Advanced Single-Cell CRISPR Screening and Transcriptome Analysis Services
Our single-cell CRISPR screening service combines pooled gene perturbation with single-cell transcriptome profiling to help you connect each genetic edit with its downstream cellular response. We support researchers in target discovery, cell-state characterization, heterogeneity analysis, and mechanism investigation, enabling a clearer understanding of gene function and perturbation effects beyond conventional bulk or phenotype-based screening.
>85% gRNA capture efficiency for reliable assignment
Compatible with CRISPRi, CRISPRa, and CRISPRko models
Advanced network inference and clustering algorithms
Unlocking Cellular Complexity with High-Resolution Perturbation
The transition from conventional bulk assays to high-resolution single-cell profiling has fundamentally revolutionized functional genomics. Traditional pooled CRISPR screens have historically relied heavily on simple, binary phenotypic readouts, such as cellular survival, proliferation rates, or basic fluorescent markers. While useful for genome-wide essentiality screening, these bulk readouts inherently mask the underlying cellular heterogeneity, averaging out the subtle but critical transcriptional noise and state shifts occurring in rare cellular sub-populations.
Our advanced single-cell CRISPR screening and transcriptome analysis service directly addresses this analytical bottleneck. By capturing both the specific genetic perturbation (the gRNA identity) and the resulting global transcriptomic profile within each individual cell simultaneously, we establish a definitive causal link between a genetic edit and its comprehensive cellular phenotype. By mapping this genotype-to-phenotype relationship at the single-cell level, researchers can identify distinct, rare sub-population responses that would otherwise be entirely lost in bulk sequencing averages.
This capability is absolutely critical for understanding complex, multidimensional biological systems. It allows you to precisely trace the downstream cascading effects of targeted gene modifications on intricate cellular pathways. Whether your primary research goal is to validate novel mechanisms of action for early-stage drug candidates or to explore sophisticated genome editing and engineering architectures, this technology provides the robust, data-driven foundation required for comprehensive cellular decoding and biomarker discovery.
Applications
Diverse Applications in Discovery & Therapeutics
The robust and versatile nature of single-cell transcriptome perturbation allows it to be deployed across a widely diverse spectrum of biomedical research fields. We consistently support academic principal investigators and biopharma research heads in applying this multi-omics technique to overcome highly complex therapeutic and developmental challenges.
CAR-T and Cell Therapy Optimization
Evaluate exactly how targeted gene knockouts or gene activations influence immune cell exhaustion, long-term persistence, and proliferative capacity in vivo or in vitro. Researchers can systematically screen multiple therapeutic targets simultaneously to engineer next-generation T cells, NK cells, or macrophages with vastly superior anti-tumor efficacy. By analyzing the transcriptomic signature of each edited cell, developers can identify interventions that prevent cells from entering terminal exhaustion states during chronic antigen exposure.
Gene Editing Effect and Safety Evaluation
Assessing the global transcriptomic consequences of specific editing events is crucial for ensuring the safety and efficacy of novel cell therapies. This high-resolution approach allows developers to confirm that the desired on-target functional effect is fully achieved without causing unintended pathway dysregulation, oncogene activation, or cellular stress responses elsewhere in the cell's regulatory network.
Drug Target Discovery in Complex Oncology
Investigate the intricate, heterogeneous interactions within the solid tumor microenvironment (TME). By perturbing suspected tumor driver genes or immune evasion regulators and analyzing the resulting cell-state shifts across thousands of individual cells, developers can uncover novel, cryptic therapeutic vulnerabilities. This is particularly valuable for overcoming acquired drug resistance mechanisms mediated by rare cancer stem cell sub-clones or immunosuppressive tumor-associated macrophages.
Cell Fate and Developmental Lineage Mapping
For regenerative medicine, induced pluripotent stem cell (iPSC) engineering, and developmental biology, analyzing rapid transcriptomic changes following transcription factor perturbation is invaluable. It helps precisely define the critical pathways and bifurcation points governing cellular differentiation, allowing researchers to optimize protocols for directing stem cells into highly specific therapeutic lineages.
