A Beginner's Guide to Single-cell RNA Sequencing

The Type of Sequencing

It's important to determine the type of sequencing you need before starting an RNA sequencing experiment. Bulk cell RNA sequencing (bulk RNA-seq) measures the average gene expression across a tissue sample, while single-cell RNA sequencing (scRNA-seq) measures gene expression at the individual cell level. Factors such as time, cost, workload, and information requirements play a crucial role in determining the appropriate sequencing method for a given study. The choice between bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) hinges on these considerations.

Bulk RNA-seq is an effective approach for comparative transcriptomics and biomarker investigations; however, it cannot adequately capture tissue heterogeneity. Conversely, scRNA-seq is well-suited for analyzing gene expression changes within specific cell populations, making it valuable for time-course studies, developmental investigations, and genealogical tracing.

When confronting samples with high cellular heterogeneity, scRNA-seq is better suited to discern the underlying changes. However, it is important to note that scRNA-seq may be more expensive, less sensitive, and potentially unnecessary if cell surface markers can be employed to sort different cell populations within the sample.

A Beginner's Guide to Single-cell RNA SequencingSingle-cell RNA sequencing. (Kim et al., 2020)

Choosing The Right Sequencing Platform

Upon deciding to employ scRNA-seq, the subsequent question arises as to which sequencing platform to utilize. Numerous platforms are available, each offering distinct advantages and trade-offs in terms of throughput and sensitivity. High-throughput sequencing enables the analysis of a large number of cells or samples, thereby facilitating the observation of overall heterogeneity or the detection of rare cell subpopulations. However, it may not be viable when sample volumes are limited or when studies necessitate the examination of full-length transcripts. Consequently, the selection of a suitable platform is contingent upon the sample type and experimental requirements.

  • Throughput vs. Sensitivity: Considerations such as throughput and sensitivity come into play when evaluating different scRNA-seq platforms. Platforms such as CEL-Seq, MARS-Seq, SMART-Seq, SCRB-Seq, Quartz-Seq, and Seq-well generally offer higher sensitivity but lower throughput. These platforms are apt for low-throughput applications where detailed characterization of individual cells is desired. Conversely, platforms like InDrops and DropSeq provide higher throughput but may compromise some sensitivity.
  • Sample Type: The choice of platform should also consider the sample type you are working with. Some platforms may be better suited for specific sample types. For example, Seq-well may work well with adherent cells or tissue sections, while InDrops and DropSeq are often used with suspension cells.
  • Experimental Needs: Consider your specific experimental goals. If you require full-length transcript information, some platforms like SMART-Seq and Quartz-Seq provide this capability. However, these methods are often lower throughput and may require more input material compared to other platforms.

Optimizing Experimental Protocols

Optimizing the experimental protocols is crucial for obtaining high-quality scRNA-seq data. Sample preparation involves dissociating tissue samples into single-cell suspensions with minimal debris and clumps. High-quality samples are essential for successful scRNA-seq, and it's important to maintain cell viability and intact membranes until barcodes are added.

When designing an experiment, factors such as the number of cells to be analyzed and sequencing depth should be considered. Generally, 50-100 cells with unique transcriptome features are needed to form clear clusters in t-SNE plots. Rarer cell populations require a higher number of cells and deeper sequencing. Cost and funding constraints also play a role in determining the platform and the number of samples to run.

Sample Preparation

  • Use gentle dissociation techniques to obtain single-cell suspensions without debris and clumps. Optimizing the dissociation protocol for each specific tissue type is crucial.
  • Maintain cell viability and intact membranes during sample preparation to ensure accurate representation of gene expression profiles.

Cell Number

  • Determine the desired number of cells to be analyzed based on the scientific question and the complexity of the cell population. Generally, 50-100 cells with unique transcriptome features are needed to identify clear clusters in t-SNE plots.
  • Rarer cell populations may require a higher number of cells to obtain sufficient coverage.
  • Consider the limitations of the scRNA-seq platform in handling large cell numbers, as this can affect experimental feasibility.

