Perturb-seq Single-Cell Transcriptomic Sequencing

Perturb-seq Single-Cell Transcriptomic Sequencing

1. Background

The integration of CRISPR gene editing technology with single-cell transcriptomic sequencing has pioneered a new paradigm in functional genomics research. Conventional CRISPR screens rely on phenotypic readouts (e.g., cell survival, fluorescent markers) and can only obtain limited endpoint information. In contrast, Perturb-seq (CRISPR perturbation transcriptomic sequencing) technology simultaneously detects gRNA identity and genome-wide transcriptomic expression profiles at the single-cell level, enabling systematic dissection of the effects of genetic perturbations on cellular states and revealing gene function, regulatory networks, and cell fate determination mechanisms[1,2].

In the field of cell therapy, Perturb-seq technology holds broad application prospects. For CAR-T cell products, it enables systematic screening of target genes that enhance persistence and reduce exhaustion; for iPSC-derived cell products, it enables dissection of key regulatory factors during differentiation; for gene editing therapeutic products, it enables evaluation of transcriptomic changes and functional status of edited cells[3,4].This strategy of combining genetic perturbation with transcriptomic readout provides a powerful tool for understanding complex cellular biological processes and optimizing cell therapy product design.

Generulor has established a comprehensive single-cell CRISPR perturbation transcriptomic sequencing and analysis platform, supporting end-to-end services from gRNA library design, cell perturbation experiments, to single-cell sequencing and bioinformatics analysis, providing systematic solutions for gene function research and cell therapy product development.

2. Principles of Single-Cell CRISPR Perturbation Transcriptomic Sequencing

The core principle of single-cell CRISPR perturbation transcriptomic sequencing (Perturb-seq) involves simultaneously capturing two categories of information at the single-cell level: (1) the gRNA sequence carried by the cell, used to determine the type of genetic perturbation the cell has undergone; (2) the genome-wide transcriptomic mRNA expression profile, reflecting cellular state changes following genetic perturbation. By correlating these two categories of information at the single-cell level, a direct causal relationship between genetic perturbation and transcriptomic response can be established.

The technical workflow comprises the following steps: first, a CRISPR perturbation library containing gRNA sequences with Poly(A) tails is constructed, target cells are transduced, and functional selection or culture is performed; subsequently, single-cell RNA sequencing is conducted on the perturbed cells using single-cell platforms such as 10x Genomics, with simultaneous capture of gRNA sequences via specific primers; sequencing data are processed through specialized bioinformatics analysis pipelines, including gRNA identity determination, transcriptome quantification, differential expression analysis, pathway enrichment analysis, and regulatory network inference, ultimately yielding transcriptomic feature profiles corresponding to each genetic perturbation.

Figure 1. Schematic workflow of single-cell CRISPR perturbation transcriptomic sequencing technology

3. Technological Innovations and Advantages of Single-Cell CRISPR Perturbation Transcriptomic Sequencing

3.1 Core Technological Innovations

3.1.1 High-Throughput Parallel Multigene Perturbation

Supporting large-scale gene function screening with significantly enhanced research efficiency:

(1) A single experiment can simultaneously perturb tens to hundreds of gene targets;

(2) Supports multiple perturbation modalities including CRISPRi (interference), CRISPRa (activation), and CRISPRko (knockout);

(3) Compatible with combinatorial perturbation designs to dissect gene–gene interaction relationships.

3.1.2 Single-Cell Resolution Transcriptomic Readout

Overcoming the limitations of conventional bulk screening to obtain comprehensive cellular state information:

(1) Independent acquisition of gRNA identity and genome-wide transcriptomic expression profiles for each cell;

(2) Identification of cellular heterogeneity and subpopulation-specific effects in perturbation responses;

(3) Support for cell trajectory analysis to dissect gene functions during dynamic processes.

3.1.3 Systematic Gene Function Annotation

Providing multi-level functional dissection and regulatory network inference:

(1) Gene function clustering and annotation based on transcriptomic features;

(2) Construction of gene regulatory networks to identify upstream and downstream regulatory relationships;

(3) Prediction of phenotypic effects and therapeutic potential of genetic perturbations.

