Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning

Mice with deficiency in Pcdh15, a gene associated with bipolar disorders, exhibit significantly elevated diurnal amplitudes of locomotion and body temperature

Genetic factors significantly affect the pathogenesis of psychiatric disorders. However, the specific pathogenic mechanisms underlying these effects are not fully understood. Recent extensive genomic studies have implicated the protocadherin-related 15 (PCDH15) gene in the onset of psychiatric disorders, such as bipolar disorder (BD). To further investigate the pathogenesis of these psychiatric disorders, we developed a mouse model lacking Pcdh15. Notably, although PCDH15 is primarily identified as the causative gene of Usher syndrome, which presents with visual and auditory impairments, our mice with Pcdh15 homozygous deletion (Pcdh15-null) did not exhibit observable structural abnormalities in either the retina or the inner ear. The Pcdh15-null mice showed very high levels of spontaneous motor activity which was too disturbed to perform standard behavioral testing. However, the Pcdh15 heterozygous deletion mice (Pcdh15-het) exhibited enhanced spontaneous locomotor activity, reduced prepulse inhibition, and diminished cliff avoidance behavior. These observations agreed with the symptoms observed in patients with various psychiatric disorders and several mouse models of psychiatric diseases. Specifically, the hyperactivity may mirror the manic episodes in BD. To obtain a more physiological, long-term quantification of the hyperactive phenotype, we implanted nano tag® sensor chips in the animals, to enable the continuous monitoring of both activity and body temperature. During the light-off period, Pcdh15-null exhibited elevated activity and body temperature compared with wild-type (WT) mice. However, we observed a decreased body temperature during the light-on period. Comprehensive brain activity was visualized using c-Fos mapping, which was assessed during the activity and temperature peak and trough. There was a stark contrast between the distribution of c-Fos expression in Pcdh15-null and WT brains during both the light-on and light-off periods. These results provide valuable insights into the neural basis of the behavioral and thermal characteristics of Pcdh15-deletion mice. Therefore, Pcdh15-deletion mice can be a novel model for BD with mania and other psychiatric disorders, with a strong genetic component that satisfies both construct and surface validity.

PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting

Introdution: During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity, investigators must often manually measure cell orientations, which is a time-consuming endeavor. To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). Methods: PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. For validation, inner ear sensory epithelia including cochleae and utricles from mice were immunostained for βII-spectrin and imaged with a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear hair cell angle measurements for accuracy using a one to five agreement scale. For utricle samples, PCPA derived measurements were directly compared against manually derived angle measurements and the concordance correlation coefficient (CCC) and Bland-Altman limits of agreement were calculated. PCPA was also tested against previously published images examining PCP in various tissues and across various species suggesting fairly broad utility. Results: PCPA was able to recognize and count 99.81% of cochlear hair cells, and was able to obtain ideally accurate planar cell polarity measurements for at least 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to 98%-100% for all users and across all samples. When PCPA's measurements were compared with manual angle measurements for E17.5 utricles there was negligible bias (<0.5°), and a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Discussion: Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5-15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 107,000 hair cells which have been identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

PCP Auto Count: A Novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting

Background

During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity investigators must often manually measure cell orientations, which is a time-consuming endeavor.

Methodology

To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. Inner ear sensory epithelia including cochleae (P4) and utricles (E17.5) from mice were immunostained for βII-spectrin and imaged on a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear angle measurements for accuracy using a 1-5 agreement scale. For utricle samples, we directly compared PCPA derived measurements against manually derived angle measurements using concordance correlation coefficients (CCC) and Bland-Altman limits of agreement. Finally, PCPA was tested against a variety of images copied from publications examining PCP in various tissues and across various species.

