The Human Development Multiomic Atlas (HDMA) is a sophisticated single-cell resource that maps chromatin accessibility and gene expression across 12 human fetal organs. Researchers utilized deep learning models to decode the complex regulatory syntax of DNA, identifying over one million regulatory elements that govern cell identity. This investigation revealed distinct "hard" and "soft" rules for how transcription factors cooperate based on their spacing and orientation. The study also discovered negative motifs that actively reduce genetic accessibility, providing a new perspective on how the genome is silenced. Finally, the atlas helps prioritize disease-associated genetic variants by predicting how specific mutations disrupt regulatory logic during development. This foundational work offers a comprehensive framework for understanding the cis-regulatory logic that drives human growth.

References:

• Liu B B, Jessa S, Kim S H, et al. Multiomics and deep learning dissect regulatory syntax in human development[J]. Nature, 2026: 1-14.

This research introduces the Human Neural Organoid Cell Atlas (HNOCA), a massive transcriptomic resource integrating over 1.7 million single cells from dozens of different scientific protocols. By consolidating data from numerous studies, researchers have created a comprehensive map that allows for a quantitative comparison between lab-grown organoids and actual developing human brain tissue. The atlas reveals that while organoids successfully replicate many early neural cell types and developmental trajectories, they often exhibit universal metabolic differences, such as increased cellular stress, regardless of the specific growth method used. This tool enables scientists to identify which brain regions are well-represented in vitro and which remain under-represented, such as the thalamus and cerebellum. Furthermore, the HNOCA serves as a standardized framework for evaluating new protocols and studying the underlying mechanisms of various neurological diseases. Ultimately, this integrated atlas provides a vital benchmark for improving the fidelity and accuracy of human brain models in regenerative medicine and drug discovery.

References:

• He Z, Dony L, Fleck J S, et al. An integrated transcriptomic cell atlas of human neural organoids[J]. Nature, 2024, 635(8039): 690-698.

This research paper describes the atomistic-resolution structure of the mouse cytoplasmic lattice (CPL), a massive protein storage organelle essential for mammalian reproduction. Using single-particle cryo-electron microscopy, scientists determined that the CPL is a 4-MDa repeating unit featuring a tripartite architecture of an external framework, linkers, and a core. The study reveals how this lattice sequesters and inhibits vital maternal factors, such as the epigenetic regulator UHRF1, GTP-bound tubulin, and ubiquitin ligase components. These stored proteins are held in a poised but inactive state to ensure their timely release during the critical oocyte-to-embryo transition. By providing a molecular blueprint of the CPL, the authors establish a framework for diagnosing and treating female infertility caused by genetic defects. Mapping over 300 clinical variants onto this structure further confirms that lattice integrity is indispensable for early embryonic development.

References:

• Chi P, Wang X, Li J, et al. Structure of the mouse cytoplasmic lattice[J]. Nature, 2026: 1-3.

References:

• Chen X, Mao Z, Kolawole E M, et al. Overcoming T cell tolerance to tumor self-antigens through catch-bond engineering[J]. Science, 2026, 391(6791): eadx3162.

The paper details the creation of the Human Endoderm-derived Organoid Cell Atlas (HEOCA), a massive integrated dataset comprising nearly one million single-cell transcriptomes. Researchers synthesized data from 55 publications and nine different tissue types to harmonize cell annotations and establish a benchmark for organoid fidelity. By comparing lab-grown tissues to primary fetal and adult references, the study reveals how different stem cell sources influence the maturity and accuracy of models like the lung and intestine. The authors introduced sc2heoca, a toolkit designed to help scientists project new data onto the atlas to evaluate novel protocols and identify off-target cells. Furthermore, the sources demonstrate the utility of this atlas in modeling diseases and biological perturbations, such as viral infections or colorectal cancer. Ultimately, this centralized resource streamlines the development of high-fidelity human biosystems for translational research and drug testing.

References:

• Xu Q, Halle L, Hediyeh-Zadeh S, et al. An integrated transcriptomic cell atlas of human endoderm-derived organoids[J]. Nature genetics, 2025, 57(5): 1201-1212.

References:

• Chen M H, Hua Y J, Li Y, et al. Sparcl1 mitigates abdominal aortic aneurysm through inhibiting lymphangiogenesis-mediated TLS formation[J]. Nature Immunology, 2026: 1-12.

This review examines the neuroimmunometabolic co-evolution in cancer, detailing how the nervous system coordinates with immune and metabolic processes to influence tumor progression. At the tumor microenvironment (TME) scale, the authors explain how peripheral neurons—including sympathetic, parasympathetic, and sensory types—reprogram local metabolism and suppress anti-tumor immunity. Expanding to the tumor macroenvironment (TMaE), the text highlights how cancer exploits brain-body interactions to disrupt systemic homeostasis and cause conditions like cancer-associated cachexia. The research identifies the peripheral nervous system as a vital communication bridge between primary tumors and distant organs, facilitating long-range immune suppression. Ultimately, the authors propose integrating spatial technologies to uncover new neuroimmune and neurometabolic therapies that could enhance existing treatments like immunotherapy.

References:

• Zhang Y, Zhuang W, Zhu H, et al. Neuroimmunometabolic co-evolution in the tumor micro-and macroenvironment[J]. Cell Metabolism, 2026.

This article introduces GASTON, an unsupervised deep learning algorithm designed to analyze complex spatial transcriptomics data. By calculating a metric called isodepth, the tool creates a topographic map of tissue slices that functions similarly to elevation on a geographical map. This innovative approach allows researchers to identify spatial domains while simultaneously modeling both smooth gene expression gradients and abrupt changes in cellular composition. The researchers demonstrate that GASTON outperforms existing computational methods in maintaining spatial coherence, particularly in structured tissues like the brain. Furthermore, the algorithm provides a unique coordinate system to study biological shifts in the tumor microenvironment, such as immune activity and metabolic changes. Ultimately, the software offers a more precise way to visualize how cells organize and communicate within their physical landscape.

References:

• Chitra U, Arnold B J, Sarkar H, et al. Mapping the topography of spatial gene expression with interpretable deep learning[J]. Nature Methods, 2025, 22(2): 298-309.

This research article from Nature Neuroscience investigates the epigenetic landscape of the adult human central nervous system at a single-nucleus level. By analyzing chromatin accessibility and specific histone modifications, researchers identified a specialized epigenetic memory in oligodendroglia and astrocytes that mirrors early developmental stages. These unique signatures, particularly at HOX gene loci, remain present in adulthood even when the genes are not actively being expressed. The study suggests that while this priming allows for rapid gene activation during tissue repair, it may also increase vulnerability to certain types of brain tumors. Ultimately, these findings provide a comprehensive regulatory blueprint of human neural cells and offer new insights into both regenerative medicine and cancer biology.

References:

• Kabbe M, Agirre E, Carlström K E, et al. Single-nucleus epigenomic profiling of the adult human central nervous system unveils epigenetic memory of developmental programs[J]. Nature Neuroscience, 2026: 1-15.

References:

• Wei X, Xu Y, Yang D, et al. Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR[J]. Nature biotechnology, 2026: 1-16.