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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: Apr 11, 2026

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

STAPLE: automating spatial transcriptomics analysis and AI interpretation.

Dmitrijs Lvovs^1,2,3, Jeffrey Quinn⁴, André Forjaz⁵

¹Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD USA.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

Summary

This summary is machine-generated.

STAPLE unifies spatial transcriptomics analysis tools into a single, modular framework. This enhances scalability, interpretation, and reproducibility for spatial biology research.

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Last Updated: Apr 11, 2026

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Published on: July 6, 2022

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Area of Science:

Computational Biology
Bioinformatics
Spatial Transcriptomics

Background:

Current spatial transcriptomics workflows are fragmented across multiple tools.
This fragmentation hinders scalability, interpretation, and reproducibility of analyses.
Separate tools exist for cell typing, neighborhood analysis, and cell-cell communication.

Purpose of the Study:

To systematize spatial transcriptomics analyses.
To create a modular framework for integrating distinct analytical methods.
To improve scalability, interpretation, and reproducibility in spatial transcriptomics.

Main Methods:

Developed STAPLE, a modular framework for spatial transcriptomics.
Unified data structures and ensured cross-tool interoperability.
Implemented end-to-end analyses with a single invocation.
Integrated a novel AI-enabled reporting layer for result synthesis.

Main Results:

STAPLE systematizes analyses across distinct spatial transcriptomics tools.
Achieved unified data structures and cross-tool interoperability.
Enabled unassisted, end-to-end analyses for rigorous and reproducible results.
AI reporting layer synthesizes quantitative results into biological summaries.

Conclusions:

STAPLE fosters rigorous, reproducible spatial transcriptomics analysis.
The framework facilitates interpretation of complex biological findings.
Enhances scalability and integration of diverse spatial biology tools.