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Related Experiment Video

Updated: Jul 24, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

MAPS: Pathologist-level cell type annotation from tissue images through machine learning.

Muhammad Shaban^1,2,3,4, Yunhao Bai⁵, Huaying Qiu⁶

¹Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.

Biorxiv : the Preprint Server for Biology

|July 10, 2023

Summary

This summary is machine-generated.

Machine learning for Analysis of Proteomics in Spatial biology (MAPS) enables fast, accurate cell type identification from spatial proteomics data. This scalable approach achieves pathologist-level precision, advancing tissue biology and disease research.

Keywords:

Cell Annotation Machine learning Multiplexed imaging Proteomics

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Last Updated: Jul 24, 2025

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Published on: September 22, 2013

Area of Science:

Spatial biology
Proteomics
Machine learning

Background:

Highly multiplexed protein imaging analyzes protein distribution in native cellular and tissue contexts.
Current cell annotation methods for high-plex spatial proteomics are resource-intensive and require expert input, limiting scalability.

Approach:

MAPS (Machine learning for Analysis of Proteomics in Spatial biology) is a novel machine learning approach for cell type identification.
MAPS processes spatial proteomics data for rapid and precise cell annotation.

Key Points:

MAPS achieves human-level accuracy in cell type identification from spatial proteomics data.
Validated on MIBI and CODEX datasets, MAPS surpasses existing annotation techniques in speed and accuracy.
Demonstrates pathologist-level precision, even for challenging cell types like tumor cells of immune origin.

Conclusions:

MAPS offers a scalable and democratized solution for machine learning-based annotation of spatial proteomics data.
The approach accelerates advancements in understanding tissue biology and disease mechanisms.