Biology subjects

Hekselman, I.

Publications and source records attributed to Hekselman, I..

7 recordsLinked to original sources

bioRxiv2023

Human subcutaneous and visceral adipocyte atlases uncover classical and specialized adipocytes and depot-specific patterns

Human adipose depots are functionally distinct. Yet, recent single-nucleus RNA-sequencing (snRNA-seq) analyses largely uncovered overlapping/similar cell-type landscapes. We hypothesized that adipocytes subtypes, differentiation trajectories, and/or intercellular communication patterns could illuminate this depot similarity-difference gap. For this, we performed snRNA-seq of human subcutaneous and visceral adipose tissue. Whereas the majority of adipocytes in both depots were classical, namely enriched in lipid metabolism pathways, we also observed specialized adipocyte subtypes that were enriched in immune-related, extracellular matrix deposition (fibrosis), vascularization/angiogenesis, or ribosomal processes. Pseudo-temporal analysis suggested a developmental trajectory from adipose progenitor cells to classical adipocytes via specialized adipocytes, suggesting that the classical state stems from loss, rather than gain, of specialized functions. Lastly, intercellular communication routes were consistent with the different inflammatory tone of the two depots. Jointly, these findings provide a high-resolution view into the contribution of cellular composition, differentiation, and intercellular communication patterns to human fat depot differences.

systems biology↗

bioRxiv2022

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,113 hereditary diseases. Overall, we identified 110 cell types affected by 714 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.

systems biology↗

bioRxiv2021

A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases

Genetic studies of Mendelian and rare diseases face the critical challenges of identifying pathogenic gene variants and their modes-of-action. Previous efforts rarely utilized the tissue-selective manifestation of these diseases for their elucidation. Here we introduce an interpretable machine learning (ML) platform that utilizes heterogeneous and large-scale tissue-aware datasets of human genes, and rigorously, concurrently and quantitatively assesses hundreds of candidate mechanisms per disease. The resulting tissue-aware ML platform is applicable in gene-specific, tissue-specific, or patient-specific modes. Application of the platform to selected Mendelian disease genes pinpointed mechanisms that lead to tissue-specific disease manifestation. When applied jointly to diseases that manifest in the same tissue, the models revealed common known and previously underappreciated factors that underlie tissue-selective disease manifestation. Lastly, we harnessed our ML platform toward genetic diagnosis of tissue-selective rare diseases. Patient-specific models of candidate disease-causing genes from 50 patients successfully prioritized the pathogenic gene in 86% of the cases, implying that the tissue-selectivity of rare diseases aids in filtering out unlikely candidate genes. Thus, interpretable tissue-aware ML models can boost mechanistic understanding and genetic diagnosis of tissue-selective heritable diseases. A webserver supporting gene prioritization is available at https://netbio.bgu.ac.il/trace/.

genetics↗

bioRxiv2020

Multi-cellular communities are perturbed in the aging human brain and with Alzheimer's disease

The role of different cell types and their interactions in Alzheimers disease (AD) is an open question. Here we pursued it by assembling a high-resolution cellular map of the aging frontal cortex by single nucleus RNA-seq of 24 individuals with different clinicopathologic characteristics. We used the map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA-seq, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including inhibitory neuronal subtypes and oligodendroglial states. We further recovered a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.

