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Related Concept Videos

Proteins: From Genes to Degradation02:11

Proteins: From Genes to Degradation

Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA molecules by RNA...
Proteins: From Genes to Degradation02:11

Proteins: From Genes to Degradation

Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA molecules by RNA...

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Related Experiment Video

Updated: Jun 9, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Histopathology-based protein multiplex generation using deep learning.

Sonali Andani1,2,3, Boqi Chen1,3,4,5, Joanna Ficek-Pascual1,3

  • 1Department of Computer Science, ETH Zurich, Zurich, Switzerland.

Nature Machine Intelligence
|August 22, 2025
PubMed
Summary
This summary is machine-generated.

HistoPlexer, a deep learning tool, creates detailed protein maps from standard H&E images, aiding tumor microenvironment analysis. This cost-effective method enhances immune subtype classification and survival prediction in cancer research.

Keywords:
Computational modelsMachine learningMelanoma

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Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
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Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
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Area of Science:

  • Computational biology
  • Pathology
  • Artificial intelligence in medicine

Background:

  • Multiplexed protein imaging is crucial for understanding tumor-microenvironment interactions but faces limitations in cost, time, and tissue accessibility.
  • Standard hematoxylin and eosin (H&E) histopathology images are widely available but lack detailed protein information.

Purpose of the Study:

  • To develop a deep learning framework, HistoPlexer, for generating spatially resolved protein multiplexes from standard H&E images.
  • To enable cost- and time-efficient characterization of the tumor microenvironment for advancing precision oncology.

Main Methods:

  • HistoPlexer utilizes a conditional generative adversarial network architecture with custom loss functions.
  • The framework jointly predicts multiple tumor and immune markers, ensuring pixel- and embedding-level similarity while minimizing slice-to-slice variations.
  • Validation involved expert assessment on metastatic melanoma samples and benchmarking on diverse, publicly available cancer datasets.

Main Results:

  • HistoPlexer-generated protein maps closely resemble experimentally derived maps and preserve key biological relationships, including protein co-localization patterns.
  • The predicted immune infiltration patterns enabled accurate stratification of tumors into distinct immune subtypes.
  • Integration of HistoPlexer-derived features improved survival prediction and immune subtype classification models compared to using H&E features alone.
  • The method demonstrated robustness and outperformed baseline approaches across various cancer types and imaging conditions.

Conclusions:

  • HistoPlexer provides a powerful, efficient method for whole-slide protein multiplex generation from routine H&E images.
  • This approach significantly enhances tumor microenvironment characterization, offering a valuable tool for precision oncology.
  • The framework's ability to stratify tumors and improve predictive model performance highlights its potential to impact clinical decision-making.