As biomedical research enters the single-cell era, high-throughput sequencing depth and high-quality data output have become the cornerstone for dissecting complex cellular landscapes. Under its OpenSeq Initiative, Cygnus Biosciences (Beijing) Co., Ltd. aims to support global researchers in exploring cutting-edge life science fields using the GP1000 （P1000） Sequencing System by providing a total of 1,000 terabytes of free sequencing data.

Recently, researchers employed the Cygnus Bio GP1000 platform to sequence libraries prepared with 10x Genomics 3' scRNA-seq technology, where the GP1000 delivered outstanding sequencing performance in complex immune cell populations.

Research Background: Comparative Analysis of T-Cell Subsets

The test samples focused on two critical T-cell states: resident memory T cells (Trm) generated during acute infection and transitory exhausted T cells (Ttrans) observed in chronic infection.

In immunological research, T-cell states determine the body’s efficacy against pathogens or tumors. Trm cells reside long-term in tissues and serve as the first line of defense against reinfection, whereas Ttrans cells represent a key transitional population as T cells evolve from functional effector states to terminal exhaustion under chronic antigen stimulation. Deciphering the transcriptomic differences between these two cell types carries profound biological significance for understanding the immunopathology of chronic viral infections and developing targeted immunotherapies [1].

Sequencing Quality: Dual Assurance of Depth and Precision

As illustrated in Figure 1, the GP1000 sequencer demonstrated exceptional data quality and output stability:

• Superior data abundance: The test generated more than 1.26 billion reads. Based on an estimated 13,504 cells, the mean reads per cell reached 94,022.
• High valid data rates: Valid Barcodes and Valid UMIs stood at 94.7% and 95.1% respectively, ensuring high data accuracy. A sequencing saturation rate of 67.5% indicated satisfactory sequencing coverage and sufficient data collection.
• Precise mapping performance: The Reads Mapped to Genome rate reached 91.00%, and the Reads Mapped to Transcriptome rate hit 75.30%. The median genes per cell stood at 4,146, with a median UMI count of 18,738, fully capturing intracellular transcriptomic information.

These metrics confirm that the GP1000 is fully compatible with mainstream single-cell library preparation platforms such as 10x Genomics, delivering high-fidelity raw data for downstream analysis.

Table 1: Quality Control Metrics from CellRanger Software

Cell Clustering: Clear Profiling of the Immune Landscape

After processing raw data generated by the GP1000, dimensionality reduction analysis using the UMAP algorithm (Figure 1) revealed distinct population clustering. The turquoise cluster represents Trm cells derived from acute infection, while the red cluster denotes Ttrans cells in chronic infection. The two cell types showed clear spatial separation in the UMAP plot, indicating significant disparities in gene expression and proving that the transcriptomic signatures captured by the GP1000 support high-resolution cell subset classification.

Figure 1: UMAP Dimensionality Reduction and Cell Typing of Single-Cell Transcriptomes

Marker Gene Validation: Accurate Identification of Functional States
Supported by the marker gene expression profiles in Figure 2, the high-quality data produced by the GP1000 not only validated canonical cell identity features but also sensitively detected transcriptional plasticity during T-cell differentiation:

• Remodeling of transcriptional regulatory programs: Data analysis showed that while Tcf7 was predominantly enriched in the Trm population, distinct Tcf7 expression signals were also observed in Ttrans cells. According to recent studies, this may indicate that a subset of Ttrans cells closely resembles progenitor exhausted (Tpex) T cells [2], aligning with the established differentiation pathway from Tpex to Ttrans and then to terminally exhausted T cells (Tex). Correspondingly, Tox, a core transcription factor of exhaustion, exhibited higher expression in Ttrans cells with low Tcf7 expression, implying further progression toward exhaustion and revealing the antagonistic effect of the two transcription factors.
• Fine characterization of complex subsets: Within the Ttrans population, Il7r was not completely silenced but displayed marked heterogeneous distribution — a portion of Tcf7-high Ttrans cells also showed high Il7r expression. This suggests the evolution of functionally distinct subpopulations within this intermediate state. High Cx3cr1 expression in Il7r-positive Ttrans cells reflects that these transitional exhausted cells retain strong effector functions [3].
• Faithful reflection of specific states: In this experimental model, Sell expression remained low in both populations, consistent with the tissue-resident nature of Trm cells and the activated transitional status of Ttrans cells.

Such refined transcriptomic differential analysis demonstrates that the GP1000 sequencing system can capture subtle fluctuations within cell populations. For Ttrans cells at dynamically transitional transcriptional stages, the GP1000 provides robust digital evidence supporting high-resolution subset delineation.

Figure 2: Marker Gene Expression Distribution

Conclusion: Join the OpenSeq Initiative to Explore the Future Together

This real-world test showcases the technological advantages of the Cygnus Bio GP1000 in complex single-cell transcriptome research. From rigorous quality control metrics to sensitive detection of intermediate cell characteristics, the GP1000 serves as a reliable tool for immunological research.

The OpenSeq Initiative is now accepting applications! We look forward to partnering with more research colleagues to unlock the mysteries of the single-cell world. Whether your work focuses on developmental biology, tumor immunity, or neuroscience, Cygnus Bio will provide powerful high-throughput sequencing support.

Contact us directly.Email: OpenSeq@cygnusbio.com

References

• [1] Wherry, E. J., & Kurachi, M. (2015). Molecular and cellular insights into T cell exhaustion. Nature Reviews Immunology.
• [2] Miller, B. C., et al. (2019). Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nature Immunology.
• [3] Zander, R., et al. (2019). CD4+ T Cell Help Is Required for the Formation of a Cytolytic CD8+ T Cell Subset that Protects against Chronic Infection and Cancer. Immunity.