Current State of Laboratory Automation in Clinical Microbiology Laboratory

Implementing enclosed sterile integrated robotic platforms to improve cell-based screening for drug discovery

At GSK, we have implemented custom integrated robotics platforms housed in bespoke biosafety enclosures to augment our capabilities in advanced cellular screening. Here we present and discuss the impact of one such system, the Cellular Automated Screening Platform (CASPer). We evaluate the benefits of implementing specific processes on CASPer that include increasing the throughput of safety screening assays and improving data integrity when testing complex in vitro 3D primary human hepatocyte models. This article provides an overview of the platforms installed and offers insight into their utilisation by presenting example workflows and quality control solutions which have been implemented. We offer perspective on the advantages of such custom integrated systems and their limitations in cellular screening for early drug discovery.

Concept of flexible no-code automation for complex sample preparation procedures

Driven by demographic changes and dwindling Science Technology Engineering Mathematics enrolments, our research introduces no-code automation as a strategic response, aimed at mitigating labor shortages while enhancing productivity and safety in the laboratory environment. Employing a user-friendly, no-code software platform, we automated a complex HPTLC assay, enabling laboratory personnel to configure and modify workflows without requiring specialized programming skills. The manuscript outlines the deployment of a collaborative robot (cobot), a programmable logic controller (PLC), and the utilization of self-developed open-source hardware components to establish automated stations for sample handling, incubation, spraying, detection, and storage within the assay process. The research addresses challenges such as the handling of fragile HPTLC plates and the seamless integration of automated stations, solved through innovative design solutions and adaptive programming methods. This investigation demonstrates the feasibility and efficiency of no-code automation in overcoming skilled labor deficits.

Improvement of bioanalytical parameters through automation: suitability of a hand-like robotic system

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy-using a robotic system (RS)-is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS.

The Impact of Laboratory Automation on the Time to Urine Microbiological Results: A Five-Year Retrospective Study

Total laboratory automation (TLA) is a valuable component of microbiology laboratories and a growing number of publications suggest the potential impact of automation in terms of analysis standardization, streaking quality, and the turnaround time (TAT). The aim of this project was to perform a detailed investigation of the impact of TLA on the workflow of commonly treated specimens such as urine. This is a retrospective observational study comparing two time periods (pre TLA versus post TLA) for urine specimen culture processing. A total of 35,864 urine specimens were plated during the pre-TLA period and 47,283 were plated during the post-TLA period. The median time from streaking to identification decreased from 22.3 h pre TLA to 21.4 h post TLA (p < 0.001), and the median time from streaking to final validation of the report decreased from 24.3 h pre TLA to 23 h post TLA (p < 0.001). Further analysis revealed that the observed differences in TAT were mainly driven by the contaminated and positive samples. Our findings demonstrate that TLA has the potential to decrease turnaround times of samples in a laboratory. Nevertheless, changes in laboratory workflow (such as extended opening hours for plate reading and antibiotic susceptibility testing or decreased incubation times) might further maximize the efficiency of TLA and optimize TATs.

The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research

Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of genetic material, making them promising candidates for various biomedical applications. The integration of artificial intelligence (AI) into VLP research has catalyzed significant advancements in understanding, production, and therapeutic applications of these nanostructures. This comprehensive review explores the collaborative utilization of AI tools, computational methodologies, and state-of-the-art technologies within the VLP domain. AI's involvement in bioinformatics facilitates sequencing and structure prediction, unraveling genetic intricacies and three-dimensional configurations of VLPs. Furthermore, AI-enabled drug discovery enables virtual screening, demonstrating promise in identifying compounds to inhibit VLP activity. In VLP production, AI optimizes processes by providing strategies for culture conditions, nutrient concentrations, and growth kinetics. AI's utilization in image analysis and electron microscopy expedites VLP recognition and quantification. Moreover, network analysis of protein-protein interactions through AI tools offers an understanding of VLP interactions. The integration of multi-omics data via AI analytics provides a comprehensive view of VLP behavior. Predictive modeling utilizing machine learning algorithms aids in forecasting VLP stability, guiding optimization efforts. Literature mining facilitated by text mining algorithms assists in summarizing information from the VLP knowledge corpus. Additionally, AI's role in laboratory automation enhances experimental efficiency. Addressing data security concerns, AI ensures the protection of sensitive information in the digital era of VLP research. This review serves as a roadmap, providing insights into AI's current and future applications in VLP research, thereby guiding innovative directions in medicine and beyond.

