Laboratory Animal Management Assistant (LAMA): a LIMS for active research colonies

Digital Management Systems in Academic Health Sciences Laboratories: A Scoping Review

Good laboratory practices (GLP) increase the quality and traceability of results in health sciences research. However, factors such as high staff turnover, insufficient resources, and a lack of training for managers may limit their implementation in research and academic laboratories. This Scoping Review aimed to identify digital tools for managing academic health sciences and experimental medicine laboratories and their relationship with good practices. Following the PRISMA-ScR 2018 criteria, a search strategy was conducted until April 2021 in the databases PUBMED, Web of Sciences, and Health Virtual Library. A critical appraisal of the selected references was conducted, followed by data charting. The search identified twenty-one eligible articles, mainly originated from high-income countries, describing the development and/or implementation of thirty-two electronic management systems. Most studies described software functionalities, while nine evaluated and discussed impacts on management, reporting both improvements in the workflow and system limitations during implementation. In general, the studies point to a contribution to different management issues related to GLP principles. In conclusion, this review identified evolving evidence that digital laboratory management systems may represent important tools in compliance with the principles of good practices in experimental medicine and health sciences research.

A REDCap application that links researchers, animal facility staff and members of the IACUC in animal health monitoring

Research studies involving animal experimentation are regulated by the Institutional Animal Care and Use Committee (IACUC). To this end, the IACUC must integrate the information provided by the investigators of each preclinical study and the veterinarians from the animal facility in order to monitor and approve the process. Using a paper-based system to collect animal health and welfare data is a common, albeit time-consuming practice, prone to transcription and reading errors, not to mention inconvenient for veterinarians and investigators wishing to make timely and collaborative decisions when animal welfare is at risk. We created a web-based monitoring system focused on animal health with the potential to improve animal welfare. The data management system is based on REDCap software, which enables data integration in order to offer a solution for animal welfare assessment. The proposed scheme includes key indicators of general health status, such as environment, physical/nutritional information, and behavioral parameters during animal breeding and experimentation, as important components of animal welfare. In addition, the system facilitates communication of this information among researchers, animal facility staff, and the IACUC. REDCap is available to non-profit organizations, and may be adapted and replicated by institutions interested in and responsible for animal care, and used in research. REDCap is an excellent tool for promoting good practices that benefit experimental animal health.

Principles and application of LIMS in mouse clinics

Large-scale systemic mouse phenotyping, as performed by mouse clinics for more than a decade, requires thousands of mice from a multitude of different mutant lines to be bred, individually tracked and subjected to phenotyping procedures according to a standardised schedule. All these efforts are typically organised in overlapping projects, running in parallel. In terms of logistics, data capture, data analysis, result visualisation and reporting, new challenges have emerged from such projects. These challenges could hardly be met with traditional methods such as pen & paper colony management, spreadsheet-based data management and manual data analysis. Hence, different Laboratory Information Management Systems (LIMS) have been developed in mouse clinics to facilitate or even enable mouse and data management in the described order of magnitude. This review shows that general principles of LIMS can be empirically deduced from LIMS used by different mouse clinics, although these have evolved differently. Supported by LIMS descriptions and lessons learned from seven mouse clinics, this review also shows that the unique LIMS environment in a particular facility strongly influences strategic LIMS decisions and LIMS development. As a major conclusion, this review states that there is no universal LIMS for the mouse research domain that fits all requirements. Still, empirically deduced general LIMS principles can serve as a master decision support template, which is provided as a hands-on tool for mouse research facilities looking for a LIMS.