3.2 Methodological Validation and Performance Metrics

Generulor has conducted comprehensive systematic validation of the single-cell CRISPR perturbation transcriptomic sequencing platform to ensure data quality and analytical accuracy:

4. Application Scenarios and Service Advantages

4.1 Application Scenarios

Single-cell CRISPR perturbation transcriptomic sequencing technology has broad applications across multiple fields:

(1) CAR-T/cell therapy product optimization: systematic screening of target genes that enhance T cell persistence and reduce exhaustion;

(2) Gene editing effect evaluation: dissection of transcriptomic changes and functional status of cells following CRISPR editing;

(3) Drug target discovery and validation: high-throughput screening of disease-related gene functions and identification of potential therapeutic targets;

(4) Cell differentiation mechanism research: dissection of key regulatory factors during iPSC/stem cell differentiation;

(5) Gene regulatory network construction: systematic dissection of regulatory relationships among transcription factors and signaling pathways;

(6) Tumor biology research: identification of key driver genes and vulnerability targets in tumor initiation and progression.

4.2 Service Advantages

(1) End-to-end service: providing one-stop solutions from gRNA library design, cell perturbation experiments, to sequencing analysis;

(2) Mature platform: based on mainstream single-cell platforms such as 10x Genomics, with a mature and stable technical system;

(3) Accredited quality management: the laboratory operates under both ISO 9001 quality management system and ISO/IEC 17025 accreditation standards;

(4) Specialized analytical team: extensive experience in single-cell data analysis and CRISPR screen analysis;

(5) Customized analysis: supporting personalized analysis and in-depth mining for client-specific requirements.

5. Exemplary Report of Single-Cell CRISPR Perturbation Transcriptomic Sequencing

Generulor provides comprehensive single-cell CRISPR perturbation transcriptomic sequencing analysis reports, encompassing foundational information including sequencing data quality assessment and cell filtering statistics. The core contents of the report include:

(1) gRNA Identity Determination and Assignment Analysis:Statistical summary of cell capture counts and distribution for each gRNA, assessment of library coverage and gRNA assignment quality, and identification of multiplex infections and unassigned cells.

Figure 2. gRNA Identity Assignment Statistics and Quality Assessment

(2) Perturbation Effect Validation Analysis:Assessment of expression changes in the target gene corresponding to each gRNA, validation of CRISPRi/ko knockdown effects or CRISPRa activation effects, and selection of effectively perturbed cell populations.Figure 4demonstrates the perturbation effect validation for 8 target genes: The first 6 genes display knockdown effects (blue: control vs. red: perturbed). The last 2 genes display activation effects (blue: control vs. green: activated).

Figure 3. Validation of Target Gene Expression Knockdown/Activation Effects

(3) Transcriptomic Feature Clustering Analysis:Dimensionality reduction and clustering analysis of transcriptomic features across different genetic perturbations, identification of gene groups with similar functional effects, and construction of gene function classification maps.

Figure 4. UMAP Clustering Analysis of Perturbation Transcriptomic Features

(4) Differential Expression and Pathway Enrichment Analysis:Differential expression analysis for each genetic perturbation, identification of significantly upregulated and downregulated genes, GO functional enrichment and KEGG pathway analysis, and dissection of the biological effects of perturbations.

Figure 5. Differentially Expressed Gene Volcano Plot and Pathway Enrichment Analysis

(5) Gene Regulatory Network Construction:Inference of gene regulatory networks based on perturbation–response relationships, identification of key transcription factors and regulatory hubs, and construction of signaling pathway regulatory models.

Figure 6. Gene Regulatory Network Visualization Analysis

(6) Cellular State Change Analysis:Assessment of the effects of genetic perturbation on cellular states, including cell cycle, apoptosis, and differentiation status, and identification of key genes influencing cell fate determination.

Figure 7. Analysis of Perturbation-Induced Cellular State Changes

6. Service Scope for Single-Cell CRISPR Perturbation Transcriptomic Sequencing

*Turnaround time: 30–45 business days for the standard workflow (including cell perturbation experiments); 20–25 business days for sequencing and analysis only20-25 business days;

7. Sample Requirements

*Note: (1) All samples must meet the quality standards described above; (2) For special cell types or experimental designs, please consult with the Generulor technical team in advance (Tel: 400-6309596; Product ordering/Technical support: service@generulor.com).

8. References

[1] Dixit A, et al. (2016). Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell, 167(7):1853-1866.

[2] Adamson B, et al. (2016). A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell, 167(7):1867-1882.

[3] Replogle JM, et al. (2022). Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell, 185(14):2559-2575.

[4] Ursu O, et al. (2022). Massively parallel phenotyping of coding variants in cancer with Perturb-seq. Nat Biotechnol, 40(6):896-905.