An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

Development and evaluation of an artificial intelligence for bacterial growth monitoring in clinical bacteriology

In clinical bacteriology laboratories, reading and processing of sterile plates remain a significant part of the routine workload (30%-40% of the plates). Here, an algorithm was developed for bacterial growth detection starting with any type of specimens and using the most common media in bacteriology. The growth prediction performance of the algorithm for automatic processing of sterile plates was evaluated not only at 18-24 h and 48 h but also at earlier timepoints toward the development of an early growth monitoring system. A total of 3,844 plates inoculated with representative clinical specimens were used. The plates were imaged 15 times, and two different microbiologists read the images randomly and independently, creating 99,944 human ground truths. The algorithm was able, at 48 h, to discriminate growth from no growth with a sensitivity of 99.80% (five false-negative [FN] plates out of 3,844) and a specificity of 91.97%. At 24 h, sensitivity and specificity reached 99.08% and 93.37%, respectively. Interestingly, during human truth reading, growth was reported as early as 4 h, while at 6 h, half of the positive plates were already showing some growth. In this context, automated early growth monitoring in case of normally sterile samples is envisioned to provide added value to the microbiologists, enabling them to prioritize reading and to communicate early detection of bacterial growth to the clinicians.

Development of an automated amplicon-based next-generation sequencing pipeline for rapid detection of bacteria and fungi directly from clinical specimens

The timely identification of microbial pathogens is essential to guide targeted antimicrobial therapy and ultimately, successful treatment of an infection. However, the yield of standard microbiology testing (SMT) is directly related to the duration of antecedent antimicrobial therapy as SMT culture methods are dependent on the recovery of viable organisms, the fastidious nature of certain pathogens, and other pre-analytic factors. In the last decade, metagenomic next-generation sequencing (mNGS) has been successfully utilized as a diagnostic tool for various applications within the clinical laboratory. However, mNGS is resource, time, and labor-intensive-requiring extensive laborious preliminary benchwork, followed by complex bioinformatic analysis. We aimed to address these shortcomings by developing a largely Automated targeted Metagenomic next-generation sequencing (tmNGS) PipeLine for rapId inFectIous disEase Diagnosis (AMPLIFIED) to detect bacteria and fungi directly from clinical specimens. Therefore, AMPLIFIED may serve as an adjunctive approach to complement SMT. This tmNGS pipeline requires less than 1 hour of hands-on time before sequencing and less than 2 hours of total processing time, including bioinformatic analysis. We performed tmNGS on 50 clinical specimens with concomitant cultures to assess feasibility and performance in the hospital laboratory. Of the 50 specimens, 34 (68%) were from true clinical infections. Specimens from cases of true infection were more often tmNGS positive compared to those from the non-infected group (82.4% vs 43.8%, respectively, P = 0.0087). Overall, the clinical sensitivity of AMPLIFIED was 54.6% with 85.7% specificity, equating to 70.6% and 75% negative and positive predictive values, respectively. AMPLIFIED represents a rapid supplementary approach to SMT; the typical time from specimen receipt to identification of potential pathogens by AMPLIFIED is roughly 24 hours which is markedly faster than the days, weeks, and months required to recover bacterial, fungal, and mycobacterial pathogens by culture, respectively.

IMPORTANCE

To our knowledge, this represents the first application of an automated sequencing and bioinformatics pipeline in an exclusively pediatric population. Next-generation sequencing is time-consuming, labor-intensive, and requires experienced personnel; perhaps contributing to hesitancy among clinical laboratories to adopt such a test. Here, we report a strong case for use by removing these barriers through near-total automation of our sequencing pipeline.

