Live‐cell imaging reveals the interplay between transcription factors, nucleosomes, and bursting

The EMBO Journal, Journal Year: 2019, Volume and Issue: 38(12)

Published: May 17, 2019

Article17 May 2019free access Source DataTransparent process Live-cell imaging reveals the interplay between transcription factors, nucleosomes, and bursting Benjamin T Donovan Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA Search for more papers by this author Anh Huynh Department of Physics, Boise Boise, ID, David A Ball Laboratory Receptor Biology Gene Expression, National Cancer Institute, NIH, Bethesda, MD, Heta P Patel Division Regulation, Netherlands Amsterdam, Michael G Poirier Departments Chemistry & Biochemistry, Biochemistry Daniel R Larson Matthew L Ferguson Corresponding Author [email protected] orcid.org/0000-0003-0760-757X Biomolecular Sciences, Tineke Lenstra orcid.org/0000-0002-4440-9962 Information Donovan1, Huynh2, Ball3,‡, Patel4, Poirier1,5, Larson3,‡, *,2,6 *,4 1Biophysics 2Department 3Laboratory 4Division 5Departments 6Biomolecular ‡This article has been contributed to US Government employees their work is in public domain *Corresponding author. Tel: +1 208 426 3722; E-mail: author, lead contact. +31 20 512 7889; EMBO J (2019)38:e100809https://doi.org/10.15252/embj.2018100809 PDFDownload PDF text main figures. Peer ReviewDownload a summary editorial decision including letters, reviewer comments responses feedback. ToolsAdd favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures Info Abstract Transcription factors show rapid reversible binding chromatin living cells, occurs sporadic bursts, but how these phenomena are related unknown. Using combination vitro vivo single-molecule approaches, we directly correlated Gal4 factor with transcriptional kinetics target genes GAL3 GAL10 yeast cells. We find that dwell time sets burst size. depends on affinity site reduced orders magnitude nucleosomes. novel platform called orbital tracking, simultaneously tracked at one locus, revealing timing correlation transcription. Collectively, our data support model which multiple RNA polymerases initiate during as long bound DNA, bursts terminate upon dissociation. Synopsis study time, nucleosome galactose-responsive budding suggests polymerase initiation bursts. start end burst. determined site. several promoter Simultaneous single endogenous locus Introduction During activation transcription, (TFs) bind specific motif sequences promoters recruit such regulators, coactivators, general eventually Understanding molecular events underlie gene requires knowledge about processes. Studies dynamics cells have shown some constitutively transcribed random (1-state model) (Zenklusen et al, 2008; 2011), whereas other short stochastic high activity where initiate, interspersed periods inactivity (2-state (Golding 2005; Chubb 2006; Zenklusen Bahar Halpern 2015; 2015). Modulation gene's can be done changing size (the number burst), duration, or frequency (Bartman 2016; Fukaya Rodriguez 2019), each different effects cell-to-cell variability population (Raser O'Shea, 2004). Recent advances technologies allow direct measurement TF level providing insight into search mechanisms (Elf 2007; Tokunaga Grimm Liu Tjian, 2018). Interpretation challenging, because position often unknown, affinities (Normanno Target also contexts, modification state nucleosomes affect accessibility sites TFs, although certain pioneer DNA even context full partially unwound (Zaret Mango, 2016). For example, GAL1/10 yeast, binds unwrapped help RSC remodeler (Floer 2010). In addition regulating accessibility, significantly reduce activators (Luo 2014). However, regulate still mostly unexplored. addition, relationship only starting emerge (Larson 2013; Senecal 2014; Loffreda 2017; Rullan major difficulty deciphering causal relationships synthesis comes from technical limitations. Although measured resolution, it challenging measure both same cell active locus. Even if location known, would limited, partial labeling experiments (Liu Additionally, there mismatch timescale (on order seconds) minutes), quantification intensity plane hampered movement. Similarly, assessing role proven difficult, since lack tools precisely control visualize around observe effect dynamics. Here, used techniques assay bursting. developed output an setting. exploited characteristics low expression small allows interest. Moreover, employed advanced 3D tracking technique track 3D, allowed first correlate precedes overlaps fluctuations coupled time. determines duration not modulated galactose signaling, instead regulates frequency. quantitative comparison rates nucleosomal indicates allowing turnover. Overall, key determinant take place associated DNA. Results Mutations upstream activating sequence reduces Several often-studied genes, GAL1 GAL10, contain UASs (upstream sequences), spacing, configuration, cooperativity may contribute Therefore, bursting, focused gene, contains UAS its promoter. Endogenous was visualized live introduction 14 PP7 repeats 5′ UTR GAL3. Upon repeat forms stem loop bacteriophage coat proteins fused fluorescent protein. loops did levels, (TS) PP7-tagged non-tagged allele heterozygous diploid showed similar amount nascent RNAs (Fig EV1A). Click here expand figure. Figure EV1. size, (related Fig 1) diploids untagged allele, colocalization TS smFISH. An example right, indicated arrow arrowhead. dynamic range distribution tagged similar, indicating PP7-tag does To prevent contributing distributions, TSs were defined nuclear spots 2.5-fold median cytoplasmic RNAs. n = 2,716 Scale bar: 2 μm. Distribution per strain driven UASwt hybridized probes. 