Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h.

AIMS/HYPOTHESIS: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes. METHODS: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-ßH1 cells, followed by a glucose-stimulated insulin secretion assay. RESULTS: In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-ßH1 cells. CONCLUSIONS/INTERPRETATION: The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production. DATA AVAILABILITY: The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .

Cognitive Complaint Types Can Correlate With Cognitive Testing, Perceived Stress, and Symptom Distress in Older Adults With Normal Cognition and Dementia.

OBJECTIVE: We examined how cognitive complaint types (CCTs) correlate with cognitive testing, perceived stress, and symptom distress in older adults with normal cognition and dementia. METHODS: Older adults (n = 259) with normal cognition, mild cognitive impairment, or mild-stage Alzheimer disease completed cognitive testing and self-report measures (Cognitive Difficulties Scale, Global Distress Index, Perceived Stress Scale). Cross-sectional analyses examined: (1) CCT composition by classification method,( 2) CCTs by diagnostic group, (3) correlations of CCTs with cognitive testing scores, and (4) correlations of CCTs with perceived stress and symptom distress. RESULTS: CCTs derived from 2 classification approaches loaded onto 4 factors: memory, attention-concentration (AC), temporal orientation, and praxis. Memory contained complaints about both memory and executive functioning. AC contained both classifications of AC complaints. Complaints about AC (AC1 and AC2) differed by diagnostic group (all P < 0.05). One of 2 classifications of AC (AC1) complaints discerned between impaired and unimpaired long-delay memory scores (both P < 0.05). In multivariable analyses, that same classification of AC (AC1) complaints correlated with higher perceived stress (both P < 0.001) but not symptom distress (both P > 0.05). CONCLUSION: CCTs showed a factor structure that was mostly robust between classification methods; however, some content-divergent CCTs shared factors, suggesting construct overlap. Relatively slight variations in content altered how CCTs correlated with diagnostic groups, perceived stress, and symptom distress. Most CCTs did not discern between impaired and unimpaired cognitive test scores. Research is needed to better understand CCTs as clinical markers and targets of clinical interventions.

Sex modifies effects of imaging and CSF biomarkers on cognitive and functional outcomes: a study of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory and functional impairments. Two of 3 patients with AD are biologically female; therefore, the biological underpinnings of this diagnosis disparity may inform interventions slowing the AD progression. To bridge this gap, we conducted analyses of 1078 male and female participants from the Alzheimer's Disease Neuroimaging Initiative to examine associations between levels of cerebral spinal fluid (CSF)/neuroimaging biomarkers and cognitive/functional outcomes. The Chow test was used to quantify sex differences by determining if biological sex affects relationships between the studied biomarkers and outcomes. Multiple magnetic resonance imaging (whole brain, entorhinal cortex, middle temporal gyrus, fusiform gyrus, hippocampus), position emission tomography (AV45), and CSF (P-TAU, TAU) biomarkers were differentially associated with cognitive and functional outcomes. Post-hoc bootstrapped and association analyses confirmed these differential effects and emphasized the necessity of using separate, sex-stratified models. The studied imaging/CSF biomarkers may account for some of the sex-based variation in AD pathophysiology. The identified sex-varying relationships between CSF/imaging biomarkers and cognitive/functional outcomes warrant future biological investigation in independent cohorts.

Double-Edged Sword: A Positive Brain Scan Result Heightens Confidence in an Alzheimer's Diagnosis but also Leads to Higher Stigma among Older Adults in a Vignette-Based Experiment.

OBJECTIVES: Early diagnosis of Alzheimer's disease (AD) using brain scans and other biomarker tests will be essential to increasing the benefits of emerging disease-modifying therapies, but AD biomarkers may have unintended negative consequences on stigma. We examined how a brain scan result affects AD diagnosis confidence and AD stigma. METHODS: The study used a vignette-based experiment with a 2×2×3 factorial design of main effects: a brain scan result as positive or negative, treatment availability and symptom stage. We sampled 1,283 adults ages 65 and older between 11 June and 3 July 2019. Participants (1) rated their confidence in an AD diagnosis in each of four medical evaluations that varied in number and type of diagnostic tools and (2) read a vignette about a fictional patient with varied characteristics before completing the Modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We examined mean diagnosis confidence by medical evaluation type. We conducted between-group comparisons of diagnosis confidence and FS-ADS scores in the positive versus negative brain scan result conditions and, in the positive condition, by symptom stage and treatment availability. RESULTS: A positive versus negative test result corresponds with higher confidence in an AD diagnosis independent of medical evaluation type (all p<0.001). A positive result correlates with stronger reactions on 6 of 7 FS-ADS domains (all p<0.001). DISCUSSION: A positive biomarker result heightens AD diagnosis confidence but also correlates with more AD stigma. Our findings inform strategies to promote early diagnosis and clinical discussions with individuals undergoing AD biomarker testing.

Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.

Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than 250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.

Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.

Alzheimer's Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed stratified genetic analyses of 1,574 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants to examine associations between levels of quantitative traits (QT's) and future diagnosis. The Chow test was employed to determine if an individual's genetic profile affects identified predictive relationships between QT's and future diagnosis. Our chow test analysis discovered that cognitive and PET-based biomarkers differentially predicted future diagnosis when stratifying on allelic dosage of AD loci. Post-hoc bootstrapped and association analyses of biomarkers confirmed differential effects, emphasizing the necessity of stratified models to realize individualized AD diagnosis prediction. This novel application of the Chow test allows for the quantification and direct comparison of genetic-based differences. Our findings, as well as the identified QT-future diagnosis relationships, warrant future investigation from a biological context.

Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data.

Brain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer's disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability.