Automated Flow Cytometry: An Alternative to Urine Culture in a Routine Clinical Microbiology Laboratory?

Advances and Progress in Automated Urine Analyzers

The clinical analysis of urine has classically focused on conventional chemical-based urinalysis and urine microscopy. Contemporary advances in both analysis subsets have started to employ new technologies such as automated image analysis, flow cytometry, and mass spectrometry. In addition to new detection technologies, current analyzers have incorporated more advanced imaging, automated sample handing, and machine learning analyses into their workflow. The most advanced semiautomated analyzers can be interfaced with hospital medical record systems, and in the point-of-care setting, smartphones can be used for image analysis. This review will discuss current technological advancements in the field of urinalysis and urine microscopy.

Label-Free Analysis of Urine Samples with In-Flow Digital Holographic Microscopy

Urinary tract infections are among the most frequent infectious diseases and require screening a great amount of urine samples from patients. However, a high percentage of samples result as negative after urine culture plate tests (CPTs), demanding a simple and fast preliminary technique to screen out the negative samples. We propose a digital holographic microscopy (DHM) method to inspect fresh urine samples flowing in a glass capillary for 3 min, recording holograms at 2 frames per second. After digital reconstruction, bacteria, white and red blood cells, epithelial cells and crystals were identified and counted, and the samples were classified as negative or positive according to clinical cutoff values. Taking the CPT as reference, we processed 180 urine samples and compared the results with those of urine flow cytometry (UFC). Using standard evaluation metrics for our screening test, we found a similar performance for DHM and UFC, indicating DHM as a suitable and fast screening technique retaining several advantages. As a benefit of DHM, the technique is label-free and does not require sample preparation. Moreover, the phase and amplitude images of the cells and other particles present in urine are digitally recorded and can serve for further investigation afterwards.

Comparison of Laboratory Diagnosis of Urinary Tract Infections Based on Leukocyte and Bacterial Parameters Using Standardized Microscopic and Flow Cytometry Methods

Background

Rapid and reliable tests are essential for the diagnostic laboratory confirmation of urinary tract infections (UTIs). Until now, UTI has been confirmed by the microbiology culture of urine, requiring at least 48-hour turnaround time (TAT), with a standardized microscopic method being widely favored. Automated urine flow cytometry, however, has recently been used to improve the rapid TAT by analyzing the urine sediment. This study therefore aimed to compare the diagnostic value of the Shih-Yung conventional microscopic and urine flow cytometry methods in the detection of leukocyte and bacterial parameters of patients with UTIs in an outpatient clinic.

Methods

A cross-sectional study was conducted on a total of 100 patients. Seventy urine samples were positive for leukocytes and nitrite chemistry, and 30 were negative for both. The measurements of urine leukocytes and bacteria were compared between Sysmex UF-5000 urine flow cytometry and the Shih-Yung method. The diagnostic value was obtained from ROC analysis of urine flow cytometry and the culture.

Results

A leukocyte cutoff value of 87.2/μL had a sensitivity and specificity of 98.33% and 95%, respectively, and 98.33% sensitivity and 75% specificity at a bacterial cutoff of 582.22/μL. Interestingly, our study identified strong and consistent agreement of leukocyte and bacterial parameters between urine flow cytometry and Shih-Yung (k = 0.959, p < 0.001 and k = 0.939, p < 0.001, respectively). Furthermore, through analyzing the dominance angle of the scattergram, a strong agreement was obtained with the culture result (k = 0.880, p < 0.001).

Conclusions

The Shih-Yung method showed consistent agreement with urine flow cytometry for the detection of leukocytes and bacteria. The use of certain cutoffs for bacterial and leukocyte parameters in urine flow cytometry demonstrated very good performance in detecting acquired symptomatic UTIs.

Medium throughput protocol for genome-based quantification of intracellular mycobacterial loads and macrophage survival during in vitro infection

Here, we present a streamlined protocol for assessing intracellular Mycobacterium tuberculosis (Mtb) loads in macrophages. This protocol describes the simultaneous assessment of macrophage viability using automated microscopy. Further, we detail the quantification of mycobacterial loads using a rapid, inexpensive, and accurate approach for mycobacterial DNA isolation from paraformaldehyde-fixed macrophages. Simultaneous assessment of the bacterial loads using internal standard and macrophage viability allows for precise quantification of the effects of perturbations on Mtb and host cells while accounting for technical artifacts. For complete details on the use and execution of this protocol, please refer to Chatterjee et al. (2021).

