Modeling, applications and challenges of inner ear organoid

Emerging trends in biosensor and microfluidics integration for inner ear theragnostics

Advancements in inner ear theragnostics are critical for addressing the pervasive challenges of diagnosing and treating hearing and balance disorders, which significantly impact quality of life. This paper reviews biosensors and devices that leverage advanced functional nanomaterials, microfabrication techniques, and nano-biotechnology to enhance theragnostic applications for the inner ear. The paper highlights the development of diverse electromechanical, electrochemical, and biomarker sensors for inner ear theragnostics. Electromechanical sensors replicate the cochlear and vestibular sensory structures through bioinspired designs, while electrochemical sensors are used to measure the level of ions and chemicals in the inner ear fluid, providing insights into the health and disease of the hearing and balance organs. Biomarker sensors focus on screening of inner ear diseases through early detection of correlated biomarkers based on point of care diagnostics. This study also examines the use of microfluidic devices with sensory elements to provide a compact and integrated model of the fluid-filled cochlea. In addition, advanced delivery strategies, including targeted drug delivery systems and nanocarriers are explored for their ability to improve the penetration and distribution of therapeutics within the inner ear. The study also highlights the importance of pharmacokinetics and post-treatment monitoring as critical indicators for assessing the efficacy of micro/nanotechnology-based theragnostic approaches. By consolidating these innovations, this work offers a comprehensive framework for advancing otology, paving the way for novel diagnostic tools, effective treatments, and future clinical applications.

Optimized inner ear organoids for efficient hair cell generation and ototoxicity response modeling

Hair cells in the mammalian cochlea are highly vulnerable to damage from drug toxicity, noise exposure, aging, and genetic mutations, with no capacity for regeneration. Progress in hair cell protection research has been limited by the scarcity of cochlear tissue and suitable in vitro models. Here, we present a novel one-step, self-organizing inner ear organoid system optimized with small molecules, which bypasses the need for multi-step expansion and forced differentiation protocols. This approach efficiently generates hair cells and supporting cells that recapitulate the molecular, cellular, and structural characteristics of the inner ear. Single-cell RNA sequencing revealed the diversity and fidelity of cell populations within the organoids. Utilizing this platform, we validated the protective effects of candidate compounds against hair cell damage, highlighting its potential as a powerful tool for drug discovery and mechanistic studies of hair cell protection.

Human organoids for rapid validation of gene variants linked to cochlear malformations

Developmental anomalies of the hearing organ, the cochlea, are diagnosed in approximately one-fourth of individuals with congenital. The majority of patients with cochlear malformations remain etiologically undiagnosed due to insufficient knowledge about underlying genes or the inability to make conclusive interpretations of identified genetic variants. We used exome sequencing for the genetic evaluation of hearing loss associated with cochlear malformations in three probands from unrelated families deafness. We subsequently generated monoclonal induced pluripotent stem cell (iPSC) lines, bearing patient-specific knockins and knockouts using CRISPR/Cas9 to assess pathogenicity of candidate variants. We detected FGF3 (p.Arg165Gly) and GREB1L (p.Cys186Arg), variants of uncertain significance in two recognized genes for deafness, and PBXIP1(p.Trp574*) in a candidate gene. Upon differentiation of iPSCs towards inner ear organoids, we observed developmental aberrations in knockout lines compared to their isogenic controls. Patient-specific single nucleotide variants (SNVs) showed similar abnormalities as the knockout lines, functionally supporting their causality in the observed phenotype. Therefore, we present human inner ear organoids as a potential tool to validate the pathogenicity of DNA variants associated with cochlear malformations.

Dexamethasone nanocrystals-embedded hydroxypropyl methylcellulose hydrogel increases cochlear delivery and attenuates hearing loss following intratympanic injection

Herein, dexamethasone (DEX) nanocrystalline suspension (NS)-embedded hydrogel (NS-G) was constructed using a hydroxypropyl methylcellulose (HPMC) polymer to enhance cochlear delivery and attenuate hearing loss following intratympanic (IT) injection. Hydrophobic steroidal nanocrystals were prepared using a bead milling technique and incorporated into a polysaccharide hydrogel. The NS-G system with HPMC (average molecular weight, 86,000 g/mol; 15 mg/mL) was characterized as follows: rod-shaped drug crystalline; particle size <300 nm; and constant complex viscosity ≤1.17 Pa·s. Pulverization of the drug particles into submicron diameters enhanced drug dissolution, while the HPMC matrix increased the residence time in the middle ear cavity, exhibiting a controlled release profile. The IT NS-G system elicited markedly enhanced and prolonged drug delivery (> 9 h) to the cochlear tissue compared with that of DEX sodium phosphate (DEX-SP), a water-soluble prodrug. In mice with kanamycin- and furosemide-induced ototoxicity, NS-G markedly enhanced hearing preservation across all frequencies (8-32 kHz), as revealed by an auditory brainstem response test, compared with both saline and DEX-SP. Moreover, treatment with NS-G showed enhanced anti-inflammatory effects, as evidenced by decreased levels of inflammation-related cytokines. Therefore, the IT administration of DEX NS-loaded HPMC hydrogels is a promising strategy for treating hearing loss.

SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach

The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syllable-Specific Temporal Encoding (SSTE) to learn vocal sequences in a reservoir of Izhikevich neurons, by forming associations between exclusive input activities and their corresponding syllables in the sequence. Our model converts the audio signals to cochleograms using the CAR-FAC model to simulate a brain-like auditory learning and memorization process. The reservoir is trained using a hardware-friendly approach to FORCE learning. Reservoir computing could yield associative memory dynamics with far less computational complexity compared to RNNs. The SSTE-based learning enables competent accuracy and stable recall of spatiotemporal sequences with fewer reservoir inputs compared with existing encodings in the literature for similar purpose, offering resource savings. The encoding points to syllable onsets and allows recalling from a desired point in the sequence, making it particularly suitable for recalling subsets of long vocal sequences. The SSTE demonstrates the capability of learning new signals without forgetting previously memorized sequences and displays robustness against occasional noise, a characteristic of real-world scenarios. The components of this model are configured to improve resource consumption and computational intensity, addressing some of the cost-efficiency issues that might arise in future implementations aiming for compactness and real-time, low-power operation. Overall, this model proposes a brain-inspired pattern generation network for vocal sequences that can be extended with other bio-inspired computations to explore their potentials for brain-like auditory perception. Future designs could inspire from this model to implement embedded devices that learn vocal sequences and recall them as needed in real-time. Such systems could acquire language and speech, operate as artificial assistants, and transcribe text to speech, in the presence of natural noise and corruption on audio data.