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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...

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Related Experiment Video

Updated: Jun 14, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial

Shijia Zhu^1,2, Naoto Kubota³, Shidan Wang⁴

¹Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA. Zhu02@UMN.edu.

Nature Communications

|August 30, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics methods struggle to profile single cells. STIE, an Expectation Maximization algorithm, integrates histology images to recover missing cells, achieving true single-cell resolution for spatial gene expression analysis.

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Last Updated: Jun 14, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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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

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

In situ capturing-based spatial transcriptomics methods use fixed-size spots that fail to precisely capture randomly located single cells.
This limitation inherently prevents transcriptome profiling at the single-cell level.

Purpose of the Study:

To develop a computational method that enables single-cell resolution and whole-slide scale analysis for spatial transcriptomics.
To accurately map gene expression to individual cells within tissue samples.

Main Methods:

Developed STIE (Spatial Transcriptomics Image Enhancement), an Expectation Maximization algorithm.
STIE aligns spatial transcriptome data with histology images based on nuclear morphology.
The algorithm recovers missing cells from approximately 70% of the gap area within spots.

Main Results:

STIE achieves real single-cell level deconvolution, convolution, and clustering for both low- and high-resolution spots.
The method demonstrates superior concordance with true cell-type-specific transcriptomic signatures compared to existing spot- and subspot-level methods.
STIE reveals insights into actual spot resolution, cell type colocalization, and spatial cell-cell interactions at the single-cell level.

Conclusions:

STIE overcomes the limitations of fixed-size spots in spatial transcriptomics, enabling accurate single-cell resolution.
The algorithm provides a powerful tool for analyzing spatial gene expression and cell-cell interactions at an unprecedented resolution.
STIE enhances the understanding of tissue architecture and cellular heterogeneity in complex biological systems.