Using computational genome mining approaches, candidateTFREs were identified within promoter sequences of highly expressedDC-specific genes or those exhibiting an upregulated expression duringDC maturation. Individual TFREs were then screened in vitro in a target DC line and off-target cell lines derived from skeletalmuscle, fibroblast, epithelial, and endothelial cells using homotypic(TFRE repeats in series) reporter constructs. Based on these data,a library of heterotypic promoter assemblies varying in the TFRE composition,copy number, and sequential arrangement was constructed and tested in vitro to identify DC-specific promoters. Analysis ofthe transcriptional activity and specificity of these promoters unraveledunderlying design rules, primarily TFRE composition, which governthe DC-specific synthetic promoter activity. Using these design rules,a second library of exclusively DC-specific promoters exhibiting variedtranscriptional activities was generated. All DC-specific syntheticpromoter assemblies exhibited >5-fold activity in the target DClinerelative to off-target cell lines, with transcriptional activitiesranging from 8 to 67% of the nonspecific human cytomegalovirus (hCMV-IE1)promoter. We show that bioinformatic analysis of a mammalian celltranscriptional landscape is an effective strategy for denovo design of cell-type-specific synthetic promoters withprecisely controllable transcriptional activities.Next-generation DNA vectors for cancerimmunotherapies and vaccinedevelopment require promoters eliciting predefined transcriptionalactivities specific to target cell types, such as dendritic cells(DCs), which underpin immune response. In this study, we describethe  Accordingly, in vivo antigenexpression in DCs has been shown to be achievable by targeting thetropism of viral/nonviral DNA vectors to DCs and/or at the transcriptionallevel—by using DC-specific promoters to drive antigen expressionin vector systems.12 However, the complex design requirements for physical targetinghave hampered the sustainable development of safe and efficaciousDNA-based vaccine vectors; therefore, transcriptional targeting remainsa critical focal point for the development of vaccines and cancerimmunotherapies.12Dendriticcells (DCs) are a heterogeneous population of professionalantigen-presenting cells (APCs) equipped to process protein antigensinto peptides which are displayed on the cell surface. This is achievedusing major histocompatibility complexes I and/or II as DCs matureand migrate to lymphoid tissues to initiate an immune response.14 DC-STAMP andDC-SIGN,15 CD11c,16 and the actin-bundling fascin gene17 havebeen used to transcriptionally target antigen expression to differentDC subsets. However, endogenous promoters are typically encoded onrelatively long stretches of DNA, thus limiting the transgene(s) sizesthat can be incorporated into DNA vectors. They also display minimalactivity compared to the hCMV-IE1 promoter, which displays a relativelynonspecific high level of transcriptional activity (TA) across mammaliancell types.18 In addition, endogenous promoterspossess a vaguely defined architecture, as they are naturally evolvedfor a diverse array of genetic functionalities.19 Notably, the heterogeneity in transcriptional factor (TF)-bindingmotif population potentially compromises the specificity of promoteractivity, particularly when evaluated in a broader range of cell types.Moreover, cell-type specificity in gene expression is highly subjective,often depending on the base cell types that specificity is comparedagainst. These fundamental drawbacks have significantly limited therepertoire of promoters available for DC-specific antigen expression.Endogenous enhancersor enhancer fragments of DC-specific genes(genomic regions upstream of DC-specific genes) have previously beendeployed to drive DC-specific antigen expression in vaccine vectors.For example, promoters of the dectin-2,via concatenation of binding motifs has been previouslydescribed for a similar myeloid cell type—macrophages21 and several nonmyeloid cell/tissue types, for example, skeletalmuscle.23 However, while these studies successfullyidentified candidate DNA-binding motifs, inadequate characterizationand standardization of the “transcriptional power” ofeach composite binding motif in a synthetic promoter context or architecturehamper the generation of large arrays of functionally relevant syntheticpromoters with predefined activity and specificity levels.Addressing these inherent challenges requires proper identification,characterization, and standardization of modular DNA-binding motifs,followed by an informed design strategy to assemble the standardizedbuilding blocks into fit-for-purpose synthetic promoters. Indeed,the construction of cell-type-specific synthetic promoters via robust in silico transcriptomicdata analysis of TF expression dynamics25 and via genome-mined overrepresented transcription factor regulatoryelements (TFREs) in endogenous promoters of genes of interest in CHOcell lines in a fashion that obviates the need to design and screenlarge libraries of randomly assembled TFREs.27 In this study, we have developed an informatics workflow that leveragesboth approaches to (i) identify modular DC-specific-binding motifsin the endogenous promoters of genes that confer DC-specific TA and(ii) define promoter assembly rules for generating DC-specific syntheticpromoters. In particular, we aimed to design DC-synthetic promoterswith negligible activity in the bulk of cell types associated withthe skeletal muscle tissue—myocytes, fibroblasts in the associatedconnective tissue, endothelial cells in the surrounding vasculature,and epithelial cells in the surrounding epidermal layer. Accordingly,we utilized the DC2.4 murine monocyte-derived DC line as a model cellline for the in vitro assay of transcriptional activitiesof in silico designed promoter constructs based onprevious studies that have validated this cell line as a powerfultool for DC research.31 The specificity of promoter activitywas evaluated by in vitro screening in cell linesC2C12, NIH-3T3, and CaCO-2 as model cell lines for myocytes, fibroblasts,and the epithelium, respectively, and HUVEC primary cells as the modelcell type for endothelial cells. Our results demonstrate that analysisof the transcriptional landscape of target cell type(s) and off-targetcell types critically underpins in silico designof functionally active cell-type specific synthetic promoters.Previously,we have demonstrated the construction of cell-type-specificpromoters www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6192) and NCBI GEO Database (accession GSE101878),32 respectively. Accordingly, we ranked the genes from eachdataset in order of mRNA quantification, then selected conserved genesexhibiting high mRNA levels across both datasets . In order to selectfor genes with high TA specifically in moDC and cDC from this dataset,we first identified genes expressed specifically in moDC and cDC basedon cell/tissue-specific expression potential analysis of the transcriptionaldata on GeneVestigator V3.33 Cell typesrelated to endothelial, epithelial cells, fibroblasts, and skeletalmuscle were set as base (off-target) cells, while moDC and cDC wereseparately set as target cell types . Next, genes common to both the highTA and specificity groups were classed as highly expressed and specificgenes in moDC and cDC . Second, we identified genes that are upregulated duringthe maturation of moDC and cDC based on the available data on DC maturationgenetic reprogramming studies by Mahn et al. .34 All profiled genes were subsequently categorizedas follows: (1) A1—highly expressed in moDC, (2) A2—expressedspecifically in moDC, (3) A1.2—highly expressed specificallyin moDC, (4) B1—highly expressed in cDC, (5) B2—expressedspecifically in cDC, (6) B1.2—highly expressed specificallyin cDC, (7) C—upregulated in moDC and cDC during maturation.Our approach todesigning moDC- and cDC-specific synthetic promoters involved theidentification of individual TF-binding motifs and cognate endogenousTFs associated with a high constitutive expression of DC-specificgenes and/or those which influence upregulation of TA during DC maturation1. To ach35 We limited endogenous promoter sequence lengthto −1000 to +100 relative to the transcriptional start siteof the genes of interest in line with common knowledge that this cistromeregion is relatively more concentrated with binding motifs which influencegene expression dynamics.36 As such, the CommonTF tool of Gene Regulation software suites of Genomatix (https://www.genomatix.de/)was used for in silico analysis of the endogenouspromoter sequences to identify common TFREs in the endogenous promotersequences of genes in each gene class of interest. A 25% thresholdof the gene set and matrix settings core similarity (1.0) and matrixsimilarity (optimized) were applied. On this note, a total of 745TFRE types common to at least 25% of promoters of each gene classwere identified .From our collectionof gene sets of interest, we first aimed toidentify TFREs commonly represented within the promoters of each geneclass. We hypothesized that these TFREs are critical to the transcriptionalphenotype of the genes in each gene class and would be key inputsfor a bottom-up approach to designing highly transcriptionally activeDC-specific synthetic promoters. Endogenous promoter sequences ofall A1.2, B1.2, and C genes were derived from a genomic region upstreamof each gene from the Eukaryotic Promoter Database (EPA).http://www.genomatix.de/matinspector.html). We next applied the tissue specificity filter function to identifydiscrete TFRE types of trans-activators specificto APCs, which is the closest available description of DCs that thetool afforded . From this in silico analysis,52 TFRE types of TFs unique to APCs were identified . In total, ∼800TFRE types (spanning ∼2000 unique TFRE sequences) were identifiedfrom in silico endogenous promoter analysis.In order to rapidly curate the literature forTFREs of cell-type-specificTFs within the promoters of each gene class, we analyzed the generalTFRE pool of all genes in the three gene classes using MatInspector,Matrix Library 11.2  parameter (1–1055), which ranks cell type TFs in a decreasingorder of abundance, to identify TFs which were not only highly abundantin the moDC, myeloid DC, and activated myeloid DC but also relativelyless abundant in the preselected off-target cell types combined . Accordingly,the top ∼10% of highly expressed TFs (top 100 TFs with SS:1–100) in (i) the moDC, (ii) myeloid or conventional DC, and(iii) activated myeloid DC were selected from this study, cumulatingin 170 candidate TFs from all 3 DC types . Non-DC specific TFs, which were alsoamong the top ∼10% of highly expressed TFs (TFs with SS: 1–100)in the key 8 off-target cell types predominant in off-target tissuesites, were excluded, leaving a total of 79 DC-specific TF candidates. Themean SS values of each TF in all three target cell types (MTSS) andthe corresponding mean SS value in all eight off-target cell types(MBSS) were also calculated, thus generating discrete SS values foreach TF in target and off-target cell types. In order