RESUMEN
Previous methods to measure protozoan numbers mostly rely on manual counting, which suffers from high variation and poor efficiency. Although advanced counting devices are available, the specialized and usually expensive machinery precludes their prevalent utilization in the regular laboratory routine. In this study, we established the ImageJ-based workflow to quantify ciliate numbers in a high-throughput manner. We conducted Tetrahymena number measurement using five different methods: particle analyzer method (PAM), find maxima method (FMM), trainable WEKA segmentation method (TWS), watershed segmentation method (WSM) and StarDist method (SDM), and compared their results with the data obtained from the manual counting. Among the five methods tested, all of them could yield decent results, but the deep-learning-based SDM displayed the best performance for Tetrahymena cell counting. The optimized methods reported in this paper provide scientists with a convenient tool to perform cell counting for Tetrahymena ecotoxicity assessment.
Asunto(s)
Tetrahymena , Recuento de Células/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Laboratorios , Aprendizaje AutomáticoRESUMEN
Image quantitation is as truthful as the reproducibility of the methods involved in experimentation, image acquisition, and analysis. Automation in image analysis is highly recommended as it grants the reproducibility of the resulting data, minimizes the bias and mistakes produced by manual intervention, and optimizes the overall time invested. This chapter focuses on the main concepts of ImageJ macro programming with special emphasis on the image analysis pipeline automation.
Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Lenguajes de Programación , Algoritmos , Conjuntos de Datos como AsuntoRESUMEN
This chapter describes an ImageJ/Fiji automated macro approach to estimate synapse densities in 2D fluorescence confocal microscopy images. The main step-by-step imaging workflow is explained, including example macro language scripts that perform all steps automatically for multiple images. Such tool provides a straightforward method for exploratory synapse screenings where hundreds to thousands of images need to be analyzed in order to render significant statistical information. The method can be adapted to any particular set of images where fixed brain slices have been immunolabeled against validated presynaptic and postsynaptic markers.
Asunto(s)
Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Animales , Encéfalo/citología , Colorantes Fluorescentes/química , Inmunohistoquímica/métodos , Proteínas de la Membrana/análisis , Proteínas de la Membrana/inmunología , Ratones , Microscopía Confocal/métodos , Neuronas/citología , Programas Informáticos , Coloración y Etiquetado/métodos , Sinapsis , Proteínas del Transporte Vesicular de Aminoácidos Inhibidores/análisis , Proteínas del Transporte Vesicular de Aminoácidos Inhibidores/inmunologíaRESUMEN
High-content screening (HCS) automates image acquisition and analysis in microscopy. This technology considers the multiple parameters contained in the images and produces statistically significant results. The recent improvements in image acquisition throughput, image analysis, and machine learning (ML) have popularized this kind of experiments, emphasizing the need for new tools and know-how to help in its design, analysis, and data interpretation. This chapter summarizes HCS recommendations for lab scale assays and provides both macros for HCS-oriented image analysis and user-friendly tools for data mining processes. All the steps described herein are oriented to a wide variety of image cell-based experiments. The workflows are illustrated with practical examples and test images. Their use is expected to help analyze thousands of images, create graphical representations, and apply machine learning models on HCS.