Transforming microbial genotyping: a robotic pipeline for genotyping bacterial strains

Microbial genotyping increasingly deals with large numbers of samples, and data are commonly evaluated by unstructured approaches, such as spread-sheets. The efficiency, reliability and throughput of genotyping would benefit from the automation of manual manipulations within the context of sophisticated data storage. We developed a medium- throughput genotyping pipeline for MultiLocus Sequence Typing (MLST) of bacterial pathogens. This pipeline was implemented through a combination of four automated liquid handling systems, a Laboratory Information Management System (LIMS) consisting of a variety of dedicated commercial operating systems and programs, including a Sample Management System, plus numerous Python scripts. All tubes and microwell racks were bar-coded and their locations and status were recorded in the LIMS. We also created a hierarchical set of items that could be used to represent bacterial species, their products and experiments. The LIMS allowed reliable, semi-automated, traceable bacterial genotyping from initial single colony isolation and sub-cultivation through DNA extraction and normalization to PCRs, sequencing and MLST sequence trace evaluation. We also describe robotic sequencing to facilitate cherrypicking of sequence dropouts. This pipeline is user-friendly, with a throughput of 96 strains within 10 working days at a total cost of < €25 per strain. Since developing this pipeline, >200,000 items were processed by two to three people. Our sophisticated automated pipeline can be implemented by a small microbiology group without extensive external support, and provides a general framework for semi-automated bacterial genotyping of large numbers of samples at low cost.

SAPling: a Scan-Add-Print barcoding database system to label and track asexual organisms

We have developed a 'Scan-Add-Print' database system, SAPling, to track and monitor asexually reproducing organisms. Using barcodes to uniquely identify each animal, we can record information on the life of the individual in a computerized database containing its entire family tree. SAPling has enabled us to carry out large-scale population dynamics experiments with thousands of planarians and keep track of each individual. The database stores information such as family connections, birth date, division date and generation. We show that SAPling can be easily adapted to other asexually reproducing organisms and has a strong potential for use in large-scale and/or long-term population and senescence studies as well as studies of clonal diversity. The software is platform-independent, designed for reliability and ease of use, and provided open source from our webpage to allow project-specific customization.

Implementation of a configurable laboratory information management system for use in cellular process development and manufacturing

BACKGROUND AIMS

Regulatory requirements for the manufacturing of cell products for clinical investigation require a significant level of record-keeping, starting early in process development and continuing through to the execution and requisite follow-up of patients on clinical trials. Central to record-keeping is the management of documentation related to patients, raw materials, processes, assays and facilities.

METHODS

To support these requirements, we evaluated several laboratory information management systems (LIMS), including their cost, flexibility, regulatory compliance, ongoing programming requirements and ability to integrate with laboratory equipment. After selecting a system, we performed a pilot study to develop a user-configurable LIMS for our laboratory in support of our pre-clinical and clinical cell-production activities. We report here on the design and utilization of this system to manage accrual with a healthy blood-donor protocol, as well as manufacturing operations for the production of a master cell bank and several patient-specific stem cell products.

RESULTS

The system was used successfully to manage blood donor eligibility, recruiting, appointments, billing and serology, and to provide annual accrual reports. Quality management reporting features of the system were used to capture, report and investigate process and equipment deviations that occurred during the production of a master cell bank and patient products.

CONCLUSIONS

Overall the system has served to support the compliance requirements of process development and phase I/II clinical trial activities for our laboratory and can be easily modified to meet the needs of similar laboratories.

Conditional Allele Mouse Planner (CAMP): software to facilitate the planning and design of breeding strategies involving mice with conditional alleles

Transgenic and conditional knockout mouse models play an important role in biomedical research and their use has grown exponentially in the last 5-10 years. Generating conditional knockouts often requires breeding multiple alleles onto the background of a single mouse or group of mice. Breeding these mice depends on parental genotype, litter size, transmission frequency, and the number of breeding rounds. Therefore, a well planned breeding strategy is critical for keeping costs to a minimum. However, designing a viable breeding strategy can be challenging. With so many different variables this would be an ideal task for a computer program. To facilitate this process, we created a Java-based program called Conditional Allele Mouse Planner (CAMP). CAMP is designed to provide an estimate of the number of breeders, amount of time, and costs associated with generating mice of a particular genotype. We provide a description of CAMP, how to use it, and offer it freely as an application.