Profiling Dynamic Patterns of Single-Cell Motility.

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n = 74 253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations.

High-motility pro-tumorigenic monocytes drive macrophage enrichment in the tumor microenvironment.

Enrichment of tumor-associated macrophages (TAMΦs) in the tumor microenvironment correlates with worse clinical outcomes in triple-negative breast cancer (TNBC) patients, prompting the development of therapies to inhibit TAMΦ infiltration. However, the lackluster efficacy of CCL2-based chemotaxis blockade in clinical trials suggests that a new understanding of monocyte/macrophage infiltration may be necessary. Here we demonstrate that random migration, and not only chemotaxis, drives macrophage tumor infiltration. We identified tumor- associated monocytes (TAMos) that display a dramatically enhanced migration capability, induced rapidly by the tumor microenvironment, that drives effective tumor infiltration, in contrast to low-motility differentiated macrophages. TAMo, not TAMΦ, promotes cancer cell proliferation through activation of the MAPK pathway. IL-6 secreted both by cancer cells and TAMo themselves enhances TAMo migration by increasing dendritic protrusion dynamics and myosin- based contractility via the JAK2/STAT3 signaling pathway. Independent from CCL2 mediated chemotaxis, IL-6 driven enhanced migration and pro-proliferative effect of TAMo were validated in a syngeneic TNBC mouse model. Depletion of IL-6 in cancer cells significantly attenuated monocyte infiltration and reversed TAMo-induced cancer cell proliferation. This work reveals the critical role random migration plays in monocyte driven TAMΦ enrichment in a tumor and pinpoints IL-6 as a potential therapeutic target in combination with CCL2 to ameliorate current strategies against TAMΦ infiltration.

qMAP enabled microanatomical mapping of human skin aging.

Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.

Quantitative morphological analysis of Deinococcus radiodurans elucidates complex dose-dependent nucleoid condensation during recovery from ionizing radiation.

The extremophile Deinococcus radiodurans maintains a highly organized and condensed nucleoid as its default state, possibly contributing to its high tolerance to ionizing radiation (IR). Previous studies of the D. radiodurans nucleoid were limited by reliance on manual image annotation and qualitative metrics. Here, we introduce a high-throughput approach to quantify the geometric properties of cells and nucleoids using confocal microscopy, digital reconstructions of cells, and computational modeling. We utilize this novel approach to investigate the dynamic process of nucleoid condensation in response to IR stress. Our quantitative analysis reveals that at the population level, exposure to IR induced nucleoid compaction and decreased the size of D. radiodurans cells. Morphological analysis and clustering identified six distinct sub-populations across all tested experimental conditions. Results indicate that exposure to IR induced fractional redistributions of cells across sub-populations to exhibit morphologies associated with greater nucleoid condensation and decreased the abundance of sub-populations associated with cell division. Nucleoid-associated proteins (NAPs) may link nucleoid compaction and stress tolerance, but their roles in regulating compaction in D. radiodurans are unknown. Imaging of genomic mutants of known and suspected NAPs that contribute to nucleoid condensation found that deletion of nucleic acid-binding proteins, not previously described as NAPs, can remodel the nucleoid by driving condensation or decondensation in the absence of stress and that IR increased the abundance of these morphological states. Thus, our integrated analysis introduces a new methodology for studying environmental influences on bacterial nucleoids and provides an opportunity to further investigate potential regulators of nucleoid condensation.IMPORTANCEDeinococcus radiodurans, an extremophile known for its stress tolerance, constitutively maintains a highly condensed nucleoid. Qualitative studies have described nucleoid behavior under a variety of conditions. However, a lack of quantitative data regarding nucleoid organization and dynamics has limited our understanding of the regulatory mechanisms controlling nucleoid organization in D. radiodurans. Here, we introduce a quantitative approach that enables high-throughput quantitative measurements of subcellular spatial characteristics in bacterial cells. Applying this to wild-type or single-protein-deficient populations of D. radiodurans subjected to ionizing radiation, we identified significant stress-responsive changes in cell shape, nucleoid organization, and morphology. These findings highlight this methodology's adaptability and capacity for quantitatively analyzing the cellular response to stressors for screening cellular proteins involved in bacterial nucleoid organization.

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts.

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three bona-fide senescence subtypes (C7, C10, C11), with C10 showing the strongest age-dependence across a cohort of fifty aging individuals. To determine the functional significance of these senescence subtypes, we profiled their responses to senotherapies, specifically focusing on Dasatinib + Quercetin (D+Q). Results indicated subtype-dependent responses, with senescent cells in C7 being most responsive to D+Q. Altogether, we provide a robust single-cell framework to identify and classify functional senescence subtypes with applications for next-generation senotherapy screens, and the potential to explain heterogeneous senescence phenotypes across biological settings based on the presence and abundance of distinct senescence subtypes.

