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Cell Specific Gene Expression

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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...

Cell Specific Gene Expression

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Related Experiment Video

Updated: Jul 4, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.

Jia Zhao, Xinning Shan, Gefei Wang

Biorxiv : the Preprint Server for Biology

|July 3, 2026

Summary

This summary is machine-generated.

CONCISE accurately infers cell-cell communication in tissues by modeling spatial data complexities. This method improves ligand-receptor interaction detection, crucial for understanding tissue organization and disease.

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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Last Updated: Jul 4, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Published on: September 5, 2025

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Area of Science:

Spatial transcriptomics
Cellular communication
Bioinformatics

Background:

Cell-cell communication is vital for tissue function and disease.
Spatial transcriptomics enables studying interactions in intact tissues.
Existing methods struggle with spatial data artifacts, leading to unreliable communication inference.

Purpose of the Study:

To develop a robust statistical method for inferring spatial ligand-receptor interactions.
To address challenges posed by spatial autocorrelation, molecular count variation, and measurement errors in spatial transcriptomics data.
To improve the accuracy and reliability of cellular communication analysis in tissues.

Main Methods:

CONCISE: A novel statistical method for spatially constrained co-expression and ligand-receptor interaction inference.
Jointly models spatial autocorrelation, molecular count variation, measurement errors, and spatial proximity.
Employs efficient moment-based parameter estimation and analytical hypothesis testing.

Main Results:

CONCISE demonstrates well-calibrated inference and robust false-positive control across simulations and real-world data.
Outperforms existing methods in detection power and reliability.
Identified novel fibroblast interactions in intestinal inflammation and tumor microenvironment signaling networks.

Conclusions:

CONCISE provides a statistically rigorous and computationally efficient approach for spatial ligand-receptor interaction inference.
Enhances the biological utility of spatial transcriptomics for disease research.
Enables deeper understanding of cellular communication in complex biological systems and disease contexts.