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Establishing a biosafety plan for a flow cytometry shared resource laboratory.

ABSTRACT: A biosafety plan is essential to establish appropriate practices for biosafety in a shared resource laboratory (SRL). A biosafety plan will contain the essential information for the use of biological samples on specific instrumentation, their apparent risks, and the steps that should be taken to mitigate these risks. Establishment of a biosafety plan can be a daunting task as the variety of pathogens that come through the SRL is highly diverse and may change over time; however, having a plan that can adapt to this variety will provide a framework for addressing concerns and educating personnel and users on biosafety practices. Using resources available at your institution and developing a robust relationship with health and safety personnel at your institution is key to generating an effective biosafety plan. Here we provide a basic underlying structure for a biosafety plan to aid SRL personnel in generating or maintaining their biosafety procedures, and provide guidance for establishing a dynamic, living biosafety plan.

SUBMITTER: Back JB

PROVIDER: S-EPMC9081124 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Establishing a biosafety plan for a flow cytometry shared resource laboratory.

Back Jessica B JB Martinez Lola L Nettenstrom Lauren L Sheerar Dagna D Thornton Sherry S

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20220117 5

A biosafety plan is essential to establish appropriate practices for biosafety in a shared resource laboratory (SRL). A biosafety plan will contain the essential information for the use of biological samples on specific instrumentation, their apparent risks, and the steps that should be taken to mitigate these risks. Establishment of a biosafety plan can be a daunting task as the variety of pathogens that come through the SRL is highly diverse and may change over time; however, having a plan tha ...[more]

PMID: 35037390

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Project description:BackgroundDespite the recent innovations in tuberculosis (TB) and multi-drug resistant TB (MDR-TB) diagnosis, culture remains vital for difficult-to-diagnose patients, baseline and end-point determination for novel vaccines and drug trials. Herein, we share our experience of establishing a BSL-3 culture facility in Uganda as well as 3-years performance indicators and post-TB vaccine trials (pioneer) and funding experience of sustaining such a facility.MethodsBetween September 2008 and April 2009, the laboratory was set-up with financial support from external partners. After an initial procedure validation phase in parallel with the National TB Reference Laboratory (NTRL) and legal approvals, the laboratory registered for external quality assessment (EQA) from the NTRL, WHO, National Health Laboratories Services (NHLS), and the College of American Pathologists (CAP). The laboratory also instituted a functional quality management system (QMS). Pioneer funding ended in 2012 and the laboratory remained in self-sustainability mode.ResultsThe laboratory achieved internationally acceptable standards in both structural and biosafety requirements. Of the 14 patient samples analyzed in the procedural validation phase, agreement for all tests with NTRL was 90% (P <0.01). It started full operations in October 2009 performing smear microscopy, culture, identification, and drug susceptibility testing (DST). The annual culture workload was 7,636, 10,242, and 2,712 inoculations for the years 2010, 2011, and 2012, respectively. Other performance indicators of TB culture laboratories were also monitored. Scores from EQA panels included smear microscopy >80% in all years from NTRL, CAP, and NHLS, and culture was 100% for CAP panels and above regional average scores for all years with NHLS. Quarterly DST scores from WHO-EQA ranged from 78% to 100% in 2010, 80% to 100% in 2011, and 90 to 100% in 2012.ConclusionsFrom our experience, it is feasible to set-up a BSL-3 TB culture laboratory with acceptable quality performance standards in resource-limited countries. With the demonstrated quality of work, the laboratory attracted more research groups and post-pioneer funding, which helped to ensure sustainability. The high skilled experts in this research laboratory also continue to provide an excellent resource for the needed national discussion of the laboratory and quality management systems.

Project description:Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

Dataset Information

Establishing a biosafety plan for a flow cytometry shared resource laboratory.

Publications

Establishing a biosafety plan for a flow cytometry shared resource laboratory.

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