This perspective discusses the current state and future directions of human neuroscience, emphasizing the convergence of large-scale neurophysiology and single-cell profiling techniques. The authors highlight new tools like Neuropixels probes for high-resolution, large-scale neural activity recording in awake patients and the advancements in single-cell omics for creating comprehensive brain-cell atlases. A key focus is the development of a workflow that integrates in vivo functional mapping of human brain tissue resected during surgery with subsequent ex vivo multi-modal cellular and functional profiling. Furthermore, the article addresses the potential clinical applications of these advances, such as informing precision medicine and gene therapies, and concludes by exploring the crucial societal and ethical implications concerning informed consent and data sharing in this burgeoning field.

References:

• Lee A T, Chang E F, Paredes M F, et al. Large-scale neurophysiology and single-cell profiling in human neuroscience[J]. Nature, 2024, 630(8017): 587-595.

The article discusses the ongoing evolution and critical importance of the Human Cell Atlas (HCA) initiative. The authors explain that the HCA is moving beyond simply collecting data to becoming an integrated resource, leveraging advances in artificial intelligence (AI) and machine learning (ML) to map the estimated 37.2 trillion cells in the human body. This comprehensive resource is envisioned in five key ways: as a cell census, a 3D multimodal and multiscale map, a genotype-to-phenotype map, a temporal map of development (4D), and ultimately, a foundation model for biology. These advanced cellular maps are considered essential for transforming medicine by providing crucial insights into health, disease mechanisms, diagnosis, and the development of more effective therapies.

References:

• Rood J E, Wynne S, Robson L, et al. The Human Cell Atlas from a cell census to a unified foundation model[J]. Nature, 2025, 637(8048): 1065-1071.

This is a review article from Nature Cell Biology that systematically explores translational regulation in the context of cellular stress biology and aging. The authors focus on how three age-associated stressors—loss of proteostasis, oxidative damage, and dysregulated nutrient sensing—modify protein synthesis. The text details the specific molecular mechanisms by which cells sense these stresses and implement corresponding adaptive or, at times, deleterious translational programs, such as activating the Integrated Stress Response (ISR) or inhibiting mTORC1 signaling. Ultimately, the review compares the translational changes observed during acute stress responses with the patterns of protein synthesis alterations that occur during healthy and unhealthy aging to better understand longevity.

References:

• Genuth N R, Dillin A. Translational regulation in stress biology[J]. Nature Cell Biology, 2025: 1-13.

The article introduces CellNavi, a novel deep learning framework designed to predict pivotal genes that drive complex cellular state transitions. CellNavi utilizes a gene graph-enhanced cell state manifold learned from vast, high-dimensional single-cell transcriptomics data, which integrates directional gene graph priors to capture the intrinsic features of cellular states. The framework's efficacy is demonstrated across diverse biological scenarios, including T cell differentiation, neurodegenerative disease pathogenesis, and determining the mechanism of action for drug compounds, consistently outperforming traditional network-based and differential gene expression analysis methods. The research highlights CellNavi's robust generalization capacity across various cell types and perturbation contexts, positioning it as a significant advancement for gene driver prediction and cell state manipulation.

References:

• Wang T, Pan Y, Ju F, et al. CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold[J]. Nature Cell Biology, 2025: 1-12.

The paper details a comprehensive study employing genome-scale Perturb-seq screens—a technique combining genetic perturbations via CRISPR interference (CRISPRi) with single-cell RNA sequencing (scRNA-seq)—to create detailed genotype-phenotype maps in K562 and RPE1 cell lines. This methodology is shown to be highly effective for the unbiased assignment of gene function and for dissecting complex cellular phenotypes, such as RNA splicing, cell differentiation, and chromosomal instability (CIN). Key discoveries include the functional assignment of poorly characterized genes in processes like ribosome biogenesis and the identification of a novel submodule of the Integrator complex. Furthermore, the large-scale data allowed researchers to uncover previously unseen, stress-specific regulation of the mitochondrial genome.

References:

• Replogle J M, Saunders R A, Pogson A N, et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq[J]. Cell, 2022, 185(14): 2559-2575. e28.