Workflow
End-to-End Workflow with Stringent QC Checkpoints
Navigating complex single-cell transcriptomic projects requires rigorous operational execution and highly standardized protocols. We offer a completely transparent, end-to-end workflow designed to minimize sample loss and maximize data integrity. If your project demands a more conventional approach for preliminary broad-scale target identification before zooming into single-cell resolution, we also provide high-throughput bulk CRISPR screening solutions.
End-to-end single-cell CRISPR screening workflow.
Our specialized single-cell screening pipeline encompasses the following key phases:
Custom gRNA Library Design and Synthesis: We collaborate closely with your team to design optimal gRNA sequences for your specific targets, ensuring high on-target specificity whether you are using interference, activation, or knockout modalities.
Cell Transduction and Perturbation: Target cells are transduced using optimized viral vectors. We carefully calibrate the multiplicity of infection (MOI) to ensure the majority of cells receive a single perturbation, minimizing background data noise.
Single-Cell Partitioning and Sequencing: Utilizing industry-standard microfluidic platforms, we perform precise single-cell partitioning. Both the gRNA transcripts and the poly-adenylated mRNAs are captured simultaneously and constructed into high-quality next-generation sequencing libraries.
Data Processing and Bioinformatics: Raw sequencing reads are immediately demultiplexed, aligned to the reference genome, and subjected to rigorous quality control before entering our advanced bioinformatics pipelines.
Critical Quality Control (QC) Metrics
We fundamentally believe that high-quality biological insights stem directly from flawless, low-noise data. Our stringent laboratory and computational QC checkpoints guarantee data reliability:
gRNA Capture Efficiency: >85% of viable cells must present a clearly detectable gRNA identity to be included in the final analysis.
Multiplet Exclusion: We actively monitor and computationally restrict multiple-infection rates, ensuring that >95% of successfully captured cells are assigned a unique, single gRNA identity (keeping the effective multiplet rate <5%).
Transcriptomic Depth: We target a median gene detection rate of >3,000 genes and >10,000 molecular tags per cell, dependent strictly on the cell type, to ensure sufficient statistical power for downstream differential and temporal analysis.
Bioinformatics
Comprehensive Bioinformatics & Demo Results
Generating high-dimensional transcriptomic data is only the first step; translating it into actionable biological intelligence is where our true service value lies. Our specialized bioinformatics team provides deep data mining far beyond standard open-source automated pipelines, ensuring your raw data is transformed into insightful, publication-ready figures.
Minimum Deliverables vs. Optional Add-ons
Standard Deliverables: Accurate gRNA identity assignment, basic sequencing quality control reports, target knockdown/activation expression validation, UMAP/t-SNE dimensional reduction and cell clustering, and Differential Gene Expression (DGE) analysis.
Optional Deep-Mining Add-ons: Gene Regulatory Network (GRN) inference, cell trajectory and pseudotime analysis, custom GO/KEGG pathway enrichment targeting specific immune or oncogenic cascades, and advanced multi-omics integration.
Typical Demo Results & Visualizations
To help you visualize the immense analytical depth of our service, we provide the following output modules in our comprehensive project reports:
gRNA Identity & Distribution: Accurate mapping of gRNA to individual cells with strict computational multiplet exclusion, usually visualized via Bar Charts showing distribution uniformity, library representation, and precise MOI control.
Target Knockdown/Activation Validation: Before exploring broad global effects, we verify the primary perturbation efficacy. Violin Plots clearly demonstrate the targeted gene's expression level in perturbed populations versus non-targeting (scrambled) controls.
Transcriptomic State Clustering: Identification of distinct cell sub-populations post-perturbation. High-resolution UMAP Scatter Plots reveal how specific genetic modifications drive cells into entirely new transcriptional states or shift the proportional balance of existing sub-clones.
