Single-cell omics

01Overview and value chain#

Markers EC: Advanced Omics & Cell Heterogeneity | OECD: Single-Cell Genomics Services | Regulator: FDA (USA), EMA (EU), NMPA (China)

Single-cell omics maps the genome, transcriptome or proteome of each individual cell rather than the bulk average of a tissue, turning a “smoothie” of mixed cells into a catalogue of distinct cell types. It is delivered overwhelmingly as a contracted service: a biotech or clinical research group sends a fresh tissue biopsy to a platform or CRO, which runs droplet-based single-cell RNA sequencing (scRNA-seq), returns a cell-by-cell gene-expression matrix, and analyses it with machine-learning pipelines. The industry-standard capture uses microfluidic gel-beads-in-emulsion (GEM) on the 10x Genomics Chromium platform, where each cell is lysed inside a droplet and tagged with a 16-nucleotide cell barcode and a 12-nucleotide unique molecular identifier (UMI); CITE-seq extends this to surface proteins via DNA-barcoded antibodies, and scATAC-seq reads chromatin accessibility per cell. The output powers oncology (rare drug-resistant tumour subclones), immunology (T-cell exhaustion and checkpoint-inhibitor response) and the international Human Cell Atlas. Deep sequencing needs at least 50,000 reads per cell, so the service bundles microfluidics, Illumina-grade sequencing and AI analysis into one CRO workflow that biopharma buys by the project.

The key directions of single-cell omics are:

1. scRNA-seq capture platforms: microfluidic droplet barcoding (10x Chromium, BD Rhapsody, Singleron) that isolates and barcodes tens of thousands of cells per run for transcriptome-wide expression.
2. Multi-omics per cell (CITE-seq, scATAC-seq): simultaneous readout of surface proteins (CITE-seq) or chromatin accessibility (scATAC-seq) alongside RNA, giving full phenotype resolution.
3. Spatial and trajectory methods: spatial transcriptomics preserves tissue location, and pseudotime algorithms (Monocle, scVelo) order static cells along a developmental trajectory.
4. AI analysis services: pipelines (Cell Ranger, Seurat, Scanpy) that demultiplex reads, cluster cells by type on UMAP maps and surface unique oncomarkers for the client.

Sectoral value chain#

[Viable cell isolation] ──> [Microfluidic GEM barcoding] ──> [NGS library assembly]
                                          │
                               (deep sequencing)
                                          │
                                          ▼
[Therapeutic-target discovery] <─── [AI clustering / Seurat] <─── [Cell Ranger pipeline]

Value chain levels#

Cross-cutting technologies of the sector:

• Microfluidic GEM barcoding: precise water-in-oil droplet generation where each droplet holds one cell and one gel bead coated with millions of primers carrying a shared cell barcode and a unique molecular identifier.
• Unique molecular identifiers (UMIs): short random tags on each mRNA that let the pipeline count original RNA molecules absolutely, cancelling out PCR amplification bias.
• Pseudotime trajectory inference: computational ordering (Monocle, scVelo) of static single cells along a virtual developmental or activation trajectory from smooth changes in their expression profiles.

02US#

The United States dominates single-cell platform and reagent supply and hosts the field’s core bioinformatics research.

10x Genomics, BD Rhapsody, Mission Bio, CZI funding#

• 10x Genomics and the Chromium platform: 10x Genomics is the dominant platform vendor for scRNA-seq, supplying Chromium Controller and Chromium X instruments and reagents to most R&D laboratories worldwide.
• BD Biosciences and Mission Bio: BD Biosciences runs the Rhapsody multi-omics platform, and Mission Bio’s Tapestri reads somatic DNA mutations and proteins per cell, used to monitor leukaemia therapy.
• Broad Institute and CZI: the Broad Institute pioneered single-cell methods (under Aviv Regev), and the Chan Zuckerberg Initiative funds the multi-billion-dollar effort to map every human cell.

03CN#

China pairs its native DNBSEQ sequencing platform with a large contracted-sequencing CRO sector to offer single-cell omics at high throughput and competitive cost.

BGI/MGI DNBSEQ, Novogene CRO services, MOST funding#

• BGI and MGI DNBSEQ: BGI Group and its MGI instrument arm run the DNBSEQ sequencing platform and offer single-cell sequencing services on deep Asian-population reference data.
• Novogene and the CRO sector: Novogene and peer contract-sequencing CROs run scRNA-seq projects end-to-end for global biopharma, bundling library prep, sequencing and bioinformatics.
• MOST and national programmes: the Ministry of Science and Technology funds single-cell atlas programmes that feed domestic drug discovery.

