Decoding the complexity of delayed wound healing following Enterococcus faecalis infection.

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.

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images.

Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability. Without proper tuning and data management, deploying machine learning models in the presence of unaccounted for corruptions leads to reduced or misleading performance. This study explores techniques to enhance model generalizability through iterative adjustments. Specifically, we investigate a detection tasks using electron microscopy images and compare models trained with different normalization and augmentation techniques. We found that models trained with Group Normalization or texture data augmentation outperform other normalization techniques and classical data augmentation, enabling them to learn more generalized features. These improvements persist even when models are trained and tested on disjoint datasets acquired through diverse data acquisition protocols. Results hold true for transformerand convolution-based detection architectures. The experiments show an impressive 29% boost in average precision, indicating significant enhancements in the model's generalizibality. This underscores the models' capacity to effectively adapt to diverse datasets and demonstrates their increased resilience in real-world applications.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Electron microscopy (EM) enables imaging at nanometer resolution 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 the 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, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.

Automated large volume sample preparation for vEM.

New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences field. Moreover, sample preparation methods are continuously being modified by investigators to improve final sample quality, increase electron density, combine imaging technologies, and minimize the introduction of artifacts into specimens under study. There are a variety of conventional bench protocols that a researcher can utilize, though most of these protocols require several days. In this work, we describe the utilization of an automated specimen processor, the mPrep™ ASP-2000™, to prepare samples for vEM that are compatible with focused ion beam scanning electron microscopy (FIB-SEM), serial block face scanning electron microscopy (SBF-SEM), and array tomography (AT). The protocols described here aimed for methods that are completed in a much shorter period of time while minimizing the exposure of the operator to hazardous and toxic chemicals and improving the reproducibility of the specimens' heavy metal staining, all without compromising the quality of the data acquired using backscattered electrons during SEM imaging. As a control, we have included a widely used sample bench protocol and have utilized it as a comparator for image quality analysis, both qualitatively and using image quality analysis metrics.

SEG: Segmentation Evaluation in absence of Ground truth labels.

Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO1- though groundbreaking in their usability and extensibility - are often unable to provide users guidance regarding the most appropriate models for their segmentation task among an endless proliferation of novel segmentation methods. Unfortunately, evaluating segmentation results on a user's dataset without ground truth labels is either purely subjective or eventually amounts to the task of performing the original, time-intensive annotation. As a consequence, researchers rely on models pre-trained on other large datasets for their unique tasks. Here, we propose a methodological approach for evaluating MTI nuclei segmentation methods in absence of ground truth labels by scoring relatively to a larger ensemble of segmentations. To avoid potential sensitivity to collective bias from the ensemble approach, we refine the ensemble via weighted average across segmentation methods, which we derive from a systematic model ablation study. First, we demonstrate a proof-of-concept and the feasibility of the proposed approach to evaluate segmentation performance in a small dataset with ground truth annotation. To validate the ensemble and demonstrate the importance of our method-specific weighting, we compare the ensemble's detection and pixel-level predictions - derived without supervision - with the data's ground truth labels. Second, we apply the methodology to an unlabeled larger tissue microarray (TMA) dataset, which includes a diverse set of breast cancer phenotypes, and provides decision guidelines for the general user to more easily choose the most suitable segmentation methods for their own dataset by systematically evaluating the performance of individual segmentation approaches in the entire dataset.

Endoplasmic reticulum stress and lipids in health and diseases.

The endoplasmic reticulum (ER) is a complex and dynamic organelle that regulates many cellular pathways, including protein synthesis, protein quality control, and lipid synthesis. When one or multiple ER roles are dysregulated and saturated, the ER enters a stress state, which, in turn, activates the highly conserved unfolded protein response (UPR). By sensing the accumulation of unfolded proteins or lipid bilayer stress (LBS) at the ER, the UPR triggers pathways to restore ER homeostasis and eventually induces apoptosis if the stress remains unresolved. In recent years, it has emerged that the UPR works intimately with other cellular pathways to maintain lipid homeostasis at the ER, and so does at cellular levels. Lipid distribution, along with lipid anabolism and catabolism, are tightly regulated, in part, by the ER. Dysfunctional and overwhelmed lipid-related pathways, independently or in combination with ER stress, can have reciprocal effects on other cellular functions, contributing to the development of diseases. In this review, we summarize the current understanding of the UPR in response to proteotoxic stress and LBS and the breadth of the functions mitigated by the UPR in different tissues and in the context of diseases.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

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.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.

