Advanced Omics Techniques for Cochlear Research

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1		Mauro Maccarrone	--	3528	2024-01-17 10:33:54	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	3528	2024-01-19 02:44:09	\|

This entry is adapted from the peer-reviewed paper 10.3390/biom13101534

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear.

omics cochlea single-cell omics spatial omics epigenomics transcriptomics genomics

1. Introduction

According to the World Health Organization (WHO), 432 million adults and 32 million children are affected by disabling hearing loss, and it is estimated that this number will increase to 700 million by 2050 ^[1]. In particular, sensorineural hearing loss (SNHL) is characterized by the deterioration of the neuro-sensory structure of the inner ear—the cochlea—and leads to irreversible hearing loss that affects communication, speech, and cognition, with a clear impact on the quality of life and severe socio-economic consequences. SNHL can be caused by either congenital or acquired factors (noise exposure, ototoxic drugs, ageing, strial or metabolic dysfunctions) ^[2]. The severity of the aetiology can range from synaptic disconnectivity of the sensory epithelium ^[3]—composed of inner (IHCs)/outer hair cells (OHCs) and supporting cells (SCs)—to critical cases of the loss of hair cells (HCs). The latter process is often followed by the degeneration of the downstream spiral ganglion neurons (SGNs) ^[4], whose axons form the auditory nerve. Although cochlear implants and hearing aids exhibit some beneficial outcomes in deaf patients, they cannot entirely replace the cochlea’s functionality ^[5]. Thus, management-based approaches must give way to disease-modifying interventions. This strategy needs a more thorough understanding of the molecular events that could eventually become novel therapeutic targets and/or diagnostic biomarkers of SNHL, to be exploited also in cochlear regeneration strategies. Thanks to the technological advancements in the field of molecular biology, recent progress has been made in identifying and characterizing novel genes involved in hearing loss ^[6], as well as new molecular mechanisms of cochlear development ^[7], degeneration, and regeneration ^[8].

2. Experimental Models in Inner Ear Research

Modelling inner ear disorders is important to understand the molecular basis of hearing, as well as the mechanisms of deafness in humans. Currently, it is only possible to study human inner ear disorders in cadavers ^[9] since sampling tissues from alive subjects would cause irreversible damage to the intricate inner ear structures. Hence, this is possible only in cases of inner ear tumors ^[10]^[11]. Moreover, non-invasive techniques, such as magnetic resonance imaging (MRI) and computerized tomography (CT), cannot lead to a detailed understanding of the inner ear pathogenesis ^[12]. Therefore, most of the models for studies on the cochlea are based on cell cultures from animals or on animal models. Figure 1 summarizes the experimental models that are currently available and used in cochlear research.

Figure 1. Schematic illustration of the available experimental models for cochlear research. The available models for cochlear research include cell lines of otocyst, HCs, organ of Corti, and stria vascularis. Explants of cochlear tissues may also be used, more recently via microfluidic chambers for organ-on-chip cultures. Cochlear organoids are an additional in vitro possibility and can be derived from induced pluripotent stem cells (iPSCs) or from embryonic stem cells (ESCs). Animal models can be generated by exposure to ototoxic drugs or by noise trauma; also, age-related and transgenic models of hearing loss have been developed. Finally, all the models may be subjected to CRISPR/Cas9 to achieve targeted gene editing. Abbreviations: IMO; Immortomouse; HC; hair cell, SC; supporting cell, iPSC; induced pluripotent stem cell, ESC; embryonic stem cell, Dmm; disproportionate micromelia; sedc, spondyloepiphyseal dysplasia congenital; USH: Usher; Cho: chondrodysplasia.

3. Omics Techniques

3.1. Introduction to Omics: Principles and Advancements

The term omics refers to a rapidly evolving and expanding group of techniques aimed at investigating pools of biological molecules of an organism, including nucleic acids, proteins, and metabolites ^[13]. Hence, the main branches of omics techniques are known as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. The most advanced omics techniques include single-cell omics and spatial omics, which allow the investigation of the molecular events occurring at a single-cell resolution and the retention of spatial information ^[14]^[15]. There are also other advanced and upcoming sequencing-based omics, such as epitranscriptomics, epiproteomics, and interactomics (DNA–RNA, RNA–RNA, RNA–protein, protein–protein, protein–metabolite), which give detailed information on the complex interactions and dynamics of regulation in a biological system ^[13]. The number of omics studies in cochlear research is relatively low compared to other sensory systems, since the sampling of the cochlear tissues has only recently advanced, and some techniques are incompatible with the small sample quantity obtained ^[16]. Nonetheless, the studies performed so far have significantly advanced the knowledge of cochlear physio-pathology.

