Biology subjects

Taherian Fard, A.

Publications and source records attributed to Taherian Fard, A..

7 recordsLinked to original sources

bioRxiv2024

Ataxia Telangiectasia patient-derived neuronal and brain organoid models reveal mitochondrial dysfunction and oxidative stress

Ataxia Telangiectasia (AT) is a rare genetic disorder caused by mutations in the ATM gene and is characterized by oxidative stress, premature ageing, and progressive neurodegeneration of the cerebellum. The molecular mechanisms driving the neurological defects AT remain unclear, mainly due to lack of human neuronal models. Here, we use AT patient-derived pluripotent stem cells (iPSCs) and iPSC-derived neurons and brain organoids to comprehensively explore mitochondrial dysfunction, oxidative stress, and senescence phenotypes. We identified mislocalisation of mitochondria, a prevailing reduction in mitochondrial membrane potential, and increased oxidative stress in AT patient-derived iPSC and neuronal cultures that was restored by ATM gene correction. Cortical brain organoids from AT patients also display extensive oxidative stress, increased levels of senescence, and impaired neuronal function that could be counteracted with antioxidant treatment. Transcriptomic analysis identified disruptions in regulatory networks related to mitochondrial function and maintenance, including alterations in the PARP/SIRT signalling axis and dysregulation of key mitophagy and mitochondrial fission-fusion processes. Our study reveals that progressive mitochondrial dysfunction and aberrant ROS production are hallmarks of AT, and lead us to conclude that ATM is a master regulator of mitochondrial homeostasis.

cell biology↗

bioRxiv2023

Assessing tissue-specific gene expression of essential genes from human and mouse.

A gene satisfies the definition of essentiality when the loss of its function compromises an organisms viability. Identifying essential genes is useful for understanding the core components that regulate a biological system and ensure its survival. Advances in gene editing techniques like CRISPR-Cas9 generate the capacity to comprehensively interrogate a genome to elucidate what genes are essential. However, these techniques are often applied in the context of a single cell line and even when studies have collated essential gene sets across multiple cell lines, this information is rarely probed at a level that incorporates multiple cell and tissue types. The recent availability of large-scale single-cell RNA-sequencing (scRNA-seq) atlases provides an unprecedented opportunity to investigate the distribution of essential gene expression in cell and tissue types. Here, we leverage information contained in benchmarking datasets, single cell tissue atlases, and databases of essential genes, and develop a computational method, scEssentials, which uses a statistical framework to report on the robustness and specificity of essential genes in human and mouse across multiple cell types. Using scEssentials, both mouse and human models showed consistently high in expression and exhibit limited variably across more than 60 cell types. We also demonstrate a substantial number of significantly correlated gene pairs within scEssentials, which produce densely connected co-expression networks with functional annotation. Furthermore, we showed high frequencies of scEssentials across 200 pathways. Finally, we develop a score to quantify the relative essentiality of genes within scEssentials, which further validates with significant association with gene mutation frequency and chromatin accessibility. Using the heterogeneous ageing process, we demonstrate the application of scEssentials and their robust gene expression profile. Only one-fifth of scEssentials showed significant ageing-related differential expression among three age groups, occurring primarily in muscle satellite cells of varying tissue origins and highly interacting brain cells. Collectively, the robustness of scEssentials serves as a reference for analysing scRNA-seq data and provides insight into the heterogeneous nature of biological processes such as ageing.

bioinformatics↗

bioRxiv2023

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

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.

neuroscience↗

bioRxiv2022

Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell ageing

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently 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. ResultsIn this study, we conducted a systematic evaluation of cell-to-cell gene expression variability using 14 different variability metrics that are commonly applied to transcriptomic data. Performance was evaluated with respect to data-specific features like sparsity and sequencing platform, biological properties like gene length, and the ability to recapitulate true levels of variability based on simulation and known biological gene sets like ribosomal genes and stably expressed genes. scran had the strongest all-round performance, and this metric was then applied to investigate the changes in cell-to-cell variability that occur during ageing. Studying ageing showcases the value of cell-to-cell variability as it is a genetically-regulated program that is influenced by stochastic processes.scRNA-seq datasets from hematopoietic stem cells (HSCs) and B lymphocytes and other cell types from this differentiation lineage were used with scran to identify the genes with consistent patterns of variable and stable expression profiles during differentiation. Furthermore, to understand the regulatory relationship for genes that were differentially-variable in their expression between young and old mice, we constructed networks using transcription factors and their known targets for HSC and B lymphocyte cells. Comparisons of these networks identified a shared TF Sfpi1 that although was seen to increase in gene expression variability in old mice versus young in both cell types, the corresponding targets were distinct and their gene expression variability had different directions between cell types. ConclusionsThrough these analyses, we highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and ageing, and emphasise the value and specificity of interpreting these findings at the level of individual cell types.

bioinformatics↗

bioRxiv2022

How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance 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 a scRNA-seq dataset, specific to the biological system under study, has 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 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 system under study and provide practical guidance for researchers looking to optimise cell similarity search for the structural properties of their own data.

bioinformatics↗

bioRxiv2022

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

BackgroundSingle 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. ResultsWe 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. ConclusionsThis 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).

bioinformatics↗

bioRxiv2022

Deconstructing replicative senescence heterogeneity of human mesenchymal stem cells at single cell resolution reveals therapeutically targetable senescent cell sub-populations

Cellular senescence is characterised by a state of permanent cell cycle arrest. It is accompanied by often variable release of the so-called senescence-associated secretory phenotype (SASP) factors, and occurs in response to a variety of triggers such as persistent DNA damage, telomere dysfunction, or oncogene activation. While cellular senescence is a recognised driver of organismal ageing, the extent of heterogeneity within and between different senescent cell populations remains largely unclear. Elucidating the drivers and extent of variability in cellular senescence states is important for discovering novel targeted seno-therapeutics and for overcoming cell expansion constraints in the cell therapy industry. Here we combine cell biological and single cell RNA-sequencing approaches to investigate heterogeneity of replicative senescence in human ESC-derived mesenchymal stem cells (esMSCs) as MSCs are the cell type of choice for the majority of current stem cell therapies and senescence of MSC is a recognized driver of organismal ageing. Our data identify three senescent subpopulations in the senescing esMSC population that differ in SASP, oncogene expression, and escape from senescence. Uncovering and defining this heterogeneity of senescence states in cultured human esMSCs allowed us to identify potential drug targets that may delay the emergence of senescent MSCs in vitro and perhaps in vivo in the future.

bioinformatics↗