to rank all79 candidate TFs in the decreasing order of specific abundance inour DC subsets of interest relative to off-target cell types, we deviseda TF specificity metric—TSSQ, which is the normalized differencebetween the mean SS of each TF in the off-target and target cell types.i of the 79 TF candidatesnormalized by the highest difference between MBSS and MTSS value ofall 79 TFs.To thisend, we queried existing literature on cell type-specific TF expressionprofile from D’Alessio interest 1.37 We uSupporting Information, Figure S2A).TFs with a TSSQ <0.1 were eliminated, leaving 69 core DC-specificTF candidates. In addition, all initially excluded non-DC-specificTFs from the list of 170 TF all also had TSSQ values <0.1 . From this selection, TFRE types (spanning 44 TFs) common toat least two of these groups were identified . Of these 44 TFs, only25 TFs with a TSSQ > 1 were selected. All 25 TFs of the identifiedTFREs were classed according to the TSSQ values as 11 class I TFs(TSSQ: 1–0.5) and 14 class II TFs (TSSQ: 0.499–0.100).Two TFs, which were not among the initially identified 69 TFs butwhich had the highest TSSQ  among TFs identifiedfrom in silico promoter analysis with common TFsand APC-related, were among the list of 25 TFs. Also, 46 class IIITFs, which were among the initial 69 TFs but were not common to TFidentified from common TF analysis and/or APC-related TFREs, wereexcluded from the pool of 69 TFs.Having identifiedcommon  high TA 1. First,trans-elements, we further screenedthe 25 TF candidates to identify (i) high-transactivation potentialTFs using TFdiff encode ,(ii) TFs involved in the transcriptional network of genes upregulatedduring DC maturation, using IPA, (iii) nucleoplasm-localized trans-activators of DC function, identity, or phenotype,using the HPA as a credible source of information . Notably,the mean of the mean SS of all 10 TFs in the 3 target cell types (MTSS)was ∼4-fold lower than the mean of their mean SS in all 8 off-targetcell types (MBSS), thus suggesting that all 10 TFs were indeed uniqueto DCs  .To improve the stringencyof our selection of ormation 1. Finall26 Accordingly,6-mer homotypic units were constructed from TFRE sequences of eachselected TF (∼40 sequences per TF) and analyzed on MatInspectorto identify accidental TFREs at TFRE–TFRE junctions or embeddedwithin individual TFRE sequence units  to groupC genes (We have previously utilized homotypic promoterscomprising 6× repeat (6-mer) of individual candidate TFRE sequencesto characterize the transcriptional power of individual TFRE syntheticpromoter building blocks prior to being utilized in the constructionof fit-for-purpose heterotypic promoters.ce units 2A. Wherece units 2A.HowevC genes 1.As sucSupporting Information, Figures S3 and S4).Accordingly, all selected homotypic promoter sequences werescreenedfor green fluorescence protein (GFP) expression in DC2.4 cells andall four off-target cells using the hCMV-IE1 promoter in the pVAX1-CMV-GFPvector as a positive control , followed by moderate values for IRF9-A,IRF8-A, IRF4-A, and SPI1-A (2–4), while IKI-A, STAT5A-A, JUNB-A,JUNB-B, BATF-A, and SPI1-B had the lowest homotypic promoter SS (<2)2C.trans-elements  with unique genetic signatures comprising distinct cis-elements  within endogenouspromoters.38 While this phenotype is naturallyencoded in endogenous promoter architectures as complex promoter frameworks/motifs,creating synthetic mimicries of these genetic signatures is a significantchallenge. The main interacting criteria for the design of cell-type-specificheterotypic synthetic promoters from distinct TFREs include (i) maintenanceof the specificity and TA of individual TFREs proportionately withinthe heterotypic promoter architecture, (ii) minimization of the accidentalbioactive DNA sequence introduction at selected TFRE–TFRE junctionsthat could compromise promoter specificity and/or activity via optimization of the linear arrangement of TFREs.Cell-type-specific endogenous promoteractivity is a complex cellular phenotype influenced by the bindinginteraction of specific in silico, the first library of 42 heterotypic promoters (library 1) usingpermutations of 12 active TFREs comprising at least 3 different TFREsequences per promoter in order to create heterogeneity in the endogenousTFs used to drive transcription from synthetic constructs. Furthermore,a number of sequence-specific design elements were employed, including(i)in silico constructed promoters from the TFRE compositionand copy number.utilizationof 2-mer TFRE units aspaired blocks of adjacent single TFRE units. This allowed for a betterfunctional modular coverage of the activity and specificity of selectedhomotypic constructs in a heterotypic environment 2A. We po(ii)39 and BATF/JUNB-IRF8/IRF4-bindingmotifs (AICE sequence motifs)40 in someendogenous promoters have been identified as the key influencers ofDC-specific expression of downstream genes. Alternatively, non-DC-specificTF–TF interaction, such as between RELA and IRF8, positivelyinfluences gene expression in a large array of cell types, includingfibroblasts and endothelial cells.41avoidanceof non-DC specific multi-TFREmotifs or colocation of TFREs whose TFs have been shown to cooperativelyinteract to create a non-DC-specific transcriptional signature inthe promoter architecture coupled with colocation of TFREs known tocooperatively signify DC-specific transcriptional signatures as asynthetic mimicry of DC-specific promoter models in endogenous promotersof genes of interest. For example, naturally occurring IRF8/IRF4-SPI1-bindingmotifs (EICE sequence motifs)(iii)where required, useof appropriate2–3 bp spacer units to eliminate unintended TFREs between adjacent2-mer units of different TFREs. We also ensured that adjacent repeatTFRE sequences were limited only to paired TFRE sequences (2-mer units)in order to avoid the formation of long repeat sequences potentiallycapable of homologous recombination.(iv)i.e., homotypicspecific activity < 2), such as SPI1-B, JUNB-B, IKI-A on the premiseof such TFREs, would result in very unspecific heterotypic promoters,particularly where they outnumber the more specific TFREs . Next, replacingthe control human CMV-IE proximal domain in the pVAX1-CMV-GFP (andretaining the 84 bp hCMV-IE1 core element), all 42 synthetic promoterswere cloned upstream of a GFP reporter gene  prior to transfection into DC2.4 and four off-targetcell types. Transfected cells were then assayed for the cellular GFPcontent at 24 h post-transfection . All 42 heterotypic promoterselicited a broad range of activity ranging from ∼1% (1/01)to ∼121% (1/42) relative to the activity in the hCMV-IE1 promoterin pVAX1-CMV-GFP in DC2.4 cells . In the off-target cells, the promoters elicited adifferent transcriptional output relative to the target DC2.4 cells.In particular, promoters 1/06, 1/07, 1/11, 1/12, 1/15, 1/16, 1/23,1/36, 1/39, 1/40, and 1/38 exhibited 8–67% activities (∼8-foldrange of activity) relative to the hCMV-IE1 promoter in DC2.4 cells,and low activities in all four off-target cells (Supporting Information S6). Conversely, promoterssuch as 1/17 and 1/35 displayed high activities in the off-targetcells and low activity in DC2.4 cells .This indicated a contrast between the transcriptional behavior exhibitedby constituent TFRE motifs in a heterotypic environment compared andtheir respective homotypic promoter activities. Second, we quantifiedthe degree to which each of the 42 promoters specifically harnessesthe DC-transcriptional landscape to actuate transcription. This metric—dendriticcell-specific promoter activity score (DCSPA score)—is calculatedas the difference between the observed RPU in DC2.4 cells and thesum of the observed relative promoter activities in all four off-targetcells  to ∼−130 (1/17) 4A. A negNij is the copynumber of a given TFRE i in a given promoter j, DCSPAj isthe DCSPA score of a given promoter j.Next, we proceeded to delineate how individualconstituent TFREsinfluence DC-specific activity in a heterotypic synthetic promoterarchitecture. We calculated the DC-specific TFRE activity (DCSTA)score of each TFRE as the sum of the product of the DCSPA score ofeach promoter and the copy number of each TFRE within each promoteracross all 42 promoters.Supporting Information, Figure S7). Other TFREs such as IRF4-A, c-Rel-B, c-Rel-A, RELA-A,STAT5A-A, and SPI1-A appear to exert the least effects on DC-specificactivities .Using the normalized DCTSA score of each TFRE , we theoretically estimatedthe resultant contribution of constituent TFREs to DCSPA, that is,the estimated DCSPA score .This strongly indicated that the observed DCSPA is primarily influencedby the TFRE composition and distinct transcriptional behaviors ofconstituent TFREs in a heterotypic environment . Also, promoter 1/01, which is highly concentrated withSPI1-A motifs (10 copies), has a negative DCSPA score and has thelowest activity (∼1%) of all 42 first library promoters . In fact, the activities of the aforementioned promoters aresignificantly lower than the individual activities of homotypic constructsof IRF4-B and SPI1-A in the target DC2.4 cells . This clearly demonstratesthat the TFRE concentration range is a limiting factor that influencesDCSPA. Furthermore, the observed pattern of promoter activities ata high concentration of different TFREs further indicates unique transcriptionalbehaviors of TFREs in a heterotypic promoter architecture.First, promoters such as 1/02, 1/03,and 1/04, which are highlyconcentrated with a high DCSTA TFRE, IRF4-B ,have very low DCSPA scores owing to very low activities in the targetDC2.4 cells , are relatively more represented across promotersexhibiting positive DCSPA scores compared to promoters exhibitingnegative DCSPA scores .Having delineated the unique transcriptionalbehavior of individual TFREs in a heterotypic environment and theunderpinning role of the TFRE composition in DCSPA, we proceeded togenerate a second library of DC-specific synthetic promoters exhibitingvaried transcriptional activities. A number of design goals were set,including (i) generating a library of exclusively DC-specific promoters,which are architecturally distinct from the first library of promoters,(ii) utilizing optimal concentrations of individual TFREs requiredfor the optimal target promoter activity and minimal off-target activitiesper promoter assembly, (iii) encoding high DC-specific transcriptionalactivities within minimal stretches of DNA, that is, minimal length.To achieve these objectives, promoters were designed predominantlywith TFREs overrepresented across first library promoters exhibitingpositive DCSPA scores. We also utilized single unpaired TFRE unitsin addition to 2-mer TFRE units as constituent building blocks fromhomotypic constructs in order to increase variability in the TFREcopy number. We aimed to attain optimal concentrations of TFRE blocksby restricting the maximum copy number of any given TFRE to six copiesin order to avoid previously observed detrimental effects of highTFRE concentrations on the promoter activity. Adhering strictly tothese design rules, we generated a second library of 16 promotersranging from 119 to 434 bp in length . Essentially, all 16 