Gene expression analysis articles from across Nature Portfolio

Microfluidics-assisted grid chips for spatial transcriptome sequencing (MAGIC-seq) is a spatial transcriptomics method combining multiple-grid microfluidic design and prefabricated DNA arrays for increased throughput and reduced cost, with applications for large fields of view and 3D spatial mapping.

In glioblastoma, malignant cells can escape surgical resection by migrating into the surrounding healthy brain tissue. Here, the authors use spatial transcriptomics to characterise the tumour core and infiltrated brain regions in glioblastoma, and show shifts in malignant cell composition and molecular pathways with potential clinical implications.

Spatial molecular imaging analysis of human pancreatic adenocarcinomas describes multicellular neighborhoods in the tumor microenvironment. Ligand–receptor analysis using optimal transport-based SCOTIA identifies interleukin-6 as a mediator of chemoresistance.

This Comment discusses the progress, limitations and best practices in applying foundation models to single-cell omics data.

Mustafa Mir reflects on a 1976 paper by McKnight and Miller, in which they developed a technique to directly visualize gene regulatory dynamics.

Homeostatic immune cells remain perpetually vigilant against pathogens. We found that baseline JAK–STAT signaling supports the characteristic transcriptional and epigenetic state of homeostatic T cells and macrophages in mice. JAK–STAT signaling under homeostatic conditions was driven by signals from healthy tissue and did not require external immune stimuli.

Cells interact with their local environment to enact global tissue function. By harnessing gene–gene covariation in cellular neighborhoods from spatial transcriptomics data, the covariance environment (COVET) niche representation and the environmental variational inference (ENVI) data integration method model phenotype–microenvironment interplay and reconstruct the spatial context of dissociated single-cell RNA sequencing datasets.

Friedländer and Gilbert introduce the study of ancient RNA of viruses, plants and animals, and how it can inform us of (pre)historic gene expression.