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.

Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes post induction. Using a primary dermal fibroblast model of senescence, we show that decreased substrate stiffness accelerates cell-cycle arrest during senescence development and regulate expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21 WAF1/CIP1 ( CDKN1a ) and p16 INK4a ( CDKN2a ) 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 p21 WAF1/CIP1 and p16 INK4a at comparable levels to induced cells on stiff substrates (4GPa). However, cells do express CDKN1a, CDKN2a, and IL6 at the RNA level across both stiff and soft substrates. When cells were transferred from soft to stiff substrates, senescent cells recover an elevated expression expressing p21 WAF1/CIP1 and p16 INK4a at levels comparable to senescence cells on stiff substrates, pointing to a mechanosensitive regulation of the senescence phenotypes. Together, our results indicate that the induction of senescence programs depends critically on the mechanical environments of cells and that senescent cells actively respond and adapt 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.

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.

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.

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.

TBL1XR1 Mutations Drive Extranodal Lymphoma by Inducing a Pro-tumorigenic Memory Fate.

The most aggressive B cell lymphomas frequently manifest extranodal distribution and carry somatic mutations in the poorly characterized gene TBL1XR1. Here, we show that TBL1XR1 mutations skew the humoral immune response toward generating abnormal immature memory B cells (MB), while impairing plasma cell differentiation. At the molecular level, TBL1XR1 mutants co-opt SMRT/HDAC3 repressor complexes toward binding the MB cell transcription factor (TF) BACH2 at the expense of the germinal center (GC) TF BCL6, leading to pre-memory transcriptional reprogramming and cell-fate bias. Upon antigen recall, TBL1XR1 mutant MB cells fail to differentiate into plasma cells and instead preferentially reenter new GC reactions, providing evidence for a cyclic reentry lymphomagenesis mechanism. Ultimately, TBL1XR1 alterations lead to a striking extranodal immunoblastic lymphoma phenotype that mimics the human disease. Both human and murine lymphomas feature expanded MB-like cell populations, consistent with a MB-cell origin and delineating an unforeseen pathway for malignant transformation of the immune system.

Mutant EZH2 Induces a Pre-malignant Lymphoma Niche by Reprogramming the Immune Response.

Follicular lymphomas (FLs) are slow-growing, indolent tumors containing extensive follicular dendritic cell (FDC) networks and recurrent EZH2 gain-of-function mutations. Paradoxically, FLs originate from highly proliferative germinal center (GC) B cells with proliferation strictly dependent on interactions with T follicular helper cells. Herein, we show that EZH2 mutations initiate FL by attenuating GC B cell requirement for T cell help and driving slow expansion of GC centrocytes that become enmeshed with and dependent on FDCs. By impairing T cell help, mutant EZH2 prevents induction of proliferative MYC programs. Thus, EZH2 mutation fosters malignant transformation by epigenetically reprograming B cells to form an aberrant immunological niche that reflects characteristic features of human FLs, explaining how indolent tumors arise from GC B cells.

Epigenetic reprogramming sensitizes immunologically silent EBV+ lymphomas to virus-directed immunotherapy.

Despite advances in T-cell immunotherapy against Epstein-Barr virus (EBV)-infected lymphomas that express the full EBV latency III program, a critical barrier has been that most EBV+ lymphomas express the latency I program, in which the single Epstein-Barr nuclear antigen (EBNA1) is produced. EBNA1 is poorly immunogenic, enabling tumors to evade immune responses. Using a high-throughput screen, we identified decitabine as a potent inducer of immunogenic EBV antigens, including LMP1, EBNA2, and EBNA3C. Induction occurs at low doses and persists after removal of decitabine. Decitabine treatment of latency I EBV+ Burkitt lymphoma (BL) sensitized cells to lysis by EBV-specific cytotoxic T cells (EBV-CTLs). In latency I BL xenografts, decitabine followed by EBV-CTLs results in T-cell homing to tumors and inhibition of tumor growth. Collectively, these results identify key epigenetic factors required for latency restriction and highlight a novel therapeutic approach to sensitize EBV+ lymphomas to immunotherapy.

Single-cell morphology encodes metastatic potential.

A central goal of precision medicine is to predict disease outcomes and design treatments based on multidimensional information from afflicted cells and tissues. Cell morphology is an emergent readout of the molecular underpinnings of a cell's functions and, thus, can be used as a method to define the functional state of an individual cell. We measured 216 features derived from cell and nucleus morphology for more than 30,000 breast cancer cells. We find that single cell-derived clones (SCCs) established from the same parental cells exhibit distinct and heritable morphological traits associated with genomic (ploidy) and transcriptomic phenotypes. Using unsupervised clustering analysis, we find that the morphological classes of SCCs predict distinct tumorigenic and metastatic potentials in vivo using multiple mouse models of breast cancer. These findings lay the groundwork for using quantitative morpho-profiling in vitro as a potentially convenient and economical method for phenotyping function in cancer in vivo.

BCL6 Evolved to Enable Stress Tolerance in Vertebrates and Is Broadly Required by Cancer Cells to Adapt to Stress.

Several lines of evidence link the canonical oncogene BCL6 to stress response. Here we demonstrate that BCL6 evolved in vertebrates as a component of the HSF1-driven stress response, which has been co-opted by the immune system to support germinal center formation and may have been decisive in the convergent evolution of humoral immunity in jawless and jawed vertebrates. We find that the highly conserved BTB corepressor binding site of BCL6 mediates stress adaptation across vertebrates. We demonstrate that pan-cancer cells hijack this stress tolerance mechanism to aberrantly express BCL6. Targeting the BCL6 BTB domain in cancer cells induces apoptosis and increases susceptibility to repeated doses of cytotoxic therapy. The chemosensitization effect upon BCL6 BTB inhibition is dependent on the derepression of TOX, implicating modulation of DNA repair as a downstream mechanism. Collectively, these data suggest a form of adaptive nononcogene addiction rooted in the natural selection of BCL6 during vertebrate evolution. SIGNIFICANCE: We demonstrate that HSF1 drives BCL6 expression to enable stress tolerance in vertebrates. We identify an HSF1-BCL6-TOX stress axis that is required by cancer cells to tolerate exposure to cytotoxic agents and points toward BCL6-targeted therapy as a way to more effectively kill a wide variety of solid tumors.This article is highlighted in the In This Issue feature, p. 565.