The work, led by Mitali U. Sheth, Wen-Lyong Qiu, Xiang Ru Ma and colleagues, addresses a gap that has widened as single-cell genomics has exploded in scale. Technologies such as single-cell RNA sequencing can now measure gene expression in hundreds of thousands of individual cells simultaneously, and single-cell ATAC-seq can profile chromatin accessibility, revealing which regulatory regions are open and potentially active in each cell. But these measurements, taken separately, do not by themselves reveal regulatory wiring. An enhancer may be accessible in a neuron, yet without evidence linking its activity to a target gene’s expression, its functional role remains speculative. Traditional approaches to establishing enhancer–gene links, such as CRISPR-based enhancer deletion or perturbation screens, are powerful but labor-intensive and difficult to scale across the enormous diversity of cell types in complex tissues.

The framework developed by the team leverages a key statistical insight: regulatory interactions leave fingerprints in the natural variation of gene expression and chromatin state across cells. If an enhancer genuinely controls a gene, then across the thousands of cells captured in a single-cell dataset, variation in that enhancer’s activity should be correlated with variation in the target gene’s expression, with effects that appear at the expected genomic distance and that are shaped by transcription factor binding motifs embedded in the enhancer sequence. By aggregating these weak signals across large cell populations and modeling them jointly, the method can distinguish true regulatory links from the many spurious correlations that arise from shared cellular states, cell cycle effects, or batch artifacts — confounders that have historically plagued correlation-based enhancer–gene inference.

A critical component of the approach is its handling of confounding at the level of cell identity. Because all genes in a given cell type tend to rise and fall together, naive correlation analysis assigns enhancers promiscuously to nearby genes, inflating the apparent regulatory network. The new method explicitly models and removes this shared component of variation, isolating the residual, enhancer-specific signal that reflects direct regulation. It also incorporates prior biological knowledge, including enhancer–promoter distance constraints, chromatin contact frequency data from technologies such as Hi-C and promoter capture assays, and sequence features that indicate which transcription factors bind a given enhancer. The result is a ranked set of candidate enhancer–gene pairs for each cell type, each accompanied by a quantitative confidence score that researchers can use to prioritize downstream experimental validation.

To benchmark the framework, the authors compared its predictions against gold-standard perturbation data — instances in which individual enhancers had been experimentally deleted or repressed and the resulting change in target gene expression measured directly. Across the validation sets, the computational predictions recovered a substantial fraction of experimentally confirmed enhancer–gene pairs while maintaining specificity, meaning they did not drown the true positives in a sea of false links. Notably, the method correctly identified many cases in which the nearest gene is not the actual target, a phenomenon that single-cell perturbation screens have increasingly shown to be common. Enhancers frequently skip over adjacent genes to contact promoters further away, and distance-based or nearest-gene assumptions systematically miss these long-range relationships.

One of the most striking demonstrations of the framework’s power comes from its application to disease genetics. When the researchers overlaid genome-wide association study summary statistics for a range of traits and diseases onto their enhancer–gene maps, they were able to trace risk variants to plausible target genes in a cell-type-specific manner. This step is where the method’s value becomes tangible for translational research. A noncoding variant associated with, say, an autoimmune disorder may lie within an enhancer active only in a specific subset of immune cells; by linking that enhancer to its gene targets computationally, researchers gain immediate hypotheses about the molecular mechanism of disease risk and the cellular context in which it operates. The maps effectively convert a list of statistically associated genomic coordinates into a functional annotation, complete with directionality and tissue relevance.

The study also illustrates how the same regulatory architecture can produce different outcomes in different cell types. The authors found that a substantial proportion of enhancers exhibit cell-type-specific target preferences: a given regulatory element may drive expression of one gene in a cortical neuron and an entirely different gene in an astrocyte, even when both cell types share the same underlying genome. This context dependence has major implications for interpreting both normal development and disease. It means that a single variant in a pleiotropic enhancer can contribute to multiple phenotypes through distinct target genes depending on where it is active, and it underscores why bulk-tissue studies, which average signals across mixed cell populations, often fail to resolve the relevant biology.

Technically, the framework operates on paired or unpaired single-cell multi-ome data, in which gene expression and chromatin accessibility are profiled either in the same individual cells or in parallel samples from the same biological condition. The method constructs a graph-based representation of cell-to-cell similarity, smooths sparse single-cell measurements over this graph to recover signal lost to dropout and shallow sequencing depth, and then evaluates enhancer–gene relationships using a regularized regression model that jointly considers all candidate enhancers within a defined genomic window around each gene. Regularization is essential: with tens of thousands of candidate enhancers competing to explain each gene’s expression, the model must penalize complexity to avoid overfitting noise. The learned weights, combined with the motif and chromatin contact priors, yield the final interaction scores. The authors have made the pipeline available to the community, and its computational design allows it to scale to the atlas-sized datasets — millions of cells spanning dozens of tissues — that are now being generated by consortia worldwide.