Benefit analysis of the auto-verification system of intelligent inspection for microorganisms

In recent years, the automatic machine for microbial identification and antibiotic susceptibility tests has been introduced into the microbiology laboratory of our hospital, but there are still many steps that need manual operation. The purpose of this study was to establish an auto-verification system for bacterial naming to improve the turnaround time (TAT) and reduce the burden on clinical laboratory technologists. After the basic interpretation of the gram staining results of microorganisms, the appearance of strain growth, etc., the 9 rules were formulated by the laboratory technologists specialized in microbiology for auto-verification of bacterial naming. The results showed that among 70,044 reports, the average pass rate of auto-verification was 68.2%, and the reason for the failure of auto-verification was further evaluated. It was found that the main causes reason the inconsistency between identification results and strain appearance rationality, the normal flora in the respiratory tract and urine that was identified, the identification limitation of the mass spectrometer, and so on. The average TAT for the preliminary report of bacterial naming was 35.2 h before, which was reduced to 31.9 h after auto-verification. In summary, after auto-verification, the laboratory could replace nearly 2/3 of manual verification and issuance of reports, reducing the daily workload of medical laboratory technologists by about 2 h. Moreover, the TAT on the preliminary identification report was reduced by 3.3 h on average, which could provide treatment evidence for clinicians in advance.

A Simplified Label-Free Method for Proteotyping Sets of Six Isolates in a Single Liquid Chromatography-High-Resolution Tandem Mass Spectrometry Analysis

Clinical diagnostics and microbiology require high-throughput identification of microorganisms. Sample multiplexing prior to detection is an attractive means to reduce analysis costs and time-to-result. Recent studies have demonstrated the discriminative power of tandem mass spectrometry-based proteotyping. This technology can rapidly identify the most likely taxonomical position of any microorganism, even uncharacterized organisms. Here, we present a simplified label-free multiplexing method to proteotype isolates by tandem mass spectrometry that can identify six microorganisms in a single 20 min analytical run. The strategy involves the production of peptide fractions with distinct hydrophobicity profiles using spin column fractionation. Assemblages of different fractions can then be analyzed using mass spectrometry. Results are subsequently interpreted based on the hydrophobic characteristics of the peptides detected, which make it possible to link each taxon identified to the initial sample. The methodology was tested on 32 distinct sets of six organisms including several worst-scenario assemblages-with differences in sample quantities or the presence of the same organisms in multiple fractions-and proved to be robust. These results pave the way for the deployment of tandem mass spectrometry-based proteotyping in microbiology laboratories.

High-Throughput Bacteriophage Testing with Potency Determination: Validation of an Automated Pipetting and Phage Drop-Off Method

The development of bacteriophages (phages) as active pharmaceutical ingredients for the treatment of patients is on its way and regulatory agencies are calling for reliable methods to assess phage potency. As the number of phage banks is increasing, so is the number of phages that need to be tested to identify therapeutic candidates. Currently, assessment of phage potency on a semi-solid medium to observe plaque-forming units is unavoidable and proves to be labor intensive when considering dozens of phage candidates. Here, we present a method based on automated pipetting and phage drop-off performed by a liquid-handling robot, allowing high-throughput testing and phage potency determination (based on phage titer and efficiency of plaquing). Ten phages were tested, individually and assembled into one cocktail, against 126 Escherichia coli strains. This automated method was compared to the reference one (manual assay) and validated in terms of reproducibility and concordance (ratio of results according to the Bland and Altman method: 0.99; Lin's concordance correlation coefficient: 0.86). We found that coefficients of variation were lower with automated pipetting (mean CV: 13.3% vs. 24.5%). Beyond speeding up the process of phage screening, this method could be used to standardize phage potency evaluation.

Conversion of a classical microbiology laboratory to a total automation laboratory enhanced by the application of lean principles

