Deconstructing heterogeneity of replicative senescence in human mesenchymal stem cells at single cell resolution.

Following prolonged cell division, mesenchymal stem cells enter replicative senescence, a state of permanent cell cycle arrest that constrains the use of this cell type in regenerative medicine applications and that in vivo substantially contributes to organismal ageing. Multiple cellular processes such as telomere dysfunction, DNA damage and oncogene activation are implicated in promoting replicative senescence, but whether mesenchymal stem cells enter different pre-senescent and senescent states has remained unclear. To address this knowledge gap, we subjected serially passaged human ESC-derived mesenchymal stem cells (esMSCs) to single cell profiling and single cell RNA-sequencing during their progressive entry into replicative senescence. We found that esMSC transitioned through newly identified pre-senescent cell states before entering into three different senescent cell states. By deconstructing this heterogeneity and temporally ordering these pre-senescent and senescent esMSC subpopulations into developmental trajectories, we identified markers and predicted drivers of these cell states. Regulatory networks that capture connections between genes at each timepoint demonstrated a loss of connectivity, and specific genes altered their gene expression distributions as cells entered senescence. Collectively, this data reconciles previous observations that identified different senescence programs within an individual cell type and should enable the design of novel senotherapeutic regimes that can overcome in vitro MSC expansion constraints or that can perhaps slow organismal ageing.

Senolytic therapy alleviates physiological human brain aging and COVID-19 neuropathology.

Aging is a major risk factor for neurodegenerative diseases, and coronavirus disease 2019 (COVID-19) is linked to severe neurological manifestations. Senescent cells contribute to brain aging, but the impact of virus-induced senescence on neuropathologies is unknown. Here we show that senescent cells accumulate in aged human brain organoids and that senolytics reduce age-related inflammation and rejuvenate transcriptomic aging clocks. In postmortem brains of patients with severe COVID-19 we observed increased senescent cell accumulation compared with age-matched controls. Exposure of human brain organoids to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induced cellular senescence, and transcriptomic analysis revealed a unique SARS-CoV-2 inflammatory signature. Senolytic treatment of infected brain organoids blocked viral replication and prevented senescence in distinct neuronal populations. In human-ACE2-overexpressing mice, senolytics improved COVID-19 clinical outcomes, promoted dopaminergic neuron survival and alleviated viral and proinflammatory gene expression. Collectively our results demonstrate an important role for cellular senescence in driving brain aging and SARS-CoV-2-induced neuropathology, and a therapeutic benefit of senolytic treatments.

How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data.

Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. RESULTS: We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. CONCLUSIONS: This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).

Leptin antagonism inhibits prostate cancer xenograft growth and progression.

Hyperleptinaemia is a well-established therapeutic side effect of drugs inhibiting the androgen axis in prostate cancer (PCa), including main stay androgen deprivation therapy (ADT) and androgen targeted therapies (ATT). Given significant crossover between the adipokine hormone signalling of leptin and multiple cancer-promoting hallmark pathways, including growth, proliferation, migration, angiogenesis, metabolism and inflammation, targeting the leptin axis is therapeutically appealing, especially in advanced PCa where current therapies fail to be curative. In this study, we uncover leptin as a novel universal target in PCa and are the first to highlight increased intratumoural leptin and leptin receptor (LEPR) expression in PCa cells and patients' tumours exposed to androgen deprivation, as is observed in patients' tumours of metastatic and castrate resistant (CRPC) PCa. We also reveal the world-first preclinical evidence that demonstrates marked efficacy of targeted leptin-signalling blockade, using Allo-aca, a potent, specific, and safe LEPR peptide antagonist. Allo-aca-suppressed tumour growth and delayed progression to CRPC in mice bearing LNCaP xenografts, with reduced tumour vascularity and altered pathways of apoptosis, transcription/translation, and energetics in tumours determined as potential mechanisms underpinning anti-tumour efficacy. We highlight LEPR blockade in combination with androgen axis inhibition represents a promising new therapeutic strategy vital in advanced PCa treatment.

Computational Methods for Single-Cell Imaging and Omics Data Integration.

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer.

