SIGNIFICANCE: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. AIM: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. APPROACH: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. RESULTS: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. CONCLUSIONS: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.
Glioma-initiating cells (GIC) are considered the underlying cause of recurrences of aggressive glioblastomas, replenishing the tumor population and undermining the efficacy of conventional chemotherapy. Here we report the discovery that inhibiting T-type voltage-gated Ca(2+) and KCa channels can effectively induce selective cell death of GIC and increase host survival in an orthotopic mouse model of human glioma. At present, the precise cellular pathways affected by the drugs affecting these channels are unknown. However, using cell-based assays and integrated proteomics, phosphoproteomics, and transcriptomics analyses, we identified the downstream signaling events these drugs affect. Changes in plasma membrane depolarization and elevated intracellular Na(+), which compromised Na(+)-dependent nutrient transport, were documented. Deficits in nutrient deficit acted in turn to trigger the unfolded protein response and the amino acid response, leading ultimately to nutrient starvation and GIC cell death. Our results suggest new therapeutic targets to attack aggressive gliomas. Cancer Res; 77(7); 1741-52. ©2017 AACR.
- MeSH
- aminokyseliny metabolismus MeSH
- biologický transport MeSH
- blokátory kalciových kanálů farmakologie MeSH
- buněčná smrt MeSH
- dihydropyridiny farmakologie MeSH
- draslíkové kanály aktivované vápníkem antagonisté a inhibitory MeSH
- gliom farmakoterapie metabolismus patologie MeSH
- lidé MeSH
- mykotoxiny farmakologie MeSH
- myši MeSH
- nádorové buněčné linie MeSH
- nádorové kmenové buňky patologie MeSH
- nádory mozku farmakoterapie metabolismus patologie MeSH
- proteomika MeSH
- signální dráha UPR účinky léků MeSH
- sodík metabolismus MeSH
- vápníkové kanály - typ T fyziologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
