SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data

The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data.

Abstract 27: Unravelling Cell-specific Interactions At The Preeclamptic Maternal-foetal Interface From Early To Late Pregnancy

Preeclamptic syndrome arrises in the fetal part of the placenta (villi). In this study, we analyse placental development by single nuclei RNA-sequencing in early and term pregnancy and draw conclusions about pathological processes in preeclampsia (PE) that originate early in gestation. We profiled the transcriptome of 101,067 nuclei obtained from a total of 12 pregnancies, spanning early, term and PE doners. Using unsupervised computational approaches, we identified 12 and 16 different cell types and states in decidua and villi, respectively. Our comprehensively identified catalogue of cell types and states aligns well with the previous single cell studies. We identified different subpopulations of syncytiotrophoblast and GATA3+/GREM2+ trophoblast stem cells (TSC) in villi. Through gestation, gene expression in cell populations from the matrisome or vascular environments show dynamic expression reflecting vascular development associated with spiral artery remodelling and concordant decidual stroma reorganisation. Global differential gene expression analysis shows that trophoblast cell types are most dysregulated in PE. Cell-cell communication analysis revealed important dysregulation between villi and decidual cell types. The secretory signalling characteristic of this trophoblastic disease may be used for early biomarker screening. Overall, this study paves the way to a deeper understanding of the early pathophysiology of PE. Figure 1: Villi (v) and decidua (d) cell clusters from early, late control and preeclampsia (PE) villi and decidua visualised as a UMAP. Datasets were integrated separately for each tissue and merged for embedding.

Cell segmentation-free inference of cell types from in situ transcriptomics data

Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

Single-Nucleus and In Situ RNA-Sequencing Reveal Cell Topographies in the Human Pancreas

BACKGROUND & AIMS

Molecular evidence of cellular heterogeneity in the human exocrine pancreas has not been yet established because of the local concentration and cascade of hydrolytic enzymes that can rapidly degrade cells and RNA upon pancreatic resection. We sought to better understand the heterogeneity and cellular composition of the pancreas in neonates and adults in healthy and diseased conditions using single-cell sequencing approaches.

METHODS

We innovated single-nucleus RNA-sequencing protocols and profiled more than 120,000 cells from pancreata of adult and neonatal human donors. We validated the single-nucleus findings using RNA fluorescence in situ hybridization, in situ sequencing, and computational approaches.

RESULTS

We created the first comprehensive atlas of human pancreas cells including epithelial and nonepithelial constituents, and uncovered 3 distinct acinar cell types, with possible implications for homeostatic and inflammatory processes of the pancreas. The comparison with neonatal single-nucleus sequencing data showed a different cellular composition of the endocrine tissue, highlighting the tissue dynamics occurring during development. By applying spatial cartography, involving cell proximity mapping through in situ sequencing, we found evidence of specific cell type neighborhoods, dynamic topographies in the endocrine and exocrine pancreas, and principles of morphologic organization of the organ. Furthermore, similar analyses in chronic pancreatitis biopsy samples showed the presence of acinar-REG⁺ cells, a reciprocal association between macrophages and activated stellate cells, and a new potential role of tuft cells in this disease.

CONCLUSIONS

Our human pancreas cell atlas can be interrogated to understand pancreatic cell biology and provides a crucial reference set for comparisons with diseased tissue samples to map the cellular foundations of pancreatic diseases.