A unified network systems approach uncovers a core program underlying T follicular helper cell differentiation

Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution

Single-cell studies are frequently designed to compare across conditions such as health and disease. However, existing computational approaches typically rely on grouping cells into discrete populations before making comparisons, which can limit resolution for detecting state-dependent changes. Here, we introduce Kompot, a statistical framework for comparative analysis of multi-condition single-cell data. Kompot quantifies both differential abundance, capturing how cells redistribute across the phenotypic space, and differential expression, identifying condition-specific transcriptional changes that may be localized, heterogeneous, or oppositely regulated across states. By modeling cell density and gene expression as continuous functions over a shared cell-state representation, Kompot enables single-cell–resolution inference with principled uncertainty estimates, without requiring predefined clusters or cell types. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. We demonstrate the utility of Kompot in disease settings by identifying cell-state and gene expression changes associated with improved efficacy of combinatorial immunotherapy in melanoma. Additionally, Kompot enables multi-sample comparative analysis by accounting for sample-to-sample heterogeneity. By capturing both global and cell-state–specific effects of perturbation, the Kompot framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, R35GM147125, R01CA292932, T32GM136534, S10OD028685 The Mark Foundation for Cancer Research, https://ror.org/00v7th354, Endeavor Award Edward P. Evans Foundation, https://ror.org/03h22gm35, Discovery Research Grant Brotman Baty Institute, https://ror.org/03jxvbk42, Pilot Award

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon’s efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

Gene regulatory networks: from correlative models to causal explanations

Gene regulatory networks (GRNs) explain how the genome controls cellular behaviour and tissue morphogenesis, serving to connect molecular mechanism to functional output. Single-cell technologies now provide descriptions of these networks with unprecedented detail, but this advance has also revealed gene regulatory systems that are too complex for our existing conceptual frameworks. GRNs, which should provide mechanistic explanations, are increasingly reduced to statistical correlations — ‘hairballs’ that fail to capture molecular causation. Here, we explore why this dilemma exists and propose a path forward. We argue that methods in ‘representation learning’ can be used to model GRNs, without needing to capture every molecular detail. For this framework, we advocate three linked principles: models must be inherently mechanistic, with structures grounded in cellular and evolutionary biology; molecular principles and constraints must be used to reduce the solution space for learning GRN models; and more sophisticated forms of experimental perturbation and synthetic biological engineering are needed to train models and test predictions. By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding.

Temporal tissue dynamics from a spatial snapshot

Physiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time1. However, methods to follow cell populations over time within the native context of a human tissue are lacking because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR), an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighbourhoods influences division rate, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data2–4, and reconstruct two fixed points of fibroblasts and macrophage interactions5,6. These fixed points correspond to hot and cold fibrosis7, in agreement with co-culture experiments that measured these dynamics directly8. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumour microenvironment, suggesting temporal flares of anticancer immune responses. Finally, we study longitudinal biopsies from a triple-negative breast cancer clinical trial3, in which OSDR predicts the collapse of the tumour cell population in responders but not in non-responders, based on early-treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.

