Single-cell sequencing (SCS) is a technology that examines genetic material from individual cells. This approach provides a high-resolution view of cellular differences within a sample. SCS allows researchers to obtain genomic, transcriptomic, or multi-omic information at the single-cell level. It has become a significant tool in biology and medicine, revealing previously hidden insights.
Beyond Bulk Sequencing
Traditional “bulk” sequencing methods analyze genetic material from thousands or millions of cells simultaneously, providing an average genetic profile for the entire cell population. This averaging can obscure variations among individual cells within the sample. For instance, bulk sequencing might miss rare cell types or subtle differences in gene activity crucial for understanding complex biological systems.
Single-cell sequencing overcomes this limitation by providing detailed information from each cell separately. This allows for the identification of cellular heterogeneity, meaning the diverse characteristics of cells within a seemingly uniform population. Observing individual cells helps researchers discover new cell types, understand how diseases develop, or determine how specific drugs affect different cells.
Preparing Individual Cells
The first step in single-cell sequencing involves obtaining a biological sample, which can range from tissue biopsies to blood samples. This sample then requires careful processing to separate it into a suspension of individual, living cells. The goal is to ensure each cell is intact.
Tissue dissociation, the process of breaking down complex tissues into single cells, can be performed using mechanical methods, enzymatic digestion, or a combination. Mechanical dissociation might involve mincing or physical disruption, while enzymatic methods use enzymes like collagenase to break down the extracellular matrix that holds cells together. The balance between sufficient cell separation and preserving cell viability is important, as excessive agitation or enzyme exposure can damage cells.
Once dissociated, various techniques are employed to isolate individual cells. Common methods include fluorescence-activated cell sorting (FACS) or magnetic-activated cell sorting (MACS), which use markers to identify and separate specific cell types. Microfluidic devices also enable precise isolation by guiding cells through tiny channels, often based on their physical properties. The isolated cells are then ready for the next stages of genetic material capture.
Capturing and Tagging Genetic Material
After individual cells are prepared, their genetic material, typically RNA for gene expression studies, needs to be isolated and marked. Each cell’s genetic material receives a unique identifier, known as a molecular barcode. This barcode is a short DNA sequence that acts like a tag, allowing researchers to trace the genetic information back to its original single cell.
Several technologies are used for capturing single cells and applying these barcodes. Microfluidic droplet-based systems, such as those used by 10x Genomics, encapsulate single cells, along with barcoded beads and reagents, within oil droplets. Each droplet acts as a tiny reaction chamber, ensuring that the genetic material from one cell is processed independently. Microwell-based platforms are another approach, where individual cells settle into microscopic wells, often on a chip, which can then be sealed for reactions.
For RNA sequencing, messenger RNA (mRNA) from the lysed cell is reverse transcribed into complementary DNA (cDNA). During this reverse transcription or a subsequent amplification step, the unique molecular barcode is incorporated into the cDNA molecule. This process also includes amplification steps, such as polymerase chain reaction (PCR), to generate enough genetic material for sequencing. The unique barcode ensures that even when genetic material from many cells is pooled later, its cellular origin remains identifiable.
Sequencing and Interpreting Data
Following the capture and tagging of genetic material, the barcoded and amplified DNA or RNA from all individual cells is combined into a single sample. This pooled sample is then subjected to high-throughput next-generation sequencing (NGS). NGS technologies rapidly read millions of DNA fragments simultaneously, generating vast amounts of sequence data.
The raw sequencing reads then undergo a multi-step computational process. First, specialized software uses the unique molecular barcodes to “demultiplex” the data, sorting reads back to their original individual cells. Next, these cell-specific reads are aligned to a reference genome, mapping them to known genes or genomic regions.
The final stage involves interpreting this complex dataset using specialized bioinformatics tools. This analysis identifies distinct cell populations by clustering cells with similar gene expression patterns. Researchers can then infer cell types and states, visualize the relationships between different cells using dimensionality reduction techniques like t-distributed stochastic neighbor embedding (t-SNE) or Uniform Manifold Approximation and Projection (UMAP), and uncover biological insights.