iGEM 2025 — Team afmu-china

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{% block title %}Notebook{% endblock %}
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* [Timeline](#timeline)
* [Achievements](#achievements)

Notebook

Timeline

AFMU China 2025 - Our synthetic biology journey from February to September

## Project Timeline Overview

### February and March: Concept Development

Project initiation, literature review, and establishing research direction for SynNotch-engineered
macrophages.

### April: Input Design

Development of liver-specific antibody targeting SLC17A2 for SynNotch system recognition domain.

### May: Antibody verify and output Design

Verification of antibody specificity and design of P65 and SIRPα shRNA output components.

### June and July: SynNotch system Construction

SynNotch system assembly, iterative optimization.

### August: Construct the Syn-M and verify Dry lab

Construct the final Syn-M plasmid and sequencing data analysis and modeling.

### September: Functional validation and LNP Development

P65 and SIRPαshRNA functional validation. lipid nanoparticle (LNP) vector design for therapeutic delivery.

Achievements

### Key Achievements

* Successfully constructed the input of liver-specific activation
* Validated functional output of P65 and SIRPα shRNA components
* Developed LNP delivery system for potential clinical translation

### February and March: Concept Development

We solidified our project concept and established the core research direction. Our team conducted
comprehensive literature reviews and identified the key challenges in liver cancer immunotherapy.

#### February 15-March 31 - Design SynNotch system and and outline approach

We defined the overall strategy for engineering macrophages using the SynNotch system and outlined the
experimental approach. This phase involved intensive brainstorming sessions, resource planning, and
establishing collaboration protocols with our advisors and partners.

### April: Input Design - Liver-Specific Antibody

During April, we focused on designing the input component of our SynNotch system. We screened liver-specific
antigens and developed software to identify cell membrane-specific antigens.

#### April 6-11 - Screening liver-specific antigens

We screened liver-specific antigens on the https://www.proteinatlas.org/humanproteome/tissue/liver website
and developed software to further screen cell membrane-specific antigens. We searched the literature on
SLC17A2 and compared it with the results of software analysis to verify the specificity of the antigen.

#### April 12-18 - Structuring prediction

We performed TMHMM-2.0 on mouse 17A2: protein transmembrane helix structure prediction and AlphaFold
structure prediction to further clarify whether it has a transmembrane structure and whether it has an ideal
extracellular segment.

#### April 19-May 20 - Preparing antibodies

The specific amino acid sequence was sent to Xi'an Haina Biotechnology Co., Ltd. to prepare Anti-slc17a2
svFV, and the company delivered three strains.

### May: Verify the antibody and determine the output

In May, we designed the output component of our SynNotch system and verified the antibodies delivered by the
company.

#### May 3-18 - Determining the output terminal

We searched the literature and visited clinicians, expert professors of molecular genetics and immunology,
and determined that the output end was P65 and SIRPαshRNA.

#### May 21-30 - Verifying the antibody

We performed immunofluorescence staining of Anti-SLC17A2 scFV on mouse hepatoma cell line Hepa1-6 and mouse
lung cancer cell line LLC after cell climbing. Screening three Anti-slc17a2 scFV strains delivered by Xi'an Haina Biotechnology Co., Ltd., and selecting high affinity and efficient antibodies.

### June and July: Constructed the Syn-M System

In June, we designed and constructed the Syn-M-INPUT Test 1 to determine the input and output, and performed
iterative verification.

In July, we designed and constructed the Syn-M-INPUT Test 2 to determine the input and output, and performed
iterative verification.

#### June 1 -16 - Constructing the antibody SynNotch system

We designed the Syn-M-INPUT Test 1 plasmid system based on specific antibodies and carried out plasmid
synthesis and lentivirus packaging by Xi'an Tsingke Biotechnology Co., Ltd.

#### June 17-31 - Input iteration

The packaged lentivirus was transfected to verify its liver-specific activation ability. In order to improve
the effect, we continue to iterate.

