Abstract
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.
Highlights
Industrial laboratories and institutional core facilities increasingly employ robotics supported by sophisticated bioinformatics for fluidic manipulations [1]
In order to organise this information, we used a commercial, generic sample management system (SMS) (ITEMTRACKER) which assigns a unique identifier (ItemID), creation date, user and location to each item and appends a new data entry for each change, ensuring tracking of the history of all changes. We extended this SMS for use in a microbiology laboratory by creating a sequential, one-tomany parent-child hierarchy of items, the top of which is a fictitious item ‘Bacteria’, with a unique strain identifier (StrainID) plus alternative designations (Fig. 1b)
High-throughput DNA extraction and sequencing was implemented in multiple core sequencing laboratories soon after the beginnings of the genomic revolution in the mid-1990’s [25], and aspects of bacterial genotyping were automated in some laboratories soon thereafter [26,27,28]
Summary
Industrial laboratories and institutional core facilities increasingly employ robotics supported by sophisticated bioinformatics for fluidic manipulations [1]. The possibilities for automation and data management are illustrated by the ‘‘Robot Scientist’’, which formulates hypotheses, designs experiments and executes them, and analyzes and stores the results without human intervention [8,9]. Such developments emphasize a growing interest in robotic systems as experimentation tools where user-friendly plasticity is at least as important as the degree of throughput. Experiments, sample locations and data are usually documented in project-specific lab books or in unstructured and uncoordinated spreadsheets Such practises can result in inefficient communication; loss of data; and issues with knowledge transfer, in research groups with rotating staff and temporary visitors. The use of a LIMS is critical when large amounts of data are processed, and supports data mining
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