In recent years, the landscape of neuroscience has been dramatically reshaped by the emergence of single-cell RNA sequencing (scRNA-seq), a powerful technology that allows researchers to explore cellular heterogeneity at unprecedented resolution. Among the most intensely studied cell types in the central nervous system (CNS) are microglia, the brain’s resident immune cells known for their multifaceted roles in development, homeostasis, and disease. The deluge of scRNA-seq datasets has led to the generation of numerous so-called microglial “subsets,” often portrayed as discrete and distinct cellular populations with specific functional attributes. However, a provocative perspective emerging from recent work by Sankowski and Prinz argues that this fragmented map of microglial identities may be more illusion than reality. Instead, they propose that microglia exist along a continuum of states, molded by a complex interplay of biological aging and molecular microenvironments, challenging the prevailing paradigm of rigid microglial classifications.
This rethink shakes the foundations of how microglial diversity is conceptualized and interpreted. The authors contend that the widespread reliance on computational clustering methods to parse high-dimensional scRNA-seq data is a double-edged sword. These algorithms, while invaluable, often impose arbitrary boundaries by demanding discrete cluster numbers. Unfortunately, such computational constraints do not necessarily reflect inherent biological divisions but risk fragmenting a fluid landscape into artificial islands. Each cluster, touted as a novel and distinct microglial subset, may instead represent snapshots along a spectrum of cellular plasticity, colored by technical noise and transitional transcriptional programs.
The implication of this insight extends beyond taxonomy. It underscores the plastic nature of microglial cells, which dynamically adapt their molecular profiles in response to local cues, age-related changes, and pathological insults. Contrasting with the entrenched model of fixed microglial states, Sankowski and Prinz envision a dynamic and multimodal framework wherein microglial identity is continuous rather than categorical. This continuum accommodates heterogeneous expression patterns without overfitting the data into contrived partitions, allowing one to grasp the nuanced gradations of microglial phenotypes and functions.
One of the technical drivers behind the proliferation of purported microglial subsets is the methodological trend in scRNA-seq data analysis focused on clustering approaches. Popular algorithms, such as k-means or Louvain community detection, yield clusters by partitioning cells into mutually exclusive groups based on transcriptomic similarity. Although instrumental for many discoveries, when applied without biological validation or consideration of cellular transition dynamics, this approach can inflate the number of reported clusters without establishing their biological significance. The authors highlight that much of the so-called “transcriptional diversity” observed is, in part, an artifact stemming from this analytical pipeline combined with stochastic variation inherent in single-cell data collection.
Furthermore, technical noise—random fluctuations in gene expression measurements—and batch effects exacerbate the challenge of discerning meaningful biological signals. These sources of variation can masquerade as distinct clusters, further complicating the interpretation of scRNA-seq results. In this context, the authors warn against overinterpretation of clustering outputs, urging the field to embrace a parsimonious perspective that respects the continuous, context-dependent nature of microglial states.
Biological aging emerges as a crucial axis along which microglial phenotypes transition. Microglia experience gradual and cumulative molecular changes over time that influence their functional capacities, including immune surveillance, synaptic pruning, and repair mechanisms. These age-related shifts are not abrupt but progressive, underscoring the inadequacy of rigid state assignments. Instead, the continuum model effectively captures this subtle evolution, relating continuous trajectories of transcriptional profiles to microglial maturation, senescence, or responses to environmental perturbations.
Cell-specific molecular contexts also play a decisive role in shaping microglial states. Variations in local CNS microenvironments—ranging from regional differences in neuronal and glial composition to distinct inflammatory milieus—imprint unique transcriptomic signatures onto microglia. Rather than defining cells as members of static subsets, the continuous framework embraces this heterogeneity as a natural consequence of microglial plasticity. The resulting landscape is a high-dimensional space where cells shift their positions dynamically, akin to travelers on a continuum guided by molecular signals and physiological demands.
