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.

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.

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.

Clinical implications of convergent procoagulant toxicity and differential antivenom efficacy in Australian elapid snake venoms.

Australian elapid snakes are some of the most venomous snakes in the world and are unique among venomous snakes in having mutated forms of the blood clotting factor X in an activated form (FXa) as a key venom component. In human bite victims, an overdose of this activated clotting enzyme results in the systemic consumption of fibrinogen due to the large amounts of endogenous thrombin generated by the conversion of prothrombin to thrombin by venom FXa. Within Australian elapids, such procoagulant venom is currently known from the tiger snake clade (Hoplocephalus, Notechis, Paroplocephalus, and Tropidechis species), brown/taipan (Oxyuranus and Pseudonaja species) clade, and the red-bellied black snake Pseudechis porphyriacus. We used a STA-R Max coagulation analyser and TEG5000 thromboelastographers to test 47 Australian elapid venoms from 19 genera against human plasma in vitro. In addition to activity being confirmed in the two clades above, FXa-driven potent procoagulant activity was found in four additional genera (Cryptophis, Demansia, Hemiaspis, and Suta). Ontogenetic changes in procoagulant function was also identified as a feature of Suta punctata venom. Phylogenetic analysis of FX sequences confirmed that snake venom FXa toxins evolved only once, that the potency of these toxins against human plasma has increased in a stepwise fashion, and that multiple convergent amplifications of procoagulant activity within Australian elapid snakes have occurred. Cofactor dependence tests revealed all procoagulant venoms in our study, except those of the tiger snake clade, to be highly calcium-dependent, whereas phospholipid dependence was less of a feature but still displayed significant variation between venoms. Antivenom testing using CSL Tiger Snake Antivenom showed broad but differential cross-reactivity against procoagulant venoms, with P. porphyriacus and S. punctata extremely well neutralised but with Cryptophis, Demansia, and Hemiaspis less well-neutralised. The relative variation was not in accordance to genetic relatedness of the species used in antivenom production (Notechis scutatus), which underscores a fundamental principle that the rapid evolution characteristic of venoms results in organismal phylogeny being a poor predictor of antivenom efficacy. Our results have direct and immediate implications for the design of clinical management plans in the event of snakebite by such lesser known Australian elapid snake species that have been revealed in this study to be as potent as the better studied, and proven lethal, species.