Hierarchical classification of immune cell transcriptomes at population-scale

Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress is constrained by the lack of independent, high-resolution benchmarks, as the routine integration of datasets introduces statistical dependencies that artificially inflate model generalizability. Here, we present the single-cell universal classification omnibus (Suco), a resource of independent, uniform expert annotations, and Compocyte, a modular hierarchical classifier. Together, they establish a framework designed for the scale of human population immunology. This approach substantially outperforms existing classifiers while facilitating expert review of ambiguous annotations. Applying Compocyte across 50 studies, including three newly generated datasets, we classified 15.6 million leukocytes from 3,965 patients. Within this expansive cohort, we identified a new tumor-associated resorptive macrophage phenotype, a non-canonical monocyte subtype in subclinical cytokine release syndrome, and the programmatic erosion of T cell memory stemness across metastatic sites. Suco and Compocyte thus provide a generalizable architecture and benchmark capable of sustaining high-resolution annotation across massive clinical cohorts. ### Competing Interest Statement CMR has consulted regarding oncology drug development with Amgen, AstraZeneca, Daiichi Sankyo, Genentech, Merck, and Novartis, and has received licensing and royalty payments for DLL3-directed therapeutics. T.W. reports stock ownership for Roche, Astra Zeneca, Bayer, Innate Pharma, Kyntra, Illumina, 10x Genomics, and Merck KGaA as well as research funding from Atrandi Biosciences, Vilnius, Lithuania; CanVirex AG, Basel Switzerland; and Institut fuer Klinische Krebsforschung GmbH, Frankfurt, Germany, and travel funding from Roche, Basel, Switzerland. S.Z. reports advisory board membership and honoraria from Amgen, Astellas, AstraZeneca, Bayer, Bristol-Myers Squibb, Daiichi Sankyo, Eisai, EUSA, Gilead, Ipsen, Johnson&Johnson, Lilly, MedSir, Medtoday, Merck, MSD, Novartis, Pfizer, Roche, Sanofi Aventis, StreamedUp, Urotrials, Urotube, Zentiva and resarch funding from Eisai. S.Z. reports clinical trial support from Amgen, AstraZeneca, AVEO, Bayer, Biontech, Bristol-Myers Squibb, Calithera, Exelixis, Gilead, Lilly, MSD, Novartis, Pfizer, Roche, Seagen/Astellas, Urotrials and travels & conference support from Amgen, Astellas, AstraZeneca, Bayer, EISAI, Ipsen, Johnson&Johnson, Merck, MSD, Pfizer. All remaining authors declare no relevant competing interests. Spanish Association Against Cancer, PI049999 Federal Ministry of Research, Technology and Space, 001001KT2322 National Cancer Institute, R35 CA263816 National Cancer Institute, U24 CA213274 National Cancer Institute, P30 CA008748 Research Council of Lithuania, P-MIP-24-93

Accurate and scalable multi-disease classification from adaptive immune repertoires

Summary BackgroundMachine learning models trained on paratope-similarity networks have shown superior accuracy compared with clonotype-based models in binary disease classification. However, the computational demands of paratope networks hinder their use on large datasets and multi-disease classification.MethodsWe reanalyzed publicly available T cell receptor (TCR) repertoire data from 1,421 donors across 15 disease groups and a large control group, encompassing approximately 81 million TCR sequences.

Mathematical Discovery of Potential Therapeutic Targets: Application to Rare Melanomas

Patients with rare types of melanoma such as acral, mucosal, or uveal melanoma, have lower survival rates than patients with cutaneous melanoma; these lower survival rates reflect the lower objective response rates to immunotherapy compared to cutaneous melanoma. Understanding tumor-immune dynamics in rare melanomas is critical for the development of new therapies and for improving response rates to current cancer therapies. Progress has been hindered by the lack of clinical data and the need for better preclinical models of rare melanomas. Canine melanoma provides a valuable comparative oncology model for rare types of human melanomas. We analyzed RNA sequencing data from canine melanoma patients and combined this with literature information to create a novel mechanistic mathematical model of melanoma-immune dynamics. Sensitivity analysis of the mathematical model indicated influential pathways in the dynamics, providing support for potential new therapeutic targets and future combinations of therapies. We share our learnings from this work, to help enable the application of this proof-of-concept workflow to other rare disease settings with sparse available data.

Osteoimmunology: shared mechanisms and crosstalk between the immune and bone systems

Osteoimmunology is an interdisciplinary field, that covers the shared mechanisms and interactions between bone cells and immune cellsReceptor activator of nuclear factor-κB (NF-κB) ligand (RANKL) is an osteoclast-differentiation factor that links the activated immune system and bone loss. In addition, abnormal bone homeostasis has been observed in various mice deficient in immunomodulatory molecules.Osteoclast differentiation is dependent on the transcription factor nuclear factor of activated T cells, cytoplasmic 1 (NFATc1), which is induced and activated by RANKL and its co-stimulatory (immunoglobulin-like) receptors.Interleukin-17 (IL-17)-producing T helper cells (TH17 cells) are the key T-cell subset that links T-cell activation and bone destruction in autoimmune arthritis.Bone cells are involved in the maintenance and mobilization of haematopoietic stem cells.Osteoimmunology is becoming increasingly important for understanding the pathogenesis of, and developing new therapeutic strategies for, diseases that affect both systems.

