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Biomaterials 156 (2018) 56e64

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Biomaterials

journal homepage: www.elsevier .com/locate/biomateria ls

A gapmer aptamer nanobiosensor for real-time monitoring oftranscription and translation in single cells

Shue Wang a, b, Yuan Xiao b, c, Donna D. Zhang d, Pak Kin Wong b, c, e, *

a Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USAb Department of Aerospace and Mechanical Engineering, The University of Arizona, Tucson, AZ 85721, USAc Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802, USAd Department of Pharmacology and Toxicology, The University of Arizona, Tucson, AZ 85721, USAe Department of Mechanical Engineering and Department of Surgery, The Pennsylvania State University, University Park, PA 16802, USA

a r t i c l e i n f o

Article history:Received 7 June 2017Received in revised form23 October 2017Accepted 21 November 2017Available online 24 November 2017

Keywords:NanobiosensorGapmerAptamerLocked nucleic acidGene expression

* Corresponding author. Department of Biomedicalnia State University, University Park, PA 16802, USA.

E-mail address: pak@engr.psu.edu (P.K. Wong).

https://doi.org/10.1016/j.biomaterials.2017.11.0260142-9612/© 2017 Elsevier Ltd. All rights reserved.

a b s t r a c t

Transcription and translation are under tight spatiotemporal regulation among cells to coordinatemulticellular organization. Methods that allow massively parallel detection of gene expression dynamicsat the single cell level are required for elucidating the complex regulatory mechanisms. Here we presenta multiplex nanobiosensor for real-time monitoring of protein and mRNA expression dynamics in livecells based on gapmer aptamers and complementary locked nucleic acid probes. Using the multiplexnanobiosensor, we quantified spatiotemporal dynamics of vascular endothelial growth factor A mRNAand protein expressions in single human endothelial cells during microvascular self-organization. Ourresults revealed distinct gene regulatory processes in the heterogeneous cell subpopulations.

© 2017 Elsevier Ltd. All rights reserved.

1. Introduction

In multicellular processes such as cellular self-organizationduring tissue development and regeneration, cells are subjected tospatiotemporal regulation directed by cell-cell communication andenvironmental stimuli for forming complex tissue architectures [1].Gene expressions in each cell are dynamically regulated throughoutvarious synthesis and degradation pathways [2,3]. The abundancesof mRNA and protein are often poorly correlated due to the diverseregulatory mechanisms, such as transcription factors, cis-regulatoryRNA elements, post-transcriptional modifications, RNA interference,RNA binding proteins, and ubiquitination [4,5]. Effective methodsfor massively parallel detection of transcription and translationdynamics at the single cell level are required for defining cell statesand elucidating the multicellular organization process.

Multiplex detection of mRNA and protein expression in a singlecell can be performed in fixed and isolated cell samples usingcombinations of proximity ligation assays, RNA-seq, digital PCR,

Engineering, The Pennsylva-

RNA in situ hybridization, and immunostaining [6e9]. Features ofcell-cell coordination and dynamic regulatory schemes in multi-cellular processes, however, are inherently lost by study of cells inisolation and fixation. Fluorescent protein tagging systems, such asMS2 and SunTag, are available for dynamic gene expression analysisin a single cell [10,11]. These techniques have been applied for real-time observation of translation of single mRNA molecules in livecells [12e14]. Nevertheless, fluorescent protein tagging systemsrequire genetic modifications, which are often impractical forstudying endogenous molecules. Transfection of multiple reporterconstructs in primary human cells with high yield is also chal-lenging for probing multicellular processes during tissue morpho-genesis and regeneration [15].

To address the need for high-throughput single cell analysis, wedeveloped a nanobiosensor for intracellular detection of mRNAexpression in live cells and tissues [16e18]. The nanobiosensorconsists of locked nucleic acid (LNA) probes and gold nanorods(GNRs). The GNR spontaneously binds to the probes to form a GNR-LNA complex and effectively quenches the fluorophores conjugatedto the LNA probe. The GNR also enables endocytic delivery of LNAprobes into live cells without transfection or microinjection, facil-itating massively parallel detection of gene expression dynamics inmulticellular communities. With a target mRNA molecule, the LNA

