Biology subjects

Peltekian, A.

Publications and source records attributed to Peltekian, A..

4 recordsLinked to original sources

bioRxiv2026

Selective gating of neural modulation through frequency- and behavior-dependent modes during cortical electrical stimulation

Electrical stimulation is widely used to modulate neuronal activity, yet its effects on neuronal circuits in vivo remain poorly understood. This, in turn, has hindered the principled design of stimulation protocols and raised questions about reproducibility that constrain the field's translational impact. Here we combine cortical sinusoidal electrical stimulation (sES) with Neuropixels recordings to characterize stimulation-driven responses in more than 2,700 well-isolated neurons across 53 brain areas in 14 behaving, head-fixed mice. We uncover two distinct, concurrent modes of neural modulation. First is a sustained, brain-wide spike-phase entrainment effect that depends on stimulation frequency: entrainment to slow stimulation is supported by non-synaptic electric field propagation while anatomical connectivity dominates entrainment to higher stimulation frequencies. Second, we find a transient, spatially localized spike-rate modulation mainly mediated through anatomical connectivity that only emerges at high stimulation frequencies by selectively recruiting inhibitory neurons. We show that the two distinct modes are differentially shaped by behavior. By identifying how stimulation frequency governs the mechanism of neural engagement and how behavioral state selectively gates brain-wide entrainment but not local inhibitory recruitment, our results provide a mechanistic foundation for designing targeted, reproducible neuromodulation strategies.

neuroscience↗

bioRxiv2026

Pathogen-specific host responses define distinct pneumonia endotypes in the human lung

Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients; 263 samples, complemented by 9 healthy controls and 25 post-COVID-19 patients, yielding [~]2.4 million single cells across 28 cell types), and extracted daily clinical data from the electronic health record (>15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.

immunology↗

bioRxiv2021

Scalable Clustering with Supervised Linkage Methods

Data clustering plays a significant role in biomedical sciences, particularly in single-cell data analysis. Researchers use clustering algorithms to group individual cells into populations that can be evaluated across different levels of disease progression, drug response, and other clinical statuses. In many cases, multiple sets of clusters must be generated to assess varying levels of cluster specificity. For example, there are many subtypes of leukocytes (e.g. T cells), whose individual preponderance and phenotype must be assessed for statistical/functional significance. In this report, we introduce a novel hierarchical density clustering algorithm (HAL-x) that uses supervised linkage methods to build a cluster hierarchy on raw single-cell data. With this new approach, HAL-x can quickly predict multiple sets of labels for immense datasets, achieving a considerable improvement in computational efficiency on large datasets compared to existing methods. We also show that cell clusters generated by HAL-x yield near-perfect F1-scores when classifying different clinical statuses based on single-cell profiles. Our hierarchical density clustering algorithm achieves high accuracy in single cell classification in a scalable, tunable and rapid manner. We make HAL-x publicly available at: https://pypi.org/project/hal-x/

bioinformatics↗

bioRxiv2020

NMDAR-mediated transcriptional control of geneexpression in the specification of interneuron subtype identity

Medial ganglionic eminence (MGE)-derived parvalbumin (PV)+, somatostatin (SST)+ and Neurogliaform (NGFC)-type cortical and hippocampal interneurons, have distinct molecular, anatomical and physiological properties. However, the molecular mechanisms regulating their diversity remain poorly understood. Here, via single-cell transcriptomics, we show that the obligate NMDA-type glutamate receptor (NMDAR) subunit gene Grin1 mediates subtype-specific transcriptional regulation of gene expression in MGE-derived interneurons, leading to altered subtype abundances. Notably, MGE-specific conditional Grin1 loss results in a systemic downregulation of diverse transcriptional, synaptogenic and membrane excitability regulatory programs. These widespread gene expression abnormalities mirror aberrations that are typically associated with neurodevelopmental disorders, particularly schizophrenia. Our study hence provides a road map for the systematic examination of NMDAR signaling in interneuron subtypes, revealing potential MGE-specific genetic targets that could instruct future therapies of psychiatric disorders.

neuroscience↗