Recent sequencing-based methods, including novel ribosome profiling, ribosome affinity purification and spatial translation methods, have enabled the measurement of translation at single-cell resolution. These developments have opened avenues for studying translation regulation in tissues and rare cell types.

Understanding the reprogramming of gene expression and metabolism is critical for the success of somatic embryogenesis-based plant tissue regeneration. Here, the authors report single-cell RNA-sequencing, spatial transcriptomics and spatial metabolomics analyses during cotton somatic embryo development.

While cotton fiber development has been extensively studied, the spatiotemporal transcriptome and metabolome landscape remain largely unknown. Here, the authors profile early stage cotton fiber cell using a combination of spatial transcriptomic, single-cell transcriptomic, and spatial metabolomic analyses.

What makes a transcription factor (TF) binding site “strong”: slow TF dissociation, or fast TF association? Kandavalli et al found that stronger sites had both slower dissociation and faster association compared to weaker sites in vivo.

In this Journal Club, Shila Ghazanfar highlights a seminal paper by Levsky et al. that paved the way for contemporary single-cell spatial transcriptomics.

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.