S. Wang et al. / Biomaterials 156 (2018) 56e64 57

probe, which is the complementary sequence, is thermodynami-cally displaced from the GNR, allowing the fluorophores to fluo-resce. The reversible binding reaction allows dynamic geneexpression analysis in live cells and tissues. The nanobiosensor hasbeen applied for investigating injury induced response in mousecornea [17], photothermal ablation induced heat shock response inlung tissues [16], dynamic regulation of Notch1-Dll4 signalingduringmicrovascular self-organization [18], the formation of leadercells during collective cell migration [19], and Nrf2 mediated che-moresistance in KRASG12D mouse lung tumor [20]. Nonetheless,multiplex single cell detection of transcriptional and translationaldynamics with high resolution during cellular self-organizationremains a challenging task.

In this study, we report a multiplex nanobiosensor for moni-toring mRNA and protein expression dynamics in live cells simul-taneously by establishing a gapmer aptamer design along withcomplementary LNA probes and GNRs. The gapmer aptamernanobiosensor is optimized for detecting intracellular proteinexpression and distribution with high specificity and stability inlive cells. The multiplex nanobiosensor is applied to monitorvascular endothelial growth factor A (VEGF-A) mRNA and proteinexpressions during microvascular self-organization. The nano-biosensor simultaneously tracks the mRNA and protein expressionsin hundreds of cells for over 20 h, resulting in more than 100,000expression data points along with spatial and morphological in-formation of individual cells in a single experiment. This techniqueenables us to monitor the dynamic VEGF protein and mRNA fromthe subcellular level to the population level. The results revealdiverse mRNA and protein expression patterns in the heteroge-neous subpopulations of endothelial cells, suggesting distinct generegulation mechanisms involved in the self-organization of themulticellular community. The capability of the nanobiosensor formassively parallel mRNA and protein detection in single live cellsprovides a versatile detection method for identifying functional cellsubpopulations and studying the gene regulatory networks inmulticellular processes.

2. Results

2.1. Intracellular protein detection with gapmer aptamer

An intracellular protein nanobiosensor was developed byincorporating molecular aptamers into the GNR-LNA nano-biosensor (Fig.1a). Unlike previous aptamer biosensors [21e23], wedesigned the aptamer sequence with LNA monomers to enableintracellular protein detection with high specificity and stability.Three aptamer probe designs including DNA aptamer, gapmeraptamer, and alternating aptamer were synthesized (Fig. 1b andSupplementary Table 1) [24]. A fluorophore (6-FAM) was conju-gated to the 50 end of each aptamer probe. In this intracellularnanobiosensor, the fluorophore-labeled aptamer is displacedfrom the GNR and fluoresces only with the existence of a targetprotein. The binding affinities of the aptamer probes with GNRwere characterized and optimized for VEGF protein detection(Supplementary Fig. 1a-b). Endocytic internalization of GNRsenabled delivery of the nanobiosensor into cells with high effi-ciency and minimal toxicity for massively parallel detection ofsingle cells and high-resolution imaging (Fig. 1c-d) [25]. Thegapmer aptamer with LNA modification in both ends of thesequence was quenched by the GNR effectively and had the highestbinding affinity to VEGF protein among all aptamer probes.Consistently, the gapmer aptamer resulted in the highest contrastfor high-resolution imaging in live human umbilical vein endo-thelial cells (HUVEC) (Supplementary Fig. 1c). The nanobiosensorwas also capable of detecting autoregulation of VEGF and

thrombin-induced VEGF expression in microvascular structuresself-assembled on basement membrane matrix (Fig. 2 andSupplementary Fig. 2). The increase in VEGF expressionwas in goodagreement with previous VEGF studies [26,27], supporting theapplicability of the aptamer nanobiosensor for intracellular proteindetection. The specificity of the gapmer aptamer was verified byVEGF knockdown with RNA interference (Supplementary Fig. 3).Since the gapmer aptamer design had the highest signal-to-noiseratio, it was utilized for detecting intracellular VEGF protein inthis study.

2.2. Simultaneous detection of mRNA and protein in single live cells

For simultaneous detection of VEGF mRNA and protein, a com-plementary nucleic acid probe sequence was designed and labeledwith a different fluorophore (TEX 615). The nucleic acid probeconsisted of alternating LNA-DNA monomers, which were previ-ously optimized for intracellular mRNA detection [16e18]. Bothfluorophores labeled on the mRNA and protein probes werequenched due to GNR's fluorescence quenching ability. Simulta-neous detection of VEGF mRNA and protein was demonstrated inHUVEC microvascular structures (Fig. 1c and SupplementaryFig. 4a). A housekeeping gene, b-actin mRNA, was also incorpo-rated to verify the uniformity of probe delivery (SupplementaryFig. 4b). The level of b-actin expression was uniform among thecells and maintained a constant level (Supplementary Fig. 5). Un-like transfection of molecular beacons and double-stranded probes[28e30], the nanobiosensor was delivered into the cytoplasmwithout nuclear accumulation (Supplementary Fig. 1c). Dynamicsingle cell analysis of the intracellular distribution and colocaliza-tion of VEGF mRNA and protein could be performed by incorpo-rating high-resolution imaging (Fig. 1d).

2.3. Intracellular imaging in heterogeneous subpopulations withdistinct phenotypes

We demonstrated the capability of the multiplex nanobiosensorfor probing microvascular self-organization over 20 h. This micro-vascular self-organization assay captures the cell migration,sprouting and elongation steps during microvascular development[31e33]. In agreement with previous studies [18,32], heteroge-neous cell subpopulations with distinct morphologies and pheno-types, including aggregating cells, sprouting cells and elongatingcells, were observed in the experiment (Fig. 3a). The aggregatingcells assembled with other cells in the nodes and maintained lowvalues of cell perimeter and area. The sprouting cells, which con-nected to other cells on one end, displayed steady increases in cellperimeter and area. In contrast, the elongating cells, which con-nected to neighboring cells on both ends, displayed rapid increasesin cell perimeter and area during microvascular self-organization.Similar microvascular networks were observed with and withoutthe nanobiosensors (Supplementary Fig. 2). This observation sug-gests the nanobiosensor does not significantly interfere with themicrovascular self-organization process.

We incorporate high-resolution imaging to investigate thespatial distribution and colocalization of VEGFmRNA and protein inthese cell subpopulations during microvascular network formation(Fig. 3b-d). Overall, the gene expression distributions in endothelialcells were non-uniform and displayed distinct signatures amongthe cell subpopulations. Transient “hotspots” of VEGF mRNA andprotein colocalization were observed in cells. In aggregating cells,the colocalization hotspots were observed throughout the experi-ment. This observation was supported quantitatively by analyzingthe pixel values above the threshold (Supplementary Fig. 6). ThePearson's correlation coefficient of aggregating cells maintained a

Fig. 1. A multiplex nanobiosensor for simultaneous detection of mRNA and protein in single cells during multicellular organization. (a) Schematic illustration of themultiplex nanobiosensor. The nucleic acid probes spontaneously bind to the gold nanorods, which quench the fluorophores conjugated on the probes. The gold nanorods facilitateinternalization of the multiplex nanobiosensor for intracellular detection. (b) Three designs of the protein nanobiosensor with DNA aptamer, gapmer aptamer, and alternatingaptamer. LNA monomers are highlighted in red. A fluorophore (6-FAM) is conjugated at the 50 end of the aptamer sequence. (c) Multiplex detection of VEGF mRNA and protein inHUVEC microvascular structures. Scale bar, 200 mm. (d) Dynamic tracking of intracellular VEGF mRNA (red) and protein (green) in a single HUVEC cell. Scale bars, 10 mm. Images arerepresentative of five independent experiments. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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high value (over 0.9) throughout the experiment. Sprouting cellsalso displayed a high level of VEGF mRNA and protein colocaliza-tion initially (correlation coefficient over 0.9). The correlation co-efficient decreased and maintained an intermediate level(correlation coefficient between 0.7 and 0.8) after the first hour ofmicrovascular self-organization (Supplementary Fig. 7). In contrast,elongating cells displayed transient dynamics of colocalizationhotspots in the experiment. Colocalization hotspots were mainlyobserved at the beginning of the experiment. The correlation co-efficient decayed rapidly in the first hour and fluctuated at a lowlevel of mRNA and protein colocalization (correlation coefficientbetween 0.5 and 0.7) compared to sprouting and aggregating cells.These observations suggest the heterogeneous subpopulationshave distinct expression patterns.