11,839 Same (B) UASmut 4,845 Heatmap individual (rows) three independent experiments. Left plots experimental GAL3, right binarized after thresholding. all combined, 324 analyzed 250 UASmut. Download figure PowerPoint visualization galactose-containing media revealed 1A, Movie EV1). less frequent than previously (Lenstra 2015), longer inactivity. determine off threshold applied traces (Materials Methods). agreement two-state (Peccoud Ycart, 1995), exponentially distributed 1B C), average 46.5 s ± 2.4 4.2 0.2 min. 1. (UAS) yeast. Example trace quantified fluorescence Traces (active) (inactive) times. Histogram (burst duration) wt mutated UAS, respectively, shorter times UAS. Errors indicate SE experiments, UASwt, Similar (A) lower Also see Average exponential fit. SD 5′PP7 3′MS2 Cross-correlation MS2 signals shows peak 37.9 1.8 delay, 137 Shaded area errors SEM. fit Poisson (gray line), supporting transcribed, image shown, yellow arrows TSs. bars: 5 (G) fits distribution, GAL3-UASmut polymerases, constitutive genes. 10,616 See EV1B C. available online Data 1 [embj2018100809-sup-0008-SDataFig1.xlsx] investigate determining pattern, PP7-GAL3 replaced (called UASmut; 1D; Liang 1996). Interestingly, analysis mutant 29.3 1.3 s, (4.2 min 3.7 0.3 1C–F). (also referred reflects visible includes window when loaded well elongation, termination, release. refer "active time" interval over "burst size" period. If post-initiation processes UASmut, reduced, likely resulting fewer initiating distinguish steps cycle, inserted orthogonal 3′ As expected, dual-color PP7-GAL3-MS2 increase followed MS2, simultaneous drop 1G, EV2). PP7-MS2 temporal cross-correlation takes transcribe middle 1H), elongation rate 64.5 3.0 bp/s (3.9 kb/min). Based length construct, construct calculated ~30 s. thus dominated little loading polymerases. facilitates few seconds, ~15 confirm mutating smFISH performed strains 1I J). normalized present strain. transcript model, expected non-bursting mutation, transcripts model. mutation results like loss summary, require high-affinity reducing through cis-acting Mutation Since suggest decrease due series assays compare sequences. First, compared two naked occupancy protein-induced enhancement (PIFE) (Hwang event enhances Cy3 fluorophore ~1.5-fold 2A). Titrating produces curve stoichiometric EV2A). Because binding, relative could titrating concentrations unlabeled competitor Wild-type effective competing labeled (IC50 4.0 0.6 nM versus 17.3 3.5 2B), change 4.3-fold 2C). 2. residence Competitive (green) (blue) motifs. Cy3/Cy5 measuring 51-bp oligos containing either bp away fluorophore). occupancy. IC50UASwt nM, IC50UASmut nM. 3. Error bars SD. Comparison 4.3 1.1 × difference KD 6.8 1.7× Experimental setup smFRET 8 nucleosome. FRET pair entry/exit region provides readout (one histone). absence Gal4, state. traps gives S1/2 7.2 0.8 48.9 10.8 respectively. showing concentrations. States using HMM (F) Binding concentration-dependent, UASmut: kon 0.011 0.002/s/nM, 0.009 0.001/s/nM. all, except 4 10 3 Gal4. dissociation ˜5-fold slower koff 0.20 0.01/s, 0.92 0.05/s. 11 SE. Scatter plot (from smFISH) measurements). levels saturates above wild-type sequence. box. mean smFISH, UASconsensus, UAS-2C, UAS-8T measurements, [embj2018100809-sup-0009-SDataFig2.xlsx] EV2. 2) Titration PIFE. With 500 pM 680 230 pM. essentially equal concentration experiment, considered assume actual much lower. ˜80% molecules [Gal4] competition seq, used. Nucleosomes reconstituted ratio 1.25:1 DNA:HO via salt gradient dialysis. Lanes samples post dialysis before sucrose purification. After dialysis, 5–30% gradients purified centrifugation. Sucrose fractions collected concentrated. 6 Cumulative sum unbound Numbers replicate number. vivo, wrapped Previous measurements ~1,000-fold therefore within energy transfer fluorophores positioned (Förster resonance transfer, FRET). state, increases distance fluorophores, efficiency 2D). 8.6-fold higher 2C E). then recording time-resolved trajectories surface-tethered 2F G). (frequency binding) (dwell time) concentration-dependent 2H I). regulated unwrapping 2H). fivefold wild type (4.96 0.25 1.09 0.06 2I stabilizes influence binding. This correlates increased vivo. generalize five 2K). previous data, production. output. saturation regulatory limit inherent test whether relevance, sought near presence galactose, i.e., induced uninduced conditions. Specifically, mapped stable fragile digestion MNase (Kubik Cells grown protected fragments (95–225 140–225 bp) (Figs EV3E). appeared flanking moved downstream, Brahma Henikoff, 2019). Fragile reported GAL1-10 2010), appear many EV3), strongly suggesting edge vivoProfiles midpoint positions MNase-seq Samples digested raffinose (raf) (gal) media. Midpoints smoothed 31 bp. move Gal4UAS, creating space additional (indicated arrow). EV3. 3) A–D. response Profiles GAL1, GAL7, GAL2, (C) GAL80, (D) GCY1. E. Profile (140–225 bp), result information: Arrows

