Pracownicy

Streszczenie:
Recent advances in single-cell technologies have revealed the dynamic and heterogeneous nature of host-pathogen interactions at the single-cell level. This review explores how cellular variability—both within clonal bacterial populations and among genetically identical host cells—gives rise to distinct infection outcomes, from pathogen clearance to persistence across multiple biological scales, from single cells to tissues and the whole organism. We highlight the conceptual and technological progress that has enabled the dissection of these interactions at single-cell resolution, including microscopy, single-cell transcriptomics, proteomics, and emerging dual RNA-seq and spatial approaches. Drawing on examples from well-characterized bacterial pathogens like Listeria monocytogenes, Salmonella enterica, and Mycobacterium tuberculosis, we discuss how stochastic gene expression, intrinsic and extrinsic factors, as well as tissue context shape the variable activation of the immune responses and ultimately determine the outcomes of host-pathogen interactions. We argue that the outcome of single-cell interactions is shaped by a combination of host states, bacterial-intrinsic features, and the local microenvironment. We further discuss how computational and mathematical modeling can integrate these heterogeneous single-cell events across spatial scales, linking intracellular variability with tissue-level pathogenesis and progression of infection. Gaining insight into and controlling these layers of variability holds promise for the development of more precise, context-dependent antimicrobial strategies.

Słowa kluczowe:
host-pathogen interactions, single-cell biology, cellular heterogeneity, infection biology, Listeria monocytogenes, Salmonella enterica, Mycobacterium tuberculosis

Streszczenie:
Advances in high-content microscopy and artificial intelligence (AI) are transforming the quantitative study of infection biology. Automated imaging platforms now enable rapid, large-scale acquisition of host-pathogen interactions across thousands of cells and multiple experimental conditions. When combined with AI-based segmentation, these workflows extract infection-relevant features such as pathogen load, intracellular localization, and host response markers at single-cell resolution. Deep-learning models have proven especially powerful, outperforming classical threshold-based methods under different imaging conditions, reducing reliance on manual annotation, and detecting rare infection outcomes. Beyond robust image analysis, these approaches generate scalable and reproducible datasets that can be integrated with computational modelling and systems biology, providing predictive insight into infection dynamics. This review highlights recent progress in AI-assisted microscopy for bacterial infection and outlines future directions toward multimodal integration, clinical translation, and open-source tool development.

Słowa kluczowe:
artificial intelligence, machine learning, deep learning, host-pathogen interactions, single-cell biology, cell-to-cell variability, cellular heterogeneity, infection biology

Streszczenie:
Esophageal squamous cell carcinoma (eSCC) accounts for more than 85% cases of esophageal cancer worldwide and the 5-year survival rate associated with metastatic eSCC is poor. This low survival rate is the consequence of a complex mechanism of resistance to therapy and tumor relapse. To effectively reduce the mortality rate of this disease, we need to better understand the molecular mechanisms underlying the development of resistance to therapy and translate that knowledge into novel approaches for cancer treatment. The circadian clock orchestrates several physiological processes through the establishment and synchronization of circadian rhythms. Since cancer cells need to fuel rapid proliferation and increased metabolic demands, the escape from circadian rhythm is relevant in tumorigenesis. Although clock related genes may be globally repressed in human eSCC samples, PER2 expression still oscillates in some human eSCC cell lines. However, the consequences of this circadian rhythm are still unclear. In the present study, we confirm that PER2 oscillations still occur in human cancer cells in vitro in spite of a deregulated circadian clock gene expression. Profiling of eSCC cells by RNAseq reveals that when PER2 expression is low, several transcripts related to apoptosis are upregulated. Consistently, treating eSCC cells with cisplatin when PER2 expression is low enhances DNA damage and leads to a higher apoptosis rate. Interestingly, this process is conserved in a mouse model of chemically-induced eSCC ex vivo. These results therefore suggest that response to therapy might be enhanced in esophageal cancers using chronotherapy.

Słowa kluczowe:
circadian clock, esophagus cancer, squamous cell carcinoma, apoptosis, chemotherapy, chronotherapy