CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging

Held, Michael; Schmitz, Michael H.A.; Fischer, Bernd; Walter, Thomas; Neumann, Beate; Olma, Michael H.; Peter, Matthias; Ellenberg, Jan; Gerlich, Daniel W.

doi:10.1038/nmeth.1486

Cited by 349 publications

(430 citation statements)

References 34 publications

Supporting

Mentioning

423

Contrasting

Order By: Relevance

“…We applied SiR-SNAP, SiR-CLIP and SiR-Halo for live-cell imaging. To demonstrate the potential of SiR-labelled proteins for multicolour imaging, these experiments were performed in a HeLa cell line that stably expressed a green fluorescence protein (GFP) fusion with a-tubulin (GFP-a-tubulin) and a mCherry fusion with histone H2B (H2B-mCherry) as markers 16 . We observed a specific fluorescence labelling of fusion proteins of SNAP-, CLIP-and Halo-tags in different organelles of living cells within 30-60 minutes, with no significant background signal (Fig.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins

et al. 2013

View full text Add to dashboard Cite

The ideal fluorescent probe for bioimaging is bright, absorbs at long wavelengths and can be implemented flexibly in living cells and in vivo. However, the design of synthetic fluorophores that combine all of these properties has proved to be extremely difficult. Here, we introduce a biocompatible near-infrared silicon-rhodamine probe that can be coupled specifically to proteins using different labelling techniques. Importantly, its high permeability and fluorogenic character permit the imaging of proteins in living cells and tissues, and its brightness and photostability make it ideally suited for live-cell super-resolution microscopy. The excellent spectroscopic properties of the probe combined with its ease of use in live-cell applications make it a powerful new tool for bioimaging.

show abstract

Section: Resultsmentioning

confidence: 99%

“…HeLa cells that expressed EGFP-a-tubulin and H2B-mCherry 16 were transfected with the appropriate construct, labelled and imaged in DMEM gfp media (Evrogen) that contained 10% FBS (Invitrogen). SNAP-tag, CLIP-tag and Halo-tag were labelled using 2.5, 3 and 1 mM substrate (0.5 h, 37 8C), respectively.…”

Section: Discussionmentioning

confidence: 99%

A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins

et al. 2013

View full text Add to dashboard Cite

show abstract

“…In the field of neurobiology, researchers have observed links between the static image of neurons and their genotypes [13]. Recent works in oncology have shown that studying the cell dynamics provides important information about its genotype [15,16]. In the same line of thinking, we propose to study if the neuron morpho-dynamics in videos 280 depend on their genotype characteristics.…”

Section: Classification Of Neurons In Videosmentioning

confidence: 99%

treeKL: A distance between high dimension empirical distributions

Lefort

Fleuret

2013

Pattern Recognition Letters

View full text Add to dashboard Cite

“…3). To avoid tedious manual adaptations of feature sets for each specific application, multi-purpose feature libraries have been developed, and these cover the needs for most cell biological assays (Jones et al, 2008;Held et al, 2010;Shariff et al, 2010).…”

Section: Feature Extractionmentioning

confidence: 99%

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer

Gerlich

2013

Journal of Cell Science

284

237

View full text Add to dashboard Cite

SummaryRecent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

show abstract

CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging

Abstract: 3

Cited by 349 publications

References 34 publications

A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins

A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins

treeKL: A distance between high dimension empirical distributions

Machine learning in cell biology – teaching computers to recognize phenotypes

Contact Info

Product

Resources

About