美国癌症纪念斯隆-凯特琳中心Caleb A. Lareau，Ronan Chaligné，美国斯坦福大学Ansuman T. Satpathy和Robert R. Stickels共同合作，近期取得重要工作进展。他们研究提出，转录本特异性富集可通过单细胞RNA测序对稀有细胞状态进行分析。相关研究成果2025年1月8日在线发表于《自然—遗传学》杂志上。

据介绍，单细胞基因组学技术加速了人们对不同情境下细胞状态异质性的理解。尽管单细胞RNA测序能够识别出表达特定标志物转录本组合的稀有细胞群，但传统的流式分选需要有能与细胞表面标志物特异性结合的高亲和力抗体，这限制了人们对这些细胞群进行深入探究的能力。此外，许多单细胞研究需要从组织中分离细胞核，这就使得无法基于细胞核外的蛋白质标志物来富集已发现的稀有细胞状态。

研究人员通过开发“基于测序的RNA荧光原位杂交可编程富集技术（PERFF-seq）解决了这些局限。这是一种可扩展的检测方法，能够对由特定RNA转录本丰度所定义的亚群进行单细胞RNA测序分析。在免疫细胞群（共计184126个细胞）以及新鲜冷冻和福尔马林固定、石蜡包埋的脑组织（共计33145个细胞核）中，研究人员证明了通过基于RNA的细胞计数所实现的可编程分选逻辑能够分离出稀有细胞群，并可通过后续的高通量单细胞基因组学分析揭示表型异质性。

附：英文原文

Title: Transcript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing

Author: 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., Chalign, Ronan, Satpathy, Ansuman T., Lareau, Caleb A.

Issue&Volume: 2025-01-08

Abstract: 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.

DOI: 10.1038/s41588-024-02036-7

Source: https://www.nature.com/articles/s41588-024-02036-7