Anatomy of the fetal membranes: insights from spinning disk confocal microscopy

PURPOSE

The fetal membranes are essential for the maintenance of pregnancy, and their integrity until parturition is critical for both fetal and maternal health. Preterm premature rupture of the membranes (pPROM) is known to be an indicator of preterm birth, but the underlying architectural and mechanical changes that lead to fetal membrane failure are not yet fully understood. The aim of this study was to gain new insights into the anatomy of the fetal membrane and to establish a tissue processing and staining protocol suitable for future prospective cohort studies.

METHODS

In this proof of principle study, we collected fetal membranes from women undergoing vaginal delivery or cesarean section. Small membrane sections were then fixed, stained for nucleic acids, actin, and collagen using fluorescent probes, and subsequently imaged in three dimensions using a spinning disk confocal microscope.

RESULTS

Four fetal membranes of different types were successfully processed and imaged after establishing a suitable protocol. Cellular and nuclear outlines are clearly visible in all cases, especially in the uppermost membrane layer. Focal membrane (micro) fractures could be identified in several samples.

CONCLUSION

The presented method proves to be well suited to determine whether and how the occurrence of membrane (micro) fractures and cellular jamming correlate with the timing of membrane rupture and the mode of delivery. In future measurements, this method could be combined with mechanical probing techniques to compare optical and mechanical sample information.

A replicating LCMV-based vaccine for the treatment of solid tumors

Harnessing the immune system to eradicate tumors requires identification and targeting of tumor antigens, including tumor-specific neoantigens and tumor-associated self-antigens. Tumor-associated antigens are subject to existing immune tolerance, which must be overcome by immunotherapies. Despite many novel immunotherapies reaching clinical trials, inducing self-antigen-specific immune responses remains challenging. Here, we systematically investigate viral-vector-based cancer vaccines encoding a tumor-associated self-antigen (TRP2) for the treatment of established melanomas in preclinical mouse models, alone or in combination with adoptive T cell therapy. We reveal that, unlike foreign antigens, tumor-associated antigens require replication of lymphocytic choriomeningitis virus (LCMV)-based vectors to break tolerance and induce effective antigen-specific CD8+ T cell responses. Immunization with a replicating LCMV vector leads to complete tumor rejection when combined with adoptive TRP2-specific T cell transfer. Importantly, immunization with replicating vectors leads to extended antigen persistence in secondary lymphoid organs, resulting in efficient T cell priming, which renders previously "cold" tumors open to immune infiltration and reprograms the tumor microenvironment to "hot." Our findings have important implications for the design of next-generation immunotherapies targeting solid cancers utilizing viral vectors and adoptive cell transfer.

Corrigendum: A review on deep learning applications in highly multiplexed tissue imaging data analysis

[This corrects the article DOI: 10.3389/fbinf.2023.1159381.].

S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue

The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S3-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer. Moreover, we use S3-CIMA to identify disease-onset-specific changes of the pancreatic tissue microenvironment in type 1 diabetes using imaging mass-cytometry data. We evaluated S3-CIMA as a powerful tool to discover novel disease-associated spatial cellular interactions from currently available and future spatial biology datasets.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Implementing deep learning models for the classification of Echinococcus multilocularis infection in human liver tissue

BACKGROUND

The histological diagnosis of alveolar echinococcosis can be challenging. Decision support models based on deep learning (DL) are increasingly used to aid pathologists, but data on the histology of tissue-invasive parasitic infections are missing. The aim of this study was to implement DL methods to classify Echinococcus multilocularis liver lesions and normal liver tissue and assess which regions and structures play the most important role in classification decisions.

METHODS

We extracted 15,756 echinococcus tiles from 28 patients using 59 whole slide images (WSI); 11,602 tiles of normal liver parenchyma from 18 patients using 33 WSI served as a control group. Different pretrained model architectures were used with a 60-20-20% random splitting. We visualized the predictions using probability-thresholded heat maps of WSI. The area-under-the-curve (AUC) value and other performance metrics were calculated. The GradCAM method was used to calculate and visualize important spatial features.

RESULTS

The models achieved a high validation and test set accuracy. The calculated AUC values were 1.0 in all models. Pericystic fibrosis and necrotic areas, as well as germinative and laminated layers of the metacestodes played an important role in decision tasks according to the superimposed GradCAM heatmaps.

CONCLUSION

Deep learning models achieved a high predictive performance in classifying E. multilocularis liver lesions. A possible next step could be to validate the model using other datasets and test it against other pathologic entities as well, such as, for example, Echinococcus granulosus infection.

