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【标题速读】【Nmeth】【2022年】【2-6月】

2023-03-04 17:24 作者:Rt_Cola  | 我要投稿

声明:本专栏主要对生命科学领域的一些期刊文章标题进行翻译,所有内容均由本人手工整理翻译。由于本人专业为生物分析相关,其他领域如果出现翻译错误请谅解。

Cryo-ExM preserves cellular ultrastructure

冷冻膨胀显微镜保留细胞超微结构

A human cell in mitosis observed using cryo-expansion microscopy (Cryo-ExM). The DNA is stained pink and the rest of the cell with an NHS ester that marks the proteome and highlights the mitochondria in black at each pole of the mitotic spindle.

使用冷冻膨胀显微镜(Cryo-Exm)观察到有丝分裂中的人类细胞。DNA染成粉红色,其余的带有NHS酯标记蛋白质组的NHS酯的细胞的其余部分,并在有丝分裂纺锤体的每个极点突出了黑色的线粒体。

1.CellRank for directed single-cell fate mapping.

用于定向单细胞命运图谱的CellRank。

2.Squidpy: a scalable framework for spatial omics analysis.

Squidpy:一个可扩展的空间omics分析框架。

3.Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.

使用MEFISTO从多模态数据中识别出时间和空间的变化模式。

4.Reprogramming the piRNA pathway for multiplexed and transgenerational gene silencing in C. elegans.

重新编程piRNA途径以实现优雅动物的多重和跨代基因沉默。

5.CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks.

CR-I-TASSER:使用深度卷积神经网络从冷冻电子显微镜密度图中组装蛋白质结构。

6.NTR 2.0: a rationally engineered prodrug-converting enzyme with substantially enhanced efficacy for targeted cell ablation.

NTR 2.0:一种合理设计的原药转化酶,对靶向细胞消融的功效大大增强。

7.Visualizing the native cellular organization by coupling cryofixation with expansion microscopy (Cryo-ExM).

通过将低温固定与膨胀显微镜(Cryo-ExM)结合起来,对原始细胞组织进行可视化。

8.Spatially resolved isotope tracing reveals tissue metabolic activity.

空间分辨率的同位素追踪揭示了组织的代谢活动。

9.A genetically encoded sensor for in vivo imaging of orexin neuropeptides.

一种基因编码的传感器,用于对奥克苏神经肽的体内成像。

10.VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology.

VascuViz:用于血管系统生物学的多模式和多尺度成像和可视化管道。

Tools and guidelines for multiplexed tissue imaging

多重组织成像的工具和指南

IBEX (iterative bleaching extends multiplexity) imaging of cell–cell interactions in a human lymph node evokes a stained glass window in a cathedral.

IBEX(迭代漂白扩展了多重性)在人淋巴结中的细胞 - 细胞相互作用的成像唤起大教堂中的彩色玻璃窗。

1.Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data.

Alevin-fry解除了对单细胞RNA-seq数据的快速、准确和节省内存的量化。

2.Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition.

确定性的scRNA-seq捕获了肠隐窝和类器官组成的变化。

3.Efficient targeted insertion of large DNA fragments without DNA donors.

在没有DNA供体的情况下高效地定向插入大的DNA片段。

4.Global profiling of phosphorylation-dependent changes in cysteine reactivity.

磷酸化依赖的半胱氨酸反应性变化的全球分析。

5.Targeted multicolor in vivo imaging over 1,000 nm enabled by nonamethine cyanines.

非甲胺基氰化物使1000纳米以上的靶向多色活体成像。

6.Isotropic super-resolution light-sheet microscopy of dynamic intracellular structures at subsecond timescales.

亚秒级时间尺度的细胞内动态结构的各向同性超分辨率光片显微镜。

COVID-19 research: methods lead the way

COVID-19研究:方法引导

Decades of accumulated methods development across diverse areas of basic biological research have facilitated a speedy scientific response to the SARS-CoV-2 virus.

几十年来,基础生物学研究不同领域的数十年来发展方法促进了对SARS-COV-2病毒的快速科学反应。

1.Antigen identification and high-throughput interaction mapping by reprogramming viral entry.

通过重新规划病毒的进入,进行抗原识别和高通量的相互作用图谱。

2.DaXi—high-resolution, large imaging volume and multi-view single-objective light-sheet microscopy.

DaXi-高分辨率、大成像量和多视角单目标光片显微镜。

3.Detecting and correcting false transients in calcium imaging.

检测和纠正钙成像中的虚假瞬态。

4.HYBRiD: hydrogel-reinforced DISCO for clearing mammalian bodies.

HYBRiD:用于清除哺乳动物尸体的水凝胶强化DISCO。

5.SLEAP: A deep learning system for multi-animal pose tracking.

SLEAP:用于多动物姿态跟踪的深度学习系统。

6.Multi-animal pose estimation, identification and tracking with DeepLabCut.

用DeepLabCut进行多动物姿势估计、识别和跟踪。

Versatile multiscale imaging of cleared tissues

清除组织的多功能多尺度成像

On the cover, an optically cleared mouse brain imaged with a hybrid open-top light-sheet microscope.

在封面上,用混合式灯具显微镜成像的光学清除的小鼠大脑。

1.Molecular spikes: a gold standard for single-cell RNA counting.

分子尖峰:单细胞RNA计数的黄金标准。

2.Alignment and integration of spatial transcriptomics data.

空间转录组学数据的排列和整合。

3.Sub-3-Å cryo-EM structure of RNA enabled by engineered homomeric self-assembly.

通过工程化同源自组装实现RNA的亚3Å低温电子显微镜结构。

4.Precision size and refractive index analysis of weakly scattering nanoparticles in polydispersions.

多分散体中弱散射纳米粒子的精确尺寸和折射率分析。

5.Correction of multiple-blinking artifacts in photoactivated localization microscopy.

光活化定位显微镜中多重闪烁伪影的校正。

6.Optimal precision and accuracy in 4Pi-STORM using dynamic spline PSF models.

使用动态花键PSF模型在4Pi-STORM中的最佳精度和准确性。

7.A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues.

一种混合的敞开式光片显微镜,用于清扫组织的多功能多尺度成像。

8.NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster.

NeuroMechFly,成年黑腹果蝇的神经机械模型。

Tools for assembling and analyzing complete genomes

组装和分析完整基因组的工具

With new tools developed by the Telomere-to-Telomere (T2T) Consortium, the human genome is revealed in greater quality and detail.

借助端粒到居组(T2T)联盟开发的新工具,人类基因组的质量和细节都更高。

1.Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies.

追逐完美:端粒到端粒基因组组装的验证和抛光策略。

2.Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation.

Merfin:通过K-mer验证改进变体过滤、组装评估和抛光。

3.Long-read mapping to repetitive reference sequences using Winnowmap2.

使用Wnowmap2对重复参考序列进行长读映射。

4.DiMeLo-seq: a long-read, single-molecule method for mapping protein–DNA interactions genome wide.

DiMeLo-seq:一种长线、单分子方法,用于绘制全基因组的蛋白质-DNA相互作用图。

5.Ab initio phasing macromolecular structures using electron-counted MicroED data.

使用电子计数的MicroED数据对大分子结构进行无源相控。

6.ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.

ScanNet:一个可解释的几何深度学习模型,用于基于结构的蛋白质结合点预测。

7.Single-domain near-infrared protein provides a scaffold for antigen-dependent fluorescent nanobodies.

单域近红外蛋白为抗原依赖性荧光纳米体提供支架。

8.Label-free nanofluidic scattering microscopy of size and mass of single diffusing molecules and nanoparticles.

无标签的纳米流体散射显微镜对单个扩散分子和纳米颗粒的尺寸和质量的观察。

9.Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA.

利用CELESTA结合蛋白质表达和空间信息,在多路原位图像中识别细胞类型。

【标题速读】【Nmeth】【2022年】【2-6月】的评论 (共 条)

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