Biology subjects

Salavaty, A.

Publications and source records attributed to Salavaty, A..

4 recordsLinked to original sources

bioRxiv2026

ClustoCell reveals cell states and their markers from single-cell transcriptomes

Accurate identification of cell types and states is essential for reliable single-cell RNA-sequencing analyses, yet current methods remain sensitive to continuous biological states, data preprocessing choices, and reference selection. Here we present ClustoCell, a reference-free method that resolves cell identity using within-cell transcriptional architecture. By stratifying gene expression of each cell into high and medium tiers, ClustoCell constructs cell-cell similarity graphs that prioritize intrinsic expression structure over global variance. Across 450 datasets spanning over 24 million cells, ClustoCell recovered expert annotations with high concordance (92%). Benchmarked against state-of-the-art methods, ClustoCell identifies more stable and coherent cell types and states, avoids excessive partitioning of closely related cells, and improves the identification of cell type-specific markers. From transcriptional structure alone, ClustoCell resolves rare and transitional cell states, distinguishes malignant from non-malignant cells, and refines expert cell annotations. Applied to immunotherapy datasets, ClustoCell uncovered coordinated pre-treatment immune circuits linking T cell states to PD-1 responsiveness in a tumour-type-specific manner. ClustoCell provides an interpretable and scalable foundation for single-cell analysis and translational profiling.

bioinformatics↗

bioRxiv2022

Systems-level investigation of mucopolysaccharidosis IIIA identifies deficient synaptic activity as a key driver of disease progression

Mucopolysaccharidoses are lysosomal storage diseases that collectively represent a major cause of lethal, treatment-refractory childhood dementias 1-7 Clinically-useful interventions are hampered due to an incomplete understanding of their neuropathological origins. Using the zebrafish sgsh model of mucopolysaccharidosis IIIA 8 (MPS IIIA, Sanfilippo syndrome A), we conducted several omics-based analyses, and developed and benchmarked a novel bioinformatic feature classification and ranking model for high-throughput datasets - ExIR - to prioritise important features in the progression of neurological manifestations of the disease. We find that the massive endolysosomal burden resulting from increased lysosomal storage of heparan sulfate and other secondarily accumulating substrates, such as sphingolipids, induces abnormal microtubule organisation and vesicle trafficking in neurons. This results in a gradual impairment of synaptic vesicle localisation at the presynaptic terminal and consequently impaired neuronal activity. Importantly, the endolysosomal phenotype in MPS IIIA zebrafish well-precedes the onset of neural pathology, though the larval MPS IIIA brain was found to be more susceptible to perturbation than wild type siblings. Collectively, these analyses demonstrate the presence of a progressive functional neurodegenerative phenotype underpinning neurological disease in MPS IIIA. Our findings provide direct mechanistic evidence linking the well-described lysosomal storage basis for MPS IIIA to its disproportionately severe neural clinical involvement, enabling development and refinement of future therapeutic interventions for this currently untreatable disorder. HighlightsO_LIMPS IIIA represents one of the most common causes of broadly fatal childhood dementia, but the mechanisms underlying disease progression are poorly understood. C_LIO_LIThe first systems-level analyses of disease state and progression in the CNS of an MPS IIIA animal model were performed. C_LIO_LIExperimental data-based Integrative Ranking (ExIR) was developed to provide unbiased prioritisation and classification of biological data as drivers, biomarkers and mediators of biological processes from high-throughput data at a systems level. C_LIO_LIApplication of ExIR to a transcriptomic and proteomic analyses of a zebrafish model of MPS IIIA implies progressive deficiencies in synaptic activity as a key driver of disease progression correlating with progressive neuronal endolysosomal burden and secondary storage diseases. C_LIO_LIA novel unifying explanation of pathobiology and progression of MPS IIIA facilitates identification of clinically targetable features and may be generalised to other neuronopathic storage disorders. C_LI

neuroscience↗

bioRxiv2022

Parallel use of pluripotent human stem cell lung and heart models provide new insights for treatment of SARS-CoV-2

SARS-CoV-2 primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe COVID-19. To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR- Cas9 mediated knock-out of ACE2, we demonstrated that angiotensin converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but further processing in lung cells required TMPRSS2 while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems. One-sentence summaryRational treatment strategies for SARS-CoV-2 derived from human PSC models

microbiology↗

bioRxiv2020

IHS: an integrative method for the identification of network hubs

Biological systems are composed of highly complex networks and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points, or hub nodes, within a network. However, none of the current methods correct for inherent positional biases which limits their applicability. In addition, none of the currently available hub detection algorithms effectively combine network centrality measures together. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures, and developed a novel algorithm termed "integrated hubness score" (IHS), which integrates the most important and commonly used network centrality measures, namely degree centrality, betweenness centrality and neighbourhood connectivity, in an unbiased way. When compared against the four most commonly used hub identification methods on four independent validated biological networks, the IHS algorithm outperformed all other assessed methods. Using this novel and universal method, researchers of any discipline can now identify the most influential network nodes.

systems biology↗