https://www.yiliao.com/index.php?action=history&feed=atom&title=ScRNA-seq
ScRNA-seq - 版本历史
2026-04-19T04:07:59Z
本wiki的该页面的版本历史
MediaWiki 1.35.1
https://www.yiliao.com/index.php?title=ScRNA-seq&diff=311131&oldid=prev
2025年12月28日 (日) 01:55 77921020
2025-12-28T01:55:07Z
<p></p>
<a href="https://www.yiliao.com/index.php?title=ScRNA-seq&diff=311131&oldid=311130">显示更改</a>
77921020
https://www.yiliao.com/index.php?title=ScRNA-seq&diff=311130&oldid=prev
77921020：建立内容为“<div style="padding: 0 2%; line-height: 1.8; color: #1e293b; font-family: 'Helvetica Neue', Helvetica, 'PingFang SC', Arial, sans-serif; background-color: #ffffff…”的新页面
2025-12-28T01:53:25Z
<p>建立内容为“<div style="padding: 0 2%; line-height: 1.8; color: #1e293b; font-family: 'Helvetica Neue', Helvetica, 'PingFang SC', Arial, sans-serif; background-color: #ffffff…”的新页面</p>
<p><b>新页面</b></p><div><div style="padding: 0 2%; line-height: 1.8; color: #1e293b; font-family: 'Helvetica Neue', Helvetica, 'PingFang SC', Arial, sans-serif; background-color: #ffffff;"><br />
<br />
<div style="margin-bottom: 20px; border-bottom: 1px solid #f1f5f9; padding-bottom: 15px;"><br />
<p style="font-size: 1.1em; margin: 10px 0; color: #334155;"><br />
<strong>scRNA-seq</strong>（Single-cell RNA sequencing）即单细胞转录组测序技术，是在单细胞水平上对全转录组进行高通量测序的分子生物学技术。它通过捕捉单个细胞中的 mRNA 信息，揭示了组织内部的[[细胞异质性]]、稀有细胞亚群以及动态的细胞分化轨迹。在[[精准医学]]领域，scRNA-seq 是解析[[肿瘤微环境]]、制定[[免疫决策]]以及辅助[[智慧医生]]进行临床分层的核心工具。<br />
</p><br />
</div><br />
<br />
<div class="medical-infobox mw-collapsible mw-collapsed" style="width: 100%; max-width: 360px; margin: 0 auto 30px auto; border: 1px solid #cbd5e1; border-radius: 12px; background-color: #ffffff; box-shadow: 0 10px 25px rgba(0,0,0,0.08); overflow: hidden;"><br />
<br />
<div style="padding: 18px 15px; color: #ffffff; background: linear-gradient(135deg, #1e3a8a 0%, #3b82f6 100%); text-align: center; cursor: pointer;"><br />
<div style="font-size: 1.25em; font-weight: bold; letter-spacing: 1px;">scRNA-seq · 表达矩阵</div><br />
<div style="font-size: 0.75em; opacity: 0.8; margin-top: 4px; white-space: nowrap;">Single-cell RNA-seq (点击展开详细数据)</div><br />
</div><br />
<br />
<div class="mw-collapsible-content"><br />
<div style="padding: 25px; text-align: center; background-color: #f8fafc;"><br />
<div style="display: inline-block; background: #ffffff; border: 1px solid #e2e8f0; border-radius: 12px; padding: 20px; box-shadow: 0 4px 6px rgba(0,0,0,0.02);"><br />
[[文件:scRNA-seq_Workflow_Visual.png|180px|scRNA-seq 技术流程示意]]<br />
</div><br />
<div style="font-size: 0.85em; color: #64748b; margin-top: 12px; font-weight: 600;">从组织离散到数字化矩阵</div><br />
</div><br />
<br />
<table style="width: 100%; border-spacing: 0; border-collapse: collapse; font-size: 0.95em;"><br />
<tr><br />
<th style="text-align: left; padding: 12px 18px; border-bottom: 1px solid #f1f5f9; color: #64748b; font-weight: 600; width: 35%; background-color: #fcfdfe;">主流平台</th><br />