This paradigm offers a conceptual bridge for integrating functional readouts with transcriptomic data. Instead of assigning microglia a fixed label, their positioning along the continuum reflects emergent properties such as activation states, phagocytic activity, or pro-inflammatory versus anti-inflammatory tendencies. Consequently, the framework can accommodate simultaneous co-existence of multiple microglial modes, reflecting the multifaceted roles these cells execute within the CNS milieu.
Adopting this dynamic and multimodal model calls for a re-evaluation of microglial nomenclature and classification standards. Current conventions that proliferate specialized terms for narrowly defined subsets potentially hinder rather than help understanding, creating a lexicon tangled by overlapping or transient states. The authors advocate for streamlined terminology that honors the continuous nature of microglial phenotypes without fragmenting them into unnecessarily discrete categories. This approach promises to foster clearer communication and interpretability across studies, accelerating scientific progress.
The significance of this shift extends into the realm of neuropathology. Microglia have been implicated in a wide array of CNS disorders, including neurodegenerative diseases like Alzheimer’s, multiple sclerosis, and brain tumors. Therapeutic strategies often aim to manipulate microglial function by targeting specific “disease-associated” subpopulations. Recognizing that microglial states form a continuum sensitizes researchers and clinicians to the complexity of targeting these cells, encouraging interventions tailored to modulate state transitions rather than eradicating ill-defined subsets.
Beyond the biological and conceptual implications, this work also touches on the evolving challenges of data analysis in the era of big single-cell omics. It underscores the need for computational methods that transcend rigid clustering to capture cellular nuances more faithfully. Techniques such as trajectory inference, density estimation, and manifold learning are gaining traction, offering tools better suited for modeling continuous cellular landscapes. Sankowski and Prinz’s framework could thus serve as a guiding principle for next-generation computational pipelines tailored for microglia and potentially other cell types.
Moreover, the authors highlight the importance of integrating multimodal data—combining transcriptomics with proteomics, epigenetics, and functional imaging—to enrich the understanding of microglial states. Such comprehensive datasets would enable mapping the continuum across different molecular layers, unraveling the complex interplay between gene expression, protein function, and cellular phenotype. This multimodal integration is poised to deepen insights into how microglia dynamically respond and adapt within the CNS environment.
Importantly, this reconceptualization does not deny the existence of microglial heterogeneity; rather, it reframes it as fluid and context-dependent rather than fixed and discrete. Microglial cells, by virtue of their immunological heritage and CNS residency, are remarkable for their adaptability. The continuum model reflects this plasticity, capturing the transient gene expression programs induced by diverse stimuli, from developmental cues to pathological challenges, all within a unified framework.
In conclusion, the proposal put forth by Sankowski and Prinz constitutes a paradigm shift in understanding microglial biology, emphasizing a dynamic and continuous spectrum of cellular states over artificially constructed clusters. This approach not only aligns better with biological reality but also encourages methodological rigor, parsimony in classification, and integrative analyses combining computational and experimental insights. As the field moves forward, embracing this continuum framework promises to refine how microglial function is interpreted, enhancing our ability to harness these pivotal cells for therapeutic benefit in CNS diseases.
The work beckons a broader reconsideration of how cell identity is conceptualized across the life sciences, pushing beyond discrete taxonomic boxes toward fluid maps capturing the rich dynamics of living systems. For a cell type as vital and versatile as microglia, this fresh lens is poised to illuminate new paths in neuroscience research, vaccine strategies, and precision medicine, marking an exciting horizon for both fundamental and translational science.
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Subject of Research: Microglial diversity and state classification in the central nervous system using single-cell transcriptomics.
Article Title: A dynamic and multimodal framework to define microglial states.
Article References:
Sankowski, R., Prinz, M. A dynamic and multimodal framework to define microglial states.
Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-01978-3
Image Credits: AI Generated
Tags: biological aging in microgliacellular heterogeneitycentral nervous system immune cellschallenges in neuroscience classificationcomputational clustering methods in neurosciencecontinuum of microglial identitiesmicroglial diversity interpretationmicroglial statesmicroglial subsetsmolecular microenvironmentsscRNA-seq data analysis limitationsSingle-Cell RNA Sequencing