The implications extend well beyond human genetics. Enhancer–gene maps of this kind provide a substrate for studying gene regulatory network evolution, allowing comparative analyses of how regulatory wiring differs between species or between healthy and diseased states. In cancer genomics, where structural variants frequently rewire enhancer–promoter contacts to activate oncogenes, cell-type-resolved regulatory maps could help identify which tumors depend on which enhancer hijacking events. In developmental biology, the framework offers a way to trace how transcriptional programs are controlled as cells progress through differentiation, capturing transient regulatory relationships that exist only in rare intermediate cell states — populations too small and too fleeting to study with bulk methods.

Limitations remain, and the authors are careful to acknowledge them. Correlation-based inference, however sophisticated, cannot fully substitute for direct perturbation; some predicted interactions will prove false when tested experimentally, and some true interactions may be missed if the relevant enhancing activity is rare or condition-specific. Single-cell datasets also under-sample rare cell types, meaning that regulatory maps for those populations will be less complete. Moreover, chromatin accessibility is an imperfect proxy for enhancer activity: an open region is not necessarily an active enhancer, and methods that read out actual enhancer transcription, such as single-cell eRNA detection, could add further resolution in the future. The integration of the computational maps with systematic CRISPR perturbation screens — using the predictions to guide which enhancers to test — represents a promising hybrid strategy that combines scale with causal certainty.

What the study ultimately delivers is a change in the default assumption researchers can make when confronted with a noncoding variant. Where the fallback was once the nearest gene, it can now be a cell-type-specific, confidence-scored list of candidate targets derived from the collective behavior of thousands of single cells. As single-cell atlases continue to grow and as perturbation technologies mature, frameworks like this one are positioned to become standard infrastructure for interpreting the regulatory genome — the layer of DNA that, despite encoding no proteins, orchestrates when, where, and how much every gene is expressed. In bridging the gap between variant catalogs and mechanism, the work brings the field measurably closer to the long-promised era of actionable noncoding genetics.

The prefrontal cortex, known for its integral role in executive functions such as decision making, social behavior, and cognitive flexibility, has long fascinated neuroscientists. Despite its critical importance, the precise temporal and spatial dynamics of molecular changes leading to its mature state have remained elusive. This study moves the field forward by harnessing multi-omic single-cell technologies to capture the nuances of both transcriptomic and chromatin landscapes as development progresses.

At the heart of this investigation lies the innovative integration of spatial transcriptomics with chromatin accessibility assays performed at the single-cell level. By doing so, the researchers have been able to correlate gene expression states with the underlying epigenetic architecture across discrete developmental time points and in the detailed anatomical context of the developing cortex. This dual approach enables an unprecedented resolution of how cellular identities are established and maintained during critical periods of brain maturation.

The researchers focused their analysis on postnatal stages — a period marked by rapid and dynamic restructuring of neural circuits. Their choice to include both human and macaque brains in the study further allows for comparative insights, enhancing our understanding of species-specific versus conserved developmental mechanisms. Such comparative work is especially important given the evolutionary proximity of macaques to humans and their widespread use as models for human brain function and disorders.

One of the most compelling findings revealed distinct trajectories of gene expression programs that are tightly coupled with changes in chromatin accessibility. This coupling appears to orchestrate the differentiation and specialization of cortical neurons and glial cells, highlighting the complexity of gene regulatory networks at play. These gene networks underpin defining features of cortical circuitry, including the establishment of synaptic connections and the pruning processes essential for functional maturation.

Moreover, the spatial dimension of the data unveiled intriguing region-specific patterns. Different prefrontal cortical subregions showed unique molecular signatures and epigenomic states, which likely correspond to their specialized roles within the wider prefrontal network. This spatial heterogeneity underscores the importance of studying brain development within an anatomical framework, as opposed to bulk analyses that often obscure such fine-grained regional distinctions.

Chromatin accessibility profiling illuminated critical zones of regulatory elements—enhancers and promoters—that dynamically change their activity during key developmental windows. Understanding when and where these regulatory elements operate provides vital clues into how transcriptional programs are modulated and how disruptions in these processes could lead to neurodevelopmental disorders.

The data also highlighted cellular heterogeneity and lineage relationships within the developing cortex. By mapping single-cell transcriptomes alongside chromatin states, the team could reconstruct developmental trajectories and pinpoint critical decision points where progenitor cells diverge into specific cortical neuron subtypes. This molecular roadmap beautifully illustrates the stepwise maturation process and could inform strategies for therapeutic interventions or stem cell-based regenerative approaches.

