Abay Tsion, Stickels Robert R, Takizawa Meril T, Nalbant Benan N, Hsieh Yu-Hsin, Hwang Sidney, Snopkowski Catherine, Yu Kenny Kwok Hei, Abou-Mrad Zaki, Tabar Viviane, Howitt Brooke E, Ludwig Leif S, Chaligné Ronan, Satpathy Ansuman T, Lareau Caleb A
Department of Pathology, Stanford University, Stanford, CA, USA.
Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
Nat Genet. 2025 Feb;57(2):451-460. doi: 10.1038/s41588-024-02036-7. Epub 2025 Jan 8.
Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing identifies rare populations that express specific marker transcript combinations, traditional flow sorting requires cell surface markers with high-fidelity antibodies, limiting our ability to interrogate these populations. In addition, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. In the present report, we addressed these limitations by developing Programmable Enrichment via RNA FlowFISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations defined by the abundance of specific RNA transcripts. Across immune populations (n = 184,126 cells) and fresh-frozen and formalin-fixed, paraffin-embedded brain tissue (n = 33,145 nuclei), we demonstrated that programmable sorting logic via RNA-based cytometry can isolate rare cell populations and uncover phenotypic heterogeneity via downstream, high-throughput, single-cell genomics analyses.
单细胞基因组学技术加速了我们在不同背景下对细胞状态异质性的理解。尽管单细胞RNA测序可识别表达特定标记转录本组合的罕见细胞群体，但传统的流式分选需要使用具有高保真抗体的细胞表面标记，这限制了我们研究这些细胞群体的能力。此外，许多单细胞研究需要从组织中分离细胞核，从而无法基于核外蛋白质标记富集已识别的罕见细胞状态。在本报告中，我们通过开发基于RNA流式荧光原位杂交测序的可编程富集技术（PERFF-seq）解决了这些限制，这是一种可扩展的检测方法，能够对由特定RNA转录本丰度定义的亚群进行单细胞RNA测序分析。在免疫细胞群体（n = 184,126个细胞）以及新鲜冷冻和福尔马林固定、石蜡包埋的脑组织（n = 33,145个细胞核）中，我们证明了基于RNA的细胞计数法的可编程分选逻辑能够分离罕见细胞群体，并通过下游的高通量单细胞基因组学分析揭示表型异质性。
