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Single-Cell RNA Sequencing Service

Q: How Many Cells Can Be Profiled in a Single-Cell RNA-seq Experiment?

MetwareBio's droplet microfluidic workflows support an estimated cell number of up to approximately 20,000 per partitioning reaction. The actual number of high-quality cells or nuclei retained in the final dataset depends on sample quality, input concentration, cell viability or nuclear integrity, recovery efficiency, multiplet rate, and quality-control filtering. For projects requiring greater cellular coverage, multiple partitioning reactions can be incorporated, allowing the total number of profiled cells at the project level to exceed 20,000.

Q: What Sequencing Strategy and Data Volume Are Provided for scRNA-seq?

MetwareBio applies platform-specific sequencing strategies and standardized data delivery targets for single-cell transcriptome profiling. The standard sequencing strategy and data volume are designed to provide sufficient depth for gene expression quantification, clustering, cell-type annotation, and downstream bioinformatics analysis. The page lists MGI DNBelab C-TaiM 4 with PE100 and 500 million reads per sample, and 10x Genomics GEM-X with PE150 and 100 Gb per sample. These are MetwareBio’s standard delivery specifications for routine single-cell RNA sequencing projects. Sequencing depth can be adjusted according to estimated cell number, sample complexity, transcriptomic diversity, and specific research objectives.

MetwareBio's single-cell RNA sequencing (scRNA-seq) service provides high-throughput gene expression profiling at single-cell resolution to resolve cellular heterogeneity and characterize cell types in biological samples. This single-cell transcriptomics workflow supports differential expression, pathway analysis, and cell-specific molecular discovery across biomedical, animal, and plant research.

2 Droplet Microfluidic Workflows for Flexible Single-Cell RNA Profiling

≥50% Cell Recovery Rate for Efficient Cell Capture and Barcoding

5–60 μm Cell Size Compatibility Across Diverse Sample Types

1,000+ Projects for Proven Single-Cell Transcriptomics Expertise

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Introduction Advantages Workflow Deliverables Experience Applications Sample Requirements FAQ

What Is Single-Cell RNA Sequencing (scRNA-seq)?

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic technology that measures gene expression at the individual-cell level. Unlike bulk RNA sequencing, which averages gene expression across mixed cell populations, scRNA-seq resolves cellular heterogeneity by revealing transcriptionally distinct cell types, cell states, and rare cell populations within complex biological samples. It enables researchers to characterize cell-specific gene expression, identify molecular markers, investigate developmental or disease-associated cell states, and uncover dynamic biological processes that may be masked in bulk transcriptomic analysis.

MetwareBio’s single-cell RNA sequencing (scRNA-seq) service provides end-to-end, high-throughput single-cell transcriptome profiling by integrating optimized single-cell preparation, sequencing, and bioinformatics analysis to characterize cellular heterogeneity at single-cell resolution. For single-cell partitioning, barcoding, and reverse transcription, we support two droplet microfluidic workflows using 10x Genomics GEM-X technology and the MGI DNBelab C-TaiM 4 system, enabling flexible processing of diverse biological samples, including single-cell suspensions, isolated nuclei, and plant protoplasts. This workflow supports single-cell transcriptomics research across biomedical, animal, and plant studies, with applications in cell atlas construction, cellular and tissue heterogeneity, developmental biology, disease mechanisms, immune microenvironments, treatment response, and plant development and stress adaptation.

MetwareBio single-cell RNA sequencing preparation systems and droplet microfluidic workflows.

(a) 10x Genomics workflow: left, Chromium X instrument for automated single-cell partitioning; middle, schematic of GEM-X droplet microfluidic generation and cell encapsulation; right, structure of the GEM-X Single Cell 3′ barcoded gel bead used for mRNA capture and molecular indexing.

