Biology subjects

Nitzan, M.

Publications and source records attributed to Nitzan, M..

At least 19 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

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↗

bioRxiv2026

A cell-type-resolved human brain atlas of microRNAs and tRNA fragments

MicroRNAs play key roles in regulating brain processes ranging from neurogenesis to neurological disease and contribute to many cell-type-specific pathways. However, technical limitations of single-cell small RNA sequencing have confined most studies to bulk analyses of whole-tissue specimens, limiting our capacity to resolve the cell-type specificity of brain microRNAs and to study their roles in depth. To generate a comprehensive, cell-type-resolved atlas of microRNAs in the human brain, we isolated neurons, astrocytes, microglia and oligodendrocytes from fresh neurosurgery-derived brain samples and profiled their small RNA repertoires by RNA sequencing. The results revealed pronounced cell-type-associated differences in microRNA profiles, identified novel cell-type-specific microRNA markers and shed new light on the contribution of their originating genomic loci to cell-type specificity. We further characterized the cell-type dependence of microRNA strand preference and isomiR processing, and identified cell-type-dependent microRNA programs associated with brain ageing. Combined with an accompanying statistical tool for cell-type signal enrichment, our atlas provides a publicly available resource for cell-type-resolved microRNA analysis in the human brain.

neuroscience↗

bioRxiv2025

Self-Organization Through Local Cell-Cell Communication Drives Intestinal Epithelial Zonation

The intestinal epithelium exhibits zonated gene expression along the crypt-villus axis, with distinct transcriptional programs in enterocytes at the villus top versus bottom. However, the mechanisms establishing these spatial patterns remain unclear. Three models could explain zonation: external gradients, cell-intrinsic temporal programs, or local self-organization. Using spatial transcriptomics and perturbations of two-dimensional intestinal organoids, we show that zonation emerges via spontaneous self-organization without mesenchymal, neural, or vascular inputs. Cell-intrinsic models were eliminated by transplanting cells into established monolayers; transplanted cells progressively adopted zonation profiles matching their new location, with strongly zonated genes showing the greatest adaptive responses. Pharmacological inhibition of EphA2 receptors disrupted zonation, revealing a previously unknown role for epithelial EphA-ephrin-A signaling in regulating enterocyte zonation. These findings demonstrate that self-organization through local epithelial cell-cell communication generates spatial patterns independently of external positional cues or cell-autonomous programs.

systems biology↗

bioRxiv2025

Coupling differential adhesion to cell signaling avoids kinetic traps to yield robust multicellular self-organization

Differential adhesion, where cells physically reorganize based on their heterogeneous adhesion preferences, is one of the major models for self-organization in development and tissue formation. However, accumulating evidence suggests that differential adhesion is many times insufficient for robust convergence to a target minimal energy multicellular structure. Here we use computational simulations and engineered synthetic cell circuits to systematically explore alternative mechanisms for programming formation of a simple two-cell type core-shell morphology. Starting with two pre-differentiated cell types with constitutively high differential adhesion leads to kinetic trapping in variable, multi-core structures. In contrast, hybrid mechanisms that gradually induce differential adhesion upon cell-cell contact signaling consistently converge to the target single-core structure, in a manner robust to variation in cell numbers, interaction energy, and noise. This work delineates intrinsic limitations of self-organizing systems based solely on differential adhesion, and shows how inducible systems provide a way to invoke the strong adhesion required to maintain a multicellular structure, while avoiding the pitfall of kinetic traps. This study illustrates how joint computational and experimental exploration of synthetic circuits can be used to probe key developmental principles and tradeoffs and inform the design of synthetic development and self-organization.

biophysics↗

bioRxiv2025

Spatiotemporal dynamics of tumor microenvironment remodelling

During tumorigenesis, interactions between tumor and stromal cells progressively remodel the tumor microenvironment (TME) towards pro-tumoral functions. Understanding early TME remodeling dynamics is therefore crucial for developing interceptive therapies. However, clinical samples typically provide isolated, late tumorigenesis snapshots. To overcome this limitation, we generated triple-negative breast cancer mice that develop multifocal, asynchronous tumors along a continuous luminal-to-basal transdifferentiation trajectory. Ordering spatial transcriptomes from 100+ ducts along this trajectory reveals the spatiotemporal dynamics of TME remodeling and underlying molecular mechanisms. Cancer-associated myofibroblasts (myCAFs) emerge as key players in advanced tumors, where they orchestrate pro-invasive remodeling of the tumor-stromal interface. myCAFs are conserved in patient-derived xenograft models and steer tumor trajectories towards invasive phenotypes when co-injected with tumor cells in syngeneic mice. Our study shows that temporal ordering of spatially-resolved disease snapshots unravels some of the molecular "forces" that, starting from the cell-of-origin, propel cells/microenvironments along a disease trajectory.

cancer biology↗

bioRxiv2025

Neighborhood-Informed Positional Information for Precise Cell Identity Specification