2.4. Dynamic gene expression profiling of VEGF mRNA and proteinin single cells

The nanobiosensor allows dynamic monitoring of the VEGFmRNA and protein expression levels at the single cell level. We,

therefore, investigated the VEGF mRNA and protein dynamics inindividual cells during the early stage of microvascular self-organization. In general, the mRNA and protein expression levelsare the results of transcription, mRNA degradation, translation, andprotein degradation. Close examination of mRNA and proteinexpression dynamics reveals diverse gene expression behaviors forthe cell subpopulations. Fig. 4 shows the expression profiles ofrepresentative cells of each subpopulation. For aggregating cells(Fig. 4a, cells 1e3), the levels of VEGF mRNA were approximatelyconstant and the protein levels increased monotonically. Asdemonstrated by a computational model of expression kinetics(Supplementary Fig. 9-10), this behavior is anticipated when thetranslational rate is constant.

Interestingly, transcriptional control was observed in other cellsubpopulations. For instance, sprouting cells displayed a transientincrease in VEGFmRNA and gradually decreased after the first hour(e.g., cells 4e5). The decrease in VEGF mRNA could be a result of areduction of transcription rate or an increase in mRNA degradationrate. A longer duration of VEGFmRNA increasewas also observed insome cells (e.g., cell 6). Despite the heterogeneous expression

Fig. 2. Optimization of aptamer probe designs for intracellular VEGF protein detection in HUVEC microvascular structures. (a) Fluorescence images characterizing VEGFprotein detection with and without VEGF treatment using DNA aptamer, gapmer aptamer and alternating aptamer. Human recombinant VEGF165 (25 ng/ml) were added after cellseeding. Fluorescence images were taken after 6 h of incubation. Scale bar: 200 mm. (b) Comparison of VEGF protein detection using the aptamer probes in HUVEC microvascularstructures. Data are expressed as mean ± s.e.m. (n ¼ 3; **P < 0.01 and ***P < 0.001; unpaired Student's t-test). (c) Thrombin induced VEGF protein expression in HUVEC micro-vascular structures. Bright field and fluorescence images of microvascular structures with and without thrombin (buffer). Human a-thrombin (1 IU/ml) were added immediatelyafter cell seeding. Images were acquired after 6 h of incubation. Fluorescence images illustrating VEGF protein detection using the gapmer aptamer nanobiosensor. Scale bar:200 mm. (d) Comparison of three aptamer probes for VEGF protein detection in microvascular structures with and without thrombin treatment. Data are expressed as mean ± s.e.m.(n ¼ 3; *P < 0.05, **P < 0.01, ***P < 0.001; unpaired Student's t-test).

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dynamics, the VEGF protein expressions generally followed theexpression dynamics of VEGF mRNA for the majority of cells. Adelay of the protein dynamics, which was presumably due to thetime scales of translation and maturation of VEGF protein, wasobserved in the cells. This observation suggests that gene expres-sion dynamics were primarily controlled at the transcription levelin these cells. For elongating cells (cells 7e9), the level of VEGFmRNA increased initially and gradually decreased after a shortduration, similar to sprouting cells. The duration ranged from30 min to over 120 min, resulting in more diverse expressionprofiles for elongating cells. Interestingly, a large variation of VEGFprotein dynamics relative to the VEGF mRNAwas also observed forelongating cells. In particular, a reduction in VEGF mRNA expres-sion level did not always result in a decrease in VEGF protein. Sincethe VEGF protein level is the combined result of translation anddegradation, the results suggested additional mechanisms oftranslational control of VEGF expression, such as modification ofthe VEGF degradation pathway (e.g., proteases), are involved inelongating cells.

