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全部共 27 篇
合成生物学基础研究Nature

Modelling mouse embryogenesis from chemically induced totipotent stem cells.

Hua Wu、Yanyan Hu、Hanzhi Yu 等DOI: 10.1038/s41586-026-11164-5
方法学
0 次引用趋势数据不足

小鼠胚胎发育模型研究获进展。为验证单一诱导性非生殖系细胞能否启动胚胎发育，研究者以化学诱导全能样干细胞（ciTotiSCs）为唯一来源，建立可连续体外培养的小鼠胚胎模型（ciToti-EMs）。该细胞自组织形成类囊胚结构，在形态和谱系分离上与天然囊胚高度相似，并跨越植入前桑葚胚、囊胚及原肠运动后阶段，含胚胎与胚外成分。模型成功经历原肠运动、胚胎轴形成、造血和早期器官发生等关键节点，整体类似E8.5天然胚胎；延长培养还形成功能心脏组织，与E10.5天然胚胎高度相当。结果表明，单一诱导细胞类型ciTotiSCs可自主重现小鼠早期胚胎发育全过程，为哺乳动物胚胎研究提供有力模型。

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完整作者

Hua Wu、Yanyan Hu、Hanzhi Yu、Nana Huang、Lu Shi、Menghan Gao、Xinghan Du、Xue Lu、Yuxia Zhang、Xiya Sun、Xiaoqing Xu、Zhimin Liu、Junsong Yuan、Wei Zhou、Qiliang Shao、Dan Wang、Yangyu Zhao、Tianhua Ma、Sheng Ding

作者机构

  • New Cornerstone Science Laboratory, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
  • Howard Hughes Medical Institute, Chevy Chase, MD, USA.
  • Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China.
  • Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
  • National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China.
  • National Center for Healthcare Quality Management in Obstetrics, Beijing, China.
  • State Key Laboratory of Female Fertility Promotion, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
  • Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China.
  • CRE Life Institute, Beijing, China.
  • Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
  • New Cornerstone Science Laboratory, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China. matianhua@tsinghua.edu.cn.
  • New Cornerstone Science Laboratory, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China. shengding@tsinghua.edu.cn.
  • Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China. shengding@tsinghua.edu.cn.

技术路线

化学诱导全能样干细胞(ciTotiSCs) → 自组织形成类囊胚结构

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:31
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临床队列方法学Nature methods

CytoVI: deep generative modeling of antibody-based single cell data.

Florian Ingelfinger、Nathan Levy、Can Ergen 等DOI: 10.1038/s41592-026-03224-5
单细胞队列研究方法学
0 次引用趋势数据不足

基于抗体的单细胞技术（如流式细胞术、质谱流式细胞术和CITE-seq）已广泛应用于临床诊断和基础研究，但其分析受到技术噪声、批次效应、平台差异和抗体组合受限等因素的影响。为此，研究提出CytoVI——一种概率生成模型，用于对基于抗体的单细胞数据进行统计严谨的统一分析。CytoVI可在单一概率模型中生成信息丰富的细胞嵌入、填补缺失测量值、进行差异蛋白表达检验并自动注释细胞。研究应用CytoVI构建了涵盖350种蛋白的B细胞成熟整合图谱，识别出与免疫球蛋白类别转换相关的蛋白；在经流式细胞术和CITE-seq分析的B细胞非霍奇金淋巴瘤患者队列中，CytoVI揭示了疾病相关的T细胞状态。CytoVI已作为开源软件发布于scvi-tools.org。

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完整作者

Florian Ingelfinger、Nathan Levy、Can Ergen、Artemy Bakulin、Alexander Becker、Pierre Boyeau、Martin Kim、Diana Ditz、Jan Dirks、Jonas Maaskola、Tobias Wertheimer、Robert Zeiser、Corinne C Widmer、Ido Amit、Nir Yosef

