One-pot shotgun cell biology

一锅霰弹枪细胞生物学

• 批准号: 371923335
• 负责人: Professor Dr. Michael Knop
• 金额: --
• 依托单位: Zentrum für Molekulare Biologie (ZMBH)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/371923335?language=en
• 关键词: pot; shotgun; cell; biology

Public Abstract - High throughput approaches are critical for systems biology. In this project we will develop new tools, instruments and algorithms for high throughput biology and the functional characterization of cellular proteomes. CRISPR/Cas is used to generate genome wide resources of clonal lines with gene-specific genomic modifications, e.g. gene deletions or fusion to fluorescent proteins. High throughput fluorescence imaging, cell sorting along with semi-supervised as well as unsupervised deep learning algorithms will be used to functionally group cell populations, to characterize complex cell mixtures and to derive functional information.The work will be performed in a collaboration of a group in molecular cell biology and instrumentation and a computer vision group. We will develop new fluorescence microscopic devices and algorithms for on-line image based cellular analysis. We will test and use the developed instruments and methods in an initial experimental study where we seek to localize as many as possible of the so far un-detected yeast proteins by employing new fluorescent proteins. With this we will complement the yeast protein localization atlas and provide a base for comprehensive analysis of the species-specific proteome.The study will also serve as a paradigm for future work in single cell analytics and with mammalian cell lines.

公共摘要-高通量方法对系统生物学至关重要。在这个项目中，我们将为高通量生物学和细胞蛋白质组的功能表征开发新的工具、仪器和算法。CRISPR/Cas用于产生具有基因特异性基因组修饰的克隆系的全基因组资源，例如基因缺失或与荧光蛋白融合。高通量荧光成像，细胞分选以及半监督和无监督深度学习算法将用于功能分组细胞群体，表征复杂细胞混合物并获得功能信息。这项工作将由分子细胞生物学和仪器仪表组和计算机视觉组合作完成。我们将开发新的荧光显微镜设备和算法，用于基于图像的在线细胞分析。我们将在最初的实验研究中测试和使用开发的仪器和方法，通过使用新的荧光蛋白，我们寻求尽可能多地定位迄今为止未检测到的酵母蛋白。这将进一步完善酵母蛋白定位图谱，为全面分析酵母蛋白组提供基础。该研究也将作为未来单细胞分析和哺乳动物细胞系工作的范例。

项目成果

Geometry-Free View Synthesis: Transformers and no 3D Priors

• DOI: 10.1109/iccv48922.2021.01409
• 发表时间: 2021-04
• 期刊: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
• 作者: Robin Rombach;Patrick Esser;B. Ommer

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

• DOI: 10.3390/mi10050311
• 发表时间: 2019-05-01
• 期刊: MICROMACHINES
• 影响因子: 3.4
• 作者: Constantinou, Iordania;Jendrusch, Michael;Knop, Michael
• 通讯作者: Knop, Michael

Unsupervised behaviour analysis and magnification (uBAM) using deep learning

• DOI: 10.1038/s42256-021-00326-x
• 发表时间: 2021-04-05
• 期刊: NATURE MACHINE INTELLIGENCE
• 影响因子: 23.8
• 作者: Brattoli, Biagio;Buchler, Uta;Ommer, Bjorn
• 通讯作者: Ommer, Bjorn

Pooled clone collections by multiplexed CRISPR-Cas12a-assisted gene tagging in yeast

• DOI: 10.1038/s41467-019-10816-7
• 发表时间: 2019-07-04
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Buchmullen, Benjamin C.;Herbst, Konrad;Knop, Michael
• 通讯作者: Knop, Michael