Tacco: Unifying Annotation Transfer and Cell Decomposition

Analyzing cellular composition in complex tissues is essential for understanding biological function and disease mechanisms. Advances in single-cell technologies have provided unprecedented resolution, yet challenges remain in accurately identifying and mapping diverse cell populations across datasets. Efficient methods are needed to integrate data from different sources while preserving meaningful biological variation.

Tacco offers a unified approach by combining annotation transfer with cell decomposition, allowing researchers to classify cells more effectively. This streamlines the interpretation of gene expression data and enhances comparability across studies.

Cellular Variation In Complex Tissues

The cellular landscape of complex tissues is shaped by genetic, epigenetic, and environmental factors, leading to significant heterogeneity in both structure and function. Within a single tissue, cells can exhibit distinct transcriptional profiles, metabolic states, and signaling interactions, even when classified under the same broad category. This variation often reflects functional specialization, developmental trajectories, or responses to microenvironmental cues. In the human brain, excitatory and inhibitory neurons display unique gene expression patterns that govern synaptic activity, while glial cells contribute to immune surveillance and metabolic support, highlighting the necessity of precise cellular characterization.

Spatial organization further complicates cellular diversity, as cells within different regions of a tissue experience distinct biochemical gradients, mechanical forces, or intercellular interactions. In the liver, hepatocytes near the portal triad are exposed to oxygen- and nutrient-rich blood, leading to metabolic zonation that influences drug metabolism and detoxification. Similarly, in epithelial tissues, basal cells serve as progenitors, while differentiated cells at the surface perform specialized barrier functions. These spatially driven differences underscore the importance of considering both intrinsic and extrinsic factors when analyzing cellular composition.

Technological advancements, particularly in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, have provided unprecedented resolution in mapping cellular variation. However, these methods reveal a continuum of cell states rather than discrete categories, challenging traditional classification approaches. In the hematopoietic system, single-cell analyses have identified transitional states between progenitor and mature immune cells, suggesting that differentiation is a gradual process rather than a series of distinct steps. This continuum complicates annotation efforts, as cells may not fit neatly into predefined categories but instead exist along a spectrum of functional states.

Molecular Insights From Expression Patterns

Gene expression patterns provide a powerful lens for deciphering cellular identity, function, and interactions. By analyzing the transcriptional landscape of individual cells, researchers can uncover regulatory networks that govern differentiation, adaptation, and pathological transformations. High-throughput sequencing technologies now allow for single-cell transcriptome profiling, revealing not only genes that define specific cell types but also dynamic shifts in expression in response to physiological and environmental stimuli. These insights are particularly valuable in tissues with pronounced heterogeneity, offering a refined understanding of functional specialization at the molecular level.

Beyond identifying cell types, expression patterns illuminate gene co-regulation and pathway activation, offering insight into the molecular mechanisms driving cellular behavior. In neural tissues, distinct transcriptional programs regulate synaptic plasticity, neurotransmitter release, and axonal growth, each contributing to the complexity of neuronal circuits. In metabolic organs, coordinated gene expression in glucose metabolism, lipid processing, and oxidative phosphorylation reflects the energetic demands of different cell populations. Computational models help infer regulatory hierarchies, identifying transcription factors that act as master regulators of cell identity and enabling targeted investigations into lineage specification and reprogramming.

Expression profiles also capture transient states that may not be evident through traditional histological or marker-based approaches. Single-cell transcriptomics has revealed intermediate cellular states that bridge developmental transitions, such as progenitor cells acquiring lineage-specific traits before committing to a defined fate. This is particularly evident in regenerative processes, where cells rapidly shift transcriptional programs in response to injury or stress. In fibrotic diseases, fibroblasts exhibit distinct gene expression signatures depending on their activation status, with some aiding tissue repair while others drive pathological remodeling. Understanding these transitional states provides opportunities for therapeutic intervention by modulating pathways that influence cellular plasticity.

Strategies For Classifying Distinct Cell Groups

Distinguishing cellular subpopulations requires computational, molecular, and statistical approaches that account for gene expression complexity. Traditional classification relied on marker genes—specific transcripts predominantly expressed in certain cell types—to delineate populations. While useful, this approach oversimplifies cellular identity, as many markers exhibit overlapping expression across multiple lineages. Advances in single-cell technologies have necessitated more sophisticated methods that integrate entire transcriptomic profiles. Clustering algorithms such as Louvain and Leiden partition cells based on expression similarities, allowing for unbiased identification of subsets without prior knowledge of expected categories. These methods leverage high-dimensional data to detect subtle transcriptional differences that might otherwise be overlooked.

Machine learning has refined classification by incorporating probabilistic models that assign cells to groups based on transcriptomic and external contextual features. Supervised learning techniques, such as support vector machines and neural networks, use labeled reference datasets to predict cell identity with increasing accuracy. Unsupervised methods, including principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), reduce data complexity to visualize relationships between cells in lower-dimensional space. These visualization techniques help distinguish discrete populations from continuous gradients of differentiation, which is particularly useful in analyzing dynamic biological processes such as development and tissue regeneration. By integrating gene regulatory network inference, researchers can also identify upstream transcription factors driving specific cellular programs, providing mechanistic insights into how different groups arise and maintain their identity.

Multimodal profiling enhances classification by incorporating additional layers of molecular information. Single-cell multi-omics combines transcriptomics with chromatin accessibility, epigenetic modifications, or protein expression to resolve ambiguities in cellular identity. For example, ATAC-seq data can reveal whether transcriptionally similar cells exhibit distinct chromatin landscapes, suggesting functional divergence despite shared gene expression patterns. Spatial transcriptomics further refines classification by mapping cells within their native tissue context, preserving information about local interactions that influence cellular phenotype. This integration of spatial and molecular features is particularly valuable in tissues where microenvironments drive functional specialization, enabling a more comprehensive understanding of how cells behave within their physiological niches.