During development cells reliably establish their identities, a process that is enabled in part by positional information encoded in gene expression patterns. Previous works showed that cells in Drosophila embryos can utilize this information to decode their position along the anterior-posterior axis with 1% accuracy. However, this precision is insufficient to uniquely determine position, leading to a positional information gap. Here, we propose a neighborhood-informed information-theoretic framework where cells integrate local gene expression information as well as information from neighboring cells. We formulate how much additional information exists in neighboring cells as a function of spatial variation in gene expression. In Drosophila embryos, we show that the additional information encoded by local neighborhoods is sufficient to uniquely specify cell identities, closing the information gap. Furthermore, neighborhood-informed decoders predict cell positions and downstream gene expression patterns more accurately than cell-independent decoders, resulting in lower decoding variability, which is maintained in mutant embryos. Our results provide a basis for the analysis of cellular decision making in the context of their microenvironments.

cell biology↗

bioRxiv2024

Identifying maximally informative signal-aware representations of single-cell data using the Information Bottleneck

Rapid advancements in single-cell RNA-sequencing (scRNA-seq) technologies revealed the richness of myriad attributes encompassing cell identity. However, the complexity of the data hinders tasks focusing on a specific biological signal. To address this challenge, we introduce bioIB, a framework based on the Information Bottleneck method, designed to extract an interpretable compressed representation of scRNA-seq data, optimally-informative with respect to a desired biological signal, such as developmental stage or disease state. Provided with cellular labels representing the signal of interest, bioIB generates weighted gene clusters, termed metagenes, that compress the data, while maximizing signal-specific information. Following the Information Bottleneck principle, bioIB identifies an optimal trade-off between data compression and retaining target information. Further, bioIB provides the hierarchical structure of the metagenes, revealing the interconnections between the corresponding biological processes and cellular populations, such as the developmental hierarchy of hematopoietic cell types. We showcase bioIBs applicability to diverse biological contexts, including Alzheimers Disease, epithelial-to-mesenchymal transition, immune development and hematopoiesis, demonstrating that the compressed representations capture signal-associated molecular pathways and expose cellular subpopulations with prominent phenotypes such as transition states and disease association.

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↗

bioRxiv2023

Mapping cells through time and space with moscot

Single-cell genomics technologies enable multimodal profiling of millions of cells across temporal and spatial dimensions. Experimental limitations prevent the measurement of all-encompassing cellular states in their native temporal dynamics or spatial tissue niche. Optimal transport theory has emerged as a powerful tool to overcome such constraints, enabling the recovery of the original cellular context. However, most algorithmic implementations currently available have not kept up the pace with increasing dataset complexity, so that current methods are unable to incorporate multimodal information or scale to single-cell atlases. Here, we introduce multi-omics single-cell optimal transport (moscot), a general and scalable framework for optimal transport applications in single-cell genomics, supporting multimodality across all applications. We demonstrate moscots ability to efficiently reconstruct developmental trajectories of 1.7 million cells of mouse embryos across 20 time points and identify driver genes for first heart field formation. The moscot formulation can be used to transport cells across spatial dimensions as well: To demonstrate this, we enrich spatial transcriptomics datasets by mapping multimodal information from single-cell profiles in a mouse liver sample, and align multiple coronal sections of the mouse brain. We then present moscot.spatiotemporal, a new approach that leverages gene expression across spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. Finally, we disentangle lineage relationships in a novel murine, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility, finding evidence for a shared ancestry between delta and epsilon cells. Moscot is available as an easy-to-use, open-source python package with extensive documentation at https://moscot-tools.org.

bioinformatics↗

bioRxiv2023

Mapping lineage-traced cells across time points with moslin

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making beyond simpler pseudotime-based approaches. However, it is currently unclear how lineage and gene expression information across experimental time points can be combined in destructive experiments, which is particularly challenging for in-vivo systems. Here we present moslin, a Fused Gromov-Wasserstein-based model to couple matching cellular profiles across time points. In contrast to existing methods, moslin leverages both intra-individual lineage relations and inter-individual gene expression similarity. We demonstrate on simulated and real data that moslin outperforms state-of-the-art approaches that use either one or both data modalities, even when the lineage information is noisy. On C. elegans embryonic development, we show how moslin, combined with trajectory inference methods, predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration. We anticipate moslin to play a crucial role in deciphering complex state change trajectories from lineage-traced single-cell data.

bioinformatics↗

bioRxiv2023

Biological representation disentanglement of single-cell data

Due to its internal state or external environment, a cells gene expression profile contains multiple signatures, simultaneously encoding information about its characteristics. Disentangling these factors of variations from single-cell data is needed to recover multiple layers of biological information and extract insight into the individual and collective behavior of cellular populations. While several recent methods were suggested for biological disentanglement, each has its limitations; they are either task-specific, cannot capture inherent nonlinear or interaction effects, cannot integrate layers of experimental data, or do not provide a general reconstruction procedure. We present biolord, a deep generative framework for disentangling known and unknown attributes in single-cell data. Biolord exposes the distinct effects of different biological processes or tissue structure on cellular gene expression. Based on that, biolord allows generating experimentally-inaccessible cell states by virtually shifting cells across time, space, and biological states. Specifically, we showcase accurate predictions of cellular responses to drug perturbations and generalization to predict responses to unseen drugs. Further, biolord disentangles spatial, temporal, and infection-related attributes and their associated gene expression signatures in a single-cell atlas of Plasmodium infection progression in the mouse liver. Biolord can handle partially labeled attributes by predicting a classification for missing labels, and hence can be used to computationally extend an infected hepatocyte population identified at a late stage of the infection to earlier stages. Biolord applies to diverse biological settings, is implemented using the scvi-tools library, and is released as open-source software at https://github.com/nitzanlab/biolord.

