Literature DB >> 29031005

Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models.

Maxime Bombrun1, Hui Gao2,3, Petter Ranefall1, Niklas Mejhert3, Peter Arner3, Carolina Wählby1.   

Abstract

Neutral lipids packed in lipid droplets (LDs) are essential as a source of fuel for organisms, and specialized storing cells, the adipocytes, provide a buffer for energy variations. Many modern-society-disorders are connected with excess accumulation or deficiency of LDs in adipose tissue. Intracellular LD number and size distribution reflect the tissue conditions, while the associated mechanisms and genes rs are still poorly understood. Large-scale genetic screens using human in vitro differentiated primary adipocytes require cell samples donated from many patients. The heterogeneity appearing between donors highlighted the need for high-throughput methods robust to individual variations. Previous image analysis algorithms failed to handle individual LDs, but focused on averages, hiding population heterogeneity. We present a new high-content analysis (HCA) technique for analysis of fat cell metabolism using data from a large-scale RNAi screen including images of more than 500 k in vitro differentiated adipocytes from three donors. The RNAi-based suppression of Perilipin 1 (PLIN1), a protein involved in the adipocyte lipid metabolism, served as a positive control, while cells treated with randomized RNA served as negative controls. We validate our segmentation by comparing our results to those of previously published methods: We also evaluate the discriminative power of different morphological features describing LD size distribution. Classification of cells as containing few large or many small LDs followed by calculating the percentage of cells in each class proved to discriminate the positive PLIN1-suppressed phenotype from the untreated negative control with an area under the receiver operating characteristic curve of 0.98. The results suggest that this HCA method offers improved segmentation and classification accuracy, and can, thus, be utilized to quantify changes in LD metabolism in response to treatment in many cell models relevant to a variety of diseases.
© 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Entities:  

Keywords:  PLIN1 targeting; adipocyte tissue; digital image processing; feature extraction; high-content analysis; lipid droplets; segmentation

Mesh:

Substances:

Year:  2017        PMID: 29031005     DOI: 10.1002/cyto.a.23265

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  5 in total

1.  PEDF regulates plasticity of a novel lipid-MTOC axis in prostate cancer-associated fibroblasts.

Authors:  Francesca Nardi; Philip Fitchev; Omar E Franco; Jelena Ivanisevic; Adrian Scheibler; Simon W Hayward; Charles B Brendler; Michael A Welte; Susan E Crawford
Journal:  J Cell Sci       Date:  2018-07-11       Impact factor: 5.285

2.  Adipocytes role in the bone marrow niche.

Authors:  Daniel A P Guerra; Ana E Paiva; Isadora F G Sena; Patrick O Azevedo; Miguel Luiz Batista; Akiva Mintz; Alexander Birbrair
Journal:  Cytometry A       Date:  2017-12-13       Impact factor: 4.355

3.  Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.

Authors:  Håkan Wieslander; Ankit Gupta; Ebba Bergman; Erik Hallström; Philip John Harrison
Journal:  PLoS One       Date:  2021-10-15       Impact factor: 3.240

Review 4.  Insights Into the Biogenesis and Emerging Functions of Lipid Droplets From Unbiased Molecular Profiling Approaches.

Authors:  Miguel Sánchez-Álvarez; Miguel Ángel Del Pozo; Marta Bosch; Albert Pol
Journal:  Front Cell Dev Biol       Date:  2022-06-08

5.  Tributyltin chloride (TBT) induces RXRA down-regulation and lipid accumulation in human liver cells.

Authors:  Fabio Stossi; Radhika D Dandekar; Hannah Johnson; Philip Lavere; Charles E Foulds; Maureen G Mancini; Michael A Mancini
Journal:  PLoS One       Date:  2019-11-11       Impact factor: 3.240

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.