BACKGROUND: Metabolic reprograming, non-mutational epigenetic changes, increased cell plasticity, and multidrug tolerance are early hallmarks of therapy resistance in cancer. In this temporary, therapy-tolerant state, cancer cells are highly sensitive to ferroptosis, a form of regulated cell death that is caused by oxidative stress through excess levels of iron-dependent peroxidation of polyunsaturated fatty acids (PUFA). However, mechanisms underpinning therapy-induced ferroptosis hypersensitivity remain to be elucidated. METHODS: We used quantitative single-cell imaging of fluorescent metabolic probes, transcriptomics, proteomics, and lipidomics to perform a longitudinal analysis of the adaptive response to androgen receptor-targeted therapies (androgen deprivation and enzalutamide) in prostate cancer (PCa). RESULTS: We discovered that cessation of cell proliferation and a robust reduction in bioenergetic processes were associated with multidrug tolerance and a strong accumulation of lipids. The gain in lipid biomass was fueled by enhanced lipid uptake through cargo non-selective (macropinocytosis, tunneling nanotubes) and cargo-selective mechanisms (lipid transporters), whereas de novo lipid synthesis was strongly reduced. Enzalutamide induced extensive lipid remodeling of all major phospholipid classes at the expense of storage lipids, leading to increased desaturation and acyl chain length of membrane lipids. The rise in membrane PUFA levels enhanced membrane fluidity and lipid peroxidation, causing hypersensitivity to glutathione peroxidase (GPX4) inhibition and ferroptosis. Combination treatments against AR and fatty acid desaturation, lipase activities, or growth medium supplementation with antioxidants or PUFAs altered GPX4 dependence. CONCLUSIONS: Our work provides mechanistic insight into processes of lipid metabolism that underpin the acquisition of therapy-induced GPX4 dependence and ferroptosis hypersensitivity to standard of care therapies in PCa. It demonstrates novel strategies to suppress the therapy-tolerant state that may have potential to delay and combat resistance to androgen receptor-targeted therapies, a currently unmet clinical challenge of advanced PCa. Since enhanced GPX4 dependence is an adaptive phenotype shared by several types of cancer in response to different therapies, our work might have universal implications for our understanding of metabolic events that underpin resistance to cancer therapies.

Quantitative Modelling of the Waddington Epigenetic Landscape.

C.H. Waddington introduced the epigenetic landscape as a metaphor to represent cellular decision-making during development. Like a population of balls rolling down a rough hillside, developing cells follow specific trajectories (valleys) and eventually come to rest in one or another low-energy state that represents a mature cell type. Waddington depicted the topography of this landscape as determined by interactions among gene products, thereby connecting genotype to phenotype. In modern terms, each point on the landscape represents a state of the underlying genetic regulatory network, which in turn is described by a gene expression profile. In this chapter we demonstrate how the mathematical formalism of Hopfield networks can be used to model this epigenetic landscape. Hopfield networks are auto-associative artificial neural networks; input patterns are stored as attractors of the network and can be recalled from noisy or incomplete inputs. The resulting models capture the temporal dynamics of a gene regulatory network, yielding quantitative insight into cellular development and phenotype.

Lipid Uptake Is an Androgen-Enhanced Lipid Supply Pathway Associated with Prostate Cancer Disease Progression and Bone Metastasis.

