Biology subjects

Huang, C.-J.

Publications and source records attributed to Huang, C.-J..

7 recordsLinked to original sources

bioRxiv2025

Tissue-guided multi-omics profiling identifies extracellular vesicle biomarkers indicative of lung pathology in acute respiratory distress syndrome

BackgroundAcute respiratory distress syndrome (ARDS) remains a lethal inflammatory lung condition lacking reliable biomarkers that reflect lung-specific pathology. Extracellular vesicles (EVs) circulate systemically and may carry molecular signals from injured organs, but the correspondence between EV cargo and lung tissue alterations remains unclear. MethodsWe established aspiration-, lipopolysaccharide (LPS)-, and COVID-19-induced murine ARDS models and applied a tissue-guided multiomics framework integrating proteomic and metabolomic analyses of lung tissue and plasma-derived EVs to identify lung-originating circulating biomarkers. ResultsFour proteins--haptoglobin (HP), inter-alpha-trypsin inhibitor heavy chains 3 and 4 (ITIH3, ITIH4), and clusterin (CLU)--were consistently upregulated in both lung tissue and plasma EVs across all ARDS etiologies. Metabolomic integration revealed dysregulation of arachidonic acid metabolism as a unifying inflammatory axis. Multiomics network analysis further distinguished etiology-specific molecular programs, including glycolytic activation in aspiration-induced, platelet aggregation in LPS-induced, and vascular smooth muscle dysregulation in COVID-19-induced ARDS. ConclusionsThis study establishes a tissue-informed EV profiling framework that links local lung pathology to systemic molecular signatures, revealing HP, ITIH3, ITIH4, CLU, and arachidonic-acid-related metabolites as potential diagnostic markers for ARDS. These findings provide a foundation for developing clinically translatable, EV-based biomarker assays for early detection and molecular subtyping of lung injury.

biochemistry↗

bioRxiv2025

The Effect of Resistance Training Volume on Individual-Level Skeletal Muscle Adaptations: A Novel Replicated Within-Participant Unilateral Trial

PurposeThere is growing emphasis on investigating heterogeneity in resistance training (RT) outcomes, likely motivated by observations of substantial gross variability in training effects. However, gross variability does not necessarily represent true inter-individual response variation (IRV) and can be obscured by measurement error, sampling variance, and biological variability. Appropriate study design and statistical analysis are required to distinguish IRV from these confounding sources of within-participant variation. Methods16 recreationally trained participants completed a novel replicated within-participant unilateral design across two 11-week training phases separated by a 6-8 week washout. Lower limbs were randomized to a low volume ([~]8 sets/week) or high volume ([~]16 sets/week) training protocol in each phase. We assessed both general (GEN; average response across conditions) and condition-specific (CON; difference between volumes) IRV for vastus lateralis cross-sectional area and leg press one-repetition maximum using a multi-stage statistical approach. ResultsHigher weekly set volumes demonstrated a detectable advantage for muscle hypertrophy (1.8 cm{superscript 2} [95% HDI: 0.29, 3.41]; 98.77% posterior probability) but not maximal strength (3.48 kg [95% HDI: -5.1, 12.15]; 80.01% posterior probability). Despite substantial gross variability, we failed to detect irrefutable evidence of meaningful IRV. Integrated methods revealed stronger evidence for GEN versus CON IRV, with correlation coefficients ranging from 0.67 to 0.7 for GEN versus 0.04 to 0.06 for CON. ConclusionsOur findings clearly illustrate that gross variability in training outcomes does not necessarily indicate true inter-individual differences, a distinction critical for both research and practice. KEY POINTSO_LIGross variability in resistance training outcomes is commonly interpreted as evidence of meaningful inter-individual response variation (IRV), but this variability often reflects confounding sources of within-participant variation rather than true inter-individual differences. C_LIO_LIUsing a novel replicated within-participant unilateral design, we investigated the effects of different weekly set volumes on changes in muscle size and maximal strength, as well as the inter-individual variation thereof. C_LIO_LIAt the group level, higher weekly set volumes produced modest but detectable benefits for muscle hypertrophy but not maximal strength. At the individual level, we failed to reveal irrefutable evidence of true IRV for either primary outcome, though there was stronger support for variability in training responses independent of weekly set volume (i.e., general IRV) compared to differential responses between volume conditions (i.e., condition-specific IRV). C_LIO_LIWhile this study has limitations, it highlights that appropriate study design and statistical analysis are essential for investigating IRV in resistance training. Our findings, along with the broader research in multiple disciplines (i.e., medicine, nutrition science, and exercise physiology), demonstrate that gross variability can purely reflect confounding sources of within-participant variation (e.g., sampling variance, measurement error, biological variability) rather than true inter-individual differences--a distinction critical for both research and practice. C_LI

