RESUMEN
Wound infections are highly prevalent and can lead to delayed or failed healing, causing significant morbidity and adverse economic impacts. These infections occur in various contexts, including diabetic foot ulcers, burns, and surgical sites. Enterococcus faecalis is often found in persistent non-healing wounds, but its contribution to chronic wounds remains understudied. To address this, we employed single-cell RNA sequencing (scRNA-seq) on infected wounds in comparison to uninfected wounds in a mouse model. Examining over 23,000 cells, we created a comprehensive single-cell atlas that captures the cellular and transcriptomic landscape of these wounds. Our analysis revealed unique transcriptional and metabolic alterations in infected wounds, elucidating the distinct molecular changes associated with bacterial infection compared to the normal wound healing process. We identified dysregulated keratinocyte and fibroblast transcriptomes in response to infection, jointly contributing to an anti-inflammatory environment. Notably, E. faecalis infection prompted a premature, incomplete epithelial-mesenchymal transition in keratinocytes. Additionally, E. faecalis infection modulated M2-like macrophage polarization by inhibiting pro-inflammatory resolution in vitro, in vivo, and in our scRNA-seq atlas. Furthermore, we discovered macrophage crosstalk with neutrophils, which regulates chemokine signaling pathways, while promoting anti-inflammatory interactions with endothelial cells. Overall, our findings offer new insights into the immunosuppressive role of E. faecalis in wound infections.
If wounds get infected, they heal much more slowly, sometimes leading to skin damage and other complications, including disseminated infections or even amputation. Infections can happen in many types of wounds, ranging from ulcers in patients with diabetes to severe burns. If infections are not cleared quickly, the wounds can become 'chronic' and are unable to heal without intervention. Enterococcus faecalis is a type of bacteria that normally lives in the gut. Within that environment, in healthy people, it is not harmful. However, if it comes into contact with wounds particularly diabetic ulcers or the site of a surgery it can cause persistent infections and prevent healing. Although researchers are beginning to understand how E. faecalis initially colonises wounds, the biological mechanisms that transform these infections into chronic wounds are still largely unknown. Celik et al. therefore set out to investigate exactly how E. faecalis interferes with wound healing. To do this, Celik et al. looked at E. faecalis-infected wounds in mice and compared them to uninfected ones. Using a genetic technique called single-cell RNA sequencing, Celik et al. were able to determine which genes were switched on in individual skin and immune cells at the site of the wounds. This in turn allowed the researchers to determine how those cells were behaving in both infected and uninfected conditions. The experiments revealed that when E. faecalis was present in wounds, several important cell types in the wounds did not behave normally. For example, although the infected skin cells still underwent a change in behaviour required for healing (called an epithelial-mesenchymal transition), the change was both premature and incomplete. In other words, the skin cells in infected wounds started changing too early and did not finish the healing process properly. E. faecalis also changed the way macrophages and neutrophils worked within the wounds. These are cells in our immune system that normally promote inflammation, a process involved in both uninfected wounds or during infections and is a key part of wound healing when properly controlled. In the E. faecalis-infected wounds, these cells' inflammatory properties were suppressed, making them less helpful for healing. These results shed new light on how E. faecalis interacts with skin cells and the immune system to disrupt wound healing. Celik et al. hope that this knowledge will allow us to find new ways to target E. faecalis infections, and ultimately develop treatments to help chronic wounds heal better and faster.
Asunto(s)
Enterococcus faecalis , Infecciones por Bacterias Grampositivas , Queratinocitos , Cicatrización de Heridas , Enterococcus faecalis/fisiología , Enterococcus faecalis/genética , Animales , Ratones , Infecciones por Bacterias Grampositivas/microbiología , Queratinocitos/microbiología , Queratinocitos/metabolismo , Macrófagos/microbiología , Macrófagos/metabolismo , Macrófagos/inmunología , Modelos Animales de Enfermedad , Infección de Heridas/microbiología , Transcriptoma , Ratones Endogámicos C57BL , Análisis de la Célula Individual , Transición Epitelial-Mesenquimal/genética , Masculino , Fibroblastos/microbiología , Fibroblastos/metabolismoRESUMEN
Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, and semi-supervised learning as well as the next steps for the mitigation of the manual segmentation bottleneck.
RESUMEN
Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.