Joseph Beechem

The paper uses spatial transcriptomics to analyze a breast tumor, identifying spatially distinct biological processes and potential therapeutic targets.

The paper uses single-cell spatial transcriptomics to analyze tau pathology in mouse brains, revealing cell-autonomous and non-autonomous effects of tau accumulation on gene expression and pathways.

The paper presents a spatial multi-omics workflow for Alzheimer's neuropathology, resolving spatially distinct molecular domains and establishing analytical guidelines.

The paper integrates spatial morpho-transcriptomics to identify morphological subtypes in pancreatic cancer cell lines and tissues, linking them to gene expression, drug resistance, and metastatic potential.

SPACE enables spatial CRISPR screening with whole-transcriptome and protein readouts at subcellular resolution, revealing novel regulatory mechanisms in tumor microenvironments.

This paper traces colorectal cancer progression by integrating spatial transcriptomics, single-nucleus RNA-sequencing, and histopathology to map cellular and molecular changes across tumor stages.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

FastReseg improves cell segmentation accuracy in spatial transcriptomics by using transcriptomic data to correct image-based segmentation errors.

This paper introduces a high-plex spatial molecular imaging technique capable of sub-cellular transcriptome analysis of FFPE tissues, achieving whole transcriptome coverage with high resolution and minimal dissociation bias.

InSituCor identifies non-trivial spatial correlations in spatial transcriptomics data by filtering out correlations explained by cell type landscapes.

This paper introduces Maxspin, an information-theoretic method for detecting spatially varying genes, showing improved accuracy across spatial transcriptomics platforms and simulations.

Insitutype types cells in spatial transcriptomics with a likelihood model over every expression value, admits image and neighbourhood priors, and is reported to cluster millions of cells quickly.

The paper introduces a statistical model-based workflow for quality control and normalization of GeoMx RNA data, outperforming conventional methods in reducing background noise and technical variability.

An automated in-situ imaging platform reads 980 RNAs and 108 proteins in FFPE tissue at subcellular resolution, with 0.0092 false calls per cell; 800,000-plus cells released publicly.

Multiplexed in situ hybridization pools against every protein-coding gene let Digital Spatial Profiling read whole transcriptomes from regions of ~10-500 cells in human and mouse tissue.

This paper introduces a new spatial transcriptomic technology, WTA, and applies it to map the cellular architecture of the developing human neocortex, showing spatial enrichment of autism-related genes.

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.

This study uses single-nucleus and spatial transcriptomics to analyze pancreatic cancer before and after neoadjuvant treatment, revealing changes in cell states and immune infiltration that may inform therapy.

SpatialDecon quantifies cell populations in spatially-resolved gene expression data, showing spatially heterogeneous immune responses in lung tumors.