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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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Updated: Jul 6, 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

Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level

Shijia Zhu^1,2, Naoto Kubota², Shidan Wang³

¹Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA.

Biorxiv : the Preprint Server for Biology

|January 8, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics often fails to profile single cells due to fixed spot sizes. STIE, a new EM algorithm, uses histology images to recover missing cells, achieving true single-cell resolution for better gene expression analysis.

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Last Updated: Jul 6, 2025

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

Author Spotlight: Nuclei Isolation from Mouse Cardiac Progenitor Cells for Epigenome and Gene Expression Profiling at Single-Cell Resolution

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Published on: May 12, 2023

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

Area of Science:

Spatial transcriptomics
Computational biology
Genomics

Background:

Current spot-based spatial transcriptomics methods struggle to profile individual cells due to fixed spot sizes and locations.
Existing approaches focus on improving spot resolution but overlook the fundamental challenge of achieving true single-cell level profiling.
High-resolution spatial transcriptomics still contains spots that cover multiple cells, limiting single-cell analysis.

Conclusions:

STIE overcomes the intrinsic limitations of spot-based spatial transcriptomics, providing single-cell resolution across whole slides.
The method highlights the importance of integrating morphological and transcriptomic data for accurate cell typing and analysis.
STIE offers a powerful tool for uncovering biological insights previously inaccessible due to the lack of single-cell resolution.