Substrate stiffness modulates the emergence and magnitude of senescence phenotypes in dermal fibroblasts.

Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes after exposure to senescence inducers. Using a primary dermal fibroblast model, we show that decreased substrate stiffness accelerates senescence-associated cell-cycle arrest and regulates the expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21WAF1/CIP1 and p16INK4a are mechanosensitive and are in-part regulated by myosin contractility through focal adhesion kinase (FAK)-ROCK signaling. Interestingly, at the protein level senescence-induced dermal fibroblasts on soft substrates (0.5 kPa) do not express p21WAF1/CIP1 and p16INK4a at comparable levels to induced cells on stiff substrates (4GPa). However, cells express CDKN1a, CDKN2a, and IL6 at the RNA level across both stiff and soft substrates. Moreover, when cells are transferred from soft to stiff substrates, senescent cells recover an elevated expression of p21WAF1/CIP1 and p16INK4a at levels comparable to senescence cells on stiff substrates, pointing to a mechanosensitive regulation of the senescence phenotype. Together, our results indicate that the emergent senescence phenotype depends critically on the local mechanical environments of cells and that senescent cells actively respond to changing mechanical cues.

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells.

Particulate matter (PM) is a ubiquitous component of air pollution that is epidemiologically linked to human pulmonary diseases. PM chemical composition varies widely, and the development of high-throughput experimental techniques enables direct profiling of cellular effects using compositionally unique PM mixtures. Here, we show that in a human bronchial epithelial cell model, exposure to three chemically distinct PM mixtures drive unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability, DNA damage responses, and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Finally, we observed that PM mixtures with higher cadmium content induced increased DNA damage and drove redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of individual cellular morphologies provides a robust, high-throughput approach to gauge the effects of environmental stressors on biological systems and score cellular susceptibilities to pollution.

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells.

Particulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.

Endothelial cell-leukemia interactions remodel drug responses, uncovering T-ALL vulnerabilities.

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive and often incurable disease. To uncover therapeutic vulnerabilities, we first developed T-ALL patient-derived tumor xenografts (PDXs) and exposed PDX cells to a library of 433 clinical-stage compounds in vitro. We identified 39 broadly active drugs with antileukemia activity. Because endothelial cells (ECs) can alter drug responses in T-ALL, we developed an EC/T-ALL coculture system. We found that ECs provide protumorigenic signals and mitigate drug responses in T-ALL PDXs. Whereas ECs broadly rescued several compounds in most models, for some drugs the rescue was restricted to individual PDXs, suggesting unique crosstalk interactions and/or intrinsic tumor features. Mechanistically, cocultured T-ALL cells and ECs underwent bidirectional transcriptomic changes at the single-cell level, highlighting distinct "education signatures." These changes were linked to bidirectional regulation of multiple pathways in T-ALL cells as well as in ECs. Remarkably, in vitro EC-educated T-ALL cells transcriptionally mirrored ex vivo splenic T-ALL at single-cell resolution. Last, 5 effective drugs from the 2 drug screenings were tested in vivo and shown to effectively delay tumor growth and dissemination thus prolonging overall survival. In sum, we developed a T-ALL/EC platform that elucidated leukemia-microenvironment interactions and identified effective compounds and therapeutic vulnerabilities.

Nature-inspired delivery of mitochondria-targeted angiotensin receptor blocker.

Mitochondria are critical regulators of cellular function and survival. We have previously demonstrated that functional angiotensin receptors embedded within the inner mitochondrial membrane modulate mitochondrial energy production and free radical generation. The expression of mitochondrial angiotensin II type-1 receptors increases during aging, with a complementary decrease in angiotensin II type-2 receptor density. To address this age-associated mitochondrial dysfunction, we have developed a mitochondria-targeted delivery system to effectively transport angiotensin type-1 receptor blocker-Losartan (mtLOS) into the inner mitochondrial membrane. We engineered mtLOS to become active within the mitochondria after cleavage by mitochondrial peptidases. Our data demonstrate effective and targeted delivery of mtLOS into the mitochondria, compared to a free Losartan, or Losartan conjugated to a scrambled mitochondrial target signal peptide, with significant shifts in mitochondrial membrane potential upon mtLOS treatment. Furthermore, engineered mitochondrial-targeting modalities could open new avenues to transport nonmitochondrial proteins into the mitochondria, such as other macromolecules and therapeutic agents.

Inhibition of Integrin αVß3 Signaling Improves the Antineoplastic Effect of Bexarotene in Cutaneous T-Cell Lymphoma.