3.2. Principles of Single-Cell Omics

The term single-cell omics refers to the process of profiling the genome, transcriptome, epigenome, proteome, and metabolome in individual cells. As a consequence, single-cell techniques were shown to be useful in several biological fields, including cancer ^[17], developmental biology ^[18], stem cell research ^[19], neuroscience ^[20], and hearing ^[8].

The first step of all these technologies is the isolation of individual cells and the setting up of libraries. Multiple methodologies have been designed to isolate single cells from pooled cell populations/tissues through a variety of techniques ^[21] that span from the most straightforward—using pipettes and cell isolation by dilution—to the more sophisticated—using advanced microfluidic technologies ^[22]. The latter include hydrodynamic trapping, droplet-based isolation, valve-based isolation, microwell-based isolation and dielectrophoresis trapping ^[23], as well as magnetic-activated cell sorting (MACS), flow-activated cell sorting (FACS) ^[21], laser capture microdissection (LCM)—which also preserves spatial context—and nanowell-based cell sorting ^[24]. Details of the isolation methods for abundant or rare cells have been described by Wang and Navin ^[25].

After the isolation of single cells, the genome, the epigenome and the transcriptome can be profiled ^[8]^[26]. Notably, single-cell multi-omics approaches have recently been developed to investigate the molecular events that occur in individual cells under physiological or pathological conditions in a wider overview, at once. An example of this cutting-edge methodology is single-cell triple-omics sequencing (scTrio-seq), which simultaneously gathers data from the genome, DNA methylome, and transcriptome of a single cell ^[27].

3.3. Spatial Omics

The study of omics at a single-cell resolution has been transformative in the identification of novel biomarkers and molecular regulators of tissues, yet single-cell omics cannot deliver information on the tissue or sub-cellular localization of the isolated cells. For this reason, spatial omics have been developed with the aim of identifying molecular events while maintaining the spatial information. Multiple spatial omics approaches exist, and they vary depending on the biomolecules of interest. In the cochlea, spatial omics are of particular relevance due to its complex anatomical architecture. Indeed, the cochlea exhibits a tonotopic organization from its base (high-frequency perception) to the apex (low-frequency perception), which requires appropriate cellular structures and expression patterns ^[28]. Moreover, different cell types are also present from the medial (i.e., greater epithelial ridge (GER), IHCs, and their associated SCs) to the lateral (i.e., Deiters’ cells, pillar cells, and OHCs) compartment of the cochlea ^[28].

4. The Role of Bioinformatics in Analyzing Omics Data

In order to fill the knowledge gap between omics data acquisition and interpretation, bioinformatics is a critical field. Numerous computational techniques have been developed to this end, including machine learning, deep learning, data mining and statistical and metaheuristic approaches, to analyze, process, interpret, and integrate omics data for both single omics and integrative multi-omics ^[29]^[30]^[31]^[32]^[33]. Machine learning and deep learning are frequently used in the research community for decoding and analyzing data, predicting disease occurrence and recurrence, calculating survival rates, and finding potential biomarkers ^[29]. Deep learning models are a sub-set of machine learning tools of high utility since they are automated and analyze large high-dimensional data sets. Deep learning is primarily based on stratified artificial neural networks, providing diverse interpretations based on the fed data. The primary neural networks in deep learning include recursive neural networks (RvNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) ^[34]. Given the recent rapid advancements in omics and the accumulation of high-throughput omics data, future efforts should be aimed towards improving current machine learning and deep learning models for multi-omic data analysis. In this context, graph neural networks (GNNs) have gained attention in recent years ^[35]: the spatial relations within and between cells can be better represented with graph models, and graph-based artificial intelligence appears to hold promise, especially with regard to the most advanced omics (i.e, spatial omics). In this context, it should be mentioned that two relevant techniques have recently been developed to analyze spatial transcriptomics data: SPAcI ^[36] and SiGra ^[37]. They both have several technical advantages over existing methods, such as an improvement in accuracy, enhancing noisy gene expression data sets, and an increased ability to adapt ^[35]^[36].