2.5. Correlation of mRNA and protein levels at the population scale

The multiplex nanobiosensor is capable of detecting VEGF pro-tein and mRNA expressions in a large number of cells simulta-neously. By developing a custom-design image analysis program,we studied the correlation of mRNA and protein expressions at the

population scale (Fig. 5a and Supplementary Fig. 11). The correla-tion between mRNA and protein expression levels were deter-mined at different time points. The data revealed significantdeviations between mRNA and protein expressions at the begin-ning of the experiment. The correlation coefficients R2 fluctuatedbetween 0.792 and 0.845 in the first hour. The computationalmodel was also applied to predict the correlations between mRNAand protein expressions using the experimental data at 5min as theinitial condition (Fig. 5b). The computational model correctly pre-dicted similar values of correlation coefficients (from 0.767 to0.8621) at the early stage of microvascular self-organization. Wethen studied the correlation between mRNA and protein expres-sions between 1 and 12 h during microvascular self-organizationusing the multiplex nanobiosensor and computational model(Fig. 5). The correlation coefficient increased gradually between 1and 12 h from 0.8330 to 0.9251. In agreement, the computationalmodel predicted an increasing trend of the correlation coefficient.The values increased from 0.8256 to 0.9972. These results collec-tively suggest that initial expression levels as well as the kinetics inprotein translation and maturation had significant effects on thecorrelation between VEGF protein and mRNA, providing a possibleexplanation for the low level of correlation at the beginning of theexperiment. For a time scale compatible with protein expressionand maturation (e.g., 1e12 h), the initial randomness of theexpression levels had a much smaller influence on the correlationbetween mRNA and protein expressions.

Fig. 3. Cell subpopulations displayed distinct morphological patterns and intracellular gene expression patterns during microvascular self-organization. (a) Morphologies ofthree distinctive cell subpopulations observed during microvascular self-organization. (bed) Aggregating cells, sprouting cells and elongating cells displayed diverse expressionlevels and distributions of VEGF mRNA and protein during microvascular self-organization. White dotted lines indicate the cell boundaries. Scale bars, 20 mm. Images are repre-sentative of five independent experiments.

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3. Discussion

In this study, a multiplex nanobiosensor is developed formonitoring intracellular mRNA and protein expression dynamics inlive cells. By incorporating LNA monomers in the aptamersequence, we circumvented the stability issue of aptamers forintracellular protein detection. Using VEGF autoregulation,thrombin stimulation, and siRNA knockdown, the binding affinity,signal-to-noise ratio and stability of the aptamer designs werecharacterized and optimized for intracellular VEGF detection inHUVEC cells. The gapmer aptamer probe with LNA monomers inboth ends of the sequence possessed the best signal-to-noise ratioand performance for intracellular protein detection. This gapmerstrategy can be applied, in principle, when a DNA or RNA aptamer isavailable. Otherwise, affinity-based selection and optimize will berequired to identify an aptamer. By incorporating the gapmeraptamer for protein detection along with an alternating LNA/DNAprobe for mRNA detection, a multiplex nanobiosensor was estab-lished for investigating VEGF expression dynamics. This multiplexnanobiosensor was capable of detecting multiple genes, such asVEGF mRNA, VEGF protein, and b-actin mRNA, in the same cell.We applied the multiplex nanobiosensor to monitor VEGF mRNAand protein expression dynamics during microvascular self-organization. The expression dynamics of VEGF mRNA and

protein at the subcellular, single cell and population levels weremonitored during microvascular self-organization.

Multiplex detection at both transcriptional and translationallevels in live cells has been a challenging task. Despite the recentdevelopment in single cell analysis, there is a lack of effective ap-proaches for simultaneous monitoring of mRNA and protein in thesame cell dynamically [34]. Current methods of single cell analysistypically do not allowmultiplex detection and are often limited to aspecific time point due to the requirement of cell fixation or lysis[6e9]. Fluorescent protein tagging systems represent a powerfulplatform for live cell imaging [10,11]. Real-time imaging of trans-lation on single mRNA transcripts in live cells was demonstrated forstudying intracellular dynamics of protein synthesis, transport, andlocalization [12e14]. Multiplex detection of a large number of cellswith fluorescent protein tagging systems, however, can be hin-dered by the requirement of genetic modifications, the availabilityof high-affinity binding motifs, and the efficiency of transfectingmultiple reporter transcripts in the same cells [15]. These issues areparticularly challenging for delicate primary human cells. On theother hand, our approach provides an effective method for moni-toring the gene expression dynamics during multicellular pro-cesses. The multiplex nanobiosensor allows not only high-resolution imaging of gene expression dynamics but alsomassively parallel detection of mRNA and protein dynamics for a

Fig. 4. Dynamics of mRNA and protein expressions in representative cells during microvascular self-organization. (aec) VEGF mRNA and protein expression dynamics in cellsthat are representative of (a) aggregating cell, (b) sprouting cell, and (c) elongating cell subpopulations. Aggregating cells maintained a constant level of VEGF mRNA and amonotonic increase of VEGF protein. Sprouting cells and elongating cells displayed transient increases in VEGF expressions. Data are representative of over 1200 cells.

