CellClassifier: supervised learning of cellular phenotypes

Rämö, Pauli; Sacher, Raphael; Snijder, Berend; Begemann, Boris; Pelkmans, Lucas

doi:10.1093/bioinformatics/btp524

Cited by 82 publications

(38 citation statements)

References 11 publications

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“…In brief, intensity and texture features were extracted from bacterial and IL-8 images. Based on these features, cells were scored for infection and IL-8 expression using CellClassifier and supervised machine learning using a Support Vector Machine based binary classifier [49]. Measurements were normalized for plate-to-plate variations and population context dependency as described in [17].…”

Section: Methodsmentioning

confidence: 99%

ALPK1 controls TIFA/TRAF6-dependent innate immunity against heptose-1,7-bisphosphate of gram-negative bacteria

et al. 2017

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During infection by invasive bacteria, epithelial cells contribute to innate immunity via the local secretion of inflammatory cytokines. These are directly produced by infected cells or by uninfected bystanders via connexin-dependent cell-cell communication. However, the cellular pathways underlying this process remain largely unknown. Here we perform a genome-wide RNA interference screen and identify TIFA and TRAF6 as central players of Shigella flexneri and Salmonella typhimurium-induced interleukin-8 expression. We show that threonine 9 and the forkhead-associated domain of TIFA are necessary for the oligomerization of TIFA in both infected and bystander cells. Subsequently, this process triggers TRAF6 oligomerization and NF-κB activation. We demonstrate that TIFA/TRAF6-dependent cytokine expression is induced by the bacterial metabolite heptose-1,7-bisphosphate (HBP). In addition, we identify alpha-kinase 1 (ALPK1) as the critical kinase responsible for TIFA oligomerization and IL-8 expression in response to infection with S. flexneri and S. typhimurium but also to Neisseria meningitidis. Altogether, these results clearly show that ALPK1 is a master regulator of innate immunity against both invasive and extracellular gram-negative bacteria.

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Section: Methodsmentioning

confidence: 99%

ALPK1 controls TIFA/TRAF6-dependent innate immunity against heptose-1,7-bisphosphate of gram-negative bacteria

et al. 2017

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“…The Generic feature descriptor (GFD) consists of several well-known image descriptors which have been used to classify images in many applications and is included in HCS bioimage tools [24-27]. A summary of these features including references and parameters is given in Table 4.…”

Section: Methodsmentioning

confidence: 99%

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening

et al. 2013

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BackgroundHigh-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images.ResultsWe propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons.ConclusionsFew automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.

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“…Although expression profiles complement and partially replaced the classical descriptors (4,6), they are not sufficient to fully define the phenotype of a cell; further cytological, morphological and histological images are still of high importance for describing and distinguishing cells in biology and medicine (43). Accordingly, CellFinder comprises gene and protein expression data as well as image data, both of which are integrated by the ontology-based data model (24).…”

Section: Data Sources and Typesmentioning

confidence: 99%

CellFinder: a cell data repository

Stachelscheid¹,

Seltmann²,

Lekschas³

et al. 2013

Nucl. Acids Res.

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CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∼900 proteins and genes. CellFinder’s data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder’s web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians.

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CellClassifier: supervised learning of cellular phenotypes

Abstract: Source code, user manual and SaveObjectSegmentation CellProfiler module available for download at www.cellclassifier.ethz.ch under the GPL license (implemented in Matlab).

Cited by 82 publications

References 11 publications

ALPK1 controls TIFA/TRAF6-dependent innate immunity against heptose-1,7-bisphosphate of gram-negative bacteria

ALPK1 controls TIFA/TRAF6-dependent innate immunity against heptose-1,7-bisphosphate of gram-negative bacteria

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening

CellFinder: a cell data repository

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