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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Single-cell sequencing in molecular diagnostics: Transformative yet untapped potential.

Duaa Elshiekh¹, Nour Farchoukh¹, Reema El Hassan¹

¹Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.

Frontiers in Genetics

|December 4, 2025

Summary

Single-cell sequencing (SCS) offers detailed cellular insights for personalized medicine. While promising for diagnostics in oncology and rare diseases, challenges in data analysis and clinical integration remain.

Keywords:

clinical molecular diagnostics data analysis challenges omics personalized treatment single-cell sequencing

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Area of Science:

Molecular Biology
Genomics
Clinical Diagnostics

Background:

Single-cell sequencing (SCS) provides high-resolution genetic and transcriptomic data.
Current research extensively uses SCS, but clinical diagnostic applications are limited.
SCS enables granular analysis of cellular heterogeneity for personalized medicine.

Purpose of the Study:

To review the potential of SCS in clinical molecular diagnostics.
To discuss SCS applications in oncology, genetics, infectious diseases, and autoimmune disorders.
To identify challenges and future directions for SCS integration into clinical practice.

Main Methods:

Review of recent advances and current uses of SCS technologies.
Analysis of integration challenges in clinical settings.
Discussion of SCS contributions to various medical fields.

Main Results:

SCS shows potential in oncology for analyzing tumor heterogeneity and personalizing treatments.
SCS aids in identifying biomarkers and understanding immune pathways in autoimmune and rare diseases.
SCS applications in infectious diseases help elucidate pathogen diversity and host-pathogen interactions.

Conclusions:

SCS offers significant opportunities for precision diagnostics and personalized treatments.
Challenges in data analysis, standardization, and clinical integration need to be addressed.
Interdisciplinary collaboration and innovative data analysis are crucial for advancing SCS in clinical diagnostics.