A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture.

Rapid recurrence is common in triple-negative breast cancer (TNBC). To better understand drivers of recurrence, we use imaging mass cytometry to characterize the tumor phenotype landscapes of 215 TNBC patients. We observe high intertumor heterogeneity with eleven tumor cell phenotypes, each of which dominates in an individual patient, and identify a tumor cell phenotype with reduced basoluminal lineage fidelity and stem-like traits that is correlated with rapid disease recurrence.

Identification and characterization of tertiary lymphoid structures in brain metastases.

Brain metastases (BrM) are the most common cancers in the brain and linked to poor prognosis. Given the high incidence and often limited treatment options, understanding the complexity of the BrM tumor microenvironment is crucial for the development of novel therapeutic strategies. We performed transcriptome-wide gene expression profiling combined with spatial immune cell profiling to characterize the tumor immune microenvironment in 95 patients with BrM from different primary tumors.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data.

Background: Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.

An end-to-end workflow for multiplexed image processing and analysis.

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion.

Identification of key amino acid residues in AtUMAMIT29 for transport of glucosinolates.

Glucosinolates are key defense compounds of plants in Brassicales order, and their accumulation in seeds is essential for the protection of the next generation. Recently, members of the Usually Multiple Amino acids Move In and Out Transporter (UMAMIT) family were shown to be essential for facilitating transport of seed-bound glucosinolates from site of synthesis within the reproductive organ to seeds. Here, we set out to identify amino acid residues responsible for glucosinolate transport activity of the main seed glucosinolate exporter UMAMIT29 in .

an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data.

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive data visualization of multiplexed imaging data is necessary for quality control and hypothesis examination. Here, we describe , an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks.

Export of defensive glucosinolates is key for their accumulation in seeds.

Plant membrane transporters controlling metabolite distribution contribute key agronomic traits. To eliminate anti-nutritional factors in edible parts of crops, the mutation of importers can block the accumulation of these factors in sink tissues. However, this often results in a substantially altered distribution pattern within the plant, whereas engineering of exporters may prevent such changes in distribution.