CRISPR-KO Genome-Wide Knockout Library

One-Stop Service from Design to Discovery — Simplifying Gene Function Research

Understanding gene function is central to decoding disease mechanisms and developing new therapies. Yet traditional knockout methods are time-consuming and ill-suited for high-throughput screening.

GeneRulor CRISPR-KO Library Service provides a complete end-to-end solution — library design to data analysis — so you can focus on science, not technical details.

Figure 1 CRISPR Knockout Principle Diagram

1. What is a CRISPR-KO Library?

A CRISPR-KO library contains thousands of sgRNAs to systematically knock out virtually all protein-coding genes, enabling rapid identification of key genes in biological processes or diseases.

2. Core Technical Principles

• Precise Target Recognition: Each sgRNA targets 5' exons near the start codon for efficient knockout

• Efficient Gene Editing: Cas9 nuclease precisely cleaves both DNA strands, generating double-strand breaks

• Permanent Functional Inactivation: NHEJ repair introduces frameshift indels for permanent gene inactivation

• High-Throughput Phenotypic Screening: Enriched or depleted sgRNAs directly reveal gene function

Figure 2 CRISPR-KO Library Core Technical Principle Diagram

3. GeneRulor One-Stop Service Advantages — Your Research Accelerator

Traditional CRISPR screens require multi-vendor coordination — adding cost, delays, and quality risks.

Choose GeneRulor — one decision covers it all. Backed by a postdoctoral R&D team, integrating five departments: Molecular Biology, Cell Biology, Viral Packaging, Bioinformatics, and NGS, we deliver seven end-to-end workflow steps：

Figure 3 CRISPR-KO Library One-Stop Service Workflow

4. Five Core Competencies

• Postdoctoral Station, Elite Team: Expert postdoctoral team for library design and data analysis

• Multi-Department Collaboration, Full-Process QC: Integrating Molecular Biology, Cell Biology, Viral Packaging, Bioinformatics, and NGS Sequencing forming a seamless service chain

• Proprietary Design Algorithms, Superior Knockout Efficiency: Proprietary gRNA algorithms achieve 60-90% knockout efficiency

• Professional Viral Packaging Platform for Hard-to-Transfect Cells: High-titer lentivirus for primary cells, neurons, immune cells, and other hard-to-transfect types

• One-Stop Service — Save Time, Effort, and Hassle: Full-cycle service from design to data; no multi-vendor coordination; 30-50% shorter timelines

5. High-Impact Publication Case Studies

5.1 Case 1: Nature 2025 — CRISPR Screens Boost CAR-T Cell Therapy

Reference: Datlinger et al. (2025). Systematic discovery of CRISPR-boosted CAR T cell immunotherapies.Nature.

Figure 4 CELLFIE Platform High-Throughput CRISPR Screening System

Background: CAR-T therapy has achieved success in hematologic malignancies, but CAR-T cell dysfunction remains a leading cause of failure. Systematically improving proliferation, persistence, and tumor killing is a major challenge.

Approach: The team developed CELLFIE — a high-throughput CRISPR platform for primary human CAR-T cells solving CAR/sgRNA/Cas9 co-delivery, with multi-dimensional phenotypic readouts.

Key Findings:

• Genome-wide screening + in vivo CROP-seq: RHOG, PRDM1, FAS knockout significantly enhance CAR-T function

• Unexpected: RHOG deficiency causes normal immunodeficiency yet enhances CAR-T — RHOG-KO cells reached 25% at day 21

• Combination screening found that RHOG+FAS dual gene knockout showed synergistic enhancement across in vivo models, CAR designs, and patient cells

Clinical Significance: This study demonstrates the enormous potential of CRISPR-KO screening for cell immunotherapy. Systematic screening revealed unexpected targets and proved combined gene editing dramatically boosts CAR-T efficacy, pointing toward next-generation "super CAR-T" development.

5.2 Case 2: 15-Gene Classifier to Predict NACT Response in Cervical Cancer

Reference: Tian X et al. (2021). A Fifteen-Gene Classifier to Predict Neoadjuvant Chemotherapy Responses in Patients with Stage IB to IIB Squamous Cervical Cancer. Adv Sci.

