Imagej citation manual#
Imagej citation license#

The user may define as many cell types as needed, and each cell type is defined by as many constraints on the features as desired. (d) Cell types are defined by a set of constraints in any of the detected features. (c) A collection of diverse features are extracted to both characterize and identify by ID number each cell. (b) Cell images are acquired and then processed for single-cell segmentation, feature extraction, and cell-type classification.

(a) Cell culture in which classification will be done to identify specific cell types. Illustration of the workflow to identify specific cell types in a cell population. Therefore, Cell-TypeAnalyzer enables users describing a cell population through a set of extracted features to identify biologically relevant similarities or variations on a sample.įigure 1.

Imagej citation license#

Cell-TypeAnalyzer is an open-source plugin under the GNU public license that works equally well under Fiji ( Reference Schindelin, Arganda-Carreras and Frise 12 ) or ImageJ ( Reference Schindelin, Rueden, Hiner and Eliceiri 13 ), offering a semiautomated cell-type classification using separated RGB channels for multiple microscopy image formats in an objective manner considerably more accurate than qualitative strategies. In this context, single-cell features extraction arises helping researchers to properly overcome these drawbacks defining cell types which will be then cataloged into groups revealing different cell states or behaviors.Ĭell-TypeAnalyzer allows the user to classify cells of interest (see Figure 1), identifying a set of cells sharing common morphological, intensity, or spatial features according to a given biologically defined class. Additionally, in fluorescence microscopy, the signal-to-noise ratio is often low and the resolution quite limited ( Reference Ferrand, Schleicher, Ehrenfeuchter, Heusermann and Biehlmaier 10 ), making automation of cell-type classification even more challenging ( Reference Smal, Loog, Niessen and Meijering 11 ). It is quite challenging to design a versatile algorithm to automatically identify different cell types on multiple fluorescent markers located on the same field ( Reference Cossarizza, Chan, Radbruch, Acs, Adam and Adam-Klages 8, Reference Vembadi, Menachery and Qasaimeh 9 ) at the single-cell level. In cell biology, distinguishing specific cell types has traditionally been a labor-intensive and subjective task since it tries to classify cells according to morphological or phenotype forms ( Reference Trapnell 7 ) using tedious laboratory procedures as visual inspection.

Imagej citation manual#

Parallel to this unprecedented progress, advances in open-source bio-image software and scientific computing ( Reference Schneider, Rasband and Eliceiri 4 ), cell counting automation, and single-particle analysis algorithms ensure reproducibility and objectivity compared to the more subjective manual analyses ( Reference Barry, Durkin, Abella and Way 5, Reference O’Brien, Hayder and Peng 6 ). Nowadays, both multi-fluorescence imaging and labeling techniques are commonly used to identify biologically relevant processes through quantitative data extraction from fluorescently labeled molecules of interest ( Reference Kervrann, Sorzano, Acton, Olivo-Marin and Unser 1 – Reference Waters 3 ). The plugin does not require any programming skill and can analyze cells in many different acquisition setups. Once the analysis is set up, it can be automatically and efficiently performed on many images. This procedure may be applied in batch mode to multiple microscopy files. Our software provides a modular and flexible strategy to perform cell classification through a wizard-like graphical user interface in which the user is intuitively guided through each step of the analysis.

Our workflow consists of (a) image preprocessing actions, data spatial calibration, and region of interest for analysis (b) segmentation to isolate cells from background (optionally including user-defined preprocessing steps helping the identification of cells) (c) extraction of features from each cell (d) filters to select relevant cells (e) definition of specific criteria to be included in the different cell types (f) cell classification and (g) flexible analysis of the results. It consists of an open-source plugin for Fiji or ImageJ to detect and classify cells in 2D images. We describe a semiautomated and versatile tool called Cell-TypeAnalyzer to avoid the time-consuming and biased manual classification of cells according to cell types. Fluorescence microscopy techniques have experienced a substantial increase in the visualization and analysis of many biological processes in life science.