(b) MGI workflow: left, DNBelab C-TaiM 4 single-cell droplet generator; middle, schematic of droplet microfluidic encapsulation of cells or nuclei with barcoded beads and reverse-transcription reagents; right, schematic of the barcoded bead and index-carrier structure used for mRNA capture and molecular labeling.

Why Choose MetwareBio’s Single-Cell Transcriptomics Service

Flexible Droplet Microfluidic Partitioning

MetwareBio supports 10x Genomics GEM-X technology and the MGI DNBelab C-TaiM 4 system for droplet-based microfluidic single-cell partitioning, molecular barcoding, and reverse transcription. The dual-workflow strategy enables flexible selection according to sample characteristics, cell size, throughput requirements, and study design.

High Cell Recovery and Throughput

Optimized single-cell preparation supports a cell recovery rate of ≥50% under qualified sample conditions, helping preserve valuable cells for downstream analysis. The workflow supports an estimated cell number of up to 20,000 per partitioning reaction, enabling efficient high-throughput scRNA-seq profiling.

Broad Cell Size Compatibility

The single-cell workflows accommodate cells approximately 5–60 μm in diameter, extending compatibility across cell populations with substantially different physical characteristics. This broad range supports single-cell transcriptome profiling of diverse primary cells, cultured cells, immune cells, and plant protoplasts.

Comprehensive Single-Cell Bioinformatics

MetwareBio provides a structured single-cell RNA-seq data analysis workflow including quality control, dimensionality reduction, clustering, cell-type annotation, marker and differential gene analysis, and functional enrichment. Advanced analyses can further include trajectory inference, cell–cell communication, pathway activity, and regulatory network analysis.

Single-Cell and Spatial Transcriptomics Integration

MetwareBio supports integrated analysis of single-cell RNA sequencing and spatial transcriptomics data, linking cell-resolved transcriptional identities with their spatial locations in intact tissues. This approach strengthens spatial cell-type annotation and enables deeper investigation of tissue heterogeneity, microenvironments, cell interactions, and spatially organized gene programs.

Extensive Single-Cell Project Experience

MetwareBio has completed 1,000+ single-cell transcriptomics projects, building extensive experience across diverse tissues, cell populations, and experimental designs. This accumulated expertise supports workflow optimization from sample preparation through sequencing and bioinformatics, helping deliver reliable data and biologically meaningful results.

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Which scRNA-Seq Platform Should You Choose?

10x Genomics GEM-X and MGI DNBelab C-TaiM 4 are two droplet-based single-cell RNA sequencing platforms that differ in cell-size compatibility, molecular barcoding strategies, sample throughput, and sequencing workflows. GEM-X uses barcoded gel beads and supports up to eight samples per chip, while DNBelab C-TaiM 4 employs a dual-bead system and offers greater flexibility for larger cells, processing up to four samples per run. For samples containing larger cells, DNBelab C-TaiM 4 may be preferable following sample assessment. For single-nucleus RNA-seq, both platforms are suitable options, with selection guided by project cost, turnaround time, and compatibility with upstream sample preparation and downstream research requirements.

Feature	10x Genomics GEM-X	MGI DNBelab C-TaiM 4
Core Technology	Droplet microfluidics with barcoded gel beads	Droplet microfluidics with a dual-bead barcoding system
RNA Profiling Strategy	Poly(A)-based 3′ gene expression profiling	Poly(A)-based 3′ gene expression profiling
Compatible Sample Types	Single-cell and single-nucleus suspensions	Single-cell and single-nucleus suspensions
Cell Size Compatibility	Generally ≤40 μm	5–60 μm
Parallel Sample Capacity	1–8 independent samples per chip	1–4 independent samples per run
Target Cell Recovery	Approximately 500–20,000 cells per sample	Up to approximately 20,000 cells per sample
Sequencing Platform	Illumina	DNBSEQ
Sequencing Data Output	100 Gbp/sample (PE150)	cDNA library: 500M reads (73.5 Gbp) /sample; Oligo library: 50M reads (3.7 Gbp) /sample
Primary Data Processing	Cell Ranger	C4Tools
Platform Selection Guidance	Suitable for projects requiring higher parallel sample capacity or established Chromium-compatible workflows	Suitable for projects using DNBSEQ-based workflows or requiring assay-specific assessment of challenging cell suspensions

