A Uniquely Scalable Approach to Sequence Tens of Millions of Single Cells Without Compromising Performance

免疫疗法(包括CAR-T细胞疗法)在有效时是特效药，但 今天的免疫疗法的应答率只有15%-20%，治疗费用仍然很高 对于大规模采用来说，这一比例高得令人望而却步。单细胞测序的最新进展使其成为关键 通过高影响力的应用程序开发下一代细胞疗法的工具，如 作为TCR免疫分析和联合CRISPR筛查。解锁这些价值 应用每项实验需要询问1000万个细胞。Illumina核心的失效 2023年的知识产权重新点燃了美国和欧盟市场的竞争，并再次加速了DNA的下降 测序成本在1-2年内使约1,000万个细胞的测序负担得起，但现有 单细胞条形码的方法不支持每个实验超过100万个细胞的吞吐量。 建议的替代方案是可扩展的，但在性能和易用性方面的权衡使它们 不适合进行翻译研究。Sansimeon的方法独特地实现了可扩展的单电池 测序，吞吐量可达100M个细胞，同时保留高度易用性和必要的细胞 翻译研究中高通量应用程序的捕获效率。站台 克服了其他Drop-Seq方法常见的吞吐量限制 在乳化之前将单个细胞与微通道中的单个珠子进行确定性配对， 不需要诸如单元散列或组合索引之类的多步骤标记工作流。

Immunotherapies (including CAR-T cell therapies) are wonder drugs when they work, but the response rates of today’s immunotherapies are only 15-20% and therapy costs remain prohibitively high for mass adoption. Recent advances in single cell sequencing make it a critical tool for the development of next generation cell therapies through high impact applications such as TCR immune profiling and pooled CRISPR screening. Unlocking the value of these applications requires interrogation of >10 million cells per experiment. Expiration of Illumina’s core IP in 2023 re-ignited competition in US & EU markets and re-accelerated the drop in DNA sequencing costs to make sequencing of ~10 million cells affordable in 1-2 years, but existing methods for single cell barcoding do not support throughput beyond 1 million cells per experiment. Proposed alternatives are scalable, but trade-offs in performance and ease-of-use make them unsuitable for translational research. Sansimeon’s approach uniquely enables scaled single cell sequencing with throughput to 100M cells, while retaining high ease-of-use and necessary cell capture efficiency for high-throughput applications in translational research. The platform overcomes throughput limitations common to other drop-seq approaches by leveraging rapid deterministic pairing of single cells with single beads in a microchannel prior to emulsification, without the need for multi-step labeling workflows such as cell-hashing or combinatorial indexing.