promotersdisplay over 5-fold greater activity in the target DC2.4 cells relativeto all off-target cells. Upon screening this second library,all 16 promoters displayed relative promoter activities ranging from∼20–∼61% in DC2.4 cells and <5% in all off-targetcells relative to the activity of the hCMV-IE1 promoter  . This demonstrates that we can precisely tune the promoterstrength in the target DC without compromising the cell-type specificityof the actuated TA. All 27 DC-specific synthetic promoters exhibitingover an 8-fold range of TA in the target DC2.4 cell line can be categorizedas weak (5–29%), moderate (30–49%), and strong (>50%)promoters (17 destin-2 promoter (3.2 kb),13 and CD11c,DC-SIGN, DC-STAMP, Langerin promoters (3–5 kb) , synthetic promotersdesigned as described, therefore, offer a significant advantage interms of transcriptional targeting and efficiency (TA per DNA length). A strong positive relationship is establishedbetween the observed relative promoter activities and the DCSPA scoresof all 27 promoters  2.15 Wher length)2. Moreovin silico and invitro preliminary screening of genome-mined TFREs in bothhomotypic and heterotypic architectures enabled rapid identificationof functionally active DNA motif sequences and prompt definition ofan optimal design space for promoter assembly. We have essentiallygenerated compact synthetic mimicries of endogenous DNA motif stretcheswhich encode DC-specific gene expression without a priori empirical knowledge of transcriptional activities of individualconstituent TFREs in an endogenous promoter environment. In particular,our approach obviates the need for intensive in vitro screening of large libraries of candidate promoter sequences, whichis a common requirement in designing synthetic promoters with user-definedfunctionalities.26 More importantly, we delineate how TFRE compositionprimarily underpins activity and specificity of DC-synthetic promoters.We have described a bottom-up approach to construct cell type-specificsynthetic promoters displaying predictable activity and specificitysolely by mining the transcriptional landscape of target and off-targetcells. A combination of in silico promoterassembly process demonstrated here could generate a larger array offunctionally relevant DC-specific synthetic promoters. This is immediatelyachievable by adopting DC-specific synthetic promoters reported hereas design archetypes. Additionally, both our informatics workflowand in silico promoter assembly process may be adoptedfor the de novo design of synthetic promoters withtranscriptional activities specific to any given cell type of interest.With over 300 TFREs  minedfrom the endogenous promoters of candidate genes from our bioinformaticspipeline, we anticipate that the NheI, KpnI, MluI, Bsu36I, and NruI unique restrictionsites within the vector in order to facilitate restriction digestion–ligationcloning. The coding sequence (CDS) of a GFP from pmax-GFP  was cloned into the vector between NheI and KpnI restriction sites. The wild-type corepromoter of the human cytomegalovirus immediate-early 1 gene (hCMV-IE1)was synthesized as a double-stranded DNA oligonucleotide (Eurofins)and inserted between Bsu36I and NheI upstream of the GFP CDS, thus replacing the original core elementin pVAX1. The final expression vector—pVAX1-CMV-GFP—wasused as the positive control vector for assaying the promoter strength. Homotypicand heterotypic promoter sequences were synthesized as double-strandeddsDNA oligonucleotides  and inserted into pVAX1-CMV-GFP,between MluI and Bsu36I sites, thusreplacing the hCMV-IE1 promoter upstream of the core promoter to createboth homotypic and heterotypic expression vectors, respectively. Endotoxin-free,transfection-grade plasmid DNA is purified using the GenElute Endotoxin-freeplasmid Midiprep kit .The pVAX1 vector , which contains a cytomegalovirus(CMV) promoter sequence, was used as the parental expression vectorfor constructing a reporter vector for promoter strength assays. Q5Site-directed Mutagenesis Kit  was used toinsert 2 in tissue culture-treated flasks in a humidified static incubator,according to the supplier’s instructions. DC2.4 cells werecultured in RPMI medium (Sigma) supplemented with glutamine (2 mM),fetal bovine serum (10%), herpes buffer solution (1×), nonessentialamino acids (1×), and β-mercaptoethanol (0.0054×),and routinely subcultured at ∼85% confluency by seeding freshmedia at 20,000 cells/cm2. NIH-3T3 cells were culturedin Dulbecco’s modified Eagle medium (DMEM) (Sigma), supplementedwith glutamine (2 mM) and calf serum (10%), and subcultured when cellswere ∼80% confluent by seeding fresh media at 30,000 cells/cm2. C2Cl2 cells were cultured in DMEM supplemented with glutamine(2 mM) and fetal bovine serum (10%) and subcultured when cells were∼50% confluent by seeding fresh media at 2000 cells/cm2. CaCO-2 cells were cultured in Eagle’s minimum essentialmedium  supplemented with glutamine (2 mM), fetal bovineserum (10%), and nonessential amino acids (1×) and routinelysubcultured when cells are ∼80% confluent by seeding freshmedia at 30,000 cells/cm2. HUVEC cells were cultured inan endothelial cell growth medium  and routinely subculturedwhen cells are ∼80% confluent by seeding fresh media at 5000cells/cm2. Cell viability and concentration were measuredusing a VI-CELL viability analyzer  based onthe trypan blue exclusion assay. Accutase  was used todetach all adherent cells prior to subculturing or transfection.DC2.4—murine monocyte-derivedDC line , NIH-3T3 (ECACC 93061524)—mouseembryonic fibroblast cell line, C2C12 (ECACC 91031101)—mousemuscle myoblast cell line, and CaCO-2 (ECACC 86010202)—humancolon epithelial cell line , and HUVEC—HumanUmbilical Vein Endothelial Cells  were used in this work.All cells were routinely cultured at 37 °C in 5% (v/v) CO2 for 24 h.All cells were transfected in a 96-well Amaxa Nucleofector System at ∼80% confluence, following the manufacturer’sprotocol and instructions. Appropriate buffers and supplements wereapplied for each cell type as follows: DC2.4 and NIH-3T3 , C2C12 and CaCO-2 , and HUVEC. Transfected cells were plated in black,flat-bottomed 96-well microplates  after transfectionand incubated at 37 °C, 5% COEx 485nm, Em 535 nm; bottom read) wasperformed using a SpectraMax iD5 microplate reader . The relative fluorescence unit (RFU) was calculated by normalizingobserved GFP fluorescence values with the fluorescence values fromcell cultures transfected with pVAX1, that is, a GFP-null vector negativecontrol. The RPU unit of all synthetic promoters in each cell typewas calculated as a percentage of RFU values of pVAX1-CMV-GFP (positivecontrol) in each cell type. GFP fluorescence imaging of live cellswas performed with an INCELL Analyzer 2000  usingchannel settings for FITC. Transfection efficiencies were measuredwith a Countess 3 Automated Cell Counter , following the manufacturer’s instructions.The GFP fluorescence measurement (


Chromatin accessibility mapping is a powerful approach and can be performed using ATAC-sequencing (ATAC-seq), whereby Tn5 transposase inserts sequencing adaptors into genomic DNA to identify differentially accessible regions of chromatin in different cell populations. It requires low sample input and can be performed and analysed relatively quickly compared with other methods. The data generated from ATAC-seq, along with other genomic approaches, can help uncover chromatin packaging and potential cis-regulatory elements that may be responsible for gene expression. Here, we describe the ATAC-seq approach and give examples from mainly vertebrate embryonic development, where such datasets have identified the highly dynamic nature of chromatin, with differing landscapes between cellular precursors for different lineages.Mapping accessible chromatin across time scales can give insights into its dynamic nature, for example during cellular differentiation and tissue or organism development. Analysis of such data can be utilised to identify functional  Genomic DNA is packaged into chromatin composed of repeating structural units called nucleosomes, where 147 bps of DNA wrap 1.6 times around an octamer of core histone proteins. The degree of nucleosome occupancy determines chromatin accessibility, which is linked to biological functions, including gene expression, since physical access to regions such as gene promoters, enhancers or silencers is essential for transcription factor (TF) binding. Chromatin accessibility is facilitated by pioneer transcription factors, which bind to nucleosomes and recruit chromatin remodelling complexes, as well as other enzymes which modify histones and control DNA methylation to release DNA from the tightly coiled nucleosome into euchromatin, reviewed in .cis-regulatory mechanism, whereby enhancer bound proteins assemble the transcription machinery to initiate transcription.During embryonic development and cell differentiation, the epigenetic and therefore chromatin landscapes change in response to developmental cues and external stimuli. Changes in access to chromatin can influence the activity of regulatory elements such as enhancers, short DNA sequences containing TF binding motifs that increase the likelihood of transcription of one or more genes through a Enhancer identification remains challenging for several reasons. Enhancers can interact with promoters that are long distances away ,5. AlthoShh) gene is controlled by two enhancers. When activated by both, Shh expression is restricted to the floor plate, but when controlled by only one enhancer, Shh transcription is confined to the limb bud [Functional characterisation of enhancers becomes difficult when several enhancers act on one gene to produce complex expression patterns. These can result from the cooperation between different enhancers with cell type or tissue-specific activities . The prelimb bud .Despite these challenges, many attempts have been made to identify enhancer elements, as this is critical in aiding our understanding of gene regulation, not only during embryonic development but also in human disease. Here, we review the use of ATAC-seq to aid enhancer discovery in dynamic systems.cis-regulatory elements and active chromatin regions. Micrococcal nuclease sequencing (MNase-seq) is an indirect assay in which the enzyme digests any unprotected DNA. Nucleosomes can be purified and the bound DNA sequenced, generating an occupancy map of nucleosomes. Where there is a lack of nucleosomes, it is inferred that chromatin was more accessible. A direct method of chromatin accessibility includes formaldehyde-assisted isolation of regulatory elements (FAIRE-seq), which crosslinks DNA–protein complexes. Subsequent shearing by sonication, and phenol–chloroform extraction isolates nucleosome free regions, which can then be sequenced. Deoxyribonuclease I sequencing (Dnase-seq) and assay for transposase accessible chromatin sequencing (ATAC-seq) methods both work by utilising enzymes that recognise and cleave nucleosome depleted regions. For comparison of these methods, see [For enhancers to act on target promoters, chromatin must be accessible to enable TF binding, thus changes in chromatin accessibility usually correlate with enhancer