#### July 1-16 - Input iteration

By consulting experts and consulting literature, we plan to replace the Hinge domain of the system from the SynNotch1 extracellular domain sequence to the CD8αhinge extracellular
domain sequence and verify it. We designed the Syn-M-INPUT Test 2 plasmid system based on Anti-SLC17A2
scFV to verify the liver-specific activation ability again. Plasmid synthesis and lentivirus packaging were
made by Xi'an Tsingke Biotechnology Co., Ltd.

#### July 17-30 - Input iteration

The packaged lentivirus was transfected and verified, and the Syn-M-INPUT Test 2 system based on
Anti-SLC17A2 scFV was co-cultured with mCherry-AML12 cells for further verification. Anti-SLC17A2 scFV was co-cultured with mCherry-AML12 cells. After activation, Syn-m was sorted by magnetic beads and sent to Beijing Biomarker Biotechnology Co., Ltd. for transcriptome sequencing.

### August: Construct the Syn-M and dry lab

In August, we construct the final Syn-M plasmid and performed sequencing data analysis and modeling.

#### August 1-17 - Construct the Syn-M and transcriptome sequencing

We identified the final version of the Syn-M plasmid and the plasmid synthesis and lentivirus packaging were made by Xi'an Tsingke Biotechnology Co., Ltd. The packaged
lentivirus was transfected and verified.

In the meanwhile we performed principal component analysis to compare intra-group and inter-group differences between the
experimental group and the control group. Then all the related genes were analyzed by mapping. Then the
enrichment analysis of the cell pathway was carried out. Further analysis of related factors.

#### August 18-30 - Analysis of sequencing results and modeling

We analyzed the transcriptome sequencing data to identify key pathways and genes affected by our SynNotch
system, providing insights for further optimization.

We simulated the liver microenvironment by modeling and constructed a mobile activation model to study whether Syn-M macrophages can move from the vascular area to the cancer area.

### September: functional validation and LNP Construction

We verified its safety through animal experiments and iterated the delivery vector LNP.

#### September 1-14 - P65 and SIRPα functional verification

We co-cultured Syn-M with AML12 cells at a ratio of 1:2 for 24 hours and then performed immunofluorescence
staining of F4/80 and P65. The concentrations of TNAFa, IL-6 and INFNr in the culture medium were detected
by ELISA kit. P65 was overexpressed in Syn-M, and QPCR was used to detect downstream related inflammatory
factors.

We co-cultured Syn-M with AML12 cells at a ratio of 1:2 for 24 hours and then performed immunofluorescence
staining of F4/80 and Sirpα. Syn-M was co-cultured with AML12 for 24 h, and Syn-M was sorted out by magnetic
bead sorting. The sorted Syn-M was co-cultured with CSFE-labeled liver cancer cells hepa1-6 and lung cancer
cells LLC (co-incubation ratio macrophage to tumor ratio is 1:2). After two hours, the phagocytosis ratio
was detected by flow cytometry.

#### September 15-30 - Designing and constructing LNP

We further iterate, design and construct lipid nanoparticle (LNP) by consulting experts and consulting
literature. This work establishes the foundation for future development and potential clinical translation
of our SynNotch-engineered macrophage therapy.

iGEM Project Notebook - AFMU China 2025

SynNotch-Engineered Macrophages for Liver Cancer Immunotherapy

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* [Overview](#overview)
* [Cycle-1](#cycle-1)
* [Cycle-2](#cycle-2)
  + [Screening of Liver-Specific Membrane Protein SLC17A2](#cycle-2-1)
  + [Preparation of Anti-SLC17A2 scFv](#cycle-2-2)
  + [Optimization and functional validation of the Syn-M based on Anti-SLC17A2 scFv](#cycle-2-3)
* [Cycle-3](#cycle-3)
* [Cycle-4](#cycle-4)
* [References](#references)

Engineering

Overview

Our project is centered around the synthetic biology "Design-Build-Test-Learn" (DBTL) cycle strategy, through which we developed a novel Syn-M cell therapy system via multiple rounds of iteration. The entire engineering process was divided into
several key cycles, encompassing the full pipeline from target screening and receptor engineering to the integration of therapeutic functional modules. This fully embodies an engineering mindset featuring modular design, theoretical construction,
closed-loop testing, and continuous optimization. Through this sustained, interlocking feedback loop, we progressively refined the outcomes of each cycle and continuously improved our cell therapy system based on empirical results.

