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

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

You might also read

Related Articles

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

Sort by

Same author

Immune-related adverse events with cancer clinical benefits: a systematic review and meta-analysis.

BMC cancer·2026

Same author

CLPTM1L modulates membrane lipid rafts to promote tumor EGFR signaling.

Life metabolism·2026

Same author

Organic Photovoltaic Cells for Reliable Energy Generation in Deep Space Environments.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Citrus-leaf microemulsion controls postharvest mold through membrane disruption and host defense enhancement.

Food chemistry: X·2026

Same author

Dual-responsive ionic liquid based foam-to-gel for precision vaginal delivery of progesterone.

Acta biomaterialia·2026

Same author

Efficacy and cost-effectiveness of intraoperative cell salvage for patients with placenta previa during cesarean section: a retrospective comparison analysis.

Open medicine (Warsaw, Poland)·2026

Same journal

Learning Moisture-Induced Damage From Vision: Diffusion Models for Real-Time Monitoring of Additive Manufacturing Processes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Intrinsic Dual-Phase Regulated GeSe<sub>2</sub> Nanoparticles Triggered by Ball-Milling Treatment for Photonic Multi-Valued Logic Circuits.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

A Plant Photoregulator-Inspired S-Type Heterojunction System for Diabetic Keratopathy via Tri-Modal Light-Driven Immunometabolic Reprogramming, Tissue Repair, and Antibacterial Activity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

eEF1G Orchestrates Translation to Ensure Meiotic Progression in Transcriptionally Quiescent Spermatocytes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Ultrasound-Recharged Sub-Nanometer Palladium Catalysts for on-Demand and Self-Terminating Bioorthogonal Prodrug Activation in Cancer Therapy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same journal

Graphene Aerogels With Spherical Pore Structure for Broad Frequency Regulation and Enhanced Low-Frequency Response.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 10, 2025

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

SURF: A Self-Supervised Deep Learning Method for Reference-Free Deconvolution in Spatial Transcriptomics.

Shuyu Liang¹, Zixia Zhou², Peng Huang¹

¹School of Information Science and Technology, Fudan University, Shanghai, 200433, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|August 27, 2025

Summary

This summary is machine-generated.

This study introduces SURF, a novel computational tool for spatial transcriptomics. SURF accurately deconvolutes gene expression data from tissue spots without needing external cell references, improving cellular-level analysis.

Keywords:

deconvolution deep learning reference‐free self‐supervised spatial transcriptomics

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Sep 10, 2025

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

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Spatial transcriptomics offers spatially resolved gene expression but struggles with cellular-level analysis due to signal consolidation in spots.
Reference-based deconvolution methods often lack necessary matched single-cell data.

Purpose of the Study:

To develop a reference-free deconvolution tool, SURF, to overcome limitations in spatial transcriptomics.
To accurately model cellular composition and gene interactions within tissue microenvironments.

Main Methods:

SURF integrates high-dimensional gene data analysis with self-supervised deep learning.
It models nonlinear gene interactions and leverages spatial relationships between spots.
The method was benchmarked on synthetic and real-world spatial transcriptomic datasets.

Main Results:

SURF consistently outperforms existing reference-free deconvolution methods.
It achieves performance exceeding reference-based methods when references are unavailable.
SURF accurately represents diverse tissue microenvironments across various resolutions and species.

Conclusions:

SURF provides a robust and accurate solution for reference-free deconvolution in spatial transcriptomics.
The tool precisely models tissue microenvironments and identifies critical biological mechanisms, such as epithelial-to-mesenchymal transition in colorectal cancer metastasis.