Single-Cell Proteomic Sequencing

Single-Cell Proteomic Sequencing

1. Background

CRISPR-Cas9 gene editing technology has become a core tool in the field of cell therapy, playing a particularly critical role in engineered immune cell therapies such as CAR-T and TCR-T. Knockout of endogenous T cell receptor (TCR) genes eliminates the risk of graft-versus-host disease, while knockout of immune checkpoint genes (e.g., PD-1) enhances antitumor activity. The CRISPR-CAR clinical trial published in 2020 first demonstrated the feasibility of simultaneously editing TCRA、TCRB, TCRB, and PDCD1—marking the entry of multiplex gene-edited cell therapy into the clinical application phase[1].

However, the safety and efficacy evaluation of gene-edited products faces new technical challenges. Conventional population-level detection methods cannot address the following critical questions: Has the knockout of the target gene been functionally validated at the protein level? Are the effects of different editing outcomes (frameshift vs. in-frame mutations) on protein expression consistent? In multiplex editing scenarios, what proportion of cells achieve simultaneous functional knockout of multiple genes? Answering these questions requires the integration of genotype and phenotype information at the single-cell level[2,3].

To address this technological demand, Generulor has introduced and optimized single-cell DNA + protein multi-omics detection technology based on the Tapestri platform. This technology enables simultaneous detection of gene editing status and corresponding protein expression levels within the same cell, achieving direct genotype–phenotype correlation analysis and providing a comprehensive quality evaluation system spanning from DNA to protein for CRISPR gene editing products[4,5].

2. Principles of Single-Cell DNA + Protein Multi-Omics Detection

The core of single-cell DNA + protein multi-omics detection technology builds upon single-cell DNA sequencing by incorporating antibody-oligonucleotide conjugate (AOC) technology for quantitative detection of cell surface proteins. Each AOC comprises a specific antibody coupled with an oligonucleotide bearing a unique barcode sequence. Upon antibody binding to the target protein, the barcode oligonucleotide is co-amplified and sequenced alongside genomic DNA, thereby enabling joint detection of genotype and protein phenotype from the same cell.

The detection workflow comprises the following steps: first, cells are stained and labeled using an AOC panel containing antibodies against target proteins; subsequently, the stained cells are co-encapsulated with lysis buffer in microdroplets for cell lysis, releasing genomic DNA and cell surface-bound AOCs; within barcode-labeled droplets, multiplex PCR simultaneously amplifies target genomic regions and protein barcode sequences; after library construction and high-throughput sequencing of amplification products, data are analyzed through the Tapestri GE analysis pipeline combined with the DAb-seq protein analysis pipeline, ultimately achieving integrated analysis of gene editing status and protein expression levels at single-cell resolution.

Figure 1. Schematic workflow of single-cell DNA + protein multi-omics detection for CRISPR gene editing (gene and protein markers in the flowchart can be adjusted according to actual experimental design; shown here as an illustrative example)

3. Technological Innovations and Advantages of Single-Cell DNA + Protein Multi-Omics

3.1 Core Technological Innovations

3.1.1 Genotype–Phenotype Integrated Analysis

Overcoming the limitations of conventional analytical methods, direct correlation between gene editing outcomes and protein expression is achieved:

(1) Simultaneous acquisition of DNA editing status and protein expression information within the same cell;

(2) Verification of whether gene knockout achieves functional silencing at the protein level;

(3) Elucidation of the differential effects of different editing types (frameshift/in-frame) on protein expression.

3.1.2 Multiplex Functional Knockout Assessment

Precise definition of target cell populations and assessment of product functional quality:

(1) Simultaneous detection of editing status and corresponding protein expression at multiple target sites;

(2) Precise quantification of the proportion of cells achieving simultaneous multigene functional knockout;

(3) Distinction between 'gene editing' and 'functional knockout,' preventing overestimation of editing product potency.

3.1.3 Cellular Immunophenotyping Analysis

Supporting multiplex protein marker detection for refined cell subpopulation classification:

(1) A 45-plex AOC panel covering major immune cell surface markers(Table 1);

(2) Supporting subpopulation classification of CD4+/CD8+ T cells, NK cells, and other subsets;

(3) Assessment of editing efficiency differences across cell subpopulations in conjunction with editing information.

