Key Takeaways
- Single-cell analysis measures molecular features in individual cells rather than averaged populations.
- Core single-cell methods include scRNA-seq, scDNA-seq, scATAC-seq, and CITE-seq. Spatial transcriptomics complements these methods and does not always achieve single-cell resolution.
- Cell isolation methods include FACS, microfluidics, and laser capture microdissection (LCM).
- Applications span cancer research, immunology, developmental biology, and drug discovery.
What Is Single-Cell Analysis?
Single-cell analysis measures gene expression, chromatin accessibility, DNA variation, or protein abundance in individual cells. When cells are pooled, a signal from a small subpopulation can be diluted by the rest of the sample.
Single-cell methods can reveal:
- Heterogeneity within tissue that appears uniform in bulk.
- Rare cell subtypes.
- Transitional cell states during differentiation or stimulation.
Estimates place the number of cells in the human body at roughly 30–37 trillion (Sender et al., 2016; Roy et al., 2018). These cells vary in type, state, and function.
Why Single-Cell Analysis Matters
Single-cell methods have identified new cell types and clarified how cells move between developmental states.
Patel and colleagues profiled 430 cells from five primary glioblastomas using scRNA-seq (Science, 2014). Within individual tumors, they reported:
- A range of stemness-related expression states.
- Mosaic expression of receptor genes such as EGFR and PDGFRA across different cells.
An association between greater intratumoral heterogeneity and poorer survival was reported when these single-cell findings were extended with bulk expression analyses, not from the five patients alone.
This mosaic pattern is not visible in bulk data, where each cell contributes to one averaged profile.
Limitation: many single-cell methods require dissociating tissue into individual cells or nuclei, which removes information about which cells were neighbors in the tissue. Microscopy-guided isolation and spatial methods can retain location information.
Key Techniques
Transcriptomic and Genomic Methods
scRNA-seq measures the transcriptome of individual cells. It is used to define cell types, activation states, and transcriptional programs. RNA capture per cell is incomplete, so scRNA-seq data are noisier than bulk RNA-seq.
scDNA-seq detects mutations and copy-number variants in individual cells. It is technically demanding because each cell contains only two copies of most genomic regions. Applications include tracking tumor subclones and clonal evolution.
scATAC-seq profiles chromatin accessibility and shows which regulatory elements, such as promoters and enhancers, are open in each cell.
Protein and Multimodal Methods
| Method | Labeling | Strength | Limitation |
|---|---|---|---|
| Flow cytometry | Fluorophore-labeled antibodies | High throughput | Spectral overlap as marker number increases |
| Mass cytometry | Metal-tagged antibodies | 40 or more parameters per cell | Fewer cells measured per second |
The multiparameter capacity of mass cytometry is described by Spitzer and Nolan (Cell, 2016).
CITE-seq uses oligonucleotide-tagged antibodies to measure surface proteins and the transcriptome in the same cell.
Spatial transcriptomics measures expression in intact tissue sections, preserving location information lost during dissociation.
The Single-Cell Analysis Workflow
Single-cell analysis workflows differ according to the technology and molecular features being measured. The five steps below describe a representative sequencing-based single-cell workflow, such as scRNA-seq, scDNA-seq, or related sequencing assays. Methods such as conventional flow cytometry, mass cytometry, and microscopy-based protein imaging use different measurement procedures and do not require next-generation sequencing.
- Isolate cells or nuclei. Common methods include FACS, microfluidic systems, microwells, and LCM. LCM isolates selected cells or regions from tissue sections under the microscope, which preserves the link to tissue morphology.
- Prepare molecular material. Lyse cells and capture RNA, DNA, or protein. Single cells contain very small amounts of material, so capture efficiency is important. Multimodal assays may use antibody-derived tags alongside nucleic-acid capture.
- Prepare sequencing libraries. Molecules may be tagged with barcodes and UMIs so reads can be traced to their cell of origin; barcoding is common but not universal across all methods.
- Sequence the libraries. Next-generation sequencing produces reads containing molecular and barcode information.
- Analyze and validate results. Normalization, clustering, differential expression, and trajectory inference turn reads into interpretable results, which should be validated against orthogonal data where appropriate.
Sequencing depth affects both cost and data quality. Haque and colleagues reported that full-length scRNA-seq libraries approach saturation around 1,000,000 reads per cell, with most genes detected by about 500,000 reads (Genome Medicine, 2017). The approximately 500,000-read figure is method-specific and is not a universal depth requirement across all single-cell assays.
Isolation from Tissue Sections
When cells of interest are defined by their location or morphology, LCM isolates them directly from FFPE or fresh-frozen sections. Laxco's Accuva Cellect Laser Capture Microdissection System provides:
- An infrared (IR) laser designed for film capture of single cells or small numbers of cells.
- An ultraviolet (UV) laser designed for cutting dense structures, such as bone or plant tissue.
- A five-step software workflow with visual quality control and documentation.
- Documented support for FFPE and fresh-frozen tissue, stained and unstained sections, immunofluorescence, and cell culture.
LCM has lower throughput than droplet-based capture. It can isolate individual cells or pooled regions, depending on the study design. Accuva Cellect isolates selected cells or regions; sequencing and other molecular assays are performed separately. LCM suits studies where tissue context determines which cells are analyzed.
Applications Across Research Fields
- Cancer research: profiling tumor heterogeneity and treatment-resistant subclones.
- Immunology: mapping activation states of T cells, B cells, and myeloid cells.
- Developmental biology: reconstructing differentiation trajectories.
- Drug discovery: profiling cellular responses to candidate compounds and identifying resistant subpopulations.
A bibliometric analysis found that single-cell sequencing publications grew from 3 in 2010 to 989 in 2019, from authors in 61 countries (Wang et al., 2021).
For proposals that include LCM, Laxco's Cellect Grant Assistance Program provides grant language and technical documentation.
Frequently Asked Questions
How is single-cell analysis different from bulk sequencing?
Bulk sequencing averages signals across many cells into one profile. Single-cell analysis measures each cell separately, preserving differences between cells and revealing rare populations.
Which methods isolate single cells before analysis?
Common approaches include FACS, droplet microfluidics, microwell systems, and laser capture microdissection. They differ in throughput, cost, and whether tissue context is retained. LCM selects cells directly from tissue sections. Pooled multi-cell captures do not yield single-cell profiles.
Which sample types does Accuva Cellect support?
Laxco lists FFPE and fresh-frozen tissue, H&E-stained and LCM-optimized stained sections, immunofluorescence, unstained samples, and cell culture. Confirm compatibility with your downstream assay during planning.
Can single-cell analysis be combined with spatial information?
Yes. Spatial transcriptomics and related spatial methods measure molecules in intact tissue. These data are often integrated with dissociated single-cell data to link cell identity with location.
