Nature Biotechnology - The right AI workflow can help you to read more widely and think more deeply — without sacrificing rigor.

Single-cell profiling provides snapshots of the heterogeneous states that characterise developmental processes, organ regeneration and progression towards disease in a complex landscape. The underlying trajectories are of pivotal interest, but existing methods for reconstructing cell state trajectories commonly neglect population sizes. However, snapshot experiments make it difficult to interpret cell flux because the observed trajectories are confounded by changes in overall population size. This ambiguity can lead to misinterpreting changes in proliferation or death rates as changes in cellular migration. We introduce pseudodynamics+ , a physics-informed neural network framework that solves high-dimensional flow equations on complex, branching landscapes. By integrating single-cell genomics with population dynamics, pseudodynamics+ estimates state- and time-dependent parameters of growth, differentiation, and diffusion. The model recapitulates proliferation bursts during T-cell maturation and, when applied to LARRY-barcoded data, predicts differentiation rates consistent with clonal behaviour. When applied to time-resolved persistent-labelling datasets of in vivo mouse bone marrow haematopoiesis, pseudodynamics+ reconstructs continuous tissue flows with dynamic parameters aligned with known molecular signatures. Notably, simulations revealed a previously unrecognised shift from megakaryocyte-biased to balanced progenitor output, explained by evolving fate preferences of progenitor states, as revealed by simulations leveraging our estimated dynamic parameters. Pseudodynamics+ therefore establishes a population-aware framework for reconstructing single-cell population dynamics and is available at <https://github.com/Gottgens-lab/pseudodynamics_plus>. ### Competing Interest Statement Yes there is potential Competing Interest. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: F.J.T. consults for Immunai, CytoReason, BioTuring, Genbio and Valinor Industries, and has ownership interest in RN.AI Therapeutics, Dermagnostix, and Cellarity. The remaining authors declare no competing interests.

Nature - Enrichment for efferocytic macrophages in the islets of Langerhans promotes the accumulation of anergic-like CD4+ T cells and dampens autoimmune diabetes.

Pilley et al. describe how metabolite levels change in 12 organs from male and female mice across five ages, spanning adolescence to old age. They reveal that organs show distinct age- and sex-specific changes in metabolism. They also develop metabolic clocks that predict putative metabolic drivers of aging, including hydroxyproline.

Time marches endlessly on … but what does that mean for the immune system? Here, investigators discuss how aging impacts the immune response and how i…

Human gut bacteria are routinely exposed to stresses, and community-level responses are difficult to predict. To interrogate these effects, we screene…

Recent studies at molecular and genomic scales have enriched our understanding of life’s most fundamental building block: the cell. However, bridging …

Nature - Mapping of spatial metabolic gradients in the mouse liver and intestine identifies fructose-induced focal derangements in liver metabolism.

Nature Cell Biology - Liu et al. present TemporalVAE, a method for integrating single-cell time course data. The model proposes a workflow to determine the biological timing of samples and its...

Single-cell omics reveals nestin-negative mesenchymal neural crest stem-like cells and key regulators for MPNST malignancy.

Autoimmune diseases arise from genetic and environmental factors that disrupt immune tolerance. Recent studies highlight the role of myeloid cell immu…