Igor Pro Analysis
Igor Pro Analysis - Related Videos
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
0 Views •
Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
08:48
Analysis of the Ambient Particulate Matter-induced Chromosomal Aberrations Using an In Vitro System
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Cited by 3 •
2016
This protocol describes techniques for the quantification and characterization of chromosomal aberrations in vitro in RAW264.7 mouse macrophages after treatment with ambient air particulate matter.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
06:24
Preparation of Nanoparticles for ToF-SIMS and XPS Analysis
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Cited by 14 •
2020
A number of different procedures for preparing nanoparticles for surface analysis are presented (drop casting, spin coating, deposition from powders, and cryofixation). We discuss the challenges, opportunities, and possible applications of each method, particularly regarding the changes in the surface properties caused by the different preparation methods.
FAQs
Waves provide structured arrays for storing measurements and related values, allowing calculations, graphs, statistical operations, and fits to use organized data rather than isolated entries. This structure is especially useful when biological experiments produce multiple measurements across time, conditions, or samples. Keeping data in waves also supports consistent transformations and makes customized analytical procedures easier to reproduce.
These tools address different stages of interpretation. Mathematical operations transform or derive values from measurements, statistical analysis supports comparisons among experimental conditions, and curve fitting estimates parameters from a selected model. Used together, they can move an investigation from raw biological measurements toward quantitative descriptions of patterns, relationships, and differences.
Programmable procedures allow researchers to customize calculations and organize repeated analytical steps into a defined workflow. Applying the same procedure to comparable datasets can reduce inconsistencies caused by manual processing and preserve how results were generated. In biological studies, this is valuable when analyzing repeated time courses, microscopy measurements, or electrophysiology recordings.
The usefulness of an analysis depends on how well the chosen operations, statistical approach, graph, or fitted model match the biological measurements and experimental comparison. Data organization also matters because waves must represent the measurements clearly. When these choices align with the question being studied, the resulting parameters, patterns, or comparisons are easier to interpret.
A typical workflow begins by organizing measurements into waves, followed by applying mathematical operations or other transformations needed for analysis. Researchers can then visualize the data, compare experimental conditions statistically, or fit a model to estimate parameters. The final stage is interpreting the resulting patterns and presenting them clearly through graphs or quantitative summaries.
The approach is relevant when experiments generate quantitative datasets that require organization, visualization, calculation, or modeling. Examples supported by the topic include time courses, microscopy measurements, dose-response experiments, and electrophysiology recordings. It is particularly useful when a study needs both standard analytical tools and customized procedures for handling complex or repeated measurements.
Depending on the analysis, investigators can identify patterns over time, compare experimental conditions, estimate parameters from fitted models, and create graphs that communicate results. These outcomes help connect measurements with biological questions without relying only on visual inspection. The same workflow can also support clearer reporting by linking quantitative conclusions to organized data and reproducible calculations.