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Integrating single-nucleus barcoding with spatial transcriptomics via Stamp-seq to reveal immunotherapy

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Stamp-seq technology offers precise spatial mapping of cell states for improved disease understanding. This method identifies a specific plasma cell community that predicts chemoimmunotherapy response in lung cancer.

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Area of Science:

  • Spatial transcriptomics
  • Cellular biology
  • Cancer research

Background:

  • Understanding spatial cell organization is crucial for development, tissue homeostasis, and disease pathology.
  • Current spatial transcriptomic methods face challenges in cell state assignment, gene detection, and cost.

Purpose of the Study:

  • To develop a novel method, Stamp-seq, for high-resolution spatial transcriptomic profiling.
  • To enable precise cell state assignment and spatial mapping with reduced cost.
  • To investigate cellular ecosystems in non-small cell lung carcinoma (NSCLC) and their response to chemoimmunotherapy.

Main Methods:

  • Development of Stamp-seq using custom DNA sequencing chips with high-density, restriction enzyme-cleavable spatial barcodes.
  • Achieving single physical cell resolution with an average 4 μm localization error.
  • Application of Stamp-seq to delineate cellular ecosystems in NSCLC and spatially resolve B-cell receptor (BCR) clonotypes.

Main Results:

  • Stamp-seq provides precise subtype classification and spatial mapping at reduced cost.
  • Identified a distinct IGHG1+ plasma cell-enriched community within chemoimmunotherapy-responsive NSCLC ecosystems.
  • Elucidated the spatiotemporal trajectory of IGHG1+ plasma cells, from origin to tumor cell contact.

Conclusions:

  • Stamp-seq enables powerful spatial cellular subtyping and molecular tracking.
  • The IGHG1+ plasma cell niche serves as a potential prognostic biomarker for chemoimmunotherapy response in NSCLC.