Language: Английский

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What’s Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism

Molecular Cell, Journal Year: 2016, Volume and Issue: 62(5), P. 788 - 802

Published: June 1, 2016

Language: Английский

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A continuum model of transcriptional bursting

eLife, Journal Year: 2016, Volume and Issue: 5

Published: Feb. 20, 2016

Transcription occurs in stochastic bursts. Early models based upon RNA hybridisation studies suggest bursting dynamics arise from alternating inactive and permissive states. Here we investigate mechanism live cells by quantitative imaging of actin gene transcription, combined with molecular genetics, simulation probabilistic modelling. In contrast to early models, our data indicate a continuum transcriptional states, slowly fluctuating initiation rate converting the between different levels activity, interspersed extended periods inactivity. We place an upper limit 40 s on lifetime fluctuations elongation rate, variations persisting order magnitude longer. TATA mutations reduce accessibility high activity leaving on- off-states unchanged. A or spectrum states potentially enables wide dynamic range for cell responses stimuli.

Language: Английский

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Transcription Dynamics in Living Cells

Annual Review of Biophysics, Journal Year: 2016, Volume and Issue: 45(1), P. 25 - 47

Published: May 5, 2016

The transcription cycle can be roughly divided into three stages: initiation, elongation, and termination. Understanding the molecular events that regulate all these stages requires a dynamic view of underlying processes. development techniques to visualize quantify in single living cells has been essential revealing kinetics. They have revealed (a) is heterogeneous between (b) discontinuous within cell. In this review, we discuss progress our quantitative understanding dynamics cells, focusing on parts cycle. We present allowing for single-cell measurements, review evidence from different organisms, how experiments broadened mechanistic regulation.

Language: Английский

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Single-cell analysis of transcription kinetics across the cell cycle

eLife, Journal Year: 2016, Volume and Issue: 5

Published: Jan. 29, 2016

Transcription is a highly stochastic process. To infer transcription kinetics for gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to prediction theoretical model. However, reliability this procedure limited because measured numbers represent integration over lifetime, contribution from multiple gene copies, and mixing cells different cell-cycle phases. We address these limitations by simultaneously quantifying nascent mature in individual cells, incorporating effects analysis statistics. demonstrate our approach on Oct4 Nanog mouse embryonic stem cells. Both genes follow similar two-state kinetics. exhibits slower ON/OFF switching, resulting increased cell-to-cell variability levels. Early cell cycle, two copies each exhibit independent activity. After replication, probability copy be active diminishes, dosage compensation.

Language: Английский

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Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

Proceedings of the National Academy of Sciences, Journal Year: 2020, Volume and Issue: 117(9), P. 4682 - 4692

Published: Feb. 18, 2020

Significance The random nature of gene expression is well established experimentally. Mathematical modeling provides a means understanding the factors leading to observed stochasticity. In this article, we extend classical two-state model stochastic mRNA dynamics include considerable number salient features single-cell biology, such as cell division, replication, maturation, dosage compensation, and growth-dependent transcription. By biologically relevant approximations, obtain expressions for time-dependent distributions protein numbers. These provide insight into how fluctuations are modified controlled by complex intracellular processes.

Language: Английский

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Polyploidy in liver development, homeostasis and disease

Nature Reviews Gastroenterology & Hepatology, Journal Year: 2020, Volume and Issue: 17(7), P. 391 - 405

Published: April 2, 2020

Language: Английский

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A single cell atlas of the human liver tumor microenvironment

Molecular Systems Biology, Journal Year: 2020, Volume and Issue: 16(12)

