From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

Chessel, Anatole; Carazo-Salas, Rafael E.

doi:10.1042/ebc20180044

Cited by 29 publications

(18 citation statements)

References 121 publications

(99 reference statements)

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“…On the computational side, deep learning is already beginning to accelerate drug discovery by tackling diverse problems in the process 151 , and image-based profiling will be among the major beneficiaries of advancements in computer vision and predictive algorithms 152 – 154 . Deep learning can process raw microscopy images to produce representations that are better suited for downstream analysis and interpretation; cells or cellular substructures can be identified more accurately 155 , 156 , and improved image-based descriptors can be derived during feature extraction 157 .…”

Section: Future Directionsmentioning

confidence: 99%

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Chandrasekaran

Ceulemans

Boyd

et al. 2020

Nat Rev Drug Discov

270

242

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Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug’s activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

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Section: Future Directionsmentioning

confidence: 99%

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Chandrasekaran

Ceulemans

Boyd

et al. 2020

Nat Rev Drug Discov

270

242

View full text Add to dashboard Cite

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“…Patterns of Ca 2+ changes in combination with voltage current recordings may allow to discriminate not only between the different taste modalities but also between compounds of the same families, such as natural versus artificial sweeteners. Novel, sophisticated imaging systems suitable to acquire multiple fluorescence signals from microplates with cellular resolution are now available and high-content imaging systems allow the acquisition of confocal images in thick tissue/probes up to a 1536 wells format with multiple lasers and dichroic filters [338,339]. In addition, optical tissue clearing protocols start also to be applied to 3D cultures, which could largely improve expression analysis in three dimensions [340].…”

Section: Development Of New In Vitro Taste Systemsmentioning

confidence: 99%

Sensing Senses: Optical Biosensors to Study Gustation

Molitor

Riedel

Hafner

et al. 2020

Sensors

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The five basic taste modalities, sweet, bitter, umami, salty and sour induce changes of Ca2+ levels, pH and/or membrane potential in taste cells of the tongue and/or in neurons that convey and decode gustatory signals to the brain. Optical biosensors, which can be either synthetic dyes or genetically encoded proteins whose fluorescence spectra depend on levels of Ca2+, pH or membrane potential, have been used in primary cells/tissues or in recombinant systems to study taste-related intra- and intercellular signaling mechanisms or to discover new ligands. Taste-evoked responses were measured by microscopy achieving high spatial and temporal resolution, while plate readers were employed for higher throughput screening. Here, these approaches making use of fluorescent optical biosensors to investigate specific taste-related questions or to screen new agonists/antagonists for the different taste modalities were reviewed systematically. Furthermore, in the context of recent developments in genetically encoded sensors, 3D cultures and imaging technologies, we propose new feasible approaches for studying taste physiology and for compound screening.

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“…High‐throughput (HTP) approaches for monitoring single‐cell phenotypes include single‐cell transcriptomics, mass spectrometry and automated imaging, among others (Ziegenhain et al , ; Chessel & Carazo Salas, ; Yin et al , ). High‐content screening, which combines HTP microscopy with multiparametric image and data analyses, provides rich phenotypic information about the spatio‐temporal properties of biological systems at the single‐cell level (Boutros et al , ; Mattiazzi Usaj et al , ; Chessel & Carazo Salas, ). Large‐scale screens have been productively combined with image analysis to explore different aspects of cell biology in yeast and in higher eukaryotes.…”

Section: Introductionmentioning

confidence: 99%

Systematic genetics and single‐cell imaging reveal widespread morphological pleiotropy and cell‐to‐cell variability

Ušaj

Sahin

Friesen

et al. 2020

Molecular Systems Biology

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Our ability to understand the genotype‐to‐phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single‐cell level. To systematically assess cell‐to‐cell phenotypic variability, we combined automated yeast genetics, high‐content screening and neural network‐based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments—endocytic patch, actin patch, late endosome and vacuole—identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single‐cell level. Our single‐cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.

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From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

Cited by 29 publications

References 121 publications

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Sensing Senses: Optical Biosensors to Study Gustation

Systematic genetics and single‐cell imaging reveal widespread morphological pleiotropy and cell‐to‐cell variability

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