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3947 Publications

Showing 3181-3190 of 3947 results
06/21/19 | Spastin tethers lipid droplets to peroxisomes and directs fatty acid trafficking through ESCRT-III.
Chang C, Weigel AV, Ioannou MS, Pasolli HA, Xu CS, Peale DR, Shtengel G, Freeman M, Hess HF, Blackstone C, Lippincott-Schwartz J
Journal of Cell Biology. 2019 Jun 21;218(8):2583-99. doi: 10.1101/544023

Lipid droplets (LDs) are neutral lipid storage organelles that transfer lipids to various organelles including peroxisomes. Here, we show that the hereditary spastic paraplegia protein M1 Spastin, a membrane-bound AAA ATPase found on LDs, coordinates fatty acid (FA) trafficking from LDs to peroxisomes through two inter-related mechanisms. First, M1 Spastin forms a tethering complex with peroxisomal ABCD1 to promote LD-peroxisome contact formation. Second, M1 Spastin recruits the membrane-shaping ESCRT-III proteins IST1 and CHMP1B to LDs via its MIT domain to facilitate LD-to-peroxisome FA trafficking, possibly through IST1 and CHMP1B modifying LD membrane morphology. Furthermore, M1 Spastin, IST1 and CHMP1B are all required to relieve LDs of lipid peroxidation. The roles of M1 Spastin in tethering LDs to peroxisomes and in recruiting ESCRT-III components to LD-peroxisome contact sites for FA trafficking may help explain the pathogenesis of diseases associated with defective FA metabolism in LDs and peroxisomes.

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Singer Lab
01/01/12 | Spatial arrangement of an RNA zipcode identifies mRNAs under post-transcriptional control.
Patel VL, Mitra S, Harris R, Buxbaum AR, Lionnet T, Brenowitz M, Girvin M, Levy M, Almo SC, Singer RH, Chao JA
Genes & Development. 2012 Jan 1;26(1):43-53. doi: 10.1101/gad.177428.111

How RNA-binding proteins recognize specific sets of target mRNAs remains poorly understood because current approaches depend primarily on sequence information. In this study, we demonstrate that specific recognition of messenger RNAs (mRNAs) by RNA-binding proteins requires the correct spatial positioning of these sequences. We characterized both the cis-acting sequence elements and the spatial restraints that define the mode of RNA binding of the zipcode-binding protein 1 (ZBP1/IMP1/IGF2BP1) to the β-actin zipcode. The third and fourth KH (hnRNP K homology) domains of ZBP1 specifically recognize a bipartite RNA element comprised of a 5' element (CGGAC) followed by a variable 3' element (C/A-CA-C/U) that must be appropriately spaced. Remarkably, the orientation of these elements is interchangeable within target transcripts bound by ZBP1. The spatial relationship of this consensus binding site identified conserved transcripts that were verified to associate with ZBP1 in vivo. The dendritic localization of one of these transcripts, spinophilin, was found to be dependent on both ZBP1 and the RNA elements recognized by ZBP1 KH34.

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01/13/16 | Spatial gene-expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons.
Cembrowski MS, Bachman JL, Wang L, Sugino K, Shields BC, Spruston N
Neuron. 2016 Jan 13:. doi: 10.1016/j.neuron.2015.12.013

Tissue and organ function has been conventionally understood in terms of the interactions among discrete and homogeneous cell types. This approach has proven difficult in neuroscience due to the marked diversity across different neuron classes, but it may be further hampered by prominent within-class variability. Here, we considered a well-defined canonical neuronal population-hippocampal CA1 pyramidal cells (CA1 PCs)-and systematically examined the extent and spatial rules of transcriptional heterogeneity. Using next-generation RNA sequencing, we identified striking variability in CA1 PCs, such that the differences within CA1 along the dorsal-ventral axis rivaled differences across distinct pyramidal neuron classes. This variability emerged from a spectrum of continuous gene-expression gradients, producing a transcriptional profile consistent with a multifarious continuum of cells. This work reveals an unexpected amount of variability within a canonical and narrowly defined neuronal population and suggests that continuous, within-class heterogeneity may be an important feature of neural circuits.

