Gene expression analysis of the cerebellar cortex in essential tremor

Robust Spatial Cell-Type Deconvolution with Qualitative Reference for Spatial Transcriptomics

Many spatially resolved transcriptomic technologies have been developed to provide gene expression profiles for spots that may contain heterogeneous mixtures of cells. To decompose cellular composition and expression levels, various deconvolution methods have been developed using single-cell RNA sequencing (scRNA-seq) data with known cell-type labels as a reference. However, in the absence of a reliable reference dataset or in the presence of heterogeneous batch effects, these methods may introduce bias. Here, a Qualitative-Reference-based Spatially-Informed Deconvolution method (QR-SIDE) is developed for multi-cellular spatial transcriptomic data. Uniquely, QR-SIDE provides a detailed map of spatial heterogeneity for individual marker genes and performs robust deconvolution by adaptively adjusting the contributions of each marker gene. Simultaneously, QR-SIDE unifies cell-type deconvolution with spatial clustering and incorporates spatial information via a Potts model to promote spatial continuity. The identified spatial domains represent a meaningful biological effect in potential tissue segments. Using simulated data and three real spatial transcriptomic datasets from the 10x Visium and ST platforms, QR-SIDE demonstrates improved accuracy and robustness in cell-type deconvolution and its superiority over established methods in recognizing and delineating spatial structures within a given context. These results can facilitate a range of downstream analyses and provide a refined understanding of cellular heterogeneity.

Axonal pathology differentially affects human Purkinje cell subtypes in the essential tremor cerebellum

The cerebellar cortex is organized into discrete regions populated by molecularly distinct Purkinje cells (PCs), the sole cortical output neurons. While studies in animal models have shown that PC subtypes differ in their vulnerability to disease, our understanding of human PC subtype and vulnerability remains limited. Here, we demonstrate that human cerebellar regions specialized for motor vs cognitive functions (lobule HV vs Crus I) contain distinct PC populations characterized by specific molecular and anatomical features, which show selective vulnerability in essential tremor (ET), a cerebellar degenerative disorder. Using a known PC subtype marker, neurofilament heavy chain (NEFH), we found that motor lobule HV contains PCs with high NEFH expression, while cognitive lobule Crus I contains PCs with low NEFH expression in post-mortem samples from healthy controls. In the same cerebella, PC axons in lobule HV were 2.2-fold thicker than those in Crus I. Across lobules, axon caliber positively correlated with NEFH expression. In ET cerebella, we identified motor lobule-specific PC axon pathology with a 1.5-fold reduction in caliber and increased axon variability in lobule HV, while Crus I axons were unaffected. Tremor severity and duration in ET correlated with axon diameter variability selectively in lobule HV PCs. Given that axonal caliber is a major determinant of neural signaling capacity, our results (1) suggest that disrupted cerebellar corticonuclear signaling is occurring in ET, (2) provide evidence of region-specific PC subtypes in the human cerebellum and offer insight into how selective PC vulnerability may contribute to the pathophysiology of cerebellar degeneration.

Tremor in the Age of Omics: An Overview of the Transcriptomic Landscape of Essential Tremor

Essential Tremor (ET) is the most common movement disorder and has a worldwide prevalence of 1%, including 5% of the population over 65 years old. It is characterized by an active, postural or kinetic tremor, primarily affecting the upper limbs, and is diagnosed based on clinical characteristics. The pathological mechanisms of ET, however, are mostly unknown. Moreover, despite its high heritability, genetic studies of ET genetics have yielded mixed results. Transcriptomics is a field that has the potential to reveal valuable insights about the processes and pathogenesis of ET thus providing an avenue for the development of more effective therapies. With the emergence of techniques such as single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq), molecular and cellular events can now be more closely examined, providing valuable insights into potential causal mechanisms. In this review, we review the growing literature on transcriptomic studies in ET, aiming to identify biological pathways involved and explore possible avenues for further ET research. We emphasized the convergence on shared of biological pathways across several studies, specifically axonal guidance and calcium signaling. These findings posit multiple hypotheses linking both pathways through the regulation of axonal and synaptic plasticity. We conclude that increasing the sample size is vital to uncover the subtleties of ET clinical and pathological heterogeneity. Additionally, integrating Multiomics approaches should provide a comprehensive understanding of the disease's pathophysiology.

Association of Gene Expression and Tremor Network Structure

BACKGROUND

Transcriptomic changes in the essential tremor (ET)-associated cerebello-thalamo-cortical "tremor network" and their association to brain structure have not been investigated.

OBJECTIVE

The aim was to characterize molecular changes associated with network-level imaging-derived phenotypes (IDP) found in ET.

METHODS

We performed an imaging-transcriptomic study in British adults using imaging-genome-wide association study summary statistics (UK Biobank "BIG40" cohort; n = 33,224, aged 40-69 years). We imputed imaging-transcriptomic associations for 184 IDPs and analyzed functional enrichment of gene modules and aggregate network-level phenotypes. Validation was performed in cerebellar-tissue RNA-sequencing data from ET patients and controls (n = 55).

