Single-Cell RNA Sequencing - RNA / BOC Sciences

What is Single-Cell RNA Sequencing?

Nearly all human cells contain the same genetic material, but the transcriptional information in each cell reflects only the unique activity of its gene set. Analyzing gene expression activity within cells is considered one of the most reliable methods for understanding cell identity, status, function, and response. Through single-cell RNA sequencing (scRNA-seq), the transcriptional profiles of millions of individual cells can be analyzed. This enables the classification, characterization, and differentiation of each cell, thereby identifying rare but functionally important cell populations. scRNA-seq is a next-generation sequencing (NGS) method used to examine the genome or transcriptome of individual cells, providing a high-resolution view to reveal intercellular variations.

What are the Steps Involved in Single-Cell RNA Sequencing?

Single-cell sequencing has achieved more complex, accurate, and high-throughput analysis, with the technical workflow mainly consisting of three key stages: library generation, sequence data preprocessing, and sequence data post-processing. Firstly, in the library generation stage, single-cell capture, reverse transcription, and cDNA amplification are the most challenging parts of library construction. Cells or nuclei are isolated and processed to capture mRNA and prepare samples suitable for sequencing. This step requires high precision because RNA must be converted into a cDNA library through lysis and reverse transcription. Common techniques for single-cell isolation and capture include limiting dilution, fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting, microfluidic systems, and laser microdissection. It is generally recommended to dissociate tissues into single-cell suspensions at 4°C to minimize gene expression changes induced by the separation process. Next, in the sequence data preprocessing stage, specific tools such as Cell Ranger are used for initial counting and allocation of data, taking into account RNA capture efficiency and potential technical variability. Finally, in the sequence data post-processing stage, data are further normalized and analyzed using clustering and dimensionality reduction techniques such as t-SNE or UMAP to identify cell types and for visualization. This entire process may require iterative optimization to ensure data quality and extract valuable biological information.

What is Single-Nuclei RNA Sequencing?

Single-nuclei RNA sequencing (snRNA-seq) is an alternative method to single-cell sequencing. snRNA-seq captures only the mRNA present in the cell nucleus. It addresses issues related to tissue preservation and cell isolation (difficulties in obtaining single-cell suspensions), is applicable to frozen samples, and minimizes artificial transcriptional stress responses. It is particularly useful for brain tissues where obtaining intact cells is challenging. This method offers several potential benefits: 1) Compared to whole cells, nuclei are easier to isolate from complex tissues and organs, 2) snRNA-seq can be widely used across eukaryotic species, and 3) it can provide insights into nucleus-specific regulatory mechanisms. However, snRNA-seq captures only transcripts within the cell nucleus and cannot capture important biological processes related to mRNA processing, RNA stability, and metabolism.

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Single-Cell RNA Sequencing vs Bulk RNA Sequencing

Bulk RNA Sequencing (RNA-seq):

It involves sequencing RNA from a population of cells, providing averaged gene expression data.
It is less expensive, less labor-intensive, and less time-consuming compared to single-cell sequencing.
Useful for transcriptome profiling and discovering novel transcripts, spliced genes, or allele-specific expression.
However, it doesn't capture the heterogeneity within the cell population.

Single-Cell RNA Sequencing (scRNA-seq):

It studies individual cells at the genomic, proteomic, transcriptomic, and metabolomic levels.
Enables the investigation of heterogeneity within cell populations and identification of biomarkers.
Although more expensive and laborious, it offers insights into cell interactions, state changes, and transitions.
Nature recognized single-cell sequencing as the Method of the Year in 2013.

In brief, single-cell RNA sequencing (scRNA-seq) proves advantageous in exploring cellular diversity and uncommon cell varieties, whereas bulk RNA sequencing offers a more economical approach, well-suited for identifying shifts in gene expression across populations. Selecting between these methods hinges on the research goals and the desired level of detail for the investigation.

What is Single-Cell RNA-Sequencing Used for?

Single-cell (SC) technologies, especially scRNA-seq, can be applied throughout the entire drug discovery and development process. By leveraging classification based on altered cellular composition and states, they enhance understanding of diseases and guide the identification of novel cellular and molecular targets. In the identification of relevant preclinical models for specific disease subtypes, the use of SC sequencing can benefit target validation and verification. Integration of CRISPR with SC sequencing in highly multiplex functional genomics screening (scCRISPR screening) can increase throughput for target validation and prioritize targets by augmenting perturbation readouts with mechanistic information. SC sequencing technologies offer insights into cell-type-specific compound effects, off-target effects, and heterogeneous responses, providing information for candidate drug selection.

Applications of single-cell RNA sequencing. (L, Slenders.; et al, 2022)

Cancer

Single-cell (SC) molecular phenotyping has been widely employed to understand cancer development. Notable examples include the use of SC technologies to identify origin cells or cells associated with prostate cancer, heterogeneous papillary renal cell carcinoma (pRCC), and Barrett's esophagus-related cells leading to esophageal adenocarcinoma. scRNA-seq reveals extensive cellular and transcriptional heterogeneity in cancer and can track the heterogeneity of cancer cells. This has been combined with immune phenotyping techniques to provide insights into the stromal immune microenvironment (ecosystems or ecotypes), characterized by unique cellular compositions, that characterize different types of tumors.

Neurodegenerative Diseases

Parkinson's disease is caused by the degeneration of dopaminergic neurons in the substantia nigra, but not all neurons producing dopamine degenerate. SC genomic analysis of human dopaminergic neurons has revealed that although there are ten transcriptionally defined dopamine subtypes in the human substantia nigra, only one population selectively degenerates in Parkinson's disease, and the transcriptional features of this population are highly enriched in the expression of genes associated with Parkinson's disease risk. New SC technologies have been developed to study the brain. Examples include Patch-seq, a powerful platform that combines scRNA-seq with patch-clamp recording, and VINE-seq based on single-nucleus RNA sequencing (snRNA-seq). These methods have been used to identify cell types selectively lost in the new cortex of Alzheimer's disease and to map vascular and perivascular cell types in the human Alzheimer's disease brain at SC resolution.

Inflammatory and Autoimmune Diseases

ScRNA-seq is used to characterize specific regulatory T cells present in spondyloarthritis and to help identify cytotoxic T cells in the synovium of psoriatic arthritis. Clonal expansion of these synovial immune cells has been confirmed by complementary TCR-seq. Peripheral blood mononuclear cell (PBMC) samples from rheumatoid arthritis patients positive for anti-cyclic citrullinated peptide (ACPA+) and negative (ACPA-) show immune relevance at the SC level in both subtypes of rheumatoid arthritis, while analysis of immune zones in skin biopsies shows distinct T cell memory residency, innate lymphoid cell, and CD8+ T cell gene signatures for common skin inflammatory diseases.

Infectious Diseases

A prominent example of using SC methods to improve understanding of infectious diseases is recent research on COVID-19 to determine the immunological correlates of disease severity in human tissues. Analysis of SC transcriptomes, surface proteomes, and T and B lymphocyte antigen receptors in PBMC samples from COVID-19 patients revealed the involvement of monocytes in platelet aggregation, clonal expansion of circulating follicular helper T cells and cytotoxic CD8+ T cells in mild disease, while in more severe cases, there was an increased ratio of CD8+ effector T cells to effector memory T cells. These findings suggest cellular components may be targeted for therapy. Additionally, combining SC transcriptomics and SC proteomics with mechanistic studies has discovered that the C3a complement protein fragment produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection promotes the differentiation of a group of CD16-expressing T cells associated with severe COVID-19.