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05/10/24 | Spatial mapping of hepatic ER and mitochondria architecture reveals zonated remodeling in fasting and obesity
Parlakgül G, Pang S, Artico LL, Min N, Cagampan E, Villa R, Goncalves RL, Lee GY, Xu CS, Hotamışlıgil GS, Arruda AP
Nat Commun. 2024 May 10;15(1):3982. doi: 10.1038/s41467-024-48272-7

The hepatocytes within the liver present an immense capacity to adapt to changes in nutrient availability. Here, by using high resolution volume electron microscopy, we map how hepatic subcellular spatial organization is regulated during nutritional fluctuations and as a function of liver zonation. We identify that fasting leads to remodeling of endoplasmic reticulum (ER) architecture in hepatocytes, characterized by the induction of single rough ER sheet around the mitochondria, which becomes larger and flatter. These alterations are enriched in periportal and mid-lobular hepatocytes but not in pericentral hepatocytes. Gain- and loss-of-function in vivo models demonstrate that the Ribosome receptor binding protein1 (RRBP1) is required to enable fasting-induced ER sheet-mitochondria interactions and to regulate hepatic fatty acid oxidation. Endogenous RRBP1 is enriched around periportal and mid-lobular regions of the liver. In obesity, ER-mitochondria interactions are distinct and fasting fails to induce rough ER sheet-mitochondrion interactions. These findings illustrate the importance of a regulated molecular architecture for hepatocyte metabolic flexibility.

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05/22/17 | Spatial Memory: Mice Quickly Learn a Safe Haven.
Egnor SE
Current Biology : CB. 2017 May 22;27(10):R388-R390. doi: 10.1016/j.cub.2017.04.007

New work on innate escape behavior shows that mice spontaneously form a spatially precise memory of the location of shelter, which is laid down quickly and updated continuously.

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10/27/22 | Spatial organization of the 3D genome encodes gene co-expression programs in single cells
Peng Dong , Shu Zhang , Liangqi Xie , Lihua Wang , Andrew L. Lemire , Arthur D. Lander , Howard Y. Chang , Zhe J. Liu
bioRxiv. 2022 Oct 27:. doi: 10.1101/2022.10.26.513917

Deconstructing the mechanism by which the 3D genome encodes genetic information to generate diverse cell types during animal development is a major challenge in biology. The contrast between the elimination of chromatin loops and domains upon Cohesin loss and the lack of downstream gene expression changes at the cell population level instigates intense debates regarding the structure-function relationship between genome organization and gene regulation. Here, by analyzing single cells after acute Cohesin removal with sequencing and spatial genome imaging techniques, we discover that, instead of dictating population-wide gene expression levels, 3D genome topology mediated by Cohesin safeguards long-range gene co-expression correlations in single cells. Notably, Cohesin loss induces gene co-activation and chromatin co-opening between active domains in cis up to tens of megabase apart, far beyond the typical length scale of enhancer-promoter communication. In addition, Cohesin separates Mediator protein hubs, prevents active genes in cis from localizing into shared hubs and blocks intersegment transfer of diverse transcriptional regulators. Together, these results support that spatial organization of the 3D genome orchestrates dynamic long-range gene and chromatin co-regulation in single living cells.

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11/18/20 | Spatial readout of visual looming in the central brain of Drosophila.
Morimoto MM, Nern A, Zhao A, Rogers EM, Wong A, Isaacson MD, Bock D, Rubin GM, Reiser MB
eLife. 2020 Nov 18;9:. doi: 10.7554/eLife.57685

Visual systems can exploit spatial correlations in the visual scene by using retinotopy. However, retinotopy is often lost, such as when visual pathways are integrated with other sensory modalities. How is spatial information processed outside of strictly visual brain areas? Here, we focused on visual looming responsive LC6 cells in , a population whose dendrites collectively cover the visual field, but whose axons form a single glomerulus-a structure without obvious retinotopic organization-in the central brain. We identified multiple cell types downstream of LC6 in the glomerulus and found that they more strongly respond to looming in different portions of the visual field, unexpectedly preserving spatial information. Through EM reconstruction of all LC6 synaptic inputs to the glomerulus, we found that LC6 and downstream cell types form circuits within the glomerulus that enable spatial readout of visual features and contralateral suppression-mechanisms that transform visual information for behavioral control.

