Biology subjects

Zakar-Polyak, E.

Publications and source records attributed to Zakar-Polyak, E..

3 recordsLinked to original sources

bioRxiv2026

Sample-specific protein-protein interaction networks inferred from transcriptomics and proteomics show high similarities

Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.

systems biology↗

bioRxiv2026

Rapid direct neuronal reprogramming of human dental pulp stem cells

Direct neuronal reprogramming offers an alternative to induced pluripotent stem cell-based differentiation by converting somatic cells directly into neurons without passage through pluripotency. However, commonly used fibroblast-based protocols are often slow and inefficient. Here, we evaluated human dental pulp stem cells (DPSCs), which originate from the cranial neural crest and possess intrinsic neurogenic potential, as a developmentally relevant source of induced neurons (iNs). Using an all-in-one lentiviral vector, we converted DPSCs into iNs within 17 days, compared with 28 days for fibroblasts reprogrammed with the same vector, and achieved significantly higher neuronal purity under the respective established protocols. Multi-omic profiling revealed coordinated suppression of mesenchymal and cell-cycle programs and induction of neuronal, synaptic, and metabolic pathways. Single-nucleus RNA sequencing resolved fibroblast-like, transitional, maturing neuronal, GABAergic-like, and alternative fates, while trajectory inference suggested divergent neuronal and non-neuronal conversion paths. Whole-cell recordings showed that a subset of DPSC-iNs developed early neuronal excitability and voltage-gated inward and outward currents. Together, our findings establish DPSCs as an accessible and developmentally relevant source for rapid direct neuronal conversion. This integrated molecular, single-nucleus, and electrophysiological characterization defines the cellular heterogeneity of DPSC-to-neuron reprogramming and provides a framework for protocol refinement and future patient-specific disease modeling.

neuroscience↗

bioRxiv2025

Human brain cell-type-specific aging clocks based on single-nuclei transcriptomics

Aging is the primary risk factor for most neurodegenerative diseases, yet the cell-type-specific progression of brain aging remains poorly understood. Here, we developed human cell-type-specific transcriptomic aging clocks using high-quality single-nucleus RNA sequencing data from post mortem human prefrontal cortex tissue of 31 donors aged 18 - 94 years, encompassing 73,941 high-quality nuclei. We observed distinct transcriptomic changes across major cell types, including upregulation of inflammatory response genes in microglia from older samples. Aging clocks trained on each major cell type accurately predicted chronological age and remained robust in independent single-nucleus RNA-sequencing datasets, underscoring their broad applicability. These findings demonstrate the feasibility of cell-type-specific transcriptomic clocks to measure biological aging in the human brain and highlight potential mechanisms of selective vulnerability in neurodegenerative diseases. We anticipate these clocks will serve as a basis for further studies in other brain regions and more diverse populations, ultimately advancing our understanding of age-related neurodegenerative processes at the single-cell level.

neuroscience↗