![overview](https://static.igem.wiki/teams/5968/engineering/1.webp)

Figure1. iGEM Engineering Cycle

Cycle-1

Design Concept of Engineered Macrophages for Liver Cancer Therapy

|  |  |
| --- | --- |
| Design | Hepatocellular carcinoma (HCC) is the sixth most common cancer globally and ranks among the top three in mortality, presenting a severe challenge for prevention and treatment. While CAR-T therapy has shown significant efficacy in hematological malignancies, its application in solid tumors like HCC is limited due to poor tumor infiltration. Our goal is to design a more effective immune cell therapy. Given that macrophages are the most abundant immune cells infiltrating the tumor microenvironment (TME), we plan to engineer a CAR-M to address the limitations of CAR-T therapy. |
| Build | Leveraging the characteristic high and specific expression of GPC3 in HCC, we designed a CAR construct linking GPC3 to CD3ζ. We hope to deliver the GPC3-CD3ζ CAR into induced pluripotent stem cells (iPSCs) using a lentiviral vector and subsequently differentiate them into mature macrophages. This approach aims to polarize the macrophages towards an M1 pro-inflammatory phenotype, enhancing their tumor-killing activity (Figure 2). |
| Test | Through discussions with our Principal Investigator (PI) and experts **[[Human Practices]](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%7D%7D)**, we recognized that while CAR-M offers unique advantages in antigen presentation and infiltration capabilities, it still faces challenges such as tumor antigen heterogeneity and the tumor microenvironment. |
| Learn | Based on the limitations identified for CAR-M, our goal for the next iteration is to design a new macrophage strategy targeting tumor antigen heterogeneity and the immunosuppressive TME. |

![Cycle-1](https://static.igem.wiki/teams/5968/engineering/2.webp)

Figure 2. GPC3-CD3ζ-CAR-M Design(Image source: Nat Immunol 2024;25:102–116)

|  |  |
| --- | --- |
| Design | Based on the first iteration results, we identified two key challenges: tumor antigen heterogeneity and the immunosuppressive TME. To address heterogeneity, we recognized the potential of switching from HCC-specific activation to liver microenvironment-specific activation. To counteract the immunosuppressive TME, we can modulate downstream gene expression post-activation. |
| Build | To realize this design concept, we needed a suitable foundational system to host these two components. Through literature review and discussions with our PI **[[Human Practices]](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%7D%7D)**, we introduced the SynNotch system. We aim to use liver-specific (rather than HCC-specific) activation (Input) to address antigen heterogeneity, and subsequently maintain the anti-cancer M1 polarized phenotype (Output) to tackle the immunosuppressive TME. |
| Test | The SynNotch system consists of an extracellular antigen recognition domain, a Notch transmembrane domain, and an intracellular transcription factor (e.g., Gal4-VP64) (Figure 3). Upon recognition of a primary target antigen, it releases the transcription factor into the nucleus, initiating the expression of user-defined downstream genes. |
| Learn | This system allows for the incorporation of desired extracellular antibodies while offering programmable output with complex logic gates. This indicated that the SynNotch system is a crucial tool for achieving liver-specific activation and intracellular gene regulation in macrophages. |

![Cycle-1](https://static.igem.wiki/teams/5968/engineering/3.webp)

Figure 3. Introduction to the SynNotch system (A) and our overall design concept (B).
Image adapted from: Cell. 2016 Feb; Cell, 164(4):780-791.