Bexarotene is a specific retinoid X receptor agonist that has been used for the treatment of cutaneous T-cell lymphoma (CTCL). Because bexarotene causes hypothyroidism, it requires the administration of levothyroxine. However, levothyroxine, in addition to its ubiquitous nuclear receptors, can activate the αVß3 integrin that is overexpressed in CTCL, potentially interfering the antineoplastic effect of bexarotene. We thus investigated the biological effect of levothyroxine in relation to bexarotene treatment. Although in isolated CTCL cells levothyroxine decreased, in an αVß3-dependent manner, the antineoplastic effect of bexarotene, levothyroxine supplementation in preclinical models was necessary to avoid suppression of lymphoma immunity. Accordingly, selective genetic and pharmacologic inhibition of integrin αVß3 improved the antineoplastic effect of bexarotene plus levothyroxine replacement while maintaining lymphoma immunity. Our results provide a mechanistic rationale for clinical testing of integrin αVß3 inhibitors as part of CTCL regimens based on bexarotene administration. TEASER: Inhibiting αVß3 integrin improves the antineoplastic effect of bexarotene while maintaining lymphoma immunity.

Deep learning identification of stiffness markers in breast cancer.

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

Histamine H4 Receptor Agonism Induces Antitumor Effects in Human T-Cell Lymphoma.

The discovery of the human histamine H4 receptor (H4R) has contributed to our understanding of the role of histamine in numerous physiological and pathological conditions, including tumor development and progression. The lymph nodes of patients with malignant lymphomas have shown to contain high levels of histamine, however, less is known regarding the expression and function of the H4R in T-cell lymphoma (TCL). In this work we demonstrate the expression of H4R isoforms (mRNA and protein) in three human aggressive TCL (OCI-Ly12, Karpas 299, and HuT78). Histamine and specific H4R agonists (VUF8430 and JNJ28610244) significantly reduced cell viability in a dose-dependent manner (p < 0.05). The combined treatment with the H4R antagonist (JNJ7777120, 10 µM) reversed the effects of the H4R ligands. Importantly, we screened a drug repurposing library of 433 FDA-approved compounds (1 µM) in combination with histamine (10 µM) in Hut78 cells. Histamine produced a favorable antitumor effect with 18 of these compounds, including the histone deacetylase inhibitor panobinostat. Apoptosis, proliferation, and oxidative stress studies confirmed the antitumoral effects of the combination. We conclude that the H4R is expressed in TCL, and it is involved in histamine-mediated responses.

Use of the p-values as a size-dependent function to address practical differences when analyzing large datasets.

Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision-making. In the largely extended null hypothesis significance testing for identifying statistically significant differences among groups of observations, a single p-value is computed from sample data. Then, it is routinely compared with a threshold, commonly set to 0.05, to assess the evidence against the hypothesis of having non-significant differences among groups, or the null hypothesis. Because the estimated p-value tends to decrease when the sample size is increased, applying this methodology to datasets with large sample sizes results in the rejection of the null hypothesis, making it not meaningful in this specific situation. We propose a new approach to detect differences based on the dependence of the p-value on the sample size. We introduce new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of datasets with large sample sizes, reducing the uncertainty in the decision about the existence of biological differences between the compared experiments. The methodology enables the graphical and quantitative characterization of the differences between the compared experiments guiding the researchers in the decision process. An in-depth study of the methodology is carried out on simulated and experimental data. Code availability at https://github.com/BIIG-UC3M/pMoSS .

Oncogenic HSP90 Facilitates Metabolic Alterations in Aggressive B-cell Lymphomas.

HSP90 is critical for maintenance of the cellular proteostasis. In cancer cells, HSP90 also becomes a nucleating site for the stabilization of multiprotein complexes including signaling pathways and transcription complexes. Here we described the role of this HSP90 form, referred to as oncogenic HSP90, in the regulation of cytosolic metabolic pathways in proliferating B-cell lymphoma cells. Oncogenic HSP90 assisted in the organization of metabolic enzymes into non-membrane-bound functional compartments. Under experimental conditions that conserved cellular proteostasis, oncogenic HSP90 coordinated and sustained multiple metabolic pathways required for energy production and maintenance of cellular biomass as well as for secretion of extracellular metabolites. Conversely, inhibition of oncogenic HSP90, in absence of apparent client protein degradation, decreased the efficiency of MYC-driven metabolic reprogramming. This study reveals that oncogenic HSP90 supports metabolism in B-cell lymphoma cells and patients with diffuse large B-cell lymphoma, providing a novel mechanism of activity for HSP90 inhibitors. SIGNIFICANCE: The oncogenic form of HSP90 organizes and maintains functional multienzymatic metabolic hubs in cancer cells, suggesting the potential of repurposing oncogenic HSP90 selective inhibitors to disrupt metabolism in lymphoma cells.

