Biology subjects

Asmann, Y. W.

Publications and source records attributed to Asmann, Y. W..

9 recordsLinked to original sources

bioRxiv2025

Single-cell multiomics reveals disrupted glial gene regulatory programs in Alzheimer's disease via interpretable machine learning

Recent development of single-cell technology across multiple omics platforms has provided new ways to obtain holistic views of cells to study disease pathobiology. Alzheimers disease (AD) is the most common form of dementia worldwide, yet the detailed understanding of its cellular and molecular mechanisms remains limited. In this study, we analyzed paired single-cell transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data from the Seattle Alzheimers Disease Brain Cell Atlas (SEA-AD) Consortium to investigate the molecular mechanisms of AD at a cell-subpopulation-specific resolution focusing on glial cells. We benchmarked various multi-omics integration methods using diverse metrics and built an analytic workflow that enabled effective batch correction and cross-modality alignment, creating a unified cell state space. Through integrative analysis of 26 human brain samples, we uncovered AD-associated gene expression and pathway changes in glial subpopulations and highlighted important transcriptomic and epigenomic signatures via functional inference and interpretable machine learning paradigms, discovering the profound involvement of the Solute Carrier proteins (SLC) family genes in multiple glial cell types. We also identified glial cell-specific regulatory programs mediated by key transcription factors such as JUN and FOSL2 in astrocytes, the Zinc Finger (ZNF) family genes in microglia, and the SOX family of transcription factors in oligodendrocytes. Our study provides a comprehensive workflow and a high-resolution view of how glial regulatory programs are disrupted in AD. Our findings offer novel insights into disease-related changes in gene regulation and suggest potential targets for further research and therapy.

genomics↗

bioRxiv2024

Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline

Single-cell sequencing technologies provide us with information at the level of individual cells. The combination of single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. The integration of both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

bioinformatics↗

bioRxiv2023

Cell-specific crosstalk proteomics reveals cathepsin B signaling as a driver of glioblastoma malignancy near the subventricular zone

Glioblastoma (GBM) is the most prevalent and aggressive malignant primary brain tumor. GBM proximal to the lateral ventricles (LVs) is more aggressive, potentially due to subventricular zone (SVZ) contact. Despite this, crosstalk between GBM and neural stem/progenitor cells (NSC/NPCs) is not well understood. Using cell-specific proteomics, we show that LV-proximal GBM prevents neuronal maturation of NSCs through induction of senescence. Additionally, GBM brain tumor initiating cells (BTICs) increase expression of CTSB upon interaction with NPCs. Lentiviral knockdown and recombinant protein experiments reveal both cell-intrinsic and soluble CTSB promote malignancy-associated phenotypes in BTICs. Soluble CTSB stalls neuronal maturation in NPCs while promoting senescence, providing a link between LV-tumor proximity and neurogenesis disruption. Finally, we show LV-proximal CTSB upregulation in patients, showing the relevance of this crosstalk in human GBM biology. These results demonstrate the value of proteomic analysis in tumor microenvironment research and provide direction for new therapeutic strategies in GBM. HighlightsO_LIPeriventricular GBM is more malignant and disrupts neurogenesis in a rodent model. C_LIO_LICell-specific proteomics elucidates tumor-promoting crosstalk between GBM and NPCs. C_LIO_LINPCs induce upregulated CTSB expression in GBM, promoting tumor progression. C_LIO_LIGBM stalls neurogenesis and promotes NPC senescence via CTSB. C_LI

cancer biology↗

bioRxiv2022

Interpreting how machine learning models make predictions in biological studies

Explainable artificial intelligence aims to interpret how the machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, the understandings of the applicability of these model explainers to biological data are still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pretrained models of predicting tissue types from transcriptomic data, and by identifying top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.

bioinformatics↗

bioRxiv2021

Conserved Architecture of Brain Transcriptome Changes between Alzheimer's Disease and Progressive Supranuclear Palsy in Pathologically Affected and Unaffected Regions

We identify a striking correlation in the directionality and magnitude of gene expression changes in brain transcriptomes between Alzheimers disease (AD) and Progressive Supranuclear Palsy (PSP). Further, the transcriptome architecture in AD and PSP is highly conserved between the temporal and cerebellar cortices, indicating highly similar transcriptional changes occur in pathologically affected and "unaffected" areas of the brain. These data have broad implications for interpreting transcriptomic data in neurodegenerative disorders.

neuroscience↗

bioRxiv2020

Leveraging selective hippocampal vulnerability among Alzheimer's disease subtypes reveals a novel tau binding partner SERPINA5

