A multimodal zebrafish developmental atlas reveals the state-transition dynamics of late-vertebrate pluripotent axial progenitors DOI Creative Commons
Merlin Lange, Alejandro Granados, Shruthi VijayKumar

и другие.

Cell, Год журнала: 2024, Номер 187(23), С. 6742 - 6759.e17

Опубликована: Окт. 24, 2024

Язык: Английский

ilastik: interactive machine learning for (bio)image analysis DOI
Stuart Berg, Dominik Kutra,

Thorben Kroeger

и другие.

Nature Methods, Год журнала: 2019, Номер 16(12), С. 1226 - 1232

Опубликована: Сен. 30, 2019

Язык: Английский

Процитировано

2769

Tissue clearing and its applications in neuroscience DOI Open Access
Hiroki R. Ueda, Ali Ertürk,

Kwanghun Chung

и другие.

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

Опубликована: Янв. 2, 2020

Язык: Английский

Процитировано

504

In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level DOI Creative Commons
Katie McDole, Léo Guignard,

Fernando Amat

и другие.

Cell, Год журнала: 2018, Номер 175(3), С. 859 - 876.e33

Опубликована: Окт. 1, 2018

Язык: Английский

Процитировано

451

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

Alexandra B. Schroeder,

Ellen T. A. Dobson, Curtis Rueden

и другие.

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

Процитировано

217

Light sheet fluorescence microscopy DOI
Ernst H. K. Stelzer, Frederic Strobl, Bo-Jui Chang

и другие.

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

Опубликована: Ноя. 3, 2021

Язык: Английский

Процитировано

214

LABKIT: Labeling and Segmentation Toolkit for Big Image Data DOI Creative Commons

Matthias Arzt,

J.R. Deschamps, Christopher Schmied

и другие.

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

Процитировано

206

An early cell shape transition drives evolutionary expansion of the human forebrain DOI Creative Commons
Silvia Benito-Kwiecinski, Stefano L. Giandomenico, Magdalena Sutcliffe

и другие.

Cell, Год журнала: 2021, Номер 184(8), С. 2084 - 2102.e19

Опубликована: Март 24, 2021

The human brain has undergone rapid expansion since humans diverged from other great apes, but the mechanism of this human-specific enlargement is still unknown. Here, we use cerebral organoids derived human, gorilla, and chimpanzee cells to study developmental mechanisms driving evolutionary expansion. We find that neuroepithelial differentiation a protracted process in involving previously unrecognized transition state characterized by change cell shape. Furthermore, show are larger due delay transition, associated with differences interkinetic nuclear migration cycle length. Comparative RNA sequencing (RNA-seq) reveals expression dynamics morphogenesis factors, including ZEB2, known epithelial-mesenchymal regulator. ZEB2 promotes its manipulation downstream signaling leads acquisition nonhuman ape architecture context vice versa, establishing an important role for shape

Язык: Английский

Процитировано

202

Virtual Reality: Beyond Visualization DOI Creative Commons
Mohamed El Beheiry,

Sébastien Doutreligne,

Clément Caporal

и другие.

Journal of Molecular Biology, Год журнала: 2019, Номер 431(7), С. 1315 - 1321

Опубликована: Фев. 10, 2019

Язык: Английский

Процитировано

159

Lineage recording in human cerebral organoids DOI Creative Commons
Zhisong He, Ashley Maynard, Akanksha Jain

и другие.

Nature Methods, Год журнала: 2021, Номер 19(1), С. 90 - 99

Опубликована: Дек. 30, 2021

Abstract Induced pluripotent stem cell (iPSC)-derived organoids provide models to study human organ development. Single-cell transcriptomics enable highly resolved descriptions of states within these systems; however, approaches are needed directly measure lineage relationships. Here we establish iTracer, a recorder that combines reporter barcodes with inducible CRISPR–Cas9 scarring and is compatible single-cell spatial transcriptomics. We apply iTracer explore clonality dynamics during cerebral organoid development identify time window fate restriction as well variation in neurogenic between progenitor neuron families. also long-term four-dimensional light-sheet microscopy for recording confirm regional the developing neuroepithelium. incorporate gene perturbation (iTracer-perturb) assess effect mosaic TSC2 mutations on Our data shed light how lineages fates established formation. More broadly, our techniques can be adapted any iPSC-derived culture system dissect alterations normal or perturbed

Язык: Английский

Процитировано

152

Chitosan and its derivatives in 3D/4D (bio) printing for tissue engineering and drug delivery applications DOI
Tarun Agarwal, Irene Chiesa, Marco Costantini

и другие.

International Journal of Biological Macromolecules, Год журнала: 2023, Номер 246, С. 125669 - 125669

Опубликована: Июль 4, 2023

Язык: Английский

Процитировано

56