Cycle-2

INPUT Module-Achieving Liver Microenvironment-Specific Activation

To achieve the preliminary input design, we needed to screen for a protein meeting the following criteria:

| Criteria | Rationale |
| --- | --- |
| Hepatocyte Localization | Hepatocytes are the primary cell type in the liver, essential for achieving liver-specific rather than HCC-specific activation. |
| High Specificity Expression | Ensures specificity and sensitivity of the input signal |
| Membrane Localization | Requirement of the SynNotch system |

The goal was to identify the target protein, obtain its single-chain variable fragment (scFv), and use it to construct the signal input part of the SynNotch receptor. Therefore, we conducted the following phases: (I) Screening of liver-specific
membrane protein SLC17A2; (II) Preparation of Anti-SLC17A2 scFv; (III) Optimization and functional validation of the Syn-M based on Anti-SLC17A2 scFv.

#### (I) Screening of Liver-Specific Membrane Protein SLC17A2

|  |  |
| --- | --- |
| Design | Our target liver microenvironment-specific antigen needed to meet three criteria: high/ exclusive expression in the liver, a hepatocyte marker protein, and membrane localization. Accordingly, we utilized our team's self-developed Software for stepwise screening. |
| Build | First, we categorized all genes into five groups using the Protein Atlas to identify proteins highly expressed in the liver. These proteins were then filtered using our Software based on the criteria of hepatocyte-specific expression and cell membrane localization. |
| Test | Screening with the Protein Atlas yielded 978 liver-highly-expressed proteins (Figure 4). Inputting these into our Software for filtering ultimately identified SLC17A2 as the optimal candidate meeting the overlapping criteria (Figure 5).**[Software](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27software%27%29%7D%7D)** . |
| Learn | After identifying SLC17A2 with its high liver-specific expression, and considering our foundational SynNotch system, we needed to further investigate whether this protein has a transmembrane structure and possesses an ideal extracellular domain for antibody development. |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/4.webp)

Figure 4. Distribution of All Genes Across Five Transcript Categories

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/5.webp)

Figure 5. Murine SLC17A2 Sequence(Resource from：
https://www.proteinatlas.org/humanproteome/tissue/liver)

|  |  |
| --- | --- |
| Design | Based on the requirements from the first iteration, we needed to predict the transmembrane and extracellular domain structures of SLC17A2. |
| Build | For this purpose, we performed TMHMM-2.0 prediction for transmembrane helix structure and AlphaFold structure prediction for murine SLC17A2. **[Model](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%7D%7D)** |
| Test | TMHMM-2.0 prediction indicated that murine SLC17A2 has 10 transmembrane helices (Figure 6). AlphaFold structure prediction revealed it possesses an ideal extracellular structure, with amino acids 48-84 being relatively conserved, representing an ideal region for antibody development (Figure 7). |
| Learn | Combining software screening and structural predictions, we identified SLC17A2 as a suitable target for constructing the corresponding SynNotch extracellular domain to achieve liver microenvironment-specific activation. |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/6.webp)

Figure 6. TMHMM-2.0 Prediction Results for Murine SLC17A2

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/7.webp)

Figure 7. AlphaFold 3D Structure Prediction of Murine SLC17A2
The red box highlights the extracellular region (amino acids 48-84).

#### (II) Preparation of Anti-SLC17A2 scFv

|  |  |
| --- | --- |
| Design | Based on the prior screening phase identifying the liver-highly-specific target protein SLC17A2, we planned to prepare its single-chain variable fragment (scFv) as the extracellular domain of the SynNotch system to achieve liver-specific activation of macrophages. |
| Build | Initially, we planned to generate high-affinity scFvs against SLC17A2 using genetic engineering techniques. However, the process of scFv development involves multiple technical challenges requiring advanced experimental skills and extensive experience, which exceeded the technical capabilities of an undergraduate team within the project timeframe. To ensure antibody quality and project timeline, we decided to outsource the preliminary scFv preparation work to a professional biotechnology company. This company possesses mature scFv molecular construction and optimization platforms, enabling efficient production and optimization of antibodies, providing our project with high-affinity scFv materials meeting experimental expectations. |
| Test | We provided the 48-84 amino acid sequence to Xi'an Haina Biotechnology Co., Ltd. for the preparation of Anti-SLC17A2 scFv. The company delivered three antibody clones (No.1, No.2, No.3). We sequenced these antibodies and tested their efficacy (Figures 8, 9). Immunofluorescence results indicated that Clone No.3 performed significantly better than the other two. |
| Learn | Building upon the assistance of the professional biotechnology company, we further confirmed the optimal efficacy of Clone No.3 antibody, successfully obtaining the most suitable scFv for the SynNotch system extracellular domain. **[Parts](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27parts%27%29%7D%7D)** |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/8.webp)

Figure 8. Sequencing results of the three Anti-SLC17A2 scFv clones.
(The red parts are antigen complementary determining region (CDR).)

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/9.webp)

Figure 9. Immunofluorescence staining with Anti-SLC17A2 scFvs on mouse liver cancer cell line Hepa1-6 and mouse lung cancer cell line LLC after cell climbing.
(A, B and C correspond to Anti-SLC17A2 scFvs No.1, No.2 and No.3 delivered by Xi'an Haina Biotechnology Co., Ltd., all conjugated with Cy-3.)

#### (III) Optimization and functional validation of the Syn-M based on Anti-SLC17A2 scFv

|  |  |
| --- | --- |
| Design | The SynNotch system consists of a ligand-binding domain (LBD), extracellular domain (ECD), transmembrane domain (TMD), juxtamembrane domain (JMD), and transcription factor (TF). Utilizing the acquired Anti-SLC17A2 scFv, we aimed to modify the corresponding domains of the SynNotch system. |
| Build | We designed the Syn-M-INPUT Test 1 system by employing the Anti-SLC17A2 scFv as the Ligand-Binding Domain (LBD), GV as the intracellular Transcription Factor (TF), and retaining the Notch 1 structure for the remaining domains (Figure 10). |
| Test | When designing the Syn-M-INPUT Test 1 system based on the mouse Notch1 human equivalent, we observed high ligand-independent signaling (i.e., basal activation) in the original design, meaning partial self-activation occurred even in the absence of the SLC17A2 signal on hepatocytes (Figure 11). |
| Learn | Encouraged by these results, we considered replacing the retained Notch1 structural components to improve signal activation efficiency and receptor sensitivity. |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/10.webp)

Figure 10. Design diagram of the Syn-M-INPUT Test 1 System.
(A: Design diagram of the Syn-M-INPUT Test 1 System; B: Structural design diagram of the plasmid used in the Syn-M-INPUT Test 1 system; C: Lentivirus packaging and cell infection schematic diagram)

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/11.webp)

Figure 11. Basal activation of the Syn-M-INPUT Test 1 System in the absence of hepatocyte SLC17A2 signal.

|  |  |
| --- | --- |
| Design | Through a literature review, we decided to adopt a systematic modular engineering approach to modify the extracellular domain (ECD) of the existing Syn-M-INPUT Test 1 system, aiming to reduce its basal self-activation level. |
| Build | We replaced the Notch1 extracellular region sequence with the CD8α hinge extracellular sequence, while retaining the mouse Notch1 transmembrane domain and cleavage site (Figure 12). Concurrently, to validate the specificity of the optimized system, we performed immunofluorescence and other experiments on the Syn-M-INPUT Test 2 system. |
| Test | The engineered SynNotch receptor (Syn-M-INPUT Test 2 system) was 810 bp shorter than the original full-length design (Syn-M-INPUT Test 1 system). Experimental results showed that the system did not self-activate in the absence of the hepatocyte SLC17A2 signal. Upon receiving the protein signal, the system displayed strong fluorescence, indicating its specific activation capability (Figure 13). |
| Learn | We replaced the ECD module component with one demonstrating superior performance (the optimized CD8α hinge). This optimized CD8α hinge SynNotch receptor was selected for subsequent development and chassis cell engineering due to its efficient, high-fidelity activation characteristics and compact sequence length. |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/12.webp)

Figure 12. Design diagram of the Syn-M-INPUT Test 2 System.

