Biology subjects

Shi, C. H.

Publications and source records attributed to Shi, C. H..

9 recordsLinked to original sources

bioRxiv2026

Spatial proximity to AD pathology defines cell-type and genotype-specific characteristics in human AD hippocampus

How amyloid-{beta} (A{beta}) plaques and tau phosphorylation spatially relate to cell response and genetic variation in Alzheimer's disease (AD) brain is currently unclear. We established a pipeline to concurrently annotate A{beta} and AT8 ptau pathology and cell-specific MERFISH expression profiles in human control and AD hippocampus. We characterized expression signatures in glia and neurons proximal or distal to A{beta} plaques, AT8 ptau-enriched neurons, or A{beta} and AT8 ptau in combination, and used spatially-defined transcriptional signatures to infer pathology-associated states in large snRNA-seq datasets lacking spatial context. We characterized variants from 120 gene loci associated with AD risk that affect cell-specific gene expression within local pathological contexts. Astrocyte APOE4/E4 expression was specifically suppressed in proximity to A{beta} plaques, whereas other APOE genotypes were unaffected by A{beta} proximity. Together, this analysis pipeline couples expression profiles in proximity to A{beta} and tau pathology, and implicates AD cis-gene variants with spatial sensitivity to AD pathology.

neuroscience↗

bioRxiv2026

EpiATLAS - a reference for human epigenomic research

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease1. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium2 (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

genomics↗

bioRxiv2026

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues

Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

bioinformatics↗

bioRxiv2025

Towards universal modeling of transcript isoform expression levels

A holy grail in computational biology is accurate modeling of transcript expression levels using epigenetic features, which would provide a quantitative way to study gene regulation in normal and disease states. Previous studies relied heavily on immortalized cell lines that exhibit properties different from cells in natural tissue environments. Most studies also quantified the expression of each gene by a single expression level, which fails to capture separate expression levels of different transcript isoforms of the same gene. In this study, making use of the latest large-scale dataset of paired transcriptomic and epigenomic data of human samples produced by the International Human Epigenome Consortium (IHEC), we computationally modeled the expression levels of individual transcript isoforms in 324 samples from 29 tissue types. We constructed the models using graph-based methods that integrate both location-specific epigenomic features and multiple types of gene-gene relationships. We found that to infer transcript isoform expression levels in a sample, a model that integrates information from many samples of other tissue types consistently outperforms a model trained on data from this sample itself, providing strong support that it is possible to construct a "universal" model that can accurately infer transcript isoform expression levels across tissue types.

bioinformatics↗

bioRxiv2025

SORLA upregulation suppresses global pathological effects in aged tauopathy mouse brain

A role for the trafficking receptor SORLA in reducing A{beta} levels has been well-established, however, relatively little is known with respect to whether and how SORLA can potentially affect tau pathology in vivo. Here, we show that transgenic SORLA upregulation (SORLA TG) can reverse pathological effects in aged PS19 (P301S tau) mouse brain, including tau phosphorylation and seeding, ventricle dilation, synapse loss, LTP impairment and glial hyperactivation. Proteomic analysis indicates reversion of PS19 profiles in PS19/SORLA TG hippocampus, including pathological changes in synapse-related proteins as well as key drivers of synaptic dysfunction such as Apoe and C1q. snRNA-seq analysis reveals suppression of PS19- signatures with SORLA upregulation, including proinflammatory induction of Plxnb1/Plxnb2 in glia. Tau seeding and aggregation, neuroinflammation, as well as PlxnB1/B2 induction are exacerbated in PS19 hippocampus with SORLA deletion. These results implicate a global role for SORLA in neuroprotection from tau toxicity in PS19 mouse brain.

neuroscience↗

bioRxiv2023

Linked-Pair Long-Read Sequencing Strategy for Targeted Resequencing and Enrichment