Published: Dec. 1, 2020

Article17 December 2020Open Access Transparent process A single cell atlas of the human liver tumor microenvironment Hassan Massalha orcid.org/0000-0002-9923-6878 Department Molecular Cell Biology, Weizmann Institute Science, Rehovot, Israel Search for more papers by this author Keren Bahar Halpern Samir Abu-Gazala General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Transplant Division, Hospital Pennsylvania, Philadelphia, PA, USA Tamar Jana Efi E Massasa Andreas Moor orcid.org/0000-0001-8715-8449 Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland Lisa Buchauer orcid.org/0000-0002-4722-8390 Milena Rozenberg Eli Pikarsky The Lautenberg Center Immunology, Research Israel-Canada, School, Ido Amit Gideon Zamir Shalev Itzkovitz Corresponding Author [email protected] orcid.org/0000-0003-0685-2522 Information Massalha1, Halpern1, Abu-Gazala2,3, Jana1, Massasa1, Moor4, Buchauer1, Rozenberg1, Pikarsky5, Amit6, Zamir2 *,1 1Department 2Department 3Transplant 4Department 5The 6Department *Corresponding author. Tel: +972 89343104; E-mail: Systems Biology (2020)16:e9682https://doi.org/10.15252/msb.20209682 PDFDownload PDF article text main figures. Peer ReviewDownload a summary editorial decision including letters, reviewer comments responses to feedback. ToolsAdd favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Malignant growth is fueled interactions between cells stromal composing microenvironment. major site tumors metastases, but molecular identities intercellular different types have not been resolved in these pathologies. Here, we apply RNA-sequencing spatial analysis malignant adjacent non-malignant tissues from five patients with cholangiocarcinoma or metastases. We find that exhibit recurring, patient-independent expression programs, reconstruct ligand–receptor map highlights recurring tumor–stroma interactions. By combining transcriptomics laser-capture microdissected regions, zonation hepatocytes sites characterize distribution each type across Our provides resource understanding malignancies may expose potential points interventions. SYNOPSIS Single methods are used generate microenvironment, exposing tumor-stroma patterns healthy tissue. presented. Recurring gene signatures found metastases cholangiocarcinomas. Tumor communicate through conserved ligand-receptor interaction network. Spatial reveal zonated liver. Introduction Cancer heterogeneous disease, exhibiting both interpatient intrapatient variability (Marusyk et al, 2012; Meacham Morrison, 2013; Patel 2014; Alizadeh 2015). do operate isolation, rather closely interact complex milieu supporting form (TME) (Polyak 2009; Hanahan Weinberg, 2011; Lambrechts 2018). These include, among others, range immune cells, cancer-associated fibroblasts (CAFs), endothelial cells. Interactions critical cancer survival (Meacham 2013). Stromal supply factors, facilitate evasion, modulate composition extracellular matrix. Given diversity TME, it essential approaches resolve their (Tirosh 2016; Puram 2017; primary (Llovet 2016). Tumors origin include hepatocellular carcinomas (Guichard 2012), cholangiocarcinomas [tumors originating cholangiocytes (Patel, Sia 2013)], hepatoblastomas. Liver often originate colorectal pancreatic cause mortality (Weinberg, atlases provided important insight into development (Camp Segal 2019; Popescu 2019), physiology (MacParland 2018; Aizarani pathology (Zhang 2019, 2020; Ramachandran Sharma 2020) modalities carcinoma information, tissue identify distinct distributions TME. Results To assemble analyzed six who underwent resection (Fig 1A, Appendix Fig S1). Three Patients hepatic two intrahepatic cholangiocarcinoma, one cyst at benign stage (Dataset EV1). dissociated measured transcriptomes using MARS-seq (Jaitin Materials Methods). In parallel, preserved microdissection (LCM) (Moor 2017, 2018) molecule fluorescence situ hybridization (smFISH) (Bahar Figure 1. Experimental scheme, tumor, non-tumor samples surgeries were scRNA-seq, frozen LCM, fixed smFISH. tSNE plot colored normalized sum pan-carcinoma markers taken al (2017). "n"—indicates number per group. 