Long-term quality assessment and monitoring of light microscope performance through accessible and reliable protocols, tools and metrics
O. Faklaris, L. Bancel-Vallee, A. Dauphin, B. Monterroso, P. Frere, D. Geny, T. Manoliu, S. de Rossi, F. P. Cordelieres, D. Schapman, R. Nitschke, J. Cau, T. Guilbert
Abstract
Reliable, reproducible and comparable results are what biology requires from microscopy. To achieve that level of confidence, monitoring the stability of the microscope performance over time with standardized quality testing routines is essential for mining quantitative data. Three levels of microscope quality control procedures should be considered: i) usage of accessible and affordable tools and samples, ii) execution of easy and fast, preferably automatized, acquisition protocols, iii) analysis of data in the most automated way possible with adequate metrics for long-term monitoring. In this paper, we test the acquisition protocols on the mainly used microscope techniques (wide-field, spinning disk and confocal microscopy) with simple quality control tools. Seven protocols specify metrics on measuring the lateral and axial resolution (Point-Spread Function) of the system, field flatness, chromatic aberrations and co-registration, illumination power monitoring and stability, stage drift and positioning repeatability and finally temporal and spatial noise sources of camera detectors. We designed an ImageJ/FiJi java plugin named MetroloJ_QC to incorporate the identified metrics and automatize the data processing for the analysis. After processing and comparing the data of microscopes from more than ten imaging facilities, we test the robustness of the metrics and the protocols by determining experimental limit values. Our results give a first extensive characterization of the quality control procedures of a light microscope, with an automated data processing and experimental limit values that can be used by core facility staff and researchers to monitor the microscope performance over time.
02 /Specification & Classification
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