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Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Updated: Jan 8, 2026

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

LARIS enables accurate and efficient ligand and receptor interaction analysis in spatial transcriptomics.

Min Dai^1,2, Tivadar Török³, Dawei Sun^3,4

¹Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Biorxiv : the Preprint Server for Biology

|December 15, 2025

Summary

We developed LARIS, a new computational method for analyzing spatial transcriptomics data to understand cell communication. LARIS accurately maps ligand-receptor interactions within tissues, revealing how cells signal to each other in development and disease.

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iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

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Area of Science:

Genomics and Molecular Biology
Computational Biology and Bioinformatics
Developmental Biology

Background:

Spatially resolved transcriptomics offers insights into intercellular communication.
Integrating spatial information into communication inference is computationally challenging.
Existing methods lack accuracy and scalability for complex spatial data.

Purpose of the Study:

To present LARIS (Ligand And Receptor Interaction analysis in Spatial transcriptomics), a novel computational method.
To accurately and efficiently identify cell type-specific, spatially restricted ligand-receptor interactions.
To enable analysis across various spatial transcriptomic technologies and biological conditions.

Main Methods:

Developed LARIS, a scalable algorithm for analyzing spatial transcriptomic data.
Incorporated spatial information to infer ligand-receptor interactions and directionality.
Validated LARIS using a simulation framework and applied it to human and mouse datasets.

Main Results:

LARIS demonstrated superior accuracy and scalability compared to existing methods in simulations.
Application to human tonsil and mouse cortex revealed key signaling pathways in tissue organization.
Identified cell type-, niche-, and condition-specific signaling dynamics across development.

Conclusions:

LARIS provides an accurate, scalable, and versatile tool for deciphering intercellular communication in spatial transcriptomics.
The method effectively uncovers molecular crosstalk shaping tissue architecture and developmental processes.
LARIS enables rapid analysis of large datasets, facilitating discovery of cell-cell signaling mechanisms.