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Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.

Yuxing Wang^1,2, Wenguan Wang³, Dongfang Liu¹

¹Department of Computer Engineering, Rochester Institute of Technology, Rochester, USA.

|October 20, 2023

Summary

GeneSegNet, a new deep learning method, enhances cell segmentation for in situ RNA data by combining gene expression and imaging. This approach improves accuracy over existing techniques, aiding in gene expression and cell morphology studies.

Keywords:

Cell segmentation Deep learning In situ hybridization Spatial transcriptomics

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Accurate cell segmentation is crucial for analyzing in situ RNA detection data.
Existing methods often fail to integrate both gene expression and imaging data effectively.
This limits the comprehensive study of cellular features.

Purpose of the Study:

To develop a novel deep learning method for cell segmentation that integrates gene expression and imaging data.
To improve the accuracy and robustness of cell segmentation in complex biological samples.
To address challenges posed by noisy training labels in segmentation tasks.

Main Methods:

Developed GeneSegNet, a deep learning model for cell segmentation.
Integrated gene expression profiles and imaging data within the model architecture.
Implemented a recursive training strategy to handle noisy training labels.

Main Results:

GeneSegNet demonstrated significantly improved cell segmentation performance.
The method outperformed existing approaches that utilize either gene expression or imaging data alone.
Successful integration of multi-modal data led to more accurate cell boundary identification.

Conclusions:

GeneSegNet offers a superior approach to cell segmentation for in situ RNA data analysis.
Integrating gene expression and imaging data enhances the understanding of cellular characteristics.
The method provides a robust solution for segmentation tasks with noisy labels.