Sequencing Depth

  • Determine the appropriate sequencing depth based on the desired sensitivity and the abundance of the transcripts of interest.
  • Deeper sequencing may be required for rare cell populations or when investigating lowly expressed genes.
  • Consider the trade-off between sequencing depth and cost, as deeper sequencing increases the overall cost per sample.

Platform Selection

  • Consider the available scRNA-seq platforms and their capabilities, such as cell throughput, cost per cell, and sensitivity.
  • Select a platform that suits the experimental needs and aligns with the available funding.
  • It's also important to consider the compatibility of the platform with downstream analysis tools and pipelines.

Experimental Replication

  • Determine the number of biological replicates required to obtain statistically robust results.
  • Replicates help to assess the reproducibility of the data and increase confidence in the findings.
  • Consider the balance between the number of replicates and the available resources.

Quality Control

  • Implement quality control steps throughout the experimental workflow to identify and address potential issues.
  • Assess sample quality using metrics such as cell viability, RNA integrity, and library complexity.
  • Monitor technical variation and batch effects to ensure data reliability.

Pilot Studies

  • Consider conducting pilot studies or using publicly available datasets to optimize the experimental protocols before performing larger-scale experiments.
  • Piloting helps identify potential challenges, refine protocols, and estimate the appropriate sample and sequencing sizes.

How To Get High Quality Sequencing Samples?

Achieving high-quality sequencing samples necessitates a meticulous and professional approach to sample preparation. By implementing strategic measures and employing advanced techniques, researchers can minimize errors, prevent degradation, and obtain reliable sequencing data. This article outlines a series of complex and professional steps to optimize the sample preparation process, ensuring the utmost integrity of nucleic acids.

Strategically Design and Streamline Workflow

Begin by carefully designing your experimental setup to minimize sample handling and processing time. A streamlined workflow reduces the risk of introducing errors or degradation during sample preparation. Emphasize efficiency and precision when selecting and arranging the necessary steps.

Maintain Optimal Low Temperatures

Throughout the entire sample preparation process, maintaining low temperatures is crucial. By doing so, researchers can preserve the integrity of nucleic acids and safeguard against degradation. Implement meticulous temperature control measures for both samples and reagents.

Optimize Dissociation Protocols

To obtain high-quality nucleic acids, it is imperative to select the most appropriate dissociation protocol for the specific sample type under investigation. Thoroughly optimize the chosen protocol to ensure maximum yield and quality of nucleic acids. This step is critical in acquiring dependable sequencing data.

Exercise Caution with Lysis Conditions

Avoid subjecting nucleic acids to harsh lysis conditions that can cause damage or introduce contaminants. Opt instead for gentle lysis buffers and conditions to minimize disruption of sample integrity. This meticulous approach ensures the preservation of nucleic acid quality.

Consider Efficient Cell Sorting Methods

When cell sorting becomes necessary, prioritize protocols and equipment that minimize processing time. Employ time-saving protocols and larger nozzles to expedite the cell sorting procedure, ultimately enhancing overall efficiency.

Minimize Centrifugation and Resuspension Steps

To mitigate the risk of sample degradation, limit the number and duration of centrifugation steps. Additionally, reduce the resuspension time to minimize exposure to environmental factors. By minimizing these steps, researchers can ensure the integrity of their samples.

Remove Debris and Contaminants

Employ filtration or density gradient centrifugation methods to eliminate debris and contaminants that could interfere with sequencing. These advanced techniques enhance the purity of samples, leading to more accurate downstream applications.

Incorporate BSA or FBS into the Final Buffer

To provide a protective environment for nucleic acids, minimize degradation, and maintain sample quality, incorporate bovine serum albumin (BSA) or fetal bovine serum (FBS) into the final buffer. These additives act as stabilizers, ensuring the preservation of nucleic acid integrity.

Perform Pilot Experiments

Before embarking on the main sample preparation, conduct pilot experiments using a small subset of samples. These preliminary trials allow researchers to optimize and troubleshoot the protocol, ensuring a smooth and successful preparation process.


  1. Kim, Doyoung, Kyung Bae Chung, and Tae-Gyun Kim. "Application of single-cell RNA sequencing on human skin: Technical evolution and challenges." Journal of dermatological science 99.2 (2020): 74-81.
* For Research Use Only. Not for use in diagnostic procedures.

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