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  1. Article ; Online: Composite mathematical modeling of calcium signaling behind neuronal cell death in Alzheimer's disease.

    Ranjan, Bobby / Chong, Ket Hing / Zheng, Jie

    BMC systems biology

    2018  Volume 12, Issue Suppl 1, Page(s) 10

    Abstract: Background: Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD is characterized by an increase in amyloid metabolism, and by the misfolding and ... ...

    Abstract Background: Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD is characterized by an increase in amyloid metabolism, and by the misfolding and deposition of β-amyloid oligomers in and around neurons in the brain. These processes remodel the calcium signaling mechanism in neurons, leading to cell death via apoptosis. Despite accumulating knowledge about the biological processes underlying AD, mathematical models to date are restricted to depicting only a small portion of the pathology.
    Results: Here, we integrated multiple mathematical models to analyze and understand the relationship among amyloid depositions, calcium signaling and mitochondrial permeability transition pore (PTP) related cell apoptosis in AD. The model was used to simulate calcium dynamics in the absence and presence of AD. In the absence of AD, i.e. without β-amyloid deposition, mitochondrial and cytosolic calcium level remains in the low resting concentration. However, our in silico simulation of the presence of AD with the β-amyloid deposition, shows an increase in the entry of calcium ions into the cell and dysregulation of Ca
    Conclusions: Our mathematical model depicting the mechanisms affecting calcium signaling in neurons can help understand AD at the systems level and has potential for diagnostic and therapeutic applications.
    MeSH term(s) Alzheimer Disease/metabolism ; Alzheimer Disease/pathology ; Amyloid/metabolism ; Calcium Signaling ; Cell Death ; Humans ; Mitochondria/metabolism ; Models, Biological ; Neurons/metabolism ; Neurons/pathology
    Chemical Substances Amyloid
    Language English
    Publishing date 2018-04-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1752-0509
    ISSN (online) 1752-0509
    DOI 10.1186/s12918-018-0529-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data.

    Ranjan, Bobby / Schmidt, Florian / Sun, Wenjie / Park, Jinyu / Honardoost, Mohammad Amin / Tan, Joanna / Arul Rayan, Nirmala / Prabhakar, Shyam

    BMC bioinformatics

    2021  Volume 22, Issue 1, Page(s) 186

    Abstract: Background: Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference ... ...

    Abstract Background: Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation.
    Results: We present SCCONSENSUS, an [Formula: see text] framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations.
    Conclusions: SCCONSENSUS combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. SCCONSENSUS is implemented in [Formula: see text] and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus .
    MeSH term(s) Cluster Analysis ; Gene Expression Profiling ; Leukocytes, Mononuclear ; RNA ; Sequence Analysis, RNA ; Single-Cell Analysis
    Chemical Substances RNA (63231-63-0)
    Language English
    Publishing date 2021-04-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-021-04028-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Systematic immune cell dysregulation and molecular subtypes revealed by single-cell RNA-seq of subjects with type 1 diabetes.

    Honardoost, Mohammad Amin / Adinatha, Andreas / Schmidt, Florian / Ranjan, Bobby / Ghaeidamini, Maryam / Arul Rayan, Nirmala / Gek Liang Lim, Michelle / Joanito, Ignasius / Xiao Xuan Lin, Quy / Rajagopalan, Deepa / Qi Mok, Shi / Hwang, You Yi / Larbi, Anis / Khor, Chiea Chuen / Foo, Roger / Boehm, Bernhard Otto / Prabhakar, Shyam

    Genome medicine

    2024  Volume 16, Issue 1, Page(s) 45

    Abstract: Background: Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting : Methods: In this cross-sectional analysis, we generated a single-cell ... ...

    Abstract Background: Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting
    Methods: In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (stage 3) cases and 31 matched controls.
    Results: We surprisingly detected profound alterations in circulatory immune cells (1784 dysregulated genes in 13 immune cell types), far exceeding the count in the comparator systemic autoimmune disease SLE. Genes upregulated in T1DM were involved in WNT signaling, interferon signaling and migration of T/NK cells, antigen presentation by B cells, and monocyte activation. A significant fraction of these differentially expressed genes were also altered in T1DM pancreatic islets. We used the single-cell data to construct a T1DM metagene z-score (TMZ score) that distinguished cases and controls and classified patients into molecular subtypes. This score correlated with known prognostic immune markers of T1DM, as well as with drug response in clinical trials.
    Conclusions: Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes, and has the potential to guide non-invasive test development and patient stratification.
    MeSH term(s) Humans ; Diabetes Mellitus, Type 1/genetics ; Leukocytes, Mononuclear/metabolism ; Cross-Sectional Studies ; Single-Cell Gene Expression Analysis ; Autoimmune Diseases
    Language English
    Publishing date 2024-03-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 2484394-5
    ISSN 1756-994X ; 1756-994X
    ISSN (online) 1756-994X
    ISSN 1756-994X
    DOI 10.1186/s13073-024-01300-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Paternal microbiome perturbations impact offspring fitness.

    Argaw-Denboba, Ayele / Schmidt, Thomas S B / Di Giacomo, Monica / Ranjan, Bobby / Devendran, Saravanan / Mastrorilli, Eleonora / Lloyd, Catrin T / Pugliese, Danilo / Paribeni, Violetta / Dabin, Juliette / Pisaniello, Alessandra / Espinola, Sergio / Crevenna, Alvaro / Ghosh, Subhanita / Humphreys, Neil / Boruc, Olga / Sarkies, Peter / Zimmermann, Michael / Bork, Peer /
    Hackett, Jamie A

    Nature

    2024  

    Abstract: The gut microbiota operates at the interface of host-environment interactions to influence human homoeostasis and metabolic ... ...

    Abstract The gut microbiota operates at the interface of host-environment interactions to influence human homoeostasis and metabolic networks
    Language English
    Publishing date 2024-05-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-024-07336-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 26: Show issues Location:
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  5. Article ; Online: RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data.

    Schmidt, Florian / Ranjan, Bobby / Lin, Quy Xiao Xuan / Krishnan, Vaidehi / Joanito, Ignasius / Honardoost, Mohammad Amin / Nawaz, Zahid / Venkatesh, Prasanna Nori / Tan, Joanna / Rayan, Nirmala Arul / Ong, Sin Tiong / Prabhakar, Shyam

    Nucleic acids research

    2021  Volume 49, Issue 15, Page(s) 8505–8519

    Abstract: The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to ... ...

    Abstract The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets.
    MeSH term(s) Algorithms ; Animals ; Arthritis, Rheumatoid/genetics ; Bone Marrow Cells/metabolism ; COVID-19/blood ; COVID-19/pathology ; Cluster Analysis ; Cohort Studies ; Datasets as Topic ; Humans ; Leukocytes, Mononuclear/metabolism ; Leukocytes, Mononuclear/pathology ; Mice ; Organ Specificity ; Quality Control ; RNA, Small Cytoplasmic/genetics ; RNA-Seq/methods ; RNA-Seq/standards ; Single-Cell Analysis/methods ; Single-Cell Analysis/standards ; Transcriptome
    Chemical Substances RNA, Small Cytoplasmic
    Language English
    Publishing date 2021-07-28
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkab632
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    Zs.A 1067: Show issues Location:
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  6. Article ; Online: DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.

    Ranjan, Bobby / Sun, Wenjie / Park, Jinyu / Mishra, Kunal / Schmidt, Florian / Xie, Ronald / Alipour, Fatemeh / Singhal, Vipul / Joanito, Ignasius / Honardoost, Mohammad Amin / Yong, Jacy Mei Yun / Koh, Ee Tzun / Leong, Khai Pang / Rayan, Nirmala Arul / Lim, Michelle Gek Liang / Prabhakar, Shyam

    Nature communications

    2021  Volume 12, Issue 1, Page(s) 5849

    Abstract: Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even ... ...

    Abstract Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.
    MeSH term(s) Algorithms ; Arthritis, Rheumatoid ; Chromatin Immunoprecipitation Sequencing ; Cluster Analysis ; Gene Expression ; Genes, Mitochondrial ; Humans ; Machine Learning ; RNA-Seq ; Research Design ; Sequence Analysis, RNA ; Single-Cell Analysis/methods ; Software
    Language English
    Publishing date 2021-10-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-021-26085-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer.

    Lee, Hae-Ock / Hong, Yourae / Etlioglu, Hakki Emre / Cho, Yong Beom / Pomella, Valentina / Van den Bosch, Ben / Vanhecke, Jasper / Verbandt, Sara / Hong, Hyekyung / Min, Jae-Woong / Kim, Nayoung / Eum, Hye Hyeon / Qian, Junbin / Boeckx, Bram / Lambrechts, Diether / Tsantoulis, Petros / De Hertogh, Gert / Chung, Woosung / Lee, Taeseob /
    An, Minae / Shin, Hyun-Tae / Joung, Je-Gun / Jung, Min-Hyeok / Ko, Gunhwan / Wirapati, Pratyaksha / Kim, Seok Hyung / Kim, Hee Cheol / Yun, Seong Hyeon / Tan, Iain Bee Huat / Ranjan, Bobby / Lee, Woo Yong / Kim, Tae-You / Choi, Jung Kyoon / Kim, Young-Joon / Prabhakar, Shyam / Tejpar, Sabine / Park, Woong-Yang

    Nature genetics

    2020  Volume 52, Issue 6, Page(s) 594–603

    Abstract: Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To ... ...

    Abstract Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To understand this cross-talk, we analyzed the transcriptome of 91,103 unsorted single cells from 23 Korean and 6 Belgian patients. Cancer cells displayed transcriptional features reminiscent of normal differentiation programs, and genetic alterations that apparently fostered immunosuppressive microenvironments directed by regulatory T cells, myofibroblasts and myeloid cells. Intercellular network reconstruction supported the association between cancer cell signatures and specific stromal or immune cell populations. Our collective view of the cellular landscape and intercellular interactions in colorectal cancer provide mechanistic information for the design of efficient immuno-oncology treatment strategies.
    MeSH term(s) Cell Lineage ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/immunology ; Colorectal Neoplasms/pathology ; Gastric Mucosa/immunology ; Gastric Mucosa/pathology ; Gene Expression Regulation, Neoplastic/immunology ; Humans ; Sequence Analysis, RNA ; Single-Cell Analysis ; Stromal Cells/pathology ; T-Lymphocytes/immunology ; T-Lymphocytes/pathology ; Tumor Microenvironment/genetics ; Tumor Microenvironment/immunology
    Language English
    Publishing date 2020-05-25
    Publishing country United States
    Document type Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 1108734-1
    ISSN 1546-1718 ; 1061-4036
    ISSN (online) 1546-1718
    ISSN 1061-4036
    DOI 10.1038/s41588-020-0636-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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