# Single-cell omics Per-cell mapping of genomes, transcriptomes and proteomes as a contracted service — moving biology from bulk-average reads to cell-by-cell atlases for drug-target discovery and patient stratification. Source: https://en.bioecon.ru/technology/single-cell-omics/ Updated: 2026-08-18 ## Overview 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 | Level | Description | Key inputs/outputs | |:---|:---|:---| | **Sample preparation** | Gentle enzymatic dissociation of a tissue biopsy into a suspension, preserving RNA stability and above 90% cell viability. | **In:** Fresh tissue, enzyme cocktails, FACS sorter.
**Out:** High-concentration suspension of viable single cells. | | **Droplet capture (GEM)** | Co-emulsification of cells with barcoded gel beads on a microfluidic chip so each cell gets a unique barcode. | **In:** Cell suspension, 10x Chromium chip, RT reagents.
**Out:** Emulsion (GEMs) of isolated droplet reactors. | | **Library assembly** | Emulsion break, cDNA PCR amplification, fragmentation and adapter ligation for sequencing. | **In:** Barcoded cDNA, PCR enzymes, Illumina adapters.
**Out:** Sequence-ready DNA library. | | **NGS sequencing** | High-throughput sequencing at deep coverage (at least 50,000 reads per cell) on Illumina NovaSeq X or MGI DNBSEQ. | **In:** Concentrated libraries, NGS instrument.
**Out:** Gigabytes of raw FASTQ reads. | | **Data processing** | FASTQ demultiplexing, read alignment to a reference genome, and UMI counting per cell. | **In:** FASTQ files, Cell Ranger / Kallisto-Bustools pipeline.
**Out:** Gene-by-cell expression matrix. | | **Bioinformatics & AI** | Noise filtering, dimensionality reduction (UMAP), cell-type clustering and pseudotime trajectory inference. | **In:** Expression matrix, Seurat / Scanpy.
**Out:** UMAP cluster map and list of unique oncomarkers. | 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. --- ## US 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. --- ## CN 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. --- ## EU 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. --- ## Leading companies and research institutes | Company / Institute | Country | Key products / platforms | Tech features | Status 2026 | |:---|:---|:---|:---|:---| | **10x Genomics** | 🇺🇸 USA | *Chromium Controller / Chromium X* | GEM droplet barcoding; industry-standard scRNA-seq platform | commercial | | **BD Biosciences** | 🇺🇸 USA | *BD Rhapsody* | Multi-omics single-cell capture | commercial | | **Mission Bio** | 🇺🇸 USA | *Tapestri* | Per-cell DNA mutation + protein readout (leukaemia MRD) | commercial | | **Singleron Biotechnologies** | 🇩🇪 Germany | Single-cell capture platform | European non-10x single-cell instrument and reagent route | commercial | | **BGI Group** | 🇨🇳 China | *DNBSEQ* single-cell service | Deep Asian-population reference; native NGS platform | commercial | | **Novogene** | 🇨🇳 China | Single-cell CRO services | End-to-end scRNA-seq library, sequencing and bioinformatics | commercial | --- ## Tech 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. --- ## Value 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.