<td style="padding: 12px 18px; border-bottom: 1px solid #f1f5f9; color: #1e293b;">10x Genomics / Drop-seq</td><br />
</tr><br />
<tr><br />
<th style="text-align: left; padding: 12px 18px; border-bottom: 1px solid #f1f5f9; color: #64748b; font-weight: 600; background-color: #fcfdfe;">关键标记</th><br />
<td style="padding: 12px 18px; border-bottom: 1px solid #f1f5f9; color: #1e293b;">[[UMI]] / Barcode</td><br />
</tr><br />
<tr><br />
<th style="text-align: left; padding: 12px 18px; color: #64748b; font-weight: 600; background-color: #fcfdfe;">主要产出</th><br />
<td style="padding: 12px 18px; color: #1e293b; font-weight: bold;">[[细胞分选图谱]]</td><br />
</tr><br />
</table><br />
</div><br />
</div><br />
<br />
<br />
<br />
<h2 style="background: linear-gradient(to right, #1e3a8a, #ffffff); color: #ffffff; padding: 8px 15px; border-radius: 4px; font-size: 1.2em; margin-top: 35px;">核心技术流程</h2><br />
<p style="margin: 15px 0;"><br />
scRNA-seq 通过物理或化学方法将组织解离为单个细胞悬液，随后经历以下关键步骤：<br />
</p><br />
<ul style="padding-left: 20px; color: #475569;"><br />
<li style="margin-bottom: 10px;"><strong>单细胞捕获：</strong> 利用微流控（Microfluidics）或液滴法将单个细胞与带有[[分子条形码]]（Barcode）的磁珠封装。</li><br />
<li style="margin-bottom: 10px;"><strong>逆转录与扩增：</strong> 在液滴内进行原位逆转录，为每个细胞的 mRNA 加上独特的“身份标签”。</li><br />
<li style="margin-bottom: 10px;"><strong>文库构建与测序：</strong> 汇总所有细胞的 cDNA 进行高通量测序，随后通过生物信息学手段还原[[基因表达矩阵]]。</li><br />
</ul><br />
<br />
<h2 style="background: linear-gradient(to right, #1e3a8a, #ffffff); color: #ffffff; padding: 8px 15px; border-radius: 4px; font-size: 1.2em; margin-top: 35px;">临床决策赋能：智慧医生的应用</h2><br />
<br />
<h3 style="color: #1e40af; border-bottom: 2px solid #dbeafe; display: inline-block; padding-bottom: 3px; margin-top: 20px;">1. 肿瘤克隆演化监测</h3><br />
<p style="margin: 10px 0;"><br />
[[智慧医生]]利用 scRNA-seq 数据实时分析患者病灶内的克隆异质性：<br />
</p><br />
<ul style="padding-left: 20px; color: #475569;"><br />
<li style="margin-bottom: 10px;"><strong>耐药预判：</strong> 识别携带 KRAS<sup>G12D</sup> 等突变但在转录水平处于“代谢休眠”状态的稀有细胞，预警[[迟发性耐药]]。</li><br />
<li style="margin-bottom: 10px;"><strong>治疗后评估：</strong> 通过[[微小残留病灶]]（MRD）的单细胞转录指纹，判断手术或化疗是否彻底清除了[[致瘤克隆]]。</li><br />
</ul><br />
<br />
<h3 style="color: #1e40af; border-bottom: 2px solid #dbeafe; display: inline-block; padding-bottom: 3px; margin-top: 20px;">2. 免疫微环境深度解码</h3><br />
<ul style="padding-left: 20px; color: #475569;"><br />
<li style="margin-bottom: 8px;"><strong>T细胞状态分层：</strong> 精确定义 [[T细胞耗竭]]、[[调节性T细胞]]（Treg）的浸润比例，辅助[[免疫治疗]]联合方案的制定。</li><br />
<li style="margin-bottom: 8px;"><strong>配体-受体交互：</strong> 构建细胞间通讯网络，识别影响 [[PD-L1]] 疗效的新型抑制位点。</li><br />
</ul><br />
<br />
<div style="overflow-x: auto; margin: 30px auto; max-width: 650px;"><br />
<table style="width: 100%; border-collapse: collapse; border: 1px solid #e2e8f0; font-size: 0.95em; text-align: left;"><br />
<tr style="background-color: #f8fafc; border-bottom: 2px solid #1e3a8a;"><br />
