Single-cell RNA sequencing (scRNA-seq) measures the activity of thousands of genes within individual cells. This technique provides a detailed snapshot of which genes are turned on or off, offering insights into cellular functions and states. Its purpose is to explore the diversity within biological samples at high resolution, moving beyond averaged measurements. By analyzing gene expression one cell at a time, researchers can uncover the unique characteristics that define different cell types and their roles within complex biological systems. This approach has transformed how scientists investigate cellular processes in health and disease.
Why Look at Single Cells?
Traditional RNA sequencing, often called “bulk” RNA-seq, analyzes genetic material from millions of cells simultaneously, providing an average gene expression profile. While useful, this averaging can obscure important differences that exist between individual cells within a population. For example, a tissue might contain various cell types, or even cells of the same type can exhibit subtle differences in their gene activity.
Single-cell RNA sequencing overcomes this limitation by examining each cell separately. This individual cell-level analysis allows researchers to uncover cell-to-cell variability that would otherwise be masked by bulk methods. It enables the identification of rare cell populations that might be functionally significant but are too few to register in an averaged sample. Furthermore, scRNA-seq helps track cellular development and disease progression by observing changes in gene expression as cells mature or undergo disease-related transformations. Understanding complex tissue heterogeneity, such as the diverse cellular landscape within a tumor, becomes possible, leading to insights into disease mechanisms and potential therapeutic targets.
Getting Cells Ready for Sequencing
The initial step involves obtaining a suspension of individual cells from a tissue or sample. This process requires careful dissociation of the tissue to separate cells while maintaining their viability and integrity. Various methods are employed for cell isolation, each suited for different sample types and experimental goals.
For instance, flow cytometry (FACS) can sort cells based on specific markers, allowing for the purification of particular cell populations. Microfluidic devices, such as those used in droplet-based technologies like 10x Genomics Chromium, are widely utilized for high-throughput single-cell capture. These systems encapsulate individual cells into tiny droplets, each containing reagents necessary for subsequent molecular reactions. Another method, micromanipulation, involves manually picking individual cells under a microscope, which is precise for rare or delicate cells but more labor-intensive. The primary goal throughout this preparation phase is to ensure that each isolated cell is viable and intact, providing high-quality RNA for accurate gene expression profiling.
Transforming Genetic Material into Data
Once individual cells are isolated, their genetic material is prepared for sequencing. This process begins with cell lysis, where the cell membrane is broken open to release the RNA molecules. Messenger RNA (mRNA), which carries the instructions for making proteins, is typically targeted for sequencing due to its poly(A) tail. Reverse transcription then converts these mRNA molecules into complementary DNA (cDNA), a more stable form, using specialized primers that often include unique molecular identifiers (UMIs) and cell-specific barcodes.
These barcodes allow researchers to identify which sequence reads originated from which individual cell, even after pooling all the cDNA for sequencing. The small amounts of cDNA are then amplified, often through polymerase chain reaction (PCR) or in vitro transcription (IVT), to generate sufficient material for sequencing. Finally, library preparation involves adding adapter sequences to the amplified cDNA fragments, which are necessary for binding to the sequencing platform. These prepared “libraries” are then loaded onto high-throughput sequencing machines, such as Illumina sequencers, to generate millions of raw sequence reads.
Making Sense of the Information
Raw sequence data generated from the sequencing machines requires extensive computational analysis, often referred to as bioinformatics. The initial steps involve quality control, where low-quality reads and data from damaged or dead cells are filtered out to ensure accuracy. Subsequently, the remaining high-quality reads are mapped back to a reference genome to determine their origin and identify the genes from which they were transcribed.
This mapping process allows for the quantification of gene expression levels for each gene within every individual cell. Specialized algorithms then process this data to group cells with similar gene expression patterns into distinct clusters, effectively identifying different cell types or states within the sample. Researchers can then identify “marker genes” that are uniquely expressed in these clusters, helping to characterize and annotate the newly discovered cell populations. This comprehensive analysis leads to biological insights, such as the discovery of novel cell types, understanding cellular differentiation pathways, or identifying mechanisms underlying disease.