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.

A new combinatorial megaplasmid library assembly method designed to screen for minimal pathways by using SCRaMbLE.

Human proteins expressed in yeast are common to enhance protein production while the expression of functional human pathways remain challenging. Here, we propose a simple and economical high-throughput gene assembly method to create a yeast megaplasmid library from human cDNA to screen for minimal human functional pathways. We introduced artificial promoters followed by symmetric loxP sites into the megaplasmids using Golden Gate assembly coupled with streptavidin-bead-based purification. The isolated high molecular weight, randomly assembled cDNA megaplasmid library may be useful for high-throughput directed evolution experiments and may be adapted for use in other model organisms.

Neuronal IRE-1 coordinates an organism-wide cold stress response by regulating fat metabolism.

Cold affects many aspects of biology, medicine, agriculture, and industry. Here, we identify a conserved endoplasmic reticulum (ER) stress response, distinct from the canonical unfolded protein response, that maintains lipid homeostasis during extreme cold. We establish that the ER stress sensor IRE-1 is critical for resistance to extreme cold and activated by cold temperature. Specifically, neuronal IRE-1 signals through JNK-1 and neuropeptide signaling to regulate lipid composition within the animal. This cold-response pathway can be bypassed by dietary supplementation with unsaturated fatty acids. Altogether, our findings define an ER-centric conserved organism-wide cold stress response, consisting of molecular neuronal sensors, effectors, and signaling moieties, which control adaptation to cold conditions in the organism. Better understanding of the molecular basis of this stress response is crucial for the optimal use of cold conditions on live organisms and manipulation of lipid saturation homeostasis, which is perturbed in human pathologies.

The unfolded protein response reverses the effects of glucose on lifespan in chemically-sterilized C. elegans.

Metabolic diseases often share common traits, including accumulation of unfolded proteins in the endoplasmic reticulum (ER). Upon ER stress, the unfolded protein response (UPR) is activated to limit cellular damage which weakens with age. Here, we show that Caenorhabditis elegans fed a bacterial diet supplemented high glucose at day 5 of adulthood (HGD-5) extends their lifespan, whereas exposed at day 1 (HGD-1) experience shortened longevity. We observed a metabolic shift only in HGD-1, while glucose and infertility synergistically prolonged the lifespan of HGD-5, independently of DAF-16. Notably, we identified that UPR stress sensors ATF-6 and PEK-1 contributed to the longevity of HGD-5 worms, while ire-1 ablation drastically increased HGD-1 lifespan. Together, we postulate that HGD activates the otherwise quiescent UPR in aged worms to overcome ageing-related stress and restore ER homeostasis. In contrast, young animals subjected to HGD provokes unresolved ER stress, conversely leading to a detrimental stress response.

Human PERK rescues unfolded protein response-deficient yeast cells.

Protein folding and quality control is tightly regulated at the endoplasmic reticulum (ER), and its disruption is associated with many diseases. In eukaryotes, the accumulation of unfolded protein in the ER is sensed by the three sensors, IRE1, PERK, and ATF6 to activate the unfolded protein response (UPR) to restore ER homeostasis. However, uncoupling the sensing of each sensor and their respective downstream pathways has been challenging as the absence of one is compensated by the remaining two sensors. Here, we report a fully functional human PERK (hPERK) chimeric protein expressed in Saccharomyces cerevisiae that could be used for high throughput screen to identify new PERK inhibitory or activating compounds as well as to characterize the PERK stress sensing mechanisms.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Spatial Profiles of Intratumoral PD-1+ Helper T Cells Predict Prognosis in Head and Neck Squamous Cell Carcinoma.

