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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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Single-Cell and Spatial Multiomics: Applications for Diseases.

Wentao Li1,2, Chao Chen1, Xin Zhu1,3

  • 1Key Laboratory of Head & Neck Cancer Translation Research of Zhejiang Province Zhejiang Cancer Hospital Hangzhou China.

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|December 17, 2025
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Single-cell and spatial multiomics offer novel ways to understand human disease mechanisms. Integrating these approaches aids in disease diagnosis, treatment planning, and advancing precision medicine.

Keywords:
deep learningmultiomicsprecision medicinesingle‐cell omicsspatial omics

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Area of Science:

  • Biomedical research
  • Genomics
  • Molecular biology

Background:

  • Human diseases involve complex molecular networks.
  • Single-cell and spatial multiomics provide new tools for disease research.
  • Integrating these multiomics approaches enhances understanding of cellular characteristics and connections in disease.

Purpose of the Study:

  • To review single-cell and spatial multiomics approaches.
  • To discuss their application in understanding disease mechanisms (e.g., cancer, neurodegenerative diseases, aging).
  • To explore the role of deep learning in analyzing multiomics data for disease insights.

Main Methods:

  • Review of single-cell and spatial multiomics techniques.
  • Analysis of deep learning applications in multiomics data.
  • Case studies on cancer, neurodegenerative diseases, and aging.

Main Results:

  • Single-cell and spatial multiomics integration offers deeper insights into disease pathogenesis.
  • Deep learning enhances the analysis of upstream pathogenic alterations and downstream molecular effects.
  • These approaches are crucial for guiding treatment strategies and risk assessment in precision medicine.

Conclusions:

  • Single-cell and spatial multiomics are powerful tools for elucidating disease pathogenesis.
  • The integration of these technologies promises significant advancements in understanding and treating human diseases.
  • The field is rapidly evolving, with deep learning playing a key role in data analysis.