physiology↗

bioRxiv2025

Forecast Padding Enhances Accuracy and Robustness of EEG-Phase-Synchronized TMS

Closed-loop neuromodulation is a promising personalized treatment for various neuropsychiatric disor-ders, delivering precise stimuli based on real-time brain signals. However, its clinical potential is currently limited by technical challenges inherent in its real-time nature. This article addresses the primary technical challenges in EEG-Phase-Synchronized Transcranial Magnetic Stimulation (TMS), including poor stimulation accuracy and inefficient biomarker detection (correlated with deadlock). These challenges arise from the vulnerability of existing algorithms to filter edge effects. Inspired by predictive coding theory in neuroscience, we propose a novel signal padding method (forecast padding) to mitigate the filter edge effect. To properly quantify the improvements that forecast padding brings about in real-world systems, we introduce a novel delay-relevant validation framework and demonstrate its reliability using experimental data from a real system. Through this framework, we demonstrate that forecast padding significantly improves both stimulation accuracy and deadlock rate. Given the pervasive impact of filter edge effects in closed-loop neuromodulation and other signal processing domains, forecast padding shows broad application potential across various fields.

bioengineering↗

bioRxiv2023

Temporal Transcriptome Analysis Uncovers Regulatory Modules Programming Embryo Development from Embryonic Morphogenesis to Post-Germination

We profiled the soybean seed embryo transcriptome across embryonic development to post-germinative development to understand gene activities and regulatory networks promoting these processes. Transcriptomic landscapes feature highly prevalent transcripts which are categorized into early and late groups with major functions of reserve accumulation and energy generation, respectively, and both functions are dominant during late reserve accumulation as the transitioning stage. During the mid-reserve accumulation, regulatory events simultaneously dominate at the transcriptional and chromatin levels, followed by the emergence of distinct mRNA populations during late reserve accumulation throughout germination. We identified diverse functions conducted by sequentially activated genes across developmental stages. Gene coexpression network analysis reveals modules associated with developmental stages, some of which are enriched in genes with functions involved in specific developmental processes. We identified an early-desiccation-associated gene module, containing most transcription factors responsive to abiotic stress, within which one transcription factor is functionally validated to demonstrate increased drought tolerance in Arabidopsis overexpression lines. Finally, we found that a subset of genes is under purifying selection, surpasses the number of their Arabidopsis germination-specific homologs and most are active before germination from embryonic morphogenesis through dormancy, suggesting a potential role in governing physical dormancy in soybean compared to physiological dormancy in Arabidopsis. Our data represent a step toward identifying genes and regulatory networks in the soybean genome facilitating developmental programs across transition phases to bridge embryonic and germinative development.

plant biology↗

bioRxiv2020

The Genomic Formation of Human Populations in East Asia

The deep population history of East Asia remains poorly understood due to a lack of ancient DNA data and sparse sampling of present-day people. We report genome-wide data from 191 individuals from Mongolia, northern China, Taiwan, the Amur River Basin and Japan dating to 6000 BCE - 1000 CE, many from contexts never previously analyzed with ancient DNA. We also report 383 present-day individuals from 46 groups mostly from the Tibetan Plateau and southern China. We document how 6000-3600 BCE people of Mongolia and the Amur River Basin were from populations that expanded over Northeast Asia, likely dispersing the ancestors of Mongolic and Tungusic languages. In a time transect of 89 Mongolians, we reveal how Yamnaya steppe pastoralist spread from the west by 3300-2900 BCE in association with the Afanasievo culture, although we also document a boy buried in an Afanasievo barrow with ancestry entirely from local Mongolian hunter-gatherers, representing a unique case of someone of entirely non-Yamnaya ancestry interred in this way. The second spread of Yamnaya-derived ancestry came via groups that harbored about a third of their ancestry from European farmers, which nearly completely displaced unmixed Yamnaya-related lineages in Mongolia in the second millennium BCE, but did not replace Afanasievo lineages in western China where Afanasievo ancestry persisted, plausibly acting as the source of the early-splitting Tocharian branch of Indo-European languages. Analyzing 20 Yellow River Basin farmers dating to [~]3000 BCE, we document a population that was a plausible vector for the spread of Sino-Tibetan languages both to the Tibetan Plateau and to the central plain where they mixed with southern agriculturalists to form the ancestors of Han Chinese. We show that the individuals in a time transect of 52 ancient Taiwan individuals spanning at least 1400 BCE to 600 CE were consistent with being nearly direct descendants of Yangtze Valley first farmers who likely spread Austronesian, Tai-Kadai and Austroasiatic languages across Southeast and South Asia and mixing with the people they encountered, contributing to a four-fold reduction of genetic differentiation during the emergence of complex societies. We finally report data from Jomon hunter-gatherers from Japan who harbored one of the earliest splitting branches of East Eurasian variation, and show an affinity among Jomon, Amur River Basin, ancient Taiwan, and Austronesian-speakers, as expected for ancestry if they all had contributions from a Late Pleistocene coastal route migration to East Asia.

genomics↗

bioRxiv2020

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. For example, at high flow speed (i.e., high throughput), the integration time of the image sensor becomes short, resulting in reduced sensitivity or pixel resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually "freezing" the motion of flowing cells on the image sensor to effectively achieve 1,000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells/s without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

cell biology↗

bioRxiv2019

Classification of platelet aggregates by agonist type

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

cell biology↗