In this report, we present linked-pair sequencing, a novel strategy to construct a long-read sequencing library such that adjacent fragments are linked with end-terminal duplications. We use the CRISPR-Cas9 nickase enzyme and a pool of multiple sgRNAs to perform non-random fragmentation of targeted long DNA molecules (>300kb) into smaller library-sized fragments (about 20 kbp) in a manner so as to retain physical linkage information (up to 1000 bp) between adjacent fragments. DNA molecules targeted for fragmentation are preferentially ligated with adaptors for sequencing, so this method can enrich targeted regions while taking advantage of the long-read sequencing platforms. This enables the sequencing of target regions with significantly lower total coverage, and the genome sequence within linker regions provides information for assembly and phasing. We demonstrated the validity and efficacy of the method first using phage and then by sequencing a panel of 100 full-length cancer-related genes (including both exons and introns) in the human genome. When the designed linkers contained heterozygous genetic variants, long haplotypes could be established. This sequencing strategy can be readily applied in both PacBio and Oxford Nanopore platforms. This economically viable approach is useful for targeted enrichment of hundreds of target genomic regions and where long no-gap contigs need deep sequencing.

genomics↗

bioRxiv2021

Quantifying full-length circular RNAs in cancer

Circular RNAs (circRNAs) are abundantly expressed in cancer. Their resistance to exonucleases enables them to have potentially stable interactions with different types of biomolecules. Alternative splicing can create different circRNA isoforms that have different sequences and unequal interaction potentials. The study of circRNA function thus requires knowledge of complete circRNA sequences. Here we describe psirc, a method that can identify full-length circRNA isoforms and quantify their expression levels from RNA sequencing data. We confirm the effectiveness and computational efficiency of psirc using both simulated and actual experimental data. Applying psirc on transcriptome profiles from nasopharyngeal carcinoma and normal nasopharynx samples, we discover and validate circRNA isoforms differentially expressed between the two groups. Compared to the assumed circular isoforms derived from linear transcript annotations, some of the alternatively spliced circular isoforms have 100 times higher expression and contain substantially fewer microRNA response elements, demonstrating the importance of quantifying full-length circRNA isoforms.

bioinformatics↗

bioRxiv2019

K-mer counting with low memory consumption enables fast clustering of single-cell sequencing data without read alignment

K-mer counting has many applications in sequencing data processing and analysis. However, sequencing errors can produce many false k-mers that substantially increase the memory requirement during counting. We propose a fast k-mer counting method, CQF-deNoise, which has a novel component for dynamically identifying and removing false k-mers while preserving counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consumed 49-76% less memory than the second best method, but still ran competitively fast. The k-mer counts from CQF-deNoise produced cell clusters from single-cell RNA-seq data highly consistent with CellRanger but required only 5% of the running time at the same memory consumption, suggesting that CQF-deNoise can be used for a preview of cell clusters for an early detection of potential data problems, before running a much more time-consuming full analysis pipeline.

bioinformatics↗

bioRxiv2018

The biosynthetic origin of psychoactive kavalactones in kava

For millennia, humans have used plants for medicinal purposes. However, our limited understanding of plant biochemistry hinders the translation of such ancient wisdom into modern pharmaceuticals1. Kava (Piper methysticum) is a medicinal plant native to the Polynesian islands with anxiolytic and analgesic properties supported by over 3,000 years of traditional use as well as numerous recent clinical trials2-5. The main psychoactive principles of kava, kavalactones, are a unique class of polyketide natural products known to interact with central nervous system through mechanisms distinct from those of the prescription psychiatric drugs benzodiazepines and opioids6,7. Here we report de novo elucidation of the biosynthetic pathway of kavalactones, consisting of seven specialized metabolic enzymes. Based on phylogenetic and crystallographic analyses, we highlight the emergence of two paralogous styrylpyrone synthases, both of which have neofunctionalized from an ancestral chalcone synthase to catalyze the formation of the kavalactone scaffold. Structurally diverse kavalactones are then biosynthesized by subsequent regio- and stereo-specific tailoring enzymes. We demonstrate the feasibility of engineering heterologous production of kavalactones and their derivatives in bacterial, yeast, and plant hosts, thus opening an avenue towards the development of new psychiatric therapeutics for anxiety disorders, which affect over 260 million people globally8.

plant biology↗