5. Genomics

5.1. Principles of Sequencing

Genomics investigates somatic and germ-line inter-individual variations in the genome. The currently most used genomics are based on sequencing for the determination of the nucleic acid sequence. Genomics has been used to identify several genetic disorders and to disclose novel alleles in multiple inherited human diseases ^[13], including hearing loss ^[38]^[39]^[40]^[41]^[42]^[43]. The first sequencing method, known as the chain-termination method, was first developed by Sanger in 1977 and was based on the capillary electrophoresis of fragmented DNA bound to a single-stranded DNA template. The main drawback of Sanger sequencing is the ability to sequence only a low amount of DNA at a time ^[44]. To date, more advanced sequencing technologies have been developed and allow massive, faster, and more precise sequencing of nucleic acids. These are next generation sequencing (NGS)—more widely used—and third generation sequencing (TGS). The primary difference between these two techniques is the DNA read length. In NGS, the DNA is cleaved in small fragments (150–1000 bp), then amplified and sequenced; instead, TGS uses single-molecule sequencing without the need for prior amplification and reads long DNA sequences at a time. Moreover, it is possible to sequence different lengths of the genome depending on the experimental purpose: targeted genes (targeted panel sequencing), whole-exome sequencing (WES) ^[40]^[41] or whole-genome sequencing (WGS) ^[42]^[43] (Figure 2).

Figure 2. Schematic illustration of genomics. DNA is isolated from cells or tissues and is fragmented in order to create DNA libraries using DNA adapters. Sequencing can then be performed on targeted sequences (panels), on the whole exosome (WES), or on the whole genome (WGS). Genomics has provided important advancements in the diagnosis and discovery of genetic hearing loss.

5.2. Single-Cell and Spatial Genomics

Single-cell DNA sequencing (scDNAseq) allows the DNA profiling of individual cells ^[45] and is generally based on NGS. The whole genome of single cells can be primarily amplified using three methods: (i) the degenerate oligonucleotide-primed PCR (DOP-PCR), (ii) the multiple displacement amplification (MDA), and (iii) the multiple annealing and looping-based amplification cycles (MALBAC) ^[46]. Recently, a single-cell WGS method based on TGS was also developed in order to sequence long reads; this is known as “single-molecule real-time sequencing of long fragments amplified through transposon insertion” (SMOOTH-seq) ^[46]. SMOOTH-seq has greatly improved the identification of structural variants (SVs) and extra-chromosomal DNA compared to NGS ^[47].

5.3. Genomic Studies Have Delivered Unprecedented Knowledge on the Genetic Background and Early Diagnosis of Inherited Hearing Loss

Genetic hearing loss affects any part of the auditory system and accounts for ~50% of the deaf population. It can be either non-syndromic (70%) ^[48]^[49] or syndromic (30%) ^[50]. The large heterogeneity of genes involved in deafness makes it difficult to study and diagnose it ^[40]. However, thanks to the advancements in genomics, to date several variants have been identified in genes associated with hearing loss. For instance, the combination of WES, qPCR, and TGS was able to unravel for the first-time novel SVs of centrosomal protein 78 (CEP78), a key gene responsible for hearing loss associated with cone–rod dystrophy (CRDHL) ^[43]. The applications of advanced genomics have also revealed new variants that have recently been outlined in important hearing loss-related genes, namely, myosin 15 A (MYO15A), otoferlin (OTOF), radixin (RDX) ^[51], TATA-box-binding protein-associated factor 1 (TAF1) ^[52], atonal BHLH transcription factor 1 (ATOH1) ^[53], and centrosomal protein 78 (CEP78) ^[43]. The discovery of novel variants represents a fundamental step forward in the understanding of the molecular basis of cochlear diseases, and indeed, it has improved the diagnosis of genetic hearing loss, as well as the prediction of its severity and prognosis.

6. Transcriptomics

Transcriptomics enable the analysis of gene expression at the RNA level, including messenger RNAs (mRNAs), transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), and other non-coding RNAs (ncRNAs) (e.g., microRNAs (miRNAs), long-non-coding RNAs (lncRNAs), and circular RNAs (circRNAs)) ^[13]^[54]. As for genomics, the currently most used transcriptomics technologies are based on sequencing (described above in Section 5.1); however, transcriptome profiling is more challenging compared to genome sequencing due to the highly dynamic nature of the transcriptome in biological processes. The sequencing of the entire transcriptome in a tissue or cell population is known as bulk-RNAseq and can be performed either with direct RNA sequencing (dRNA-seq) or with cDNA sequencing ^[55]. A schematic overview of the transcriptomics techniques is shown in Figure 3.

