Tissue clearing and its applications in neuroscience

Nature reviews. Neuroscience, Год журнала: 2020, Номер 21(2), С. 61 - 79

Опубликована: Янв. 2, 2020

Язык: Английский

---

Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution

Science, Год журнала: 2019, Номер 363(6424)

Опубликована: Янв. 18, 2019

Optical and electron microscopy have made tremendous inroads toward understanding the complexity of brain. However, optical offers insufficient resolution to reveal subcellular details, lacks throughput molecular contrast visualize specific constituents over millimeter-scale or larger dimensions. We combined expansion lattice light-sheet image nanoscale spatial relationships between proteins across thickness mouse cortex entire Drosophila These included synaptic at dendritic spines, myelination along axons, presynaptic densities dopaminergic neurons in every fly brain region. The technology should enable statistically rich, large-scale studies neural development, sexual dimorphism, degree stereotypy, structural correlations behavior activity, all with contrast.

Язык: Английский

---

Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature

Cell, Год журнала: 2020, Номер 180(4), С. 780 - 795.e25

Опубликована: Фев. 1, 2020

The cerebral vasculature is a dense network of arteries, capillaries, and veins. Quantifying variations the vascular organization across individuals, brain regions, or disease models challenging. We used immunolabeling tissue clearing to image adult mouse brains developed pipeline segment terabyte-sized multichannel images from light sheet microscopy, enabling construction, analysis, visualization graphs composed over 100 million vessel segments. generated datasets 20 brains, with labeled veins, capillaries according their anatomical regions. characterized highlighting local adaptations functional correlates. propose classification cortical regions based on topology. Finally, we analysed brain-wide rearrangements in animal congenital deafness ischemic stroke, revealing that plasticity remodeling adopt diverging rules different models.

Язык: Английский

---

Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0

Nature Methods, Год журнала: 2019, Номер 16(10), С. 1054 - 1062

Опубликована: Сен. 27, 2019

Язык: Английский

---

The mesoSPIM initiative: open-source light-sheet microscopes for imaging cleared tissue

Nature Methods, Год журнала: 2019, Номер 16(11), С. 1105 - 1108

Опубликована: Сен. 16, 2019

Язык: Английский

---

Spatial components of molecular tissue biology

Nature Biotechnology, Год журнала: 2022, Номер 40(3), С. 308 - 318

Опубликована: Фев. 7, 2022

Язык: Английский

---

Probabilistic cell typing enables fine mapping of closely related cell types in situ

Nature Methods, Год журнала: 2019, Номер 17(1), С. 101 - 106

Опубликована: Ноя. 18, 2019

Язык: Английский

---

The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis

Protein Science, Год журнала: 2020, Номер 30(1), С. 234 - 249

Опубликована: Ноя. 9, 2020

Abstract For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting larger multidimensional datasets, must adapt. ImageJ is an open‐source image analysis platform that has aided researchers with a variety of applications, driven mainly by engaged collaborative user developer communities. The close collaboration between programmers users resulted adaptations accommodate new challenges address the needs ImageJ's diverse base. consists many components, some relevant primarily developers vast collection user‐centric plugins. It available forms, including widely used Fiji distribution. We refer this entire codebase community as ecosystem. Here we review core features ecosystem highlight how responded technology advancements plugins tools recent years. These been developed several areas such visualization, segmentation, tracking biological entities large, complex datasets. Moreover, capabilities deep learning are being added ImageJ, reflecting shift bioimage towards exploiting artificial intelligence. facilitated profound architectural changes brought about ImageJ2 project. Therefore, also discuss contributions enhancing processing interoperability

Язык: Английский

---

Light sheet fluorescence microscopy

Nature Reviews Methods Primers, Год журнала: 2021, Номер 1(1)

Опубликована: Ноя. 3, 2021

Язык: Английский

---

LABKIT: Labeling and Segmentation Toolkit for Big Image Data

Frontiers in Computer Science, Год журнала: 2022, Номер 4

Опубликована: Фев. 10, 2022

We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated routines that can be rapidly applied single- multi-channel images as well timelapse movies in 2D or 3D. LABKIT is specifically designed work efficiently on big data enables users consumer laptops conveniently with multiple-terabyte images. This efficiency achieved by using ImgLib2 BigDataViewer memory efficient fast implementation random forest based pixel classification algorithm foundation our software. Optionally we harness power graphics processing units (GPU) gain additional runtime performance. install virtually all workstations. Additionally, compatible high performance computing (HPC) clusters distributed The ability classifiers trained via ImageJ macro language integrate this functionality step workflows. Finally, comes rich online resources such tutorials examples will help familiarize themselves available features how best number practical real-world use-cases.

Язык: Английский

---