neuroscience↗

bioRxiv2020

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Here, we used human tissue RNA-sequencing profiles to analyze the expression and organization of chaperones across 29 main tissues. We found that relative to protein-coding genes, chaperones were significantly more ubiquitously and highly expressed across all tissues. Nevertheless, differential expression analysis revealed that most chaperones were up- or down-regulated in certain tissues, suggesting that they have tissue-specific roles. In agreement, chaperones that were upregulated in skeletal muscle were highly enriched in mouse myoblasts and in nematodes muscle tissue, and overlapped significantly with chaperones that are causal for muscle diseases. We also identified a distinct subset of chaperones that formed a uniformly-expressed, cross-family core group conducting basic cellular functions that was significantly more essential for cell survival. Altogether, this suggests a layered architecture of chaperones across tissues that is composed of shared core elements that are complemented by variable elements which give rise to tissue-specific functions and sensitivities, thereby contributing to the tissue-specificity of protein misfolding diseases. Significance StatementProtein misfolding diseases, such as neurodegenerative disorders and myopathies, are often manifested in a specific tissue or even a specific cell type. Enigmatically, however, they are typically caused by mutations in widely expressed proteins. Here we focused on chaperones, the main and basic components of the protein-folding machinery of cells. Computational analyses of large scale tissue transcriptomes unveils that the chaperone system is composed of core essential elements that are uniformly expressed across tissues, and of variable elements that are differentially expressed in a tissue-specific manner. This organization allows each tissue to fit the quality control system to its specific requirements and illuminates the mechanisms that underlie a tissues susceptibility to protein-misfolding diseases.

systems biology↗

bioRxiv2019

Differential network analysis of human tissue interactomes highlights tissue-selective processes and genetic disorder genes

MotivationDifferential network analysis, designed to highlight interaction changes between conditions, is an important paradigm in network biology. However, network analysis methods have been typically designed to compare between few conditions, were rarely applied to protein interaction networks (interactomes). Moreover, large-scale benchmarks for their evaluation have been lacking.\n\nResultsHere, we assess five network analysis methods by applying them to 34 human tissues interactomes. For this, we created a manually-curated benchmark of 6,499 tissue-specific, gene ontology biological processes, and analyzed the ability of each method to expose these tissue-process associations. The four differential network analysis methods outperformed the non-differential, expression-based method (AUCs of 0.82-0.9 versus 0.69, respectively). We then created another benchmark, of 1,527 tissue-specific disease cases, and analyzed the ability of differential network analysis methods to highlight additional disease-related genes. Compared to a non-differential subnetworks surrounding a known disease-causing gene, the extremely-differential subnetwork (top 1%) was significantly enriched for additional disease-causing genes in 18.6% of the cases (p[≤]10e-3). In 5/10 tissues tested, including Muscle, nerve and heart tissues (p = 2.54E-05, 2.71E-04, 3.63E-19), such enrichments were highly significant.\n\nSummaryAltogether, our study demonstrates that differential network analysis of human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. Moreover, it offers expansive manually-curated datasets of tissue-selective processes and diseases that could serve for benchmark and for analyses in many other studies.\n\nContactestiyl@bgu.ac.il

bioinformatics↗

bioRxiv2017

Role of duplicate genes in determining the tissue-selectivity of hereditary diseases

A longstanding puzzle in human genetics is what limits the clinical manifestation of hundreds of hereditary diseases to certain tissues or cell types, while their causal genes are present and expressed throughout the human body. Here we considered a possible role for paralogs of causal genes in affecting this tissue selectivity. It has been shown across organisms that paralogs can compensate for the loss of each other. We hypothesized that specifically in the disease tissue causal genes and their paralogs are imbalanced, leading to insufficient compensation and to the emergence of disease phenotypes. While demonstrated previously in the context of few specific diseases, this hypothesis was never assessed quantitatively at large-scale. For this, we analyzed functional relationships between causal genes and their paralogs associated with 112 tissue-selective hereditary diseases. To test our hypothesis we used several large-scale omics datasets, including RNA sequencing profiles of over 30 different human tissues. Indeed, the expression of causal genes and their paralogs was significantly imbalanced in their disease tissues compared to unaffected tissues. Imbalanced expression was evident across different disease tissues, and was common to causal genes with single or multiple paralogs. This imbalance was driven by significant upregulation of the causal gene in its disease tissue, often combined with significant down-regulation of a paralog. Nevertheless, in additional 20% of the causal genes, a paralog alone was significantly down-regulated in the disease tissue. Our results suggest that dosage relationships between paralogs affect the phenotypic outcome of germline aberrations, adding paralogs as important modifiers of disease manifestation.

systems biology↗