<th style="padding: 15px; border: 1px solid #e2e8f0; color: #1e3a8a; width: 30%;">分析维度</th><br />
<th style="padding: 15px; border: 1px solid #e2e8f0; color: #1e3a8a;">临床决策产出</th><br />
</tr><br />
<tr><br />
<td style="padding: 12px; border: 1px solid #e2e8f0; background: #fcfdfe; font-weight: bold;">细胞类型聚类</td><br />
<td style="padding: 12px; border: 1px solid #e2e8f0;">识别[[驱动基因]]突变所在的特定细胞群。</td><br />
</tr><br />
<tr><br />
<td style="padding: 12px; border: 1px solid #e2e8f0; background: #fcfdfe; font-weight: bold;">差异表达分析</td><br />
<td style="padding: 12px; border: 1px solid #e2e8f0;">锁定不同临床分期下的特征[[生物标志物]]。</td><br />
</tr><br />
</table><br />
</div><br />
<br />
<h2 style="background: linear-gradient(to right, #1e3a8a, #ffffff); color: #ffffff; padding: 8px 15px; border-radius: 4px; font-size: 1.2em; margin-top: 35px;">经典参考文献与学术点评</h2><br />
<div style="margin-top: 15px; border-top: 2px solid #f1f5f9; padding-top: 15px;"><br />
<div style="margin-bottom: 20px;"><br />
<p style="font-size: 0.9em; color: #1e293b; font-weight: bold; margin-bottom: 5px;">[1] Tang F, et al. "mRNA-Seq analysis of a single cell." <em>Nature Methods</em>. 2009.</p><br />
<p style="font-size: 0.85em; color: #64748b; background: #f8fafc; padding: 10px; border-radius: 6px; border-left: 4px solid #3b82f6;"><br />
<strong>点评：</strong>该研究是全球首个单细胞转录组测序的成功尝试，利用改良的单管扩增法实现了单个细胞的测序，彻底打破了 Bulk-seq 的均值限制。<br />
</p><br />
</div><br />
<br />
<div style="margin-bottom: 20px;"><br />
<p style="font-size: 0.9em; color: #1e293b; font-weight: bold; margin-bottom: 5px;">[2] Macosko EZ, et al. "Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets." <em>Cell</em>. 2015.</p><br />
<p style="font-size: 0.85em; color: #64748b; background: #f8fafc; padding: 10px; border-radius: 6px; border-left: 4px solid #3b82f6;"><br />
<strong>点评：</strong>Drop-seq 技术的里程碑文献。首次证明了液滴微流控技术可以大规模并行处理成千上万个细胞，极大地降低了单细胞测序的成本并提升了通量。<br />
</p><br />
</div><br />
<br />
<div style="margin-bottom: 20px;"><br />
<p style="font-size: 0.9em; color: #1e293b; font-weight: bold; margin-bottom: 5px;">[3] Zheng GXY, et al. "Massively parallel digital transcriptional profiling of single cells." <em>Nature Communications</em>. 2017.</p><br />
<p style="font-size: 0.85em; color: #64748b; background: #f8fafc; padding: 10px; border-radius: 6px; border-left: 4px solid #3b82f6;"><br />
<strong>点评：</strong>10x Genomics 核心技术的系统性论述。该论文确立了目前临床最通用的微流控油包水技术标准，使 scRNA-seq 具备了向临床常规诊断转化的稳定性。<br />
</p><br />
</div><br />
</div><br />
<br />
<div style="margin: 40px 0; border: 1px solid #1e3a8a; border-radius: 8px; overflow: hidden; font-size: 0.9em;"><br />
<div style="background-color: #1e3a8a; color: #ffffff; text-align: center; font-weight: bold; padding: 12px;">scRNA-seq 导航</div><br />
<div style="padding: 15px; background: #ffffff; line-height: 2; text-align: center;"><br />
[[单细胞测序]] • [[智慧医生]] • [[全息图谱]] • [[肿瘤异质性]] • [[精准决策]]<br />
</div><br />
</div><br />
<br />
</div></div>
77921020