Long-term Live Single Cell Quantification of Transcription Factor Dynamics

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. SCI-4-SCI-4
Author(s):  
Timm Schroeder
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Protein ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Molecular Control ◽  
Endogenous Gene ◽  
Stem And Progenitor Cells

Abstract Hematopoiesis is highly complex and dynamic, and consist of large numbers of different cells expressing many molecules. Despite intensive research, many long-standing questions in hematopoiesis research remain disputed. One major reason is the fact that we usually only analyze populations of cells - rather than individual cells - at very few time points of an experiment. Tracking of individual cells would be an extremely powerful approach to improve our understanding of molecular cell fate control. We are therefore developing imaging systems to follow the fate of single cells over many generations. We program new software to help recording and displaying the divisional history, position, properties, interaction, etc. of all individual cells over many generations. In addition, novel microfluidics devices are designed and produced to allow improved observation and manipulation of cells. Our technologies allow continuous long-term quantification of protein expression or activity in living cells. Among other approaches, we generate knock in models expressing transcription factor to fluorescent protein fusions from endogenous gene loci. This enables non-invasive long-term live quantification of transcription factor protein dynamics in single stem and progenitor cells throughout their differentiation. The resulting novel kind of continuous quantitative single cell data is used for the generation and falsification of models describing the molecular control of hematopoietic cell fates. Disclosures No relevant conflicts of interest to declare.

Combined Single Cell Lineage and Transcriptome Sequencing Unveils Cell-Autonomous Regulators of Hematopoietic Stem Cell Fate

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 446-446
Author(s):  
Alejo E Rodriguez-Fraticelli ◽  
Caleb S Weinreb ◽  
Allon Moshe Klein ◽  
Shou-Wen Wang ◽  
Fernando D Camargo
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Conflicts Of Interest ◽  
Large Fraction ◽  
Cell Lineage ◽  
Gene Signature ◽  
Hematopoietic Stem ◽  
Cell State

Blood regeneration upon transplantation relies on the activity of long-term repopulating hematopoietic stem cells (LT-HSCs). One of the major controversies in hematopoiesis relates to the apparently different properties that HSCs have in transplantation versus unperturbed settings. In unperturbed steady state hematopoiesis, the most potent HSCs appear to be mostly dormant, and only producing platelet-lineage cells. In turn, upon transplant, even a single transplanted HSC can actively divide and regenerate hundreds of millions of blood progenitors of all lineages. It would thus appear that HSCs have different fundamental properties in each study system. However, most transplantation studies have only tracked the lineage output of the transplanted HSC clones, and rarely the regeneration of the HSC compartment itself. In addition, clonal assays have not been performed at sufficient resolution to fully capture the diversity and clonal complexity of the regenerated HSC compartment. Here, we have used expressible barcodes, which can be sequenced in conventional single cell RNAseq assays, to simultaneously record the functional outcomes and transcriptional states of thousands of HSCs. Our analysis revealed multiple clonal HSC behaviors following transplantation that drastically differ in their differentiation activity, lineage-bias and self-renewal. Surprisingly, we witnessed a large fraction of clones that efficiently repopulate the HSC compartment but show limited contribution to differentiated progeny. Furthermore, these inactive clones have increased competitive multilineage serial repopulating capacity, implying that shortly after transplant a subset of clones reestablishes the native-like LT-HSC behaviors. Our results also argue that this clonal distribution of labor is controlled by cell autonomous, heritable properties (i.e. the epigenetic cell state). Then, using only our clonal readouts to segregate single HSC transcriptomes, we unveiled the transcriptional signatures that associated with unique HSC outcomes (platelet bias, clonal expansion, dormancy, etc.) and unraveled, for the first time, a gene signature for functional long-term serially repopulating clones. We interrogated the drivers of this cell state using an in vivo inducible CRISPR screening and identified 5 novel regulators that are required to regenerate the HSC compartment in a cell autonomous fashion. In conclusion, we demonstrate that functional LT-HSCs share more similar properties in native and transplantation hematopoiesis than previously expected. Consequently, we unveil a definition of the essential, common functional properties of HSCs and the molecular programs that control them. Figure 1 Disclosures No relevant conflicts of interest to declare.