scRNA-seq Experimental Workflow

MetwareBio’s single-cell RNA sequencing service follows a standardized workflow from sample preparation to bioinformatics analysis. Samples first undergo quality assessment and are prepared as qualified single-cell or single-nucleus suspensions according to tissue characteristics. Cells or nuclei are then partitioned using droplet microfluidics for molecular barcoding and reverse transcription, followed by library preparation and high-throughput sequencing. Sequencing data are subsequently processed through comprehensive single-cell bioinformatics analysis to generate cell-resolved transcriptomic profiles for downstream biological interpretation.

Single-cell RNA sequencing workflow from sample preparation to bioinformatics analysis — Experimental Workflow of MetwareBio’s Single-Cell RNA Sequencing

Single-Cell Transcriptome Analysis and Data Deliverables

MetwareBio provides comprehensive single-cell RNA sequencing (scRNA-seq) data analysis and deliverables through a structured workflow that progresses from raw sequencing data processing to biological interpretation. Standard analysis includes data quality control and filtering, expression matrix generation, unsupervised clustering, marker gene identification, cell-type annotation, and differential gene analysis. For multi-sample studies, the workflow further supports comparative clustering, differential expression analysis between groups, and functional enrichment analysis to identify biologically meaningful changes across conditions. Depending on study design and data characteristics, advanced analyses can also be incorporated, including trajectory inference, RNA velocity, cell–cell communication, inferred CNV analysis, transcription factor regulatory analysis, and other customized analyses. Deliverables typically include raw data, processed expression matrices, quality control summaries, clustering and annotation results, differential analysis results, enrichment analysis, publication-ready figures, and a structured bioinformatics report for downstream interpretation and publication support.

Cell Subtype Annotation

Pseudotime Analysis

RNA Velocity Analysis

Plant Cell–Cell Communication

In Silico Gene Knockout Analysis

Gene Co-expression Network Analysis

Single-Cell Gene Expression Profiling Experience and Performance

MetwareBio has extensive experience in single-cell RNA sequencing, having analyzed over 2,000 samples across diverse species and tissue types. We optimize tissue-specific sample preparation protocols for both single-cell dissociation and nuclei isolation, combining rigorous quality control with standardized workflows to ensure reliable data generation. Our standard delivery benchmarks include an estimated 5,000 or more cells/nuclei per sample, a median of over 1,000 detected genes per cell/nucleus, and a read mapping rate above 70%. The table below presents representative project results demonstrating our expertise across different biological samples.

