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Related Experiment Video

Updated: Jan 16, 2026

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

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Full-length spatial transcriptome strategy based on robust and low-cost target tissue capture.

Ying Zhou1, Qinyu Ge1, Moxin Li1

  • 1State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.

Biosensors & Bioelectronics
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

A new method, MSN-seq, offers a low-cost and simple approach to spatial transcriptomic analysis. This technique enhances full-length mRNA transcriptome profiling, making advanced spatial transcriptomics more accessible for research.

Keywords:
GlioblastomaMSN-seqSmart-seq2Spatial transcriptomics

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Area of Science:

  • Molecular Biology
  • Genomics
  • Neuroscience

Background:

  • Smart-seq2 and Laser Capture Microdissection (LCM) are established methods for spatial transcriptomic profiling.
  • These methods are limited by high costs and operational complexity, restricting their widespread use.

Purpose of the Study:

  • To develop a cost-effective and user-friendly method for full-length spatial transcriptome analysis.
  • To improve accessibility of spatial transcriptomics for researchers without specialized equipment or skills.

Main Methods:

  • Developed MSN-seq, integrating microneedle-based sampling with a modified Smart-seq2 protocol.
  • Utilized reusable steel needles for multi-cellular tissue spot capture, optimizing Smart-seq2 performance.
  • Streamlined experimental procedures for enhanced transcriptomic capture efficiency.

Main Results:

  • MSN-seq successfully captured spatial transcriptomic data from brain and retinal tissues in Parkinson's disease and retinal degeneration models.
  • Analysis of glioblastoma model mice revealed early invasive mechanisms and immune cell subset enrichment.
  • Demonstrated reduced costs and improved efficiency compared to traditional methods.

Conclusions:

  • MSN-seq provides a robust, low-cost, and efficient strategy for spatial transcriptome acquisition.
  • The method simplifies experimental workflows, increasing accessibility for spatial transcriptomic studies.
  • MSN-seq enables detailed analysis of tissue microenvironments and disease mechanisms.