10x Single Cell Techniques: Steps and Multiome Insights

Single-cell analysis has transformed biology by enabling researchers to study gene expression and chromatin accessibility at the individual cell level. This precision reveals cellular heterogeneity, disease mechanisms, and developmental processes that bulk sequencing cannot capture. Among the leading platforms for single-cell studies is 10x Genomics, which uses microfluidic partitioning to isolate cells for molecular profiling.

Understanding these techniques is essential for optimizing experiments and interpreting data effectively.

Microfluidic Partitioning Steps

10x Genomics’ single-cell technology relies on a microfluidic partitioning system that isolates individual cells into nanoliter-scale droplets. A microfluidic chip controls the flow of aqueous and oil phases to ensure each droplet contains a single cell and a barcoded gel bead. The efficiency of this encapsulation depends on cell concentration, fluid viscosity, and emulsion stability, which must be optimized to minimize doublets and maximize resolution.

Cells suspended in the aqueous phase enter microfluidic channels, where they encounter barcoded beads preloaded with oligonucleotides. These Gel Beads-in-Emulsion (GEMs) enable unique tagging of each cell’s RNA or chromatin fragments. The formation of GEMs relies on precise hydrodynamic forces and surfactant chemistry to maintain droplet stability. Disruptions, such as excessive shear stress or improper reagent mixing, can cause droplet coalescence, compromising data integrity.

After encapsulation, the system lyses cells within each droplet, releasing molecular contents while preserving compartmentalization. This step is sensitive to cell membrane composition, requiring tailored lysis conditions for different cell types. Maintaining compartmentalization ensures transcripts or chromatin fragments remain linked to their respective barcodes, preventing cross-contamination.

Barcoding And Transcript Capture

Once encapsulated, RNA transcripts are tagged through a barcoding process. Each gel bead carries a unique barcode sequence conjugated to oligonucleotides designed for transcript capture. These oligonucleotides include a poly(dT) sequence to hybridize with polyadenylated mRNA, a unique molecular identifier (UMI) to track individual transcript copies, and a cell barcode to distinguish RNA pools. This system ensures accurate identification of transcripts during sequencing, preventing signal ambiguity.

Transcript capture efficiency depends on hybridization kinetics between mRNA and barcoded oligonucleotides. Factors like transcript abundance, secondary structures, and sequence composition affect binding affinity, influencing capture rates. Reverse transcription follows, requiring enzymes with high processivity and low error rates to ensure cDNA accurately reflects the original RNA. Deviations in enzyme performance or reaction conditions can introduce biases, particularly in low-abundance transcripts.

After reverse transcription, cDNA remains linked to its original barcode, preserving single-cell resolution. However, incomplete synthesis and template switching can cause barcode misassignments. Optimizing reaction temperatures, enzyme concentrations, and primer designs helps mitigate these issues. UMIs further correct amplification biases by distinguishing true biological signals from PCR duplicates, improving quantification accuracy.

Library Construction Fundamentals

After cDNA synthesis, the material is prepared for sequencing through library construction, which involves fragmenting cDNA, adding sequencing adapters, and selectively amplifying fragments. Fragmentation must produce uniform sizes to ensure efficient sequencing and data quality. Enzymatic fragmentation is commonly used for its ability to generate consistent fragment distributions while preserving barcode integrity. Reaction conditions, including enzyme concentration and incubation time, must be carefully controlled to prevent degradation or incomplete fragmentation.

Adapter ligation follows, enabling sequencing platforms to recognize and process cDNA. Ligation efficiency depends on template purity, ATP availability, and ligase activity. Inefficiencies can lead to adapter dimers or low library complexity, reducing transcript diversity. Size selection techniques like SPRI bead purification remove unwanted byproducts and enrich properly ligated fragments, ensuring high-quality cDNA molecules progress to amplification.

PCR amplification generates sufficient material for sequencing while maintaining transcript proportionality. However, amplification bias can skew representation. Cycle number optimization balances yield and uniformity, while high-fidelity polymerases minimize sequence errors. UMIs incorporated during barcoding help correct amplification artifacts, improving quantification accuracy.

Single Cell Multiome Techniques

Advancements in single-cell technology now allow simultaneous profiling of multiple molecular layers within the same cell, providing a more integrated view of cellular function. Single-cell multiome techniques combine transcriptomic and epigenomic data, revealing how gene expression is regulated by chromatin accessibility. This approach is particularly valuable for studying cellular differentiation, disease progression, and regulatory networks.

By capturing both RNA and chromatin accessibility from the same cell, researchers can directly link transcriptional activity to regulatory elements like enhancers and promoters. Single-cell ATAC-seq (Assay for Transposase-Accessible Chromatin) is commonly integrated with RNA-seq in multiome workflows. A transposase enzyme inserts sequencing adapters into open chromatin regions, and barcoded oligonucleotides capture transcriptomic data. Computational methods align these datasets, connecting gene expression patterns with chromatin landscapes to infer regulatory interactions.

Cell Subtype Identification

Identifying cell subtypes within heterogeneous populations is a key application of single-cell analysis, revealing functional diversity and lineage relationships. Single-cell RNA sequencing (scRNA-seq) classifies cells based on gene expression, uncovering distinct transcriptional signatures. Dimensionality reduction techniques like principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) visualize cellular heterogeneity, helping researchers identify clusters with shared gene expression patterns. These clusters are annotated using known marker genes to distinguish closely related subpopulations.

Integrating chromatin accessibility profiles from single-cell ATAC-seq enhances subtype identification by mapping open chromatin regions, which define cell identity. This is particularly useful for distinguishing progenitor cells from differentiated states, as epigenetic changes often precede transcriptional shifts. Computational tools like latent semantic indexing (LSI) and graph-based clustering refine classification by incorporating chromatin accessibility into clustering frameworks.

Multiome data further strengthens subtype identification by linking regulatory elements to active transcription, providing a comprehensive view of how cellular identities are maintained and altered in response to physiological cues.