Differential Expression Analysis: Identifying which downstream genes are significantly upregulated or downregulated as a direct or indirect consequence of the primary edit. Volcano Plots highlight these statistically significant transcriptomic shifts across the entire genome.
Pathway Enrichment: Mapping the specifically disrupted genes to known biological pathways. Bubble Charts demonstrate the biological magnitude and statistical significance of the affected signaling cascades, such as identifying a stark drop in oxidative phosphorylation or an increase in inflammatory cytokine production.
Gene Regulatory Network (GRN) Inference: Moving beyond simple linear correlations, we map master hub regulators and their downstream targets. Network Graphs reveal the hierarchical structure of gene regulation altered by the CRISPR perturbation, allowing you to identify the "master switches" driving a specific phenotype.
Cell Trajectory Analysis: Assessing exactly how gene perturbation alters developmental fates or continuous activation states. Pseudotime Plots trace the dynamic shifts in cell differentiation over simulated time, highlighting the exact temporal node where a genetic knockout derails normal cellular maturation.
To ensure the highest quality sequencing data and robust bioinformatic outputs, strict adherence to our sample preparation guidelines is highly required. We evaluate every sample upon receipt to confirm it meets our baseline quality thresholds before initiating the workflow.
Sample Type
Minimum Input Requirement
Viability Requirement
Delivery State
Shipping Condition
Cultured Cell Lines
≥ 1 × 106 cells per vial
≥ 85%
Single-cell suspension (no clumps)
Dry Ice (pre-evaluated)
Primary Immune Cells
≥ 2 × 106 cells per vial
≥ 85%
Highly purified single-cell suspension
Dry Ice
Sequencing Ready Libraries
≥ 20 μl volume
N/A (QC via Bioanalyzer)
Purified DNA/RNA library
Dry Ice
Note: For specific primary cells, highly sensitive stem cells, or fragile developmental models, please explicitly consult our technical team beforehand to discuss customized enzymatic dissociation protocols or specialized fixation alternatives.
Technology Comparison
Technology Comparison: Optimizing Your CRISPR Strategy
Selecting the right screening framework is absolutely paramount for achieving your specific research goals. Below is a strategic comparison between single-cell and conventional bulk screening approaches to help you critically evaluate the optimal fit for your next project.
Comparison Dimension
Bulk CRISPR Screening
Single-Cell CRISPR Screening
Resolution
Population average
Single-cell precision
Heterogeneity Capture
Low (masks rare sub-populations)
Extremely High (identifies rare cell states)
Output Data Complexity
Primary survival/phenotype markers
Global transcriptomic profiles per cell
Multiplexing Capacity
Massive (Genome-wide)
High (Hundreds to thousands of targets)
Ideal Use Case
Broad target discovery, essentiality screens
Deep mechanistic studies, pathway mapping, TME
Solution Selection Strategy: CRISPRko, CRISPRi, or CRISPRa?
Choosing the right perturbation modality is equally critical to the success of your screen. Our specialized experts guide you through selecting the most appropriate system based entirely on your biological question:
Choose CRISPRko (Knockout) when: You need to definitively validate essential survival genes, establish complete loss-of-function phenotypes, or determine the ultimate druggability of a specific target for small-molecule inhibition.
Choose CRISPRi (Interference) when: You are actively evaluating highly sensitive regulatory elements, enhancers, non-coding RNAs, or when a complete genetic knockout would result in immediate cellular lethality, thereby preventing the successful capture of transcriptomic data.
Choose CRISPRa (Activation) when: You aim to identify potent gain-of-function phenotypes, study drug resistance mechanisms via target overexpression, or attempt to engineer immune cells with significantly enhanced surface receptor expression profiles.
