A Deep Learning Model for Automated Segmentation of Fluorescence Cell images

Deep learning techniques bring together key advantages in biomedical image segmentation. They speed up the process, increase the reproducibility, and reduce the workload in segmentation and classification. Deep learning techniques can be used for analysing cell concentration, cell viability, as well as the size and form of each cell. In this study, we develop a deep learning model for automated segmentation of fluorescence cell images, and apply it to fluorescence images recorded with a home-built epi-fluorescence microscope. A deep neural network model based on U-Net architecture was built using a publicly available dataset of cell nuclei images [1]. A model accuracy of 97.3% was reached at the end of model training. Fluorescence cell images acquired with our home-built microscope were then segmented using the developed model. 141 of 151 cells in 5 images were successfully segmented, revealing a segmentation success rate of 93.4%. This deep learning model can be extended to the analysis of different cell types and cell viability

Data for a proteomic analysis of p53-independent induction of apoptosis by bortezomib

This data article contains data related to the research article entitled, “A proteomic analysis of p53-independent induction of apoptosis by bortezomib in 4T1 breast cancer cell line” by Yerlikaya et al. [1]. The research article presented 2-DE and nLC-MS/MS based proteomic analysis of proteasome inhibitor bortezomib-induced changes in the expression of cellular proteins. The report showed that GRP78 and TCEB2 were over-expressed in response to treatment with bortezomib for 24 h. In addition, the report demonstrated that Hsp70, the 26S proteasome non-ATPase regulatory subunit 14 and sequestosome 1 were increased at least 2 fold in p53-deficient 4T1 cells. The data here show for the first time the increased expressions of Card10, Dffb, Traf3 and Trp53bp2 in response to inhibition of the 26S proteasome. The information presented here also shows that both Traf1 and Xiap (a member of IAPs) are also downregulated simultaneously upon proteasomal inhibition. The increases in the level of Card10 and Trp53bp2 proteins were verified by Western blot analysis in response to varying concentrations of bortezomib for 24 h

A proteomic analysis of p53-independent induction of apoptosis by bortezomib in 4T1 breast cancer cell line

WOS: 000347582200022PubMed ID: 25305590The 26S proteasome is a proteolytic enzyme found in both cytoplasm and nucleus. In this study, we examined the differential expression of proteasome inhibitor bortezomib-induced proteins in p53-deficient 4T1 cells. It was found that GRP78 and TCEB2 were over-expressed in response to treatment with bortezomib for 24 h. Next, we analyzed the expression of intracellular proteins in response to treatment with 100 nM bortezomib for 24 h by label-free LC-MS/MS. These analyses showed that Hsp70, the 26S proteasome non-ATPase regulatory subunit 14 and sequestosome 1 were increased at least 2 fold in p53-deficient 4T1 cells. The proteins identified by label-free LC-MS/MS were then analyzed by Ingenuity Pathway Analysis (IPA) Tool to determine biological networks affected by inhibition of the 26S proteasome. The analysis results showed that post-translational modifications, protein folding, DNA replication, energy production and nucleic acid metabolism were found to be among the top functions affected by the 26S proteasome inhibition. The biological network analysis indicated that ubiquitin may be the central regulator of the pathways modulated after bortezomib-treatment. Further investigation of the mechanism of the proteins modulated in response to the proteasomal inhibition may lead to the design of more effective and novel therapeutic strategies for cancer. Biological significance Although the proteasome inhibitor bortezomib is approved and used for the treatment of human cancer (multiple myeloma), the mechanism of action is not entirely understood. A number of studies showed that proteasome inhibitors induced apoptosis through upregulation of tumor suppressor protein p53. However, the role of tumor suppressor protein p53 in bortezomib-induced apoptosis is controversial and not well-understood. The tumor suppressor p53 is mutated in at least 50% of human cancers and is strongly induced by proteasomal inhibition. Some also reported that the proteasome inhibitor can induce apoptosis in a p53-independent manner. Also, it is reported that Noxa, a target of p53, is induced in response to proteasomal inhibition in a p53-independent manner. However, we have also previously reported that neither Puma nor Noxa are induced by proteasomal inhibition in p53-null 4T1 breast cancer cells, which is commonly used for in vivo breast cancer tumor models. The current results provided additional targets of proteasome inhibitor bortezomib and may therefore help in understanding the p53-independent mechanism of apoptosis induction by proteasome inhibitors. In addition, the results presented in this current study report for the first time that proteasomal subunit Psmd14, anti-apoptotic GRP78, anti apoptotic protein Card10, Dffb, Traf3 and Trp53bp2 are regulated and overexpressed in response to proteasome inhibitor bortezomib in p53-deficient 4T1 cells. Therefore, novel therapeutic strategies targeting these anti-apoptotic or pro-apoptotic proteins as well as inhibiting the proteasome simultaneously may be more effective against cancer cells. The proteins identified here present new avenues for the development of anti-cancer drugs.Scientific and Technological Research Council of Turkey (TUBITAK) [SBAG 109S035]This study was financially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with Project no SBAG 109S035. We are grateful to Dr. Bruce A. Stanley (Pennsylvania State University, College of Medicine) for critical reading and suggestions during the preparation of the manuscript