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06/05/24 | Spatial Single-cell Analysis Decodes Cortical Layer and Area Specification
Qian X, Coleman K, Jiang S, Kriz AJ, Marciano JH, Luo C, Cai C, Manam MD, Caglayan E, Otani A, Ghosh U, Shao DD, Andersen RE, Neil JE, Johnson R, LeFevre A, Hecht JL, Miller MB, Sun L, Stringer C, Li M, Walsh CA
bioRxiv. 2024 Jun 05:. doi: 10.1101/2024.06.05.597673

The human cerebral cortex, pivotal for advanced cognitive functions, is composed of six distinct layers and dozens of functionally specialized areas. The layers and areas are distinguished both molecularly, by diverse neuronal and glial cell subtypes, and structurally, through intricate spatial organization3,4. While single-cell transcriptomics studies have advanced molecular characterization of human cortical development, a critical gap exists due to the loss of spatial context during cell dissociation. Here, we utilized multiplexed error-robust fluorescence in situ hybridization (MERFISH)9, augmented with deep-learning-based cell segmentation, to examine the molecular, cellular, and cytoarchitectural development of human fetal cortex with spatially resolved single-cell resolution. Our extensive spatial atlas, encompassing 16 million single cells, spans eight cortical areas across four time points in the second and third trimesters. We uncovered an early establishment of the six-layer structure, identifiable in the laminar distribution of excitatory neuronal subtypes by mid-gestation, long before the emergence of cytoarchitectural layers. Notably, while anterior-posterior gradients of neuronal subtypes were generally observed in most cortical areas, a striking exception was the sharp molecular border between primary (V1) and secondary visual cortices (V2) at gestational week 20. Here we discovered an abrupt binary shift in neuronal subtype specification at the earliest stages, challenging the notion that continuous morphogen gradients dictate mid-gestation cortical arealization. Moreover, integrating single-nuclei RNA-sequencing and in situ whole transcriptomics revealed an early upregulation of synaptogenesis in V1-specific Layer 4 neurons, suggesting a role of synaptogenesis in this discrete border formation. Collectively, our findings underscore the crucial role of spatial relationships in determining the molecular specification of cortical layers and areas. This work not only provides a valuable resource for the field, but also establishes a spatially resolved single-cell analysis paradigm that paves the way for a comprehensive developmental atlas of the human brain.

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08/16/21 | Spatially patterned excitatory neuron subtypes and projections of the claustrum.
Erwin SR, Bristow BN, Sullivan KE, Kendrick RM, Marriott B, Wang L, Clements J, Lemire AL, Jackson J, Cembrowski MS
eLife. 2021 Aug 16;10:. doi: 10.7554/eLife.68967

The claustrum is a functionally and structurally complex brain region, whose very spatial extent remains debated. Histochemical-based approaches typically treat the claustrum as a relatively narrow anatomical region that primarily projects to the neocortex, whereas circuit-based approaches can suggest a broader claustrum region containing projections to the neocortex and other regions. Here, in the mouse, we took a bottom-up and cell-type-specific approach to complement and possibly unite these seemingly disparate conclusions. Using single-cell RNA-sequencing, we found that the claustrum comprises two excitatory neuron subtypes that are differentiable from the surrounding cortex. Multicolor retrograde tracing in conjunction with 12-channel multiplexed in situ hybridization revealed a core-shell spatial arrangement of these subtypes, as well as differential downstream targets. Thus, the claustrum comprises excitatory neuron subtypes with distinct molecular and projection properties, whose spatial patterns reflect the narrower and broader claustral extents debated in previous research. This subtype-specific heterogeneity likely shapes the functional complexity of the claustrum.

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01/03/16 | Spatially resolved proteomic mapping in living cells with the engineered peroxidase APEX2
Hung V, Udeshi ND, Lam SS, Loh KH, Cox KJ, Pedram K, Carr SA, Ting AY
Nature Protocols. Jan-03-2016;11(3):456 - 475. doi: 10.1038/nprot.2016.018

This protocol describes a method to obtain spatially resolved proteomic maps of specific compartments within living mammalian cells. An engineered peroxidase, APEX2, is genetically targeted to a cellular region of interest. Upon the addition of hydrogen peroxide for 1 min to cells preloaded with a biotin-phenol substrate, APEX2 generates biotin-phenoxyl radicals that covalently tag proximal endogenous proteins. Cells are then lysed, and biotinylated proteins are enriched with streptavidin beads and identified by mass spectrometry. We describe the generation of an appropriate APEX2 fusion construct, proteomic sample preparation, and mass spectrometric data acquisition and analysis. A two-state stable isotope labeling by amino acids in cell culture (SILAC) protocol is used for proteomic mapping of membrane-enclosed cellular compartments from which APEX2-generated biotin-phenoxyl radicals cannot escape. For mapping of open cellular regions, we instead use a 'ratiometric' three-state SILAC protocol for high spatial specificity. Isotopic labeling of proteins takes 5–7 cell doublings. Generation of the biotinylated proteomic sample takes 1 d, acquiring the mass spectrometric data takes 2–5 d and analysis of the data to obtain the final proteomic list takes 1 week.

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