A practical guide to targeted single-cell RNA sequencing technologies.

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Current single-cell RNA sequencing (scRNA-seq) methods detect only 10-40% of cellular transcripts.
• Existing high-throughput scRNA-seq methods primarily capture untranslated regions, losing internal transcript detail.

Purpose of the Study:

• To outline biases in scRNA-seq protocols that restrict transcript and region detection.
• To review targeted sequencing solutions for enhancing scRNA-seq data.
• To provide a decision tree for selecting appropriate targeted methods.

Main Methods:

• Review of scRNA-seq protocol biases.
• Categorization of targeted sequencing solutions based on protocol step.
• Development of a decision-making framework for method selection.

Main Results:

• Identification of key biases across scRNA-seq protocol steps.
• Classification of targeted sequencing approaches into five categories.
• Presentation of a decision tree to guide experimental design.

Conclusions:

• Targeted sequencing offers solutions to overcome scRNA-seq limitations.
• Understanding protocol biases is crucial for selecting optimal methods.
• The decision tree aids researchers in improving transcript and region detection in scRNA-seq studies.