RESULTS

Among 237,896 individual predicted gene expression levels for 6063 unique genes/transcripts, we detected 2269 genome-wide significant associations (Bonferroni P < 2.102e-7, 0.95%). These were concentrated in intracellular volume fraction measures of white matter pathways and in genes with putative links to tremor (MAPT, ARL17A, KANSL1, SPPL2C, LRRC37A4P, PLEKHM1, and FMNL1). Whole-tremor-network cortical thickness was associated with a gene module linked to mitochondrial organization and protein quality control (r = 0.91, P = 2e-70), whereas white-gray T1-weighted magnetic resonance imaging (MRI) contrast in the tremor network was associated with a gene module linked to sphingolipid synthesis and ethanolamine metabolism (r = -0.90, P = 2e-68). Imputed association effect sizes and RNA-sequencing log-fold change in the validation dataset were significantly correlated for cerebellar peduncular diffusion MRI phenotypes, and there was a close overlap of significant associations between both datasets for gray matter phenotypes (χ2 = 6.40, P = 0.006).

CONCLUSIONS

The identified genes and processes are potential treatment targets for ET, and our results help characterize molecular changes that could in future be used for patient treatment selection or prognosis prediction. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Gene Expression Analysis of Laser-Captured Purkinje Cells in the Essential Tremor Cerebellum

Essential tremor (ET) is a common, progressive neurological disease characterized by an 8-12-Hz kinetic tremor. Despite its high prevalence, the patho-mechanisms of tremor in ET are not fully known. Through comprehensive studies in postmortem brains, we identified major morphological changes in the ET cerebellum that reflect cellular damage in Purkinje cells (PCs), suggesting that PC damage is central to ET pathogenesis. We previously performed a transcriptome analysis in ET cerebellar cortex, identifying candidate genes and several dysregulated pathways. To directly target PCs, we purified RNA from PCs isolated by laser capture microdissection and performed the first ever PC-specific RNA-sequencing analysis in ET versus controls. Frozen postmortem cerebellar cortex from 24 ETs and 16 controls underwent laser capture microdissection, obtaining ≥2000 PCs per sample. RNA transcriptome was analyzed via differential gene expression, principal component analysis (PCA), and gene set enrichment analyses (GSEA). We identified 36 differentially expressed genes, encompassing multiple cellular processes. Some ET (13/24) had greater dysregulation of these genes and segregated from most controls and remaining ETs in PCA. Characterization of genes/pathways enriched in this PCA and GSEA identified multiple pathway dysregulations in ET, including RNA processing/splicing, synapse organization/ion transport, and oxidative stress/inflammation. Furthermore, a different set of pathways characterized marked heterogeneity among ET patients. Our data indicate a range of possible mechanisms for the pathogenesis of ET. Significant heterogeneity among ET combined with dysregulation of multiple cellular processes supports the notion that ET is a family of disorders rather than one disease entity.

Analysis of subcellular RNA fractions demonstrates significant genetic regulation of gene expression in human brain post-transcriptionally

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

Defective cerebellar ryanodine receptor type 1 and endoplasmic reticulum calcium 'leak' in tremor pathophysiology

Essential Tremor (ET) is a prevalent neurological disease characterized by an 8-10 Hz action tremor. Molecular mechanisms of ET remain poorly understood. Clinical data suggest the importance of the cerebellum in disease pathophysiology, and pathological studies indicate Purkinje Cells (PCs) incur damage. Our recent cerebellar cortex and PC-specific transcriptome studies identified alterations in calcium (Ca2+) signaling pathways that included ryanodine receptor type 1 (RyR1) in ET. RyR1 is an intracellular Ca2+ release channel located on the Endoplasmic Reticulum (ER), and in cerebellum is predominantly expressed in PCs. Under stress conditions, RyR1 undergoes several post-translational modifications (protein kinase A [PKA] phosphorylation, oxidation, nitrosylation), coupled with depletion of the channel-stabilizing binding partner calstabin1, which collectively characterize a "leaky channel" biochemical signature. In this study, we found markedly increased PKA phosphorylation at the RyR1-S2844 site, increased RyR1 oxidation and nitrosylation, and calstabin1 depletion from the RyR1 complex in postmortem ET cerebellum. Decreased calstabin1-RyR1-binding affinity correlated with loss of PCs and climbing fiber-PC synapses in ET. This 'leaky' RyR1 signature was not seen in control or Parkinson's disease cerebellum. Microsomes from postmortem cerebellum demonstrated excessive ER Ca2+ leak in ET vs. controls, attenuated by channel stabilization. We further studied the role of RyR1 in tremor using a mouse model harboring a RyR1 point mutation that mimics constitutive site-specific PKA phosphorylation (RyR1-S2844D). RyR1-S2844D homozygous mice develop a 10 Hz action tremor and robust abnormal oscillatory activity in cerebellar physiological recordings. Intra-cerebellar microinfusion of RyR1 agonist or antagonist, respectively, increased or decreased tremor amplitude in RyR1-S2844D mice, supporting a direct role of cerebellar RyR1 leakiness for tremor generation. Treating RyR1-S2844D mice with a novel RyR1 channel-stabilizing compound, Rycal, effectively dampened cerebellar oscillatory activity, suppressed tremor, and normalized cerebellar RyR1-calstabin1 binding. These data collectively support that stress-associated ER Ca2+ leak via RyR1 may contribute to tremor pathophysiology.