Selective vulnerability is a central concept to the myriad of devastating neurodegenerative disorders. Although hippocampus and cortex are selectively vulnerable in Alzheimers disease (AD), the degree of involvement lies along a spectrum that we previously defined as AD subtypes revealing distinct clinical correlates. To operationalize heterogeneity of disease spectrum, we classified corticolimbic patterns of neurofibrillary tangles to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. Using a multidisciplinary approach, we uncovered disease-relevant hippocampal gene expression changes. Biological relevance was prioritized using machine learning and several levels of human validation. This resulted in five genes highly predictive of neuropathologically diagnosed AD: SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Deeper investigation revealed SERPINA5 to be a novel tau binding partner that may represent a "tipping point" in the dynamic maturity of neurofibrillary tangles. Our study highlights the importance of embracing heterogeneity of the human brain to yield promising gene candidates as exampled by SERPINA5.

neuroscience↗

bioRxiv2020

Identification of novel Alzheimer's disease genes co-expressed with TREM2

By analyzing whole-exome data from the Alzheimers disease sequencing project (ADSP), we identify a set of 4 genes that show highly significant association with Alzheimers disease (AD). These genes were identified within a human TREM2 co-expression network using a novel approach wherein prioritized polygenic score analyses were performed sequentially to identify significant polygenic components. Two of the 4 genes (TREM2, RIN3) have previously been linked to AD and two (ATP8B4, IL17RA) are novel. Like TREM2, the 2 novel AD genes are selectively expressed in human microglial cells. The most significant variants in ATP8B4 and IL17RA are non-synonymous variants with strong effects comparable to the APOE {varepsilon}4 and {varepsilon}2 alleles. These protein-altering variants will provide unique opportunities to further explore the biological role of microglial cells in AD and help inform future immune modulatory therapeutic development for AD.

genetics↗

bioRxiv2020

Deciphering cellular transcriptional alterations in Alzheimer's disease brains

Large-scale brain bulk-RNAseq studies identified molecular pathways implicated in Alzheimers disease (AD), however these findings can be confounded by cellular composition changes in bulk-tissue. To identify cell intrinsic gene expression alterations of individual cell types, we designed a bioinformatics pipeline and analyzed three AD and control bulk-RNAseq datasets of temporal and dorsolateral prefrontal cortex from 685 brain samples. We detected cell-proportion changes in AD brains that are robustly replicable across the three independently assessed cohorts. We applied three different algorithms including our in-house algorithm to identify cell intrinsic differentially expressed genes in individual cell types (CI-DEGs). We assessed the performance of all algorithms by comparison to single nucleus RNAseq data. We identified consensus CI-DEGs that are common to multiple brain regions. Despite significant overlap between consensus CI-DEGs and bulk-DEGs, many CI-DEGs were absent from bulk-DEGs. Consensus CI-DEGs and their enriched GO terms include genes and pathways previously implicated in AD or neurodegeneration, as well as novel ones. We demonstrated that the detection of CI-DEGs through computational deconvolution methods is promising and highlight remaining challenges. These findings provide novel insights into cell-intrinsic transcriptional changes of individual cell types in AD and may refine discovery and modeling of molecular targets that drive this complex disease.

neuroscience↗

bioRxiv2019

Contextual Classifications of Cancer Driver Genes

Functions of cancer driver genes depend on cellular contexts that vary substantially across tissues and organs. Distinguishing oncogenes (OGs) and tumor suppressor genes (TSGs) for each cancer type is critical to identifying clinically actionable targets. However, current resources for context-aware classifications of cancer drivers are limited. In this study, we show that the direction and magnitude of somatic selection of missense and truncating mutations of a gene are suggestive of its contextual activities. By integrating these features with ratiometric and conservation measures, we developed a computational method to categorize OGs and TSGs using exome sequencing data. This new method, named genes under selection in tumors (GUST) shows an overall accuracy of 0.94 when tested on manually curated benchmarks. Application of GUST to 10,172 tumor exomes of 33 cancer types identified 98 OGs and 179 TSGs, >70% of which promote tumorigenesis in only one cancer type. In broad-spectrum drivers shared across multiple cancer types, we found heterogeneous mutational hotspots modifying distinct functional domains, implicating the synchrony of convergent and divergent disease mechanisms. We further discovered two novel OGs and 28 novel TSGs with high confidence. The GUST program is available at https://github.com/liliulab/gust. A database with pre-computed classifications is available at https://liliulab.shinyapps.io/gust

bioinformatics↗