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  • 1
    Publication Date: 2015-04-02
    Description: In the context of most induced pluripotent stem (iPS) cell reprogramming methods, heterogeneous populations of non-productive and staggered productive intermediates arise at different reprogramming time points. Despite recent reports claiming substantially increased reprogramming efficiencies using genetically modified donor cells, prospectively isolating distinct reprogramming intermediates remains an important goal to decipher reprogramming mechanisms. Previous attempts to identify surface markers of intermediate cell populations were based on the assumption that, during reprogramming, cells progressively lose donor cell identity and gradually acquire iPS cell properties. Here we report that iPS cell and epithelial markers, such as SSEA1 and EpCAM, respectively, are not predictive of reprogramming during early phases. Instead, in a systematic functional surface marker screen, we find that early reprogramming-prone cells express a unique set of surface markers, including CD73, CD49d and CD200, that are absent in both fibroblasts and iPS cells. Single-cell mass cytometry and prospective isolation show that these distinct intermediates are transient and bridge the gap between donor cell silencing and pluripotency marker acquisition during the early, presumably stochastic, reprogramming phase. Expression profiling reveals early upregulation of the transcriptional regulators Nr0b1 and Etv5 in this reprogramming state, preceding activation of key pluripotency regulators such as Rex1 (also known as Zfp42), Dppa2, Nanog and Sox2. Both factors are required for the generation of the early intermediate state and fully reprogrammed iPS cells, and thus represent some of the earliest known regulators of iPS cell induction. Our study deconvolutes the first steps in a hierarchical series of events that lead to pluripotency acquisition.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4441548/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4441548/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lujan, Ernesto -- Zunder, Eli R -- Ng, Yi Han -- Goronzy, Isabel N -- Nolan, Garry P -- Wernig, Marius -- F32 GM093508-01/GM/NIGMS NIH HHS/ -- RC4 NS073015/NS/NINDS NIH HHS/ -- England -- Nature. 2015 May 21;521(7552):352-6. doi: 10.1038/nature14274. Epub 2015 Apr 1.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California 94305, USA [2] Department of Genetics, Stanford University, Stanford, California 94305, USA [3] Department of Pathology, Stanford University, Stanford, California 94305, USA. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, California 94305, USA. ; 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California 94305, USA [2] Department of Pathology, Stanford University, Stanford, California 94305, USA [3] Department of Microbiology and Immunology, Stanford University, Stanford, California 94305, USA. ; 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California 94305, USA [2] Department of Pathology, Stanford University, Stanford, California 94305, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25830878" target="_blank"〉PubMed〈/a〉
    Keywords: 5'-Nucleotidase/metabolism ; Animals ; Antigens, CD/metabolism ; Antigens, CD15/metabolism ; Antigens, Neoplasm/metabolism ; Biomarkers/analysis/metabolism ; Cell Adhesion Molecules/metabolism ; *Cell Separation ; Cellular Reprogramming/*physiology ; DAX-1 Orphan Nuclear Receptor/metabolism ; DNA-Binding Proteins/metabolism ; Epithelial Cells/metabolism ; Fibroblasts/cytology/metabolism ; *Flow Cytometry ; Gene Expression Profiling ; Homeodomain Proteins/metabolism ; Induced Pluripotent Stem Cells/*cytology/*metabolism ; Integrin alpha4/metabolism ; Mice ; Nuclear Proteins/metabolism ; SOXB1 Transcription Factors/metabolism ; Time Factors ; Transcription Factors/analysis/*metabolism
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2011-05-10
    Description: Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell "mass cytometry" to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273988/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3273988/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Bendall, Sean C -- Simonds, Erin F -- Qiu, Peng -- Amir, El-ad D -- Krutzik, Peter O -- Finck, Rachel -- Bruggner, Robert V -- Melamed, Rachel -- Trejo, Angelica -- Ornatsky, Olga I -- Balderas, Robert S -- Plevritis, Sylvia K -- Sachs, Karen -- Pe'er, Dana -- Tanner, Scott D -- Nolan, Garry P -- 1R01CA130826/CA/NCI NIH HHS/ -- 272200700038C/PHS HHS/ -- 5U54 CA143907/CA/NCI NIH HHS/ -- HHSN268201000034C/HV/NHLBI NIH HHS/ -- N0I-HV-00242/HV/NHLBI NIH HHS/ -- P01 CA034233/CA/NCI NIH HHS/ -- PN2 EY018228/EY/NEI NIH HHS/ -- R01 CA130826/CA/NCI NIH HHS/ -- R01 CA130826-04/CA/NCI NIH HHS/ -- RB2-01592/PHS HHS/ -- U19 AI057229/AI/NIAID NIH HHS/ -- U54 CA149145/CA/NCI NIH HHS/ -- New York, N.Y. -- Science. 2011 May 6;332(6030):687-96. doi: 10.1126/science.1198704.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21551058" target="_blank"〉PubMed〈/a〉
    Keywords: Algorithms ; Antibodies ; Antigens, Surface/analysis ; B-Lymphocytes/drug effects/immunology/metabolism ; Bone Marrow Cells/cytology/*drug effects/*immunology/metabolism ; Cytokines/metabolism ; Dasatinib ; Flow Cytometry/*methods ; Hematopoiesis ; Humans ; Immunophenotyping ; Lanthanoid Series Elements ; Leukocytes, Mononuclear/drug effects/immunology/metabolism ; Lymphocyte Activation ; Lymphocyte Subsets/*drug effects/*immunology/metabolism ; Mass Spectrometry ; Phosphorylation ; Protein Kinase Inhibitors/pharmacology ; Protein-Tyrosine Kinases/antagonists & inhibitors ; Pyrimidines/*pharmacology ; *Signal Transduction/drug effects ; Single-Cell Analysis/*methods ; T-Lymphocytes/drug effects/immunology/metabolism ; Thiazoles/*pharmacology ; Transition Elements
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2014-10-25
    Description: Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naive and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naive cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naive cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4334155/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4334155/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Krishnaswamy, Smita -- Spitzer, Matthew H -- Mingueneau, Michael -- Bendall, Sean C -- Litvin, Oren -- Stone, Erica -- Pe'er, Dana -- Nolan, Garry P -- 1K01DK095008/DK/NIDDK NIH HHS/ -- 1R01CA130826/CA/NCI NIH HHS/ -- 1U54CA121852-01A1/CA/NCI NIH HHS/ -- CA 09-011/CA/NCI NIH HHS/ -- HHSN268201000034C/HV/NHLBI NIH HHS/ -- HHSN272200700038C/PHS HHS/ -- HV-10-05/HV/NHLBI NIH HHS/ -- K01 DK095008/DK/NIDDK NIH HHS/ -- P01 CA034233/CA/NCI NIH HHS/ -- R00 GM104148/GM/NIGMS NIH HHS/ -- R01 CA130826/CA/NCI NIH HHS/ -- S10RR027582-01/RR/NCRR NIH HHS/ -- U19 AI057229/AI/NIAID NIH HHS/ -- U19 AI100627/AI/NIAID NIH HHS/ -- U54 CA149145/CA/NCI NIH HHS/ -- New York, N.Y. -- Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. Epub 2014 Oct 23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA. ; Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA. ; Molecular Biology Section, Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA. ; Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. dpeer@biology.columbia.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25342659" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; CD4-Positive T-Lymphocytes/*immunology ; Computer Simulation ; Image Cytometry ; Male ; Mice ; Mice, Mutant Strains ; Mitogen-Activated Protein Kinase 1/genetics ; Receptors, Antigen, T-Cell/*metabolism ; Ribosomal Protein S6/metabolism ; Signal Transduction ; Single-Cell Analysis/*methods ; Systems Biology/*methods ; eIF-2 Kinase/metabolism
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 4
    Publication Date: 2015-07-15
    Description: Immune cells function in an interacting hierarchy that coordinates the activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537647/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537647/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Spitzer, Matthew H -- Gherardini, Pier Federico -- Fragiadakis, Gabriela K -- Bhattacharya, Nupur -- Yuan, Robert T -- Hotson, Andrew N -- Finck, Rachel -- Carmi, Yaron -- Zunder, Eli R -- Fantl, Wendy J -- Bendall, Sean C -- Engleman, Edgar G -- Nolan, Garry P -- 1R01CA130826/CA/NCI NIH HHS/ -- 1R01GM109836/GM/NIGMS NIH HHS/ -- 1R01NS089533/NS/NINDS NIH HHS/ -- 1U19AI100627/AI/NIAID NIH HHS/ -- 201303028/PHS HHS/ -- 5-24927/PHS HHS/ -- 5R01AI073724/AI/NIAID NIH HHS/ -- 5U54CA143907/CA/NCI NIH HHS/ -- 7500108142/PHS HHS/ -- F31 CA189331/CA/NCI NIH HHS/ -- F31CA189331/CA/NCI NIH HHS/ -- F32 GM093508/GM/NIGMS NIH HHS/ -- F32 GM093508-01/GM/NIGMS NIH HHS/ -- HHSF223201210194C/PHS HHS/ -- HHSN268201000034C/HV/NHLBI NIH HHS/ -- HHSN272200700038C/AI/NIAID NIH HHS/ -- HHSN272200700038C/PHS HHS/ -- HHSN272201200028C/PHS HHS/ -- K99 GM104148/GM/NIGMS NIH HHS/ -- K99GM104148-01/GM/NIGMS NIH HHS/ -- N01-HV-00242/HV/NHLBI NIH HHS/ -- P01 CA034233/CA/NCI NIH HHS/ -- P01 CA034233-22A1/CA/NCI NIH HHS/ -- PN2 EY018228/EY/NEI NIH HHS/ -- PN2EY018228 0158 G KB065/EY/NEI NIH HHS/ -- R01 AI073724/AI/NIAID NIH HHS/ -- R01 CA130826/CA/NCI NIH HHS/ -- R01 CA184968/CA/NCI NIH HHS/ -- R01 GM109836/GM/NIGMS NIH HHS/ -- R01 NS089533/NS/NINDS NIH HHS/ -- R01CA184968/CA/NCI NIH HHS/ -- R33 CA183654/CA/NCI NIH HHS/ -- R33 CA183692/CA/NCI NIH HHS/ -- RFA CA 09-009/CA/NCI NIH HHS/ -- RFA CA 09-011/CA/NCI NIH HHS/ -- T32 GM007276/GM/NIGMS NIH HHS/ -- T32GM007276/GM/NIGMS NIH HHS/ -- U19 AI057229/AI/NIAID NIH HHS/ -- U19 AI100627/AI/NIAID NIH HHS/ -- U54 CA149145/CA/NCI NIH HHS/ -- U54CA149145/CA/NCI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2015 Jul 10;349(6244):1259425. doi: 10.1126/science.1259425.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. Department of Pathology, Stanford University, Stanford, CA 94305, USA. Program in Immunology, Stanford University, Stanford, CA 94305, USA. gnolan@stanford.edu matthew.spitzer@stanford.edu. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. ; Department of Pathology, Stanford University, Stanford, CA 94305, USA. ; Department of Pathology, Stanford University, Stanford, CA 94305, USA. Program in Immunology, Stanford University, Stanford, CA 94305, USA. ; Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Stanford University, Stanford, CA 94305, USA. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. Program in Immunology, Stanford University, Stanford, CA 94305, USA. gnolan@stanford.edu matthew.spitzer@stanford.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26160952" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Bone Marrow/immunology ; Circadian Rhythm/immunology ; Flow Cytometry ; Genetic Variation ; Humans ; Immune System/*cytology/*immunology ; Mice ; Mice, Inbred C57BL ; Models, Biological ; Phenotype ; Reference Standards
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 5
    Publication Date: 2018-07-27
    Description: Retrieving high-content gene-expression information while retaining three-dimensional (3D) positional anatomy at cellular resolution has been difficult, limiting integrative understanding of structure and function in complex biological tissues. We developed and applied a technology for 3D intact-tissue RNA sequencing, termed STARmap (spatially-resolved transcript amplicon readout mapping), which integrates hydrogel-tissue chemistry, targeted signal amplification, and in situ sequencing. The capabilities of STARmap were tested by mapping 160 to 1020 genes simultaneously in sections of mouse brain at single-cell resolution with high efficiency, accuracy, and reproducibility. Moving to thick tissue blocks, we observed a molecularly defined gradient distribution of excitatory-neuron subtypes across cubic millimeter–scale volumes (〉30,000 cells) and a short-range 3D self-clustering in many inhibitory-neuron subtypes that could be identified and described with 3D STARmap.
    Keywords: Molecular Biology, Neuroscience, Online Only
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Geosciences , Computer Science , Medicine , Natural Sciences in General , Physics
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