The yeast FIT2 homologs are necessary to maintain cellular proteostasis and membrane lipid homeostasis.

Lipid droplets (LDs) are implicated in conditions of lipid and protein dysregulation. The fat storage-inducing transmembrane (FIT; also known as FITM) family induces LD formation. Here, we establish a model system to study the role of the Saccharomyces cerevisiae FIT homologues (ScFIT), SCS3 and YFT2, in the proteostasis and stress response pathways. While LD biogenesis and basal endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) remain unaltered in ScFIT mutants, SCS3 was found to be essential for proper stress-induced UPR activation and for viability in the absence of the sole yeast UPR transducer IRE1 Owing to not having a functional UPR, cells with mutated SCS3 exhibited an accumulation of triacylglycerol within the ER along with aberrant LD morphology, suggesting that there is a UPR-dependent compensatory mechanism that acts to mitigate lack of SCS3 Additionally, SCS3 was necessary to maintain phospholipid homeostasis. Strikingly, global protein ubiquitylation and the turnover of both ER and cytoplasmic misfolded proteins is impaired in ScFITΔ cells, while a screen for interacting partners of Scs3 identifies components of the proteostatic machinery as putative targets. Together, our data support a model where ScFITs play an important role in lipid metabolism and proteostasis beyond their defined roles in LD biogenesis.This article has an associated First Person interview with the first author of the paper.

From the unfolded protein response to metabolic diseases - lipids under the spotlight.

The unfolded protein response (UPR) is classically viewed as a stress response pathway to maintain protein homeostasis at the endoplasmic reticulum (ER). However, it has recently emerged that the UPR can be directly activated by lipid perturbation, independently of misfolded proteins. Comprising primarily phospholipids, sphingolipids and sterols, individual membranes can contain hundreds of distinct lipids. Even with such complexity, lipid distribution in a cell is tightly regulated by mechanisms that remain incompletely understood. It is therefore unsurprising that lipid dysregulation can be a key factor in disease development. Recent advances in analysis of lipids and their regulators have revealed remarkable mechanisms and connections to other cellular pathways including the UPR. In this Review, we summarize the current understanding in UPR transducers functioning as lipid sensors and the interplay between lipid metabolism and ER homeostasis in the context of metabolic diseases. We attempt to provide a framework consisting of a few key principles to integrate the different lines of evidence and explain this rather complicated mechanism.

Lipid bilayer stress-activated IRE-1 modulates autophagy during endoplasmic reticulum stress.

Metabolic disorders, such as non-alcoholic fatty liver disease (NAFLD), are emerging as epidemics that affect the global population. One facet of these disorders is attributed to the disturbance of membrane lipid composition. Perturbation of endoplasmic reticulum (ER) homeostasis through alteration in membrane phospholipids activates the unfolded protein response (UPR) and causes dramatic transcriptional and translational changes in the cell. To restore cellular homeostasis, the three highly conserved UPR transducers ATF6, IRE1 (also known as ERN1 in mammals) and PERK (also known as EIF2AK3 in mammals) mediate adaptive responses upon ER stress. The homeostatic UPR cascade is well characterised under conditions of proteotoxic stress, but much less so under lipid bilayer stress-induced UPR. Here, we show that disrupted phosphatidylcholine (PC) synthesis in Caenorhabditiselegans causes lipid bilayer stress, lipid droplet accumulation and ER stress induction. Transcriptional profiling of PC-deficient worms revealed a unique subset of genes regulated in a UPR-dependent manner that is independent from proteotoxic stress. Among these, we show that autophagy is modulated through the conserved IRE-1-XBP-1 axis, strongly suggesting of the importance of autophagy in maintaining cellular homeostasis during the lipid bilayer stress-induced UPR.

The membrane stress response buffers lethal effects of lipid disequilibrium by reprogramming the protein homeostasis network.

