Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

You might also read

Related Articles

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

Sort by
Same author

TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.

PLoS computational biology·2026
Same author

GMHAN: A Heterogeneous Graph Attention Framework for Prioritizing Coding and Non-coding Driver Genes.

Bioinformatics (Oxford, England)·2026
Same author

spAttClu: a spatial domain clustering model leveraging spatially weighted graph attention and contrastive learning.

Bioinformatics (Oxford, England)·2026
Same author

A complex network analysis of heavy air pollution transport in NW China's urban agglomerations.

Journal of environmental management·2026
Same author

Author Correction: Extreme phonon anharmonicity underpins superionic diffusion and ultralow thermal conductivity in argyrodite Ag8SnSe6.

Nature materials·2026
Same author

scTACL: a multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis.

Bioinformatics (Oxford, England)·2026
Same journal

Srr2-dependent SOX2 levels govern the chromatin and transcriptional landscape of adult neural stem cell fate decisions in mouse.

Genome biology·2026
Same journal

Tumoral switch in NUMB splicing changes essential transcription pathways and induces malignant properties in tumour cells.

Genome biology·2026
Same journal

MLMarker: a machine learning framework for tissue inference and biomarker discovery.

Genome biology·2026
Same journal

UnionLoops: a workflow for calling chromatin loops across related Hi-C datasets with improved specificity, precision, and sensitivity.

Genome biology·2026
Same journal

Spatiotemporal transcriptomic analysis during cold ischemic injury to the murine kidney reveals compartment-specific changes.

Genome biology·2026
Same journal

GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Genome biology·2026
See all related articles
  1. Home
  3. Sinter3d: Continuous 3d Reconstruction Of Spatial Transcriptomics Via Implicit Neural Representations.
  1. Home
  3. Sinter3d: Continuous 3d Reconstruction Of Spatial Transcriptomics Via Implicit Neural Representations.

Related Experiment Video

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

SINTER3D: continuous 3D reconstruction of spatial transcriptomics via implicit neural representations.

Tianjiao Zhang1, Shenghe Li1, Hongfei Zhang1

  • 1School of Computer Science and Artificial Intelligence, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, 150040, China.

Genome Biology
|June 18, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed SINTER3D, a novel framework for 3D spatial transcriptomics reconstruction. This method effectively interpolates gene expression data, improving 3D molecular structure analysis.

Keywords:
Cell type deconvolutionImplicit neural representationMulti-slice integrationSpatial domain identificationSpatial transcriptomicsThree-dimensional reconstructionVirtual section generation

More Related Videos

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Related Experiment Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics provides gene expression data with spatial context.
  • Current 3D reconstruction methods struggle with large gaps between tissue sections and independent gene interpolation.
  • Accurate 3D molecular structure reconstruction is crucial for understanding tissue organization and function.

Purpose of the Study:

  • To develop an advanced framework for 3D spatial transcriptomics reconstruction.
  • To overcome limitations of existing methods in interpolating gene expression data across large inter-section gaps.
  • To enable more accurate and comprehensive 3D molecular mapping of tissues.

Main Methods:

  • Developed SINTER3D, an implicit neural representation-based framework.
  • Modeled gene expression as continuous functions of 3D coordinates for joint interpolation of multiple genes.
  • Applied the framework to diverse biological datasets including brain, heart, embryo, and cancer tissues.

    • Main Results:

    • SINTER3D demonstrated superior performance compared to existing methods across multiple datasets.
    • The framework successfully reconstructed biologically meaningful 3D molecular structures.
    • Enabled accurate virtual section generation, spatial-domain identification, and cell-type deconvolution.

    Conclusions:

    • SINTER3D offers a significant advancement in 3D spatial transcriptomics reconstruction.
    • The implicit neural representation approach effectively addresses challenges in data interpolation and 3D modeling.
    • This method enhances the ability to analyze and interpret complex 3D molecular landscapes in various biological contexts.