Figure 3. Schematic illustration of transcriptomics. Transcriptomics can be performed at a bulk, single-cell, or spatial resolution. In bulk RNAseq, total RNA is extracted from the tissue and can be directly sequenced or converted into cDNA and then sequenced. In scRNA-seq, the sequencing of the RNA is limited to single cells that are isolated from the tissue and analyzed individually. In spatial transcriptomics, the transcriptome may be analyzed with imaging-based methods, using fluorescent labeled probes which bind to the RNA on tissue slides, followed by microscopic analysis; spatial transcriptomics may also be performed through sequencing-based methods using arrays of barcoded probes or microdissection of target tissue areas, both followed by sequencing. Array-based spatial transcriptomics have not yet been applied in cochlear research. The other transcriptomics techniques have provided important new insights into the gene regulatory networks of the cochlea, under both physiological and pathological conditions. Abbreviations: Bulk RNA seq, Bulk RNA sequencing; scRNA-seq, single-cell RNA sequencing.

7. Epigenomics

7.1. Principles of Epigenomics

The term epigenomics refers to the techniques used to investigate the epigenome, which is the set of regulatory processes that modify the activity of gene expression without modifications in the DNA sequence. Epigenomics can be classified depending on the target: DNA methylation, histone modifications, chromatin accessibility, and chromosome interactions. The methodologies to study bulk epigenomics can be further classified as “array-based” and “sequencing-based” techniques ^[56]. Array-based technologies use hybridization with pre-designed microarrays, while sequencing-based methods use techniques like NGS. DNA methylation is an epigenetic mark where methyl groups are added to the cytosine bases of the DNA. It is important to highlight that to investigate DNA methylation, a required first step is the exposure of the methylated DNA through one of the following methods: (i) DNA digestion by methylation-sensitive restriction enzymes (MSREs) ^[57], (ii) affinity enrichment of DNA by antibodies targeting methylated CpGs ^[58], or (iii) conversion of unmethylated cytosines to uracil by bisulfite treatment ^[57]. To date, the bisulfite sequencing (BS-seq) method is considered the gold-standard technique for studies on DNA methylation because of its single-base resolution ^[56]. Histones can be modified primarily through acetylation, phosphorylation, methylation, and other miscellaneous modifications ^[59]. One of the most used techniques for monitoring histone modifications is chromatin immunoprecipitation (ChIP), in which the histone modifications of interest are targeted by antibodies. The cleavage under targets and release using nuclease (CUT&RUN) ^[60] and cleavage under targets and tagmentation (CUT&TAG) ^[61] methods are additional techniques used for the analysis of histone modifications; both rely on the same principle of recognizing DNA-bound proteins of interest through specific antibodies. The chromatin is highly dynamic, allowing regulators (enhancers, promoters, and chromatin-binding factors, among others) to have multiple physical interactions with DNA, thereby playing an important role in regulating gene expression. Multiple techniques for chromatin accessibility studies have also been developed. Among these, the most recent is the accessible chromatin using sequencing technology (ATAC-seq). It employs tagmentation (inserting adapter sequences by using the hyperactive mutant Tn5 transposase) to open target regions of the chromatin, which are then amplified and sequenced ^[62]. Other widely used techniques for chromatin accessibility include the DNase I hyper-sensitive sites sequencing (DNAse-seq) ^[63], the micrococcal nuclease digestion with deep sequencing (MNase-seq) ^[64], and the formaldehyde-assisted identification of regulatory elements followed by sequencing (FAIRE-seq) ^[65]. The higher-order organization of the nucleus is also important for the epigenetic regulation of cellular processes; hence, techniques able to analyze chromosomal interactions have also been developed. They include the chromatin conformation capture technique (3C), Hi-C, the chromatin interaction analysis by paired-end tag sequencing (ChIA PET), and the proximity ligation-assisted ChIP-seq (PLAC-seq) ^[56].

7.2. Single-Cell Epigenomics

Single-cell epigenomics enable a detailed analysis of the epigenetic regulation at a single-cell resolution, which includes single-cell DNA methylation profiling, single-cell chromatin mapping, single-cell Hi-C, and single-cell replication dynamics ^[66]. Several methods for single-cell DNA methylation profiling exist, the most recent of which are “single-cell combinatorial indexing for methylation analysis” (sci-MET) and “single-cell CGI methylation sequencing” (scCGI-seq) ^[56]^[67]^[68]. Histone modifications in single cells can also be studied using sc-ChIP-seq, single-cell droplet-based chromatin immunoprecipitation (drop-ChIP) ^[26], single-cell chromatin immune-cleavage sequencing technique (scChIC-seq), antibody-guided chromatin tagmentation sequencing (ACT-seq), combinatorial barcoding and targeted chromatin release (COBATCH), and single-cell chromatin integration labeling sequencing (scChIL-seq) ^[56]. Finally, single-cell chromatin accessibility can be investigated using scDNAse-seq and scATAC-seq. The available single-cell epigenetic methods have been recently reviewed in detail ^[66]^[69].

