Biology subjects

Chan, Y. H.

Publications and source records attributed to Chan, Y. H..

8 recordsLinked to original sources

bioRxiv2026

Immunohistochemistry-compatible gel-assisted mass spectrometry imaging

While lipids are fundamental to cellular signaling and structure, probing native lipid compositions and distributions across complex tissue architectures at single-cell resolution remains challenging due to inherent trade-offs in spatial resolution, sensitivity, and molecular coverage in current spatial lipidomics methods. Here, we report a multimodal imaging approach that seamlessly integrates gold-standard immunohistochemical labeling with gel-assisted mass spectrometry imaging to enable cell-type-specific, single-cell spatial lipidomics with modern instrumentation. Using intact brain tissue as a testbed, we demonstrate in situ measurement of cerebellar Purkinje cells and spatially mapped lipids across timepoints and cerebellar subregions within the pathological landscape of a neurodegenerative, lysosomal storage disorder. With single-cell lipidomic profiling, we delineated spatiotemporally distinct accumulation of specific glycosphingolipids and phospholipids within Purkinje cells and non-Purkinje cells in the diseased brain. Furthermore, unsupervised single-cell lipidomic clustering elucidated disease-progression-and subregion-dependent molecular divergence between healthy and neurodegenerative states within the cerebellum.

neuroscience↗

bioRxiv2025

Encoding and Decoding of Brain Dynamic Functional Connectivity for ADHD Diagnosis

Recent studies have demonstrated strong associations between the changes in dynamic functional connectivity (FC) and both behavioral and cognitive functions. The sliding window technique is the most widely used method for evaluating dynamic FC; however, it faces two key challenges: distributional shifts across windows and high dimensionality, as FC is computed across windows of the entire time series. To address these issues, we propose BRAINMAP (Bi-level Representation using Attention for INterpretability with Mamba-Aided Prediction) to model the dynamic FC of the brain. BRAINMAP employs the Optimal Transport technique to correct distributional shifts across sliding windows and leverages Graph Neural Networks (GNNs) in conjunction with a hybrid approach that integrates an attention mechanism and the Mamba block to effectively capture spatiotemporal features for functional MR images. Finally, we introduce a novel Top-K sliding window feature selection algorithm to induce sparsity in dynamic FC. We conducted an extensive evaluation of our model for diagnosing Attention Deficit Hyperactivity Disorder (ADHD) using three resting-state fMRI datasets: ADHD-200, UCLA, and CNI-TLC, which comprise a total of 447 subjects with ADHD and 845 typically developing controls. Our architecture outperformed existing state-of-the-art dynamic FC models in ADHD detection, achieving improvements ranging from 3% to 12% across the three datasets. We demonstrate that our proposed model produces robust biomarkers, most notably the connection between the dorsal attention network and the visual network. Using an association study, we further establish the clinical relevance of the identified biomarkers.

neuroscience↗

bioRxiv2023

Predicting gender from structural and functional connectomes via brain and population graph neural networks

Gender differences in terms of structural and functional organization of the human brain have been extensively studied, but existing works have mostly been limited to single modalities. In this paper, we propose a graph attention network architecture (BrainGAT) that uses informative subjectlevel features extracted from multimodal brain graphs to construct a population graph for gender classification. We show that while the extracted subject-level features can be directly used for classification, using these graph embeddings to construct a population graph further improves model performance. On the gender classification task, BrainGAT outperforms baseline models and existing multimodal modeling approaches, achieving an accuracy of 83.13% on the Human Connectome Project dataset. Salient connections highlighted by BrainGAT include connections between the inferior parietal and dorsolateral prefrontal areas of the cortex for females, while connections within the posterior cingulate cortex are highly salient for males. In sum, BrainGAT enables multimodal data to be modelled via population graphs in a parameter-efficient way. Clinical RelevanceSeveral neurological conditions exhibit significant differences across genders and multimodal studies on these diseases are increasingly prevalent. This work highlights gender differences of multimodal connectomes in neurotypical settings. These insights could help to separate multimodal disease biomarkers from fundamental gender differences.

