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Related Experiment Video

Updated: Dec 17, 2025

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

Published on: June 9, 2020

Omics technologies for kidney disease research.

Dan Wang^1,2, Jiayi Yang^1,2, Jinjin Fan^1,2

¹Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Anatomical Record (Hoboken, N.J. : 2007)

|June 28, 2020

Summary

Omics technologies like genomics and proteomics are transforming disease research. This review explores their potential for identifying kidney disease mechanisms and biomarkers to improve patient care.

Keywords:

biomarkers genomics kidney disease proteomics transcriptomics

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Area of Science:

Biomedical research
Genomics
Transcriptomics
Proteomics

Background:

Omics-based technologies are revolutionizing human and experimental disease research.
These advanced techniques offer new insights into complex biological systems.

Purpose of the Study:

To review the potential of omics technologies in kidney disease research.
To highlight their role in identifying pathogenic mechanisms and biomarkers.
To discuss improvements in diagnosis, prognosis, monitoring, and treatment of kidney disease.

Main Methods:

Literature review of omics-based studies in kidney disease.
Analysis of genomics, transcriptomics, and proteomics applications.
Synthesis of findings related to disease mechanisms and biomarkers.

Main Results:

Omics technologies provide powerful tools for understanding kidney disease.
Identification of novel pathogenic mechanisms and potential biomarkers is feasible.
These advancements can lead to personalized approaches in nephrology.

Conclusions:

Omics technologies hold significant promise for advancing kidney disease research and clinical practice.
Further integration of omics data can enhance diagnostic and therapeutic strategies.
The future of nephrology will likely be shaped by omics-driven discoveries.