CellProfiler 4: improvements in speed, utility and usability

Abstract

Background Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements. Results Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines. Conclusions CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler’s powerful computational tools in the coming years.

A User-Friendly Approach for Routine Histopathological and Morphometric Analysis of Skeletal Muscle Using CellProfiler Software

“…It can distinguish subtle changes and measure multiple properties at once. Moreover, it can perform batch analysis (thousands of images), and the latest version (CellProfiler 4) has been demonstrated to be faster and less tedious than previous ones [23]. Finally, the use of CellProfiler hints at the possibility to build user-friendly tools that are able to adapt and perform their tasks without needing to use long and more complex tools based on machine learning or deep learning.…”

“…CellProfiler, developed by the Carpenter Lab at the Broad Institute of Harvard and MIT, is open-access software and available for Windows and macOS [20,21]. CellProfiler code was written using Python [22], and an updated, faster version of CellProfiler was recently released (CellProfiler 4) [23]. Java (www.java.com, accessed on 20 October 2018) installation and update are required prior to CellProfiler installation.…”

“…In the current manuscript, we propose a method to perform image analysis of muscle sections by using pipelines built with CellProfiler software, which have been recently implemented and updated to CellProfiler 4 [23]. Specifically, we present the data obtained by using a pipeline, which we named MyoProfiler, to measure MFD, CNF, PNF, cell localization and the number of macrophages in muscle sections from mdx mice compared with wild-type ones.…”

Adjusting the neuron to astrocyte ratio with cytostatics in hippocampal cell cultures from postnatal rats: A comparison of cytarabino furanoside (AraC) and 5-fluoro-2’-deoxyuridine (FUdR)

“…Images were taken with a CCD camera (IX51: XC10, Olympus Soft Imaging Solutions; X81: F-View II, Soft Imaging System) and Olympus cellSens Standard software (version 1.15) at the IX51 microscope and Cell M software (version 3.1, Olympus) at the IX81 microscope. The total number of cells as well as the numbers of neurons, astrocytes, and unspecified cells of 10 random fields of view with dimensions of 443.8 μm times 334.8 μm were counted and classified using the open-source software CellProfiler (version 4.2.1, Broad Institute, www.cellprofiler.org) [20][21][22][23] and CellProfiler Analyst (version 3.0.4, Broad Institute, www.cellprofileranalyst.org) [24][25][26][27]. A more detailed description of the applied CellProfiler pipelines and CellProfiler Analyst classification model can be found in the S2 File.…”

Pituitary Gonadotropin Gene Expression During Induced Onset of Postsmolt Maturation in Male Atlantic Salmon: In Vivo and Tissue Culture Studies

“…First, a pixel classification of the nuclei was performed with Ilastik (38) to separate nuclei from background. Using the probability maps produced in Ilastik, nuclei were then segmented in CellProfiler (39)(40)(41) and propagation outward from the nuclei was used to locate the cell boundaries. Cell cytoplasm was located as the cell boundary minus the nuclei boundary, and any positive signal inside the cytoplasm was measured by the ''MeasureObjectIntensity'' and ''MeasureGranularity'' modules in CellProfiler.…”