Antimicrobial resistance among bacteria isolated from urinary tract infections in females in Namibia, 2016-2017

Background

The emergence of antimicrobial resistance (AMR) among bacterial pathogens demands a local understanding of the epidemiological situation. This information is needed both for clinical treatment decision-making purposes as well as for the revision of current care guidelines. Clinical AMR data from Namibia is sparse, whilst urinary tract infections remain not only widespread but they disproportionally affect females. This paper aims to describe the national antimicrobial resistance situation of major bacterial uropathogens in females within the 14 Namibian regions.

Method

Retrospective countrywide information on clinical urine cultures performed in females in Namibia in 2016–2017 was obtained from the national public health laboratory, Namibia Institute of Pathology (NIP). The data set included both microbiological findings as well as antimicrobial susceptibility test (AST) results. The AST was done as per the Clinical and Laboratory Standards Institute (CLSI) guidelines. Resistance to 3rd generation cephalosporins was indicative of Extended Spectrum-ß-lactamase (ESBL) production. Data analysis was done with WHONET using expert interpretation rules.

Results

In total, 22,259 urinary cultures were performed, of which 13,673 (61.4%) were culture positive. Gram-negative bacterial species accounted for 72.6% of the findings. The most common pathogens identified were Escherichia coli, Klebsiella pneumoniae and Proteus mirabilis. Most of these were from young females, with a median age ranging from 28 to 32 years for the various pathogens. Resistance to ampicillin was 77.7% in E. coli and 84.9% in K. pneumoniae. In E. coli, resistance to 1^st line empiric therapy antibiotic, nitrofurantoin, was below 13%, except for one region that showed 59.2% resistance. Resistance to third generation cephalosporin (3GC) was used as a proxy for ESBL production. By year 2017, 3GC resistance was 22%, 31.4% and 8.3% for E. coli, K. pneumoniae and P. mirabilis, respectively.

Conclusion

We report high resistance to ampicillin, quinolones and sulfamethoxazole-trimethoprim amongst E. coli. Resistance rates to third-generation cephalosporins was also concerningly high at 22%. Resistance to carbapenems was low. However, superiority of nitrofurantoin was found, which provides rational support for the usefulness of nitrofurantoin as an empiric therapy regimen for the treatment of urinary tract infections in this setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-022-01066-2.

Toilet-based continuous health monitoring using urine

Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections

Due to the high prevalence of patients attending with urinary tract infection (UTI) symptoms, the use of flow-cytometry as a rapid screening tool to avoid unnecessary cultures is becoming a widely used system in clinical practice. However, the recommended cut-points applied in flow-cytometry systems differ substantially among authors, making it difficult to obtain reliable conclusions. Here, we present FlowUTI, a shiny web-application created to establish optimal cut-off values in flow-cytometry for different UTI markers, such as bacterial or leukocyte counts, in urine from patients with UTI symptoms. This application provides a user-friendly graphical interface to perform robust statistical analysis without a specific training. Two datasets are analyzed in this manuscript: one composed of 204 urine samples from neonates and infants (≤3 months old) attended in the emergency department with suspected UTI; and the second dataset including 1174 urines samples from an elderly population attended at the primary care level. The source code is available on GitHub (https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI). The web application can be executed locally from the R console. Alternatively, it can be freely accessed at https://covidiario.shinyapps.io/flowuti/. FlowUTI provides an easy-to-use environment for evaluating the efficiency of the urinary screening process with flow-cytometry, reducing the computational burden associated with this kind of analysis.

Comparison of Urine Flow Cytometry on the UF-1000i System and Urine Culture of Urine Samples from Urological Patients

Introduction

The aims of this study were to evaluate urine flow cytometry (UFC) as a tool to screen urine samples of urological patients for bacteriuria and to compare UFC and dipstick analysis with urine culture in a patient cohort at a urological department of a university hospital.

Methods and Material

We screened 662 urine samples from urological patients (75.2% male; 80.7% inpatients; mean age 58 years). UFC results were compared to microbiological urine culture.