Figure 5 15-Gene Classifier for Cervical Cancer NACT Response

Background: Approximately 30-40% of cervical squamous carcinoma patients are NACT-insensitive. Predicting response before treatment is a key precision medicine challenge.

Approach: Using pre-treatment biopsies, the team applied genome-wide expression profiling and ML to construct a 15-gene NACT-response classifier.

Key Findings:

• 15 biomarkers identified spanning cell cycle, DNA repair, drug metabolism, and tumor microenvironment

• The 15-gene classifier achieved high accuracy (>80%) in distinguishing chemo-sensitive from resistant patients

• Independent validation confirmed robustness and reproducibility

• Distinct expression profiles between responders and non-responders suggest different treatment strategies

Clinical Significance: This provides a methodological reference for efficacy prediction in cervical and other solid tumors.

5.3 Key Insights

These two publications showcase the value of genomic technologies in tumor precision medicine from complementary angles:

5.3.1 Innovative Applications of CRISPR Screening

• Unbiased Screening Breaks Cognitive Limits: RHOG's discovery overturned conventional wisdom — a gene causing immunodeficiency in normal immunity powerfully enhances CAR-T function, highlighting the irreplaceable value of systematic screening

• Combination Strategy for Synergistic Enhancement: RHOG+FAS dual KO showed far superior therapeutic improvement vs. single-gene editing, demonstrating multi-target combination potential

• In Vivo Validation for Clinical Translation: Validated across multiple tumor models, CAR designs, and patient-derived cells to ensure clinical relevanc

5.3.2 Precision Prediction via Gene Expression Profiling

• Molecular Subtyping Guides Treatment Decisions: The 15-gene classifier identifies chemo-sensitive vs. resistant patients before treatment, embodying "diagnose first, treat second" precision medicine

• Multi-Dimensional Biomarker Integration: Multi-pathway integration (cell cycle, DNA repair, metabolism) outperforms single biomarkers

• Clinical Practicality and Accessibility: Pre-treatment biopsy testing is clinically actionable for personalized treatment planning

5.4 Shared Insights

Both studies advance tumor precision medicine: functional screens find treatment targets; expression profiling enables patient stratification.

GeneRulor's one-stop CRISPR-KO Service is designed to help your research reach this level — clearing every technical hurdle so you can focus on the science.

6. Technical Advantages Summary

• Genome-wide Coverage: All protein-coding genes in human, mouse, or other organisms; or custom gene sets

• High Knockout Efficiency: NHEJ mutations up to 60-90% efficiency for reliable inactivation

• Stable Phenotype: Permanent modification with stable, heritable phenotypes

• High-Throughput Screening: Thousands of genes evaluated simultaneously

• Cost-Effectiveness: Significantly lower time and cost vs. traditional methods

7. Why Choose GeneRulor?

In functional genomics, technology is a tool. The real value is translating techniques into reliable discoveries. Choose GeneRulor for a true research partner:

• Postdoctoral R&D station with deep expertise for your project

• Five integrated departments covering every step

• Every project is unique — personalized design, not one-size-fits-all

• Proven platforms and extensive project experience for complex cell types and demands

• Full-process QC and one-stop service from design to data, no multi-vendor hassle

Partner with us to turn your ideas into high-quality research — from top publications to clinical translation.

References

[1] Datlinger, P., Pankevich, E. V., Arnold, R., et al. (2025). Systematic discovery of CRISPR-boosted CAR T cell immunotherapies. Nature.

[2] Shifrut, E., Carnevale, J., Tobin, V., et al. (2018). Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell, 175(7), 1958-1971.

[3] Shalem, O., Sanjana, N. E., & Zhang, F. (2015). High-throughput functional genomics using CRISPR–Cas9. Nature Reviews Genetics, 16(5), 299-311.

[4] Doench, J. G., et al. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), 184-191.

[5] Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014). Genetic screens in human cells using the CRISPR-Cas9 system. Science, 343(6166), 80-84.

[6] Sanson, K. R., et al. (2018). Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nature Communications, 9(1), 5416.

[7] Tian X, Wang X, Cui Z, et al. (2021）. A Fifteen-Gene Classifier to Predict Neoadjuvant Chemotherapy Responses in Patients with Stage IB to IIBSquamous Cervical Cancer. Adv Sci, 8(10):2001978.