Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, remains a global health concern despite vaccines, neutralizing antibodies, and antiviral drugs. The emergence of viral mutations that diminish the effectiveness of current interventions underscores the importance of alternative, host-directed strategies. Here, we show that pharmacological inhibition or knockdown of host N-myristoyltransferase 1 (NMT1), one of the two human enzymes that mediates protein N-myristoylation, significantly impairs SARS-CoV-2, Vesicular Stomatitis Virus (VSV) and Respiratory syncytial virus (RSV) infections. We demonstrate the antiviral efficacy and safety of this host-directed therapeutic strategy across multiple viral tropic sites, including human lung adenocarcinoma cell lines, primary nasal epithelial cells, and human choroid plexus-cortical brain organoids. NMT1 inhibition triggers a Golgi-bypassing pathway for SARS-CoV-2 progeny virion egress, through endoplasmic reticulum and lysosomal structures, which leads to perturbed progeny virion composition and spike maturation, impairing progeny virion infectivity.

A gene is defined as essential when its functional loss compromises an organism's viability. Identifying essential genes is critical for identifying the components that regulate a biological system. Advances in gene editing techniques like CRISPR-Cas9 provide a capacity to interrogate a genome to elucidate the genes that are essential. However, these techniques are often applied for a single-cell line and rarely probed at a level of a tissue or organ. The recent availability of large-scale single-cell RNA-sequencing (scRNA-seq) atlases provides an unprecedented opportunity to investigate essential gene expression in a more comprehensive context. Our study leverages information from benchmarking datasets, single-cell tissue atlases, and databases of essential genes, to develop a method, scEssentials, that uses a statistical framework to investigate the robustness and specificity of essential genes across multiple cell types. Using scEssentials, mouse and human models showed consistently high expression and exhibited limited variability across more than 60 cell types. We demonstrate a substantial number of significantly correlated gene pairs that produce densely connected co-expression networks with functional annotation. Finally, we develop a score to quantify the relative essentiality of genes within scEssentials, further validating their significant association with gene mutation frequency and chromatin accessibility. Using ageing as an application, we demonstrate how scEssentials identifies robust gene expression profiles. Only one-fifth of scEssentials genes showed significant ageing-related differential expression among age groups. Collectively, the robustness of scEssentials serves as a reference for analysing scRNA-seq data and provides insight into the heterogeneous nature such as ageing.

Human cell reprogramming traditionally involves time-intensive, multistage, costly tissue culture polystyrene-based cell culture practices that ultimately produce low numbers of reprogrammed cells of variable quality. Previous studies have shown that very soft 2- and 3-dimensional hydrogel substrates/matrices (of stiffnesses ≤ 1 kPa) can drive ~2× improvements in human cell reprogramming outcomes. Unfortunately, these similarly complex multistage protocols lack intrinsic scalability, and, furthermore, the associated underlying molecular mechanisms remain to be fully elucidated, limiting the potential to further maximize reprogramming outcomes. In screening the largest range of polyacrylamide (pAAm) hydrogels of varying stiffness to date (1 kPa to 1.3 MPa), we have found that a medium stiffness gel (~100 kPa) increased the overall number of reprogrammed cells by up to 10-fold (10×), accelerated reprogramming kinetics, improved both early and late phases of reprogramming, and produced induced pluripotent stem cells (iPSCs) having more naïve characteristics and lower remnant transgene expression, compared to the gold standard tissue culture polystyrene practice. Functionalization of these pAAm hydrogels with poly-l-dopamine enabled, for the first-time, continuous, single-step reprogramming of fibroblasts to iPSCs on hydrogel substrates (noting that even the tissue culture polystyrene practice is a 2-stage process). Comparative RNA sequencing analyses coupled with experimental validation revealed that a novel reprogramming regulator, protein phosphatase and actin regulator 3, up-regulated under the gel condition at a very early time point, was responsible for the observed enhanced reprogramming outcomes. This study provides a novel culture protocol and substrate for continuous hydrogel-based cell reprogramming and previously unattained clarity of the underlying mechanisms via which substrate stiffness modulates reprogramming kinetics and iPSC quality outcomes.

Background Redox signaling caused by knockdown (KD) of Glutathione Peroxidase 2 (GPx2) in the PyMT mammary tumour model promotes metastasis via phenotypic and metabolic reprogramming. However, the tumour cell subpopulations and transcriptional regulators governing these processes remained unknown. Methods We used single-cell transcriptomics to decipher the tumour cell subpopulations stimulated by GPx2 KD in the PyMT mammary tumour and paired pulmonary metastases. We analyzed the EMT spectrum across the various tumour cell clusters using pseudotime trajectory analysis and elucidated the transcriptional and metabolic regulation of the hybrid EMT state. Results Integration of single-cell transcriptomics between the PyMT/GPx2 KD primary tumour and paired lung metastases unraveled a basal/mesenchymal-like cluster and several luminal-like clusters spanning an EMT spectrum. Interestingly, the luminal clusters at the primary tumour gained mesenchymal gene expression, resulting in epithelial/mesenchymal subpopulations fueled by oxidative phosphorylation (OXPHOS) and glycolysis. By contrast, at distant metastasis, the basal/mesenchymal-like cluster gained luminal and mesenchymal gene expression, resulting in a hybrid subpopulation using OXPHOS, supporting adaptive plasticity. Furthermore, p63 was dramatically upregulated in all hybrid clusters, implying a role in regulating partial EMT and MET at primary and distant sites, respectively. Importantly, these effects were reversed by HIF1α loss or GPx2 gain of function, resulting in metastasis suppression. Conclusions Collectively, these results underscored a dramatic effect of redox signaling on p63 activation by HIF1α, underlying phenotypic and metabolic plasticity leading to mammary tumour metastasis.

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.

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 homogenous 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 over-dispersion 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 helps 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 Bioconductor R package ( https://github.com/Malindrie/scShapes ).

Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation. We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks. We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.

Aging is the primary risk factor for most neurodegenerative diseases, and recently coronavirus disease 2019 (COVID-19) has been associated with severe neurological manifestations that can eventually impact neurodegenerative conditions in the long-term. The progressive accumulation of senescent cells in vivo strongly contributes to brain aging and neurodegenerative co-morbidities but the impact of virus-induced senescence in the aetiology of neuropathologies is unknown. Here, we show that senescent cells accumulate in physiologically aged brain organoids of human origin and that senolytic treatment reduces inflammation and cellular senescence; for which we found that combined treatment with the senolytic drugs dasatinib and quercetin rejuvenates transcriptomic human brain aging clocks. We further interrogated brain frontal cortex regions in postmortem patients who succumbed to severe COVID-19 and observed increased accumulation of senescent cells as compared to age-matched control brains from non-COVID-affected individuals. Moreover, we show that exposure of human brain organoids to SARS-CoV-2 evoked cellular senescence, and that spatial transcriptomic sequencing of virus-induced senescent cells identified a unique SARS-CoV-2 variant-specific inflammatory signature that is different from endogenous naturally-emerging senescent cells. Importantly, following SARS-CoV-2 infection of human brain organoids, treatment with senolytics blocked viral retention and prevented the emergence of senescent corticothalamic and GABAergic neurons. Furthermore, we demonstrate in human ACE2 overexpressing mice that senolytic treatment ameliorates COVID-19 brain pathology following infection with SARS-CoV-2. In vivo treatment with senolytics improved SARS-CoV-2 clinical phenotype and survival, alleviated brain senescence and reactive astrogliosis, promoted survival of dopaminergic neurons, and reduced viral and senescence-associated secretory phenotype gene expression in the brain. Collectively, our findings demonstrate SARS-CoV-2 can trigger cellular senescence in the brain, and that senolytic therapy mitigates senescence-driven brain aging and multiple neuropathological sequelae caused by neurotropic viruses, including SARS-CoV-2.

Cell reprogramming involves time-intensive, costly processes that ultimately produce low numbers of reprogrammed cells of variable quality. By screening a range of polyacrylamide hydrogels (pAAm gels) of varying stiffness (1 kPA – 1.3 MPa) we found that a gel of medium stiffness significantly increases the overall number of reprogrammed cells by up to ten-fold with accelerated reprogramming kinetics, as compared to the standard Tissue Culture PolyStyrene (TCPS)-based protocol. We observe that though the gel improves both early and late phases of reprogramming, improvement in the late (reprogramming prone population maturation) phase is more pronounced and produces iPSCs having different characteristics and lower remnant transgene expression than those produced on TCPS. Comparative RNA-Seq analyses coupled with experimental validation reveals that modulation of Bone Morphogenic Protein (BMP) signalling by a novel reprogramming regulator, Phactr3, upregulated in the gel at an earliest time-point without the influence of transcription factors used for reprogramming, plays a crucial role in the improvement in the early reprogramming kinetics and overall reprogramming outcomes. This study provides new insights into the mechanism via which substrate stiffness modulates reprogramming kinetics and iPSC quality outcomes, opening new avenues for producing higher numbers of quality iPSCs or other reprogrammed cells at shorter timescales.

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.