17 Seurat clusters hepatocytes, (liver sinusoidal cells—LSEC, vascular cells—LVEC, cells—LVECt), mesenchymal (Stellate fibroblasts—CAFs, Pericytes, smooth muscle cells—vSMC), (Kupffer scar-associated macrophages—SAMs, monocytes 1—TM1, cDC1, cDC2, T B cells), proliferating Heatmap showing marker genes (Materials Expression maximal all types. Download figure PowerPoint included 7,947 4,140 3,807 1B). did show histological signs fibrosis, exception patient p2 Methods, Dataset formed clusters, which annotated based on known recent cirrhotic livers (Ramachandran 2019) 1C). Notably, mixture (Appendix S1), demonstrating signatures. Cells several non-parenchymal populations—hepatic stellate (vSMC), Kupffer (LSEC), (LVEC), cholangiocytes, latter clustering marked KRT8, KRT18, EPCAM (Puram 1B) diverse TME populations, fibroblasts, Carcinoma exhibited differences metastatic S1E). Genes elevated higher cholangiocyte Beta-defensin 1 (DEFB1) (Harada 2004) FGFR2. metastasis Cadherin (CDH17) (Panarelli 2012) adhesion molecules CEACAM5 CEACAM6, previously shown correlate colonization (Powell extracted global unique 1D, Datasets EV2 EV3). validated panel 12 smFISH S2). common question whether reconstructed stable regard numbers sample (Mereu 2020). This particularly cancer, due profound levels heterogeneity assessed stability obtained our sampled end, mean subsamples patients, equally sized as controls. compared subsets data those full atlas. gain correlations, when adding new strongly curtailed most beyond three converged correlations subsampling than S3). An was cluster, where changed added thus demonstrates that, while high variability, exhibits uniform patients. Differences matching within same enabled identification populations compose 2). von Willebrand factor VWA1, encoding glycoprotein extravasation (Terraube 2007), well SOX17 (Yang INSR (Nowak-Sliwinska promote angiogenesis 2A). predominantly macrophages (SAMs) 2B). express CD9 TREM2, suppressor (Tang CAPG GPNMB. GSEA Subramanian (2005) SAM resulted significant enrichment apical junction complement system. Their lipid-associated genes, such PLIN2 LPL, overlapping recently identified SPP1+ (LAMs) mouse fatty (Remmerie mononuclear phagocyte composed expressing C1QB, MARCO, CD5L, CD163 S4). Tregs, CTLA4 FOXP3, whereas T-cell cytotoxic CCL5, GZMK, NKG7 2C). divisions suggest recruitment immune-suppressive macrophages, demonstrated other (Lambrechts Binnewies Additional conventional dendritic (cDC1 cDC2), (TM1), FCN1 S100A12 1C, 2. A–D. Top-left—tSNE boxes demarcate clusters. Dashed labels indicate panels A–C. (A) Volcano differential (DGE) samples. (B) DGE phagocytes (C) (D) classified (cholangiocarcinoma dark purple light purple). Wilcoxon rank-sum tests P-values, Benjamini–Hochberg multiple hypotheses correction compute q-values. Labeled dots names selected differentially expressed further phagocytes, 2D). Endothelial etiologies. contrast, up-regulation chemokines CCL4, CCL4L2, CCL3L3 remodeling MMP19, MMP12, HS3ST2 Diversity four 3, S5A). Hepatic retinol binding protein (RBP1), Myosin-11 (MYH11) abundant 3A, S1C). Mesenchymal Cancer-associated matrix (ECM) COL1A1, LUM, BGN, larger cluster. second cluster classic pericytes, periendothelial roles regulating integrity (Armulik 2011). RGS5 CSPG4, neuron-glial