Single Cell Proteomics Reveals Novel Cytokine-Producing Function of Hematopoietic Stem and Progenitor Cells

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 26-26
Author(s):  
Jimmy L. Zhao ◽  
Chao Ma ◽  
Ryan O'Connell ◽  
Dinesh S. Rao ◽  
James Heath ◽  
...  
Keyword(s):  
Stem Cells ◽  
Immune Response ◽  
Single Cell ◽  
Progenitor Cells ◽  
Cytokine Production ◽  
Conflicts Of Interest ◽  
Severe Neutropenia ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells

Abstract Abstract 26 During infection, hematopoietic stem and progenitor cells (HSPCs) are called upon to proliferate and differentiate to produce more innate and adaptive immune cells to combat infection. Traditionally, HSPCs are thought to respond to depletion of downstream hematopoietic cells during infection. More recent evidence suggests that HSPCs may respond directly to infection and pro-inflammatory cytokines. However, little is known about the direct immune response of HSPCs and the molecular signaling regulating this response upon sensing an infection. In this study, we have combined transgenic and genetic knockout mouse models with a novel single cell barcode proteomics microchip technology to tackle these questions. We show that although long-term hematopoietic stem cells (HSCs) (defined by Lineage-cKit+Sca1+CD150+CD48-) do not secrete cytokines upon toll-like receptor (TLR) stimulation, short-term HSCs and multipotent progenitor cells (MPPs) (defined by Lineage-cKit+Sca1+, referred to as LKS thereafter) can produce copious amounts of cytokines upon direct TLR-4 and TLR-2 stimulation, indicating that LKS cells can directly participate in an immune response by producing a myriad of cytokines, upon a bacterial infection. Within the population of LKS cells we detect multiple functional subsets of cells, specialized in producing myeloid-like, lymphoid-like or both types of cytokines. Moreover, we show that the cytokine production by LKS cells is regulated by the NF-κB activity, as p50-deficient LKS cells show reduced cytokine production while microRNA-146a (miR-146a)-deficient LKS cells show significantly increased cytokine production. As long-term HSCs differentiate, they start to gain effector immune function much earlier than we had originally anticipated. In light of this finding, we should start to view the stepwise differentiation scheme of HSCs, and perhaps all other stem cells, as a strategy to sequentially gain functional capacity, instead of simply losing stemness and self-renewal ability. The remarkable ability of LKS cells to produce copious amounts of cytokines in response to bacteria may provide some protective immunity during severe neutropenia and lymphopenia or in the early stage of HSC transplantation. This study further extends the functions of NF-κB to include the regulation of primitive hematopoietic stem and progenitor cells and provides direct evidence of the bacteria-responding ability of HSPCs through the TLR/NF-κB axis. The single cell barcode proteomics technology can be widely applied to study proteomics of other rare cells or heterogeneous cell population at a single cell level. Disclosures: No relevant conflicts of interest to declare.

Single-Cell Analysis of Lymphoid-Primed Multipotent Progenitors (LMPPs) Reveal Alterations in Lineage Commitment during Aging

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 244-244
Author(s):  
Sneha Borikar ◽  
Vivek Philip ◽  
Jennifer J. Trowbridge
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Cell Fate ◽  
Progenitor Cell ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Differentiation Potential ◽  
Increased Risk