Table 1 Mission Bio (Tapestri) Heme Oncology Protein Panel (45-plex; 42 surface markers + 3 isotype controls)

3.1.4 Editing Kinetics Monitoring

Supporting multi-time point longitudinal analysis to reveal the temporal relationship between editing and protein changes:

(1) Tracking genotypic and protein phenotypic changes at different time points post-editing;

(2) Assessment of the time window for protein knockout to reach steady state;

(3) Monitoring adaptive change trends in translocation-bearing cells.

3.2 Methodological Validation and Performance Metrics

Generulor conducted comprehensive systematic validation using primary T cells edited at three target sites—TCRA, TCRB, and PDCD1—combined with isogenic clonal cell line validation data, confirming the detection performance of the single-cell DNA + protein multi-omics platform:

4. Application Scenarios and Service Advantages

4.1 Application Scenarios

Single-cell DNA + protein multi-omics technology has broad application scenarios throughout the development and regulatory processes of CRISPR gene-edited cell products:

(1) Functional quality control of CAR-T/TCR-T products: verification of functional knockout efficiency of targets such as TCR and PD-1 at the protein level;

(2) Potency assessment of multiplex-edited products: precise definition of the proportion of 'target cells' achieving simultaneous multigene functional knockout;

(3) Editing strategy optimization: comparison of the effects of different gRNA designs and editing conditions on functional knockout efficiency;

(4) IND filing support for gene editing products: provision of regulatory-grade data packages with genotype–phenotype integrated analysis;

(5) End-to-end quality monitoring of clinical samples: supporting a complete quality evaluation system from gene editing to protein function;

(6) Cell subpopulation classification and editing differential analysis: assessing editing characteristic differences across subpopulations such as CD4+/CD8+ T cells.

4.2 Service Advantages

(1) Technological leadership: the first professional service provider in China offering single-cell DNA + protein multi-omics CRISPR editing analysis;

(2) End-to-end evaluation: a comprehensive quality assessment system spanning from gene editing to protein function, ensuring the accuracy of product potency evaluation;

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

(4) Specialized analytical pipeline: an integrated analysis pipeline based on scEDIT and DAb-seq, providing comprehensive multi-omics data interpretation;

(5) Extensive track record: successfully assisted multiple leading companies in completing functional quality evaluation of engineered immune cell products.

5. Exemplary Report of Single-Cell DNA + Protein Multi-Omics Detection

Generulor provides comprehensive single-cell DNA + protein multi-omics CRISPR editing analysis reports compliant with regulatory requirements, encompassing foundational information including DNA and protein sequencing data quality assessment and cell capture statistics.Figure 2 illustrates the bioinformatics analysis pipeline.

This flowchart systematically illustrates the overall analytical pathway for single-cell CRISPR gene editing safety assessment. Centered on the target gene intended for editing, potential off-target sites are predicted in silico based on sequence features, algorithmic models, and literature reports, with high-probability off-target sites selected for inclusion in the detection scope. On this basis, a customized amplicon panel covering both on-target and off-target sites is designed, followed by single-cell sequencing library construction and data acquisition. Upon obtaining sequencing data, multi-layered bioinformatics analysis is conducted: single-cell editing status is resolved through scEDIT, quantitatively assessing editing efficiency, allelic status, and multiplex editing co-occurrence at both on-target and off-target sites, with visualization analysis illustrating the distribution characteristics of cell populations with different editing states; concurrently, utilizing the intermediate read files generated by scEDIT, a customized analytical pipeline is constructed for detection and quantification of potential chromosomal translocation events, with particular emphasis on evaluating structural variation risks between on-target and off-target sites. Building upon these results, a comprehensive risk stratification of off-target events is performed by integrating off-target editing efficiency and the functional risk of associated genes, culminating in an integrated single-cell gene editing safety assessment report that provides the basis for risk evaluation and decision-making for gene editing products.Additionally, surface marker quantitative analysis can be performed in conjunction with single-cell protein data: protein expression profiling for each cell is conducted based on AOC/antibody tags and correlated with genotype results obtained from scEDIT (genotype–phenotype), enabling verification of protein-level knockout/expression changes and their correlation with editing efficiency, thereby strengthening the evidence chain for functional-level safety assessment.

