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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 microarray-based...

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Updated: Jul 7, 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

Agentic AI for Spatial Omics.

Gianna Pavilion¹, Cathrine Mbigidde¹, Rafael Tubelleza^1,2

¹Frazer Institute, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Woolloongabba, QLD, Australia.

Immunology and Cell Biology

|July 5, 2026

Summary

Agentic artificial intelligence (AI) systems are advancing spatial omics analysis. Defining appropriate autonomy is key for progress, balancing automation with accountability and reproducibility.

Keywords:

agentic AI computational biology large language models multi‐agent systems spatial omics

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Published on: September 5, 2025

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Last Updated: Jul 7, 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

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

Computational biology
Artificial intelligence
Genomics

Background:

Agentic artificial intelligence (AI) systems are increasingly applied to complex biological data analysis.
Spatial omics technologies generate high-resolution molecular data within tissue contexts.
Integrating AI with spatial omics presents unique challenges and opportunities.

Purpose of the Study:

To summarize recent advancements in agentic AI for spatial omics.
To analyze the trade-offs between autonomy and accountability in AI systems.
To explore the balance between adaptability and reproducibility in AI-driven omics research.

Main Methods:

Review of current agentic AI methodologies in spatial omics.
Comparative analysis of AI systems based on autonomy, accountability, adaptability, and reproducibility.
Discussion of the implications for future AI development in biological sciences.

Main Results:

Identification of key trends and challenges in agentic AI for spatial omics.
Framework for evaluating AI systems based on the autonomy-accountability and adaptability-reproducibility tensions.
Emerging strategies for responsible AI deployment in omics data analysis.

Conclusions:

Progress in AI for spatial omics relies on strategic implementation of autonomy, not just automation.
Careful consideration of accountability and reproducibility is crucial for reliable AI tools.
Future directions emphasize human-AI collaboration for robust spatial omics insights.