Probes for INS

Despite the physiological significance of ESR2, a lack of well-validated detection systems for ESR2 proteins has hindered progress in ESR2 research. Thus, recent identification of a specific anti-human ESR2 monoclonal antibody (PPZ0506) and its specific cross-reactivity against mouse and rat ESR2 proteins heightened momenta toward development of appropriate immunohistochemical detection systems for rodent ESR2 proteins. Building upon our previous optimization of ESR2 immunohistochemical detection in rats using PPZ0506, in this study, we further aimed to optimize mouse-on-mouse immunohistochemical detection using PPZ0506. Our assessment of several staining conditions using paraffin-embedded ovary sections revealed that intense heat-induced antigen retrieval, appropriate blocking, and appropriate antibody dilutions were necessary for optimization of mouse-on-mouse immunohistochemistry. Subsequently, we applied the optimized immunostaining method to determine expression profiles of mouse ESR2 proteins in peripheral tissues and brain subregions. Our analyses revealed more localized distribution of mouse ESR2 proteins than previously assumed. Moreover, comparison of these results with those obtained in humans and rats using PPZ0506 revealed interspecies differences in ESR2 expression. We expect that our optimized methodology for immunohistochemical staining of mouse ESR2 proteins will help researchers to solve multiple lines of controversial evidence concerning ESR2 expression.

Recent years have seen a dramatic improvement in RNA and DNA sequencing technologies allowing the analysis of gene expression and chromatin conformation at the single-cell or nuclei level. This permitted to evidence that cells of the human brain may have different genomes, the different cell types living in a tumor or during its development, and many other biological features, promising significant future biomedical and clinical impacts. In this chapter, we will develop the concept of single-cell or nucleus RNA sequencing discussing methods and applications in the field of muscle pathologies. We will focus on all the three types of muscles: skeletal muscle is particularly important to sustain the body and regulate the metabolism, cardiac muscle is fundamental for blood movement within vessels and oxygen and nutrient distribution, and smooth muscle is involved in the maintenance of blood pressure and in the movement of the bolus within the intestine.

In the last decade, single cell RNAsequencing (scRNAseq) has made significant advances in obtaining quantitative geneexpression of individual cells and identifying previously uncharacterized cell types and functions. However, scRNAseq technologies have the intrinsic limitation of losing original positional information during tissue dissociation into single cells. Spatial localization of cells in tissue microenvironments is essential to further identify cell types, elucidate the complex celltocell communication and spatial division of labor among cells, and explore the relationship between the tissue microenvironments imbalance and the development and progression of diseases. Tissuelevel systems biology requires obtaining wholegenome expression profiles while retaining the spatial positional information of cells. Spatial transcriptomics emerged at the proper time and developed rapidly in recent years. In this review, we introduced the common techniques of spatial transcriptomics and the integrated applications of spatial transcriptomics with other omics (spatialomics). Finally, we summarized current applications and future opportunities in the field of kidney diseases.

Sarcomas are rare malignancies, the number of reports is limited, and this rarity makes further research difficult even though liposarcoma is one of major sarcomas. 2D cell culture remains an important role in establishing basic tumor biology research, but its various shortcomings and limitations are still of concern, and it is now well-accepted that the behavior of 3D-cultured cells is more reflective of in vivo cellular responses compared to 2D models. This study aimed to establish 3D cell culture of liposarcomas using two different methods: scaffold-based (Matrigel extracellular matrix [ECM] scaffold method) and scaffold-free (Ultra-low attachment [ULA] plate). Lipo246, Lipo224 and Lipo863 cell lines were cultured, and distinctive differences in structures were observed in Matrigel 3D model: Lipo224 and Lipo863 formed spheroids, whereas Lipo246 grew radially without forming spheres. In ULA plate approaches, all cell lines formed spheroids, but Lipo224 and Lipo863 spheroids showed bigger size and looser aggregation than Lipo246. Formalin fixed, paraffin embedded (FFPE) blocks were obtained from all 3D models, confirming the spheroid structures. The expression of MDM2, Ki-67 positivity and MDM2 amplification were confirmed by IHC and DNAscope , respectively. Protein and DNA were extracted from all samples and MDM2 upregulation was confirmed by western blot and qPCR analysis. After treatment with MDM2 inhibitor SAR405838, DDLPS spheroids demonstrated different sensitivity patterns from 2D models. Taken together, we believed that 3D models would have a possibility to provide us a new predictability of efficacy and toxicity, and considered as one important process in in vitro pre-clinical phase prior to moving forward to clinical trials.

Although RNA plays a vital role in the process of gene expression, it is less used as an in situ biomarker for clinical diagnostics compared to DNA and protein. This is mainly due to technical challenges caused by the low expression level and easy degradation of RNA molecules themselves. To tackle this issue, methods that are sensitive and specific are needed. Here we present an RNA single molecule chromogenic in situ hybridization assay based on DNA probe proximity ligation and rolling circle amplification. When the DNA probes hybridize into close proximity on the RNA molecules, they form V shape structure and mediate the circularization of circle probes. Thus, our method was termed vsmCISH. We not only successfully applied our method to assess HER2 RNA mRNA expression status in invasive breast cancer tissue, but also to investigate the utility of albumin mRNA ISH for differentiating primary from metastatic liver cancer. The promising results on clinical samples indicates the great potential of our method to be applied in the diagnosis of disease using RNA biomarkers.