A central event in the pathogenesis of motor neuron disease (MND) is the loss of neuromuscular junctions (NMJs), yet the mechanisms that lead to this event in MND remain to be fully elucidated. Maintenance of the NMJ relies upon neural agrin (n-agrin) which, when released from the nerve terminal, activates the postsynaptic Muscle Specific Kinase (MuSK) signaling complex to stabilize clusters of acetylcholine receptors. Here, we report that muscle from MND patients has an increased proportion of slow fibers and muscle fibers with smaller diameter. Muscle cells cultured from MND biopsies failed to form large clusters of acetylcholine receptors in response to either non-MND human motor axons or n-agrin. Furthermore, levels of expression of MuSK, and MuSK-complex components: LRP4, Caveolin-3, and Dok7 differed between muscle cells cultured from MND patients compared to those from non-MND controls. To our knowledge, this is the first time a fault in the n-agrin-LRP4-MuSK signaling pathway has been identified in muscle from MND patients. Our results highlight the n-agrin-LRP4-MuSK signaling pathway as a potential therapeutic target to prolong muscle function in MND.

The processes regulating tumor metastasis are multivariate and complex. Redox regulation of the tumor phenotype by GPx2 knockdown (KD) in breast cancer led us to uncover dramatic effects on spontaneous metastasis. Analysis of single cell RNA sequencing (scRNAseq) data from GPx2 KD tumor and control tumor, revealed that both tumors were comprised of several luminal-like tumor cell clusters and one mesenchymal-like cell cluster (cluster 3). Notably, GPx2 KD promoted a significant increase in the size of mesenchymal cells (cluster 3) relative to control, which might be due to the stimulation of epithelial-to-mesenchymal transition (EMT) in response to GPx2 loss. In support of this view, GPx2 KD stimulated an increase in mRNA expression of basal/mesenchymal (KRT5, KRT14, KRT17, Vimentin, Twist1, Twist2, CDH2) genes and a decrease in mRNA expression of epithelial/luminal (Cldn7 and Epcam) genes, especially in cluster 3. Moreover, GPx2 KD upregulated mRNA expression of basal/mesenchymal (Twist2, CDH2, and KRT14) genes in most luminal-like clusters expressing epithelial/luminal (Epcam, Cldn3/7, CDH1, KRT8/18) genes, implying these clusters may be undergoing EMT transition in a hybrid epithelial/mesenchymal state in response to GPx2 loss. Validation of these data in cell lines and tumors showed that GPx2 KD dramatically enhanced EMT via activation of ROS/HIF1α-mediated signaling. Importantly, these effects were reversed by GPx2 re-expression or HIF1α inhibition, which was capable of suppressing EMT and metastasis. Collectively, these results indicate that GPx2 loss promotes breast cancer metastasis by stimulating EMT due to HIF1α signaling, highlighting the impact of GPx2 and HIF1 on therapeutic intervention in metastasis. Citation Format: Zuen Ren, Huizhi Liang, Malindrie Dharmaratne, Miriam Morales-Gallego, Atefeh Taherian Fard, Jessica Mar, Kimita Suyama, Outhiriaradjou Benard, Michael B. Prystowsky, Larry Norton, Rachel B. Hazan. Loss of glutathione peroxidase 2 promotes epithelial to mesenchymal transition and breast cancer metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 968.

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.

Chromatin accessibility influences gene regulation governing cell identity that characterizes cell types, cell states and cell differentiation. Multimodal assays for jointly profiling the transcriptome and epigenome on the same cell provides the opportunity to identify genes that define cell identity by simultaneously analyzing gene expression and chromatin accessibility. However, current methods do not take into account the distribution shape profile of ATAC-seq peaks thereby potentially ignoring biologically rich source of information. Here, we present a novel computational approach which models the distribution shape profile of ATAC-seq promoter peaks to identify genes that are associated with cell identity. This is achieved by modelling the ATAC-seq promoter peaks using Bayesian Dirichlet Process Gaussian Mixture Models, and a generalized linear model is then built to predict gene expression using the cell memberships from modes in ATAC-seq peaks. Modelling the distribution shape profile of ATAC-seq promoter peaks reveal a vast majority of the promoter peaks are captured by at least two modes. We show that our framework identifies a predictive link between promoter peak modes and gene expression and thereby identify subset of genes that have a multi-modal predictive chromatin signature that are enriched for functionally related biological processes indicative of regulation at the cell type level. Our study demonstrates that integrating the distribution shape profile of ATAC-seq promoter peaks to study gene regulation identifies genes that play a role in defining cell identity and functionality, that were not discovered through standard approaches.

Abstract Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity. Our approach identified the common ageing regulator mTOR, as significantly associated with a mixture model profile but this gene was not detected through standard differential expression analysis using edgeR. Genes identified by the standard approaches like edgeR and the mixture model-based approach were found to be enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes that are identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.