Lipid composition can differ widely among organelles and even between leaflets of a membrane. Lipid homeostasis is critical because disequilibrium can have disease outcomes. Despite their importance, mechanisms maintaining lipid homeostasis remain poorly understood. Here, we establish a model system to study the global effects of lipid imbalance. Quantitative lipid profiling was integral to monitor changes to lipid composition and for system validation. Applying global transcriptional and proteomic analyses, a dramatically altered biochemical landscape was revealed from adaptive cells. The resulting composite regulation we term the "membrane stress response" (MSR) confers compensation, not through restoration of lipid composition, but by remodeling the protein homeostasis network. To validate its physiological significance, we analyzed the unfolded protein response (UPR), one facet of the MSR and a key regulator of protein homeostasis. We demonstrate that the UPR maintains protein biogenesis, quality control, and membrane integrity-functions otherwise lethally compromised in lipid dysregulated cells.

Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity.

Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare TH 17 cells, further enabling sub-population analysis into TH 1-like and TH 2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of TH 2-like TH 17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, TH 2-like TH 17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b+ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration. © 2019 International Society for Advancement of Cytometry.

Linkage between the bacterial acid stress and stringent responses: the structure of the inducible lysine decarboxylase.

The Escherichia coli inducible lysine decarboxylase, LdcI/CadA, together with the inner-membrane lysine-cadaverine antiporter, CadB, provide cells with protection against mild acidic conditions (pH∼5). To gain a better understanding of the molecular processes underlying the acid stress response, the X-ray crystal structure of LdcI was determined. The structure revealed that the protein is an oligomer of five dimers that associate to form a decamer. Surprisingly, LdcI was found to co-crystallize with the stringent response effector molecule ppGpp, also known as the alarmone, with 10 ppGpp molecules in the decamer. ppGpp is known to mediate the stringent response, which occurs in response to nutrient deprivation. The alarmone strongly inhibited LdcI enzymatic activity. This inhibition is important for modulating the consumption of lysine in cells during acid stress under nutrient limiting conditions. Hence, our data provide direct evidence for a link between the bacterial acid stress and stringent responses.

The unfolded protein response supports cellular robustness as a broad-spectrum compensatory pathway.

Stress pathways monitor intracellular systems and deploy a range of regulatory mechanisms in response to stress. One of the best-characterized pathways, the unfolded protein response (UPR), is responsible for maintaining endoplasmic reticulum (ER) homeostasis. The highly conserved Ire1 branch regulates hundreds of gene targets by activating a UPR-specific transcription factor. To understand how the UPR manages ER stress, a unique genetic approach was applied to reveal how the system corrects disequilibria. The data show that the UPR can address a wide range of dysfunctions that are otherwise lethal if not for its intervention. Transcriptional profiling of stress-alleviated cells shows that the program can be modulated, not just in signal amplitude, but also through differential target gene expression depending on the stress. The breadth of the functions mitigated by the UPR further supports its role as a major mechanism maintaining systems robustness.

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.

Learning landmarks and routes in multi-floored buildings.

Existing studies have proposed that humans preferentially memorize buildings as a collection of floors. Yet this might stem from the fact that environments were also explored by floors. We have studied this potential bias with a learning and recognition experiment. We have detected a positive influence of the learning route - by floors, and also crucially by columns - on spatial memory performances.

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.

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.

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.

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.

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.

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.

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.

Routing misfolded proteins through the multivesicular body (MVB) pathway protects against proteotoxicity.

The secretory pathway maintains multiple quality control checkpoints. Initially, endoplasmic reticulum-associated degradation pathways monitor protein folding to retain and eliminate aberrant products. Despite its broad client range, some molecules escape detection and traffic to Golgi membranes. There, a poorly understood mechanism termed Golgi quality control routes aberrant proteins for lysosomal/vacuolar degradation. To better understand Golgi quality control, we examined the processing of the obligate substrate Wsc1p. Misfolded Wsc1p does not use routes of typical vacuolar membrane proteins. Instead, it partitions into intralumenal vesicles of the multivesicular body (MVB) pathway, mediated by the E3 ubiquitin ligase Rsp5p. Its subsequent transport to the vacuolar lumen is essential for complete molecule breakdown. Surprisingly, the transport mode plays a second crucial function in neutralizing potential substrate toxicity. Eliminating the MVB sorting signal diverted molecules to the vacuolar limiting membrane, resulting in the generation of toxic by-products. These data demonstrate a new role of the MVB pathway in protein quality control.

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.

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.