Abstract During aging, the hematopoietic compartment undergoes lineage skewing, biased toward myeloid differentiation at the expense of lymphoid differentiation. This skewing clinically presents as impaired adaptive immunity and an increased risk of myeloproliferative disorders. However, little is known of the regulatory mechanisms underlying these changes in differentiation potential due in part to the inadequacy of current analytic techniques to evaluate lineage potency of individual progenitor cells. Recent demonstration that long-lived hematopoietic progenitor cells drive steady-state hematopoiesis has shifted focus onto the progenitor cell compartment to understand clonal dynamics of native hematopoiesis. Here, we critically assess the functional and molecular alterations in the multipotent progenitor cell pool with aging at the single-cell level. We developed novel in vitro and in vivo assays to define the heterogeneity of the LMPP population and test cell-fate potential from single cells. Our results demonstrate, for the first time, distinct, intrinsic lineage potential of single in vitro LMPPs at the cellular and molecular level. We find that clonal alterations in the lymphoid-primed multipotent progenitor (LMPP) compartment contributes to the functional alterations in hematopoiesis observed during aging. Unbiased single-cell transcriptome analysis reveals that true multipotential clones and lymphoid-restricted clones are reduced with aging, while bipotential and myeloid-restricted clones are modestly expanded. Furthermore, myeloid-restricted clones gain myc driver signatures, molecularly identifying clones emerging during aging that are susceptible to transformation. Our study reveals that aging alters the clonal composition of multipotential progenitor cells, directly contributing to the global loss of the lymphoid compartment and increased susceptibility to myeloid transformation. Disclosures No relevant conflicts of interest to declare.

scholarly journals Mouse Erythroid Cells Originate from a Megakaryocyte Precursor in Common Myeloid Progenitors

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 337-337
Author(s):  
Elisabeth F Heuston ◽  
Bethan Psaila ◽  
Cheryl A Keller ◽  
NISC Comparative SequencingProgram ◽  
Stacie M Anderson ◽  
...  
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Qpcr Assay ◽  
Gene Set Enrichment Analysis ◽  
Erythroid Cells ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells ◽  
Myeloid Progenitors

The hierarchical model of hematopoiesis posits that hematopoietic stem and progenitor cells produce common myeloid progenitors (CMP). CMP can become granulocyte/monocyte progenitors (GMP) or bipotential megakaryocyte/erythroid progenitors (MEP). MEP can produce megakaryocytic (Mk) or erythroid (Ery) cells. However, we and others have shown that early mouse and human progenitor populations express many Mk genes (Heuston, Epig. Chrom., 2016), while single cell studies have identified lineage-specific colony forming cells in progenitor populations thought to be multipotent (Psaila, Genome Biol., 2016). To identify the earliest mouse Ery and Mk cells, we performed single cell RNASeq on 10000 stem and progenitor cells (Lin-Sca1+Kit+), 12000 CMP (Lin-Sca1-Kit+CD16/32-CD34+), 6000 MEP (Lin-Sca1-Kit+CD16/32-CD34-) and 8000 GMP (Lin-Sca1-Kit+CD16/32+CD34+). TSNE analysis of expression in the 4 populations identified 33 clusters, which were correlated to biological functions using gene set enrichment analysis. In LSK, no cells with an Ery RNA profile were found, while 56% of cells co-expressed Mk-associated (e.g., Meis1, Fli1) and lymphoid genes. In CMP, 12% of the cells co-expressed Ery (e.g., Gata1, Fog1) and Mk (e.g., Pf4, Cd41) genes, while 23% had an Mk-specific profile (e.g., Fli1, Cd41) enriched for platelet biology processes (p< 3E-18). Unlike traditional models, over 94% of MEP had Ery RNA profiles enriched for ribosome synthesis and heme-biology processes (p< 4E-10). To establish developmental relationships, we performed pseudotime analysis using the Monocle and Scanpy software packages. These programs model differentiation by mapping similar transcriptomes together. Map nodes indicate lineage commitment points and cells further from a node are more differentiated. Combined analysis of LSK, CMP, and MEP generated a model with a single node and 2 trajectories. LSK with Mk and lymphoid RNA profiles diverged at the node, as did 14% of CMP. 31% of CMP with an Mk RNA profile were downstream of the node. Further downstream were cells with mixed Ery/Mk profiles, and furthest from the node were MEP with Ery profiles. A separate pseudotime analysis of CMP only 2 trajectories: one with decreasing Mk- and increasing Ery RNA profiles, and a second with an early Mk endomitotic RNA profile. Pseudotime analysis of MEP only identified a linear trajectory: cells at one end expressed early Ery RNA profiles, and cells at the other end had RNA profiles similar to those of burst-forming unit-erythroid (BFU-E). We generated a predictive set of RNAs for each TSNE cluster. We used index-sorting with 11 markers (Kit, Sca1, CD34, CD16/32, CD36, CD41, CD48, CD123, CD150, CD9, Flk2) to isolate single cells for custom high-throughput multiplex qPCR. This allowed confirmation of cell frequency within TSNE clusters while identifying surface markers for prospective isolation of cell subsets. We focused on 2 populations: CMP-E, which had an Ery RNA profile (10% of clustered CMP and 12% of CMP in the qPCR assay), and CMP-MkE, which had Mk and Ery RNA profiles (12% of clustered CMP and 13% of CMP in the qPCR assay). We prospectively isolated CMP-E and CMP-MkE to compare RNASeq profiles, ATACSeq profiles, and colony forming ability against those of bulk CMP, Ery, and Mk. In CMP-E, 54% of RNAs were expressed in both CMP and ERY, while 41% were expressed only in CMP (p < 6E-72). In contrast, 41% of CMP-E ATACSeq peaks were present in CMP and ERY, while 57% of CMP-E peaks were present only in CMP (p < 1E-3). We conclude that in CMP-E, the RNASeq profile is more erythroid than the ATACSeq profile. In CMP-MkE, 89% of RNAs were expressed in both CMP and Mk, while 7% were expressed only in CMP (p < 8E-190). Likewise, 88% of CMP-MkE ATACSeq peaks were present in both CMP and Mk, while 3% were present only in CMP (p < 1E-3). We conclude that in CMP-MkE, the RNASeq and ATACSeq profiles are equivalent. In soft agar assays, 21% of CMP-E and 3% of CMP-MkE colonies contained BFU-E, compared to 9% of control colonies. We conclude that the CMP-E and CMP-MkE populations are skewed towards the ERY and MK lineages, but are not erythro-megakaryocyte restricted. Our data support a model in which there are two megakaryocyte precursor populations and no erythroid populations in LSK. A third megakaryocyte population in CMP gives rise to erythroid cells. Finally, our data show that transcriptional changes precede chromatin accessibility changes in the earliest erythroid cells. Disclosures No relevant conflicts of interest to declare.

scholarly journals Transcriptome Based Projection of Single Cells to Uncover Development and Heterogeneity of Abnormal Hematopoietic Cells

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2520-2520
Author(s):  
Parashar Dhapola ◽  
Mikael Sommarin ◽  
Mohamed Eldeeb ◽  
Amol Ugale ◽  
David Bryder ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Conflicts Of Interest ◽  
Single Cells ◽  
Hematopoietic Cells ◽  
Leukemic Cells ◽  
Target Cells ◽  
Cell Identity ◽  
Large Patient ◽  
Low Dimensional

Single-cell transcriptomics (scRNA-Seq) has accelerated the investigation of hematopoietic differentiation. Based on scRNA-Seq data, more refined models of lineage determination in stem- and progenitor cells are now available. Despite such advances, characterizing leukemic cells using single-cell approaches remains challenging. The conventional strategies of scRNA-Seq analysis map all cells on the same low dimensional space using approaches like tSNE and UMAP. However, when used for comparing normal and leukemic cells, such methods are often inadequate as the transcriptome of the leukemic cells has systematically diverged, resulting in irrelevant separation of leukemic subpopulations from their healthy counterpart. Here, we have developed a new computational approach bundled into a tool called Nabo (nabo.readthedocs.io) that has the capacity to directly compare cells that are otherwise unalignable. First, Nabo creates a shared nearest neighbor graph of the reference population, and the heterogeneity of this population is subsequently defined by performing clustering on the graph and calculating a low dimensional representation using t-SNE or UMAP. Nabo then calculates the similarity of incoming cells from a target population to each cell in the reference graph using a modified Canberra metric. The reference cells with higher similarity to the target cells obtain higher mapping scores. The built-in classifier is used to assign each target cell a reference cluster identity. We tested Nabo's accuracy on control datasets and found that Nabo's performance in terms of accuracy and robustness of projection is comparable to state-of-art methods. Moreover, Nabo is a generalized domain adaptation algorithm and hence can perform classification of target cells that are arbitrarily dissimilar to reference cells. Nabo could identify the cell-identity of sorted CD19+ B cells, CD14+ monocytes and CD56+ by projecting these unlabeled cells onto labelled peripheral blood mononuclear cells with an average specificity higher than 0.98. The general applicability of Nabo was demonstrated by successfully integrating pancreatic cells, sequenced in three different studies using different sequencing chemistries with comparable or better accuracy than existing methods. Also, it was conclusively demonstrated that Nabo can predict the identity of human HSPC subpopulations to the same accuracy as can be achieved by established cell-surface markers. Having Nabo at hand, we aimed to uncover the heterogeneity of hematopoietic cells from different stages of AML. Nabo showed that AML cells lacked the heterogeneity of normal CD34+ cells and were devoid of cells with HSC gene signature. A large patient-to-patient variability was found where leukemic cells mapped to distinct stages of myeloid progenitors. To ask whether this variability could reflect differences in leukemia-initiating cell identity, we induced leukemia in murine granulocyte-monocyte-lymphoid progenitors (GMLPs) using an inducible model for MLL-ENL-driven AML. On projection, more than 70% of MLL-ENL-activated cells mapped to a distinct Flt3+ subpopulation present within healthy GMLPs. Statistical validity of this projection was verified using two novel null models for testing cell projections: 1) ablated node model, wherein the mapping strength of target cells are evaluated after removal of high mapping score source nodes, and 2) high entropy features model, which rules out the background noise effect. By separating Flt3+ and Flt3- cells prior to activation of the fusion gene and performing in vitro replating assays, we could demonstrate that Flt3+ GMLPs contained 3-4 fold more leukemia-initiating cells (1/1.34 cells) than Flt3- GMLPs (1/4.89 cells), indicating that leukemia-initiating cells within GMLPs express Flt3. Taken together, Nabo represents a robust cell projection strategy for relevant analysis of scRNA-Seq data that permits an interpretable inference of cross-population relationships. Nabo is designed to compare disparate cellular populations by using the heterogeneity of one population as a point of reference allowing for cell-type specification even following perturbations that have resulted in large molecular changes to the cells of interest. As such, Nabo has critical implementation for delineation of leukemia heterogeneity and identification of leukemia-initiating cell population. Disclosures No relevant conflicts of interest to declare.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals More than efficacy revealed by single-cell analysis of antiviral therapeutics

2019 ◽  
Author(s):  
Wu Liu ◽  
Mehmet U. Caglar ◽  
Zhangming Mao ◽  
Andrew Woodman ◽  
Jamie J. Arnold ◽  
...  
Keyword(s):  
Single Cell ◽  
Drug Targets ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
Viral Population ◽  
Cell Analysis ◽  
Toxic Dose ◽  
Time Required

SUMMARYDevelopment of antiviral therapeutics emphasizes minimization of the effective dose and maximization of the toxic dose, first in cell culture and later in animal models. Long-term success of an antiviral therapeutic is determined not only by its efficacy but also by the duration of time required for drug-resistance to evolve. We have developed a microfluidic device comprised of ~6000 wells, with each well containing a microstructure to capture single cells. We have used this device to characterize enterovirus inhibitors with distinct mechanisms of action. In contrast to population methods, single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates reveals not only efficacy but also properties of the members of the viral population most sensitive to the drug, the stage of the lifecycle most affected by the drug, and perhaps even if the drug targets an interaction of the virus with its host.

Abstract 39: Fate Of Renin Cell Progenitors In The Developing Kidney Vasculature

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Alexandre Martini ◽  
Ariel R Gomez ◽  
Maria Luisa Sequeira Lopez
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Protein ◽  
Enrichment Analysis ◽  
Cell Fate Decisions ◽  
Mural Cells ◽  
Developing Kidney ◽  
Green Fluorescent ◽  
Old Mice ◽  
Accessible Chromatin

The unique spatial arregement of the kidney arterioles is an essential event for its development. However, the mechanisms that govern this process are still poorly understood. During nephrogenesis, a group of stromal cells expressing the Forkhead Box D1 ( FoxD1 ) transcription factor (TF) will give rise to the metanephric progenitors for the mural cells of the kidneys arteries and arterioles. We aim to identify the core TFs involved in the cell fate along the differentiaton pathways of the developing kidney vasculature. Therefore, we generated Foxd1-cre; mTmG mice, whose Foxd1 derivative cells are labeled with green fluorescent protein (GFP). GFP+ cells were isolated from 5 (P5) or 30 (P30) days old mice kidneys, and processed either for single-cell RNA-Seq (scRNA-Seq) or for single-cell Assay for Transposase-Accessible Chromatin (scATAC-Seq ). The top5 highly expressed TFs on scRNA-Seq at P5 are: Tcf21, Zeb2, Meis2, Cebpd and Nme3 (p_adjusted_value(padj)= 0, 3.8E-187, 3.9E-180, 4E-172, 4.1E-172 and 3.2E-154, respectively). They are involved in developmental processes and cell proliferation. At P30, the top5 highly expressed TFs are: Atf3, klf2, Fos, Nr4a2 and Junb (padj= 4.2E-294, 2.1E-200, 3.5E-182, 1.7E-52 and 0.2E-24, respectively). They are implicated with calcium-signaling pathway and inflammation. Additionally, scATAC-Seq identifies regions of accessible chromatin for pontential TFs binding, leading to changes in gene expression content and cell identity. At P30, scATAC-Seq showed differential accessible regions with subsequent putative motif enrichment analysis for the TF N4a2 (padj: 4E-297). This is in accordance with our scRNA-Seq results and might play a role in the Foxd1 progenitors cell fate decisions. Our results tracks the fate of the Foxd1+ cells during the kidney vasculature assembly and suggest a new transcription factors network that might play a role to orchestrate cell fate decisions during kidney vascular development.

scholarly journals Retrograde Transport of Transcription Factor NF-κB in Living Neurons

2000 ◽  
Vol 276 (15) ◽  
pp. 11821-11829 ◽  
Author(s):  
Henning Wellmann ◽  
Barbara Kaltschmidt ◽  
Christian Kaltschmidt
Keyword(s):  
Transcription Factor ◽  
Green Fluorescent Protein ◽  
Fluorescent Protein ◽  
Retrograde Transport ◽  
Receptor Activation ◽  
Transcriptional Changes ◽  
Localization Signal ◽  
Green Fluorescent ◽  
Living Neurons

The mechanism by which signals such as those produced by glutamate are transferred to the nucleus may involve direct transport of an activated transcription factor to trigger long-term transcriptional changes. Ionotropic glutamate receptor activation or depolarization activates transcription factor NF-κB and leads to translocation of NF-κB from the cytoplasm to the nucleus. We investigated the dynamics of NF-κB translocation in living neurons by tracing the NF-κB subunit RelA (p65) with jellyfish green fluorescent protein. We found that green fluorescent protein-RelA was located in either the nucleus or cytoplasm and neurites, depending on the coexpression of the cognate inhibitor of NF-κB, IκB-α. Stimulation with glutamate, kainate, or potassium chloride resulted in a redistribution of NF-κB from neurites to the nucleus. This transport depended on an intact nuclear localization signal on RelA. Thus, in addition to its role as a transcription factor, NF-κB may be a signal transducer, transmitting transient glutamatergic signals from distant sites to the nucleus.

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