Single-Cell Trajectories Reconstruction, Exploration and Mapping of Omics Data with STREAM
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RBP-J Signaling − Cells Through Notch Novel IRF8-Controlled
Sca-1+Lin−CD117− Mesenchymal Stem/Stromal Cells Induce the Generation of Novel IRF8-Controlled Regulatory Dendritic Cells through Notch −RBP-J Signaling This information is current as of September 25, 2021. Xingxia Liu, Shaoda Ren, Chaozhuo Ge, Kai Cheng, Martin Zenke, Armand Keating and Robert C. H. Zhao J Immunol 2015; 194:4298-4308; Prepublished online 30 March 2015; doi: 10.4049/jimmunol.1402641 Downloaded from http://www.jimmunol.org/content/194/9/4298 Supplementary http://www.jimmunol.org/content/suppl/2015/03/28/jimmunol.140264 http://www.jimmunol.org/ Material 1.DCSupplemental References This article cites 59 articles, 19 of which you can access for free at: http://www.jimmunol.org/content/194/9/4298.full#ref-list-1 Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision by guest on September 25, 2021 • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2015 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Sca-1+Lin2CD1172 Mesenchymal Stem/Stromal Cells Induce the Generation of Novel IRF8-Controlled Regulatory Dendritic Cells through Notch–RBP-J Signaling Xingxia Liu,*,1 Shaoda Ren,*,1 Chaozhuo Ge,* Kai Cheng,* Martin Zenke,† Armand Keating,‡,x and Robert C. -
CDC2 Mediates Progestin Initiated Endometrial Stromal Cell Proliferation: a PR Signaling to Gene Expression Independently of Its Binding to Chromatin
CDC2 Mediates Progestin Initiated Endometrial Stromal Cell Proliferation: A PR Signaling to Gene Expression Independently of Its Binding to Chromatin Griselda Vallejo1, Alejandro D. La Greca1., Inti C. Tarifa-Reischle1., Ana C. Mestre-Citrinovitz1, Cecilia Ballare´ 2, Miguel Beato2,3, Patricia Saragu¨ eta1* 1 Instituto de Biologı´a y Medicina Experimental, IByME-Conicet, Buenos Aires, Argentina, 2 Centre de Regulacio´ Geno`mica, (CRG), Barcelona, Spain, 3 University Pompeu Fabra (UPF), Barcelona, Spain Abstract Although non-genomic steroid receptor pathways have been studied over the past decade, little is known about the direct gene expression changes that take place as a consequence of their activation. Progesterone controls proliferation of rat endometrial stromal cells during the peri-implantation phase of pregnancy. We showed that picomolar concentration of progestin R5020 mimics this control in UIII endometrial stromal cells via ERK1-2 and AKT activation mediated by interaction of Progesterone Receptor (PR) with Estrogen Receptor beta (ERb) and without transcriptional activity of endogenous PR and ER. Here we identify early downstream targets of cytoplasmic PR signaling and their possible role in endometrial stromal cell proliferation. Microarray analysis of global gene expression changes in UIII cells treated for 45 min with progestin identified 97 up- and 341 down-regulated genes. The most over-represented molecular functions were transcription factors and regulatory factors associated with cell proliferation and cell cycle, a large fraction of which were repressors down-regulated by hormone. Further analysis verified that progestins regulate Ccnd1, JunD, Usf1, Gfi1, Cyr61, and Cdkn1b through PR- mediated activation of ligand-free ER, ERK1-2 or AKT, in the absence of genomic PR binding. -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
Genome-Wide Assessment of REST Binding Profiles Reveals Distinctions Between Human and Mouse Hippocampus
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.07.192229; this version posted July 7, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Genome-wide assessment of REST binding profiles reveals distinctions between human and mouse hippocampus James C. McGann1,2#, Michael Spinner1#, Saurabh K. Garg1,3#, Karin Mullendorf1,4, Randall L. Woltjer5 and Gail Mandel1*. 1 Vollum Institute, Oregon Health and Science University, Portland, OR, USA. 2 Cancer Early Detection Advanced Research Center, Oregon Health and Science Institute, Portland, OR, USA. 3 Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 4 Universidad San Sebastian, Puerto Montt, Chile 5 Department of Pathology, Division of Neuropathology, Oregon Health and Science University, Portland, OR, USA. *Corresponding author # These authors contributed equally Abstract Background The transcriptional repressor, RE1 Silencing Transcription Factor (REST), recognized historically as a master regulator of neuronal gene expression during mouse development, has recently been ascribed roles in human aging and neurodegenerative disorders. However, REST’s role in healthy adult human brain, and how faithfully mouse models reproduce REST function in human brain, is not known. Results Here, we present the first genome-wide binding profile for REST in both mouse and human postnatal hippocampus. We find the majority of REST-bound sites in human hippocampus are unique compared to both mouse hippocampus and to all other reported human ENCODE cell types. -