Results

PCPA was able to recognize and count 99.81% of cochlear hair cells (n = 1,1541 hair cells) in a sample set, and was able to obtain ideally accurate planar cell polarity measurements for over 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to >98%. When manual angle measurements for E17.5 utricles were compared, PCPA's measurements fell within -9 to +10 degrees of manually obtained mean angle measures with a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90'000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

Enlightening brain energy metabolism

Brain tissue metabolism is distributed across several cell types and subcellular compartments, which activate at different times and with different temporal patterns. The introduction of genetically-encoded fluorescent indicators that are imaged using time-lapse microscopy has opened the possibility of studying brain metabolism at cellular and sub-cellular levels. There are indicators for sugars, monocarboxylates, Krebs cycle intermediates, amino acids, cofactors, and energy nucleotides, which inform about relative levels, concentrations and fluxes. This review offers a brief survey of the metabolic indicators that have been validated in brain cells, with some illustrative examples from the literature. Whereas only a small fraction of the metabolome is currently accessible to fluorescent probes, there are grounds to be optimistic about coming developments and the application of these tools to the study of brain disease.

The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light 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. 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. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus 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, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image.

Three-dimensional mouse cochlea imaging based on the modified Sca/eS using confocal microscopy

The three-dimensional stria vascularis (SV) and cochlear blood vessel structure is essential for inner ear function. Here, modified Sca/eS, a sorbitol-based optical-clearing method, was reported to visualize SV and vascular structure in the intact mouse cochlea. Cochlear macrophages as well as perivascular-resident macrophage-like melanocytes were detected as GFP-positive cells of the CX3CR1^+/GFP mice. This study's method was effective in elucidating inner ear function under both physiological and pathological conditions.

Visualizing physiological parameters in cells and tissues using genetically encoded indicators for metabolites

The study of metabolism is undergoing a renaissance. Since the year 2002, over 50 genetically-encoded fluorescent indicators (GEFIs) have been introduced, capable of monitoring metabolites with high spatial/temporal resolution using fluorescence microscopy. Indicators are fusion proteins that change their fluorescence upon binding a specific metabolite. There are indicators for sugars, monocarboxylates, Krebs cycle intermediates, amino acids, cofactors, and energy nucleotides. They permit monitoring relative levels, concentrations, and fluxes in living systems. At a minimum they report relative levels and, in some cases, absolute concentrations may be obtained by performing ad hoc calibration protocols. Proper data collection, processing, and interpretation are critical to take full advantage of these new tools. This review offers a survey of the metabolic indicators that have been validated in mammalian systems. Minimally invasive, these indicators have been instrumental for the purposes of confirmation, rebuttal and discovery. We envision that this powerful technology will foster metabolic physiology.

A deep learning approach to quantify auditory hair cells

Hearing loss affects millions of people worldwide. Yet, there are still no curative therapies for sensorineural hearing loss. Frequent causes of sensorineural hearing loss are due to damage or loss of the sensory hair cells, the spiral ganglion neurons, or the synapses between them. Culturing the organ of Corti allows the study of all these structures in an experimental model, which is easy to manipulate. Therefore, the in vitro culture of the neonatal mammalian organ of Corti remains a frequently used experimental system, in which hair cell survival is routinely assessed. However, the analysis of the surviving hair cells is commonly performed via manual counting, which is a time-consuming process and the inter-rater reliability can be an issue. Here, we describe a deep learning approach to quantify hair cell survival in the murine organ of Corti explants. We used StarDist, a publicly available platform and plugin for Fiji (Fiji is just ImageJ), to train and apply our own custom deep learning model. We successfully validated our model in untreated, cisplatin, and gentamicin treated organ of Corti explants. Therefore, deep learning is a valuable approach for quantifying hair cell survival in organ of Corti explants. Moreover, we also demonstrate how the publicly available Fiji plugin StarDist can be efficiently used for this purpose.