Human and Animal Single-Cell RNA-seq Project Experience

Sample Type	Input Type	Platform	Estimated Number of Cells/Nuclei	Median Genes per Cell/Nucleus	Reads Mapped to Genome (%)
Human Brain	Single Nuclei	10x Genomics GEM-X	11,344	1,456	89.60%
Human PBMCs	Single Cells	MGI DNBelab C-TaiM 4	13,285	1,723	93.63%
Human Glioma	Single Nuclei	10x Genomics GEM-X	17,156	1,600	94.00%
Human Liver	Single Nuclei	10x Genomics GEM-X	9,913	1,612	92.60%
Human Synovium	Single Cells	10x Genomics GEM-X	16,270	2,843	95.60%
Human Carotid Aneurysm	Single Nuclei	10x Genomics GEM-X	15,926	2,234	95.70%
Human Rectum	Single Nuclei	MGI DNBelab C-TaiM 4	9,247	1,477	87.06%
Mouse Substantia Nigra	Single Nuclei	10x Genomics GEM-X	25,057	1,604	93.60%
Mouse Striatum	Single Nuclei	10x Genomics GEM-X	27,509	1,444	94.20%
Mouse Colon	Single Cells	MGI DNBelab C-TaiM 4	15,335	1,804	92.18%
Mouse Spleen	Single Cells	MGI DNBelab C-TaiM 4	15,833	2,003	97.01%
Mouse Eye	Single Nuclei	MGI DNBelab C-TaiM 4	25,813	2,819	93.74%
Mouse Placenta	Single Cells	MGI DNBelab C-TaiM 4	17,219	1,935	92.46%
Mouse Cornea	Single Cells	10x Genomics GEM-X	13,636	3,622	94.80%
Mouse Lung	Single Cells	10x Genomics GEM-X	13,493	3,293	94.60%
Mouse Hippocampus	Single Nuclei	MGI DNBelab C-TaiM 4	20,302	2,631	96.04%
Mouse Small Intestine	Single Cells	10x Genomics GEM-X	12,368	2,276	93.60%
Mouse Brain	Single Nuclei	10x Genomics GEM-X	18,744	1,894	94.80%
Mouse Bone Marrow	Single Cells	MGI DNBelab C-TaiM 4	9,672	1,942	96.28%
Mouse Pancreas	Single Cells	MGI DNBelab C-TaiM 4	12,173	2,197	92.73%
Goat Ovary	Single Nuclei	MGI DNBelab C-TaiM 4	15,433	1,393	95.58%
Rabbit Skin	Single Cells	MGI DNBelab C-TaiM 4	12,654	1,545	91.13%

Plant Single-Cell RNA-seq Project Experience

Sample Type	Input Type	Platform	Estimated Number of Cells/Nuclei	Median Genes per Cell/Nucleus	Reads Mapped to Genome (%)
Maize Leaf	Single Cells	MGI DNBelab C-TaiM 4	16,286	1,508	81.83%
Lettuce Root	Single Nuclei	MGI DNBelab C-TaiM 4	11,626	1,249	85.14%
Loquat Fruit	Single Nuclei	MGI DNBelab C-TaiM 4	15,327	2,526	88.21%
Poplar Stem	Single Nuclei	MGI DNBelab C-TaiM 4	15,891	1,751	88.10%
Grape Fruit	Single Nuclei	MGI DNBelab C-TaiM 4	12,508	1,341	94.31%
Cherry Root	Single Nuclei	MGI DNBelab C-TaiM 4	14,724	1,323	81.54%
Tomato Leaf	Single Nuclei	10x Genomics GEM-X	7,319	1,315	91.22%
Atractylodes lancea Stem	Single Nuclei	10x Genomics GEM-X	13,744	1,997	88.60%
Arabidopsis Leaf	Single Nuclei	MGI DNBelab C-TaiM 4	18,738	1,591	97.34%

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Single-Cell Transcriptomics Applications

Cell Atlas Construction and Cellular Heterogeneity

Single-cell RNA sequencing enables comprehensive characterization of cell populations within complex tissues, supporting cell atlas construction, cell-type identification, rare cell discovery, and cellular heterogeneity analysis. It is widely used to define tissue composition and uncover previously unrecognized cellular subsets across normal, developmental, and pathological samples.

Development, Differentiation, and Cell-State Dynamics

scRNA-seq reveals transcriptional changes associated with cell differentiation, lineage commitment, maturation, and cell-state transitions. By resolving intermediate and transient cell populations, it supports studies of embryonic development, organogenesis, stem cell biology, regeneration, and developmental processes in both animal and plant systems.

Disease Mechanisms and Treatment Response

Single-cell transcriptomics helps dissect disease-associated cellular changes that may be obscured in bulk RNA sequencing. Applications include cancer, neurological disorders, inflammation, cardiovascular and metabolic diseases, enabling investigation of disease progression, drug response, therapeutic resistance, and treatment-associated changes in specific cell populations.

