Biology subjects

Karin, J.

Publications and source records attributed to Karin, J..

5 recordsLinked to original sources

bioRxiv2026

Uncovering cross-signal interactions in single-cell and spatial omics

Individual cells simultaneously encode multiple biological signals in their gene expression profiles, including spatial position, circadian phase, and disease state. These signals can be entangled in complex ways, including circadian patterns that vary along disease progression and cell cycle dynamics that shift along developmental trajectories. While methods to disentangle such signals have been recently developed, characterizing the interaction itself between multicellular signals along with its effects on tissue function, localizing cellular subpopulations which exhibit signal interactions, and identifying potential candidate genes which drive such interactions, all remain major challenges. We present CRISP which characterizes signal interactions in single-cell gene expression data based on the correspondence between the multiple, probabilistic signal-specific projections of the data. Across simulations with tunable couplings and four single-cell and spatial transcriptomic datasets, CRISP recovers diverse biological signal interactions and localizes them to informative cellular subpopulations and groups of associated genes; CRISP distinguishes healthy from fibrotic liver samples and localizes the loss of zonation-position coupling to individual fibrotic hepatocytes, resolves the asymmetric coupling of TGF-{beta} response, EMT, and cell-cycle phase to specific cell subpopulations, and flags transcriptionally delayed hepatocytes in Plasmodium liver infection. Beyond quantifying and localizing signal interactions, CRISP can refine existing labels, uncover continuous cellular transitions and reveal condition-associated genes.

bioinformatics↗

bioRxiv2026

Tissue geometry patterns epidermal stem cell fate in murine interfollicular skin

How tissues maintain spatial patterns of stem cell renewal and differentiation despite continuous cellular turnover remains poorly understood. Here, we show that microscopic undulations of the dermal-epidermal junction (DEJ) are associated with distinct stem cell behaviors in the mouse epidermis. Using longitudinal intravital imaging and quantitative analysis of cell morphology and lineage dynamics, we find that local DEJ curvature predicts the subsequent fate of daughter cells up to three days before mother cell division. Cells residing in concave valleys preferentially produce proliferating descendants, whereas convex ridges are associated with increased differentiation. These spatial biases are reflected in the expression of stem- and differentiation-associated markers and accumulate into differences in long-term clonal expansion. Remarkably, DEJ geometry remains stable for weeks, persisting across multiple generations of epidermal cells. Moreover, human epidermal equivalents grown on devitalized dermis recapitulate the preferential retention of basal layer cells in valleys, indicating that living dermal cells are not required for this spatial organization. Our findings suggest that stable tissue architecture can pattern stem cell fate probabilities across generations, contributing to functional stem cell heterogeneity.

developmental biology↗

bioRxiv2026

Decoding Condition-Specific Cellular Crosstalk in Spatial Omics via Bilinear Edge Classification

Tissues are multicellular structured communities whose function emerges from a combination of individual cellular characteristics along with their corresponding spatial configuration, affecting their interactions and response patterns. During processes such as disease progression or aging, tissues can undergo structural reorganization, including changes in co-localization of different cell types, assembly or destruction of functional niches, and disruption of intercellular communication axes. Such changes can manifest primarily in the spatial reorganization of cells rather than in the transcriptional states of individual cells. While computational tools for spatial transcriptomics have made significant progress in characterizing tissue architecture, most approaches for characterizing changes in tissue states across biological conditions operate at the level of individual cells or rely on discrete cell type labels, thus limiting the ability to detect coordinated transcriptional changes between neighboring cells that distinguish one condition from another. We present CO_SCPLOWASEIC_SCPLOW, an interpretable bilinear edge classification framework comparing graphs across conditions, which directly models condition-specific cell-cell interactions in spatial omics data by focusing on interactions (edges), rather than cells (nodes), as the fundamental unit of inference. To capture such condition-specific signals, we leverage a model whose inductive bias aligns with cellular interactions through coordinated gene-gene relationships of neighboring cells, with learned weights that are directly interpretable as condition-specific gene-pair contributions. CO_SCPLOWASEIC_SCPLOW enables the discovery of condition-associated multicellular interactions and spatial expression programs, and characterizes the loss of multicellular function and structure. We evaluate CO_SCPLOWASEIC_SCPLOW on both controlled semi-synthetic spatial perturbations, where it outperforms node-level, edge-level, and graph neural network baselines, as well as on three real-world spatial omics datasets, where CO_SCPLOWASEIC_SCPLOW enabled extracting underlying cell-cell interactions and interaction-informative genes and cells. Applied to mammalian liver fibrosis, atherosclerosis, and brain aging, CO_SCPLOWASEIC_SCPLOW reveals biologically meaningful spatial reorganization, including the shift from endothelial-to macrophage-dominated networks in atherosclerotic plaques, disruption of hepatocyte zonation in fibrosis, and oligodendrocyte-microglia crosstalk in aging white matter.

bioinformatics↗

bioRxiv2024

Interpreting single-cell and spatial omics data using deep networks training dynamics

Single-cell and spatial genomics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations, or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations based on noisy, sparse, and high-dimensional data. Here, we show that incongruencies between cells and their input annotations can be identified by analyzing a rich but overlooked source of information: the difficulty of training a deep neural network to assign each cell to its input annotation, or annotation trainability. Furthermore, we demonstrate that annotation trainability encodes meaningful biological signals. Based on this observation, we introduce the concept of signal-aware graph embedding, which facilitates downstream analysis of diverse biological signals in single-cell and spatial omics data, such as the identification of cellular communities corresponding to a target signal. We developed Annotatability, a publicly-available implementation of annotation-trainability analysis. We address key challenges in the interpretation of genomic data, demonstrated over seven single-cell RNA-sequencing and spatial omics datasets, including auditing and rectifying erroneous cell annotations, identifying intermediate cell states, delineating complex temporal trajectories along development, characterizing cell diversity in diseased tissue, identifying disease-related genes, assessing treatment effectiveness, and identifying rare healthy-like cell populations. These results underscore the broad applicability of annotation-trainability analysis via Annotatability for unraveling cellular diversity and interpreting collective cell behaviors in health and disease.

bioinformatics↗

bioRxiv2022

scPrisma: inference, filtering and enhancement of periodic signals in single-cell data using spectral template matching

Single-cell RNA-sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is however challenging due to technical and biological noise, and as the cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational framework that utilizes topological priors to decouple, enhance, and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We demonstrate scPrismas use across diverse biological systems and tasks, including analysis and manipulation of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas, and circadian rhythm in the suprachiasmatic nucleus in the brain. We further show how scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type, and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrismas flexibility in utilizing diverse prior knowledge, and inference of topologically-informative genes. scPrisma can be used both as a stand-alone workflow for signal analysis, and, as it does not embed the data to lower dimensions, as a prior step for downstream single-cell analysis.

bioinformatics↗