Gene Expression Nebulas
基于标准化流程分析的转录图谱综合数据库

Gene Expression Nebulas

多物种转录图谱整合数据库

PRJNA407490: Single cell transcriptomic profiling of pluripotent stem cell-derived SCGB3A2+ airway epithelium reveals fate plasticity* - Fluidigm C1: bronchospheres day 27*

来源: NCBI / GSE103919
提交时间: Sep 15 2017
释放时间: Feb 28 2018
最后更新时间: May 15 2019

概要: Many lung diseases involve alterations in the cellular identity of the lung epithelium. Improved insight into airway development and the changes to cellular identity that result from abnormal signaling may therefore improve understanding of the etiology of these complex diseases. We have previously described a protocol to generate epithelial airway spheres from human pluripotent stem cells (hPSCs), but still poorly understand the identity, development, and heterogeneity of these early airway cells. Here we use novel murine and human PSC lines to study developing secretory cells derived in vitro from hPSCs. Using an SCGB3A2CherryPicker (SC) reporter system together with population-based or single-cell global transcriptomic profiling we track, purify, and analyze hPSC-derived putative secretory airway progenitors and find that SC+ cells are enriched for expression of airway epithelial markers, including secretory cell markers, SCGB3A2 and SCGB1A1. Unexpectedly, some SC+ human cells also co-express distal type 2 cell genes, including SFTPC, ABCA3, NAPSA, CTSH and functional lamellar bodies, suggesting a significant level of plasticity within the hPSC-derived secretory population relative to concurrently generated proximal airway TP63+ cells or distal alveolar SFTPC+ cells. We establish that this plasticity is minimized by inhibiting low levels of endogenous canonical Wnt signaling post-lung specification, thus depleting the co-expressed type 2 cell program. Taken together, these findings suggest that, similar to in vivo mouse genetic models and diseased human lungs, hPSC-derived airway cells exhibit cellular plasticity in response to signaling cues, providing a human model system in which to study cellular identity in a disease-relevant context.

项目整体设计: Single cell transcriptomic profiling of pluripotent stem cell-derived SCGB3A2+ airway epithelium reveals fate plasticity

GEN 数据集:
GEND000219
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生长方案: -
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提取方案: Day 27 hPSC-derived airway spheres were dissociated to single cells and sorted for viability (calcein blue+) and SCGB3A2CherryPicker expression as described above. CherryPicker- cells were stained with CellTracker Green CMFDA (ThermoFisher) and re-sorted for dye uptake into CherryPicker+ sorted cells at a ratio of 1:2. Fluidigm C1 machine and a 96-well C1 integrated fluidics circuit (IFC) were used to capture cells from this preparation. Post-capture, 70 cells were captured (out of 96 possible wells) and each captured cell was identified as CherryPicker+ (CellTracker Green-) or CherryPicker- (CellTracker Green+) for later analysis. These cells were then lysed and RNA was converted to cDNA and amplified according to the detailed Fluidigm protocol (^sing C1 to Generate Single-Cell cDNA Libraries for mRNA Sequencing^ Fluidigm, PN 100-7168).
建库方案: Cells were barcoded and a sequencing library prepared using the Illumina Nextera XT DNA kit and preparation protocol from Fluidigm. cDNA concentration was evaluated using Quant-iT PicoGreen dsDNA Assay (Life Technologies) on a Tecan Infinite M1000 Microplate Reader. Sequencing was performed on one pooled, barcoded sample with 75 base pair paired-end reads (150 cycles) with 130 million total reads in one lane of a flow cell using an Illumina NextSeq 500.
测序信息
分子类型: poly(A)+ RNA
库的片段类型: PAIRED
库的链类型: -
测序平台: ILLUMINA
测序仪型号: Illumina NextSeq 500
链特异性: Unspecific
样本
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数据来源 GEN样本编号 GEN数据集编号 系列编号 项目编号 样本编号 样本名称 生物样本编号 样本访问号 实验访问号 释放时间 提交时间 最后更新时间 物种 种族 族裔 年龄 年龄单位 性别 来源名称 组织 细胞类型 细胞亚型 细胞系 疾病 疾病状态 发育阶段 突变/变异 表型 Condition Detail 生长方案 处理方案 提取方案 建库方案 分子类型 库的片段类型 链特异性 库的链类型 加标(Spike-In) 测序方法 测序平台 测序仪型号 细胞数 测序片段数 碱基数 平均测序片段长度_1 平均测序片段长度_2 唯一比对率 多重比对率 覆盖度
文章
Single-Cell Transcriptomic Profiling of Pluripotent Stem Cell-Derived SCGB3A2+ Airway Epithelium.
Stem cell reports . 2018-04-12 [PMID: 29657097]