Laboratory automation in microbiology improves productivity and reduces sample turnaround times (TATs). However, its full potential can be unlocked through the optimization of workflows by adopting lean principles. This study aimed to explore the relative impact of laboratory automation and continuous improvement events (CIEs) on productivity and TATs. Laboratory automation took place in November 2020 and consisted of the introduction of WASPLab and VITEK MS systems. CIEs were run in May and September 2021. Before the conversion, the laboratory processed about ~492 samples on weekdays and had 10 full-time equivalent (FTE) staff for a productivity of 49 samples/FTE/day. In March 2021, after laboratory automation, the caseload went up to ~621 while the FTEs decreased to 8.5, accounting for productivity improvement to 73 samples/FTE/day. The hypothetical productivity went up to 110 samples/FTE/day following CIEs, meaning that the laboratory could at that point deal with a caseload increase to ~935 with unchanged FTEs. Laboratory conversion also led to an improvement in TATs for all sample types. For vaginal swabs and urine samples, median TATs decreased from 70.3 h [interquartile range (IQR): 63.5-93.1] and 73.7 h (IQR: 35.6-50.7) to 48.2 h (IQR: 44.8-67.7) and 40.0 h (IQR: 35.6-50.7), respectively. Automation alone was responsible for 37.2% and 75.8% of TAT reduction, respectively, while the remaining reduction of 62.8% and 24.2%, respectively, was achieved due to CIEs. The laboratory reached productivity and TAT goals predefined by the management after CIEs. In conclusion, automation substantially improved productivity and TATs, while the subsequent implementation of lean management further unlocked the potential of laboratory automation.IMPORTANCEIn this study, we combined total laboratory automation with lean management to show that appropriate laboratory work organization enhanced the benefit of the automation and substantially contributed to productivity improvements. Globally, the rapid availability of accurate results in the setting of a clinical microbiology laboratory is part of patient-centered approaches to treat infections and helps the implementation of antibiotic stewardship programs backed by the World Health Organization. Locally, from the point of view of laboratory management, it is important to find ways of maximizing the benefits of the use of technology, as total laboratory automation is an expensive investment.

Preparing for laboratory automation and consolidation-Establishing the validity of pediatric-like low-volume urine samples in boric-acid containing tubes

Microbiological services consolidation has increased the usage of preservative-containing urine tubes, potentially inhibiting pathogens in low-volume pediatric urine samples, yielding false-negative results. Our study demonstrates comparable growth with 1 ml versus the recommended 3 ml urine, following different shipping intervals. We advocate for regulators to consider similar large-scale validations, ensuring results' consistency.

Multicenter evaluation of the Selux Next-Generation Phenotyping antimicrobial susceptibility testing system

The Selux Next-Generation Phenotyping (NGP) system (Charlestown, MA) is a new antimicrobial susceptibility testing system that utilizes two sequential assays performed on all wells of doubling dilution series to determine MICs. A multicenter evaluation of the performance of the Selux NGP system compared with reference broth microdilution was conducted following FDA recommendations and using FDA-defined breakpoints. A total of 2,488 clinical and challenge isolates were included; gram-negative isolates were tested against 24 antimicrobials, and gram-positive isolates were tested against 15 antimicrobials. Data is provided for all organism-antimicrobial combinations evaluated, including those that did and did not meet FDA performance requirements. Overall very major error and major error rates were less than 1% (31/3,805 and 107/15,606, respectively), essential agreement and categorical agreement were >95%, reproducibility was ≥95%, and the average time-to-result (from time of assay start to time of MIC result) was 5.65 hours.

Evaluation of PhenoMATRIX and PhenoMATRIX PLUS for the screening of MRSA from nasal and inguinal/perineal swabs using chromogenic media

The objective of this study was to assess the clinical performances of PhenoMATRIX and PhenoMATRIX PLUS for the screening of methicillin-resistant Staphylococcus aureus (MRSA) from nasal and inguinal/perineal ESwabs using chromogenic media. The automated performances were compared to the manual reading. Additionally, we evaluated PhenoMATRIX PLUS for the automatic release of the negative results to the Laboratory Information System (LIS) and the automatic discharge of the negative plates from the incubators. A total of 6,771 non-duplicate specimens were used by PhenoMATRIX as a machine learning model. The validation of these settings was performed on 17,223 non-duplicate specimens. The MRSA positivity rate was 5% (866/17,223). Validated settings were then used by PhenoMATRIX PLUS on another 1,409 non-duplicate specimens. The sensitivities of PhenoMATRIX and PhenoMATRIX PLUS were 99.8% [95% confidence interval (CI), 99.2%-99.9%] and 100% (95% CI, 92.1%-100%), respectively. The specificities of PhenoMATRIX and PhenoMATRIX PLUS were 99.1% (95% CI, 99.0%-99.2%) and 95.2% (95% CI, 93.8%-96.1%), respectively. All the 1,297 MRSA-negative specimens analyzed by PhenoMATRIX PLUS were automatically released and sent to the LIS immediately after availability of the culture image on the WASPLab (100% accuracy). All negative media plates were automatically discarded. PhenoMATRIX PLUS decreases the time spent by technologists on negative plates and ensures optimal usage of the incubators' capacity.