activity. Therefore, chromatin accessibility assays have been used to identify ods, see .Compared with these assays, which require a large starting cell count and are time consuming , ATAC-seq  takes lein silico, within accessible peaks; see for example [In brief, the ATAC-seq protocol utilises a hyperactive prokaryotic transposase 5 (Tn5) enzyme to cleave open regions of chromatin and insert sequencing adaptors, a process known as tagmentation . Samples example . The pro example  , with two protocols published at the same time. One relies on the physical isolation of individual cells by microfluidic chambers , followed by tagmentation, library generation and sequencing . AlternaAs it is technically more demanding, fewer scATAC-seq experiments have been performed. In addition, minor sub-populations can be hard to detect and the bioinformatic analysis of scATAC-seq is more time consuming  — for a Despite quickly and accurately identifying regions of open chromatin, ATAC-seq cannot on its own identify the type of regulatory element, such as an enhancer, silencer, or promoter region. However, coupled with other genomic techniques ATAC-seq is very useful to identify active regulatory elements in different cell types.Epigenetic marks that characterise enhancer regions include various histone modifications. Mapping these features by ChIP-seq, or Cut and tag techniques , can detFurthermore, multi-omic experiments can combine ATAC-seq with other genomic techniques in the same sample. This is advantageous when working from limited starting material. For example, combining RNA-seq with ATAC-seq enables simultaneous profiling of chromatin dynamics and changes in gene expression patterns. Sci-CAR uses a pooled barcode method , other mATAC-seq is a powerful tool in assessing the chromatin landscape of different cell and tissue types. When used in combination with other genomic approaches, it can provide insight into the epigenome of specific cell populations. Since its introduction, ATAC-seq has been optimised for different purposes. For example, the Omni-ATAC protocol has been adapted for use with a wide variety of tissue types, including snap-frozen tissues, and yields a higher number of peaks and reduced mitochondrial reads ,41.Investigating the accessible chromatin landscape is of particular interest when looking at dynamic changes. For example, during the complex process of embryo development gene expression patterns are changing rapidly and are associated with changes in cell states during cell fate acquisition and differentiation programmes. With ATAC-seq it is possible to generate sufficient replicates for comparisons between different samples, such as developmental time points or tissues. This facilitates atlas-projects for multiple tissues, as for example in mouse  and zebrIn Drosophila embryos, gene-regulatory networks (GRN) that pattern the anterior–posterior axis are well characterised and involve gap and pair-rule genes. ATAC-seq was performed on cellular blastoderm-stage embryos, shortly after zygotic genome activation. Seven well characterised enhancers were used to tag blastoderm nuclei for affinity purification. The regional variation in chromatin accessibility observed, correlated with regulatory activity of axis patterning enhancers, suggesting mechanisms by which transcriptional activator and repressor proteins modulate enhancer accessibility .Chromatin accessibility dynamics have also been investigated using a developmental series of Drosophila embryos. Three major embryonic stages were examined using scATAC . InteresATAC-seq during early zebrafish development, from zygotic genome activation to the onset of lineage specification, showed that chromatin accessibility increased at regulatory elements, often preceding transcription of the associated genes . Loss ofAn important transition, discovered in Xenopus, is the loss of competence to respond to dorsalising Wnt signals in late blastula stages . This isin vitro and in vivo, providing insights into features that could be important for successful preimplantation development [In mammalian systems, chromatin accessibility also increases during genome activation in bovine embryos . In bothelopment ,50. The elopment .in vivo, in hindbrain or spinal cord progenitors suggesting that chromatin accessibility preconfigures region-specific neural fates [Several studies have examined chromatin accessibility during vertebrate neural and neural crest differentiation using embryonic stem cells, zebrafish, mouse and chick embryos ,28,52–54in vivo, by chick embryo electroporation. The differential activity of these enhancers uncovered heterogeneity at the regulatory level and distinct vagal NC populations already predetermined prior to neural tube delamination.The dorsal-most aspect of the neural tube generates the neural crest (NC), a vertebrate-specific population of cells. Different NC sub-populations arise along the axis, characterised by distinct migratory patterns and developmental potential, generating diverse cell types, including craniofacial bones, septa of the heart, neurons and glia of the peripheral nervous system, and pigment cells of the skin. Multiple genomics approaches, including ATAC-seq and RNA-seq, have elucidated the epigenomic mechanisms that govern (NC) formation and establish their axial level identity . Worin vivo biotinylation of FoxD3 with ChIP-seq showed that FoxD3 initially acts as a pioneer factor for NC specification genes, before switching to a transcriptional repressor function [The same group used genetic labelling in zebrafish combined with FACS isolation of NC populations from wildtype or FoxD3 mutants. These were subjected to RNA-seq, ATAC-seq and H3K27ac ChIP-seq. Combining function .In vitro differentiation of human ESC into cranial NC cells combined with detection of epigenome marks highlighted differentially accessible ATAC-peaks that were overlapping with p300 and H3K27ac/H3K4me histone marks. This identified three putative NC specific enhancers located 1.45 Mbp distant to the chondrogenic TF, Sox9, in a region that is deleted in PRS patients. Transgenic LacZ-reporters confirmed enhancer activity in mouse craniofacial development, and ATAC-seq of hESC-derived cranial NC cells confirmed accessibility during a restricted window of development prior to chondrogenic differentiation.The importance of enhancer discovery and functional characterisation is illustrated by a study looking at the congenital craniofacial disorder, Pierre Robin Syndrome (PRS) . In vitrin vivo, of a region 23 kb upstream of Sox10, a key TF for NC development that is also up-regulated in melanoma initiation.Similarly, ATAC-Seq analysis of multiple zebrafish melanoma tumours identified a developmental enhancer that is also important in cancer . Accessiin vivo. Mutagenesis of TF binding sites in enhancer-reporter plasmids, followed by epigenome editing of endogenous enhancer elements confirms their functional importance [As highlighted in the examples above, ESC differentiation protocols and transgenic reporter systems in Drosophila, zebrafish and mouse provide excellent assays for validation of candidate enhancers and their functional analysis. The avian system is also very useful for this purpose due to the ease with which embryos can be obtained and manipulated. Following identification of differentially accessible candidate regulatory elements, electroporation of enhancer-reporter plasmids ,58 combiportance ,59.in silico footprint analysis revealed the differential coverage for a number of TFs, known to be involved in axis patterning or cell differentiation, such as CDX2, HOX paralogs, PAX3, TWIST2 or LEF1. Correlation of accessible chromatin with expressed genes helped to identify candidate regulatory elements, which were validated in vivo. Electroporation of fluorescent enhancer reporters into early chick embryos demonstrated the restricted activity of enhancers located upstream of TCF15 and MEOX1 genes. Time-lapse imaging could detect the onset of enhancer activation in vivo and the importance of candidate TF motifs was confirmed by mutation analysis. CRISPR-mediated epigenome editing led to loss of gene expression and phenotypic changes confirming the importance of the enhancers for vertebrate axis development [We recently characterised the open chromatin landscape in vertebrate paraxial mesoderm in the chicken embryo . As the elopment .Morphological changes in the skeletal elements of tetrapod limbs are a fascinating model for evolutionary adaptations, and GRNs underlying limb outgrowth and development have been studied extensively in chick and mice ,61. The The heart and associated vessels are crucial for survival, and several studies have examined epigenetic changes driving cell fate transitions during cardiovascular development and regeneration ,64–67. CPostnatally, Isl1 is also expressed in cardiac pacemaker cells (PC) a specialised type of cardiomyocyte located in the sinoatrial node (SAN) and critical to initiate the heartbeat. Comparative ATAC-seq was used to investigate accessible chromatin that governs PC-specific gene expression . FACS ofIn the mouse, cardiac regenerative capacity is limited to neonatal stages. However, in adult zebrafish several tissues can regenerate, and this has been used to determine chromatin accessibility, including in the heart following cryoinjury or ventricular resection injury ,67, or tTransgenesis in zebrafish is a versatile approach and fluorescently labelled endothelial cells (fli1a:egfp) were recently used to discover active enhancers . ATAC-sein vivo models.Here we highlighted the ease with which chromatin accessibility can be characterised using ATAC-seq. Combination of ATAC-seq with other genome wide approaches, in particular transcriptomics and epigenome modifications, enhances its power. The chosen examples illustrate detection of differential chromatin accessibility in a number of dynamic biological systems. We emphasise the importance to validate candidate regions of interest, which is crucial to confirm their functional relevance, but is inevitably more involved when using in silico and thus provide information for further functional testing and validation.The genome-wide capture and analysis of chromatin accessibility by ATAC-seq is highly feasible across many species and tissue types. Differential analyses identify regions with restricted spatio-temporal accessibility. Contingent on sufficient sequencing depth, ATAC-seq can elucidate TF footprints ATAC-seq in combination with transcriptomics and computational analyses can indicate epigenome-transcriptome interactions to elucidate gene regulatory networks (GRN). Isolation of cells by tagging or FACS limits heterogeneity and helps to define discreet sub-populations for analysis.The combined use of multiple genomics techniques, such as ATAC-seq for chromatin accessibility, ChIP-seq for epigenome marks and RNA-seq for gene expression profiling, is a powerful approach to study the regulation of the epigenome during dynamic processes. The approaches can be applied to study normal embryo development or in scenarios where there is an experimental treatment, genetic mutation or disease model.