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large number of cells. This technology may also open new oppor-tunities for detecting disease biomarkers and characterizing rarecell populations, e.g. cancer stem cells and circulating tumor cells,in clinical samples. Further research will be required to translatethe single cell analysis technology to monitor disease progression,measure therapeutic responses, and predict the clinical outcome.

The multiplex nanobiosensor revealed unexpected gene regu-latory mechanisms in the cell subpopulations during microvascularself-organization. Autonomous organization of angioblasts andvascular progenitors with distinct phenotypic behaviors wasobserved during microvascular development for decades [35].Nevertheless, the molecular processes driving the distinct pheno-types during the self-organization process remained elusive. Weaddressed these fundamental questions by performing dynamicgene expression analysis along with phenotypic characterization ofhuman endothelial cells. As indicated by colocalization hotspotsinside the cells, single cell expression dynamics, and correlationanalysis at the population level, VEGF was highly dynamic duringthe early stage of the microvascular self-organization process. Thecombination of experimental and computational analyses suggestthat VEGF expressions were controlled at multiple levels anddepended on the subpopulations. Aggregating cells maintained arelatively constant level of VEGFmRNA and expressed VEGF proteincontinuously in the early stage of the process. This observation is ingood agreement with the suggested roles of aggregating cells inattracting cells at the centers of the autocrine gradients duringmicrovascular self-organization [33]. On the other hand, sproutingand elongating cells displayed transient expression dynamicscontrolled at both transcriptional and translational levels, as

indicated by single cell expression dynamics and intracellular geneexpression patterns. The single cell dynamic data along with thecomputational model also shed light on the apparent discrepancyof the mRNA and protein expressions at the beginning of the pro-cess. The expression kinetics of transient genes and initialrandomness in the expression level may potentially explain thepoor correlation of mRNA and protein in other studies. Collectively,our results suggest areas of systematic investigation of the mech-anisms of transcriptional and translational control and underscorethe significance of dynamic multiplex detection of single cells forinvestigating complex multicellular processes.

4. Materials and methods

4.1. Multiplex nanobiosensor design

The multiplex nanobiosensor consists of GNR, mRNA probes,and aptamer probes. The mRNA probe is a single-stranded 20-basenucleotide sequence with alternating LNA/DNA monomers. ThemRNA probe was labeled with a fluorophore (TEX615) at the 50 endfor mRNA detection. The design procedure of mRNA probe wasreported previously [36]. Briefly, the LNA probe was designed to becomplementary to a loop region of the target mRNA structure. Thesecondary structures, binding affinity, and specificity were opti-mized using the mFold server and NCBI Basic Local AlignmentSearch Tool (BLAST) database. The aptamers are nucleic acid se-quences identified using an iterative enrichment technique. Oligoswith high affinity and specificity to the target protein or cell areisolated from a large random sequence pool with multiple rounds

Fig. 5. Correlation between mRNA and protein expressions at the population level during microvascular self-organization. (a) Correlation of experimentally measured mRNAand protein expressions at different time points. The mRNA and protein levels were determined by the fluorescence intensity. The intensity values were normalized between 0 and1 for comparison. The correlation coefficients were 0.8446, 0.8125, 0.7916, 0.8424, 0.8212, 0.833, 0.8552, 0.8612, 0.8827, and 0.9251, respectively. (b) The correlation between mRNAand protein levels using the computational model. The initial conditions were acquired from experimental results. The correlation coefficients were 0.8325, 0.8405, 0.8621, 0.8073,0.767, 0.8256, 0.9203, 0.9874, 0.995, and 0.9972 respectively.