Mucosal dendritic cells shape mucosal immunity

Dendritic cells (DCs) are key modulators that shape the immune system. In mucosal tissues, DCs act as surveillance systems to sense infection and also function as professional antigen-presenting cells that stimulate the differentiation of naive T and B cells. On the basis of their molecular expression, DCs can be divided into several subsets with unique functions. In this review, we focus on intestinal DC subsets and their function in bridging the innate signaling and adaptive immune systems to maintain the homeostasis of the intestinal immune environment. We also review the current strategies for manipulating mucosal DCs for the development of efficient mucosal vaccines to protect against infectious diseases.

Osteoimmunology: The Conceptual Framework Unifying the Immune and Skeletal Systems

The immune and skeletal systems share a variety of molecules, including cytokines, chemokines, hormones, receptors, and transcription factors. Bone cells interact with immune cells under physiological and pathological conditions. Osteoimmunology was created as a new interdisciplinary field in large part to highlight the shared molecules and reciprocal interactions between the two systems in both heath and disease. Receptor activator of NF-κB ligand (RANKL) plays an essential role not only in the development of immune organs and bones, but also in autoimmune diseases affecting bone, thus effectively comprising the molecule that links the two systems. Here we review the function, gene regulation, and signal transduction of osteoimmune molecules, including RANKL, in the context of osteoclastogenesis as well as multiple other regulatory functions. Osteoimmunology has become indispensable for understanding the pathogenesis of a number of diseases such as rheumatoid arthritis (RA). We review the various osteoimmune pathologies, including the bone destruction in RA, in which pathogenic helper T cell subsets [such as IL-17-expressing helper T (Th17) cells] induce bone erosion through aberrant RANKL expression. We also focus on cellular interactions and the identification of the communication factors in the bone marrow, discussing the contribution of bone cells to the maintenance and regulation of hematopoietic stem and progenitors cells. Thus the time has come for a basic reappraisal of the framework for understanding both the immune and bone systems. The concept of a unified osteoimmune system will be absolutely indispensable for basic and translational approaches to diseases related to bone and/or the immune system.

The evolving concept of cell identity in the single cell era

Summary: This Spotlight explores emerging technologies that are enabling the systematic and unbiased quantification of cell identity, and how these efforts will enable the construction of high-resolution, dynamic cell atlases.

Patient-derived model capturing hypoxia and extracellular matrix remodelling of immunologically cold high-grade serous tumours

High-grade serous carcinoma tumours present poor survival rates, often associated with immunologically excluded environments driven by hypoxia and extensive extracellular matrix remodelling that disrupt tumour-stromal-immune interactions. Current experimental models fail to fully capture these microenvironmental features, limiting understanding of tumour-immune dynamics and drug development. Here, we present bioengineered patient-derived tumour-immune models to mimic physiologically relevant oxygen levels and extracellular matrix remodelling. Cancer cells are co-cultured with cancer-associated fibroblasts within human plasma-3D matrices or grown on decellularized human ovaries. Immune cells are either included within the 3D constructs to study multi-cellular interactions or challenged to infiltrate the matrices. We demonstrate that intratumoural hypoxia acts as a friend and a foe enhancing the activation and cytotoxicity of CD8 + T cells while inducing stromal/matrix dysregulation associated with impaired immune infiltration. Targeting TGF-β signalling attenuates the hypoxia-driven stromal-mediated immune exclusion. These relevant models may aid the development of targeted therapies to transform immunologically cold tumours into immunogenic to benefit female patients.

VAPOR: A variational autoencoder with transport operators to disentangle cellular gene expression dynamics of co-occurring biological processes in time and space

Abstract Single-cell and spatial transcriptomics enable the analysis of cellular states and dynamics in gene expression, revealing how diverse biological processes relate to these states over time and space. To study these dynamics, trajectory inference methods order cells along computationally inferred paths to reconstruct gradual transitions in cell states. However, by encouraging smooth and continuous trajectories, these approaches tend to conflate co-occurring processes-such as proliferation, maturation, and spatial organization-that are jointly reflected in gene expression, potentially overlooking process-specific gene expression dynamics. To address this, we developed VAPOR, which integrates a variational autoencoder with transport operators to model and disentangle cellular gene expression dynamics for potentially co-occurring biological processes. VAPOR inputs single-cell (or spatial) gene expression data into a variational autoencoder (VAE) to learn the latent states of cells and then models their latent dynamics as an ordinary differential equation. The latent dynamics are further decomposed into process-specific components parameterized by transport operators (TOs) and their corresponding process weights. Each TO defines a process-specific dynamics, and its weight for each cell quantifies the process's contribution to the cell dynamics. After assessment by simulation studies, we applied VAPOR with benchmarking to real data, including time-course scRNA-seq from postconceptual human brain development, spatial transcriptomics of the mouse hippocampus, and cross-species scRNA-seq spanning human and macaque first-trimester forebrain development. In these applications, VAPOR has identified a variety of temporal and spatial co-occurring processes, such as cell cycle, gliogenesis, neurogenesis, and neuronal migration, along with associated dynamic genes, including those species-specific to human and macaque development. VAPOR is available as an open-source tool for general-purpose use. ### Competing Interest Statement The authors have declared no competing interest.

