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In Utero Electroporation of Multiaddressable Genome-Integrating Color MAGIC Markers to Individualize Cortical Mouse Astrocytes

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Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.

Alex Olar^1,2, Teadora Tyler³, Paulina Hoppa³

¹Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.

Scientific Data

|January 19, 2024

Summary

This study introduces a new dataset of human brain tissue images for accurately detecting and quantifying astrocytes, crucial glial cells. This resource aids in developing automated tools for neurological disorder research.

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Area of Science:

Neuroscience
Cell Biology
Digital Pathology

Background:

Astrocytes, a type of glial cell, are vital for neuronal function, and their altered morphology and density are implicated in neurological disorders.
Current methods for astrocyte detection and density measurement are labor-intensive and not scalable for large-scale studies.
Aldehyde dehydrogenase 1 family member L1 (ALDH1L1) and glial fibrillary acidic protein (GFAP) are key markers for astrocyte identification.

Purpose of the Study:

To present a novel, comprehensive dataset of human brain tissue images for astrocyte research.
To facilitate the development of automated tools for astrocyte detection and quantification.
To enable deeper investigation into astrocyte density distribution and its relevance to neuropathology.

Main Methods:

A dataset of digital whole slide images from human brain tissues stained for ALDH1L1 and GFAP was created.
Images were partitioned into 8730 patches of 500x500 pixels.
The dataset includes patches with pixel sizes of 0.5019/pixel and 0.3557/pixel, sourced from 16 slides and 8 patients.

Main Results:

The dataset comprises 2323 ALDH1L1 and 4714 GFAP patches at 0.5019/pixel resolution, and 1382 ALDH1L1 and 311 GFAP patches at 0.3557/pixel resolution.
This curated collection of human brain samples provides a valuable resource for computational pathology.
The data supports the advancement of glial cell detection and quantification methodologies.

Conclusions:

The introduced dataset is pivotal for advancing automated astrocyte detection and quantification, including deep learning approaches.
It offers a platform for exploring astrocyte functionality and density variations in the human brain.
This resource has the potential to significantly broaden neuropathological study horizons and guide new diagnostic and treatment strategies for neurological disorders.