How human cytomegalovirus (HCMV) infection impacts the transcription of the host genome remains incompletely understood. Here, we examine the global consequences of infection of primary human foreskin fibroblasts (HFFs) on transcription by RNA polymerase I, II, and III over the course of a lytic infection using PRO-Seq. The expected rapid induction of innate immune response genes is observed with specific subsets of genes exhibiting dissimilar expression kinetics. We find minimal effects on Pol II initiation, but increased rates of the release of paused Pol II into productive elongation are detected by 24 h postinfection and pronounced at late times postinfection. Pol I transcription increases during infection and we provide evidence for a potential Pol I elongation control mechanism. Pol III transcription of tRNA genes is dramatically altered, with many induced and some repressed. All effects are partially dependent on viral genome replication, suggesting a link to viral mRNA levels and/or a viral early–late or late gene product. Changes in tRNA transcription are connected to distinct alterations in the chromatin state around tRNA genes, which were probed with high-resolution DFF-ChIP. Additionally, evidence is provided that the Pol III PIC stably contacts an upstream −1 nucleosome. Finally, we compared and contrasted our HCMV data with results from published experiments with HSV-1, EBV, KSHV, and MHV68. We report disparate effects on Pol II transcription and potentially similar effects on Pol III transcription. Eukaryotic nuclear transcription is primarily executed by three distinct RNA polymerases, Pol I, Pol II, and Pol III. Pol I synthesizes abundant ribosomal RNA (rRNA) from clusters of rDNA repeats, accounting for approximately 30–40% of ongoing transcription in the nucleus . Pol II Mammalian dsDNA viruses that replicate in the nucleus rely on host RNA polymerases to execute their gene expression programs. Here, most attention has been directed towards the mechanisms by which viruses co-opt host Pol II to direct the transcription of their protein-coding and long noncoding RNA genes. The human herpesviruses are dsDNA viruses that replicate largely within the host cell nucleus and purpose Pol II for the transcription of the viral genome. Decades of herpesvirus research have revealed principles of regulated Pol II initiation at viral gene promoters, which are frequently similar in structure and composition to host gene promoters, and are engaged by host and viral factors in a coordinated manner to drive a temporal cascade of viral gene expression ,11,12,13Infection by herpesviruses can lead to large-scale alterations of host transcription. Alpha and gamma herpesviruses employ host shut-off mechanisms that favor productive viral gene expression. In the case of herpes simplex virus I (HSV-1), a model member of the alphaherpesvirus subfamily, the major lytic transactivator ICP4 drives a competition between the host and viral promoters for Pol II and its initiation machinery that indiscriminately diminishes transcription initiation at host promoters by late times postinfection . In addiHuman cytomegalovirus (HCMV) is the prototypical member of the betaherpesvirus subfamily. HCMV is a nearly ubiquitous pathogen that infects more than half of the world’s population and persists lifelong in its hosts. Although infection by HCMV is typically subclinical, immunocompromised populations are at risk of unchecked HCMV replication, which can lead to systemic infection and life-threatening disease . MoreovePrimary human foreskin fibroblasts (HFFs) were derived from deidentified, discarded newborn infant foreskins. HFFs were maintained in Minimum Essential Medium  that was supplemented with 5% fetal bovine serum  and 1% penicillin–streptomycin . All infections were performed with contact-inhibited HFF and TB40/E BAC4 virus at an MOI of 3 as previously described . Cells wSpike-in quantitative PRO-Seq data analyzed in this study from TB40/E infected primary HFF were previously published and were prepared exactly as described in . PRO-CapDFF-ChIP data analyzed in this study from TB40/E infected primary HFFs were previously published. Sample processing and library construction were carried out exactly as described by Spector et al. . Data arhttps://github.com/FelixKrueger/TrimGalore/releases/tag/0.6.6 (accessed on 3 March 2019)), strand-specific alignment of paired-end sequences with bowtie v1.2.3 (available online: http://bowtie-bio.sourceforge.net/index.shtml (accessed on 5 July 2019)), UMI-based removal of PCR duplicates with dedup (available online: https://github.com/P-TEFb/dedup (accessed on 14 February 2019)), and subsequent conversion of the deduplicated, aligned data into bedGraphs and bigwigs. Read coverage at each position was normalized by multiplication of spike-in correction factors. Spike-in correction factors were computed based on total reads in the library and reads mapping to the spike-in genome, as previously described [Stranded PRO-Seq data tracks viewable with the UCSC genome browser (human genome assembly hg38) were generated as previously described , in a prescribed , all dethttps://github.com/meierjl/truQuant (accessed on 31 August 2020)). The bedtools coverage program v2.27.1 [https://bedtools.readthedocs.io/en/latest/index.html (accessed on 14 December 2017)) was used to determine the total number of 5′ or 3′ ends in pause region or gene body intervals, respectively. The gene body counts for all infected time points were divided by the count from the uninfected data to calculate the fold change. Genes with their associated fold changes at various infected time points were hierarchically clustered using the pheatmap library in R. Pearson correlation was chosen as the clustering distance metric and the number of clusters was retained at 10. The number of clusters was chosen using the within-cluster sum of squares (wcss) method in Python’s sklearn.cluster package and KMeans library. All genes and their associated fold change profiles were scaled independently using the z-score formula and colored using the pheatmap function in R. Average gene body changes for all clusters were calculated using a Python script by averaging fold changes across time points and plotted as line graphs using MS Excel. Gene sets for specific clusters were plotted independently using the pheatmap function in R.The pause region and gene body intervals of 11,593 genes transcribed in the human genome after 72 hpi HCMV infection were generated from the HFF PRO-Cap dataset using the truQuant program with default settings , and then dividing this number by ratio of the number of 3′ ends in the gene body over the gene body length for each gene (based downstream of the pause region to the annotated CPS). Pause ratios for all genes in each sample were plotted as boxplot in MS excel.https://github.com/P-TEFb/fragMap (accessed on 13 August 2021)).The sum of H3K4me3 read densities across genomic intervals between −500 and +3000 bp from the MaxTSS of all genes in clusters 1, 3, and the repressed set were generated using bedtools coverage program. Read densities were plotted using MS Excel. FragMaps were generated from the HFF uninfected and 48 hpi H3K4me3 fragments of size between 18 bp and 400 bp that were present in the genomic intervals between −/+500 bp from the MaxTSSs of genes in the repressed set using the fragMap program (available online: http://gtrnadb.ucsc.edu/ (accessed on 1 December 2021)). The mature 5′ ends of tRNAs were used for further analysis. In total, 235 tRNA genes out of 429 were used to compare tRNA transcription between uninfected and infected time points. These genes exhibited most initiation from the position upstream of their mature 5′ end. Strand specific transcription of tRNA genes was measured at their 1 bp upstream position using bedtools coverage program [An annotation of 429 tRNA genes was downloaded from the genomic tRNA database (available online:  program . Scatterhttps://github.com/P-TEFb/Heatmap (accessed on 4 March 2022)). The 5′ and 3′ ends of HFF PRO-Seq uninfected and 48 hpi DMSO-treated datasets were generated following the steps mentioned in our metaplots tutorial (available online: https://github.com/P-TEFb/Metaplot-and-Counting-fragments (accessed on 3 March 2022)). Parameters for these heatmaps were strand = yes, order = DESC, avgrows = 1, width = 2, height = 4, black = user-specified , and gamma = 0.5.Heatmaps of −50 bp and +200 bp genomic intervals centered on the mature 5′ ends of 235 tRNA genes were created using the heatmap program ). Input regions were bed files containing 400 bp promoter regions padded around the 150 bp pause region  for genes in clusters 1, 3, 4, and 8. The background file consisted of such promoter regions from all 11,594 active gene promoters. Transcription factor motifs enriched in experimental regions versus the background were determined using the rgt-motifanalysis matching function with script --input-files input/experimental.bed input/Background.bed --organism hg38 --remove-strand-duplicates. Next, the statistical significance of enrichment over background was measured using the rgt-motifanalysis enrichment function, which performed a Fisher’s exact test for each enriched transcription factor binding motif.Motif enrichment was performed using a previously documented motif analysis software   in contact-inhibited HFF and performed spike-in quantitative PRO-Seq. Libraries were prepared from uninfected cells and cells infected for 4, 12, 24, 48, and 72 hpi, which sample the early-stage of lytic infection , a mid-stage of infection close to the onset of viral genome replication (24 hpi), and late-stage  that follows the onset of genome replication. Additional samples in which the uninfected or infected cells were treated for the final hour prior to harvest with Flavopiridol, a P-TEFb inhibitor that blocks Pol II pause-release , were prThe total number of sequenced reads in each sample, numbers of reads mapping to the host, viral, and spike-in (Spodoptera) genomes, percentage of reads mapping to the viral genome, and spike-in normalization factors for each sample are shown in Intriguingly, we found that the trends in the pause region and gene body counts, plotted as a fold change over the uninfected control for each example gene D.These patterns were representative of dozens of other genes induced by infection, many of which are known ISGs. The transcriptional mechanisms that control the induction of ISGs have been intensely investigated, but remain incompletely understood and are complex, involving numerous transcription factors (TFs) operating concertedly and dynamically across time ,52,53. TThe observation that gene body transcription trended upwards during late HCMV infection was unexpected and further explored. The median fold change in the pause region and gene body counts along the infection time course, as well as the median fold change in the number of pause region counts in the presence of Flavo which blocked the release into productive elongation, was plotted A. ClearlNext, we sought to address whether HCMV infection drove changes in the chromatin state of differentially transcribed genes. We recently reported DFF-ChIP for H3K4me3, an epigenetic modification that was enriched on the positioned nucleosomes flanking active Pol II promoters, in uninfected HFFs and HFFs infected for 48 h with HCMV TB40/E. DFF-ChIP utilizes the dsDNA-specific endonuclease DFF to digest chromatin prior to IP . ImportaBy contrast, 86 genes were observed to undergo a significant repression