Thus, image and image analysis quality requirements for time-lapse microscopy are more stringent and, due to the volume of data, automated quality control is more necessary. However, time-lapse imaging is acutely susceptible to many artifacts that negatively affect the proper identification and tracking of cells the appearance of such anomalies in a single frame can ruin an entire time series. The combination of automated imaging and large-scale, high-content, live-cell experiments is capable of delivering large amounts of data in very little time. Time-lapse assays probe biological questions that can only be investigated by observing the dynamic behavior of organisms, cells, organelles, or molecular assemblies over time. ConclusionsĬellProfiler Tracer is a useful, free tool for inspection and quality control of object tracking data, available from. Tracer allows multi-parametric morphological data to be visualized on object tracks, providing visualizations that have already been validated within the scientific community for time-lapse experiments, and combining them with simple graph-based measures for highlighting possible tracking artifacts. We present CellProfiler Tracer, a free and open-source tool that complements the object tracking functionality of the CellProfiler biological image analysis package. This makes quality assessment and algorithm adjustment a substantial challenge, particularly when dealing with hundreds of time-lapse movies collected in a high-throughput manner. Algorithms for cell tracking are widely available what researchers have been missing is a single open-source software package to visualize standard tracking output (from software like CellProfiler) in a way that allows convenient assessment of track quality, especially for researchers tuning tracking parameters for high-content time-lapse experiments. Thus, the Muscle2View is a viable tool for researchers aiming to quantify immunohistochemical variables from skeletal muscle biopsies.ĬellProfiler capillary-to-fiber interface high-content immunohistochemistry image analysis skeletal muscle biopsy.Time-lapse analysis of cellular images is an important and growing need in biology. In addition to fiber typing, myonuclei counting, and the quantification of fiber type-specific morphological measurements, the Muscle2View pipeline can identify the complex capillary-to-fiber network from a batch of images within minutes. NEW & NOTEWORTHY Here, we developed a freely available CellProfiler-based pipeline termed Muscle2View, which provides unbiased, high-content analysis of muscle cross-sectional immunohistochemistry images. This robust analysis is done in one single run within a user-friendly and flexible environment based on the free and widely used image software CellProfiler. In addition to the classical morphological measurements, the Muscle2View can identify the complex capillary-to-fiber network and myonuclear density in a fiber type-specific manner. Collectively, we demonstrate that the Muscle2View pipeline can provide unbiased and high-content analysis of muscle cross-sectional immunohistochemistry images. For several variables, however, there were differences (5-15%) between values computed by manual counting and Muscle2View, suggesting that the methods should not necessarily be used interchangeably. When comparing the Muscle2View pipeline to manual or semiautomatic analysis, overall the results revealed strong correlations. The novel identification of the capillary-to-fiber interface allowed for the calculation of microvascular factors such as capillary contacts (CC), individual capillary-to-fiber ratio (C/Fi), and capillary-to-fiber perimeter exchange (CFPE) index. Provided that the images are of sufficient quality and the settings are configured for the specific study, the pipeline allows for automatic and unsupervised analysis of fiber borders, myonuclei, capillaries, and morphometric parameters in a fiber type-specific manner from large batches of images in <10 min/tissue sample. Here, we developed Muscle2View, a free CellProfiler-based pipeline that integrates all key fiber-morphological variables, including the novel quantification of the capillary-to-fiber interface, in one single tool. Because manual immunohistochemical analysis of features such as skeletal muscle fiber typing, capillaries, myonuclei, and fiber size-related parameters is time consuming and prone to user subjectivity, automatic computational methods could allow for faster and more objective evaluation.
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