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

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by

Same author

An Exploratory Transcriptomic Classification Model for Psoriasis Based on Apoptosis-Associated and Proliferation-Apoptosis-Coupled Genes Using Explainable Machine Learning.

International journal of molecular sciences·2026

Same author

Complete chloroplast genomes provide insights into evolution and phylogenetic relationships of <i>Stachyurus</i> (Stachyuraceae).

Frontiers in plant science·2026

Same author

A microfluidic lung cancer-vascular tumor-on-a-chip model for precise evaluation of anti-invasion therapeutics.

Clinical and experimental medicine·2026

Same author

Surface Energy Modulation of NiO<sub><i>x</i></sub> through In Situ Click-Cross-Linked Networks for Air-Processed Flexible Blue Perovskite Light-Emitting Diodes.

The journal of physical chemistry. A·2026

Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026

Same author

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

bioRxiv : the preprint server for biology·2026

Same journal

A unified analysis of cell type- and trajectory-associated pathways in single-cell data using Phoenix.

Genome research·2026

Same journal

Resf1 is required for proper placental development and configuration of trophoblast cell-specific heterochromatin.

Genome research·2026

Same journal

Telomere-driven replicative crisis is driven by large-scale changes in genomic architecture.

Genome research·2026

Same journal

Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD.

Genome research·2026

Same journal

Spatially resolved profiling of steroid nuclear receptors reveals a role for the disordered N-terminal domains in genome targeting and AP-1 interaction.

Genome research·2026

Same journal

Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.

Genome research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 19, 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

Partial alignment of multislice spatially resolved transcriptomics data.

Xinhao Liu¹, Ron Zeira², Benjamin J Raphael³

¹Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA.

Genome Research

|August 8, 2023

Summary

This summary is machine-generated.

PASTE2 enables 3D reconstruction of spatial gene expression by partially aligning multiple 2D tissue slices. This method overcomes limitations of existing approaches, improving spatial transcriptomics data integration and analysis.

More Related Videos

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Related Experiment Videos

Last Updated: Jul 19, 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

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) provides gene expression data within tissue slices.
Current SRT methods analyze 2D slices, losing 3D spatial information.
Integrating data from multiple slices is challenging due to alignment issues and tissue morphology changes.

Purpose of the Study:

To develop a novel method for partial alignment and 3D reconstruction of multislice SRT data.
To enable accurate integration of spatial transcriptomics data across multiple tissue slices.
To overcome limitations of existing methods that assume complete slice overlap or preserved morphology.

Main Methods:

Introduction of PASTE2, a method for partial alignment and 3D reconstruction of multislice SRT datasets.
Formulation of a novel partial fused Gromov-Wasserstein optimal transport problem, solved via a conditional gradient algorithm.
Inclusion of a model selection procedure for estimating slice overlap fraction and optional use of histological image data.

Main Results:

PASTE2 achieves more accurate alignments compared to existing methods on both simulated and real data.
Successful reconstruction of a 3D gene expression map in a *Drosophila* embryo using 16 Stereo-seq slices.
Demonstrated accurate alignment of multislice datasets from diverse SRT technologies.

Conclusions:

PASTE2 effectively integrates multislice spatial transcriptomics data, recovering lost 3D information.
The method facilitates detailed studies of spatial gene expression patterns.
PASTE2 enhances the utility of SRT technologies for various biological applications.