Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells

Brooks, Patrick Terrence; Munthe‐Fog, Lea; Rieneck, Klaus; Clausen, Frederik Banch; Rivera, Olga Ballesteros; Haastrup, Eva; Fischer‐Nielsen, Anne; Svalgaard, Jesper Dyrendom

doi:10.1080/21623945.2021.2000696

Cited by 3 publications

(4 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, gene sets relating to the transcription factors Peroxisome proliferator- activated receptor gamma (PPARG) and CCAAT/enhancer-binding protein alpha (CEBPA) were significantly enriched in the dataset (Figure F). Both PPARG and CEBPA have previously been characterized to play important roles in the metabolism of lipids, confirming the ability of TeaProt to identify known up-stream regulators . Performing GSEA using urPTMdb gene sets reveals an enrichment of HMGylation substrates in the Western diet-fed mice, as the gene set with the lowest q -value (normalized enrichment score (NES): 2.25, adjusted p -value 1.1 × 10 –8 ) (Figure G) followed by lysine glutarylation (NES: 2.13, adjusted p -value 6.1 × 10 –6 ) (data not shown).…”

Section: Resultssupporting

confidence: 57%

“…Both PPARG and CEBPA have previously been characterized to play important roles in the metabolism of lipids, confirming the ability of TeaProt to identify known up-stream regulators. 22 Performing GSEA using urPTMdb gene sets reveals an enrichment of HMGylation substrates in the Western diet-fed mice, as the gene set with the lowest q -value (normalized enrichment score (NES): 2.25, adjusted p -value 1.1 × 10 –8 ) ( Figure 3 G) followed by lysine glutarylation (NES: 2.13, adjusted p -value 6.1 × 10 –6 ) (data not shown). HMGylation is a fascinating nonenzymatic PTM that has been validated in recent years by Wagner et al 23 HMGylation is similar to glutarylation with significant overlap between the HMGylome and Glutarylome.…”

Section: Resultsmentioning

confidence: 96%

See 1 more Smart Citation

urPTMdb/TeaProt: Upstream and Downstream Proteomics Analysis

2022

View full text Add to dashboard Cite

We have developed the underrepresented posttranslational modification (PTM) database (urPTMdb), a PTM gene set database to accelerate the discovery of enriched protein modifications in experimental data. urPTMdb provides curated lists of proteins reported to be substrates of underrepresented modifications. Their enrichment in proteomics datasets can reveal unexpected PTM regulations. urPTMdb can be implemented in existing workflows, or used in TeaProt, an online Shiny tool that integrates upstream transcription factor enrichment analysis with downstream pathway analysis through an easy-to-use interactive interface. TeaProt annotates user-uploaded data with drug−gene interactions, subcellular localizations, phenotypic functions, gene− disease associations, and enzyme−gene interactions. TeaProt enables gene set enrichment analysis (GSEA) to discover enrichments in gene sets from various resources, including MSigDB, CHEA, and urPTMdb. We demonstrate the utility of urPTMdb and TeaProt through the analysis of a previously published Western diet-induced remodeling of the tongue proteome, which revealed altered cellular processes associated with energy metabolism, interferon alpha/gamma response, adipogenesis, HMGylation substrate enrichment, and transcription regulation through PPARG and CEBPA. Additionally, we analyzed the interactome of ADP-ribose glycohydrolase TARG1, a key enzyme that removes mono-ADP-ribosylation. This analysis identified an enrichment of ADPribosylation, ribosomal proteins, and proteins localized in the nucleoli and endoplasmic reticulum. TeaProt and urPTMdb are accessible at https://tea.coffeeprot.com/.

show abstract

Section: Resultssupporting

confidence: 57%

Section: Resultsmentioning

confidence: 96%

urPTMdb/TeaProt: Upstream and Downstream Proteomics Analysis

2022

View full text Add to dashboard Cite

show abstract

“…Improvements in microscopy, computational capabilities, and data analysis have enabled high-throughput, high-content approaches from endpoint 2D microscopy images to 3D microscopy images. This approach has been engaged in various cell biology research activities ( 28 – 30 ), specifically for stem cell characterization ( 31 – 33 ) and differentiation pattern ( 34 – 37 ), disease modeling ( 38 , 39 ), and drug screening and discovery ( 40 , 41 ). The advantages of this technique are that it is non-invasive, high-throughput, and consistent, which can save time and costs once automated.…”

Section: Deep Learning For Stem Cell Researchmentioning

confidence: 99%

Deep learning models for cancer stem cell detection: a brief review

Chen

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are a subset of tumor cells that persist within tumors as a distinct population. They drive tumor initiation, relapse, and metastasis through self-renewal and differentiation into multiple cell types, similar to typical stem cell processes. Despite their importance, the morphological features of CSCs have been poorly understood. Recent advances in artificial intelligence (AI) technology have provided automated recognition of biological images of various stem cells, including CSCs, leading to a surge in deep learning research in this field. This mini-review explores the emerging trend of deep learning research in the field of CSCs. It introduces diverse convolutional neural network (CNN)-based deep learning models for stem cell research and discusses the application of deep learning for CSC research. Finally, it provides perspectives and limitations in the field of deep learning-based stem cell research.

show abstract

“…Brooks et al . [ 40 ] investigated the kinetics of adipogenesis using a live-cell imaging system of human ASCs combined with a deep learning-based detection tool. During adipogenesis, images were obtained by a deep learning system to annotate regions of interest using algorithms that detect the adipose area, total cell area, and lipid droplets, which were later compared with the morphological changes and genetic expression of ASCs at different time points during differentiation.…”

Section: Live Imagingmentioning

confidence: 99%

Emerging technologies in adipose tissue research

Avtanski,

Hadzi-Petrushev,

Josifovska

et al. 2023

Adipocyte

View full text Add to dashboard Cite

Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel technologies in biomedical research and aims to identify areas where these technologies can make the most significant contribution to adipose tissue research. It discusses the impact of cutting-edge technologies such as single-cell sequencing, multi-omics analyses, spatial transcriptomics, live imaging, 3D tissue engineering, microbiome analysis, in vivo imaging, and artificial intelligence/machine learning. As these technologies continue to evolve, we can expect them to play an increasingly important role in advancing our understanding of adipose tissue and improving the treatment of related diseases.

show abstract

Application of a deep learning-based image analysis and live-cell imaging system for quantifying adipogenic differentiation kinetics of adipose-derived stem/stromal cells

Cited by 3 publications

References 44 publications

urPTMdb/TeaProt: Upstream and Downstream Proteomics Analysis

urPTMdb/TeaProt: Upstream and Downstream Proteomics Analysis

Deep learning models for cancer stem cell detection: a brief review

Emerging technologies in adipose tissue research

Contact Info

Product

Resources

About