A Cluster Specific Latent Dirichlet Allocation Model for Trajectory Clustering in Crowded Videos
A CLUSTER SPECIFIC LATENT DIRICHLET ALLOCATION MODEL FOR TRAJECTORY CLUSTERING IN CROWDED VIDEOS ∗ Jialing Zou, Yanting Cui, Fang Wan, Qixiang Ye, Jianbin Jiao University of Chinese Academy of Sciences, Beijing 101408, China. ∗ [email protected] ABSTRACT Trajectory analysis in crowded video scenes is challeng- ing as trajectories obtained by existing tracking algorithms are often fragmented. In this paper, we propose a new approach to do trajectory inference and clustering on fragmented tra- jectories, by exploring a cluster specif c Latent Dirichlet Al- location(CLDA) model. LDA models are widely used to learn middle level trajectory features and perform trajectory infer- ence. However, they often require scene priors in the learn- ing or inference process. Our cluster specif c LDA model ad- dresses this issue by using manifold based clustering as ini- tialization and iterative statistical inference as optimization. The output middle level features of CLDA are input to a clus- (a) (b) tering algorithm to obtain trajectory clusters. Experiments on Fig. 1. (a) Illustration of the LDA based topic extraction in a public dataset show the effectiveness of our approach. original feature space (up) and a manifold embedding space Index Terms— Latent Dirichlet Allocation, Manifold, (down). (b) Illustration of correspondence between trajectory Trajectory clustering clusters and the manifold embedding. 1. INTRODUCTION [6], Wang et al. propose two Euclidean similarity measures, and perform trajectories with the def ned measures. In [7], Trajectory clustering is a video analysis task whose goal is Hu et al. introduce a dimensional spectral clustering method. to assign individual trajectories with common cluster labels, Trajectories are projected to a lower space through eigenvalue with applications in activity surveillance, traff c f ow estima- factorization, and are clustered in the lower sub-space with a tion and emergency response [1], [2]. -
Alignment of Time-Course Single-Cell RNA-Seq Data with CAPITAL
bioRxiv preprint doi: https://doi.org/10.1101/859751; this version posted November 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Alignment of time-course single-cell RNA-seq data with CAPITAL Reiichi Sugihara1, Yuki Kato1;,∗ Tomoya Mori2 and Yukio Kawahara1 1 Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0871, Japan 2 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan Abstract Recent techniques on single-cell RNA sequencing have boosted transcriptome-wide observa- tion of gene expression dynamics of time-course data at a single-cell scale. Typical examples of such analysis include inference of a pseudotime cell trajectory, and comparison of pseudotime trajectories between different experimental conditions will tell us how feature genes regulate a dy- namic cellular process. Existing methods for comparing pseudotime trajectories, however, force users to select trajectories to be compared because they can deal only with simple linear trajec- tories, leading to the possibility of making a biased interpretation. Here we present CAPITAL, a method for comparing pseudotime trajectories with tree alignment whereby trajectories including branching can be compared without any knowledge of paths to be compared. Computational tests on time-series public data indicate that CAPITAL can align non-linear pseudotime trajectories and reveal gene expression dynamics. 1 Introduction Single-cell RNA-sequencing (scRNA-seq) has enabled us to scrutinize gene expression of dynamic cellular processes such as differentiation, reprogramming and cell death. -
Functional Annotation of Human Long Noncoding Rnas Using Chromatin