Background: Functional interactions between immune cells and neoplastic cells in the tumor immune microenvironment have been actively pursued for both biomarker discovery for patient stratification, as well as therapeutic anti-cancer targets to improve clinical outcomes. Although accumulating evidence indicates that intratumoral infiltration of immune cells has prognostic significance, limited information is available on the spatial infiltration patterns of immune cells within intratumoral regions. This study aimed to understand the intratumoral heterogeneity and spatial distribution of immune cell infiltrates associated with cell phenotypes and prognosis in head and neck squamous cell carcinoma (HNSCC). Methods: A total of 88 specimens of oropharyngeal squamous cell carcinoma, categorized into discovery (n = 38) and validation cohorts (n = 51), were analyzed for immune contexture by multiplexed immunohistochemistry (IHC) and image cytometry-based quantification. Tissue segmentation was performed according to a mathematical morphological approach using neoplastic cell IHC images to dissect intratumoral regions into tumor cell nests versus intratumoral stroma. Results: Tissue segmentation revealed heterogeneity in intratumoral T cells, varying from tumor cell nest-polarized to intratumoral stroma-polarized distributions. Leukocyte composition analysis revealed higher ratios of TH1/TH2 in tumor cell nests with higher percentages of helper T cells, B cells, and CD66b+ granulocytes within intratumoral stroma. A discovery and validation approach revealed a high density of programmed death receptor-1 (PD-1)+ helper T cells in tumor cell nests as a negative prognostic factor for short overall survival. CD163+ tumor-associated macrophages (TAM) provided the strongest correlation with PD-1+ helper T cells, and cases with a high density of PD-1+ helper T cells and CD163+ TAM had a significantly shorter overall survival than other cases. Conclusion: This study reveals the significance of analyzing intratumoral cell nests and reports that an immune microenvironment with a high density of PD-1+ helper T cells in tumoral cell nests is a poor prognostic factor for HNSCC.

A high-throughput genetic screening protocol to measure lipid bilayer stress-induced unfolded protein response in Saccharomyces cerevisiae.

The endoplasmic reticulum (ER) stress is defined by the accumulation of unfolded proteins at the ER and perturbation at the ER membrane, known as lipid bilayer stress (LBS). In turn, ER stress triggers the unfolded protein response (UPR) to restore ER homeostasis. Here, we provide a modified protocol based on the synthetic genetic array analysis in Saccharomyces cerevisiae to identify genetic perturbations that induce the UPR by LBS. This method is adaptable to other canonical stress pathways. For complete details on the use and execution of this protocol, please refer to Ho et al. (2020), Jonikas et al. (2009) and Baryshnikova et al. (2010).

Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms.

The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and present the first cross-validation of breast cancer cell phenotypes derived by using two different MTI platforms. Finally, we demonstrate an integrative phenotypic and spatial analysis revealing BC subtype-specific features.

Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System.

The drivers of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) transition are poorly understood. Here, we conducted an integrated genomic, transcriptomic, and whole-slide image analysis to evaluate changes in copy-number profiles, mutational profiles, expression, neoantigen load, and topology in 6 cases of matched pure DCIS and recurrent IDC. We demonstrate through combined copy-number and mutational analysis that recurrent IDC can be genetically related to its pure DCIS despite long latency periods and therapeutic interventions. Immune "hot" and "cold" tumors can arise as early as DCIS and are subtype-specific. Topologic analysis showed a similar degree of pan-leukocyte-tumor mixing in both DCIS and IDC but differ when assessing specific immune subpopulations such as CD4 T cells and CD68 macrophages. Tumor-specific copy-number aberrations in MHC-I presentation machinery and losses in 3p, 4q, and 5p are associated with differences in immune signaling in estrogen receptor (ER)-negative IDC. Common oncogenic hotspot mutations in genes including TP53 and PIK3CA are predicted to be neoantigens yet are paradoxically conserved during the DCIS-to-IDC transition, and are associated with differences in immune signaling. We highlight both tumor and immune-specific changes in the transition of pure DCIS to IDC, including genetic changes in tumor cells that may have a role in modulating immune function and assist in immune escape, driving the transition to IDC. IMPLICATIONS: We demonstrate that the in situ to IDC evolutionary bottleneck is shaped by both tumor and immune cells.

Functional cooperativity between the trigger factor chaperone and the ClpXP proteolytic complex.

A functional association is uncovered between the ribosome-associated trigger factor (TF) chaperone and the ClpXP degradation complex. Bioinformatic analyses demonstrate conservation of the close proximity of tig, the gene coding for TF, and genes coding for ClpXP, suggesting a functional interaction. The effect of TF on ClpXP-dependent degradation varies based on the nature of substrate. While degradation of some substrates are slowed down or are unaffected by TF, surprisingly, TF increases the degradation rate of a third class of substrates. These include λ phage replication protein λO, master regulator of stationary phase RpoS, and SsrA-tagged proteins. Globally, TF acts to enhance the degradation of about 2% of newly synthesized proteins. TF is found to interact through multiple sites with ClpX in a highly dynamic fashion to promote protein degradation. This chaperone-protease cooperation constitutes a unique and likely ancestral aspect of cellular protein homeostasis in which TF acts as an adaptor for ClpXP.