Biology subjects

LIU, T.

Publications and source records attributed to LIU, T..

4 recordsLinked to original sources

bioRxiv2026

Device-embedded accelerometry complements neural signals for tracking parkinsonian motor states

Adaptive deep brain stimulation (aDBS) relies on physiological biomarkers to infer motor state and guide therapeutic stimulation in Parkinsons disease. However, neural biomarkers may themselves be altered by stimulation, potentially limiting their utility for closed-loop control. We address this limitation by testing whether DBS device-embedded accelerometers can accurately track Parkinsonian motor state across stimulation conditions. We analysed over 1,900 hours of chronic recordings of subthalamic nucleus (STN), sensorimotor cortical and device-embedded accelerometry signals acquired before and during continuous STN stimulation, alongside continuous wearable assessments of bradykinesia and dyskinesia. Across stimulation conditions, accelerometry-derived features robustly tracked motor symptom severity and outperformed neural features for symptom decoding. Mechanistically, total STN beta power - a widely used biomarker for aDBS - proved less informative because it conflates periodic and aperiodic neural processes with opposing relationships to motor state. Under active stimulation, periodic beta activity showed reduced coupling to symptom severity, whereas STN aperiodic activity, cortical periodic activity and cortico-subthalamic coherence remained comparatively stable. Together, these findings demonstrate that neural and behavioural biomarkers exhibit differential robustness during deep brain stimulation and identify device-embedded accelerometry as a robust behavioural biomarker of motor state, motivating its use in next-generation adaptive DBS systems.

bioengineering↗

bioRxiv2026

Pancreatic Duct Cells as a Potential Source for Human Islet Neogenesis: Insights from Imaging Mass Cytometry

The question of whether islet neogenesis occurs in adult humans has been a subject of long-standing debate. To explore the characteristics of islet endocrine cells associated with pancreatic ducts, we employed imaging mass cytometry to examine pancreatic tissues from individuals across different age groups, including those with prediabetes or type 2 diabetes (T2D). Our analysis revealed the presence of all five pancreatic islet endocrine cell types, along with two types of non-hormone-expressing endocrine cells, located within or immediately adjacent to the ducts. These cells were most abundant in infancy, with a gradual decline observed through adulthood. Notably, ductal {beta} cells predominated in infancy, whereas ductal cells became more prevalent in adulthood, and significantly increased in the group aged over 60 years. Obesity further increased the ductal {beta} cells in the subjects aged over 60 years. Under prediabetic and T2D conditions, an increase in all duct-related endocrine cells was observed. These findings indicate that ductal cells may serve as a reservoir for new pancreatic endocrine cells, offering potential insights into the promotion of endogenous {beta} cell regeneration in diabetic patients. Highlights{bigcirc} Characterization of various islet endocrine cell types related to ducts in human pancreas. {bigcirc}The insulin-positive cells are the dominant cells among all duct-related islet endocrine cell types during the infancy period, however, the glucagon-positive cells become the dominant cells in adulthood. {bigcirc}T2D, Obesity, and aging are involved in the increase in the number of duct-related endocrine cells.

developmental biology↗

bioRxiv2026

A spatial-temporal atlas of human islet pathophysiology identifies a size-dependent trajectory from compensation to decompensation

Type 2 diabetes (T2D) is characterized by progressive islet dysfunction, yet the transition from functional adaptation to failure remains poorly defined within the native tissue architecture. Using multiplex imaging mass cytometry, we systematically analyzed human pancreatic islets across a spectrum of non-diabetic (ND), prediabetic (PreD), and T2D donors, revealing that pathological remodeling is profoundly size-dependent. This remodeling is manifested as a coordinated evolution of subpopulation abundance, endocrine cell proportions, structural integrity, and protein expression profiles, revealing that islets of different sizes undergo divergent fates during disease progression. We identified a size-dependent vulnerability spectrum where medium-and large-sized islets (>100 m in diameter) serve as the primary histopathological correlates of glycemic failure (HbA1c), exhibiting early density loss and structural disintegration. In contrast, small islets (30-100 {micro}m in diameter) exhibited compensatory hormone upregulation during PreD. Notably, diverging from the death of the small paradigm in Type 1 Diabetes, our data reveal a significant expansion of extra-islet endocrine clusters (EECs) that initiates during the compensatory stage, effectively preceding the onset of overt hyperglycemia. Finally, t-SNE clustering reconstructed a continuous trajectory of islet remodeling, capturing the phenotypic evolution of islets from normoglycemia through compensation to clinical decompensation. This study provides a high-resolution atlas of islet pathophysiology, offering new insights into T2D progression. HighlightsO_LIIslet remodeling during T2D progression is critically dependent on islet size. C_LIO_LIMedium and large islets density negatively correlates with HbA1c. C_LIO_LISmall islets exhibit resilience to metabolic stress during T2D progression. C_LIO_LIEECs expansion suggests a process of -cell-biased neogenesis. C_LIO_LIt-SNE maps the islet trajectory from compensation to decompensation. C_LI

pathology↗

bioRxiv2024

A Comprehensive Framework for Spatio-Temporal Analysis of DNA Damage Foci in Tumor Spheroids.

The evaluation of DNA damage response, particularly DNA damage foci formation, is crucial for understanding tumor biology and assessing the impacts of various drugs. We have developed a sophisticated semi-automated image analysis pipeline which generates quantitative map of the spatiotemporal distribution of DNA damage foci within live tumor spheroids. Our framework seamlessly integrates live imaging of tumor spheroids via Light Sheet Fluorescence Microscopy with a DNA damage foci formation assay using a genetically encoded fluorescently labeled DNA damage sensor. By combining advanced imaging techniques with computational tools, our framework offers a powerful tool for studying DNA damage response mechanisms in complex 3D cellular environments. MOTIVATIONThe motivation of this work is to propose a comprehensive framework that facilitates the study of DNA repair mechanisms within 3D contexts, specifically using tumor spheroid models. By integrating advanced imaging technologies and genetically encoded fluorescent sensors, our goal is to offer researchers a robust methodology for observing and analyzing DNA damage dynamics in realistic tissue-like environments. This framework is designed to enhance accessibility and streamline data processing, thereby empowering the scientific community to investigate DNA repair processes in 3D with greater precision and efficiency.

cell biology↗