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.13.426305; this version posted January 14, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Funconal annotaon of human long noncoding RNAs using chroman conformaon data Saumya Agrawal1, Tanvir Alam2, Masaru Koido1,3, Ivan V. Kulakovskiy4,5, Jessica Severin1, Imad ABugessaisa1, Andrey Buyan5,6, Josee Dos&e7, Masayoshi Itoh1,8, Naoto Kondo9, Yunjing Li10, Mickaël Mendez11, Jordan A. Ramilowski1,12, Ken Yagi1, Kayoko Yasuzawa1, CHi Wai Yip1, Yasushi Okazaki1, MicHael M. Ho9man11,13,14,15, Lisa Strug10, CHung CHau Hon1, CHikashi Terao1, Takeya Kasukawa1, Vsevolod J. Makeev4,16, Jay W. SHin1, Piero Carninci1, MicHiel JL de Hoon1 1RIKEN Center for Integra&ve Medical Sciences, YokoHama, Japan. 2College of Science and Engineering, Hamad Bin KHalifa University, DoHa, Qatar. 3Ins&tute of Medical Science, THe University of Tokyo, Tokyo, Japan. 4Vavilov Ins&tute of General Gene&cs, Russian Academy of Sciences, Moscow, Russia. 5Ins&tute of Protein ResearcH, Russian Academy of Sciences, PusHcHino, Russia. 6Faculty of Bioengineering and Bioinforma&cs, Lomonosov Moscow State University, Moscow, Russia. 7Department of BiocHemistry, Rosalind and Morris Goodman Cancer ResearcH Center, McGill University, Montréal, QuéBec, Canada. 8RIKEN Preven&ve Medicine and Diagnosis Innova&on Program, Wako, Japan. 9RIKEN Center for Life Science TecHnologies, YokoHama, Japan. 10Division of Biosta&s&cs, Dalla Lana ScHool of PuBlic HealtH, University of Toronto, Toronto, Ontario, Canada. 11Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. 12Advanced Medical ResearcH Center, YokoHama City University, YokoHama, Japan. -
The Master Regulator Gene PRDM2 Controls C2C12 Myoblasts
Open Access Insights in Biology and Medicine Research Article The master regulator gene PRDM2 controls C2C12 myoblasts proliferation ISSN 2639-6769 and Differentiation switch and PRDM4 and PRDM10 expression Di Zazzo Erika1#, Bartollino Silvia1*# and Moncharmont Bruno1 1Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Italy #These authors contributed equally to this work *Address for Correspondence: Silvia Bartollino, Abstract Department of Medicine and Health Sciences, “V. Tiberio”, University of Molise Via F. De Sanctis, The Positive Regulatory Domain (PRDM) protein family gene is involved in a spectrum variety of biological 86100 Campobasso, Italy, Tel.: +39 0874404886, processes, including proliferation, differentiation and apoptosis: its member seem to be transcriptional E-mail: [email protected] regulators highly cell type and tissue peculiar, towards histones modifi cations or recruitment of specifi c Submitted: 11 August 2017 interaction patters to modify the expression of target genes. In this study we analyzed the expression profi le of Approved: 20 September 2017 different member of PRDM gene family focusing our attention on the role of PRDM2, PRDM4 and PRDM10 genes Published: 25 September 2017 in mouse C2C12 cell line, during the differentiation of myoblasts into myotubes and speculate about the role of the protein Retinoblastoma protein-interacting zinc fi nger protein 1-RIZ1, coded by PRDM2 gene, as a regulator Copyright: 2017 Di Zazzo E, et al. This is an open access article distributed under the of the proliferation/differentiation switch. Creative Commons Attribution License, which permits unrestricted use, distribution, and Results showed a reduction of PRDM2, PRDM4 and PRDM10 expression level during the commitment of the reproduction in any medium, provided the differentiation of myoblasts into myotubes. -
Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells
This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Ubaid Ullah, Ullah; Andrabi, Syed Bilal Ahmad; Tripathi, Subhash Kumar; Dirasantha, Obaiah; Kanduri, Kartiek; Rautio, Sini; Gross, Catharina C.; Lehtimäki, Sari; Bala, Kanchan; Tuomisto, Johanna; Bhatia, Urvashi; Chakroborty, Deepankar; Elo, Laura L.; Lähdesmäki, Harri; Wiendl, Heinz; Rasool, Omid; Lahesmaa, Riitta Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells Published in: Cell Reports DOI: 10.1016/j.celrep.2018.01.070 Published: 20/02/2018 Document Version Publisher's PDF, also known as Version of record Published under the following license: CC BY-NC-ND Please cite the original version: Ubaid Ullah, U., Andrabi, S. B. A., Tripathi, S. K., Dirasantha, O., Kanduri, K., Rautio, S., Gross, C. C., Lehtimäki, S., Bala, K., Tuomisto, J., Bhatia, U., Chakroborty, D., Elo, L. L., Lähdesmäki, H., Wiendl, H., Rasool, O., & Lahesmaa, R. (2018). Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells. Cell Reports, 22(8), 2094-2106. https://doi.org/10.1016/j.celrep.2018.01.070 This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. -
Trajectory Inference Across Multiple Conditions with Condiments
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.09.433671; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Trajectory inference across multiple conditions with condiments: 2 differential topology, progression, differentiation, and expression 1;2 3;4 3 Hector Roux de B´ezieux , Koen Van den Berge , Kelly Street5;6;7;8, Sandrine Dudoit1;2;3;7;8 1 Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA 2 Center for Computational Biology, University of California, Berkeley, CA, USA 3 Department of Statistics, University of California, Berkeley, CA, USA 4 Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium 5 Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA 6 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA 7 These authors contributed equally. 8 To whom correspondence should be addressed: [email protected], [email protected] 4 March 9, 2021 5 Abstract 6 In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, 7 allowing the investigation of developmental processes, such as embryogenesis and cellular differenti- 8 ation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping 9 cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, 10 e.g., for the differentiation of stem cells into mature cell types. -
Inferring Cellular Trajectories from Scrna-Seq Using Pseudocell Tracer
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.26.173179; this version posted June 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. TITLE Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer AUTHORS Derek Reiman1#, Heping Xu2,3# , Andrew Sonin4, Dianyu Chen2,3, Harinder Singh5* and Aly A. Khan4* AFFILIATIONS 1University of Illinois at Chicago, Department of Bioengineering, Chicago, IL, 2Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China, 3Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China, 4University of Chicago, Department of Pathology, Chicago, IL, 5University of Pittsburgh, Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, Pittsburgh, PA #These authors contributed equally *Correspondence: [email protected]; [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.06.26.173179; this version posted June 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. ABSTRACT Single cell RNA sequencing (scRNA-seq) can be used to infer a temporal ordering of dynamic cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. -
Keep Your Fingers Off My DNA: Protein-Protein Interactions
1 2 Keep your fingers off my DNA: 3 protein-protein interactions mediated by C2H2 zinc finger domains 4 5 6 a scholarly review 7 8 9 10 11 Kathryn J. Brayer1 and David J. Segal2* 12 13 14 15 16 17 1Department of Pharmacology and Toxicology, College of Pharmacy, University of Arizona, 18 Tucson, AZ, 85721. 19 2Genome Center and Department of Pharmacology, University of California, Davis, CA, 95616. 20 21 22 23 24 *To whom correspondence should be addressed: 25 David J. Segal, Ph.D. 26 University of California, Davis 27 Genome Center/Pharmacology 28 4513 GBSF 29 451 E. Health Sciences Dr. 30 Davis, CA 95616 31 Tel: 530-754-9134 32 Fax: 530-754-9658 33 Email: [email protected] 34 35 36 Running header: C2H2 ZF interactions with proteins 37 38 Keywords: transcription factors, protein-DNA interactions, protein chemistry, structural biology, 39 functional annotations 40 41 Abstract: 154 words 42 Body Text: 5863 words 43 Figures: 9 44 Tables: 5 45 References: 165 46 C2H2 ZF interactions with proteins Brayer and Segal - review 46 ABSTRACT 47 Cys2-His2 (C2H2) zinc finger domains were originally identified as DNA binding 48 domains, and uncharacterized domains are typically assumed to function in DNA binding. 49 However, a growing body of evidence suggests an important and widespread role for these 50 domains in protein binding. There are even examples of zinc fingers that support both DNA and 51 protein interactions, which can be found in well-known DNA-binding proteins such as Sp1, 52 Zif268, and YY1. C2H2 protein-protein interactions are proving to be more abundant than 53 previously appreciated, more plastic than their DNA-binding counterparts, and more variable and 54 complex in their interactions surfaces.