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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Regulated mRNA Transport

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Related Experiment Video

Updated: Jan 9, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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SPICEiST: subcellular RNA pattern enhances cell clustering of imaging-based spatial transcriptomics.

Sungwoo Bae1, Yuchang Seong2, Dongjoo Lee2

  • 1Portrai, Inc., 57, Seongsui-ro 22-gil, Seongdong-gu, Seoul, 04798, Republic of Korea. sungwoo.bae@portrai.io.

Genomics & Informatics
|December 2, 2025
PubMed
Summary

SPICEiST enhances spatial transcriptomics cell clustering by integrating subcellular transcript patterns. This method improves cell state distinction, especially with small gene panels, offering new insights into tumor heterogeneity.

Keywords:
ClusteringGene panelGraph autoencoderImaging-based spatial transcriptomicsSubcellular gene expressionTumor microenvironment

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Imaging-based spatial transcriptomics (ST) offers single-cell resolution gene expression with spatial context.
  • Limitations include small gene panels and segmentation challenges.
  • Subcellular transcript distribution patterns are underexplored for ST analysis.

Purpose of the Study:

  • To introduce SPICEiST, a graph autoencoder framework.
  • To integrate subcellular transcript patterns with cell-level expression for improved ST cell clustering.
  • To overcome limitations of small gene panels and segmentation in ST.

Main Methods:

  • Developed a graph autoencoder framework (SPICEiST).
  • Integrated subcellular transcript distribution with cell-level gene expression profiles.
  • Evaluated performance on cancer datasets with varying gene panel sizes.

Main Results:

  • SPICEiST consistently outperformed conventional methods in cell clustering.
  • Performance gains were notable with small gene panels (approx. 300 genes).
  • SPICEiST revealed more spatially intermixed cell clusters, reflecting tumor microenvironment complexity.

Conclusions:

  • Leveraging subcellular transcript patterns addresses ST limitations, especially for small gene panels.
  • SPICEiST enhances cell clustering accuracy and provides insights into tumor heterogeneity.
  • The framework offers a novel approach for analyzing complex biological systems using ST data.