Essential tremor: Clinical perspectives and pathophysiology

Essential tremor (ET) is one of the most common neurological disorders and can be highly disabling. In recent years, studies on the clinical perspectives and pathophysiology have advanced our understanding of ET. Specifically, clinical heterogeneity of ET, with co-existence of tremor and other neurological features such as dystonia, ataxia, and cognitive dysfunction, has been identified. The cerebellum has been found to be the key brain region for tremor generation, and structural alterations of the cerebellum have been extensively studied in ET. Finally, four main ET pathophysiologies have been proposed: 1) environmental exposures to β-carboline alkaloids and the consequent olivocerebellar hyper-excitation, 2) cerebellar GABA deficiency, 3) climbing fiber synaptic pathology with related cerebellar oscillatory activity, 4) extra-cerebellar oscillatory activity. While these four theories are not mutually exclusive, they can represent distinctive ET subtypes, indicating multiple types of abnormal brain circuitry can lead to action tremor. This article is part of the Special Issue "Tremor" edited by Daniel D. Truong, Mark Hallett, and Aasef Shaikh.

Association of Essential Tremor With Novel Risk Loci: A Genome-Wide Association Study and Meta-analysis

Question

Can common genetic variants associated with essential tremor (ET) be identified?

Findings

In this genome-wide association study and meta-analysis including genetic data on 483 054 individuals, 5 genome-wide significant loci were associated with risk of ET and common variants were associated with approximately 18% of ET heritability.

Meaning

Findings of this study may help identify new genes and inform ET biology.

Importance

Essential tremor (ET) is one of the most common movement disorders, affecting 5% of the general population older than 65 years. Common variants are thought to contribute toward susceptibility to ET, but no variants have been robustly identified.

Objective

To identify common genetic factors associated with risk of ET.

Design, Setting, and Participants

Case-control genome-wide association study. Inverse-variance meta-analysis was used to combine cohorts. Multicenter samples collected from European populations were collected from January 2010 to September 2019 as part of an ongoing study. Included patients were clinically diagnosed with or reported having ET. Control individuals were not diagnosed with or reported to have ET. Of 485 250 individuals, data for 483 054 passed data quality control and were used.

Main Outcomes and Measures

Genotypes of common variants associated with risk of ET.

Results

Of the 483 054 individuals included, there were 7177 with ET (3693 [51.46%] female; mean [SD] age, 62.66 [15.12] years), and 475 877 control individuals (253 785 [53.33%] female; mean [SD] age, 56.40 [17.6] years). Five independent genome-wide significant loci and were identified and were associated with approximately 18% of ET heritability. Functional analyses found significant enrichment in the cerebellar hemisphere, cerebellum, and axonogenesis pathways. Genetic correlation (r), which measures the degree of genetic overlap, revealed significant common variant overlap with Parkinson disease (r, 0.28; P = 2.38 × 10⁻⁸) and depression (r, 0.12; P = 9.78 × 10⁻⁴). A separate fine-mapping of transcriptome-wide association hits identified genes such as BACE2, LRRN2, DHRS13, and LINC00323 in disease-relevant brain regions, such as the cerebellum.

Conclusions and Relevance

The results of this genome-wide association study suggest that a portion of ET heritability can be explained by common genetic variation and can help identify new common genetic risk factors for ET.

Genomic Markers for Essential Tremor

There are many reports suggesting an important role of genetic factors in the etiopathogenesis of essential tremor (ET), encouraging continuing the research for possible genetic markers. Linkage studies in families with ET have identified 4 genes/loci for familial ET, although the responsible gene(s) have not been identified. Genome-wide association studies (GWAS) described several variants in LINGO1, SLC1A2, STK32B, PPARGC1A, and CTNNA3, related with ET, but none of them have been confirmed in replication studies. In addition, the case-control association studies performed for candidate variants have not convincingly linked any gene with the risk for ET. Exome studies described the association of several genes with familial ET (FUS, HTRA2, TENM4, SORT1, SCN11A, NOTCH2NLC, NOS3, KCNS2, HAPLN4, USP46, CACNA1G, SLIT3, CCDC183, MMP10, and GPR151), but they were found only in singular families and, again, not found in other families or other populations, suggesting that some can be private polymorphisms. The search for responsible genes for ET is still ongoing.

Imaging Tremor Quantification for Neurological Disease Diagnosis

In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.