of gene body transcription across consecutive time points of the infection time course. For example, the transcription of the normally active COL1A2 gene was repressed more than five-fold at 48 hpi compared to the uninfected control B. RepresRibosomal RNA is synthesized by Pol I, which transcribes a single 45S precursor RNA that is, subsequently, processed to yield the 18S, 5.8S, and 28S ribosomal RNAs; 45S is encoded within an rDNA repeat that exists as a tandem array found on the short arms of chromosomes 13, 14, 15, 21, and 22. In the present hg38 assembly, eight rDNA repeats are annotated. Due to the remarkable abundance of ribosomal RNA in cells, mature 18S, 5.8S, and 28S rRNAs can contaminate PRO-Seq libraries despite extensive selection for biotinylated nascent transcripts, but we found that thorough washing eliminated most of this contaminating background signal over mature rRNA. To eliminate all background, the analysis of Pol I transcription was restricted to the 45S promoter region and 5′ portion of 45S that is not retained as a mature transcript. Initiation by Pol I occurred at a single sharp start site. Pol I is not thought to undergo promoter-proximal pausing like Pol II. However, unexpectedly, we observed an accumulation of transcripts all starting at the 5′ end of the 45S gene, extending up to about 200 bp downstream. This was reminiscent of Pol II pausing, though the transcripts associated with Pol I were about 2–3 times longer than typically seen for paused Pol II A. Very iWe recently reported DFF-ChIP for TATA-binding protein (TBP) , a core In light of recent reports suggesting that infection by both alpha- and gammaherpesviruses induces tRNA expression ,28,31, wDramatic changes in the transcription of most tRNAs were observed over the course of infection. For example, the transcription of tRNA-Met-CAT-5-1 was induced more than 400-fold at 48 hpi. Major increases in PRO-Seq signal were detected over the TSS and downstream of the mature tRNA 3′ end A, left. We noticed that some tRNAs transcripts had 5′ end signals coinciding with the mature 5′ end of the tRNA . Many trTo view the global effects of HCMV infection on tRNA transcription, scatterplots of read coverage at the 5′ upstream position, representing initiation, were generated comparing the uninfected control to each infection time point C. The daTo view the effects on tRNA transcription on a gene-by-gene basis, heatmaps of tRNA 5′ and 3′ ends were generated spanning a region of −50 to +200 bp relative to the mature 5′ ends E. The geNext, we investigated whether changes in Pol III transcription induced by infection were associated with changes in the chromatin structure around Pol III-transcribed genes, once more drawing upon our recently published DFF-ChIP data for H3K4me3 and TBP . SimilarHere, we investigated the transcriptional effects of lytic HCMV infection in HFFs using PRO-Seq, which profiles nascent transcripts being generated by Pol I, II, and III. Our study provided surprising new insights, revealing, most significantly, that late HCMV infection was associated with a nearly global increase in the rate of the release of Pol II into productive elongation, that infection was seemingly linked to a change in Pol I transcription of the 45S rRNA at the level of elongation, and that, like HSV-1 and MHV68 ,28, HCMVOur PRO-Seq data unexpectedly showed that that levels of Pol II productive elongation were substantially increased at late times postinfection , while levels of initiation appeared to be largely unaffected. This observation is in alignment with the idea that HCMV does not shut off transcription of host genes as is thought to occur in the context of HSV-1, KSHV, and MHV68 infection. The trend was observed at most actively transcribed genes A,B and aOur PRO-Seq data unexpectedly suggested that Pol I transcription of the 45S rRNA is impacted by HCMV infection. Relatively few studies have investigated Pol I initiation and elongation in cells using next-generation sequencing approaches, which is in part related to limitations of existing annotations of rDNA repeats. Our data suggests that there is an enrichment of elongating Pol I at the 5′ end of the 45S gene, which is reminiscent of Pol II pausing, and that late HCMV infection was associated with a decrease in this 5′ pileup and increased levels of Pol I elongation downstream. This effect also appeared to be dependent on viral replication, increased levels of viral mRNA, and/or a viral factor, as treatment with PFA led to its partial reversal. These data suggest that HCMV may drive productive rRNA transcription and perhaps downstream ribosome biogenesis to facilitate robust translation of viral mRNAs in a competitive environment where host mRNAs remain abundant. Additionally, our DFF-ChIP data for TBP sharply demarcated the boundaries of Pol I PICs in the 45S promoter region, and unexpectedly revealed PICs downstream of 45S that drive low levels of transcription initiation, are intimately associated with TTF1 termination sites, and could be involved in insulating tandem rDNA repeats from upstream Pol I transcription.Finally, our PRO-Seq data revealed that HCMV dramatically induced the transcription of tRNAs. Our use of PRO-Seq to address effects on tRNA transcription is novel. Standard RNA-Seq approaches fail to accurately quantify the abundance of mature tRNAs due to extensive base modifications and secondary structure, which are both refractory to reverse transcription. Resultantly, methods that partially overcome these obstacles and enable a quantitative assessment of the levels of both mature tRNAs and pre-tRNAs have been developed ,71,79,80Interestingly, recent reports have suggested that HSV-1 and MHV68 generally lead to a greater increase in levels of pre-tRNAs than mature tRNAs ,28. It iFinally, our DFF-ChIP data provided several new insights regarding the chromatin status of tRNA genes and how it is impacted by infection. We observed that some induced tRNA genes exhibited gains in H3K4me3 at proximal nucleosomes, while tRNAs that were associated with H3K4me3 nucleosomes downstream exhibited a shift in the downstream nucleosomes to a more distal position in association with tRNA induction. The latter may be the result of increased levels of Pol III elongation downstream of the tRNA. Finally, our DFF-ChIP data resolved a striking connection between Pol III PICs and the upstream −1 nucleosome. At induced tRNAs, the profile of H3K4me3 signal appeared to suggest stronger positioning of the −1 nucleosome and increased contact with the Pol III PIC. A caveat to these interpretations is that H3K4me3 is likely substoichiometric with respect to nucleosomes flanking tRNA genes. As such, future studies should explore changes in the tRNA chromatin structure using approaches that are agnostic to the epigenetic status. Overall, our results positioned PRO-Seq and DFF-ChIP as useful tools for investigating the dynamics of tRNA transcription and chromatin.In this study, we showed that HCMV infection elicited major changes in transcription by Pol I, II, and III using PRO-Seq. Our investigation of Pol II transcription unexpectedly revealed a substantial increase in the rates of Pol II pause release at late times postinfection that was reflected at most genes. This is in contrast with the effects of alpha and gammaherpesviruses, which are thought to shut off host Pol II transcription. We showed that Pol I transcription was characterized by an apparent block to elongation at the 5′ end of the 45S gene that diminished during late infection, where increased levels of elongating Pol I were detected downstream. Finally, we reported on massive changes in Pol III transcription of tRNA genes, similar to recent reports on HSV-1 and MHV68 ,28. Hund


The regulation of transcription is a complex process that involves binding of transcription factors (TFs) to specific sequences, recruitment of cofactors and chromatin remodelers, assembly of the pre-initiation complex and recruitment of RNA polymerase II. Increasing evidence suggests that TFs are highly dynamic and interact only transiently with DNA. Single molecule microscopy techniques are powerful approaches for tracking individual TF molecules as they diffuse in the nucleus and interact with DNA. Here we employ multifocus microscopy and highly inclined laminated optical sheet microscopy to track TF dynamics in response to perturbations in labile zinc inside cells. We sought to define whether zinc-dependent TFs sense changes in the labile zinc pool by determining whether their dynamics and DNA binding can be modulated by zinc. We used fluorescently tagged versions of the glucocorticoid receptor (GR), with two C4 zinc finger domains, and CCCTC-binding factor (CTCF), with eleven C2H2 zinc finger domains. We found that GR was largely insensitive to perturbations of zinc, whereas CTCF was significantly affected by zinc depletion and its dwell time was affected by zinc elevation. These results indicate that at least some transcription factors are sensitive to zinc dynamics, revealing a potential new layer of transcriptional regulation. The activities of these TFs are tightly regulated by the cell. TFs are often inactive until they get activated by signals such as post-translational modification  or translocation from the cytosol to the nucleus4. While much of the early work probing TF DNA binding properties was done in vitro, new technology has made it possible to probe these interactions in situ. One such technology is next-generation sequencing. Chromatin immunoprecipitation with sequencing (ChIP-seq) reveals with relatively good accuracy the sequences at which a given TF is bound. Additionally, computational techniques have been developed that use chromatin accessibility data from ATAC-seq to infer TF binding within regions of open chromatin6. However, these techniques have their drawbacks. First and foremost, these techniques only provide snapshots of what a particular TF is doing at a given treatment time. Additionally, most genomics assays are bulk assays that report averages from thousands of cells, which masks cell-to-cell heterogeneity8. Such heterogeneity can be critical to understanding diseases such as cancer.Transcription factors (TFs) regulate gene expression through a tightly organized series of events, ultimately leading to the production of RNA by RNA Polymerase (RNAP). While all transcription requires a core group of general TFs to initiate transcription and guide RNAP to its target, many genes require additional cell-type specific TFs that bind to regions of DNA known as enhancers around the target gene to further stimulate or repress gene expression9 and the genome editing technique CRISPR11. Additionally, the development of robust protein labeling systems, such as HaloTag and its corresponding ligands13, has allowed researchers to expand into the SM field to monitor TFs in live cells. SM microscopy allows for evaluation of diffusion coefficients, search mechanisms, and dwell (binding) times for individual TFs, revealing intracellular and even intercellular differences amongst populations of TFs. This can also be paired with other imaging tools, such as those that study RNA production15, to further interrogate the functional consequences of TF activities.Microscopy, namely single molecule (SM) microscopy, fills the single cell and single TF niche where genomics assays lag. While a previous barrier to studying TF activity was access to and expression of a fluorescent variant of the TF of interest, this has become much easier of late with the advent of overexpression systems such as the PiggyBac transposon system16. Although the precise coordination of the zinc ion (Zn2+) can vary, all of these TFs share the common attribute that the binding of Zn2+ stabilizes the proper fold of the zinc finger domain and thereby enables interaction with DNA17. It is well established that zinc finger TFs require their Zn2+ cofactor to bind DNA, but whether these TFs are sensitive to physiological changes in the labile Zn2+ pool in cells has not been examined. Multiple studies have shown that the labile Zn2+ pool in the cytosol and nucleus of mammalian cells is on the order of hundreds of picomolar23, but that the level of Zn2+ changes with an ever-expanding number of cellular processes. For example, it has been shown that cells experience changes in the labile Zn2+ pool during immune cell activation24, development26, neuronal stimulation27, and the cell cycle19, and that these fluctuations are important for cell physiology. Evaluating whether zinc-binding proteins, such as transcription factors, sense these fluctuations in Zn2+ is an important step in dissecting the mechanism of how Zn2+ fluctuations alter cell physiology.Zinc finger TFs are among the most ubiquitous TF families in human cells, with nearly half (868/1792) of all predicted TFs utilizing zinc fingers to interact with genomic targets2+ pool is the metal-responsive transcription factor (MTF1). MTF1 senses high Zn2+ through its array of six zinc fingers and in the presence of high Zn2+, translocates to the nucleus to regulate metal buffering proteins  and metal transporters. The apparent dissociation constant (KD) for Zn2+ in the full length MTF1 protein was found to be 31 pM28. However, biochemical studies30 have shown that Zn2+ fingers five and six are the most reactive and have the weakest affinity for cobalt (which is often used to measure affinities of Zn2+ binding proteins17), suggesting that these may sense much higher levels of Zn2+. Most TFs that bind Zn2+ have KDs in the hundreds of picomolar range. For example, it’s been shown that the nuclear hormone receptors glucocorticoid receptor (GR) and estrogen receptor (ER) contain two C4 Zn2+ fingers bind Zn2+ with KDs of 316 and 501 pM, respectively31. Despite the fact that these KDs are similar to the concentration of the labile Zn2+ pool, these TFs are generally not thought to be metal responsive. However, this has not been tested in live cells.The only known TF to exhibit sensitivity to the cellular Zn2+ pool. We used fluorescently tagged versions of GR, with two C4 Zn2+ finger domains, and CCCTC-binding factor (CTCF), with eleven C2H2 Zn2+ finger domains, and single molecule fluorescence microscopy to monitor their mobility within live cells. We found that CTCF, but not GR, shows increases in the mean squared displacement and apparent diffusion coefficient when cellular Zn2+ is chelated with Tris(2-pyridylmethyl)amine (TPA), suggesting that CTCF is more dynamic when cellular Zn2+ is low. Both Zn2+ and TPA decreased the dwell times for CTCF. On the other hand, GR was largely unaffected by perturbation of Zn2+. These results suggest that some TFs are sensitive to changes in the labile Zn2+ pool while others are not.In this work, we sought to determine whether zinc finger TFs are susceptible to changes in the labile Zn2+ to bind DNA in vitro, an open question is whether the Zn2+ occupancy and hence DNA binding capacity in cells is dependent on levels of cellular Zn2+. We applied 3D single molecule microscopy to investigate whether zinc finger transcription factors have altered mobility when Zn2+ is perturbed, where mobility is routinely used as a proxy for DNA binding36. Specifically, we used multifocus microscopy (MFM) that allows for simultaneous acquisition of particles in 9 Z-planes separated by approximately 430 nm 37. This enabled us to track labeled transcription factors at rapid acquisition rates (25 Hz) and did not result in truncated trajectories as they diffused along the Z-axis in the nucleus, a common limitation of 2D single molecule tracking.While it is well established that zinc finger transcription factors require Zn34, presumably because GR is more strongly associated with target sites on DNA. U2OS cells expressing HaloTag-GR (stable overexpression) were stained with JF549-HaloTag ligand and treated with 100 nM of either dexamethasone or hydrocortisone for 30 min. Figure 2/s and 0.289 ± 0.019 µm2/s for hydrocortisone and dexamethasone, respectively). These values are the diffusion coefficients for the entire population and most previous studies38 divide the populations into “fast” diffusing and “slow” diffusing populations. Therefore, we cannot directly compare these values to previous literature values. However, our observation that hydrocortisone results in more diffusive GR compared to dexamethasone is consistent with previous literature reports and confirms that our analysis is valid for analyzing TF dynamics. Additionally, we calculated the dwell (residence) times for GR treated with the above hormones. Our rationale was that particles with longer dwell times may correspond to those that are bound to DNA rather than freely diffusing or experiencing short, transient interactions39. We used the calculated MSDs from each trajectory to identify displacements of less than 200 nm that corresponded to bound particles. This 200 nm threshold has previously been used as a conservative estimate of general chromatin movement, as measured using fluorescently-labeled histone H2B34. Additionally, binding events had to last at least 8 frames (320 ms) to be considered bound; this eliminated transient, non-specific interactions that were sometimes observed. We found that hydrocortisone had slightly shorter dwell times than dexamethasone  treated with the known activator dexamethasone or hydrocortisone. Previously it has been shown that dexamethasone is a more potent activator of GR than hydrocortisone and this leads to longer dwell times2+ alters dynamics of candidate TFs, we continued to interrogate GR as it contains two C4 Zn2+ finger domains. Additionally, we selected the CCTCC-binding factor (CTCF), a chromatin binding protein that contains eleven C2H2 Zn2+ finger domains. Both transcription factors have been previously characterized by 2D single molecule microscopy and have DNA dwell times that span an approximate order of magnitude . U2OS cells stably expressing HaloTag-GR or HaloTag-CTCF (CRISPR-edited endogenous expression) were treated with either 50 µM of the Zn2+ chelator TPA to deplete free Zn2+, 30 µM ZnCl2 to increase free Zn2+, or a media-only control for 30 min. Previously, we have shown that these perturbations decrease labile Zn2+ to < 1 pM or increase intracellular labile Zn2+ to 30 nM, respectively40. Under our imaging conditions, we acquired between 493–1080 total tracks for HaloTag-GR and between 5065–12,770 tracks for HaloTag-CTCF . A similar analysis for HaloTag-CTCF revealed that the MSDs upon Zn2+ depletion were significantly greater than either the control or Zn2+ treatment . These results suggest that the dynamics of GR are minimally sensitive to changes in the labile Zn2+ pool, while CTCF is sensitive to Zn2+ depletion.We observed that HaloTag-GR was much more diffusive Fig. a, left t2+, 6.1% larger in high Zn2+) and superdiffusive particles  compared to control conditions  and superdiffusive particles (202% larger compared to control)  and no change for superdiffusive particles.The above analysis gives the diffusion coefficient for the entire population of molecules. However, proteins in cells are more apt to exhibit anomalous diffusion rather than purely Brownian motion, and sub-populations with different diffusion properties have been observed. The sub-populations of molecules with different diffusion behavior can be inferred by using the MSD curves to extract the entclass1pt{minima36 and 70 seconds32 respectively. Therefore, to determine whether the dwell times for GR and CTCF are altered by labile Zn2+, we performed a similar experiment using a Nikon N-STORM microscope in highly-inclined laminated optical sheet (HILO)41 mode with longer acquisition times and substantially less laser intensity. Using HILO we were able to image for 5 min at 10 Hz  and 20–30 min at 2 Hz  rather than the 1–2 min acquisition periods on the MFM. Additionally, these conditions allowed us to bias all detected particles towards bound particles, rather than freely diffusing particles. As such, we calculated the particle dwell time to be equal to As noted in Supplemental Figure 2 resulted in very small changes in dwell times relative to the control  is fit to a power law  acquisition in 3D due to simultaneous acquisition of multiple Z-planes. Thus, MFM is ideally suited for measuring fast TF characteristics such as diffusion coefficients. On the other hand, measurement of dwell times requires much longer measurement windows to observe the dynamics and heterogeneity of binding. HILO imaging is well-suited for such measurements and enables measurement of dwell times and fraction bound. Conveniently, many standard microscopes that are capable of total internal reflection fluorescent (TIRF) microscopy can be modulated to perform HILO imaging.2+ binding proteins, and nearly half (44%) are involved in transcriptional regulation44. While it is widely accepted that these proteins need their essential Zn2+ cofactor to function, very little is known about the metal status of these proteins in vivo, and whether these proteins are sensitive to changes in the labile Zn2+ pool. In mammalian cells, labile Zn2+ concentrations are in the hundreds of picomolar range40. While KD values have only been measured for a small subset of zinc-binding proteins, the majority have KD values in the low pM to nM range17, raising the possibility that some fraction of the zinc-proteome may be only partially saturated with Zn2+. There is increasing evidence that there are fluctuations in the labile Zn2+ pool that can range from low pM to nM during activation of immune cells24, development45, neuronal stimulation27, and the cell cycle19. Whether these fluctuations affect proteins in the zinc-proteome has not been examined.Bioinformatics studies predict that up to 10% of the human genome encodes for Zn2+ dependent TFs sense perturbations in the labile Zn2+ pool. We found that HaloTag-CTCF was sensitive to decreases in the labile Zn2+ pool, where treatment with the Zn2+ chelator TPA led to increased mean squared displacement, an increase in the diffusion coefficient (> 200%), decreased dwell times, and a decrease in the fraction bound compared to control conditions. All of these results suggest that CTCF is more mobile in low Zn2+ conditions. The effect of elevated Zn2+ on CTCF was more subtle: there was a small increase in the diffusion coefficient (9.1%) and a decrease in the dwell time, but no change in the fraction bound. Perturbation of Zn2+ (either increase or decrease) had very little effect on GR; there were small changes in the mean squared displacement and diffusion coefficient (5–20% increase) and a slight decrease in dwell time for both treatments. These results suggest that GR is minimally affected by changes in the labile Zn2+ pool, while CTCF is primarily affected by decreases in Zn2+ and to a lesser extent by increases in Zn2+.In this work, we explored whether two Zn2+ binding to the respective TFs upon Zn2+ perturbation, or could result from global changes in chromatin as a consequence of Zn2+ perturbation that affect CTCF more potently than GR. With respect to direct