A new contrastive learning framework, CONCORD, produced denoised, batch‑integrated cell representations that preserve differentiation trajectories and other biological structures across diverse single‑cell datasets. Published in Nature Biotechnology, the method uses principled mini‑batch sampling to reconcile study heterogeneity and revealed coherent cell‑state maps across multiple biological contexts. The authors demonstrated that CONCORD improves resolution of biological features such as cell‑cycle loops and lineage branches, enabling cross‑study comparisons crucial for translational single‑cell applications and multi‑center biomarker discovery.