作者机构

  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel. florian.ingelfinger@uniklinik-freiburg.de.
  • Department of Internal Medicine I, Medical Center-University of Freiburg, Freiburg, Germany. florian.ingelfinger@uniklinik-freiburg.de.
  • German Cancer Consortium (DKTK) Partner Site Freiburg, a partnership between DKFZ and Medical Center-University of Freiburg, Heidelberg, Germany. florian.ingelfinger@uniklinik-freiburg.de.
  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel.
  • Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.
  • Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
  • Division of Hematology and Laboratory Medicine, University Hospital Basel, Basel, Switzerland.
  • Department of Internal Medicine I, Medical Center-University of Freiburg, Freiburg, Germany.
  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel. ido.amit@weizmann.ac.il.
  • Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel. nir.yosef@weizmann.ac.il.
  • Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA. nir.yosef@weizmann.ac.il.

技术路线

抗体单细胞数据（流式细胞术、质谱流式、CITE-seq） → 统一概率生成模型分析（细胞嵌入生成、缺失测量插补、差异蛋白表达检验、自动细胞注释）

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:02
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公共数据库基础研究Cell

CD4 T cells convert transient responses to KRAS inhibition to durable remissions in pancreatic cancer.

Li Qiang、Megan T Hoffman、Jung-Ho Chun 等DOI: 10.1016/j.cell.2026.08.045
MIMIC肿瘤与癌症方法学
1 次引用趋势数据不足

胰腺导管腺癌对多数疗法（包括免疫治疗）耐受，单纯通过免疫检查点阻断重振CD8 T细胞不足以诱导长期持久缓解。KRAS抑制剂虽显示临床前景，但获得性耐药常见。研究在小鼠中建模KRAS抑制剂应答与复发，发现IL-21细胞因子模拟物21h10可诱导长期持久缓解，而化疗或联合检查点阻断无此效果。其疗效依赖Th1极化的CD4 T细胞，不依赖CD8 T细胞或肿瘤细胞MHC I类分子表达。经2型经典树突状细胞（cDC2）致敏的CD4 T细胞产生IFN-γ，促进巨噬细胞介导的肿瘤细胞吞噬。人PDAC标本体外经21h10处理可诱导浸润T细胞产生IFN-γ。表明IL-21激发的CD4 T细胞在小鼠及可能在人中发挥抗肿瘤活性，将KRAS抑制的短暂应答转化为持久缓解。

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编辑短评

该研究在小鼠模型中系统揭示IL-21模拟物21h10可将KRAS抑制剂短暂响应转化为持久缓解，机制上依赖cDC2启动的Th1样CD4 T细胞分泌IFN-γ并促进巨噬细胞吞噬，而非CD8 T细胞或肿瘤MHC I类分子。人PDAC离体实验仅显示IFN-γ诱导，样本量与临床验证有限，向人体转化仍需体内试验支持。

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完整作者

Li Qiang、Megan T Hoffman、Jung-Ho Chun、Brendan Parent、Felix Hambitzer、Frank Peprah、Courtney T S Kureshi、Birkley S Lim、Eugena Chang、Michael J Walsh、Julissa G Tello、Tavus Atajanova、Hojeong Shin、Corey Perkins、Rakeeb Kureshi、Yaniris Molina-Aponte、James M Dougan、Chong Zuo、Lauren Brais、Thomas E Clancy、James M Cleary、Jason L Hornick、Brandon M Huffman、Joseph D Mancias、George Molina、Mark Fairweather、Jonathan A Nowak、Kimberly J Perez、Douglas A Rubinson、Sarah Slater、Ritchell van Dams、Jiping Wang、Brian M Wolpin、Kwok-Kin Wong、Harshabad Singh、Andrew J Aguirre、David Baker、Michael Dougan、Stephanie K Dougan

作者机构

  • Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Immunology, Harvard Medical School, Boston, MA 02115, USA.
  • Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Biochemistry, University of Washington School of Medicine, Seattle, WA 98195, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
  • Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
  • Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Department of Surgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
  • Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
  • Department of Radiation Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
  • Laura and Isaac Perlmutter Cancer Center, New York University Langone Medical Center, New York, NY 10016, USA.
  • Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Department of Biochemistry, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
  • Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
  • Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Immunology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: stephanie_dougan@dfci.harvard.edu.

