Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution.

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, spatially resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. Integrating spatial information from MERFISH with the more complete transcriptome produced by single-nucleus RNA sequencing (snRNA-seq), also reveals zonally enriched receptor-ligand interactions.

The Psychological Effects of Guillain-Barré Syndrome: A Grounded Theory Study.

Guillain-Barré syndrome is a rare neurological condition. Research has increased our understanding of the etiology, prognosis, and effective medical treatment of the illness. There is a lack of understanding regarding the psychological effects and what could help patients.

Identifying spatially variable genes by projecting to morphologically relevant curves.

Spatial transcriptomics enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the sample. A common objective in spatial transcriptomics data analysis is to identify spatially variable genes within predefined cell types or regions within the tissue. However, these regions are often implicitly one-dimensional, making standard two-dimensional coordinate-based methods less effective as they overlook the underlying tissue organization.

Highly Multiplexed Spatial Transcriptomics in Bacteria.

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence hybridization (MERFISH) to create bacterial-MERFISH.

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments.