Browsing by Subject "scRNA-seq"

Now showing 1 - 9 of 9
  • Thumbnail Image
    Item
    Celltyper: A Single-Cell Sequencing Marker Gene Tool Suite
    (2023-05) Paisley, Brianna Meadow; Liu, Yunlong; Yan, Jingwen; Cao, Sha; Wang, Juexin; Carfagna, Mark
    Single-cell RNA-sequencing (scRNA-seq) has enabled researchers to study interindividual cellular heterogeneity, to explore disease impact on cellular composition of tissue, and to identify novel cell subtypes. However, a major challenge in scRNA-seq analysis is to identify the cell type of individual cells. Accurate cell type identification is crucial for any scRNA-seq analysis to be valid as incorrect cell type assignment will reduce statistical robustness and may lead to incorrect biological conclusions. Therefore, accurate and comprehensive cell type assignment is necessary for reliable biological insights into scRNA-seq datasets. With over 200 distinct cell types in humans alone, the concept of cell identity is large. Even within the same cell type there exists heterogeneity due to cell cycle phase, cell state, cell subtypes, cell health and the tissue microenvironment. This makes cell type classification a complicated biological problem requiring bioinformatics. One approach to classify cell type identity is using marker genes. Marker genes are genes specific for one or a few cell types. When coupled with bioinformatic methods, marker genes show promise of improving cell type classification. However, current scRNA-seq classification methods and databases use marker genes that are non-specific across sources, samples, and/or species leading to bias and errors. Furthermore, many existing tools require manual intervention by the user to provide training datasets or the expected number and name of cell types, which can introduce selection bias. The selection bias negatively impacts the accuracy of cell type classification methods as the model cannot extrapolate outside of the user inputs even when it is biologically meaningful to do so. In this dissertation I developed CellTypeR, a suite of tools to explore the biology governing cell identity in a “normal” state for humans and mice. The work presented here accomplishes three aims: 1. Develop an ontology standardized database of published marker gene literature; 2. Develop and apply a marker gene classification algorithm; and 3. Create user interface and input data structure for scRNA-seq cell type prediction.
  • Thumbnail Image
    Item
    Cholesterol Sulfotransferase SULT2B1b Modulates Sensitivity to Death Receptor Ligand TNFα in Castration-Resistant Prostate Cancer
    (American Association for Cancer Research, 2019-06) Vickman, Renee E.; Yang, Jiang; Lanman, Nadia A.; Cresswell, Gregory M.; Zheng, Faye; Zhang, Chi; Doerge, R. W.; Crist, Scott A.; Mesecar, Andrew D.; Hu, Chang-Deng; Ratliff, Timothy L.; Medical and Molecular Genetics, School of Medicine
    Cholesterol sulfotransferase, SULT2B1b, has been demonstrated to modulate both androgen receptor activity and cell growth properties. However, the mechanism(s) by which SULT2B1b alters these properties within prostate cancer cells has not been described. Furthermore, specific advantages of SULT2B1b expression in prostate cancer cells is not understood. In these studies, single-cell mRNA sequencing (scRNA-seq) was conducted to compare the transcriptomes of SULT2B1b knockdown (KD) versus Control KD LNCaP cells. Over 2,000 differentially expressed (DE) genes were identified along with alterations in numerous canonical pathways, including the death receptor signaling pathway. The studies herein demonstrate that SULT2B1b KD increases tumor necrosis factor alpha (TNF) expression in prostate cancer cells and results in NF-κB activation in a TNF-dependent manner. More importantly, SULT2B1b KD significantly enhances TNF-mediated apoptosis in both TNF-sensitive LNCaP cells and TNF-resistant C4–2 cells. Overexpression of SULT2B1b in LNCaP cells also decreases sensitivity to TNF-mediated cell death, suggesting that SULT2B1b modulates pathways dictating the TNF sensitivity capacity of prostate cancer cells. Probing human prostate cancer patient datasets further support this work by providing evidence that SULT2B1b expression is inversely correlated with TNF-related genes, including TNF, CD40LG, FADD, and NFKB1. Together, these data provide evidence that SULT2B1b expression in prostate cancer cells enhances resistance to TNF and may provide a growth advantage. In addition, targeting SULT2B1b may induce an enhanced therapeutic response to TNF treatment in advanced prostate cancer.
  • Thumbnail Image
    Item
    CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal
    (MDPI, 2024-07-05) Lyu, Zhen; Dahal, Sabin; Zeng, Shuai; Wang, Juexin; Xu, Dong; Joshi, Trupti; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.
  • Thumbnail Image
    Item
    Defining developmental trajectories of prosensory cells in human inner ear organoids at single-cell resolution
    (The Company of Biologists, 2023) Ueda, Yoshitomo; Nakamura, Takashi; Nie, Jing; Solivais, Alexander J.; Hoffman, John R.; Daye, Becca J.; Hashino, Eri; Otolaryngology -- Head and Neck Surgery, School of Medicine
    The inner ear sensory epithelia contain mechanosensitive hair cells and supporting cells. Both cell types arise from SOX2-expressing prosensory cells, but the mechanisms underlying the diversification of these cell lineages remain unclear. To determine the transcriptional trajectory of prosensory cells, we established a SOX2-2A-ntdTomato human embryonic stem cell line using CRISPR/Cas9, and performed single-cell RNA-sequencing analyses with SOX2-positive cells isolated from inner ear organoids at various time points between differentiation days 20 and 60. Our pseudotime analysis suggests that vestibular type II hair cells arise primarily from supporting cells, rather than bi-fated prosensory cells in organoids. Moreover, ion channel- and ion-transporter-related gene sets were enriched in supporting cells versus prosensory cells, whereas Wnt signaling-related gene sets were enriched in hair cells versus supporting cells. These findings provide valuable insights into how prosensory cells give rise to hair cells and supporting cells during human inner ear development, and may provide a clue to promote hair cell regeneration from resident supporting cells in individuals with hearing loss or balance disorders.
  • Thumbnail Image
    Item
    Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease
    (BMC, 2022-02-01) Johnson, Travis S.; Yu, Christina Y.; Huang, Zhi; Xu, Siwen; Wang, Tongxin; Dong, Chuanpeng; Shao, Wei; Zaid, Mohammad Abu; Huang, Xiaoqing; Wang, Yijie; Bartlett, Christopher; Zhang, Yan; Walker, Brian A.; Liu, Yunlong; Huang, Kun; Zhang, Jie; Medicine, School of Medicine
    We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression.
  • Thumbnail Image
    Item
    Generating high-fidelity cochlear organoids from human pluripotent stem cells
    (Elsevier, 2023) Moore, Stephen T.; Nakamura, Takashi; Nie, Jing; Solivais, Alexander J.; Aristizábal-Ramírez, Isabel; Ueda, Yoshitomo; Manikandan, Mayakannan; Reddy, V. Shweta; Romano, Daniel R.; Hoffman, John R.; Perrin, Benjamin J.; Nelson, Rick F.; Frolenkov, Gregory I.; Chuva de Sousa Lopes, Susana M.; Hashino, Eri; Otolaryngology -- Head and Neck Surgery, School of Medicine
    Mechanosensitive hair cells in the cochlea are responsible for hearing but are vulnerable to damage by genetic mutations and environmental insults. The paucity of human cochlear tissues makes it difficult to study cochlear hair cells. Organoids offer a compelling platform to study scarce tissues in vitro; however, derivation of cochlear cell types has proven non-trivial. Here, using 3D cultures of human pluripotent stem cells, we sought to replicate key differentiation cues of cochlear specification. We found that timed modulations of Sonic Hedgehog and WNT signaling promote ventral gene expression in otic progenitors. Ventralized otic progenitors subsequently give rise to elaborately patterned epithelia containing hair cells with morphology, marker expression, and functional properties consistent with both outer and inner hair cells in the cochlea. These results suggest that early morphogenic cues are sufficient to drive cochlear induction and establish an unprecedented system to model the human auditory organ.
  • Thumbnail Image
    Item
    Integrating single-cell and spatial transcriptomics reveals endoplasmic reticulum stress-related CAF subpopulations associated with chordoma progression
    (Oxford University Press, 2024) Zhang, Tao-Lan; Xia, Chao; Zheng, Bo-Wen; Hu, Hai-Hong; Jiang, Ling-Xiang; Escobar, David; Zheng, Bo-Yv; Chen, Tian-Dong; Li, Jing; Lv, Guo-Hua; Huang, Wei; Yan, Yi-Guo; Zou, Ming-Xiang; Radiation Oncology, School of Medicine
    Background: With cancer-associated fibroblasts (CAFs) as the main cell type, the rich myxoid stromal components in chordoma tissues may likely contribute to its development and progression. Methods: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, bulk RNA-seq, and multiplexed quantitative immunofluorescence (QIF) were used to dissect the heterogeneity, spatial distribution, and clinical implication of CAFs in chordoma. Results: We sequenced here 72 097 single cells from 3 primary and 3 recurrent tumor samples, as well as 3 nucleus pulposus samples as controls using scRNA-seq. We identified a unique cluster of CAF in recurrent tumors that highly expressed hypoxic genes and was functionally enriched in endoplasmic reticulum stress (ERS). Pseudotime trajectory and cell communication analyses showed that this ERS-CAF subpopulation originated from normal fibroblasts and widely interacted with tumoral and immune cells. Analyzing the bulk RNA-seq data from 126 patients, we found that the ERS-CAF signature score was associated with the invasion and poor prognosis of chordoma. By integrating the results of scRNA-seq with spatial transcriptomics, we demonstrated the existence of ERS-CAF in chordoma tissues and revealed that this CAF subtype displayed the most proximity to its surrounding tumor cells. In subsequent QIF validation involving 105 additional patients, we confirmed that ERS-CAF was abundant in the chordoma microenvironment and located close to tumor cells. Furthermore, both ERS-CAF density and its distance to tumor cells were correlated with tumor malignant phenotype and adverse patient outcomes. Conclusions: These findings depict the CAF landscape for chordoma and may provide insights into the development of novel treatment approaches.
  • Thumbnail Image
    Item
    LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection
    (Oxford Academic, 2019-04) Johnson, Travis S.; Wang, Tongxin; Huang, Zhi; Yu, Christina Y.; Wu, Yi; Han, Yatong; Zhang, Yan; Huang, Kun; Zhang, Jie; Medicine, School of Medicine
    Motivation Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to (i) remove systematic biases, (ii) model multiple datasets with ambiguous labels and (iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously and generalizability beyond a single model type. Results We developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets (even from different species) and applied our framework on simulated, pancreas and brain scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent cell subtype labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy simulated 1 datasets: 90%; simulated 2 datasets: 94%; pancreas datasets: 88% and brain datasets: 66%) using LAmbDA Feedforward 1 Layer Neural Network with bagging. This method achieved higher weighted accuracy in labeling cellular subtypes than two other state-of-the-art methods, scmap and CaSTLe in brain (66% versus 60% and 32%). Furthermore, it achieved better performance in correctly predicting ambiguous cellular subtype labels across datasets in 88% of test cases compared with CaSTLe (63%), scmap (50%) and MetaNeighbor (50%). LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing.
  • Thumbnail Image
    Item
    Ref-1 redox activity alters cancer cell metabolism in pancreatic cancer: exploiting this novel finding as a potential target
    (BMC, 2021-08-10) Gampala, Silpa; Shah, Fenil; Lu, Xiaoyu; Moon, Hye-ran; Babb, Olivia; Umesh Ganesh, Nikkitha; Sandusky, George; Hulsey, Emily; Armstrong, Lee; Mosely, Amber L.; Han, Bumsoo; Ivan, Mircea; Yeh, Jing-Ruey Joanna; Kelley, Mark R.; Zhang, Chi; Fishel, Melissa L.; Pediatrics, School of Medicine
    Background: Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy. Redox factor-1 (Ref-1), a redox signaling protein, regulates the conversion of several transcription factors (TFs), including HIF-1α, STAT3 and NFκB from an oxidized to reduced state leading to enhancement of their DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia. Methods: scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model and validated using proteomics and qRT-PCR. The identified Ref-1's role in mitochondrial function was confirmed using mitochondrial function assays, qRT-PCR, western blotting and NADP assay. Further, the effect of Ref-1 redox function inhibition against pancreatic cancer metabolism was assayed using 3D co-culture in vitro and xenograft studies in vivo. Results: Distinct transcriptional variation in central metabolism, cell cycle, apoptosis, immune response, and genes downstream of a series of signaling pathways and transcriptional regulatory factors were identified in Ref-1 knockdown vs Scrambled control from the scRNA-seq data. Mitochondrial DEG subsets downregulated with Ref-1 knockdown were significantly reduced following Ref-1 redox inhibition and more dramatically in combination with Devimistat in vitro. Mitochondrial function assays demonstrated that Ref-1 knockdown and Ref-1 redox signaling inhibition decreased utilization of TCA cycle substrates and slowed the growth of pancreatic cancer co-culture spheroids. In Ref-1 knockdown cells, a higher flux rate of NADP + consuming reactions was observed suggesting the less availability of NADP + and a higher level of oxidative stress in these cells. In vivo xenograft studies demonstrated that tumor reduction was potent with Ref-1 redox inhibitor similar to Devimistat. Conclusion: Ref-1 redox signaling inhibition conclusively alters cancer cell metabolism by causing TCA cycle dysfunction while also reducing the pancreatic tumor growth in vitro as well as in vivo.