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of selection. For instance, Systematic Evolution of Ligands byExponential Enrichment (SELEX) is one of the iterative enrichmentmethods used to identify aptamers [37]. In our study, the aptamersequence (50-30, CAATTGGGCCCGTCCGTATGGTGGGT) for VEGFprotein detection was acquired from the literature [24]. Threeprotein probes, DNA probe, gapmer probe, and alternating probe,were designed by adjusting the number and location of LNAmonomers (Supplementary Table 1). To characterize the uniformityof probe loading, three different probes, VEGF mRNA, VEGF proteinand b-actin mRNA, were designed with different fluorophores(Supplementary Table 2). To minimize fluorescence bleed-throughbetween fluorophores, two fluorophores that are further apart (6-FAM and TEX 615) were utilized to detect VEGF protein andmRNA (Supplementary Table 3). The gapmer aptamer probe waslabeled with a fluorophore (6-FAM) at the 50 end for VEGF proteindetection, the alternating LNA/DNA probe was labeled with a flu-orophore (TEX 615) for VEGF mRNA detection (SupplementaryTable 3). A b-actin mRNA probe was designed as a control. AllLNA probes and corresponding target DNA sequences for calibra-tion were synthesized by Exiqon Inc.

4.2. Preparation of multiplex nanobiosensor

GNRs with 10 nm axial diameter and 67 nm length were ac-quired from Nanopartz, Inc. GNRs were modified with MUTAB with

positive surface charge. All the probes (aptamer probes and mRNAprobes) were prepared in 1� TriseEDTA buffer at a concentration of100 nM. The probes were incubated at 95 �C for 5 min in a waterbath and cooled down to 70 �C over the course of 1 h. GNRs wereincubated with the probes at 70 �C for 30 min and cooled down toroom temperature slowly. The GNR probes were then incubatedwith cells at a concentration of 2 � 1011 particles/ml for 4 h forcellular uptake when the cells reached about 80% confluency.

4.3. Cell culture and reagents

Human umbilical vein endothelial cells (HUVECs, Lonza) werecultured in EBM-2 medium (endothelial growth basal medium,Lonza) and supplemented with 2% fetal bovine serum (FBS), 0.1%human epidermal growth factor, 0.1% R3-insulin-like growthfactor-1, 0.1% ascorbic acid, 0.04% hydrocortisone, 0.4% humanfibroblast growth factor b, 0.1% heparin, and 0.1% gentamicin/amphotericin B. The cells were cultured in an incubator at 37 �Cwith 5% CO [2] with medium change every two days. The cells werewashed using 1� PBS and harvested using 0.25% Trypsin-EDTA(Invitrogen) when the cells became confluent. HUVECs from pas-sage 2e7 were used in the experiments. For siRNA experiments,HUVECs were seeded at a density of 1� 105 cells/mL with a volumeof 2 mL in 6-well plate, and cultured overnight. The cells were thentransfected with 20 nM siRNA from Qiagen (Valencia, CA, USA)

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using the Lipofectamine LTX Reagent (Fisher Scientific), followingthe manufacturer's instructions, and incubated for 48 h. Cells werethen incubated with the multiplex nanobiosensor for endocyticuptake. Cells were imaged after 4 h of incubation.

4.4. In vitro microvascular organization

Growth factor reduced Matrigel (Corning® Matrigel® Matrix)was thawed overnight on ice and added to glass-bottom 24-wellplates (MatTek Corporation). After 30 min incubation at 37 �C forgelation, HUVECs were seeded onto the solidified gel at a density of250 cell/mm2. The 24-well plate was placed in a microscope incu-bator (Okolab) equipped for live-cell imaging. SpatiotemporalmRNA and protein expression dynamics were then monitoredduring microvascular self-organization.

