Single-cell sequencing platform
Decode biology
one cell at a time.
Generate high-resolution genomic, transcriptomic and epigenomic profiles from individual cells. Transform cellular heterogeneity into measurable biological insight with scalable single-cell sequencing workflows.
01 → N
Cell-level resolution
4+
Omics modalities
∞
Biological questions
Who we are
From complex samples
From complex samples
to cellular clarity.
Single-cell sequencing enables researchers to move beyond averaged measurements and investigate molecular differences between individual cells. This makes it possible to resolve cellular states, rare populations, lineage relationships and regulatory programs that can be hidden in bulk measurements.
Our approach brings together high-throughput sequencing technologies, robust sample workflows and computational analysis to support research across complex biological systems.
Our mission
Make single-cell data more accessible, reproducible and informative for researchers exploring biology at cellular resolution.
Make single-cell data more accessible, reproducible and informative for researchers exploring biology at cellular resolution.
Applications
One technology.
One technology.
Many biological questions.
Single-cell approaches can resolve heterogeneous populations across oncology, immunology, developmental biology, microbiology and neuroscience.
01
Cancer Research
Characterize tumor heterogeneity, identify rare cellular populations and investigate molecular programs associated with tumor evolution.
02
Immunology
Resolve immune-cell subpopulations and examine transcriptional states and cellular responses within complex samples.
03
Development
Follow changes in gene expression and cellular identity during differentiation and developmental processes.
04
Microbiology
Investigate microbial heterogeneity and molecular interactions between microorganisms and their surrounding environments.
05
Neuroscience
Explore molecular signatures of neuronal populations and distinguish cell states across complex neural tissues.
Technology
Four windows into
Four windows into
cellular biology.
Different sequencing modalities capture complementary molecular layers. Integrating these measurements can provide a richer picture of cellular identity, state and regulation.
Single-Cell Genomics
Characterize genomic variation at cellular resolution, including mutations, copy-number alterations and structural variation.
Single-Cell Transcriptomics
Measure RNA expression in individual cells to identify cellular identities, states and transcriptional programs.
Single-Cell Epigenomics
Investigate chromatin accessibility and other regulatory features to understand mechanisms controlling gene activity.
Single-Cell Multi-Omics
Integrate multiple molecular layers to connect genomic variation, regulatory activity and gene expression within cellular populations.
Experimental workflow
Sample → cellular insight
A typical single-cell sequencing workflow moves from sample preparation and cell or nuclei isolation through molecular barcoding, library preparation, sequencing and computational interpretation.
01
Sample
Tissue, culture or biological sample preparation.
02
Isolation
Single cells or nuclei are enriched and captured.
03
Barcoding
Molecules receive cellular identifiers.
04
Sequencing
Prepared libraries are sequenced at scale.
05
Insights
Bioinformatics transforms reads into biological maps.
Experimental intelligence
Quality begins
Quality begins
before sequencing.
Reliable single-cell sequencing depends on careful sample handling, molecular quality control and consistent experimental conditions. Monitoring upstream variables helps reduce avoidable variability before sequencing.
RNA Preparation • Quality Control
Workflow monitored
During a two-day RNA preparation workflow, observations from quality-control measurements highlighted variability associated with equipment performance and temperature transitions.
Subsequent quality assessment revealed reduced RNA yield and degradation. Because RNA integrity can influence downstream sequencing performance, the workflow was scheduled for repetition under improved experimental conditions.
High-resolution documentation of critical reagents and experimental stages can also support traceability and reproducibility throughout the workflow.
Initial RNA yield
42%
Target workflow consistency
91%
Sequencing readiness
Improving
Data & interpretation
Turn sequencing reads into
Turn sequencing reads into
biological maps.
After sequencing, cellular barcodes allow reads to be associated with their cells of origin. Computational workflows can then quantify molecular features, assess quality, cluster cells and reveal cellular populations and states.
Cellular heterogeneity
Instead of one averaged signal from an entire population, single-cell sequencing generates molecular profiles that can be compared across individual cells.
Multi-layer biology
Genomic, transcriptomic and epigenomic measurements can be analyzed individually or integrated to investigate relationships between molecular layers.
The result is a more resolved representation of cellular diversity and biological state.
Research documentation
Explore the experimental data.
Experimental Results & Graphical Data
A dedicated research report can bring together experimental observations, quality-control measurements, sequencing results and graphical analyses in one documented workflow.