7.3. Spatial Epigenomics

To fully appreciate the influence of epigenetic variations in patho-physiological processes, it is essential to know their spatial context. However, the development of spatial epigenomics techniques has been challenging for a long time due to the limited spatial resolution available ^[70]^[71]. The first spatial epigenomic technology was developed in 2021 and is now beginning to open new possibilities in the field of biology and medicine. The first spatial epigenomic technique that has been developed is the “high-spatial-resolution chromatin modification state profiling by sequencing” (hsrChST-seq). It is based on the spatial transcriptomic technique DBiT-seq, in which there is a combination of CUT&TAG and tissue deterministic barcoding with fluorescence microscopy ^[72]. Another technique developed later on to resolve chromatin accessibility spatially is the spatial-ATAC seq, which is based on the combination of in situ Tn5 transposase chemistry with microfluidic deterministic barcoding ^[73]. Recently, it has been possible to analyze the active and inactive promoters/enhancers associated with histone modifications in single cells while maintaining spatial information thanks to the advent of epigenomic MERFISH ^[73]. Epigenomic MERFISH combines CUT&TAG and MERFISH (a spatial epigenomic technique for the analysis of histone modifications) ^[74].

7.4. Epigenetic Profiling of the Cochlea Has Provided New Insights into the Mechanisms Whereby Genes Responsible for Auditory Function Are Regulated

Hearing loss can be caused by epigenetic alterations or by mutations in the genes encoding for the epigenetic machinery, affecting DNA methylation dynamics ^[75]^[76]^[77], histone modifications ^[78]^[79]^[80], and chromatin remodeling ^[76]^[81]^[82]. Thus, investigating epigenetic mechanisms could eventually pave the way towards new approaches to therapeutics. To date, most of the studies on the cochlear epigenome are based on bulk epigenomic profiling, and only a few were performed with single-cell epigenomics, namely scATAC-seq ^[83]^[84]. Instead, spatial epigenomics has not yet been applied in this field, though the epigenomics studies conducted until now have given us profound insights into the regulatory mechanisms of development, trans-differentiation, and regeneration of the auditory system. The application of ChIP-seq and ChIP-qPCR has led to the identification of fundamental epigenetic modifications in the promoters of two key genes involved in SGN differentiation (Cdk2 and NeuroD1), which affects the binding of the regulatory transcription factor neurogenin 1 (neurog1) ^[85]. Also, ChIP-qPCR allowed for the description of the histone modifications associated with the epigenetic regulation of atonal bHLH transcription factor 1 (Atoh1), which is an evolutionarily conserved transcription factor for the development of the auditory system ^[86]. Yet, histone modifications of Atoh1, which are characteristic of HCs during their development, are suppressed in the same cells after birth, but they persist in perinatal SCs. This is an important finding, which gives new information on the mechanisms underlying the regenerative potential of SCs ^[86].

8. Conclusions

Overall, advanced genomics, epigenomics, and transcriptomics techniques represent the state-of-the-art approaches in cochlear research and are providing unprecedented knowledge on the molecular basis of cochlear patho-physiology. There are various open questions in the field of cochlear research that could be addressed through the application of omic techniques. For instance, in the context of the heterogeneity of the cochlear tissue, as of now, eight different SC populations with distinct morphologies have been identified, but little is known about their contribution to auditory function ^[87]. The application of single-cell and spatial transcriptomic technologies could provide a map of cell-type-specific gene expression patterns that can help to hypothesize the potential role of these cells in auditory processes. In addition, the application of single-cell epigenomics could enhance our understanding of the important cell-type-specific gene regulatory networks that can broaden our knowledge on the putative role of SC in cochlear homeostasis. Another active area of research in hearing is the mechanisms underlying ototoxicity. For instance, cisplatin, one of the primary chemotherapeutic agents utilized in oncology, has debilitating effects on hearing function and, hence, quality of life. Although efforts have been undertaken to understand the molecular underpinnings behind cisplatin-induced ototoxicity, to date, no effective therapeutics have been approved to counteract such adverse effects ^[88]. This necessitates a thorough molecular comprehension of the ototoxic processes from cisplatin trafficking to downstream signaling. The application of single-cell and spatial omics can give insights into the consequences of those ototoxic compounds. For instance, genomics would provide advanced knowledge on the cell-type-specific mutations and their location, transcriptomics on the cell-type-specific biomarkers expressed under ototoxic conditions, and epigenomics on putative gene regulatory dynamics in ototoxicity, which can all provide novel pharmacological/gene therapy targets not only for HCs (where most of the research is focused on ^[89]) but also for SCs, which are known for their role in ototoxicity ^[90].

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