Gene expression analysis articles from across Nature Portfolio

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.

Adipose tissue adapts to overnutrition in a complex process, wherein specialized immune cells remove and replace dysfunctional and stressed adipocytes with new fat cells. Here, the authors show that the deletion of TM4SF19 expressed in lipid-associated macrophages, enhances the clearance of dying adipocytes, thereby improving local and systemic insulin sensitivity as well as energy expenditure.

In this Resource article, the authors integrate genomic, bioinformatic and flow cytometric data from human bone marrow to provide an atlas of hematopoietic progenitor cell states in health and disease.

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.

The Farm Animal GTEx project introduces a new resource for pigs, in which they map genetic variation to differences in gene expression across thousands of samples.

Kylie R. James discusses a paper by Moor et al. that described a transcriptional gradient along the intestinal axis using single-cell RNA sequencing.

Genetic and environmental diversity are major drivers of macrophage transcriptional responses, but the mechanisms that underlie the relative contributions of gene-by-environment interactions to transcriptional responses of tissue macrophages are unclear. We defined the relative effect of cis regulation, cell-autonomous trans regulation, and non-cell-autonomous trans regulation of Kupffer cell gene expression.

We conducted a comprehensive long-read RNA sequencing (RNA-seq) benchmarking experiment by combining spike-ins and in silico mixtures to establish a ground-truth dataset. We used long- and short-read RNA-seq technology to deeply sequence samples and compared the performance of a range of analysis tools on these data.