Figure 2. Single-Cell DNA and Protein Sequencing-Based CRISPR Gene Editing Data Analysis Pipeline Schematic

(1) Based on the client's specific requirements and experimental objectives, the report may encompass the following core contents: Gene Editing Efficiency and Zygosity Analysis:Provides editing efficiency statistics for each target site at the cell and allele levels, differentiating the proportions of unedited, monoallelically edited, and biallelically edited cells, while simultaneously displaying the corresponding trends in protein expression levels.In addition to standard single-time-point analysis, multi-time-point editing analysis can also be performed (Figure 3) to achieve the following objectives:

·Validation of genotype–phenotype correlation - Confirming the correspondence between editing status and protein expression

·Assessment of editing stability and cellular dynamics - Monitoring editing efficiency peak, stability, and cellular adaptability

·Optimization of therapeutic product harvest timing - Determining the optimal cell harvest window

Figure 3. Dynamic Changes in Editing Efficiency and Protein Expression Across Multiple Time Points

(2) Genotype–Protein Phenotype Correlation Analysis: Displays the quantitative correlation between different editing states (unedited, monoallelic frameshift, biallelic frameshift, biallelic in-frame) and target protein expression levels, verifying the functional effects of gene knockout.

Figure 4. Genotype–Protein Phenotype Correlation Violin Plot Analysis

(3) Multiplex Functional Knockout Assessment: Precise quantification of the proportion of cells achieving simultaneous multigene functional knockout (defined as biallelic frameshift mutations with negative protein expression), assessing the true potency of the product.

Figure 5. Statistical Chart of Multiplex Functional Knockout Cell Proportions

(4) Off-Target Activity and Protein Function Analysis: Detection of off-target editing events and their distribution within the target functionally knocked-out cell population, assessing the potential impact of the proportion of cells harboring off-target edits on product safety.

Figure 6. Off-Target Distribution Analysis Within Functionally Knocked-Out Cell Populations

(5) Chromosomal Translocation and Protein Function Monitoring: For translocations with high editing efficiency but potential risks, protein expression data can be used to analyze the immunophenotypic characteristics of translocation-bearing cells. Longitudinal tracking studies have shown that some translocation-bearing cells may be eliminated through natural selection in vivo, thus their impact on long-term product safety is relatively limited.

Figure 7. Multi-Time Point Dynamic Monitoring of Translocation Events

(6) Cell Subpopulation Classification and Editing Characteristic Analysis: Cell subpopulation classification based on protein markers (e.g., CD4+/CD8+ T cells), analyzing editing efficiency and functional knockout differences across subpopulations.

Figure 8. UMAP Clustering Analysis of Editing Characteristics Across Cell Subpopulations

6. Service Scope for Single-Cell DNA + Protein Multi-Omics Detection

*Turnaround time: standard workflow30-40 business days;

7. Sample Requirements

*Note: (1) All samples must meet the quality standards described above; (2) Protein detection has higher requirements for cell freshness; fresh samples are preferentially recommended; (3) For special sample types, please consult with the Generulor technical team in advance (Tel: 400-6309596; Product ordering/Technical support: service@generulor.com).

8. References

[1] Stadtmauer EA, et al. (2020). CRISPR-engineered T cells in patients with refractory cancer. Science, 367(6481):eaba7365.

[2] ten Hacken E, et al. (2020). High throughput single-cell detection of multiplex CRISPR-edited gene modifications. Genome Biology, 21:266.

[3] Kalter N, et al. (2025). Precise measurement of CRISPR genome editing outcomes through single-cell DNA sequencing. Mol Ther Methods Clin Dev, 33:101449.

[4] Ruff DW, et al. (2022). High-Throughput Multimodal Single-Cell Targeted DNA and Surface Protein Analysis Using the Mission Bio Tapestri Platform. Methods Mol Biol, 2386:171-188.

[5] Moshref M, et al. (2024). Assessing a single-cell multi-omic analytic platform to characterize ex vivo-engineered T-cell therapy products. Front Bioeng Biotechnol, 12:1417070.