Biology subjects

Indzhykulian, A. A.

Publications and source records attributed to Indzhykulian, A. A..

3 recordsLinked to original sources

bioRxiv2021

The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into two subtypes: outer hair cells (OHCs) which amplify sound-induced mechanical vibration, and inner hair cells (IHCs) which convert vibrations into electrical signals for interpretation by the brain. One row of IHCs and three rows of OHCs are arranged tonotopically; cells at a particular location respond best to a specific frequency which decreases from base to apex of the cochlea. Loss of hair cells at a specific place affects hearing performance at the corresponding tonotopic frequency. To better understand the underlying cause of hearing loss in patients (or experimental animals) a plot of hair cell survival along the cochlear frequency map, known as a cochleogram, can be generated post-mortem, involving manually counting thousands of cells. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Current microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Here, we present a machine learning-based hair cell analysis toolbox for the comprehensive analysis of whole cochleae (or smaller regions of interest). The Hair Cell Analysis Toolbox (HCAT) is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs vs OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep-learning-based detection tasks in other types of biological tissue: with some training data, HCATs core codebase can be trained to develop a custom deep learning detection model for any object on an image.

neuroscience↗

bioRxiv2021

Aging predisposes B cells to malignancy by activating c-Myc and perturbing the genome and epigenome

While cancer is an age-related disease, many cancer studies utilize younger animal models. Here, we uncover how a cancer, B-cell lymphoma, develops as a consequence of a naturally aged system. We show that this malignancy is associated with increased cell size, splenomegaly, and a newly discovered age-associated clonal B-cell (ACBC) population. Driven by exogenous c-Myc activation, hypermethylated promoters and somatic mutations, ACBC cells clonally expand independent of germinal centers (IgM+) and show increased biological age and hypomethylation in partially methylated domains related to mitotic solo-CpGs. Epigenetic changes in transformed mouse B cells are enriched for changes observed in human B-cell lymphomas. Mechanistically, the data suggest that cancerous ACBC cells originate from age-associated B cells, in part involving CD22 protein signaling fostered by the aging microenvironment. Transplantation assays demonstrate that ACBC evolve to become self-sufficient and support malignancy when transferred into young recipients. Inhibition of mTOR or c-Myc in old mice attenuates premalignant changes in B cells during aging and emerges as a therapeutic strategy to delay the onset of age-related lymphoma. Together, we show how aging contributes to B-cell lymphoma through a previously unrecognized mechanism involving cell-intrinsic changes and the aged microenvironment, characterize a model that captures the origin and progression of spontaneous cancer during aging and identify candidate interventions against age-associated lymphoma.

cancer biology↗

bioRxiv2020

Mechanical overstimulation causes acute injury followed by fast recovery in lateral-line neuromasts of larval zebrafish

Excess noise damages sensory hair cells, resulting in loss of synaptic connections with auditory nerves and, in some cases, hair-cell death. The cellular mechanisms underlying mechanically induced hair-cell damage and subsequent repair are not completely understood. Hair cells in neuromasts of larval zebrafish are structurally and functionally comparable to mammalian hair cells but undergo robust regeneration following ototoxic damage. We therefore developed a model for mechanically induced hair-cell damage in this highly tractable system. Free swimming larvae exposed to strong water wave stimulus for 2 hours displayed mechanical injury to neuromasts, including afferent neurite retraction, damaged hair bundles, and reduced mechanotransduction. Synapse loss was observed in apparently intact exposed neuromasts, and this loss was exacerbated by inhibiting glutamate uptake. Mechanical damage also elicited an inflammatory response and macrophage recruitment. Remarkably, neuromast hair-cell morphology and mechanotransduction recovered within hours following exposure, suggesting severely damaged neuromasts undergo repair. Our results indicate functional changes and synapse loss in mechanically damaged lateral-line neuromasts that share key features of damage observed in noise-exposed mammalian ear. Yet, unlike the mammalian ear, mechanical damage to neuromasts is rapidly reversible.

neuroscience↗