Search Results (6)

Regulating tumor invasion and metastasis is pivotal for improving cancer patient prognosis. While cell migration is a key factor in these processes, the non-targeted effects of chemoradiotherapy on cell motility remain poorly understood. In this study, we employed HeLa-FUCCI cells—a cervical cancer-derived HeLa cell line integrated with the Fluorescent Ubiquitination-Based Cell Cycle Indicator (FUCCI) probe, enabling the visualization of cell cycle phases—to investigate the radiation-induced impacts, including non-targeted effects, on cell migration. To create irradiated (In-field) and non-irradiated (out-of-field) regions, half of the culture dish was shielded with a lead block during irradiation. Cells were then exposed to 2 Gy X-rays, with or without cisplatin. Following irradiation, the cells were subjected to time-lapse imaging at 15 min intervals for 24 h, and the acquired data were analyzed using cell segmentation and tracking algorithms, Cellpose 2.0 and TrackMate 7. Without cisplatin, the migration velocity and total distance traveled of Out-of-field cells were significantly reduced compared to controls, suggesting a suppressive bystander signal. In contrast, with cisplatin treatment, these parameters significantly increased in both In-field and Out-of-field cells. This suggests that chemoradiotherapy may inadvertently enhance tumor cell motility outside the target volume, a critical finding with significant implications for therapeutic outcomes. Full article

The automatic segmentation of cell images plays a critical role in medicine and biology, as it enables faster and more accurate analysis and diagnosis. Traditional machine learning faces challenges since it requires transferring sensitive data from laboratories to the cloud, with possible risks and limitations due to patients’ privacy, data-sharing regulations, or laboratory privacy guidelines. Federated learning addresses data-sharing issues by introducing a decentralized approach that removes the need for laboratories’ data sharing. The learning task is divided among the participating clients, with each training a global model situated on the cloud with its local dataset. This guarantees privacy by only transmitting updated model weights to the cloud. In this study, the centralized learning approach for cell segmentation is compared with the federated one, demonstrating that they achieve similar performances. Stemming from a benchmarking of available cell segmentation models, Cellpose, having shown better recall and precision ($F1=0.84$) than U-Net ($F1=0.50$) and StarDist ($F1=0.12$), was used as the baseline for a federated learning testbench implementation. The results show that both binary segmentation and multi-class segmentation metrics remain high when employing both the centralized solution ($F1=0.86$) and the federated solution ($F{1}_{2clients}=0.86$). These results were also stable across an increasing number of clients and a reduced number of local data samples ($F{1}_{4clients}=0.87$, $F{1}_{16clients}=0.86$), proving the effectiveness of central aggregation on the cloud of locally trained models. Full article

Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning. Full article

With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue (n = 102) was explored using image analysis and GeoMx spatial omics profiling of 69 proteins and 1812 mRNAs. Tumor cells, T helper (T_H) cells and cytotoxic (T_C) cells of early (CD57−) and late (CD57+) differentiation stage were analyzed. An image analysis workflow was developed based on fine-tuned Cellpose models for cell segmentation and classification. T_C and CD57+ subsets of T cells were enriched in tumor-rich compared to tumor-sparse regions. Tumor-sparse regions had a higher expression of several key immune suppressive proteins, tentatively controlling T-cell expansion in regions close to the tumor. We revealed that T cells in late differentiation stages (CD57+) are enriched among MCL infiltrating T cells and are predictive of an increased expression of immune suppressive markers. CD47, IDO1 and CTLA-4 were identified as potential targets for patients with T-cell-rich MCL TIME, while GITR might be a feasible target for MCL patients with sparse T-cell infiltration. In subgroups of patients with a high degree of CD57+ T_C-cell infiltration, several immune checkpoint inhibitors, including TIGIT, PD-L1 and LAG3 were increased, emphasizing the immune-suppressive features of this highly differentiated T-cell subset not previously described in MCL. Full article

Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy. Full article

Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function. Full article