技术路线

小鼠KRASi响应/复发模型 → 与化疗或检查点阻断联合方案对比分析

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:04
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临床队列综述Nature medicine

On-premise medical AI agents for reliable clinical decision-making.

Li Zhang、Georg Wölflein、Dyke Ferber 等DOI: 10.1038/s41591-026-04609-x
MIMIC机器学习风险模型成人方法学
2 次引用趋势数据不足

研究开发并评估了一种完全本地部署的临床人工智能代理，将本地运行控制与多视角可靠性框架结合，以支持选择性自主决策。该代理在两项基于MIMIC-IV的基准测试中，分别于七疾病任务和四疾病任务上达到90.04%和83.8%的准确率，主要基准接近云端基线。为评估决策时可靠性，研究量化了诊断与推理的内部似然、语言及行为稳定性指标。诊断行为一致性对正确性的判别力最强（AUC=0.860），在压力测试下仍具信息性（AUC=0.875）。当一致性阈值为0.90时，49.4%的病例被保留，诊断准确率达98.9%。结论表明，决策时可靠性信号可识别低风险子集供自主处理，其余转交人工审核，为机构治理下的临床代理提供了实用框架。

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完整作者

Li Zhang、Georg Wölflein、Dyke Ferber、Junhao Liang、Zunamys I Carrero、Xuewei Wu、Julien Vibert、Jan Clusmann、Lino Möhrmann、Elena E Möhrmann、Catharina Wichmann、Fabian Wolf、Tim Lenz、Jakob Nikolas Kather

作者机构

  • Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
  • Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Drug Development Department, Gustave Roussy, Villejuif, France.
  • Université Paris-Saclay, Gustave Roussy, INSERM U981, IHU PRISM, Villejuif, France.
  • Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Department of Gynecology and Obstetrics, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany.
  • Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.
  • Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. kather.jn@tu-dresden.de.
  • Department of Medicine I, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany. kather.jn@tu-dresden.de.
  • Pathology & Data Analytics, Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK. kather.jn@tu-dresden.de.

技术路线

数据/样本：两个基于MIMIC-IV的基准（七疾病任务和四疾病任务） → 分析：开发并评估一个完全本地部署的临床智能体，耦合本地操作控制与多视角可靠性框架；量化诊断和推理的内部似然、语言和行为稳定性指标；评估诊断正确性的判别能力（AUC）及压力测试下的信息性；设定一致性阈值评估保留病例比例及诊断准确率

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:04
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公共数据库方法学Nature genetics

All of Us diversity and scale yield context-dependent improvements in polygenic prediction.

Kristin Tsuo、Zhuozheng Shi、Tian Ge 等DOI: 10.1038/s41588-026-02734-4
UK Biobank风险模型多中心多种族方法学
2 次引用趋势数据不足

利用“All of Us”研究计划245,388例全基因组序列及英国生物银行数据，针对32项性状和疾病构建多祖先多基因风险评分，并在“All of Us”多祖先参与者中评估祖先背景、方法和遗传结构对评分性能的影响。结果显示，增加“All of Us”多样性可提高若干性状的评分准确性，尤其在代表性不足人群中；但将“All of Us”与英国生物银行荟萃分析以最大化样本量并非普遍最优，对多基因性较低的性状，仅用“All of Us”训练在非洲祖先参与者中表现最佳，提示祖先富集效应。个体评分准确性随与发现全基因组关联研究祖先分歧增加而线性下降，多祖先训练数据可减弱该衰减。结论强调，更具代表性的生物样本库有助于实现公平的多基因风险评分性能。

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完整作者

Kristin Tsuo、Zhuozheng Shi、Tian Ge、Ravi Mandla、Kangcheng Hou、Yi Ding、Bogdan Pasaniuc、Ying Wang、Alicia R Martin

作者机构

  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. ktsuo@broadinstitute.org.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ktsuo@broadinstitute.org.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ktsuo@broadinstitute.org.
  • Graduate Program in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
  • Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. yiwang@broadinstitute.org.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. yiwang@broadinstitute.org.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. yiwang@broadinstitute.org.
  • Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. armartin@broadinstitute.org.
  • Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. armartin@broadinstitute.org.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. armartin@broadinstitute.org.