Immune Microenvironment and Cell Interactions

scRNA-seq is widely used to characterize immune cell composition, activation states, tumor–immune interactions, inflammatory responses, and cell–cell communication. These analyses help reveal how distinct cell populations coordinate within tissue microenvironments and identify signaling pathways or cellular subsets associated with immunity, disease progression, and therapeutic response.

Stress Adaptation and Functional Plasticity

Single-cell and single-nucleus transcriptomics enable cell-type-specific investigation of responses to environmental and physiological perturbations. Applications include injury, hypoxia, infection, metabolic stress, and regeneration in biomedical research, as well as drought, salinity, temperature, nutrient limitation, pathogen response, development, and agronomic trait regulation in plant research.

Single-Cell RNA Sequencing Sample Requirements and Shipping Guidelines

Proper sample collection, preparation, and cold-chain transportation are essential for reliable single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq). Please follow the sample requirements below to maintain RNA integrity and support high-quality downstream analysis.

Accepted Sample Formats

Fresh-frozen tissues are accepted for routine single-cell or single-nucleus transcriptomic analysis.

Sample Amount Requirements

Human and Animal Tissues
10 mg for RNA extraction and quality control
100 mg for the formal experiment; for dense, calcified, or other difficult-to-process tissues, such as bone tissue, approximately 300 mg is recommended
Plant Tissues
100 mg for RNA extraction and quality control
900 mg for the formal experiment; for aged, highly lignified, starch-rich, or polysaccharide-rich tissues, such as lignified stems and tubers, approximately 3–5 g is recommended

Sample Storage and Shipping

Once sufficient tissue has been collected, snap-freeze samples in liquid nitrogen for at least 30 minutes to ensure complete freezing.
Human and animal tissues: Store in liquid nitrogen before shipment.
Plant tissues: Store at −80°C before shipment.
For optimal sample quality, storage before analysis is recommended for no longer than one month. Ship frozen samples with sufficient dry ice and ensure that they remain completely frozen upon arrival.

Frequently Asked Questions About Single-Cell RNA Sequencing

1. What Is the Difference Between Single-Cell RNA Sequencing and Bulk RNA-seq?

Single-cell RNA sequencing (scRNA-seq) profiles gene expression at the individual-cell level, whereas bulk RNA-seq measures the average transcriptional signal across all cells in a sample. By separating mixed tissues into transcriptionally distinct cell populations, scRNA-seq can identify cell types, cell states, rare populations, and cell-specific molecular changes that may be masked by bulk transcriptomic analysis. This makes single-cell transcriptomics particularly valuable for studying cellular heterogeneity, development, disease mechanisms, immune microenvironments, and treatment response.

2. scRNA-seq vs. snRNA-seq: Which Method Is Better for My Samples?

scRNA-seq analyzes RNA from intact cells, whereas single-nucleus RNA sequencing (snRNA-seq) profiles RNA from isolated nuclei. scRNA-seq is generally preferred when high-quality viable single-cell suspensions can be obtained, while snRNA-seq is especially useful for frozen, fragile, fibrous, highly differentiated, or difficult-to-dissociate tissues. The optimal strategy depends on sample condition, tissue properties, biological objectives, and whether intact cells can be recovered without introducing substantial dissociation bias.

Feature	scRNA-seq	snRNA-seq
Analyzed Material	Intact single cells	Isolated nuclei
RNA Profile	Cellular RNA, including cytoplasmic and nuclear transcripts	Nuclear-enriched RNA, including more pre-mRNA
Preferred Sample Condition	Fresh tissues or high-quality viable cell suspensions	Fresh or frozen tissues
Sample Processing	Requires tissue dissociation into intact cells	Requires nuclei isolation
Dissociation Effects	More sensitive to dissociation-induced stress and recovery bias	Generally reduces extensive dissociation-related effects
Difficult Tissues	May be challenging for fragile, fibrous, or poorly dissociable tissues	Well suited to difficult-to-dissociate tissues
Plant Samples	Requires preparation of suitable protoplasts	Generally recommended for most plant tissues
Best Suited For	Samples yielding representative, viable single-cell suspensions	Frozen, complex, fibrous, plant, or challenging tissues

