Biology subjects

Huang, S. X.

Publications and source records attributed to Huang, S. X..

5 recordsLinked to original sources

bioRxiv2026

Opposing Experiences Shape Parvalbumin Interneuron Plasticity through Distinct Molecular Programs

Life experiences profoundly influence brain function, yet how opposing experiences are encoded remains unclear. Here, we show that opposing experiences, chronic stress (CS) and voluntary running (VR), induce largely distinct molecular programs rather than a simple bidirectional modulation of a shared molecular program in parvalbumin interneurons (PVIs). PVI-specific translational profiling across frontal cortex and dorsal and ventral hippocampus reveals that experience-dependent gene expression is organized into brain region-specific architectures, which are further structured into distinct co-expression modules. We uncovered CS-induced upregulation of histone acetyltransferase KAT6A and H3K23 acetylation specifically in PVIs of the frontal cortex accompanied by altered genome-wide redistribution of acetylated H3K23, including genes implicated in neuropsychiatric disorders. Targeted elevation of KAT6A in PVIs recapitulates CS-induced morphological and behavioral alterations. Together, our findings reveal that opposing experiences engage distinct and region-specific molecular programs in PVIs and identify a stress-responsive epigenetic pathway underlying inhibitory circuit plasticity.

neuroscience↗

bioRxiv2025

Generation of Region-specific Airway Basal Stem Cells from Human Pluripotent Stem Cells via Regulation of BMP-NOGGIN Axis

Basal cells (BCs) are the primary stem cell population of adult human airways and a key target for hPSC-based models of airway development, disease and regenerative medicine. Recent studies have revealed substantial regional differences between human proximal and distal airway cell types, including BCs. Here, we show that the NOGGIN-BMP signaling axis governs proximal-distal patterning of hPSC-derived lung progenitors leading to the generation of region-specific induced BCs (iBCs). Continuous BMP inhibition through NOGGIN, coupled with tapered WNT activation, generates proximal iBCs that molecularly and functionally resemble human proximal airway BCs and differentiate in vitro and in vivo into the full repertoire of specialized proximal airway cell types, including ionocytes and pulmonary neuroendocrine cells. In sharp contrast, BMP activation with tapered WNT generates distal airway-like cells and distal BCs with limited ionocyte differentiation potential. Progeny of proximal iBCs derived from G551D CFTR mutant hiPSCs also recapitulate a cystic fibrosis-associated ionocyte phenotype.

cell biology↗

bioRxiv2023

Quantitative Geometric Modeling of Blood Cells from X-ray Histotomograms of Whole Zebrafish Larvae

Tissue phenotyping is foundational to understanding and assessing the cellular aspects of disease in organismal context and an important adjunct to molecular studies in the dissection of gene function, chemical effects, and disease. As a first step toward computational tissue phenotyping, we explore the potential of cellular phenotyping from 3-Dimensional (3D), 0.74 {micro}m isotropic voxel resolution, whole zebrafish larval images derived from X-ray histotomography, a form of micro-CT customized for histopathology. As proof of principle towards computational tissue phenotyping of cells, we created a semi-automated mechanism for the segmentation of blood cells in the vascular spaces of zebrafish larvae, followed by modeling and extraction of quantitative geometric parameters. Manually segmented cells were used to train a random forest classifier for blood cells, enabling the use of a generalized cellular segmentation algorithm for the accurate segmentation of blood cells. These models were used to create an automated data segmentation and analysis pipeline to guide the steps in a 3D workflow including blood cell region prediction, cell boundary extraction, and statistical characterization of 3D geometric and cytological features. We were able to distinguish blood cells at two stages in development (4- and 5-days-post-fertilization) and wild-type vs. polA2 huli hutu (hht) mutants. The application of geometric modeling across cell types to and across organisms and sample types may comprise a valuable foundation for computational phenotyping that is more open, informative, rapid, objective, and reproducible.

systems biology↗

bioRxiv2023

Sinogram Domain Angular Upsampling of Sparse-View Micro-CT with Dense Residual Hierarchical Transformer and Noise-Aware Loss

Reduced angular sampling is a key strategy for increasing scanning efficiency of micron-scale computed tomography (micro-CT). Despite boosting throughput, this strategy introduces noise and artifacts due to undersampling. In this work, we present a solution to this issue, by proposing a novel Dense Residual Hierarchical Transformer (DRHT) network to recover high-quality sinograms from 2 x, 4x and 8x undersampled scans. DRHT is trained to utilize limited information available from sparsely angular sampled scans and once trained, it can be applied to recover higher-resolution sinograms from shorter scan sessions. Our proposed DRHT model aggregates the benefits of a hierarchical-multi-scale structure along with the combination of local and global feature extraction through dense residual convolutional blocks and non-overlapping window transformer blocks respectively. We also propose a novel noise-aware loss function named KL-L1 to improve sinogram restoration to full resolution. KL-L1, a weighted combination of pixel-level and distribution-level cost functions, leverages inconsistencies in noise distribution and uses learnable spatial weights to improve the training of the DRHT model. We present ablation studies and evaluations of our method against other state-of-the-art (SOTA) models over multiple datasets. Our proposed DRHT network achieves an average increase in peak signal to noise ratio (PSNR) of 17.73dB and a structural similarity index (SSIM) of 0.161, for 8x upsampling, across the three unique datasets, compared to their respective Bicubic interpolated versions. This novel approach can be utilized to decrease radiation exposure to patients and reduce imaging time for large-scale CT imaging projects.

bioengineering↗

bioRxiv2021

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum. In this paper, we present a machine learning analysis on Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped viruses, and 99% for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups (e.g. amide, amino acid, carboxylic acid) we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. The accurate and interpretable machine learning model developed for Raman virus identification presents promising potential in a real-time virus detection system. Significance StatementA portable micro-fluidic platform for virus capture promises rapid enrichment and label-free optical identification of viruses by Raman spectroscopy. A large Raman dataset collected on a variety of viruses enables the training of machine learning (ML) models capable of highly accurate and sensitive virus identification. The trained ML models can then be integrated with the portable device to provide real-time virus detection and identification capability. We validate this conceptual framework by presenting highly accurate virus type and subtype identification results using a convolutional neural network to classify Raman spectra of viruses.

biophysics↗