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Tagging and Fusion Proteins

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

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Related Experiment Video

Updated: May 6, 2026

Cell-cell Fusion of Genome Edited Cell Lines for Perturbation of Cellular Structure and Function

Cell-cell Fusion of Genome Edited Cell Lines for Perturbation of Cellular Structure and Function

Published on: December 7, 2019

CellFuse Enables Multimodal Integration of Single-Cell and Spatial Proteomics Data for Systems-Level Analysis in

Abhishek Koladiya¹, Zinaida Good^2,3,4, Sricharan Reddy Varra¹

¹Division of Hematology, Oncology, and Stem Cell Transplant and Regenerative Medicine, Department of Pediatrics, Stanford University, Stanford, California.

Cancer Research

|May 5, 2026

Summary

CellFuse integrates diverse proteomic data for cancer research. This deep learning framework accurately identifies cell types and reconstructs tumor microenvironments, advancing our understanding of cancer biology.

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Last Updated: May 6, 2026

Cell-cell Fusion of Genome Edited Cell Lines for Perturbation of Cellular Structure and Function

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A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

Published on: July 3, 2013

Area of Science:

Single-cell and spatial omics technologies
Cancer biology and immunology
Computational biology and bioinformatics

Background:

Single-cell and spatial analyses are crucial for understanding cancer.
Integrating diverse single-cell datasets accelerates cancer research.
Existing integration methods struggle with low-dimensional proteomic data.

Purpose of the Study:

To develop a deep learning framework, CellFuse, for integrating antibody-based proteomic datasets.
To enable cross-modality cell type prediction and label transfer across different tumor samples and experimental conditions.
To improve the analysis of both liquid and solid tumors using multi-modal proteomic data.

Main Methods:

Developed CellFuse, a deep learning framework utilizing supervised contrastive learning.
Applied CellFuse to unify high-dimensional cytometry, CITE-seq, and spatial proteomics data.
Validated CellFuse on datasets from peripheral blood, bone marrow, lymphoma, and solid tumors.

Main Results:

CellFuse created a shared embedding space for accurate cross-modality cell type prediction.
The framework outperformed existing methods in identifying rare malignant and immune cell populations.
CellFuse successfully reconstructed spatial tumor microenvironments and identified cell-cell interactions linked to treatment response.

Conclusions:

CellFuse offers a scalable, modality-agnostic solution for integrating proteomic datasets.
The framework aids in uncovering prognostic cell states and understanding the tumor-immune ecosystem.
CellFuse has significant translational relevance for advancing cancer discoveries.