3. For Plant Samples, Is snRNA-seq or Protoplast-Based scRNA-seq Recommended?

For most plant tissues, MetwareBio generally recommends single-nucleus RNA sequencing (snRNA-seq) rather than protoplast-based scRNA-seq. Plant cell walls require enzymatic digestion to generate protoplasts, and digestion efficiency can vary substantially among species, tissues, and cell types. Protoplasting may also introduce transcriptional stress and preferential recovery of certain cell populations. Nuclei isolation avoids extensive cell-wall digestion and is therefore particularly suitable for frozen, aged, lignified, starch-rich, polysaccharide-rich, or otherwise difficult-to-dissociate plant tissues. Protoplast-based scRNA-seq can still be considered when a high-quality and representative protoplast suspension can be reliably prepared. The final strategy should be selected according to plant species, tissue characteristics, sample condition, and research objectives.

4. What Sample Types Are Suitable for Single-Cell RNA Sequencing?

MetwareBio supports diverse sample formats for single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), including fresh tissues, fresh-frozen tissues, prepared cell suspensions, and liquid samples. Fresh tissues, cell suspensions, and liquid samples require time-sensitive transportation and should generally arrive at the laboratory within 24 hours for immediate processing. If delivery within this time window is not feasible, we generally recommend snap-freezing the tissue and submitting it as a fresh-frozen sample for single-nucleus RNA sequencing, which provides a more practical and reliable option for samples that cannot maintain sufficient cell viability during transportation.

5. What Quality Control Criteria Are Required for scRNA-seq and snRNA-seq?

Rigorous sample and suspension quality control is essential for reliable single-cell RNA sequencing data. MetwareBio performs QC at both the tissue/RNA level and the cell or nuclei suspension level before droplet microfluidic processing.

For tissue-level QC, extracted RNA should have an RIN ≧ 7.0. Cell or nuclei suspensions are evaluated by microscopy and automated counting, with the following standard criteria:

Total cell or nuclei count: ≧ 100,000
Cell viability: ≧ 80% for single-cell suspensions
Clumping rate: <25%
Cell or nuclei diameter: 7–60 μm
Microscopy: evaluation of cellular or nuclear integrity, aggregation, debris, and other impurities

For nuclei-based workflows, nuclear integrity and suspension quality are evaluated rather than conventional cell viability.

6. How Many Cells Can Be Profiled in a Single-Cell RNA-seq Experiment?

MetwareBio's droplet microfluidic workflows support an estimated cell number of up to approximately 20,000 per partitioning reaction. The actual number of high-quality cells or nuclei retained in the final dataset depends on sample quality, input concentration, cell viability or nuclear integrity, recovery efficiency, multiplet rate, and quality-control filtering. For projects requiring greater cellular coverage, multiple partitioning reactions can be incorporated, allowing the total number of profiled cells at the project level to exceed 20,000.

7. What Sequencing Strategy and Data Volume Are Provided for scRNA-seq?

MetwareBio applies platform-specific sequencing strategies and standardized data delivery targets for single-cell transcriptome profiling. The standard sequencing strategy and data volume are designed to provide sufficient depth for gene expression quantification, clustering, cell-type annotation, and downstream bioinformatics analysis.

Single-Cell Workflow	Sequencing Strategy	Standard Data Delivery
MGI DNBelab C-TaiM 4	PE100	500 million reads per sample
10x Genomics GEM-X	PE150	100 Gb per sample

These are MetwareBio’s standard delivery specifications for routine single-cell RNA sequencing projects. Sequencing depth can be adjusted according to estimated cell number, sample complexity, transcriptomic diversity, and specific research objectives.