技术路线

数据/样本：All of Us研究项目245,388例全基因组序列与UK Biobank数据；分析：开发32个性状/疾病的多祖先多基因风险评分（PRS），评估祖先背景、方法学和遗传结构对AoU参与者PRS性能的影响。

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:04
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公共数据库临床研究Science translational medicine

Machine learning-driven spleen imaging and genomics uncover a splenic connection to coronary artery disease.

Meghana Kamineni、Vineet Raghu、Zhanqing Hua 等DOI: 10.1126/scitranslmed.aeh2517
UK Biobank心血管疾病机器学习深度学习方法学
0 次引用趋势数据不足

利用深度学习从英国生物银行42059名及Mass General Brigham生物银行2745名参与者的腹部MRI中提取107个脾脏影像组学特征，其中10个特征与冠状动脉疾病相关。对这些特征的基因组关联分析识别出219个位点，包括9p21；该位点变异与脾脏连续纹理区域异质性等特征相关。研究MRI结果内部一致，但外部临床验证因成像方案差异和患者临床异质性面临转化挑战。研究结合深度学习与基因组学，揭示脾脏可能参与冠状动脉疾病，并强调研究与临床影像组学间的转化差距。

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编辑短评

该研究整合腹部MRI深度学习影像组学与基因组学，在大型队列中提取脾脏特征并关联CAD，进而识别219个基因座（含9p21），为脾脏参与CAD提供探索性框架。但关联分析不能确立因果，影像组学特征生物学可解释性有限，外部临床验证暴露协议与人群异质性导致的转化缺口，结论宜定位为假说生成。

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完整作者

Meghana Kamineni、Vineet Raghu、Zhanqing Hua、Haodong Tian、Buu Truong、Ahmed Alaa、Art Schuermans、Sam Friedman、Christopher Reeder、Romit Bhattacharya、Peter Libby、Patrick T Ellinor、Mahnaz Maddah、Anthony Philippakis、Whitney Hornsby、Zhi Yu、Pradeep Natarajan

作者机构

  • Harvard Medical School, Boston, MA 02115, USA.
  • Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
  • Artificial Intelligence in Medicine Program, Mass General Brigham, Boston, MA 02114, USA.
  • Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
  • Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Heart and Vascular Institute, Mass General Brigham, Boston, MA 02114, USA.
  • Computational Precision Health Program, University of California, Berkeley, Berkeley, CA 94720, USA.
  • Computational Precision Health Program, University of California, San Francisco, San Francisco, CA 94143, USA.
  • Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
  • Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Layer Health, Boston, MA 02111, USA.
  • Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
  • Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
  • Google Ventures, Cambridge, MA 02142, USA.
  • Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
  • Personalized Medicine, Mass General Brigham, Boston, MA 02114, USA.
  • Amgen Inc., Thousand Oaks, CA 91320, USA.

技术路线

数据/样本：UK Biobank 42,059例与MGBB 2,745例腹部MRI；分析：深度学习提取107个脾脏影像组学特征→与CAD关联分析→对CAD相关特征行全基因组关联分析；验证：研究MRI内部一致，外部临床验证发现影像协议差异和临床异质性带来转化挑战

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:05
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合成生物学基础研究Nucleic acids research

Modular synthetic cross-kingdom promoters enable coordinated expression in Escherichia coli and Saccharomyces cerevisiae.

