A computational framework allows the unsupervised analysis of samples from single cell data across conditions, inference of multicellular programs associated with disease, and meta-analysis of independent patient cohorts.

Quantitative proteomics reveals the diversity and heterogeneity of nuclear proteomes in mammalian organs and identifies the molecular features of ageing.

Genetic effects on body weight in mice are highly heterogeneous, dynamic, and nonlinear with respect to genomic background, age, and diet.

Allele-specific expression, natural antisense transcripts, and developmental gene expression, but not RNA editing or developmental hourglass, act in concert to shape the transcriptome during the fruiting body formation of complex multicellular fungi.

The expansion of our understanding about the regulation of iron metabolism, erythropoiesis, and the crosstalk between them has enabled delineating the pathophysiology of multiple diseases and provided rationale for novel therapeutic interventions.

Combining computational and experimental approaches reveals that the loss of a maternal transcription factor influences sex-biased differential splicing in the early zygotic transcriptome at genes that are critical for normal developmental processes.

Infection and metabolic syndrome lead to a loss of molecular regulation, and changes in molecular correlations are under genetic control as revealed by the presence of correlation quantitative trait loci.

Micromanipulation experiments demonstrate that HP1α strengthens interphase and mitotic chromosome mechanics and maintains nuclear shape by chromatin crosslinking, separate from histone methylation.

Single-molecule tracking at scale, analyzing millions of cells and thousands of compounds per day, demonstrates that measuring protein motion provides mechanistic insights relevant to drug discovery.