8 research outputs found
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Cell Shape and Treatment Duration: How They Influence a Cancer Cell's Response to TNF
The purpose of my research was to investigate the sources of heterogeneity in cellular decisions that are based on both external and internal cues. I used the signaling network induced by tumor necrosis factor (TNF) as a model system to examine how the duration of a stimulus and cell shape may affects signaling and cellular decisions. First, using a microfluidic device to achieve fine control of the ligand delivery to cells, my colleagues and I found that the duration of TNF stimulation is a factor that coordinates cell fate decisions in single cells. Specifically, we found that a few seconds of exposure to TNF is sufficient to activate the NF-κB pathway and induce apoptotic cell death and that, strikingly, a 1-min pulse of TNF can be more effective at killing cells than a 1-hour pulse. Second, to study the effects of cell shape, I used a two-pronged approach. Initially, I used live-cell imaging and single-molecule fluorescence in situ hybridization (smFISH) to examine whether descriptors of cell shape correlate with NF-κB nuclear translocation and NF-κB-dependent transcription in unperturbed populations of single cells. Next, I used surface micro-patterning to impose different geometry and degrees of spreading on cells and examine NF-κB-dependent transcription in these cells. I found that descriptors that quantify cell spreading, such as cell area and nuclear area, correlate with NF-κB nuclear translocation and NF-κB-dependent transcription. In addition, imposing bigger amount of spreading on cells increased the transcript abundance for two NF-κB-dependent genes, A20 and IκBα. In contrast, the relationships between geometry-related cell shape descriptors and NF- κB-dependent transcription are more subtle and complex. Importantly, despite observing the correlations between cell spreading and NF-κB activity, I found that the effects of cell shape on NF-κB dynamics and on NF-κB-dependent transcription were small. Together, my investigations of TNF-induced signaling have shown that while the duration of treatment encodes information used in the TNF-induced cell death decision, NF-κB dynamics and NF-κB-dependent transcription are quite robust to changes in cell shape.Medical Science
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NF-κB signalling and cell fate decisions in response to a short pulse of tumour necrosis factor
In tissues and tumours, cell behaviours are regulated by multiple time-varying signals. While in the laboratory cells are often exposed to a stimulus for the duration of the experiment, in vivo exposures may be much shorter. In this study, we monitored NF-κB and caspase signalling in human cancer cells treated with a short pulse of Tumour Necrosis Factor (TNF). TNF is an inflammatory cytokine that can induce both the pro-survival NF-κB-driven gene transcription pathway and the pro-apoptotic caspase pathway. We find that a few seconds of exposure to TNF is sufficient to activate the NF-κB pathway in HeLa cells and induce apoptotic cell death in both HeLa and Kym-1 cells. Strikingly, a 1-min pulse of TNF can be more effective at killing than a 1-hour pulse, indicating that in addition to TNF concentration, duration of exposure also coordinates cell fate decisions
Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study
This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens. A two-stage deep neural network was developed based on the 3D residual network and atrous convolution module to diagnose benign and malignant GGNs (Task1) and classify between invasive adenocarcinoma (IA) and non-IA for these malignant GGNs (Task2). A multi-reader multi-case observer study with six board-certified radiologists’ (average experience 11 years, range 2–28 years) participation was conducted to evaluate the model capability. DNN yielded area under the receiver operating characteristic curve (AUC) values of 0.76 ± 0.03 (95% confidence interval (CI): (0.69, 0.82)) and 0.96 ± 0.02 (95% CI: (0.92, 0.98)) for Task1 and Task2, which were equivalent to or higher than radiologists in the senior group with average AUC values of 0.76 and 0.95, respectively (p > 0.05). With the CT image slice thickness increasing from 1.15 mm ± 0.36 to 1.73 mm ± 0.64, DNN performance decreased 0.08 and 0.22 for the two tasks. The results demonstrated (1) a positive trend between the diagnostic performance and radiologist’s experience, (2) the DNN yielded equivalent or even higher performance in comparison with senior radiologists, and (3) low image resolution decreased model performance in predicting the risks of GGNs. Once tested prospectively in clinical practice, the DNN could have the potential to assist doctors in precision diagnosis and treatment of early lung adenocarcinoma