Immunoporosis: Immunology of Osteoporosis—Role of T Cells

The role of immune system in various bone pathologies such as osteoporosis, osteoarthritis and rheumatoid arthritis is now well established. This had led to the emergence of a modern field of systems biology called as osteoimmunology, an integrated research between fields of immunology and bone biology under one umbrella. Osteoporosis is one of the most common inflammatory bone-loss conditions with more than 200 million individuals affected worldwide. T helper cells along with various other immune cells are major players involved in bone homeostasis. In the present review, we specifically discuss the role of various defined T lymphocyte subsets (Th cells comprising Th1, Th2, Th9, Th17, Th22, regulatory T cells, follicular helper T cells, natural killer T cells, γδ T cells and CD8+ T cells) in the pathophysiology of osteoporosis. The study of the specific role of immune system in osteoporosis has now been proposed by our group as “Immunoporosis: The immunology of osteoporosis” with special emphasis on the role of various subsets of T lymphocytes. The establishment of this new field had been the need of the hour due to the emergence of novel roles of various T cell lymphocytes in accelerated bone loss observed during osteoporosis. Activated T cells either directly or indirectly through the secretion of various cytokines and factors modulate bone health and thereby regulate bone remodelling. Various studies have summarized the role of inflammation in pathogenesis of osteoporosis, but very few reports had delineated the precise role of various T cell subsets in the pathobiology of osteoporosis. The present review thus for the first time clearly highlights and summarizes the role of various T lymphocytes in the development and pathophysiology of osteoporosis, giving birth to a new field of biology termed as “Immunoporosis”. This novel field will thus provide an overview of the nexus between the cellular components of both bone and immune systems, responsible for the observed bone loss in osteoporosis. A molecular insight into the upcoming and novel field of immunoporosis would thus lead to development of innovative approaches for the prevention and treatment of osteoporosis.

Mathematical Modeling of the Gut–Bone Axis and Implications of Butyrate Treatment on Osteoimmunology

Butyrate, a short-chain fatty acid produced by the gut microbiota, has pivotal roles in the regulation of the immune system. Recent studies have revealed that butyrate increases the differentiation of peripheral regulatory T cells in the gut–bone axis and promotes osteoblasts’ bone forming activity. However, the mechanism of the therapeutic benefit of butyrate in bone remodeling remains incompletely understood. Here, we develop a multicompartment mathematical model to quantitatively predict the contribution of butyrate on the expansion of regulatory T cells in the gut, blood, and bone compartments. We investigate the interplay between regulatory T cell-derived TGF-β and CD8+ T cell-derived Wnt-10b with changes in gut butyrate concentration. In addition, we connect our model to a detailed model of bone metabolism to study the impacts of butyrate and Wnt-10b on trabecular bone volume. Our results indicate both direct and indirect immune-mediated impacts of butyrate on bone metabolism.

OSTEOIMMUNOLOGY: Interplay Between the Immune System and Bone Metabolism

Studies of bone and the immune system have converged in recent years under the banner of osteoimmunology. The immune system is spawned in the bone marrow reservoir, and investigators now recognize that important niches also exist there for memory lymphocytes. At the same time, various factors produced during immune responses are capable of profoundly affecting regulation of bone. Mechanisms have evolved to prevent excessive interference by the immune system with bone homeostasis, yet pathologic bone loss is a common sequela associated with autoimmunity and cancer. There are also developmental links, or parallels, between bone and the immune system. Cells that regulate bone turnover share a common precursor with inflammatory immune cells and may restrict themselves anatomically, in part by utilizing a signaling network analogous to lymphocyte costimulation. Efforts are currently under way to further characterize how these two organ systems overlap and to develop therapeutic strategies that benefit from this understanding.