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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Proximogram-A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and

Santhoshi N Krishnan¹, Sunjong Ji², Ahmed M Elhossiny¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Computers in Biology and Medicine

|September 10, 2024

Summary

This summary is machine-generated.

We developed Proximogram, a novel graph representation integrating omics and spatial data. This method enhances disease classification by capturing cellular interactions and spatial architecture, aiding in identifying diagnostic markers.

Keywords:

Graph convolutional networks: GCN Graph theory Omics Pancreatic cancer Spatial analysis

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational biology
Bioinformatics
Systems biology

Background:

Patient-derived multimodal biological data is increasingly available.
Integrating diverse data types (omics, spatial) is crucial for disease understanding.
Current methods may not optimally leverage combined biological data.

Purpose of the Study:

To propose Proximogram, a novel graph-based representation for joint embedding of omics and spatial data.
To evaluate the efficacy of Proximogram in disease classification.
To identify key biological features driving disease classification.

Main Methods:

Generated proximograms from multiplexed immunofluorescence images and single-cell RNA-seq data.
Utilized data from patients with normal pancreas, chronic pancreatitis (CP), and pancreatic ductal adenocarcinoma (PDAC).
Applied graph deep-learning models using proximograms as input.

Main Results:

Proximograms integrated structural information from single-cell signaling and spatial cell architecture.
Graph deep-learning models using proximograms showed improved classification performance compared to simpler spatial graphs.
The enhanced discriminatory power highlights the value of integrated data.

Conclusions:

Proximogram offers a powerful approach for joint analysis of multimodal biological data.
Integrating spatial and omics data via Proximogram improves disease classification accuracy.
This approach can identify significant diagnostic markers for pancreatic diseases.