We made 2 Z-based in situ hybridization (ISH) probes for the detection of rabbit hemorrhagic disease virus 2 (RHDV2; Lagovirus GI.2) nucleic acid in formalin-fixed, paraffin-embedded tissues from European rabbits (Oryctolagus cuniculus) that had died during an outbreak of RHD in Washington, USA. One probe system was made for detection of negative-sense RNA (i.e., the replicative intermediate RNA for the virus), and the other probe system was constructed for detection of genomic and mRNA of the virus (viral mRNA). Tissue sets were tested separately, and the viral mRNA probe system highlighted much broader tissue distribution than that of the replicative intermediate RNA probe system. The latter was limited to liver, lung, kidney, spleen, myocardium, and occasional endothelial staining, whereas signal for the viral mRNA was seen in many more tissues. The difference in distribution suggests that innate phagocytic activity of various cell types may cause overestimation of viral replication sites when utilizing ISH of single-stranded, positive-sense viruses.

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Here, we provide detailed procedures for a variety of multiparameter fluorescence microscopy imaging methods to explore the spatial organization of DC in tissues and to dissect how DC migrate, communicate, and mediate their multiple functional roles in immunity in a variety of tissue settings. The protocols presented here entail approaches to study DC dynamics and T cell cross-talk by intravital microscopy, large-scale visualization, identification, and quantitative analysis of DC subsets and their functions by multiparameter fluorescence microscopy of fixed tissue sections, and an approach to study DC interactions with tissue cells in a 3D cell culture model. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Artificial intelligence (AI) is now a powerful tool which can be applied to significantly improve the safety de-risking process early in discovery, with AI-driven  pipelines of biotechs expanding at a very fast rate. Data from screening studies with DNA-encoded libraries together with high throughput in silico data are screened through AI-enabled computational platforms. These platforms leverage a wide range of in vitro and in vivo models and along with computational predictive models to help identify targets, predicting ‘druggable’ characteristics and target selectivity of molecules from a vast  space. In terms of safety, AI can also be used to predict potential interactions and by leveraging publicly available data or proprietary databases can predict potential on- and off-target safety liabilities. A major advantage of AI systems is that they include an active learning loop, referred to as machine learning, which helps to improve the accuracy of prediction and to identify advanceable lead series or candidate molecules leading to  a very high success rate, which improves as more data is gathered. Critically AI can also be used to screen billions of molecules virtually, reducing costs and  resource requirements and improving the discovery process by more efficient use of molecular biology, public and private databases and other resources.

Public and private genomic sequencing initiatives generate ever-increasing amounts of genomic data creating a need for improved solutions for genomics data processing (Stephens et al.PLoS Biol 13:e1002195, 2015). The Sentieon Genomics software enables rapid and accurate analysis of next-generation sequence data. In this work, we present a typical use of the Sentieon Genomics software for germline variant calling. The Sentieon germline variant calling pipeline produces more accurate results than other tools on third-party benchmarks (Katherine et al. Front Genet 10:736, 2019; Shen et al. bioRxiv, 885517, 2019) in one tenth the time of comparable pipelines. Parts of this guide come from the official Sentieon Genomics software manual in https://support.sentieon.com/manual (Sentieon. Sentieon Genomics software manual, n.d.) and from the official Sentieon Genomics software application notes in https://support.sentieon.com/appnotes  (Sentieon. Sentieon Genomics software application notes, n.d.) and are republished with permission. For additional details and advanced usage instructions of the Sentieon tools, refer to the software manual.

How parasites develop and survive, and how they stimulate or modulate host immune responses are important in understanding disease pathology and for the design of new control strategies. Microarray analysis and bulk RNA sequencing have provided a wealth of data on gene expression as parasites develop through different life-cycle stages and on host cell responses to infection. These techniques have enabled gene expression in the whole organism or host tissue to be detailed, but do not take account of the heterogeneity between cells of different types or developmental stages, nor the spatial organisation of these cells. Single-cell RNA-seq (scRNA-seq) adds a new dimension to studying parasite biology and host immunity by enabling gene profiling at the individual cell level. Here we review the application of scRNA-seq to establish gene expression cell atlases for multicellular helminths and to explore the expansion and molecular profile of individual host cell types involved in parasite immunity and tissue repair. Studying host-parasite interactions in vivo is challenging and we conclude this review by briefly discussing the applications of organoids (stem-cell derived mini-tissues) to examine host-parasite interactions at the local level, and as a potential system to study parasite development in vitro. Organoid technology and its applications have developed rapidly, and the elegant studies performed to date support the use of organoids as an alternative in vitro system for research on helminth parasites.

Taming gene expression variability is critical for robust pattern formation during embryonic development. Here, we describe an optimized protocol for single-molecule fluorescence in situ hybridization and immunohistochemistry in zebrafish embryos. We detail how to count segmentation clock RNAs and calculate their variability among neighboring cells. This approach is easily adaptable to count RNA numbers of any gene and calculate transcriptional variability among neighboring cells in diverse biological settings. For complete details on the use and execution of this protocol, please refer to Keskin et al. (2018),1 Zinani et al. (2021),2 and Zinani et al. (2022).3.