antigen 2 (NG2) some suggested DES (Nehls 1992) ANPEP (Kumar 2017), specifically pericytes context Importantly, almost absent demonstrate RGS5+ indeed PDGFB, expected 3B C). CAFs COL1A1 resided farther away D, 3. A. Key (RBP1, RGS5, MYH11). Light gray denote Dark B. Left—Representative image p1 stained localization pericytes. Scale bar 10 µm. lines mark shortest distance (2a) (2b) (1). Middle—zoom-in (1) left panel, blood vessel like structure PDGFB (magenta) wrapped (green). consecutive layers 2.5 Right—zoom-in (2a b) distant signal RGS5. DAPI nuclei staining. C, D. Violin vessels low/high (n = 358 n 360, respectively) 359 359, respectively). "p" P-value determined test. Empty circles medians over repeats. E. Schematic representation top-ranked (bona-fide) detected NicheNet sorted prior LVECt F. Pathway bona-fide EV4) Enrichr tool. Images representative images out eight independent experiments Paracrine juxtacrine attached be proper vascularization (Annika 2005). unbiased signaling pathways could affect physically interacting applied (Browaeys computational method predicts induction downstream target 3E, EV4). via JAG1,2-NOTCH3, PDGFB-PDGFRB S5B), SLIT2-ROBO1,4 S5C) ANGPT2-TEK 3E). ligands receptors mediating cell–pericyte cross-talk enriched pathways, angiogenesis, chemokine cytokine 3F). highly dependent provide factors enhance survival. turn, secrete sensed (Zhou 2017). facilitated an parsed database (Ramilowski 2015) pairs, proteins specific hand SAMs hubs, representing 49.3% carcinoma–TME 4A). focused recurred least 4B, EV5). resulting interactome network highlighted modules, large module, modules centered around ERBB, HGF-MET, TGFbeta, FGF, IGF, VEGFA, lipid trafficking WNT planar polarity module 4B). 4. Human delineates Summary total 20% red box. Network sites. Node colors ligands/receptors enriched. Gray arrows color indicates Zscore shaded. Included significantly appeared Dot-plot highlight shared motifs max (A). For gene, dot size represents fraction positive Top—CAFs comodulate carcinoma–stroma interaction. produce DCN modulates CAF-SAMs-expressed ligand HGF carcinoma-expressed receptor MET. Bottom—CAFs CTHRC1 WNT5A ligand, CAFs, SAMS, TM1 FZD5. Strategy computing scores tumor. score computed products average randomizing real manner preserves outgoing incoming (Scorerand). ratio randomized constitutes score. increases increasing stage. Analysis 383 TCGA (LIHC) (CHOL). median largest consisted proteins. ECM has shaped assembly degrading proteins, collectively matrisome (Naba Varol Sagi, Features stiffness porosity optimal cellular contacts, maximize accessibility control exclusion (Binnewies Within produced collagens laminins, integrin scRNA-seq identifying secreting components S6). Planar (WNT-PCP) pathway invasion (Wang, 2009). PCP activated non-canonical Wnt morphogens, WNT5A, 4B–D). CTHRC1, secreted collagen triple helix filament forms stabilizes its tumor-expressed receptor-FZD (Yamamoto 2008), CAFs. Thus, jointly WNT-PCP signaling. observed similar cooperation MET module. driver (De Silva HGF, activating MET, DCN, decorin protein, turn inhibits HGF-MET (Goldoni revealed additional role interactor carcinoma-specific EGFR summary, details connectivity correlates severity convey selective advantage assess hypothesis, examined cohort bulk-sequenced 4E F). first consists summed degree-preserving random networks 4E). normalization important, since simply reflect receptors, oncogenes, coordinated receptors. increased along stages 4F). severity. identifies solid reside zones oxygen levels, nutrient availability, morphogen concentrations. can Itzkovitz, 2017) result spatially organ, repeating anatomical units termed lobules, polarized centripetal bloo