bioinformatics↗

bioRxiv2023

Uncovering hidden biological processes by probabilistic filtering of single-cell data

Elucidating underlying biological processes in single-cell data is an ongoing challenge and the number of methods that recapitulate dominant signals in such data has increased significantly. However, cellular populations encode multiple biological attributes, related to their spatial configuration, temporal trajectories, cell-cell interactions, and responses to environmental cues, which may be overshadowed by the dominant signal and thus much harder to recover. To approach this task, we developed SiFT (SIgnal FilTering), a method for filtering biological signals in single-cell data, thus uncovering underlying processes of interest. Utilizing existing prior knowledge and reconstruction tools for a specific biological signal, such as spatial structure, SiFT filters the signal and uncovers additional biological attributes. SiFT is applicable to a wide range of tasks, from the removal of unwanted variation in the data as a pre-processing step to revealing hidden biological structures. Applied for pre-processing, SiFT outperforms state-of-the-art methods for the removal of nuisance signals and cell cycle effects. To recover underlying biological structure, we use existing prior knowledge regarding liver zonation to filter the spatial signal from single-cell liver data thereby enhancing the temporal circadian signal the cells are encoding. Lastly, we showcase the applicability of SiFT in the case-control setting for studying COVID-19 disease. Filtering the healthy signal, based on reference samples from healthy donors, exposes disease-related dynamics in COVID-19 data and highlights disease informative cells and their underlying disease response pathways.

bioinformatics↗

bioRxiv2022

Emergence of division of labor in tissues through cell interactions and spatial cues

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions vs. instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA-seq and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

systems biology↗

bioRxiv2022

TACCO: Unified annotation transfer and decomposition of cell identities for single-cell and spatial omics

Rapid advances in single-cell-, spatial-, and multi-omics, allow us to profile cellular ecosystems in tissues at unprecedented resolution, scale, and depth. However, both technical limitations, such as low spatial resolution and biological variations, such as continuous spectra of cell states, often render these data imperfect representations of cellular systems, best captured as continuous mixtures over cells or molecules. Based on this conceptual insight, we build a versatile framework, TACCO (Transfer of Annotations to Cells and their COmbinations) that extends an Optimal Transport-based core by different wrappers or boosters to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatio-molecular tissue structure at the cell and molecular level, and resolve differentiation trajectories. TACCO excels in speed, scalability, and adaptability, while successfully outperforming benchmarks across diverse synthetic and biological datasets. Along with highly optimized visualization and analysis functions, TACCO forms a comprehensive integrated framework for studies of high-dimensional, high-resolution biology.

bioinformatics↗

bioRxiv2022

Integrative single cell and spatial transcriptomics of colorectal cancer reveals multicellular functional units that support tumor progression

While advances in single cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single cell RNA-seq, spatial transcriptomics by Slide-seq, and in situ multiplex RNA analysis, to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled inducible genetic CRC mouse models that recapitulate key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Comparing to scRNA-seq and Slide-seq data from human CRC, we find that both cell composition and layout features were conserved between the species, with mouse neighborhoods correlating with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease. Our work offers a comprehensive framework that is applicable across various tissues, tumors, and disease conditions, with tools for the extrapolation of findings from experimental mouse models to human diseases.

cancer biology↗

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↗

bioRxiv2020

Programming cell growth into different cluster shapes using diffusible signals

Recent advances in genetic engineering technologies has made it possible to construct artificial genetic circuits and use them to control how cells respond to their surroundings. This has been used to generate spatial patterns of differential gene expression. In addition to the spatial arrangement of different cell types, another important aspect of spatial structure lies in the overall shape of the group of cells. However, the question of how cells can be programmed, and how complex the rules need to be, to achieve a desired tissue morphology has received less attention. In this paper, we attempt to address these questions by developing a mathematical model to study how cells can use diffusion-mediated local rules to grow into clusters with different shapes. Within our model, cells are allowed to secrete diffusible chemicals which can either directly regulate the growth rate of cells ( growth regulator), or indirectly affect growth by changing the secretion rate or the effect of other growth regulators. We find that (1) a single growth inhibitor can be used to grow a rod-like structure, (2) multiple growth regulators are required to grow multiple protrusions, and (3) the length and shape of each protrusion can be controlled using growth-threshold regulators. Based on these regulatory schemes, we also postulate how the range of achievable structures scales with the number of signals: (A) the maximum possible number of protrusions increases exponentially with the number of growth inhibitors involved, and (B) to control the growth of each set of protrusions, it is necessary to have an independent threshold regulator. Together, these experimentally-testable findings illustrate how our approach can be used to guide the design of regulatory circuits for achieving a desired target structure.

systems biology↗