Importantly, the study offers a critical resource: an integrated spatiotemporal atlas of transcriptomic and epigenetic landscapes in the developing primate brain. This reference atlas stands to profoundly impact future efforts to understand neurodevelopmental disorders such as autism spectrum disorder, schizophrenia, and other cognitive dysfunctions rooted in early brain development. It provides a benchmark against which pathological changes can be measured.

In terms of methodology, the combination of high-throughput sequencing technologies and spatial transcriptomics platforms represents a powerful convergence of technological advances. These approaches allow not only the profiling of thousands of individual cells but also their localization within intact tissue architecture. The researchers’ data analysis pipeline, leveraging advanced computational frameworks, ensures robust integration and interpretation of these complex datasets.

Additionally, by studying both human and macaque postnatal development, the authors contribute to evolutionary neuroscience. Their comparative approach enables identification of conserved regulatory circuits as well as species-specific adaptations that may underlie unique cognitive and behavioral traits of primates. This sets the stage for unraveling the evolutionary pressures shaping higher-order brain functions.

This work also has implications for understanding the timing of critical developmental periods. The spatiotemporal maps provide insights into when certain gene regulatory programs are activated or repressed, which correlates with known windows of heightened plasticity and vulnerability in the prefrontal cortex. Such knowledge could inform the timing of interventions to optimize neurodevelopmental outcomes.

Notably, the dual focus on transcriptomics and chromatin accessibility helps disentangle cause-and-effect relationships, clarifying whether gene expression changes are driven by epigenetic remodeling or vice versa. This mechanistic clarity goes beyond correlation and lays the groundwork for targeted manipulation of gene regulatory elements in future studies.

While the study concentrates on the postnatal phase, the authors acknowledge that extending these analyses to prenatal stages and later adulthood could provide a full developmental continuum. Moreover, including pathological samples from individuals with neurodevelopmental disorders could reveal specific molecular derailments contributing to disease.

In summary, this landmark study pushes the frontier of brain developmental biology by integrating spatial and temporal dimensions of gene regulation at single-cell resolution in primates. The resulting comprehensive atlas of transcriptomic and chromatin accessibility landscapes in the postnatal prefrontal cortex offers a transformative tool for neuroscience research, with far-reaching implications for understanding brain evolution, development, and disease.

The findings highlight the intricate choreography of genetic and epigenetic factors guiding the maturation of the prefrontal cortex. This work not only deepens our insight into fundamental brain biology but also underscores the power of multi-omic single-cell approaches to unlock the mysteries of complex neuronal systems. As the field continues to evolve, such integrative studies are poised to reshape our understanding of the brain’s developmental blueprint and open new avenues for therapeutic innovation.

Subject of Research: Postnatal development of the human and macaque prefrontal cortex using single-cell transcriptomics and chromatin accessibility profiling.

Article Title: Single-cell spatiotemporal transcriptomic and chromatin accessibility profiling in developing postnatal human and macaque prefrontal cortex.

Article References:
Zhang, J., Li, M., Wang, M. et al. Single-cell spatiotemporal transcriptomic and chromatin accessibility profiling in developing postnatal human and macaque prefrontal cortex. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-02150-7

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41593-025-02150-7

This study leverages cutting-edge single-nucleus transcriptomics and chromatin accessibility profiling to intricately compare neuronal populations across different cortical regions in adult mice and extend these comparisons to other tetrapod species. By dissecting the gene expression patterns and epigenetic landscapes at a single-cell resolution, the researchers reveal a molecular portrait that challenges long-standing assumptions about cortical architecture and its evolutionary trajectory.

A striking conclusion emerges from their data: while the neocortex’s glutamatergic neurons—commonly associated with excitatory signaling—segregate into discrete types with distinct transcriptomic identities, the piriform cortex neurons display a continuous spectrum of gene expression. This discovery suggests that the piriform cortex, despite its simpler laminar organization, possesses a different organizational logic, more fluid yet equally complex, that underlies its functional capacity.

The implications of this continuous variation provoke a re-examination of how cortical areas are architecturally and functionally specialized. Unlike the neocortex, which boasts six well-defined layers with corresponding functional specializations, the piriform cortex’s three-layered structure appears to support a more integrated and less sharply divided neuronal identity landscape. This hints at fundamentally different modes of information processing and plasticity, likely related to the piriform cortex’s role in olfactory perception and associative learning.

Delving deeper into epigenetic regulation, the study identifies that distinct subsets of both piriform and neocortical glutamatergic neurons, despite sharing conserved transcriptomic profiles, exhibit area-specific chromatin accessibility patterns. This area-specific epigenetic modulation suggests that the same genetic programs are differentially tuned across cortical regions to meet unique functional demands. Such chromatin states could provide a versatile regulatory mechanism enabling diverse neuronal behaviors within conserved cell types.

