Aging- and alcohol-associated spatial transcriptomic signature in mouse acute pancreatitis reveals heterogeneity of inflammation and potential pathogenic factors.

The rapidly aging population is consuming more alcohol, leading to increased alcohol-associated acute pancreatitis (AAP) with high mortality. However, the mechanisms remain undefined, and currently there are no effective therapies available. This study aims to elucidate aging- and alcohol-associated spatial transcriptomic signature by establishing an aging AAP mouse model and applying Visium spatial transcriptomics for understanding of the mechanisms in the context of the pancreatic tissue. Upon alcohol diet feeding and caerulein treatment, aging mice (18 months) developed significantly more severe AAP with 5.0-fold increase of injury score and 2.4-fold increase of amylase compared to young mice (3 months). Via Visium spatial transcriptomics, eight distinct tissue clusters were revealed from aggregated transcriptomes of aging and young AAP mice: five acinar, two stromal, and one islet, which were then merged into three clusters: acinar, stromal, and islet for the comparative analysis. Compared to young AAP mice, > 1300 differentially expressed genes (DEGs) and approximately 3000 differentially regulated pathways were identified in aging AAP mice. The top five DEGs upregulated in aging AAP mice include Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp with heterogeneous distributions among the clusters. Taken together, this study demonstrates spatial heterogeneity of inflammatory processes in aging AAP mice, offering novel insights into the mechanisms and potential drivers for AAP development. KEY MESSAGES: Mechanisms regarding high mortality of AAP in aging remain undefined. An aging AAP mouse model was developed recapturing clinical exhibition in humans. Spatial transcriptomics identified contrasted DEGs in aging vs. young AAP mice. Top five DEGs were Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp in aging vs. young AAP mice. Our findings shed insights for identification of molecular drivers in aging AAP.

Spatial Transcriptomics in Kidney Tissue.

Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.

Protecting RNA quality for spatial transcriptomics while improving immunofluorescent staining quality.

In comparison to bulk sequencing or single cell sequencing, spatial transcriptomics preserves the spatial information in tissue slices and can even be mapped to immunofluorescent stainings, allowing translation of gene expression information into their spatial context. This enables to unravel complex interactions of neighboring cells or to link cell morphology to transcriptome data. The 10× Genomics Visium platform offers to combine spatial transcriptomics with immunofluorescent staining of cryo-sectioned tissue slices. We applied this technique to fresh frozen mouse brain slices and developed a protocol that still protects RNA quality while improving buffers for immunofluorescent staining. We investigated the impact of various parameters, including fixation time and buffer composition, on RNA quality and antibody binding. Here, we propose an improved version of the manufacturer protocol, which does not alter RNA quality and facilitates the use of multiple additional antibodies that were not compatible with the manufacturer protocol before. Finally, we discuss the influence of various staining parameters, which contribute to the development of application specific staining protocols.

VistoSeg: Processing utilities for high-resolution images for spatially resolved transcriptomics data.

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

Full-Length Spatial Transcriptomics Reveals the Unexplored Isoform Diversity of the Myocardium Post-MI.

We introduce Single-cell Nanopore Spatial Transcriptomics (scNaST), a software suite to facilitate the analysis of spatial gene expression from second- and third-generation sequencing, allowing to generate a full-length near-single-cell transcriptional landscape of the tissue microenvironment. Taking advantage of the Visium Spatial platform, we adapted a strategy recently developed to assign barcodes to long-read single-cell sequencing data for spatial capture technology. Here, we demonstrate our workflow using four short axis sections of the mouse heart following myocardial infarction. We constructed a de novo transcriptome using long-read data, and successfully assigned 19,794 transcript isoforms in total, including clinically-relevant, but yet uncharacterized modes of transcription, such as intron retention or antisense overlapping transcription. We showed a higher transcriptome complexity in the healthy regions, and identified intron retention as a mode of transcription associated with the infarct area. Our data revealed a clear regional isoform switching among differentially used transcripts for genes involved in cardiac muscle contraction and tissue morphogenesis. Molecular signatures involved in cardiac remodeling integrated with morphological context may support the development of new therapeutics towards the treatment of heart failure and the reduction of cardiac complications.