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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

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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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Subcellular Fractionation01:32

Subcellular Fractionation

7.3K
The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...
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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
07:30

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

Published on: December 7, 2019

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

Abhishek Koladiya1, Zinaida Good2,3,4, 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
PubMed
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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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.