Results

The accuracy in using the UFC-based parameters for detecting cultural bacteriuria was 91.99% and 88.97% for ≥10⁵ colony-forming units (CFU)/mL and ≥10⁴ CFU/mL, respectively. UFC and leukocyte dipstick analysis measured leukocyturia similarly (Pearson correlation coefficient 0.87, p value <0.01%), but dipstick analysis scored less accurately on bacteriuria (accuracy 59.37% and 62.69%). UFC remained effective in subgroup analysis of patients of both sexes and with different urological conditions with its overall use only slightly impaired when assessing gross hematuria (NPV 84.62% for ≥10⁴ CFU/mL). UFC also reliably removed those urine samples below cutoffs with negative predictive values of 99.28% for ≥10⁵ CFU/mL and 95.86% for ≥10⁴ CFU/mL.

Conclusion

Counting bacteria with UFC is an accurate and rapid method to determine significant bacteriuria in urological patients and is superior to dipstick analysis or indirect surrogate parameters such as leukocyturia. When UFC is available, we recommend it to be used for the diagnosis of bacteriuria over findings obtained by dipstick analysis.

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations

Background

Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use.

Methods

Species identification and colony-forming unit (CFU/ml) quantification from bacterial cultures were matched to corresponding cellular (leucocytes/epithelial cells) and bacteria counts per μl. Results comprise samples analysed between 2013 and 2015 for which urine culture (reference standard) and UFC and urine test strip data (index tests, Sysmex UX-2000) were available.

Results

47,572 urine samples of 26,256 patients were analysed. Bacteria counts used to predict bacterial growth of ≥10⁵ CFU/ml showed an accuracy with an area under the receiver operating characteristic curve of > 93% compared to 82% using leukocyte counts. The relevant bacteriuria rule-out cut-off of 50 bacteria/μl reached a negative predictive value of 98, 91 and 89% and the rule-in cut-off of 250 bacteria/μl identified relevant bacteriuria with an overall positive predictive value of 67, 72 and 73% for microbiologically defined bacteriuria thresholds of 10⁵, 10⁴ or 10³ CFU/ml, respectively. Measured epithelial cell counts by UFC could not identify contaminated urine.

Conclusions

Prediction of a relevant bacterial growth by bacteria counts was most accurate and was a better predictor than leucocyte counts independently of the source of the urine and the medical specialty ordering the test (medical, surgical or others).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05893-3.

Opportunities for the application of real-time bacterial cell analysis using flow cytometry for the advancement of sterilization microbiology

Medical devices provide critical care and diagnostic applications through patient contact. Sterility assurance level (SAL) may be defined as the probability of a single viable micro-organism occurring on an item after a sterilization process. Sterilization microbiology often relies upon using an overkill validation method where a 12-log reduction in recalcitrant bacterial endospore population occurs during the process that exploits conventional laboratory-based culture media for enumeration. This timely review explores key assumptions underpinning use of conventional culture-based methods in sterilization microbiology. Consideration is given to how such methods may limit the ability to fully appreciate the inactivation kinetics of a sterilization process such as vaporized hydrogen peroxide (VH2O2) sterilization, and consequently design efficient sterilization processes. Specific use of the real-time flow cytometry (FCM) is described by way of elucidating the practical relevance of these limitation factors with implications and opportunities for the sterilization industry discussed. Application of FCM to address these culture-based limitation factors will inform real-time kinetic inactivation modelling and unlock potential to embrace emerging opportunities for pharma, medical device and sterilization industries including potentially disruptive applications that may involve reduced usage of sterilant.

Quantitative determination of leukocyte esterase with a paper-based device

The commercially-available colorimetric urine dipstic for the early detection of urinary tract infection (UTI) has several limitations. The quantitative determination of urinary leukocyte esterase (LE) for predicting UTI remains uncertain. This study presents a paper-based analytical device to detect LE (LE-PAD) as a point-of-care quantitative test for UTI. The LE-PAD is composed of a coating of mixed 3-(N-tosyl-L-alaninyloxy)-5-phenylpyrrole (PE) and 1-diazo-2-naphthol-4-sulfonic acid (DAS) deposited onto a silver conducting film (Ag film). The LE/urine reacts with the PE and DAS, and the resulting products in turn react with the silver coating, causing a change in resistivity. The quantitative calibration curve was established in this study and has been used to analyse urine samples from inpatients with urinary catheters (n = 21). The results revealed that the level of LE determined by LE-PADs was predictive of UTI diagnosis with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.704-1.000). Using an appropriate cut-off value, the sensitivity and specificity of UTI diagnosis by LE-PAD were 87.5% and 92.3%, while the LE-positivities of urine dipstics were 62.5% and 76.9%, respectively. For UTI diagnosis, the LE-PAD demonstrated positive and negative likelihood ratios of 11.38 and 0.14, suggesting that the novel LE-PAD is a reliable test.