4.5. Imaging and data analysis

For VEGF protein detection, bright-field and fluorescence im-ages were captured using an inverted microscope (Nikon, TE2000-U) with an HQ2 CCD camera (SensiCamQE, Cook Cork.). All fluo-rescence images of endothelial cells were taken with the samesettings with a 1 s exposure time for comparison. For simultaneousdetection of VEGF mRNA and protein expression dynamics, time-lapse microscopy of microvascular self-organization was per-formed using a confocal single molecule detection platform (LeicaTCS SP8) with an interval of 5 min. The Z-stack images were ac-quired with an optical slice thickness of 1 mm and separate expo-sures of two different channels (6-FAM, excitation: 488 nm,emission: 535 nm; TEX 615, excitation: 591 nm, emission: 613 nm).Experiments were repeated independently at least three times.Data collection and imaging analysis were performed using LeicaApplication Suit and Matlab. The mRNA and protein levels werequantified by measuring the fluorescence intensity. A custom-design Matlab image processing program was used to quantifysingle cell intensity at different channels. Briefly, the original im-ages were converted to grayscale intensity images. A flat, disk-shaped structural element was created and morphological opera-tions were applied to remove the background noise. Cell segmen-tation was then performed using an adaptive threshold. Theintensities of each cell at different channels were then calculatedindividually. The scatterplot and histogram of VEGF mRNA andprotein expression were generated accordingly.

4.6. Western blot

Cell lysates were collected in radioimmunoprecipitation buffer(RIPA buffer). Samples were subjected to 12% SDS-PAGE followed bytransfer to PVDF membranes. Blotted membranes were incubatedwith rabbit anti-VEGF primary antibodies (Santa Cruz Bio-technologies, Santa Cruz, CA, USA) in blocking buffer overnight at4 �C, followed by incubationwith alkaline-phosphatase-conjugatedsecondary antibodies (Sigma-Aldrich, St. Louis, MO, USA) for 1 h atroom temperature. The blots were resolved using Western bluestabilized substrate (Promega, Madison, WI, USA).

4.7. Computational model of gene expression dynamics

A computational model was developed to study the dynamics ofmRNA and protein expressions. In this model, the mRNA is syn-thesized at a rate of km and degraded at a rate of dm. The proteinsynthesis process has two steps. ThemRNAs are first translated intoimmature protein at a rate of kp and degraded at a rate of dp. Theimmature protein will then fold and form mature functional pro-tein at a rate of kmp. The mature protein is degraded at a rate of dmp.

The kinetics of the synthesis process of mRNA and protein can bedescribed by the following differential equations (1)e(3).

dmdt

¼ km � dm$½m� (1)

dpdt

¼ kp$½m� � kmp$½p� � dp$½p� (2)

dpmdt

¼ kmp$½p� � dmp$½pm� (3)

Here, dm/dt, dp/dt, and dpm/dt are the rates of change of mRNA[m], immature protein [p], and mature protein [pm]. km, kp and kmp

are the first order transcription rate, translation rate and proteinmaturation rate, respectively. dm, dp, and dmp are the mRNA, imma-ture protein, and mature protein first order degradation rates. ThemRNA degradation rate is inversely to the mRNA half-life tm,dm ¼ ln(2)/tm. The protein degradation rate is the inverse of proteinhalf-life, dp ¼ ln(2)/tp. As indicated in equation (1), the mRNA level[m], depends on the transcription rate and the mRNA half-life. Theprotein level depends on the number of mRNAs, translational rate,and protein half-life. This model can be solved analytically. ThemRNA half-life and protein half-life can be acquired according toprevious literature [38e40]. The transcription rate and translationrate were determined based on the experimental data. First, themRNA and protein levels were quantified by measuring averagefluorescence intensity using the nanobiosensor and calibration. Ameasurement of the relative amount of labeled mRNAs and proteinsover time revealed the time scale of mRNA synthesis and proteinsynthesis. The simulation curves were then fitted by adjusting thetranscription rate km and translation rate kp. All the numbers werenormalized from 0 to 1 for comparison. The reaction rates wereassumed to be constant for studying the effect of transcriptional andtranslational control on the mRNA and protein expression levels.

4.8. Statistical analysis

Data are presented as mean ± s.e.m. Experiments were con-ducted in triplicate, and repeated at least three independent times.Student's t-tests were performed to analyze statistical significancebetween experimental groups. For comparing multiple groups, aone-way analysis of variance and Tukey's post hoc test were used.Statistically significant P values were assigned as follows: *,P < 0.05; **, P < 0.01 or ***, P < 0.001.

Conflicts of interest

The authors declare no competing financial interests.

Acknowledgments

This work was supported by National Institutes of Health Di-rector's New Innovator Award (DP2OD007161).

Appendix A. Supplementary data

Supplementary data related to this article can be found athttps://doi.org/10.1016/j.biomaterials.2017.11.026.

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