小柯机器人

美国纪念斯隆凯特林癌症研究所Dana Pe’er等研究人员合作发现，SEACells可从单细胞基因组学数据中推断出转录和表观基因组的细胞状态。这一研究成果于2023年3月27日在线发表在国际学术期刊《自然—生物技术》上。

研究人员提出了细胞状态的单细胞聚合（SEACells），这是一种识别metacells的算法，它克服了单细胞数据的稀疏性，同时保留了被传统细胞聚类掩盖的异质性。SEACells在识别RNA和转座酶可接触染色质（ATAC）模式的全面、紧凑和分离良好的元细胞方面优于现有算法，跨越了具有离散细胞类型和连续轨迹的数据集。研究人员展示了使用SEACells来改善基因峰值关联，计算ATAC基因得分，并推断分化过程中关键调节因子的活动。metacells水平的分析可以扩展到大型数据集，特别适合于病人队列，其中每个病人的聚集提供了更强大的数据整合单元。

研究人员使用metacells来揭示了造血分化过程中的表达动态和染色质图谱的逐步重构，并在2019年冠状病毒疾病（COVID-19）患者队列中独特地识别与疾病发病和严重程度相关的CD4 T细胞分化和激活状态。

据了解，metacells是来自单细胞测序数据的细胞分组，代表了高度细化、不同的细胞状态。

附：英文原文

Title: SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

Author: Persad, Sitara, Choo, Zi-Ning, Dien, Christine, Sohail, Noor, Masilionis, Ignas, Chalign, Ronan, Nawy, Tal, Brown, Chrysothemis C., Sharma, Roshan, Peer, Itsik, Setty, Manu, Peer, Dana

Issue&Volume: 2023-03-27

Abstract: Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene–peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

DOI: 10.1038/s41587-023-01716-9

Source: https://www.nature.com/articles/s41587-023-01716-9

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex