DNA Tetrahedra-Lipids Complex Design Optimization for Efficient Blood-Brain Barrier Cellular Uptake

Authors

Grigorii A. Rudakov [1], Chengde Mao[2], Gregory T. Knipp[3], Leopold N. Green[1], Tamara L. Kinzer-Ursem[1]

[1] Weldon School of Biomedical Engineering, College of Engineering, Purdue University, West Lafayette, IN

[2] Department of Chemistry, Purdue University, College of Science, Purdue University, West Lafayette, IN

[3] Department of Industrial and Physical Pharmacy, College of Pharmacy, Purdue University, West Lafayette, IN

Three Sentences Explanation

Delivering things to the brain is hard - a very selective barrier between blood and the brain doesn't let the toxic substances which can kill us but also prevents us from delivering drugs and other useful things. Our team is working on making nanoparticles made of DNA, because they are reliable in size and extremely biocompatible, to turn them into an efficient delivery vehicle. We are adopting the approach described in (8) but applying it to cells from the blood-brain barrier to test if adding lipids to our DNA nanoparticles would enhance potential delivery efficiency to the brain.

Abstract

Overcoming the challenges in treating or studying neurodegenerative disorders is hindered by the limited permeability of the Blood-Brain Barrier to non-specific or large molecules (>2 nm) (1,2). Notably, DNA nanomaterials, particularly DNA tetrahedra, emerge as promising and highly biocompatible molecules, attributed to their design tunability(3), uniform size(4), and positive outcomes in bioapplications(5–7). However, the Blood-Brain Barrier penetration is impeded by the strong proton gradient, reducing DNA tetrahedra uptake due to the high negative charge of the DNA phosphate backbone. To enhance uptake, a promising strategy involves adding cationic lipids to the nanostructures, shielding the negative charge and increasing material lipophilicity. We hypothesize that incorporating cationic lipids will enhance the Blood-Brain Barrier permeability for DNA tetrahedra. Our study replicates the method outlined in (8) but focuses on human brain endothelial cells HBEC-5i, human astrocytes, and human brain vascular pericytes to mimic the actual Blood-Brain Barrier. Additionally, we examine the impact of cationic lipids on DNA tetrahedra uptake using our in vitro human Blood-Brain Barrier model developed by Dr. Gregory Knipp (9). DNA tetrahedra synthesis is already established in our laboratory. We aim to assess nanomaterial stability and concentration in solution and in vitro by measuring Cyanine 3 and Cyanine 5 dyes' fluorescence intensity and Fluorescence Resonance Energy Transfer between them when the tetrahedra are intact. The measurement methodology is established in our laboratory. Our study introduces a novel application of existing materials to target cell lines and a Blood-Brain Barrier model that has not been previously tested, holding significant potential for advancements in bionanotechnology. This research brings us closer to efficient and safe drug delivery and brain probing by evaluating the uptake of the DNA tetrahedra-cationic lipids complex through a sophisticated in vitro human Blood-Brain Barrier model.

Disclaimer: this post is a Work in Progress (WiP).

Patience and feedback

Introduction

Problem Statement

Problem Statement. According to the 2022 WHO position paper, 1 in 3 people will develop a neurological disorder at some point in their life, making neurological disorders the leading cause of disability and the second leading cause of death worldwide. In the USA, Alzheimer’s Disease is one of the leading causes of death with 120,000 cases in 2021(10). Over the last 10 years, the National Institutes of Health (NIH) have more than doubled their investments in Neurodegenerative Disorders research from 4 to 9 billion dollars annually (Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC) by NIH). The number of publications on NDDs research has grown exponentially since the ealry 2000’s(11) (see Figure 1). This data shows that there is a high demand for NDDs treatments, the funding has been growing, and the interest in the research community is significant. The question is - despite all the mentioned factors, why there has been realtively slow progress in NDDs treatment(10)? While being a significant threat to the population’s well-being, treatment of neurological disorders poses a great challenge due to multiple factors(1,2):

Lack of understanding of the causes of NDDs.
Early diagnosis is a challenge due to late symptoms onset.
Lack of accurate models, meaning the inaccuracy of the existing animal models.
Hard to test hypotheses in vivo due to serious Centers for Disease Control and Prevention (CDC) regulations of testing in humans.

Most of the abovementioned reasons arise from the brain targeting issues(12) and extremely low permeability of the Blood-Brain Barrier(13,14).

Figure 1: Number of Publications on NDDs Yearly. Adapted from (11).

BBB plays a crucial role in protecting the brain from potential toxins and foreign substances. It does this by selectively regulating the passage of molecules based on their size, chemical identity, electrical charge, and structure(15,16), making the drug delivery into the brain challenging for the researchers(2).

Drugs and Other Systems to Cross the BBB. The most promising method to deliver the drugs to the brain is by using nanomaterials due to great passive diffusivity and ability to protect drugs of interest to be transported(17). Among the nanomaterials used to transport drugs through BBB, such as polymeric(18) and gold(19) nanoparticles, lipid-based nanomaterials(20), dendrimers(21), and others, DNA nanomaterials pose a great interest because they combine uniformity of sizes(4), biocompatibility(22), stability(23), and ability to be engineered for a particular task(3).

Engineering DNA Nanostructures. Among many DNA-based nanomaterials, Tetrahedral DNA Nanomaterials (TDNs) have been under the most focus. Tetrahedron is the most stable structure among polyhedra(24) and has higher cellular uptake thanks to sharper angles and less negative surface charge(25,26). But despite numerous proof-of-concept studies conducted to prove that TDNs can penetrate the BBB quite efficiently(5–7), no studies of optimizing the TDNs design are present in the literature.

Gap Statement

To summarize both the Introduction, NDDs research and treatment progress is challenged by a lack of understanding of the diseases, inability of early diagnosis, lack of accurate models, and difficulties in testing hypotheses in vivo. Currently, there is no solution to overcome these NDDs research and treatment challenges. But they can be significantly improved by developing a method to deliver compounds into the brain. However, brain delivery is a challenge mainly due to extremely low BBB permeability and high specificity. Among the existing approaches, DD vehicles show the biggest promise in overcoming this challenge. But the limitations of existing DD vehicles do not allow a sufficient solution to brain DD yet. Among the DD vehicles, polymeric NPs, and specifically DNA TDNs, fit the solution requirements the best, but only a few DNA TDNs designs were tested in the literature. The most promising work described in (8) functionalizes the DNA TDNs with cationic lipids and significantly improve their permeability into the cells, but this approach has not been tested on BBB cells. This leads us to the gap directly addressed by this work:

As the most promising solution to the brain DD challenge for NDDs treatment and research, no one tried to optimize the design of DNA TDNs for brain DD through the BBB, and specifically functionalize them with the cationic lipids.

Hypothesis and Aims

We hypothesize that incorporating cationic lipids will enhance the Blood-Brain Barrier permeability for DNA tetrahedra.

Aim 1: Quantify the relationship between the cationic lipids load of DNA tetrahedra nanoparticles and the permeability of the BBB cells.

Aim 2: Quantify the relationship between the cationic lipids load of DNA tetrahedra nanoparticles and the permeability of the BBB in vitro model.

Materials/Methods

Disclaimer. The reader may notice a difference in the depth of detail between the methods. While some methods are already established and validated, the others (especially in vitro) are yet to be worked through. In such cases, the expected methods procedures are described.

DNA Nanostructures Design

Figure 2: DNA Schematic. This figure was made in Biorender.

DNA. The basis of DNA Nanotechnology is the Watson-Crick pairing of DNA bases together based on the number of hydrogen bonds they can form and the morphology of potential connections. For the easiness of the spatial understanding of the matter, the schematic of DNA helix with sizes is given in Figure 2. By designing the DNA strands with partially complementary parts, one can achieve the assembly of custom-shaped DNA materials. Another fundamental concept of synthesis of complex DNA materials
is the utilization of symmetries to assemble the structures from identical motifs (see Figure 3).

Figure 3: DNA Nanomaterials Assembly Schematic. This figure was made in Biorender.

GC Content. Guanine (G) and cytosine (C) make three hydrogen bonds between each other, unlike adenine (A) and thymine (T) which make two. This makes the structures or parts of the structures stronger or weaker depending on GC content (the percentage of G’s and C’s in the sequence). In the designed TDNs, the GC content is increased in the sticky ends and nicks areas to increase the adhesion between the motifs and sequences. Additionally, the GC content is increased in the points of mechanical stress, such as cross-over areas and the motifs vertices, to increase the stability of the structures.

Sticky Ends are free-hanging ssDNA sequences complementary to a similarly hanging ssDNA sequence of another motif. The minimal length of a sticky end is chosen as 4 nt but extended where possible to increase the stability of binding.

Figure 4: DNA Crossover Schematic (top) and 3D Model (bottom).

DNA Crossovers are dsDNA molecules with an even number of strands changing the helices as continuing the direction of the original structure (Figure 4). In this work, DNA crossovers are mainly used to bring together and hold in place the dsDNA helices in d-TDNs edges, the crossover points are chosen for helices to exchange the DNA strands at the closest spatial proximity. We use both Double Antiparallel crossovers with even and odd number of DNA helices half-turns between the crossover points (DAE and DAO), as proven to be the
most stable(27).

Cyanine Dyes DNA Strands. High-Performance Liquid Chromatography (HPLC) pre-purified DNA strands with Cy3 and Cy5 in the middle of the strand or at 5’ or 3’ ends are ordered from IDT, diluted in water, and stored at -20°C. The tubes were covered with aluminum foil to avoid light bleaching.

Sequence Design. The DNA structures are first drawn as schematics with all the design ideas outlined above in mind. To ensure that the structures morphologies are realistic, Tiamat software is used to make a 3D model. The sequences are then assigned and tested with IDT OligoAnalyzer online tool for undesired self-complementarity, hairpins formation, and pairwise complementarity. The Gibbs free energies of unwanted assemblies are kept above -5 kcal/mol, while the desired parts of the structures are kept below -15 kcal/mol where possible. If these criteria are not met, the sequences are redesigned until the assembly energies criteria are met. Additionally, the sequences are kept under 100 nt length if possible to keep the DNA degradation at a low rate.

Naming Convention. The strands’ naming convention goes as follows (see Figure 5):
s-TDNs - SE_LX_Y-Z, where:
X is the length of the edge in terms of full dsDNA turns (10.5 nt or 3.57 nm);
Y is the number of the design;
Z is the number of the strand.
d-TDNs - DE_LX_Y-Z, where:
X is the length of the edge in terms of full dsDNA turns (10.5 nt or 3.57 nm);
Y is the number of the design;
Z is the number of the strand.
Dyes - DY-Z, where:
Y is the number of the design;
Z is the number of the strand.

Figure 5: Designed DNA strands naming schematic. This figure was made in Biorender.

Additionally, the strands may contain the keywords to demonstrate their purpose or modification, such as:
C3 - Cy3 dye;
C5 - Cy5 dye;
p - phosphate group attached to the 3’ end.
For C3 and C5, additional number in front of the keyword (e.g. 5C3) provides the information of where the dye is located (which end), otherwise, it is assumed that the dye is in the middle of the strand.

Experimental Methods

DNA Nanostructures Assembly and Tests

Strands Purity. All DNA strands are ordered from IDT pre-purified with PAGE. Every strand longer than 20 nt is analyzed via dPAGE for purity. The strands are considered pure if the band of interest is the only significant one on the gel.

Slow Annealing. By default, the strands are mixed in desired proportions in 50 nM concentrations in TAE/Mg buffer in 200-1000 μL, warmed up to 95°C and placed in a glass dish with 1 L of water at 95°C in a Styrofoam box to cool down to room temperature over two days (48 hours). Then, the samples are brought to the cold room and left for 30 minutes to reach 4°C. The temperature is monitored with a custom-made Arduino-based system with a thermistor attached directly to the bottom of the glass dish. An example of the temperature decrease can be found in Figure 6. The salt and DNA strands concentrations may be altered, which is specified in the experiment description.

Regular Annealing. By default, the strands are mixed in desired proportions in 200 nM concentrations in TAE/Mg buffer in 200-1000 μL, warmed up to 95°C for 5 minutes, and placed in a heating block at 65°C for 30 minutes, 50°C for 30 minutes, 37°C for 30 minutes, and on a bench for 30 minutes to reach room temperature. Then, the samples are brought to a cold room and left for 30 minutes to reach 4°C if necessary. The salt and DNA strands concentrations may be altered, which is specified in the experiment description.

Quick Annealing. By default, the strands are mixed in desired proportions in 200 nM concentrations in TAE/Mg buffer in 200-1000 μL, warmed up to 95°C for 5 minutes, and placed on a bench for 30 minutes to reach room temperature. Then, the samples are brought to a cold room and left for 30 minutes to reach 4°C if necessary. The salt and DNA strands concentrations may be altered, which is specified in the experiment description.

Figure 6: Slow Annealing Temperature versus Time Plot.

Assembly Efficiency Measurement. First, the strands are added one by one to each other at low DNA concentration (50 nM) and annealed to observe the ladder-like increase in resistance to diffusion through the gel. Then, the desired band is identified based on the literature and mobility decrease due to larger sizes and DNA NMs generally slower mobility due to DNA helices mechanical strength. An example of such gel can be found in Figure 7. Due to the complexity of identifying correctly the composition of higher bands, the formula used to measure the assembly efficiency does not account for the differences of the number of strands in each band (1):

$\eta_a=\frac{I_{TDN}}{I_{lane}}$ , (1)

where ITDN is the intensity of the band of interest,
Ilane is the intensity of the whole lane.

Ligation efficiency and stability in solution are measured based on dPAGE results similarly.

Stability in solution. For the stability tests, we incubate TDNs for 4 hours incubation in the presence of exonucleases I and III or 10% FBS at 37 °C and analyze them with dPAGE.The formula to measure the portion of intact TDNs is similar to (1). The results are verified by comparison with the FRET signals intensity at known concentrations of intact and denatured TDNs.

Figure 7: An Example of DNA Assembly nPAGE. 4% nPAGE at 4°C.

DNA Nanostructures Ligation. Two ligation methods are used in this study: chemical and enzymatic ligations. In chemical ligation, of 50 to 1000 μL of 50 to 1000 nM of 3’ end phosphorylated DNA samples (purchased from IDT) are first annealed in 100 mM 2-(N-morpholino)ethanesulfonic Acid (MES) buffer (pH 6.0) and 5 mM MnCl2 Then EDC HCl is added to 250 mM concentration and the samples are incubated at room temperature for 18 hours. In enzymatic ligation, the DNA oligonucleotides are first phosphorylated by mixing 0.5 to 2 μM of DNA with 33333 U/nmol of DNA of T4 Kinase (New England Biolabs (NEB)) in TAE/Mg and T4 Ligase Buffer (NEB) in 50 to 200 μL and incubated for 1 hour at 37°C. Then, phosphorylated DNA is mixed with other strands and 2∗106 U/nmol of DNA of T4 Ligase (NEB) in TAE/Mg and incubate at room temperature for 18 hours. An example of DNA ligation is shown in Figure 8.

Figure 8: An Example of DNA Ligation and Exonuclease Treatment Results (dPAGE). Enz stands for enzymatic, Chem - for chemical. 20% dPAGE at 55°C.

DNA Exonuclease Treatment. DNA samples of 0.1 to 1 μM concentration are mixed with 33 U/μg of DNA exonuclease III (NEB) in NEBuffer (NEB) and incubated at 37°C for 2 hours. The reaction is stopped by adding Ethylenediaminetetraacetic Acid (EDTA) to 12 mM and incubating at 60°C for 1 hour. An example of DNA exonuclease treatment is shown in Figure 30.

nPAGE. Native PAGE is performed in 3.5 to 20% acrylamide and TAE/Mg buffer. The sample is run for two hours at 80 V in the cold room at 4°C. The results of the gel are analyzed with ImageJ.

dPAGE. Denaturing PAGE is performed in 5 to 20% acrylamide, 8 M urea, and TBE buffer. The sample is run for two hours at 80 V in a water bath at 50°C. The results of the gel are analyzed with ImageJ.

Figure 9: FRET Signal Measurement Validation Experiment Results.

Buffers. TBE buffer is 89 mM tris base, 89 nM boric acid, and 2 mM EDTA. TAE/Mg buffer is 40 mM tris base, 20 mM acetic acid, 2 mM EDTA, and 12.5 mM magnesium acetate. TA/Na buffer is 40 mM tris base, 20 mM acetic acid, and 400 mM sodium acetate.

PAGE Staining. The gels are placed in 60 mL of deionized water with 2 μL of 10 mg/mL ethidium bromide and left on a shaking rack for 15 minutes.

FRET Signal Reading. In this work, we utilized two methods of reading the FRET signal. The first method of direct FRET signal reading (laser method) is performed in Dr. Kevin Webb Laboratory at a custom-made pico laser instrument. The samples (with or without Cy3 and Cy5 dyes) are diluted to 0.1 to 2 μM concentrations and 100 μL of the samples are dropped on a glass slide. The sample is illuminated with a 555 nm picosecond laser and the emission at 569 nm is read at different times after the excitation. The data is averaged over at least 1000 full emission events and the lifetime of the exponentially decaying signal is measured. The second indirect FRET signal measurement method (plate reader method) is performed by exciting Cy3 dye at 555 nm and reading the Cy5 emission at 670 nm in a Synergy H1 Hybrid multi-mode microplate reader of 200 μL of 1 to 1000 nM samples. The data is analyzed in JMP statistical software Section 5.3.2. To validate the plate reader method, alone and equimolar mixes of D 1-1, 1C3, 2, and 2C5 at 1, 10, 100, and 1000 nM in TAE/Mg or 80% formamide were quickly annealed and added in triplicates to a 96-well plate with a transparent flat bottom. The results of the analysis showed competition (presence of both D 1-1C3 and 2C5 strands; the presence of D 1-1C3, 2C5, and 1 or 2 strands; absence of either or both D 1-1C3 and 2C5) to be the most significant factor, together with the presence of D 1-1C3 or 2C5 and DNA concentration to be significant factors, while buffer kind and the presence of D 1-1 and 2 did not affect the reading (Figure 9).

DNA Nanostructures Concentration and Stability Measurements with Cyanine Dyes. The intensities of the TDNs-containing samples (in solution, in vitro, and in vivo) are compared with negative and positive (if possible) controls of known concentration. The concentration is determined by exciting and reading the emission of Cy3 dye, the stability - by measuring the FRET signal. The formula for the percentage of uptaken or stable TDNs (in comparison with the positive control) is (2):

$\eta_s=\frac{I_{TDN}-I_{neg}}{I_{pos}-I_{neg}}$ , (2)
where ITDN is the intensity of the TDNs-loaded signal (Cy3 fluorescence or FRET),
Ineg is the intensity of the negative control sample,
Ipos is the intensity of the positive control sample.

In Vitro Experiments

Obtaining the Cell Lines. HEK293 cell line ispurchased from Sigma Aldrich, HBEC-5i cells are bought from the American Type Culture Collection (ATCC) (Manassas, VA, USA), human astrocytes and human brain vascular pericytes (iPSC) are obtained from ScienCell Research Laboratories (Carlsbad CA, USA).

Cell Staining. TDNs-containing cells are incubated in DAPI, DiO, and/or LDH (concentrations are chosen according to the manufacturer instructions) for 1 hour at 37°C with gentle agitation.

Cellular Uptake Experiments. Full cellular uptake properties protocol can be found here - (28). We seed the cells (HEK293 / human astrocytes) onto a 24-well plate; after 24 hours and washing with Phosphate-Buffered Saline (PBS), we stain the cells with DAPI and DiO, and incubate the cells in TDNs-PBS solution at various concentrations (1 to 1000 nM), for different time intervals (0 to 2 hours) in triplicates. We excite Cy3, Cy5, DAPI, and DiO and measure their respective emission fluorescence signals via a plate reader. Additionally, we measure FRET signal. The pictures are analyzed with ImageJ. The media is then analyzed for the presence of LDH by using LDH Reaction Mix, and OD450nm is measured at a plate reader The intensities are analyzed and compared with the controls (no cells, no TDNs, no cells and TDNs, cells lysate with Tween-20).

BBB In Vitro Model Assembly. The experimental approach was taken from here - (9). We utilize Dr. Knipp’s optimized conditions for the tightest BBB model. First, the cells are seeded on a Tanswell filter covered with poly-L-lysine. After seeding both human astrocytes and human brain vascular pericytes, letting each incubate for 48 hours after the consequential seeding, the media is changed to complete endothelial cells medium and seed the human brain endothelial cells HBEC-5i, allowing them to grow for 9 days before the permeability measurements. Cultures are maintained in complete endothelial cells medium with medium changes every other day following endothelial cell plating.

BBB In Vitro Model Uptake Experiments. The BBB model is incubated with various concentrations of TDNs-PBS (1 to 1000 nM) for multiple time periods (0 to 2 hours) in three replicates each, collect the filtration product, and measure the TDNs concentration and stability. The intensities are analyzed and compared with the controls (no BBB model, no TDNs, no BBB model, and TDNs). The positive control is Cy3-labelled HSA from
NanoCS, and for the negative control - CyDye Near-IR labeled antibodies.

Delivery Efficiency in vitro or in vivo is measured by this formula (3):
$\eta_d=\eta_s*\eta_u*N_l$ , (3)
where ηs is the portion of stable TDNs,
ηu is the portion of uptaken TDNs,
Nl is the number of spots to load the drug and depends on the structure and size of TDNs.

Other Methods. For the cellular uptake and TDNs stability measurements see Section 5.2.

Statistics Approach to the Design of Experiments and Software Analysis Methods

Statistical Software

Figure 10: An Example of ImageJ PAGE Analysis.

Gel Analysis with ImageJ. ImageJ is used to analyze the intensity of the bands in PAGE. The lanes are isolated and plotted, the background signal is substracted, and the integral intensity of each band measured.
Then, the bands are compared to each other (see 1). Figure 10 gives an example of such an analysis.

Statistical Analysis. JMP is used as the main statistical analysis software. ANOVA , pairwise t-tests, Tukey’s tests, and other statistical methods are performed two-way if appropriate, with 95% confidence intervals, and p-values threshold of 0.05.

Preliminary Data

DNA Tetrahedra Assembly

By now we have the DNA TDN structure tested and assembled (Dr. Chengde Mao's design), see Figure 11 below.

Figure 11. A schematic of DNA TDN motif with 4 full DNA helix turns between the vertices (above) and the results of 3.5% nPAGE at 4°C. The bands correlate well with the mobilities described in the literature.

We were also able to achieve ~90% assembly yield of the structure in 500 nM after slow annealing in TA/Na buffer, which is crucial for in vitro tests. See Figure 12 for the data.

Figure 12. 3.5% nPAGE at 4°C of slowly annealed DNA TDN.

Cyanine Dyes Stability and Concentration Measurements

We started with a model of cyanine dyes implements in the DNA oligonucleotides to develop the methodology of measuring the concentration and stability of DNA TDNs. The data presented in Figure 13 proves that the dyes do not interfere with the DNA helix assembly and that we can reliably correlate the fluorescence and FRET signals with the concentration and the stability of the model.

Figure 13. Cyanine dyes containing DNA oligonucleotides schematic (top), the results of introducing the competing D1-2 strand with no Cy5 dye and analyzed by 20% nPAGE at 25 °C (combined UV channel (green) and visible (red)), and the results of statistical analysis of the gel and plate reader data (bottom).

Budget (WiP)

Costs

Most of the money will be spend on purchasing:

Oligos for the DNA TDNs synthesis through IDT;
Consumables for in silico and in vitro experiments, such as gloves, buffer components, tubes, well plates for cell culturing, and etc.

Acknowledgments

Appreciation. I would like to thank Dr. Tamara Kinzer-Ursem for her knowledge, help, and support, Dr. Leopold Green for his active input, help, and support, Dr. Gregory Knipp for his enthusiastic skepticism, advice, knowledge, help, and support, Dr. Chengde Mao for showing me the world of DNA Nanomaterials, knowledge, help, and support. I also would like to send my appreciation to Paula-Marie Ivey and Dr. Kevin Webb for their help and support in developing the FRET signal measurement methodology and data collection and Dr. Karin Ejendal, Eugene Kim, and the other lab members of Dr. Kinzer-Ursem Laboratory for their feedback.

ChatGPT. I would like to also acknowledge the use of ChatGPT in text formatting.

Biorender. Commerical Individual License. Invoice number 7EA5CEBD-0001.

Updates

2024-02-12:

Added Three Sentences Explanation of the project;
Adjusted the references and figures numbers;
Adjusted the language.

2024-06-07:

A major update here - https://www.researchhub.com/post/2559/dna-tetrahedra-lipids-complex-design-optimization-for-efficient-blood-brain-barrier-cellular-uptake-20240603-update .

References:

1. Noble W. Challenges in neurodegeneration research. Front Psychiatry [Internet]. 2010 [cited 2023 Oct 14]; Available from: http://journal.frontiersin.org/article/10.3389/fpsyt.2010.00007/abstract

2. Dokholyan NV, Mohs RC, Bateman RJ. Challenges and progress in research, diagnostics, and therapeutics in Alzheimer’s disease and related dementias. Alzheimers Dement Transl Res Clin Interv [Internet]. 2022 Jan [cited 2023 Jul 29];8(1). Available from: https://onlinelibrary.wiley.com/doi/10.1002/trc2.12330

3. He Y, Ye T, Su M, Zhang C, Ribbe AE, Jiang W, et al. Hierarchical self-assembly of DNA into symmetric supramolecular polyhedra. Nature. 2008 Mar;452(7184):198–201.

4. Liu Z, Tian C, Yu J, Li Y, Jiang W, Mao C. Self-Assembly of Responsive Multilayered DNA Nanocages. J Am Chem Soc. 2015 Feb 11;137(5):1730–3.

5. Chen X, Liu D, Wu Y, Yao H, Xia Q, Yang Y. Investigation of the Transporting Behavior of Framework DNA Nano‐Devices Across the Artificial Blood‐Brain Barrier (BBB). ChemBioChem [Internet]. 2022 Nov 4 [cited 2023 May 29];23(21). Available from: https://onlinelibrary.wiley.com/doi/10.1002/cbic.202200459

6. Kusmierz CD, Callmann CE, Kudruk S, Distler ME, Mirkin CA. Transferrin Aptamers Increase the In Vivo Blood–Brain Barrier Targeting of Protein Spherical Nucleic Acids. Bioconjug Chem. 2022 Oct 19;33(10):1803–10.

7. Langlois NI, Ma KY, Clark HA. Nucleic acid nanostructures for in vivo applications: The influence of morphology on biological fate. Appl Phys Rev. 2023 Mar;10(1):011304.

8. Kansara K, Singh R, Yadav P, Kumar A, Bhatia D. Lipid modification of DNA nanocages enhances cellular uptake, migration, and in vivo uptake [Internet]. Bioengineering; 2023 May [cited 2023 Jul 4]. Available from: http://biorxiv.org/lookup/doi/10.1101/2023.05.06.539685

9. Knipp GT, Lubin KE. Design of experiment based optimization of an in vitro direct contact triculture blood brain barrier model for permeability screening. Pharm Pharmacol Int J. 2021 Jul 28;9(4):143–58.

10. Young AB. Four Decades of Neurodegenerative Disease Research: How Far We Have Come! J Neurosci. 2009 Oct 14;29(41):12722–8.

11. El Masri J, Dankar R, El Masri D, Chanbour H, El Hage S, Salameh P. The Arab Countries’ Contribution to the Research of Neurodegenerative Disorders. Cureus [Internet]. 2021 Aug 31 [cited 2024 Feb 12]; Available from: https://www.cureus.com/articles/69489-the-arab-countries-contribution-to-the-research-of-neurodegenerative-disorders

12. Vashi K, Y. Pathak Y. Challenges in targeting to brain and brain tumors. In: Nanocarriers for Drug-Targeting Brain Tumors [Internet]. Elsevier; 2022 [cited 2023 May 29]. p. 51–68. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780323907736000099

13. Dong X. Current Strategies for Brain Drug Delivery. Theranostics. 2018;8(6):1481–93.

14. Terstappen GC, Meyer AH, Bell RD, Zhang W. Strategies for delivering therapeutics across the blood–brain barrier. Nat Rev Drug Discov. 2021 May;20(5):362–83.

15. Ohta S, Kikuchi E, Ishijima A, Azuma T, Sakuma I, Ito T. Investigating the optimum size of nanoparticles for their delivery into the brain assisted by focused ultrasound-induced blood–brain barrier opening. Sci Rep. 2020 Dec;10(1):18220.

16. Zhao P, Zhang N, An Z. Engineering antibody and protein therapeutics to cross the blood–brain barrier. Antib Ther. 2022 Oct 1;5(4):311–31.

17. Xie J, Shen Z, Anraku Y, Kataoka K, Chen X. Nanomaterial-based blood-brain-barrier (BBB) crossing strategies. Biomaterials. 2019 Dec;224:119491.

18. Annu, Sartaj A, Qamar Z, Md S, Alhakamy NA, Baboota S, et al. An Insight to Brain Targeting Utilizing Polymeric Nanoparticles: Effective Treatment Modalities for Neurological Disorders and Brain Tumor. Front Bioeng Biotechnol. 2022 Feb 3;10:788128.

19. Lasagna-Reeves C, Gonzalez-Romero D, Barria MA, Olmedo I, Clos A, Sadagopa Ramanujam VM, et al. Bioaccumulation and toxicity of gold nanoparticles after repeated administration in mice. Biochem Biophys Res Commun. 2010 Mar;393(4):649–55.

20. Vieira D, Gamarra L. Getting into the brain: liposome-based strategies for effective drug delivery across the blood–brain barrier. Int J Nanomedicine. 2016 Oct;Volume 11:5381–414.

21. Ikawati M. Paraffin embedding for mouse tissue v1 [Internet]. 2018 Mar [cited 2023 Jul 25]. Available from: https://www.protocols.io/view/paraffin-embedding-for-mouse-tissue-nw5dfg6

22. Tam DY, Ho JWT, Chan MS, Lau CH, Chang TJH, Leung HM, et al. Penetrating the Blood–Brain Barrier by Self-Assembled 3D DNA Nanocages as Drug Delivery Vehicles for Brain Cancer Therapy. ACS Appl Mater Interfaces. 2020 Jun 19;acsami.0c02957.

23. Kim KR, Kim HY, Lee YD, Ha JS, Kang JH, Jeong H, et al. Self-assembled mirror DNA nanostructures for tumor-specific delivery of anticancer drugs. J Controlled Release. 2016 Dec;243:121–31.

24. Carter MLJ, Rusling DA, Gurr S, Brown T, Fox KR. Stability of the different arms of a DNA tetrahedron and its interaction with a minor groove ligand. Biophys Chem. 2020 Jan;256:106270.

25. Ding H, Li J, Chen N, Hu X, Yang X, Guo L, et al. DNA Nanostructure-Programmed Like-Charge Attraction at the Cell-Membrane Interface. ACS Cent Sci. 2018 Oct 24;4(10):1344–51.

26. Peng X, Fang S, Ji B, Li M, Song J, Qiu L, et al. DNA Nanostructure-Programmed Cell Entry via Corner Angle-Mediated Molecular Interaction with Membrane Receptors. Nano Lett. 2021 Aug 25;21(16):6946–51.

27. Fu TJ, Seeman NC. DNA double-crossover molecules. Biochemistry. 1993 Apr 1;32(13):3211–20.

28. Kim KR, Kang SJ, Lee AY, Hwang D, Park M, Park H, et al. Highly tumor-specific DNA nanostructures discovered by in vivo screening of a nucleic acid cage library and their applications in tumor-targeted drug delivery. Biomaterials. 2019 Mar;195:1–12.

DNA Tetrahedra-Lipids Complex Design Optimization for Efficient Blood-Brain Barrier Cellular Uptake

Preprint

Preprint

Preprint

Preprint

DNA Tetrahedra-Lipids Complex Design Optimization for Efficient Blood-Brain Barrier Cellular Uptake

Preprint

Preprint