach to Urinary Sediment Examination. Milano: Elsevier, 2013.
5. Kouri T, Fogazzi G, Hallander H, Hofmann W, Guder WG, editors. European Urinalysis Guidelines. Scand J Clin Lab Invest 2000; 60 (Suppl 231): 1-96.
6. Clinical and Laboratory Standard Institute (ex NCCLS). Document GP16-A3 – Urinalysis; Approved guideline, 3rd ed. Wayne, PA: CLSI, 2009.
7. T. Kouri, A. Gyory, R.M. Rowan. ISLH Recommended Reference Procedure for the enumeration of Particles in Urine. Laboratory Hematology 9:58-63, 2003.
8. Haber MH, Galagan K, Blomberg D, Glassy EF, Ward PCJ, editors. Color Atlas of Urinary Sediment; An Illustrated Field Guide Based on Proficiency Testing. Chicago: CAP Press, 2010.
9. Brody L, Webster MC, Kark RM. Identification of elements of urinary sediment with phase-contrast microscopy. JAMA 1968; 206: 1777-1781.
10. Spencer E. and Pedersen Ib. Hand Atlas of the Urinary Sediment. Bright-field, Phase-Contrast, and Polarized Light. Copenhagen: Munksgaard, 1971.
More information on UriSed 3 is available from the manufacturer:
77 Elektronika Kft., Budapest, HUNGARY
Email: sales@e77.hu, web: en.e77.hu
The author
Erzsébet Nagy MD,
Honorary Associate Professor
Head Phisician of Central Laboratory; Hospitaller Brothers of St. John of God Hospital, Budapest
Introduction
The correct identification and characterization of pathogens is essential for the successful treatment of infections and safety of patients. However, not every pathogen can be successfully cultured and the available molecular tests, mainly focusing on specific pathogens, are inadequate to detect novel genetic features in emerging pathogens. Undetected pathogens can spread easily through a hospital, resulting in a possible outbreak and putting patients admitted to hospitals at a higher risk for infections.
In recent decades, molecular diagnostic tests have improved rapidly and their role in clinical microbiology laboratories became progressively more important [1]. The turnaround time from receiving a sample to the final diagnostic result has been drastically reduced. Molecular methods, such as real-time polymerase chain reaction (PCR), Sanger sequencing and next-generation sequencing (NGS), make it possible to detect non-culturable micro-organisms. Nevertheless, some of these technologies, such as real-time PCR, require knowledge of the genomes of the micro-organisms. In addition, bioinformatics expertise is often needed to interpret the results.
This paper addresses the use of Sanger sequencing and whole genome sequencing (WGS) in the clinical microbiology laboratory for the characterization of pathogens and outbreak management, as it is used in the University Medical Center Groningen (UMCG), one of the largest university hospitals in The Netherlands. The clinical microbiology laboratory at the UMCG receives around 5750 samples per year for detailed molecular analysis, of which approximately 1500 samples are analysed by NGS [2].
Sanger sequencing
Sanger sequencing is used to answer different molecular questions, such as the identification of bacteria and fungi in patient material or pure cultures, and the identification of mutations in specific genomic regions of interest in bacteria or viruses. In general, Sanger sequencing is used to investigate a short DNA sequence (± 500 bp) after amplification of the region of interest by PCR. After amplification, two different sequence reactions (forward and reverse) are performed and can be used to identify bacterial or fungal species based on the analyses of the sequenced 16S ribosomal DNA (rDNA) and 18S rDNA of the internal transcribed spacer (ITS) region, respectively [2].
One of the disadvantages of Sanger sequencing is that species identification in clinical materials containing more than one species is difficult, if not impossible. Furthermore, the costs and the labour needed for investigating multiple genomic regions of interest makes this method of limited use in modern clinical microbiology laboratories.
Next-generation sequencing (NGS)
NGS determines the whole genome sequence of different pathogens in one single sequencing run. This technology allows sample multiplexing and, thus, simultaneously provides genomic sequence information on diverse pathogens isolated from different patients. NGS also allows determination of microbial genomes in complex multi-species patient samples by shotgun metagenomics (third generation sequencing) [3]. In comparison to Sanger sequencing, NGS is a considerable improvement owing to the usage of one protocol for all pathogens [4]. A schematic overview of the general workflow used for the sequence analysis in the UMCG is shown in Figure 1.
Using NGS, the whole genome of a pathogen is sequenced in a random way. As benchtop next-generation sequencers can sequence DNA fragments between 100 and 1000 bases, the genome is fragmented before sequencing [5, 6]. Third generation sequencers are an exception to this, as they can handle larger fragments of over 200 kb [2]. NGS requires the preparation of libraries, in which fragments of DNA or RNA are linked to adapters and barcodes. At a later stage, this enables the identification of the sequenced fragments (reads) to the pathogens. After fragmentation, clonal amplification, normalization and a sequencing run is performed. For this, a robust preparation of libraries and standardized protocols are key [3].
Software for data analysis
A huge challenge for the introduction of NGS in a clinical setting is the data analysis. This requires specific software as well as scientific knowledge to interpret the results. There are, so far, only a few user-friendly software packages available to perform data analyses with little bioinformatics knowledge. However, the costs of these software packages is relatively high. However, there a numerous freely available software packages to answer different scientific questions, but knowledge of bioinformatics is often required [2].
