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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Published on: April 8, 2016

Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer.

Eduard Chelebian¹, Christophe Avenel¹, Kimmo Kartasalo²

¹Science for Life Laboratory, Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden.

|October 13, 2021

Summary

Artificial intelligence (AI) analyzed prostate cancer slide morphology and spatial transcriptomics data. This approach links visual patterns to molecular profiles, revealing new insights into cancer heterogeneity.

Keywords:

deep learning morphological features prostate cancer spatial transcriptomics

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miRNA Expression Analyses in Prostate Cancer Clinical Tissues

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

Published on: September 8, 2015

Area of Science:

Oncology
Computational Biology
Pathology

Background:

Prostate cancer exhibits significant molecular and morphological heterogeneity, complicating research.
Understanding the interplay between morphology and molecular patterns is crucial for advancing prostate cancer research.

Purpose of the Study:

To develop an artificial intelligence (AI)-driven method for analyzing morphological and molecular data in prostate cancer.
To investigate the connection between morphological changes and underlying molecular patterns in multi-focal prostate cancer.

Main Methods:

Utilized AI, specifically convolutional neural networks, to extract morphological features from hematoxylin and eosin (H&E)-stained prostatectomy slides.
Employed spatial transcriptomics (ST) to obtain spatially resolved gene expression data.
Applied dimensionality reduction techniques to both morphological and molecular data for integrated analysis.

Main Results:

AI successfully identified and clustered regions based on morphology, correlating with manual annotations.
Morphological patterns were found to be associated with specific molecular profiles.
The study demonstrated the ability to predict the spatial variation of individual genes using morphological data.

Conclusions:

The developed AI-based workflow effectively integrates morphological and molecular data from prostate cancer.
This approach facilitates unsupervised studies to explore the complex heterogeneity of prostate cancer.
The findings offer a novel method for gaining deeper insights into the relationship between visual and genetic characteristics of cancer.