De novo lipogenesis is a well-described androgen receptor (AR)-regulated metabolic pathway that supports prostate cancer tumor growth by providing fuel, membrane material, and steroid hormone precursor. In contrast, our current understanding of lipid supply from uptake of exogenous lipids and its regulation by AR is limited, and exogenous lipids may play a much more significant role in prostate cancer and disease progression than previously thought. By applying advanced automated quantitative fluorescence microscopy, we provide the most comprehensive functional analysis of lipid uptake in cancer cells to date and demonstrate that treatment of AR-positive prostate cancer cell lines with androgens results in significantly increased cellular uptake of fatty acids, cholesterol, and low-density lipoprotein particles. Consistent with a direct, regulatory role of AR in this process, androgen-enhanced lipid uptake can be blocked by the AR-antagonist enzalutamide, but is independent of proliferation and cell-cycle progression. This work for the first time comprehensively delineates the lipid transporter landscape in prostate cancer cell lines and patient samples by analysis of transcriptomics and proteomics data, including the plasma membrane proteome. We show that androgen exposure or deprivation regulates the expression of multiple lipid transporters in prostate cancer cell lines and tumor xenografts and that mRNA and protein expression of lipid transporters is enhanced in bone metastatic disease when compared with primary, localized prostate cancer. Our findings provide a strong rationale to investigate lipid uptake as a therapeutic cotarget in the fight against advanced prostate cancer in combination with inhibitors of lipogenesis to delay disease progression and metastasis. IMPLICATIONS: Prostate cancer exhibits metabolic plasticity in acquiring lipids from uptake and lipogenesis at different disease stages, indicating potential therapeutic benefit by cotargeting lipid supply.

Modeling the Attractor Landscape of Disease Progression: a Network-Based Approach.

Genome-wide regulatory networks enable cells to function, develop, and survive. Perturbation of these networks can lead to appearance of a disease phenotype. Inspired by Conrad Waddington's epigenetic landscape of cell development, we use a Hopfield network formalism to construct an attractor landscape model of disease progression based on protein- or gene-correlation networks of Parkinson's disease, glioma, and colorectal cancer. Attractors in this landscape correspond to normal and disease states of the cell. We introduce approaches to estimate the size and robustness of these attractors, and take a network-based approach to study their biological features such as the key genes and their functions associated with the attractors. Our results show that the attractor of cancer cells is wider than the attractor of normal cells, suggesting a heterogeneous nature of cancer. Perturbation analysis shows that robustness depends on characteristics of the input data (number of samples per time-point, and the fraction which converge to an attractor). We identify unique gene interactions at each stage, which reflect the temporal rewiring of the gene regulatory network (GRN) with disease progression. Our model of the attractor landscape, constructed from large-scale gene expression profiles of individual patients, captures snapshots of disease progression and identifies gene interactions specific to different stages, opening the way for development of stage-specific therapeutic strategies.

Breast cancer classification: linking molecular mechanisms to disease prognosis.

Breast cancer was traditionally perceived as a single disease; however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of ∼550 breast tumours from The Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios covering molecular characteristics, biomarker status, tumour stages and survival patterns. We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer.

Comparative analysis of organophosphate degrading enzymes from diverse species.

Different types of organophosphorous compounds constitute most potent pesticides. These chemicals attack the nervous system of living organisms causing death. Different organisms produce enzymes to degrade these chemicals. These enzymes are present in simple microorganisms from archaea, bacteria to complex eukaryotes like humans. A comparison of representative eight shortlisted enzymes involved in the degradation and inactivation of organophosphates from a wide range of organisms was performed to infer the basis of their common functionality. There is little sequence homology in these enzymes which results in divergent tertiary structures. The only feature that these enzymes seem to share is their amino acid composition. However, structural analysis has shown no significant similarities among this functionally similar group of organophosphate degrading enzymes.

In silico comparative genome analysis of malaria parasite Plasmodium falciparum and Plasmodium vivax chromosome 4.

Malarial parasite has long been a subject of research for a large community of scientists and has yet to be conquered. One of the main obstacles to effectively control this disease is rapidly evolving genetic structure of Plasmodium parasite itself. In this study, we focused on chromosome 4 of the Plasmodium falciparum and Plasmodium vivax species and carried out comparative studies of genes that are responsible for antigenic variation in respective species. Comparative analysis of genes responsible for antigenic variation (var and vir genes in P. falciparum and P. vivax, respectively) showed significant difference in their respective nucleotide sequence lengths as well as amino acid composition. The possible association of exon's length on pathogenecity of respective Plasmodium species was also investigated, and analysis of gene structure showed that on the whole, exon lengths in P. falciparum are larger compared to P. vivax. Analysis of tandem repeats across the genome has shown that the size of repetitive sequences has a direct effect on chromosomes length, which can also be a potential reason for P. falciparum's greater variability and hence pathogenecity than P. vivax.