Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage.

Senescent Syncytiotrophoblast Secretion During Early Onset Preeclampsia.

Background: Preeclampsia is a severe hypertensive disorder in pregnancy that causes preterm delivery, maternal and fetal morbidity, mortality, and life-long sequelae. Understanding the pathogenesis of preeclampsia is a critical first step toward protecting mother and child from this syndrome and increased risk of cardiovascular disease later in life. However, effective early predictive tests and therapies for preeclampsia are scarce.

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 . SRT methods can resolve gene expression up to single-molecule resolution.

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.

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.