A Timer for analyzing temporally dynamic changes in transcription during differentiation in vivo
A Timer for analyzing temporally dynamic changes in transcription during differentiation in vivo
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用于分析体内分化过程中转录的时间动态变化的计时器
DOI：
10.1101/217687
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Bending D
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文献类型：
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作者：
Bending D
细胞分化如何通过时间协调的分子机制的活动逐渐发生是细胞生物学中的一个中心问题（Kohwi和Doe，2013; Kurd和Robey，2016）。然而，在单细胞水平上研究体内机制是具有挑战性的，因为单个细胞是不同步的，并且是异质的，在体内不同的时间和频率接收关键信号。没有现有的技术可以系统地分析体内单个细胞的分化和活性的时间动态。活体显微术可用于分析微环境中的细胞（Koechlein等人，2016），但不适合系统地分析快速迁移通过组织的细胞，如T细胞。单细胞测序可以提供"伪时间"，但这不是顾名思义的时间测量;相反，它是在选定的分析时间点测量样品之间的转录相似性（Trapnell等人，2014年）。流式细胞术适用于确定单个细胞的分化阶段，但目前的方法不能应用于研究单个细胞如何顺序地分化成更成熟的阶段，因为来自单个细胞的数据目前不编码时间信息（Hoppe等人，2014年）。因此，非常需要一种新的技术来实验性地分析体内单个细胞在关键分化事件之后的时间流逝或时域。这种新技术将有益于细胞生物学的所有领域，但它对于在体内生理条件下研究T细胞特别有用，其中信号传导的时间和频率对其分化至关重要。T细胞迁移通过身体（Krummel等人，2016），并且它们的活化和分化状态几乎完全通过流式细胞术分析来确定（Fujii et al.，2016年）。在T细胞中，T细胞受体（TCR）信号传导触发它们的活化和分化（Cantrell，2015），并且是胸腺T细胞选择的中心决定因素（Kurd和Robey，2016），包括阴性选择（Stepanek等人，2014）和调节性T（Treg）细胞选择（Picca等人，2006）和外周中的抗原识别（Cantrell，2015）。尽管已经全面和定量地分析了以秒为时间尺度的近端TCR信号传导的时间动力学（Roncagalli等人，2014; Stepanek等人，2014），仍然不清楚用于激活和分化的转录机制如何在体内随时间响应TCR信号。这种转录机制可用于新的报告系统，以分析抗原识别后T细胞活化和分化的动力学。
It is a central question in cell biology how cellular differentiation progressively occurs through the activities of temporally coordinated molecular mechanisms (Kohwi and Doe, 2013; Kurd and Robey, 2016). It is, however, challenging to investigate in vivo mechanisms at the single-cell level because individual cells are not synchronized and are heterogeneous, receiving key signaling at different times and frequencies in the body. No existing technologies can systematically analyze the temporal dynamics of differentiation and activities of individual cells in vivo. Intravital microscopy is useful for analyzing cells in microenvironments (Koechlein et al., 2016) but is not suitable for systematically analyzing cells that rapidly migrate through tissues such as T cells. Single-cell sequencing can provide “pseudotime,” but this is not the measurement of time as the name implies; rather, it is a measurement of the transcriptional similarities between samples at chosen analysis time points (Trapnell et al., 2014). Flow cytometry is suitable for determining the differentiation stage of individual cells, but current methods cannot be applied to investigate how individual cells sequentially differentiate into more mature stages as data from individual cells do not currently encode time information (Hoppe et al., 2014). There is thus a great need for a new technology to experimentally analyze the passage of time after a key differentiation event, or the time domain, of individual cells in vivo. Such a new technology would benefit all areas of cellular biology, but it would be particularly useful for the study of T cells under physiological conditions in vivo, where both the time and frequency of signaling are critical to their differentiation. T cells migrate through the body (Krummel et al., 2016), and their activation and differentiation statuses are almost exclusively determined by flow cytometric analysis (Fujii et al., 2016). In T cells, T cell receptor (TCR) signaling triggers their activation and differentiation (Cantrell, 2015) and is the central determinant of thymic T cell selection (Kurd and Robey, 2016), including negative selection (Stepanek et al., 2014) and regulatory T (Treg) cell selection (Picca et al., 2006) and antigen recognition in the periphery (Cantrell, 2015). Although the temporal dynamics of proximal TCR signaling, which are in the timescale of seconds, have been comprehensively and quantitatively analyzed (Roncagalli et al., 2014; Stepanek et al., 2014), it is still unclear how transcriptional mechanisms for activation and differentiation respond to TCR signals over time in vivo. Such a transcriptional mechanism may be used for a new reporter system to analyze the dynamics of T cell activation and differentiation upon antigen recognition.