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Updated: Jun 11, 2025

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

Deep learning pipeline for automated cell profiling from cyclic imaging.

Christian Landeros^1,2, Juhyun Oh^1,3, Ralph Weissleder^4,5,6

¹Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA, 02114, USA.

Scientific Reports

|October 9, 2024

Summary

This summary is machine-generated.

CycloNET is a new computational pipeline that rapidly analyzes cyclic immunofluorescence images. This tool enables faster, single-cell resolution insights into complex biological systems and disease pathology.

Keywords:

Cell segmentation Cyclic microscopy Imaging analysis Machine learning Software

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Last Updated: Jun 11, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

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Published on: June 30, 2017

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Area of Science:

Biomedical Imaging
Computational Biology
Immunofluorescence Microscopy

Background:

Cyclic fluorescence microscopy allows simultaneous detection of multiple targets, enhancing understanding of cellular interactions and signaling.
Analyzing large cyclic immunofluorescence datasets is time-consuming, hindering biological discovery.

Purpose of the Study:

To develop an automated computational pipeline, CycloNET, for efficient analysis of cyclic immunofluorescence raw images.
To enable rapid, single-cell resolution profiling from complex imaging datasets.

Main Methods:

CycloNET pipeline automates pre-processing of raw fluorescent images.
It corrects for translation errors between imaging cycles.
A pre-trained neural network segments cells and generates single-cell molecular profiles.

Main Results:

CycloNET processed a large-scale dataset (22 human samples) in 10 minutes.
The pipeline achieved single-cell resolution, identifying rare immune cell clusters.
Applied to head and neck squamous cell carcinoma patient samples.

Conclusions:

CycloNET significantly accelerates the analysis of cyclic immunofluorescence data.
This rapid pipeline facilitates deeper understanding of cellular processes and disease.
Potential applications in developmental biology, pathology, and personalized medicine.