So-Hee Son、Soo Young Moon、Nan-Yeong An 等DOI: 10.1093/nar/gkag868
方法学
0 次引用趋势数据不足

合成生物学与代谢工程日益需要在亲缘关系较远的生物间实现可预测、可互操作的基因表达，但启动子结构与转录逻辑的跨界差异限制了通用表达平台的开发。为此，研究设计了一组模块化杂合启动子，将细菌-10/-35基序和Shine-Dalgarno序列与极简酵母TATA盒和Kozak序列整合，以保证转录和翻译兼容性，从而在大肠杆菌和酿酒酵母中实现可调且定量一致的基因表达。该启动子组支持弱、中、强表达，且在不同物种间具有较高相对一致性。将其应用于有价值色素前脱氧紫罗兰素的生物合成途径时，杂合启动子实现了两种宿主中的协调生产。该工作建立了广泛兼容的启动子架构，为跨界、多宿主合成生物学提供了基础工具包。

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完整作者

So-Hee Son、Soo Young Moon、Nan-Yeong An、Ju Young Lee

作者机构

  • Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
  • Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
  • KI for BioInnovation, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

技术路线

设计模块化杂合启动子（整合细菌-10/-35基序、Shine-Dalgarno序列与酵母最小TATA盒、Kozak序列） → 在大肠杆菌和酿酒酵母中表征弱、中、强表达及跨物种相对一致性

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:31
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合成生物学基础研究Nature genetics

Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.

Shenzhi Chen、Vincent Loubiere、Ethan W Hollingsworth 等DOI: 10.1038/s41588-026-02729-1
深度学习成人方法学
1 次引用趋势数据不足

研究以小鼠胚胎为对象，旨在设计组织特异性哺乳动物增强子。方法上，利用精选染色质可及性数据训练紧凑卷积神经网络，并通过迁移学习在已验证的人和小鼠增强子上微调模型。据此设计心脏、肢体和中枢神经系统15个合成增强子，均能在预期靶组织中发挥作用。结果表明，哺乳动物增强子功能可仅由DNA序列可靠推断，借助适度训练集即可预测性从头设计组织特异性合成增强子，为体内哺乳动物基因表达的可编程控制提供通用框架。

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完整作者

Shenzhi Chen、Vincent Loubiere、Ethan W Hollingsworth、Ken Murakami、Nikolaus Mandlburger、Sandra H Jacinto、Atrin Dizehchi、Jacob Schreiber、Evgeny Z Kvon、Alexander Stark

作者机构

  • Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
  • Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria.
  • Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA.
  • Medical Scientist Training Program, University of California Irvine, Irvine, CA, USA.
  • Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, USA.
  • Department of Systems Biology, University of California Irvine, Irvine, CA, USA.
  • Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria. stark@starklab.org.
  • Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria. stark@starklab.org.

技术路线

数据/样本：整理染色质可及性数据及已验证的人和小鼠增强子；分析：训练紧凑卷积神经网络，并通过迁移学习微调；设计15个针对小鼠胚胎心脏、肢体和中枢神经系统的合成增强子；验证：在目标组织中检测到所有设计增强子均具有活性。

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/29 12:31
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合成生物学基础研究Nature biotechnology

High-throughput synthesis of DNA fragments by molecular self-assembly of overlapping oligonucleotides.

Zhiguang Wu、Zhengyang Sun、Shuangying Jiang 等DOI: 10.1038/s41587-026-03266-2
方法学
2 次引用趋势数据不足

DNA合成技术的局限是合成生物学的基本瓶颈。本研究提出一种由杂交驱动的高通量基因合成方法，称为分子自组装诱导克隆。该方法通过将体外重叠DNA片段的正交自组装与体内宿主细胞的DNA修复机制相结合，克服了基因间的分子交叉干扰和寡核苷酸错配。组装后的目标片段被细胞摄取后作为模板用于回收和克隆。研究采用基于微芯片的寡核苷酸合成，在简单的一锅反应中可生产超过1000个不同的基因片段。近零错配是并行合成的不可或缺特征，寡核苷酸合成错误保持在恒定但可控的水平。研究还构建了工业酶PETase的大规模变体文库，并发现了比金标准酶效力更高的变体。

