SEACells: Breakthrough for Transcriptional & Epigenomic Data

Analyzing cellular diversity at the single-cell level has revolutionized biomedical research, but interpreting complex transcriptional and epigenomic landscapes remains a challenge. Traditional clustering methods often overlook subtle yet biologically meaningful cell states, limiting our understanding of cellular behavior in health and disease.

SEACells, a new framework, enhances resolution by partitioning cells into coherent groups that capture both transcriptional and epigenomic variations. This approach refines single-cell data analysis, offering deeper insights into cellular identity and function.

Fundamentals Of Single-Cell Partitioning

Understanding cellular heterogeneity requires methods that accurately group cells based on shared molecular characteristics while preserving biologically relevant distinctions. Traditional clustering techniques, such as k-means or hierarchical clustering, often rely on global transcriptomic similarities, which can obscure rare or transitional cell states. Advanced approaches, including graph-based clustering and probabilistic models, attempt to refine these groupings but still struggle to balance resolution with biological interpretability.

SEACells introduces “metacells”—aggregated units representing transcriptionally and epigenetically coherent populations. Unlike conventional methods that assign each cell to a discrete cluster, SEACells constructs a low-dimensional representation of cellular states, capturing both dominant and intermediate populations. By combining geometric optimization and density-based partitioning, SEACells mitigates noise while preserving the granularity needed to detect subtle variations.

A key advantage of SEACells is its ability to model cellular transitions, making it particularly useful in dynamic systems such as differentiation or disease progression. Traditional clustering methods impose rigid boundaries between cell types, making it difficult to track gradual gene expression changes. SEACells overcomes this limitation by allowing cells to contribute to multiple metacells based on transcriptional proximity, effectively capturing lineage relationships and intermediate states.

Transcriptional Context In Cell Grouping

Grouping cells based on transcriptional similarities requires capturing not just discrete expression profiles but also the nuanced relationships between genes. Single-cell RNA sequencing (scRNA-seq) provides a high-resolution snapshot of gene activity, but raw transcriptomic data is noisy due to technical variability and biological stochasticity. SEACells refines this process by constructing metacells that integrate transcriptional states, reducing noise while preserving meaningful variation.

A major challenge in transcriptional clustering is distinguishing between transient states and stable cell identities. Traditional clustering algorithms struggle to resolve intermediate populations, often leading to an oversimplified view of cellular landscapes. SEACells positions metacells in a way that respects transcriptional gradients, ensuring that cells undergoing gradual gene expression shifts—such as those in differentiation pathways—are not forced into rigid categories but instead contribute probabilistically to multiple metacells.

This framework is particularly valuable in tissues with high cellular plasticity. In developmental biology, progenitor cells exhibit overlapping transcriptional signatures as they transition toward distinct lineages. Standard clustering methods may incorrectly assign these cells to a single category, obscuring critical regulatory events. SEACells preserves these transitional states, allowing researchers to map differentiation hierarchies with greater fidelity.

Epigenomic Dimensions Of Clustering

Gene expression is tightly regulated by epigenetic mechanisms that influence chromatin accessibility, DNA methylation, and histone modifications. These regulatory layers shape cellular identity, making them indispensable for defining biologically meaningful cell clusters. SEACells integrates epigenomic information to refine cell-state delineation, ensuring that clusters reflect both transcriptional profiles and regulatory landscapes.

Chromatin accessibility, measured by assays such as ATAC-seq, plays a central role in identifying regulatory elements governing gene expression. SEACells enhances clustering resolution by incorporating this data, allowing researchers to distinguish cells based on shared regulatory architecture rather than just gene activity. For example, two cells with similar transcriptomes may belong to different functional states if their chromatin landscapes differ significantly.

Histone modifications further contribute to the complexity of cellular states by influencing chromatin compaction and transcriptional accessibility. SEACells accounts for these modifications by identifying metacells with shared epigenetic signatures, ensuring that clustering reflects both active and repressive regulatory elements. In developmental systems, poised enhancers marked by H3K4me1 and H3K27ac can distinguish progenitor cells primed for activation from those in a fully differentiated state. Incorporating these markers provides a more nuanced view of cell-state transitions, capturing regulatory potential that may not yet be evident at the transcriptional level.

Approaches For Delineating Cell States

Capturing the full spectrum of cell states requires analytical strategies that account for transitional and context-dependent identities. Cells exist along a continuum, influenced by microenvironmental signals and intrinsic regulatory programs. Computational frameworks must balance granularity with biological validity, ensuring that identified states reflect functional realities rather than artifacts of data processing. SEACells refines this process by leveraging geometric optimization to generate metacells that maintain transcriptional and epigenomic coherence.

Machine learning techniques, particularly manifold learning methods like Uniform Manifold Approximation and Projection (UMAP) and diffusion maps, help visualize cell states in a lower-dimensional space. These methods reveal underlying trajectories, but their reliance on global structures can obscure rare populations. SEACells mitigates this issue by incorporating density-based partitioning, ensuring that both dominant and intermediate states are preserved. By allowing cells to contribute probabilistically to multiple metacells, SEACells models dynamic transitions more effectively than rigid clustering approaches.

Detection Of Tissue-Specific Subpopulations

Identifying distinct cellular subpopulations within complex tissues requires analytical methods that capture both shared and unique molecular features. Many tissues consist of a heterogeneous mix of cell types, each contributing to the overall function of the organ or system. Traditional clustering approaches often struggle to resolve rare or transient populations, particularly when these cells exist along a spectrum of differentiation or activation states. SEACells enhances this process by partitioning cells into metacells that reflect tissue-specific transcriptional and epigenomic signatures, allowing researchers to detect subtle subpopulations that might otherwise be overlooked.

One of SEACells’ primary advantages in tissue analysis is its ability to integrate high-dimensional data while preserving biological context. In the liver, hepatocytes exhibit distinct transcriptional programs depending on their zonation within the hepatic lobule. Standard clustering techniques may fail to capture these spatial variations, whereas SEACells can define metacells corresponding to functionally distinct hepatocyte populations. Similarly, in the brain, where glial cells and neurons interact dynamically, SEACells can identify transcriptionally unique subtypes of astrocytes or microglia that play specialized roles in neuroinflammation or synaptic regulation. By leveraging both transcriptomic and epigenomic data, SEACells provides a more refined view of tissue architecture, helping researchers delineate functionally significant subpopulations with greater precision.