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Related Experiment Video

Updated: Feb 23, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Data-analysis strategies for image-based cell profiling.

Juan C Caicedo¹, Sam Cooper², Florian Heigwer³

¹Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

|September 1, 2017

Summary

This summary is machine-generated.

This study presents best practices for creating high-quality image-based cell profiles. These methods enable large-scale biological discovery through detailed cell morphology analysis.

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Published on: June 30, 2017

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Last Updated: Feb 23, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

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Published on: March 1, 2017

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Area of Science:

Cell Biology
Bioimaging
High-Content Screening

Background:

Image-based cell profiling quantifies phenotypic differences in cell populations.
It is a high-throughput strategy for large-scale biological studies using perturbations.
The workflow involves image acquisition, processing, and analysis.

Purpose of the Study:

To outline essential steps for generating high-quality image-based morphological profiles.
To recommend proven techniques for each stage of image-based cell profiling data analysis.
To provide guidance based on global laboratory experiences for biological discovery.

Main Methods:

Image acquisition using high-throughput microscopy.
Image processing and computational analysis for feature extraction.
Standardization of techniques across different experimental designs.

Main Results:

A curated set of recommended techniques for image-based cell profiling.
Consideration of various biological goals and laboratory preferences.
Strategies to enhance the quality and reliability of morphological profiles.

Conclusions:

Adoption of recommended techniques can improve image-based cell profiling.
This approach facilitates large-scale biological discovery.
Standardized methodologies enhance the reproducibility of cell morphology studies.