Can I benefit from laboratory automation? A decision aid for the successful introduction of laboratory automation

The large volumes of samples to be analysed every day would be impossible to manage without laboratory automation. As laboratory procedures have progressed, so have the tasks of laboratory personnel. With this feature article, we would like to provide (bio)chemical practitioners with little or no knowledge of laboratory automation with a guide to help them decide whether to implement laboratory automation and find a suitable system. Especially in small- and medium-sized laboratories, operating a laboratory system means having bioanalytical knowledge, but also being familiar with the technical aspects. However, time, budget and personnel limitations allow little opportunity for personnel to get into the depths of laboratory automation. This includes not only the operation, but also the decision to purchase an automation system. Hasty investments do not only result in slow or non-existent cost recovery, but also occupy valuable laboratory space. We have structured the article as a decision tree, so readers can selectively read chapters that apply to their individual situation. This flexible approach allows each reader to create a personal reading flow tailored to their specific needs. We tried to address a variety of perspectives on the topic, including people who are either supportive or sceptical of laboratory automation, personnel who want or need to automate specific processes, those who are unsure whether to automate and those who are interested in automation but do not know which areas to prioritize. We also help to make a decision whether to reactivate or discard already existing and unused laboratory equipment.

Implementing laboratory automation for next-generation sequencing: benefits and challenges for library preparation

In the wake of COVID-19, the importance of next-generation sequencing (NGS) for diagnostic testing and surveillance-based screening has never been more evident. Considering this, continued investment is critical to ensure more public health laboratories can adopt these advanced molecular technologies. However, many facilities may face potential barriers such as limited staff available to routinely prepare, test, and analyze samples, lack of expertise or experience in sequencing, difficulties in assay standardization, and an inability to handle throughput within expected turnaround times. Workflow automation provides an opportunity to overcome many of these challenges. By identifying these types of sustainable solutions, laboratories can begin to utilize more advanced molecular-based approaches for routine testing. Nevertheless, the introduction of automation, while valuable, does not come without its own challenges. This perspective article aims to highlight the benefits and difficulties of implementing laboratory automation used for sequencing. We discuss strategies for implementation, including things to consider when selecting instrumentation, how to approach validations, staff training, and troubleshooting.

24/7 workflow for bloodstream infection diagnostics in microbiology laboratories: the first step to improve clinical management

OBJECTIVES

We aimed to evaluate the impact of an uninterrupted workflow regarding blood cultures on turnaround time and antibiotic prescription.

METHODS

Monomicrobial episodes of bacteremia were retrospectively evaluated before and after a continuous 24/7 workflow was implemented in our clinical microbiology laboratory (pre- and post-intervention periods; PREIP and POSTIP). Primary outcome was the time from specimen collection to the first change in antibiotic therapy. Secondary outcomes included the time from specimen collection to effective antibiotic therapy and to antibiotic susceptibility testing results (or turnaround time), as well as hospital length of stay and all-cause mortality at 30 days.

RESULTS

A total of 548 episodes of bacteremia were included in the final analysis. There was no difference in PREIP and POSTIP regarding patient characteristics and causative bacteria. In POSTIP, the mean time to the first change in antibiotic therapy was reduced by 10.4 h (p<0.001). The time to effective antibiotic therapy and the turnaround time were respectively reduced by 4.8 h (p<0.001) and 5.1 h (p=0.006) in POSTIP. There was no difference in mean hospital length of stay or mortality between the two groups.

CONCLUSIONS

Around the clock processing of blood cultures allows for a reduction in turnaround time, which in turn reduces the delay until effective antibiotic therapy prescription.