neuroscience↗

bioRxiv2023

Gel-assisted mass spectrometry imaging

Compatible with label-free detection and quantification, mass spectrometry imaging (MSI) is a powerful tool for spatial investigation of biomolecules in intact specimens. Yet, the spatial resolution of MSI is limited by the methods physical and instrumental constraints, which often preclude it from single-cell and subcellular applications. By taking advantage of the reversible interaction of analytes with superabsorbent hydrogels, we developed a sample preparation and imaging workflow named Gel-Assisted Mass Spectrometry Imaging (GAMSI) to overcome these limits. With GAMSI, the spatial resolution of lipid and protein MALDI-MSI can be enhanced severalfold without changing the existing mass spectrometry hardware and analysis pipeline. This approach will further enhance the accessibility to (sub)cellular-scale MALDI-MSI-based spatial omics.

biochemistry↗

bioRxiv2020

Whole blood immunophenotyping uncovers immature neutrophil-to-VD2 T-cell ratio as an early prognostic marker for severe COVID-19

SARS-CoV-2 is the novel coronavirus responsible for the current COVID-19 pandemic. Severe complications are observed only in a small proportion of infected patients but the cellular mechanisms underlying this progression are still unknown. Comprehensive flow cytometry of whole blood samples from 54 COVID-19 patients revealed a dramatic increase in the number of immature neutrophils. This increase strongly correlated with disease severity and was associated with elevated IL-6 and IP-10 levels, two key players in the cytokine storm. The most pronounced decrease in cell counts was observed for CD8 T-cells and VD2 {gamma}{delta} T-cells, which both exhibited increased differentiation and activation. ROC analysis revealed that the count ratio of immature neutrophils to CD8 or VD2 T-cells predicts pneumonia onset (0.9071) as well as hypoxia onset (0.8908) with high sensitivity and specificity. It would thus be a useful prognostic marker for preventive patient management and improved healthcare resource management.

immunology↗

bioRxiv2020

Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

Neuroscientific knowledge points to the presence of redundancy in the correlations of brains functional activity. These redundancies can be removed to mitigate the problem of overfitting when deep neural network (DNN) models are used to classify neuroimaging datasets. We propose an algorithm that removes insignificant nodes of DNNs in a layerwise manner and then adds a subset of correlated features in a single shot. When performing experiments with functional MRI datasets for classifying patients from healthy controls, we were able to obtain simpler and more generalizable DNNs. The obtained DNNs maintained a similar performance as the full network with only around 2% of the initial trainable parameters. Further, we used the trained network to identify salient brain regions and connections from functional connectome for multiple brain disorders. The identified biomarkers were found to closely correspond to previously known disease biomarkers. The proposed methods have cross-modal applications in obtaining leaner DNNs that seem to fit the data better. The corresponding code is available at https://github.com/SCSE-Biomedical-Computing-Group/LEAN_CLIP.

bioinformatics↗

bioRxiv2019

Trimester-specific Zika virus infection affects placental responses in women

Zika virus (ZIKV) infection during pregnancy is associated with neurologic birth defects, but the effects on placental development are unclear. Full-term placentas from three women, each infected with ZIKV during specific pregnancy trimesters, were harvested for anatomic, immunologic and transcriptomic analysis. In this study, each woman exhibited a unique immune response, but they collectively diverged from healthy controls with raised IL-1RA, IP-10, EGF and RANTES expression, and neutrophil numbers during the acute infection phase. Although ZIKV NS3 antigens co-localized to placental Hofbauer cells, the placentas showed no anatomical defects. Transcriptomic analysis of samples from the placentas revealed that infection during trimester 1 caused a disparate cellular response centered on differential eIF2 signaling, mitochondrial dysfunction and oxidative phosphorylation. These findings should translate to improve clinical prenatal screening procedures for virus-infected pregnant patients.

immunology↗

bioRxiv2019

Decoding brain functional connectivity implicated in AD and MCI

Deep neural networks have been demonstrated to extract high level features from neuroimaging data when classifying brain states. Identifying salient features characterizing brain states further refines the focus of clinicians and allows design of better diagnostic systems. We demonstrate this while performing classification of resting-state functional magnetic resonance imaging (fMRI) scans of patients suffering from Alzheimers Disease (AD) and Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI). We use a 5-layer feed-forward deep neural network (DNN) to derive relevance scores of input features and show that an empirically selected subset of features improves accuracy scores for patient classification. The common distinctive salient brain regions were in the uncus and medial temporal lobe which closely correspond with previous studies. The proposed methods have cross-modal applications with several neuropsychiatric disorders.

neuroscience↗