Language: Английский

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RNA velocity unraveled

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(9), P. e1010492 - e1010492

Published: Sept. 12, 2022

We perform a thorough analysis of RNA velocity methods, with view towards understanding the suitability various assumptions underlying popular implementations. In addition to providing self-contained exposition mathematics, we undertake simulations and controlled experiments on biological datasets assess workflow sensitivity parameter choices biology. Finally, argue for more rigorous approach velocity, present framework Markovian that points directions improvement mitigation current problems.

Language: Английский

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Modulation of transcriptional burst frequency by histone acetylation

Proceedings of the National Academy of Sciences, Journal Year: 2018, Volume and Issue: 115(27), P. 7153 - 7158

Published: June 18, 2018

Many mammalian genes are transcribed during short bursts of variable frequencies and sizes that substantially contribute to cell-to-cell variability. However, which molecular mechanisms determine bursting properties remains unclear. To probe putative mechanisms, we combined temporal analysis transcription along the circadian cycle with multiple genomic reporter integrations, using both short-lived luciferase live microscopy single-molecule RNA-FISH. Using Bmal1 promoter as our model, observed rhythmic resulted predominantly from variations in burst frequency, while position changed size. Thus, frequency size independently modulated transcription. We then found histone-acetylation level covaried being greatest at peak expression lowest trough expression, remaining unaffected by location. In addition, specific deletions ROR-responsive elements led constitutively elevated histone acetylation frequency. investigated suggested link between dCas9p300-targeted modulation acetylation, revealing levels influence more than The correlation was also endogenous embryonic stem cell fate genes. data suggest acetylation-mediated control is a common mechanism gene expression.

Language: Английский

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