Tissue clearing technique: Recent progress and biomedical applications

Organisms are inherently three dimensional, thus comprehensive understanding of the complicated biological system requires analysis of organs or even whole bodies in the context of three dimensions. However, this is a tremendous task since the biological specimens are naturally opaque, a major obstacle in whole-body and whole-organ imaging. Tissue clearing technique provides a prospective solution and has become a powerful tool for three-dimensional imaging and quantification of organisms. Tissue clearing technique aims to make tissue transparent by minimizing light scattering and light absorption, thus allowing deep imaging of large volume samples. When combined with diverse molecular labeling methods and high-throughput optical sectioning microscopes, tissue clearing technique enables whole-body and whole-organ imaging at cellular or subcellular resolution, providing detailed and comprehensive information about the intact biological systems. Here, we give an overview of recent progress and biomedical applications of tissue clearing technique. We introduce the mechanisms and basic principles of tissue clearing, and summarize the current tissue clearing methods. Moreover, the available imaging techniques and software packages for data processing are also presented. Finally, we introduce the recent advances in applications of tissue clearing in biomedical fields. Tissue clearing contributes to the investigation of structure-function relationships in intact mammalian organs, and opens new avenues for cellular and molecular mapping of intact human organs. We hope this review contributes to a better understanding of tissue clearing technique and can help researchers to select the best-suited clearing protocol for their experiments.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Three-dimensional imaging of intact porcine cochlea using tissue clearing and custom-built light-sheet microscopy

Hearing loss is a prevalent disorder that affects people of all ages. On top of the existing hearing aids and cochlear implants, there is a growing effort to regenerate functional tissues and restore hearing. However, studying and evaluating these regenerative medicine approaches in a big animal model (e.g. pigs) whose anatomy, physiology, and organ size are similar to a human is challenging. In big animal models, the cochlea is bulky, intricate, and veiled in a dense and craggy otic capsule. These facts complicate 3D microscopic analysis that is vital in the cochlea, where structure-function relation is time and again manifested. To allow 3D imaging of an intact cochlea of newborn and juvenile pigs with a volume up to ∼ 250 mm3, we adapted the BoneClear tissue clearing technique, which renders the bone transparent. The transparent cochleae were then imaged with cellular resolution and in a timely fashion, which prevented bubble formation and tissue degradation, using an adaptive custom-built light-sheet fluorescence microscope. The adaptive light-sheet microscope compensated for deflections of the illumination beam by changing the angles of the beam and translating the detection objective while acquiring images. Using this combination of techniques, macroscopic and microscopic properties of the cochlea were extracted, including the density of hair cells, frequency maps, and lower frequency limits. Consequently, the proposed platform could support the growing effort to regenerate cochlear tissues and assist with basic research to advance cures for hearing impairments.

Biomedical Applications of Tissue Clearing and Three-Dimensional Imaging in Health and Disease

Three-dimensional (3D) optical imaging techniques can expand our knowledge about physiological and pathological processes that cannot be fully understood with 2D approaches. Standard diagnostic tests frequently are not sufficient to unequivocally determine the presence of a pathological condition. Whole-organ optical imaging requires tissue transparency, which can be achieved by using tissue clearing procedures enabling deeper image acquisition and therefore making possible the analysis of large-scale biological tissue samples. Here, we review currently available clearing agents, methods, and their application in imaging of physiological or pathological conditions in different animal and human organs. We also compare different optical tissue clearing methods discussing their advantages and disadvantages and review the use of different 3D imaging techniques for the visualization and image acquisition of cleared tissues. The use of optical tissue clearing resources for large-scale biological tissues 3D imaging paves the way for future applications in translational and clinical research.

A Novel Technique for Imaging and Analysis of Hair Cells in the Organ of Corti Using Modified Sca/eS and Machine Learning

Here, we describe a sorbitol-based optical clearing method, called modified Sca/eS that can be used to image all hair cells (HCs) in the mouse cochlea. This modification of Sca/eS is defined by three steps: decalcification, de-lipidation, and refractive index matching, which can all be completed within 72 h. Furthermore, we established automated analysis programs that perform machine learning-based pattern recognition. These programs generate 1) a linearized image of HCs, 2) the coordinates of HCs, 3) a holocochleogram, and 4) clusters of HC loss. In summary, a novel approach that integrates modified Sca/eS and programs based on machine learning facilitates quantitative and comprehensive analysis of the physiological and pathological properties of all HCs.