Direct Regulation of Hyperpolarization-Activated Cyclic-Nucleotide Gated (HCN1) Channels by Cannabinoids

Cannabinoids are a broad class of molecules that act primarily on neurons, affecting pain sensation, appetite, mood, learning, and memory. In addition to interacting with specific cannabinoid receptors (CBRs), cannabinoids can directly modulate the function of various ion channels. Here, we examine whether cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), the most prevalent phytocannabinoids in Cannabis sativa, can regulate the function of hyperpolarization-activated cyclic-nucleotide-gated (HCN1) channels independently of CBRs. HCN1 channels were expressed in Xenopus oocytes since they do not express CBRs, and the effects of cannabinoid treatment on HCN1 currents were examined by a two-electrode voltage clamp. We observe opposing effects of CBD and THC on HCN1 current, with CBD acting to stimulate HCN1 function, while THC inhibited current. These effects persist in HCN1 channels lacking the cyclic-nucleotide binding domain (HCN1ΔCNBD). However, changes to membrane fluidity, examined by treating cells with TX-100, inhibited HCN1 current had more pronounced effects on the voltage-dependence and kinetics of activation than THC, suggesting this is not the primary mechanism of HCN1 regulation by cannabinoids. Our findings may contribute to the overall understanding of how cannabinoids may act as promising therapeutic molecules for the treatment of several neurological disorders in which HCN function is disturbed.

[Interfacial behaviour of glycoconjugated tetraphenylporphyrins and their interaction with biomimetic models of the cell membrane]

INTRODUCTION

Porphyrins are photosensitizers usable in photodynamic therapy. Although these molecules are clinically effective, their low water solubility and their lack of specificity are major drawbacks to their development. Our study was aimed at analysing the interfacial behaviour of glycoconjugated tetraphenylporphyrins newly synthesized at the Curie Institute, and their interaction with model membranes bearing a specific lectin mimicking a mannose membrane receptor in retinoblastoma.

MATERIAL AND METHODS

The interfacial behaviour of the porphyrins was analysed by surface pressure measurements, and their specific interaction with the lectin, by dynamic light scattering (liposomes) and the quartz crystal microbalance technique (supported bilayers).

RESULTS

All porphyrin derivatives were able to organize at the air/liquid interface. The dendrimeric compounds formed more stable monolayers than the others, and generally showed good mixing properties with the phospholipid used for liposome preparation. In the presence of concanavalin A, the porphyrin bearing-liposomes behaved differently depending on the nature (mannosylated or not) of the porphyrins.

DISCUSSION

The interfacial behaviour of the tetraphenylporphyrins is directly related to the orientation of the tetrapyrrolic macrocycle controlled by the grafted groups. Incorporated into a liposome bilayer, glycodendrimeric porphyrins expose their sugar moieties at the vesicle surface. The spacer length plays a crucial role by increasing sugars freedom and enhancing glycosylated liposomes interaction with the lectin.

CONCLUSION

Compared to the other studied compounds, the glycodendrimeric porphyrins seem very promising compounds and are now evaluated on cell cultures.

Evaluation of the specific interactions between glycodendrimeric porphyrins, free or incorporated into liposomes, and concanavalin A by fluorescence spectroscopy, surface pressure, and QCM-D measurements

In photodynamic therapy, the specificity of a photosensitizer and its penetration into tumor cells are crucial. We have analyzed the ability of newly synthesized meso-(tetraphenyl)porphyrins to be recognized by a model of mannose-specific proteins overexpressed at the surface of retinoblastoma cells. The specific interaction of porphyrin with Con A was studied by surface pressure measurements, fluorescence spectroscopy, dynamic light scattering, and QCM-D. The extent of porphyrins binding to Con A was highly dependent upon their chemical structure. Glycodendrimeric porphyrins showed the higher binding constant to Con A. The length of the spacer separating the sugar from the tetrapyrrolic ring appeared to be crucial in controlling the interaction of the compounds with the lectin in solution or immobilized onto a solid substrate. The methodology used proved to be efficient for the selection of potentially active compounds. The glycodendrimeric porphyrins, especially the derivative having the longer spacer, interacted more significantly with the lectin than the compound devoid of any sugar.

Effect of cholesterol and sugar on the penetration of glycodendrimeric phenylporphyrins into biomimetic models of retinoblastoma cells membranes

Photodynamic therapy (PDT) is considered one efficient treatment against retinoblastoma. The specificity of a photosensitizer and its penetration into cancerous cells are crucial for achieving tumor necrosis. The selection of photosensitizers such as porphyrin derivatives by tumor cells thus depends to a large extent on their ability to interact with the biological membrane. In this work, we have studied by surface pressure measurements and fluorescence spectroscopy the interaction between three newly synthesized dendrimeric phenylporphyrins and monolayers or liposomes with increasing cholesterol content mimicking the retinoblastoma cell membrane. The morphology of phospholipid-cholesterol-porphyrin mixed monolayers was also analyzed by Brewster angle microscopy. The results showed that the increase in cholesterol content in the model membranes had almost no effect on the effective penetration of the drugs into the lipid layers. Conversely, the chemical structure of the glycodendrimeric phenylporphyrins and the presence of sugar moieties especially appeared to play a crucial role. Although the non-glycoconjugated phenylporphyrin penetrated to a greater extent than glycodendrimeric ones into the liposome membrane, this could be achieved at a high lipid/porphyrin ratio only. Glycodendrimeric porphyrins exhibited improved surface properties compared to the non-glycoconjugated derivative and could penetrate into lipid layers even at low lipid/porphyrin ratios and high surface pressures. Our work highlights the role in the passive diffusion of porphyrins into biomimetic cancer cell membranes, of complex interactions among the lipid molecules, the sugar moieties, and the hydrophobic macrocycle of the porphyrins.