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Related Experiment Video

Updated: Sep 12, 2025

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

Published on: March 24, 2023

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics data.

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, CA.

Biorxiv : the Preprint Server for Biology

|August 8, 2025

Summary

CellFuse integrates single-cell proteomic data from different platforms, even with limited shared markers. This deep learning framework improves cell type prediction and data integration across diverse biological samples.

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Area of Science:

Single-cell biology
Proteomics
Computational biology

Background:

Single-cell and spatial proteomic technologies offer complementary data but lack unified measurement platforms.
Existing integration methods struggle with low-dimensional proteomic data due to limited shared features, often optimized for transcriptomics.

Purpose of the Study:

To develop a deep learning framework, CellFuse, for modality-agnostic integration of single-cell proteomic data.
To address challenges in integrating datasets with limited feature overlap and varying experimental conditions.

Main Methods:

Developed CellFuse, a deep learning framework utilizing supervised contrastive learning.
Learned a shared embedding space to enable cross-modal and cross-condition data integration.
Evaluated performance on diverse datasets including PBMCs, bone marrow, lymphoma, and tumor tissues.

Main Results:

CellFuse demonstrated superior integration quality and runtime efficiency compared to existing methods.
Achieved accurate cell type prediction and robust performance with missing markers and rare cell types.
Showcased strong cross-dataset comparison results, highlighting scalability and fidelity.

Conclusions:

CellFuse provides a powerful, versatile tool for high-fidelity single-cell data integration.
Enables seamless analysis across different proteomic modalities and experimental settings.
Facilitates advancements in basic and translational single-cell research.