Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Molecular Factors Affecting Cell Division

Molecular Factors Affecting Cell Division

Several external and internal factors influence the initiation and inhibition of cell division. For instance, the death of nearby cells or the release of human growth hormone (hGH) promotes cell division. In contrast, lack of hGH or crowding of cells can inhibit cell division.
Several proteins function as internal regulators to ensure each cell cycle stage is completed faithfully before proceeding to the next. Regulator molecules may act directly or influence the activity or production of other...

B Cell Activation and Differentiation

B Cell Activation and Differentiation

The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...

Overview of Cell-Matrix Interactions

Overview of Cell-Matrix Interactions

The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...

Molecular Orbital Theory II

Molecular Orbital Theory II

Molecular Orbital Energy Diagrams

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Discovery of Tcf7 regulators with clonally-resolved CRISPR screens identifies Trim28 as a mediator of CD8 T cell differentiation in tumors.

bioRxiv : the preprint server for biology·2026

Same author

Clonal dynamics of germinal center refueling by secondary immunization.

bioRxiv : the preprint server for biology·2026

Same author

Replaying germinal center evolution on a quantified affinity landscape.

Cell·2026

Same author

ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.

Nature communications·2026

Same author

A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy.

bioRxiv : the preprint server for biology·2026

Same author

One-step generation of T-cell receptor knock-in mice in the TCRβ locus.

The EMBO journal·2026

Search research articles

Related Experiment Video

Updated: Feb 13, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Predictive modeling of molecular activity underlying physical cell-cell interactions.

Tamjeed Azad¹, Sarah K Walker², Gabriel D Victora³

¹Department of Computer Science, Princeton University, Princeton, NJ, USA.

Cell Reports Methods

|February 11, 2026

Summary

We developed Gloss, a new computational framework, to link gene activity with cell-cell interaction strength measured by LIPSTIC. This method enhances understanding of cellular communication in various biological contexts.

Keywords:

CP: computational biology CP: systems biology LIPSTIC cell-cell interactions group lasso machine learning proximity labeling scRNA-seq single-cell transcriptomics

More Related Videos

Interactive Molecular Model Assembly with 3D Printing

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Related Experiment Videos

Last Updated: Feb 13, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Interactive Molecular Model Assembly with 3D Printing

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Area of Science:

Immunology
Computational Biology
Genomics

Background:

Cell-cell interactions are crucial for tissue function and disease development.
Labeling immune partnerships by sortagging intercellular contacts (LIPSTIC) quantifies direct physical interactions.
Single-cell RNA sequencing (scRNA-seq) profiles intracellular transcriptomes.

Purpose of the Study:

To present Gloss (group lasso on scRNA-seq), a predictive modeling framework.
To systematically link gene and pathway activity to LIPSTIC-measured interaction strength.
To provide a computational framework for analyzing LIPSTIC+scRNA-seq data.

Main Methods:

Developed the Gloss predictive modeling framework.
Applied group lasso regression to link gene expression with interaction data.
Validated Gloss against correlation-based and standard regression approaches.

Main Results:

Gloss outperforms existing methods in linking gene activity to cell interaction strength.
Identified molecular features of myeloid-T cell interactions in anti-Ctla4 immunotherapy.
Described interactions between T cell subpopulations during viral infection.

Conclusions:

Gloss is an interpretable and effective framework for analyzing LIPSTIC+scRNA-seq data.
The framework prioritizes genes and pathways driving cellular communication.
Gloss advances the study of cell-cell interactions in health and disease.