Clinical and Biological Subtypes of B-cell Lymphoma Revealed by Microenvironmental Signatures.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment. SIGNIFICANCE: In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307.

A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.

Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( https://github.com/kukionfr/VAMPIRE_open ). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.

Fractional re-distribution among cell motility states during ageing.

Ageing in humans is associated with the decreased capacity to regulate cell physiology. Cellular properties, such as cell morphology and mechanics, encode ageing information, and can therefore be used as robust biomarkers of ageing. Using a panel of dermal fibroblasts derived from healthy donors spanning a wide age range, we observe an age-associated decrease in cell motility. By taking advantage of the single-cell nature of our motility data, we classified cells based on spatial and activity patterns to define age-dependent motility states. We show that the age-dependent decrease in cell motility is not due to the reduced motility of all cells, but results from the fractional re-distribution among motility states. These findings highlight an important feature of ageing cells characterized by a reduction of cellular heterogeneity in older adults relative to post-adolescent/adults. Furthermore, these results point to a mechanistic framework of ageing, with potential applications in deciphering emergent ageing phenotypes and biomarker development.

Identification of MALT1 feedback mechanisms enables rational design of potent antilymphoma regimens for ABC-DLBCL.

MALT1 inhibitors are promising therapeutic agents for B-cell lymphomas that are dependent on constitutive or aberrant signaling pathways. However, a potential limitation for signal transduction-targeted therapies is the occurrence of feedback mechanisms that enable escape from the full impact of such drugs. Here, we used a functional genomics screen in activated B-cell-like (ABC) diffuse large B-cell lymphoma (DLBCL) cells treated with a small molecule irreversible inhibitor of MALT1 to identify genes that might confer resistance or enhance the activity of MALT1 inhibition (MALT1i). We find that loss of B-cell receptor (BCR)- and phosphatidylinositol 3-kinase (PI3K)-activating proteins enhanced sensitivity, whereas loss of negative regulators of these pathways (eg, TRAF2, TNFAIP3) promoted resistance. These findings were validated by knockdown of individual genes and a combinatorial drug screen focused on BCR and PI3K pathway-targeting drugs. Among these, the most potent combinatorial effect was observed with PI3Kδ inhibitors against ABC-DLBCLs in vitro and in vivo, but that led to an adaptive increase in phosphorylated S6 and eventual disease progression. Along these lines, MALT1i promoted increased MTORC1 activity and phosphorylation of S6K1-T389 and S6-S235/6, an effect that was only partially blocked by PI3Kδ inhibition in vitro and in vivo. In contrast, simultaneous inhibition of MALT1 and MTORC1 prevented S6 phosphorylation, yielded potent activity against DLBCL cell lines and primary patient specimens, and resulted in more profound tumor regression and significantly improved survival of ABC-DLBCLs in vivo compared with PI3K inhibitors. These findings provide a basis for maximal therapeutic impact of MALT1 inhibitors in the clinic, by disrupting feedback mechanisms that might otherwise limit their efficacy.

Histone H1 loss drives lymphoma by disrupting 3D chromatin architecture.

Linker histone H1 proteins bind to nucleosomes and facilitate chromatin compaction1, although their biological functions are poorly understood. Mutations in the genes that encode H1 isoforms B-E (H1B, H1C, H1D and H1E; also known as H1-5, H1-2, H1-3 and H1-4, respectively) are highly recurrent in B cell lymphomas, but the pathogenic relevance of these mutations to cancer and the mechanisms that are involved are unknown. Here we show that lymphoma-associated H1 alleles are genetic driver mutations in lymphomas. Disruption of H1 function results in a profound architectural remodelling of the genome, which is characterized by large-scale yet focal shifts of chromatin from a compacted to a relaxed state. This decompaction drives distinct changes in epigenetic states, primarily owing to a gain of histone H3 dimethylation at lysine 36 (H3K36me2) and/or loss of repressive H3 trimethylation at lysine 27 (H3K27me3). These changes unlock the expression of stem cell genes that are normally silenced during early development. In mice, loss of H1c and H1e (also known as H1f2 and H1f4, respectively) conferred germinal centre B cells with enhanced fitness and self-renewal properties, ultimately leading to aggressive lymphomas with an increased repopulating potential. Collectively, our data indicate that H1 proteins are normally required to sequester early developmental genes into architecturally inaccessible genomic compartments. We also establish H1 as a bona fide tumour suppressor and show that mutations in H1 drive malignant transformation primarily through three-dimensional genome reorganization, which leads to epigenetic reprogramming and derepression of developmentally silenced genes.