Zn2+ binding, the number and type of Zn2+ fingers used by these two proteins differ, with CTCF having 11 C2H2 Zn2+ fingers and GR having 2 C4 Zn2+ fingers. While the literature has shown that the mode of Zn2+ binding does not necessarily dictate the relative affinity of Zn2+ for isolated zinc  fingers17, most affinity measurements are carried out on individual zinc fingers and the binding properties could change in the context of the full length protein due to the larger second coordination sphere. CTCF has been shown to utilize different combinations of its Zn2+ fingers to bind distinct DNA sequences, allowing it to bind 80,000 + sites in the human genome46. While there is likely some redundancy in which of CTCF’s Zn2+ fingers are binding to DNA, it is plausible that perturbing the Zn2+ status of even one of these domains may affect the ability of CTCF to adequately bind its genomic targets. It is important to note that the dwell times we observe do not necessarily indicate functional binding events. As noted, CTCF can potentially bind 80,000 + sites in the genome, and GR ChIP-seq experiments have shown 10,000 + potential binding sites across multiple cell types47. However, of the ~ 7000 sites found in A549 (lung adenocarcinoma) cells, only 928 (13.5%) of these sites were shown to truly be glucocorticoid responsive. It is therefore plausible that some of the binding events we observed via single molecule microscopy may be dominated by transient, non-specific interactions that occur when a TF is searching for its target rather than specific, productive binding events. This is where novel quantitative methods and kinetic models for assessing TF dwell time, such as fitting distributions to a power law to account for a continuum of binding affinities42, are needed to deconvolve the complexity of transcriptional regulation. With respect to global changes in chromatin, we cannot rule out the possibility that perturbation of Zn2+ leads to changes in chromatin architecture. Besides TFs, Zn2+ is a required cofactor for histone acetyltransferases, histone deacetylases, histone demethylases, and DNA methyltransferases48. It may be that the sheer number of Zn2+ binding proteins in the nucleus all contribute to changes in chromatin accessibility or compaction that alter the dynamics of TFs. CTCF, as one of the key chromatin architecture proteins, may therefore be more strongly affected by perturbations in Zn2+ than GR, perhaps because of the number of genomic binding sites. Whether the effect of Zn2+ is direct (modulating the Zn2+ occupancy of the TF of interest) or indirect (changing chromatin architecture in way that affects some TFs more than others), the implication of our results is that some TFs are affected by physiological changes in the labile Zn2+ pool and hence their function could be altered during Zn2+ fluctuations.The differences in mobility, diffusion, and dwell time between GR and CTCF could result from changes in Zn2+. While further studies are necessary to determine whether changes in TF dynamics and mobility are correlated with altered function, our results show for the first time that canonical zinc-finger TFs (outside of MTF1) can be sensitive to changes in the labile Zn2+ pool. Given the noted advantages of SM microscopy, it would be valuable to pair these techniques with other fluorescent microscopy techniques to assess the downstream consequences of this sensing. GR, as a canonical TF, could be paired with the incorporation of promoter arrays to more readily assess specific binding34, or it could be coupled with nascent RNA imaging15 to examine the consequences on its target gene products. CTCF, as a regulator of chromatin architecture, could be paired with microscopy techniques that measure chromatin compaction51 to see if this is perturbed with changes in Zn2+. These tools will better allow us to understand precisely how changes in TF dynamics translate into changes in TF function.Our work suggests that a subset of TFs may be sensitive to changes in cellular Zn52.To generate PiggyBac-GR-HaloTag, PB-CMV-MCS-EF1α-Puro (System Bioscience #PB510B-1) was linearized using EcoRI and BamHI. The GR insert was amplified from pk7-GR-GFP (Addgene #15534) to generate overhangs with both PB-CMV-MCS-EF1α-Puro and the HaloTag. The HaloTag insert was amplified from pcDNA3.1-3xFLAG-HaloTag-2xNLS  using the primers listed in the Key Resources table to generate overhangs with GR and PB-CMV-MCS-EF1α-Puro. Linear fragments were then assembled into the final plasmid using a homemade Gibson assembly master mix.32.All cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (ThermoFisher #12800082) containing 10% FBS (SigmaAldrich #F0926)) and 1% penicillin/streptomycin (ThermoFisher #15140–200). All live cell single molecule tracking experiments were carried out in FluoroBrite DMEM (ThermoFisher #A1896701). To generate stable U-2 OS HaloTag-GR and H2B-HaloTag cells cells, wildtype U-2 OS cells (ATCC #HTB-96) were transfected with 1 µg of either PiggyBac-HaloTag GR or PiggyBac-H2B-HaloTag , 250 µg of Super PiggyBac Transposase (System BioSciences #PB200A-1), and 3 µL of TransIt LT1 (Mirus #MIR2305). Stable clones were selected by growing in DMEM containing 0.5 µg/µL puromycin (Sigma-Aldrich #P8833-25MG) for 7 days, after which they were transferred to normal DMEM. Cells expressing endogenous HaloTag-CTCF were a gift from Anders Serj Hansen (MIT)2+, cells were treated with either 30 µM ZnCl2 (Sigma-Aldrich # 39059-100ML-F) or 50 µM Tris(2-pyridylmethyl)amine  for 30 min prior to imaging. For experiments involving HaloTag-GR, Zn2+ perturbations were followed by hormone activation with either 100 nM dexamethasone (Sigma-Aldrich #D4902-100MG) or 100 nM hydrocortisone (Sigma-Aldrich #H4001-1G).To manipulate labile Zn37. Briefly, an epi-fluorescent microscope equipped with a multi-focal diffraction grating (MFG) allows for the collection of 9 aberration-corrected focal planes. Chromatic aberrations are corrected using a separate chromatic correction grating and prism. The MFG allows for a total axial detection depth of approximately 4 µm. For each experimental day, a calibration with TetraSpeck 0.2 um fluorescent beads (ThermoFisher #T7279) was used to determine the precise Z-spacing between each focal plane, with an average ΔZ of 430 nm. Additionally, the calibration allowed for measurement of the point spread function of the microscope, which allowed for image deconvolution (see below). All images were acquired using a 100 × 1.45 NA TIRF objective (Nikon), a 561 nm laser , a Di01-R405/488/561/635 dichroic (Semrock), a FF01-593/40 (Semrock) emission filter, and an iXon3-DU897E EMCCD (Andor Technologies).Three dimensional single particle tracking was performed on the Multifocus Microscope (MFM) at the Janelia Advanced Imaging Center (HHMI)Image acquisition: Cells were labeled with 1 µM of JaneliaFluor (JF) 549 HaloTag ligand (Janelia Research Campus) for 5 min at 37 °C, rinsed three times with Dulbecco's phosphate-buffered saline (D-PBS), and then incubated for 30 min in FluoroBrite DMEM. Image acquisition was performed using a 561 nm laser at typical irradiance of 3-4 kW/cm2, with 40 ms exposure times for an effective frame rate of 25 Hz. Movies, on average, were acquired for 2 min (3000 frames).Post processing: Following acquisition, movies were cropped and laterally registered using a pre-determined affine transformation determined via TetraSpeck bead data (described above) to convert the 3 × 3 image into a 9 Z-plane stack, spanning approximately 3.8 µm in the Z-dimension. To improve signal-to-noise ratio and particle localization, local background was subtracted using a rolling ball correction (radius = 7 px), followed by 5 iterations of the Richardson-Lucy deconvolution algorithm within MATLAB R2019a (Mathworks). Following deconvolution, images were smoothed using a Gaussian filter (radius = 0.7 px) to improve particle detection. Additionally, the first 500 frames of each movie were removed, as these tended to have dense labeling that did not allow for robust tracking.3D particle tracking: Particle trajectories were generated using the MosaicSuite ImageJ plugin53 with the following parameters: radius = 3 px; cutoff = 0.001; threshold = 750; max link range = 1 frame; max displacement = 500 nm; dynamics = Brownian. Additionally, any tracks that did not exist for at least 10 frames (400 ms) were discarded.Diffusion analysis: Diffusion coefficients for HaloTag-GR and HaloTag-CTCF were calculated by first computing the mean squared displacement (MSD) for each trajectory across the entire length of the track. The first 9 displacements (corresponding to the first 10 frames of each trajectory) of all tracks in each condition were subsequently averaged to generate an aggregate MSD curve across the population. This curve was then fit to the equation MSD is the mean squared displacement, Equipment: 2D single particle images were acquired on a Nikon N-STORM imaging system equipped with a Nikon TI-E microscope, a Nikon CFI Apo TIRF 100X oil immersion objective (1.49 NA), a N-STORM 647 nm laser (Agilent), an iXon 897 Ultra EMCCD (Andor Technologies), and a cage incubator (Okolab).Image acquisition: Cells were stained with JaneliaFluor (JF) 646 HaloTag ligand at either 100 pM  for 1 min or 1 nM  for 5 min, rinsed three times with D-PBS, and then incubated for 30 min in FluoroBrite DMEM prior to Zn2+ perturbations. Image acquisition occurred with low laser intensities (5–10%) and 100 ms exposure times. Frame rates were chosen to bias towards only detecting bound particles: for HaloTag-CTCF, effective frame rate = 2 Hz; for HaloTag-GR, effective frame rate = 10 Hz. Because CTCF is known to have long residence times on DNA, these movies were collected on average for 20 min (2400 frames), while movies for GR were typically acquired for 2–5 min.Post processing: Movies were post-processed within Nikon Elements to subtract background using the rolling ball method (radius = 50), and then subjected to 5 iterations of the Richardson-Lucy deconvolution algorithm within Nikon Elements. Following deconvolution, images were smoothed using a Gaussian filter (radius = 0.7 px) to improve particle detection. Additionally, the first 500 frames of each movie were excluded, as these tended to have dense labeling that did not allow for robust tracking.2D particle tracking: Particle trajectories were generated using the MosaicSuite ImageJ plugin with the following parameters: radius = 3 px; cutoff = 0; absolute threshold = 500–1000, depending on the experiment; max link range = 1 frame; max displacement = 300 nm; dynamics = Brownian. Additionally, any tracks that did not exist for at least 5 frames  were excluded from further analysis.Dwell time analysis: Because the effective frame rate of each movie and tracking parameters were biased towards only detecting bound molecules, we inferred that the only particles detected were bound. Therefore, we calculated dwell time as the length of the track divided by the frame rate, and the aggregate dwell times were used to generate dwell time survival curves. These were then fit to a biexponential decay 32. Therefore, we used H2B dwell time data acquired at the same frame rate (10 Hz for GR, 2 Hz for CTCF; Supplemental Figure 42. Briefly, the raw H2B-HaloTag dwell time survival curves were fit to a triple exponential of the form:For fitting the dwell times to a power law, photobleaching was first corrected using H2B-HaloTag as previously describedHere, The survival distribution was then fit to a Power law, Supplementary Information.