04EU#

The European Union contributes a native single-cell platform and strong academic hubs, under EMA-quality oversight of the data and reagents used in regulated research.

Singleron platform, academic hubs, EMA oversight#

• Singleron Biotechnologies: the German single-cell company supplies its own capture platform and reagents, giving Europe a non-10x instrument route to single-cell services.
• Academic hubs: European genomics institutes contribute to the Human Cell Atlas and to spatial-transcriptomics method development.
• Quality oversight: the EMA applies data-integrity and reagent-quality expectations to single-cell readouts used in regulated drug-discovery submissions.

05Leading companies and research institutes#

06Tech stack and innovations#

The single-cell-omics service stack joins a microfluidic capture engine, multi-omics chemistry and an AI analysis layer into one CRO workflow.

1. Microfluidic GEM capture and UMI barcoding:
   • Droplet platforms (10x Chromium, BD Rhapsody, Singleron) co-encapsulate one cell and one barcoded gel bead per droplet; the bead carries a 16-nt cell barcode and a 12-nt UMI on every primer.
   • UMIs let the pipeline count original RNA molecules absolutely, removing PCR amplification bias so expression values are quantitative across cells.
2. Multi-omics per cell (CITE-seq, scATAC-seq):
   • CITE-seq labels surface proteins with DNA-barcoded antibodies that are read alongside the mRNA, giving full phenotype in the same cell, while scATAC-seq maps open-chromatin regions.
   • Mission Bio’s Tapestri extends per-cell readout to somatic DNA mutations plus proteins, used for minimal-residual-disease monitoring in leukaemia.
3. AI analysis and trajectory inference:
   • The Cell Ranger pipeline demultiplexes FASTQ reads, aligns them to the genome and builds the gene-by-cell matrix that downstream tools consume.
   • Seurat and Scanpy reduce dimensionality (UMAP), cluster cells into types and infer pseudotime trajectories (Monocle, scVelo), delivering the client a UMAP cluster map and a list of unique oncomarkers.

07Value chains and production pipelines#

CRO pipeline for a single-cell RNA-seq service project (ISO 9001 / CLIA-compatible data handling)#

┌───────────────────────────┐      ┌───────────────────────────┐
│ 1. Tissue dissociation &  │ ───> │ 2. Microfluidic GEM       │
│    viable-cell isolation  │      │    barcoding              │
└───────────────────────────┘      └───────────────────────────┘
                                                  │
                                                  ▼
┌───────────────────────────┐      ┌───────────────────────────┐
│ 4. Deep NGS sequencing    │ <─── │ 3. Library assembly & QC  │
│    (≥50,000 reads/cell)   │      │                           │
└───────────────────────────┘      └───────────────────────────┘
              │
              ▼
┌───────────────────────────┐      ┌───────────────────────────┐
│ 5. Cell Ranger processing │ ───> │ 6. AI clustering & report │
│    → expression matrix    │      │    (Seurat / UMAP)        │
└───────────────────────────┘      └───────────────────────────┘

Stage 1: Tissue dissociation and viable-cell isolation

A fresh tissue biopsy is gently enzymatically dissociated into a single-cell suspension, keeping RNA stable and cell viability above 90%, with dead cells removed on a FACS sorter.

Stage 2: Microfluidic GEM barcoding

The suspension is loaded onto a 10x Chromium (or BD Rhapsody / Singleron) chip; cells are co-emulsified with barcoded gel beads so each cell is lyssed and tagged with a unique cell barcode and UMIs inside its droplet.

Stage 3: Library assembly and QC

The emulsion is broken, the barcoded cDNA is PCR-amplified, fragmented and ligated with sequencing adapters, and the finished library is QC’d for size distribution and concentration.

Stage 4: Deep NGS sequencing

The library is sequenced at deep coverage — at least 50,000 reads per cell — on an Illumina NovaSeq X or MGI DNBSEQ, producing gigabytes of raw FASTQ data.

Stage 5: Cell Ranger processing to expression matrix

The Cell Ranger pipeline demultiplexes the FASTQ files, aligns reads to the reference genome, counts UMIs per cell and outputs the gene-by-cell expression matrix.

Stage 6: AI clustering and client report

Seurat or Scanpy filters noise, runs UMAP dimensionality reduction and clusters cells into types, and the CRO delivers a cluster map, marker-gene lists and pseudotime trajectories to the client.