One of the more unexpected findings of the study is the identification of a substantial population of immature neurons within the adult piriform cortex. These immature neurons resemble earlier developmental stages, suggesting ongoing neurogenesis or delayed maturation processes in this cortical region, which are notably absent or minimal in the neocortex at comparable stages. This raises intriguing questions about the potential for structural and functional plasticity in the adult olfactory cortex and its role in sensory adaptation or learning.

Moreover, the research highlights an intriguing divergence in piriform cortex neurons between laboratory mice and their wild-derived counterparts. While neocortical glutamatergic cells display remarkable transcriptomic consistency regardless of environmental or genetic variability, piriform neurons vary significantly, indicating a heightened sensitivity to environmental cues or evolutionary pressures. This plasticity in piriform cortex neurons underscores the adaptability of the olfactory system, potentially reflecting its evolutionary role in survival and environmental interaction.

Expanding the comparative scope, the team examined the transcriptomic profiles of piriform cortex neurons against homologous neurons in reptiles and amphibians, including turtles, lizards, and salamanders. Remarkably, they found a closer molecular resemblance of piriform neurons to these ancestral cortical neurons than to neocortical neurons, despite the hundreds of millions of years separating these lineages. This offers compelling molecular evidence supporting the theory that the olfactory cortex has conserved ancestral cortical features that predate the emergence of the more complex neocortex.

Such ancestral molecular signatures highlight the olfactory cortex as a living fossil within the brain, retaining primordial characteristics that have been largely lost or transformed in the neocortex. This evolutionary insight not only informs our understanding of cortical origins but also posits the olfactory cortex as a valuable model for exploring fundamental neuronal traits and their diversification over evolutionary timescales.

Throughout the study, the application of single-nucleus analyses enables an unprecedented resolution of neuronal identity, capturing nuances in gene expression and chromatin dynamics inaccessible by bulk tissue methods. This approach reveals the cellular heterogeneity underpinning cortical function and evolutionary history and sets the stage for future exploration of how molecular diversity translates into circuit properties and behavior.

The findings provoke broader considerations about how cortical complexity arises from underlying molecular mechanisms. The contrast between discrete neuronal types in the neocortex and continuous variation in the piriform cortex intimates different developmental programs and regulatory networks. Such diversity in organizational logic might reflect adaptation to distinct computational requirements—the high acuity and modular architecture of the neocortex versus the integrative and flexible processing necessary for olfactory function.

Furthermore, the observation of immature neurons persisting in the adult piriform cortex aligns with emerging concepts of adult neurogenesis and cellular plasticity in sensory areas. This plasticity could facilitate the dynamic remodeling of olfactory circuits in response to environmental stimuli, underpinning learning and memory associated with smell. Understanding the molecular cues governing this plasticity could open avenues for regenerative therapies and neurocognitive enhancement.

The epigenetic distinctions between områder-specific neurons emphasize the importance of chromatin remodeling in shaping neuronal identity beyond the genome. Such mechanisms might enable rapid adaptation to environmental changes or developmental signals, fine-tuning neuronal function in a context-dependent manner. Future studies exploring the causative role of these epigenetic landscapes will deepen our mechanistic understanding of cortical specialization.

Importantly, this research integrates cross-species comparisons that root cortical cell types within a broad evolutionary framework. By linking mammalian piriform cortex neurons to those of reptiles and amphibians, it underscores the continuity of certain neural traits and identifies conserved molecular programs that may be foundational to vertebrate brain organization. This evolutionary perspective enriches our grasp of how the brain’s complexity evolved in a stepwise fashion, with ancestral circuits co-opted and elaborated upon.

The study’s multidisciplinary approach—combining transcriptomics, epigenetics, developmental biology, and comparative genomics—exemplifies the power of integrative neuroscience. It illuminates not only the molecular diversity within a single species but also the evolutionary forces shaping cortical architecture. This comprehensive view is crucial for unraveling the origins of neural diversity and its implications for brain function, adaptation, and disease.

In sum, the investigation reveals that, despite millions of years of concurrent evolution with the neocortex, the olfactory cortex retains distinct molecular identities rooted in ancestral cortical structures. This discovery reframes our understanding of cortical evolution and underscores the olfactory cortex’s unique role as a window into the brain’s past and a guide for its future exploration. As neuroscience continues to bridge molecular and systems-level insights, studies like this will be pivotal in decoding the enigma of brain complexity and diversity.

Subject of Research: Single-cell genomics of cortical neurons comparing mouse piriform (olfactory) cortex and neocortex, with evolutionary analysis across tetrapods.

Article Title: Single-cell genomics of the mouse olfactory cortex reveals contrasts with neocortex and ancestral signatures of cell type evolution.

Article References:
Zeppilli, S., Gurrola, A.O., Demetci, P. et al. Single-cell genomics of the mouse olfactory cortex reveals contrasts with neocortex and ancestral signatures of cell type evolution. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-01924-3

Image Credits: AI Generated