The paper introduces SpatialZoomer, a novel computational framework designed for the multi-scale analysis of single-cell resolution spatial transcriptomics (ST) data. This spectral graph-based method utilizes a set of low-pass filters to effectively extract spatial molecular features at various resolutions, including the single-cell, niche, and domain scales. A key innovation is the automatic identification of "critical" scales by partitioning a cross-scale similarity map using dynamic programming. The authors demonstrate SpatialZoomer's utility through applications such as detecting disease-progression signals in Alzheimer’s Disease and identifying spatially dependent cell subtypes and complex tissue architectures, like the stress-proliferation-EMT tri-layer organization in cancer. Overall, the tool offers a scalable, computationally efficient approach to understanding how molecular signals and spatial context shape cellular heterogeneity.

References:

• Li X, Fan Y, Han Y, et al. SpatialZoomer: multi-scale feature analysis of spatial transcriptomics[J]. bioRxiv, 2025: 2025.09. 08.674870.

The paper introduces FiCS Perturb-seq, an innovative, industrialized platform designed to overcome challenges in scalability and consistency when generating large-scale perturbation data for biological research. This new method integrates chemical fixation, cryopreservation, and automation to produce high-quality, reproducible Perturb-seq datasets, which are essential for training next-generation biological foundation models that aim to predict causal genetic effects. The authors release a significant public resource, X-Atlas/Orion, the largest publicly available Perturb-seq atlas, which contains data targeting nearly all human protein-coding genes across eight million cells. Crucially, the research demonstrates that sgRNA abundance serves as a reliable proxy for gene knockdown efficiency, allowing for the quantification of dose-dependent genetic effects to improve the predictive power of computational models. Ultimately, the FiCS Perturb-seq platform is presented as a crucial technological advancement for accelerating the construction of robust, causal predictive biology models.

References:

• Huang A C, Hsieh T H S, Zhu J, et al. X-Atlas/Orion: Genome-wide Perturb-seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models[J]. bioRxiv, 2025: 2025.06. 11.659105.

The paper present a comprehensive single-cell immune atlas comparing immune architecture and activity in second-trimester fetuses and adults across multiple organs. This research challenges the idea of fetal immune quiescence by uncovering widespread memory and activated T cells, alongside evidence of T-cell receptor (TCR) clonal sharing across various organs, including barrier sites. The study identifies specific immune tolerance mechanisms in the fetus, notably involving ARG1+ neutrophils and the PTGES3-PTGER4 signaling pathway, which actively suppress T cell activation. Furthermore, it details the widespread distribution of functional hematopoietic stem cells (HSCs) in both traditional and non-canonical organs, underscoring a dynamic, systemic immune maturation process during fetal development.

References:

• He S, Luo C L, Luo T, et al. Systemic immune activity occurs during human immune system maturation[J]. Cell, 2025.

The paper introduces Squidiff, a novel computational framework based on a conditional diffusion model with a semantic encoder, designed to predict single-cell transcriptomic changes in response to various environmental stimuli. This predictive model addresses the limitations of previous methods by learning complex, high-resolution dynamic cellular states across different scenarios, including cell differentiation, gene perturbations, and drug responses. The authors demonstrate Squidiff’s efficacy by applying it to blood vessel organoid (BVO) development, successfully modeling the effects of both neutron irradiation and the radioprotective drug G-CSF. Overall, Squidiff provides an in-silico tool for screening molecular landscapes and inferring transient cell state transitions, aiding in the discovery of regulatory principles governing cell fate.

References:

• He S, Zhu Y, Tavakol D N, et al. Squidiff: Predicting cellular development and responses to perturbations using a diffusion model[J]. Nature Methods, 2025: 1-13.

The article introduces a novel, cost-effective method called "compressed Perturb-seq" for large-scale genetic screening of regulatory circuits. This technique modifies the conventional Perturb-seq method by employing algorithmic compressed sensing principles, allowing for measurement of random combinations of genetic perturbations (either through "cell-pooling" or "guide-pooling") and subsequent computational decompression using a new inference method, FR-Perturb. The authors demonstrate that compressed Perturb-seq achieves accuracy comparable to conventional methods while offering substantial reductions in cost and cell count, with guide-pooling specifically enhancing the ability to detect second-order genetic interactions. The study applies this framework to analyze the regulatory circuitry of the LPS response in human macrophage cells, identifying both known and novel immune regulators and integrating findings with human disease data from GWAS and eQTL studies.

References:

• Yao D, Binan L, Bezney J, et al. Scalable genetic screening for regulatory circuits using compressed Perturb-seq[J]. Nature biotechnology, 2024, 42(8): 1282-1295.