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完整作者

Zhiguang Wu、Zhengyang Sun、Shuangying Jiang、Yaqi Wang、Tanxi Bai、Cheng Zeng、Zelin Cai、Chenghao Su、Xiandi Zhang、Yunan Chen、Yue Shen、Chenyou Zhu、Ye Xiang、Fang Fang、Ninuo Xia、Dongsheng Liu、Junbiao Dai、Bryan Wei

作者机构

  • School of Life Sciences, Tsinghua University, Beijing, China.
  • Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
  • Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China.
  • BGI Research, Changzhou, China.
  • Engineering Research Center of Advanced Rare Earth Materials (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China.
  • Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, Center for Infectious Disease Research, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China.
  • Reproductive and Genetic Center, The First Affiliated Hospital of USTC, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
  • CodeR Therapeutics, Ltd, Hefei, China. nxia007@gmail.com.
  • Engineering Research Center of Advanced Rare Earth Materials (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing, China. dongsheng.liu@polyu.edu.hk.
  • Department of Chemistry, The Hong Kong Polytechnic University, Hong Kong, China. dongsheng.liu@polyu.edu.hk.
  • Research Institute for Future Food, The Hong Kong Polytechnic University, Hong Kong, China. dongsheng.liu@polyu.edu.hk.
  • PolyU Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China. dongsheng.liu@polyu.edu.hk.
  • Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. daijunbiao@caas.cn.
  • Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China. daijunbiao@caas.cn.
  • School of Life Sciences, Tsinghua University, Beijing, China. bw@tsinghua.edu.cn.
  • Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China. bw@tsinghua.edu.cn.
  • Center for Life Sciences and Artificial Intelligence, School of Life Sciences, Tsinghua University, Beijing, China. bw@tsinghua.edu.cn.

技术路线

微芯片寡核苷酸合成 → 重叠DNA片段体外正交自组装

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 05:59
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合成生物学基础研究Nucleic acids research

Multi-level precise regulation of gene transcription in the yeast Saccharomyces cerevisiae based on light-sensitive CRISPR/Cas systems.

Yaokun Liang、Xianghui Qi、Song Gao 等DOI: 10.1093/nar/gkag719
方法学
0 次引用趋势数据不足

基于CRISPR的基因转录调控是构建酿酒酵母合成基因电路的重要工具，但现有策略多局限于单一调控层面，灵敏度有待提高。本研究将翻译水平和翻译后水平的基因调控方法与光遗传学控制模式相结合，实现对dCas9蛋白的高灵敏多层级精准调控，从而灵活调节靶基因转录水平。利用该策略调控荧光蛋白转录水平，mCherry荧光强度较无调控时最高提升2.58倍。该基于CRISPR的多层级光遗传学系统有助于理解基因调控网络，并为合成生物学设计稳健的基因电路提供支持。

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完整作者

Yaokun Liang、Xianghui Qi、Song Gao、Yunjiao Wang、Guoshi Kan、Leonid N Valentovich、Jingjing Guo、Yingfeng An

作者机构

  • College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang 110866, Liaoning, China.
  • Shenyang Key Laboratory of Microbial Resource Mining and Molecular Breeding, Liaoning 110000, Shenyang, China.
  • School of Life Sciences, Guangzhou University, Guangzhou, Guangdong 510006, China.
  • Institute of Microbiology, National Academy of Sciences of Belarus, Minsk 220000, Belarus.
  • Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence, Macao Polytechnic University, Macau 999078, China.

技术路线

以荧光蛋白为报告基因→基于光敏CRISPR/dCas9系统，整合翻译与翻译后水平的调控模式，对dCas9蛋白实施多层级精准调控，分析目标基因转录水平变化（mCherry荧光强度较无调控提升最高2.58倍）

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年，仅展示最近 5 个完整年度。

数据来源：openalex更新时间：2026/10/30 06:00
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