Cell Profiler Software
Cell Profiler Software - Related Videos
12:30
Glycan Profiling of Plant Cell Wall Polymers using Microarrays
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Cited by 26 •
2012
A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.
10:20
Single-cell Profiling of Developing and Mature Retinal Neurons
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Cited by 13 •
2012
A method for the isolation of single retinal cells and subsequent amplification of their cDNAs is described. Single-cell transcriptomics reveals the degree of cellular heterogeneity present in a tissue and uncovers new marker genes for rare cell populations. The accompanying protocol can be adjusted to suit many different cell types.
16:24
Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells
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Cited by 9 •
2014
Molecular signaling through both estrogen and microRNAs are critical in breast cancer development and growth. Estrogen activates the estrogen receptors, which are transcription factors. Many transcription factors can regulate the expression of microRNAs, and estrogen-regulated microRNAs can be profiled using different large-scale techniques.
10:41
VisualEyes: A Modular Software System for Oculomotor Experimentation
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Cited by 28 •
2011
Neural control and cognitive processes can be studied through eye movements. The VisualEyes software allows an operator to program stimuli on two computer screens independently using a simple, custom scripting language. The system can stimulate tandem eye movements (saccades and smooth pursuit) or opposing eye movements (vergence) or any combination.
08:23
Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes
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Cited by 13 •
2011
Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.
FAQs
Its modular pipeline divides image analysis into ordered operations, such as loading images, correcting illumination, identifying objects, separating nearby cells or nuclei, and calculating measurements. Each module contributes a defined processing step, allowing users to assemble workflows that match their biological question. This structure also makes the analysis sequence easier to reproduce across image datasets.
Uneven illumination can make equivalent regions appear to have different brightness, complicating object identification and intensity measurements. CellProfiler can correct this variation before subsequent analysis steps, helping the workflow distinguish biological signal from image-wide lighting differences. The correction is therefore especially relevant when researchers compare cellular intensity or localization across many microscopy images.
Once cells or nuclei are identified and separated, the workflow can quantify features including size, shape, intensity, and texture. These measurements convert visual differences into numerical variables that can be compared among objects or image groups. In biology, the resulting data can support analysis of morphology, protein localization, and variation within a cell population.
Automated measurement applies the same configured analysis steps across large image datasets, whereas manual scoring depends on repeated visual assessment. By reducing manual scoring, the approach helps produce more objective and reproducible measurements. Researchers can then use numerical results to examine population heterogeneity or treatment responses rather than relying only on qualitative impressions from selected images.
A typical workflow begins by loading microscopy images, followed by illumination correction when needed. The pipeline then identifies and separates objects such as cells or nuclei, calculates selected features, and produces quantitative measurements for later statistical analysis. Researchers can tailor the sequence to the structures and measurements relevant to their biological investigation.
The platform is useful when studies require quantitative analysis of many biological images, including investigations of cell morphology, protein localization, disease-related changes, and responses to treatments. It also supports drug-screening and other imaging-based research. Its measurements help connect cellular appearance with statistical analysis, making image-derived observations more suitable for systematic comparison.