After high-throughput sequencing, the reads can be assembled, either by mapping or de novo assembly [2]. Software packages, such as CLC Genomics Workbench (Qiagen), SPAdes and Velvet, can be used for assembly. The genetic relatedness between isolates can be investigated using a gene-by-gene approach using multi-locus sequence typing (MLST), core genome MLST (cgMLST) or whole genome MLST (wgMLST) using SeqSphere+ (Ridom), Bionummerics (Biomérieux), or online tools, such as Enterobase (https://enterobase.warwick.ac.uk) and BIGSdb (http://bigsdb.readthedocs.io). Currently, it is still a matter of debate how many alleles two genomes may differ by to call them genetically related. The same problem applies for comparing two genomes by single nucleotide polymorphism (SNP) typing.
There are a number of web-based tools to perform additional NGS analysis [2]. One of them is the website of the Centre for Genomic Epidemiology (www.genomicepidemiology.org) that can be used for the detection of resistance and virulence genes. Another web-based tool is the Rapid Annotation using Subsystem Technology (RAST) website (http://rast.nmpdr.org) for annotating bacterial genomes.
One of the advantages of web-based tools is that, in general, no knowledge of bioinformatics is necessary. However, a disadvantage may be the lack of tweaking the software settings while performing the analysis. In addition, it may be necessary to confirm the results obtained through web-based tools using other methods [2].
NGS in clinical microbiology
NGS is already applied in several medical microbiology laboratories where it is used for outbreak management, molecular case findings, characterization and surveillance of pathogens, for example [2].
Indeed NGS can be extremely useful in outbreak detection, by monitoring the evolution and dynamics of multi-drug resistant pathogens [7]. A number of studies have highlighted the effectiveness of WGS-based typing for assessing of (newly) emerging pathogens. In our hospital, NGS was used for the characterization of a newly emerging CTX-M-15 producing Klebsiella pneumoniae clone [8]. Transmission of this K. pneumoniae strain between patients has been traced using genomic phylogenetic analysis (Fig. 2). In addition, the study showed the usefulness of a unique marker PCR, in which a clone-specific PCR was developed to investigate the transmission between patients [4].
In addition to tracing and characterizing outbreaks, NGS can be used for the implementation of control measures to avoid the spread of resistance bacteria [9]. An outbreak of a colistin-resistant carbapenemase-producing K. pneumoniae (KPC) with inter-institutional spread in The Netherlands was identified and characterized using NGS and, partially based on these findings, controlled by transferring all positive patients to a separate location [9].
Furthermore, NGS data stored in databases can be used to search retrospectively for molecular case studies. A study from Bathoorn et al. showed that a New Delhi Metallo-?-lactamas-5 (NDM-5)-producing K. pneumoniae was isolated from a Dutch patient. Molecular case findings showed that the Dutch strain is clonally related to strains isolated from four Danish patients in 2014. There was no obvious epidemiological link between the cases in the Dutch and Danish hospitals [10].
These studies and many others highlight the importance of NGS in clinical microbiology. NGS can be used either as a highly discriminatory tool to discriminate between bacterial clones with specific features and to use the information for patient management, infection prevention and evolutionary studies [2] or to characterize bacterial isolates in more detail [8]. Furthermore, web-based databases can be in silico screened retrospectively for the presence of novel (antibiotic-resistance) genes.
Conclusion and outlook
Using NGS, one laboratory protocol can be used to generate sequencing data from samples obtained from different sources. After data analysis, information on the presence of virulence factors and antibiotic resistance genes, as well as other relevant genes are obtained. In addition, NGS makes it possible to standardize typing methods, although cut-off values regarding cgMLST, wgMLST and SNP analysis have to be established internationally in order to distinguish related or unrelated isolates and being able to compare results between laboratories. In the next few years, the role of NGS will surely increase in medical microbiology laboratories, both for research as well as for molecular diagnostic purposes, infection prevention and molecular-epidemiological investigations.
Nonetheless, improvement of the NGS workflow is still needed, focusing on easier and faster ways of library preparation, shorter run-times and further reduction in costs. Furthermore, automatic pipelines for data analyses and easy to use software have to be developed. In addition, the development of proficiency testing panels are important for external quality controls. Only with implementation of the above items at local, (inter)regional and international level will broad use of NGS be allowed in clinical microbiological laboratories for patient and infection control management, including defining a tailor-made antibiotic therapy for each patient, leading to personalized microbiology.
Acknowledgement
A full version of this work is published in the review ‘Application of next generation sequencing in clinical microbiology and infection prevention’, Journal of biotechnology 2017; 243: 16–24.
References
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The authors
Sigrid Rosema BSc; Ruud H. Deurenberg PhD; Monica A. Chlebowicz PhD; Silvia García-Cobos PhD; Alida C. M. Veloo PhD; Alexander W. Friedrich MD, PhD; John W. A. Rossen PhD, MMM
Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, The Netherlands
*Corresponding author
E-mail: j.w.a.rossen@rug.nl
November 2025
Prins Hendrikstraat 1
5611HH Eindhoven
The Netherlands
info@clinlabint.com
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