Case Study
Case Studies: From Data to Target Validation
Real-world application consistently demonstrates the transformative analytical power of this technology. By seamlessly integrating targeted genetic editing with rich multi-omics readouts, researchers can successfully dissect intricate regulatory mechanisms within the tumor microenvironment and optimize next-generation immunotherapies.
Understanding the highly complex ecosystem of cancer and exactly how therapeutic immune cells interact with resistant tumor cells remains a major challenge in modern precision genomics. Conventional bulk sequencing inherently fails to capture the subtle, rare, and transient cell states that often drive tumor immune evasion or acquired therapy resistance in solid malignancies.
Utilizing highly multiplexed single-cell CRISPR screening, researchers can systematically introduce specific, targeted genetic perturbations into complex cell models simulating the human tumor microenvironment. Following viral transduction, selection, and high-throughput single-cell RNA sequencing, advanced transcriptomic dimensional reduction analysis is performed to track precisely how each specific gRNA perturbation alters the cell's functional state and differentiation trajectory.
As strongly highlighted in recent comprehensive evaluations of the field, visualizing these targeted perturbations allows researchers to accurately pinpoint crucial regulatory hubs that control immune suppression. For example, the detailed analysis within Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology explicitly illustrates how combining CRISPR with single-cell omics seamlessly maps the functional consequences of gene editing across diverse, heterogeneous cellular subsets in complex cancer models. This includes tracking the exact transcriptional shift of exhausted T cells attempting to regain effector function after specific checkpoint gene knockdowns.
This highly robust analytical framework enables the precise, statistically backed identification of novel genetic drivers of immune evasion. It provides a direct, data-rich pathway to translating complex genomic datasets into actionable, highly specific clinical targets. Once these novel targets are confidently identified via single-cell screening, researchers seamlessly transition to downstream targeted CRISPR validation to confirm therapeutic viability in in vivo models.
FAQ
Frequently Asked Questions
1. How do you consistently distinguish true biological perturbation signals from background technical noise and multiplets in single-cell data?
We implement exceptionally strict laboratory handling protocols to maintain a consistently low Multiplicity of Infection (MOI) during the viral transduction phase. Bioinformatically, we utilize highly advanced computational algorithms that actively detect and permanently filter out cell barcodes containing multiple unique gRNA transcripts or abnormally high molecular tag counts. This rigorous dual-gating process ensures that the downstream transcriptomic profiles we analyze correspond strictly and exclusively to a single genetic perturbation event.
2. Can you assist with the design and synthesis of custom gRNA libraries for my specific target list?
Yes, absolutely. Our comprehensive end-to-end service includes fully customized library design. You simply provide us with your curated list of gene targets, genetic loci, or pathways of interest, and our expert bioinformatics team will design highly specific gRNAs with mathematically minimized off-target effects. These libraries are specifically optimized for your chosen modality—whether it be CRISPRi, CRISPRa, or standard CRISPRko.
3. What specific bioinformatics output formats are provided, and can my internal team re-analyze the raw data?
We deliver comprehensive, interactive HTML and PDF reports alongside high-resolution, publication-ready figures. More importantly for advanced research teams, we provide all standard raw data formats (such as raw FASTQ files) and deeply processed feature matrices (like gene-barcode expression matrices and explicit gRNA assignment tables). This guarantees your internal biostatistics teams or computational biologists can perform secondary independent analyses, run proprietary algorithms, or integrate the dataset seamlessly into larger corporate data repositories.
4. How does the turnaround time for single-cell screening compare to standard bulk screening?
While single-cell sequencing requires more intricate library preparation and significantly more intensive computational processing than bulk screens, our optimized pipelines maintain highly competitive timelines. Generally, the bioinformatics phase requires slightly more time to ensure precise clustering and network inference, but the overall project timeline is actively managed to support your rapid research and development cycles.
All services and products described herein are for Research Use Only (RUO). They are not intended for use in diagnostic procedures, clinical decision-making, or any direct therapeutic interventions.
For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.
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