Recent Developments in the Application of Flow Cytometry to Advance our Understanding of Mycobacterium tuberculosis Physiology and Pathogenesis

The ability of the bacterial pathogen Mycobacterium tuberculosis to adapt and survive within human cells to disseminate to other individuals and cause active disease is poorly understood. Research supports that as M. tuberculosis adapts to stressors encountered in the host, it exhibits variable physiological and metabolic states that are time and niche-dependent. Challenges associated with effective treatment and eradication of tuberculosis (TB) are in part attributed to our lack of understanding of these different mycobacterial phenotypes. This is mainly due to a lack of suitable tools to effectively identify/detect heterogeneous bacterial populations, which may include small, difficult-to-culture subpopulations. Importantly, flow cytometry allows rapid and affordable multiparametric measurements of physical and chemical characteristics of single cells, without the need to preculture cells. Here, we summarize current knowledge of flow cytometry applications that have advanced our understanding of the physiology of M. tuberculosis during TB disease. Specifically, we review how host-associated stressors influence bacterial characteristics such as metabolic activity, membrane potential, redox status and the mycobacterial cell wall. Further, we highlight that flow cytometry offers unprecedented opportunities for insight into bacterial population heterogeneity, which is increasingly appreciated as an important determinant of disease outcome. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

The Use of Motion Analysis as Particle Biomarkers in Lensless Optofluidic Projection Imaging for Point of Care Urine Analysis

Urine testing is an essential clinical diagnostic tool. The presence of urine sediments, typically analyzed through microscopic urinalysis or cell culture, can be indicative of many diseases, including bacterial, parasitic, and yeast infections, as well as more serious conditions like bladder cancer. Current urine analysis diagnostic methods are usually centralized and limited by high cost, inconvenience, and poor sensitivity. Here, we developed a lensless projection imaging optofluidic platform with motion-based particle analysis to rapidly detect urinary constituents without the need for concentration or amplification through culture. A removable microfluidics channel ensures that urine samples do not cross contaminate and the lens-free projection video is captured and processed by a low-cost integrated microcomputer. A motion tracking and analysis algorithm is developed to identify and track moving objects in the flow. Their motion characteristics are used as biomarkers to detect different urine species in near real-time. The results show that this technology is capable of detection of red and white blood cells, Trichomonas vaginalis, crystals, casts, yeast and bacteria. This cost-effective device has the potential to be implemented for timely, point-of-care detection of a wide range of disorders in hospitals, clinics, long-term care homes, and in resource-limited regions.

Progress in Automated Urinalysis

New technological advances have paved the way for significant progress in automated urinalysis. Quantitative reading of urinary test strips using reflectometry has become possible, while complementary metal oxide semiconductor (CMOS) technology has enhanced analytical sensitivity and shown promise in microalbuminuria testing. Microscopy-based urine particle analysis has greatly progressed over the past decades, enabling high throughput in clinical laboratories. Urinary flow cytometry is an alternative for automated microscopy, and more thorough analysis of flow cytometric data has enabled rapid differentiation of urinary microorganisms. Integration of dilution parameters (e.g., creatinine, specific gravity, and conductivity) in urine test strip readers and urine particle flow cytometers enables correction for urinary dilution, which improves result interpretation. Automated urinalysis can be used for urinary tract screening and for diagnosing and monitoring a broad variety of nephrological and urological conditions; newer applications show promising results for early detection of urothelial cancer. Concomitantly, the introduction of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has enabled fast identification of urinary pathogens. Automation and workflow simplification have led to mechanical integration of test strip readers and particle analysis in urinalysis. As the information obtained by urinalysis is complex, the introduction of expert systems may further reduce analytical errors and improve the quality of sediment and test strip analysis. With the introduction of laboratory-on-a-chip approaches and the use of microfluidics, new affordable applications for quantitative urinalysis and readout on cell phones may become available. In this review, we present the main recent developments in automated urinalysis and future perspectives.