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/13.webp)

Figure 13. Fluorescence Microscopy Detection after Co-culture of the Anti-SLC17A2 scFv-based SynNotch-test-Version2 System with mCherry-AML12 Cells
(Macrophage: AML12 cell ratio = 1:2, co-culture time = 24h).

Cycle-3

OUTPUT Module-Constructing a P65-SIRPα shRNA Bicistronic System

|  |  |
| --- | --- |
| Design | To address the other major challenge-the immunosuppressive tumor microenvironment-we needed to regulate intracellular gene expression in macrophages to promote the expression of pro-inflammatory and anti-cancer related genes. |
| Build | Literature review indicated that P65 (RelA) is a key anti-cancer pro-inflammatory target (Figure 14). Therefore, we selected it as the regulatory target for macrophage gene expression. Upon activation in the liver environment, the GV domain from the SynNotch intracellular segment is cleaved and enters the nucleus, enhancing P65 expression. This action, via the NF-κB pathway, induces the secretion of inflammatory factors like TNF-α and IL-1β, potentially reversing immunosuppressive M2 macrophages to an anti-tumor M1 phenotype, recruiting CD8+ T cells, enhancing anti-tumor immunity, and improving the immune microenvironment. |
| Test | Through communication with our PI **[[HP]](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%7D%7D)**, we learned that the SIRPα-CD47 pathway is a crucial axis for tumor cell immune evasion (Figure 15). Tumor cells express CD47, which binds to SIRPα on macrophages, transmitting a "don't eat me" signal, contributing to the formation of an immunosuppressive TME. |
| Learn | Therefore, we considered dual regulation of both SIRPα and P65 in macrophages, aiming to upregulate anti-tumor gene expression and downregulate pro-tumor gene expression, achieving a synergistic anti-tumor effect. |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/14.webp)

Figure 14. P65/NF-κB Pathway((Image source: immunopharmacology.2021;101,Pt B:108255.))

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/15.webp)

Figure 15. SIRPα-CD47 Pathway((Image source: J Hepatol. 2022 Dec;77(6):1598-1606.))

|  |  |
| --- | --- |
| Design | To achieve simultaneous upregulation of anti-tumor genes and downregulation of pro-tumor genes, we identified that the silencing and degradation effect of shRNA could indirectly inhibit SIRPα expression. Accordingly, after GV enters the nucleus and synchronously activates the expression of both P65 and SIRPα shRNA, upregulation of P65 and downregulation of SIRPα can be achieved. |
| Build | Since mRNA and shRNA are transcribed by RNA Polymerase II (Pol II) and Pol III respectively, we designed a bicistronic system. The SIRPα shRNA was flanked by 5' and 3' miRNA flanking sequences (Figure 16), enabling it to utilize Pol II transcription linkage sites, thus allowing synchronous transcription of P65 and SIRPα shRNA. |
| Test | To verify the regulation of P65 and SIRPα gene expression, we performed immunofluorescence detection on Syn-M cells. Results showed that compared to the control group, the Syn-M group exhibited upregulated P65 expression and downregulated SIRPα expression (Figures 17, 18). |
| Learn | Through theoretical design and a series of validation experiments, we preliminarily achieved the goal of synergistic anti-tumor action via upregulation of anti-tumor and downregulation of pro-tumor signals. Our final design schematic is as follows: the INPUT module achieves liver-specific activation based on Anti-SLC17A2 scFv, and the OUTPUT module simultaneously expresses P65 and SIRPα shRNA to function synergistically against the immunosuppressive microenvironment. We named this system "Synergy" (also be referred to as Syn-M when describing experimental results). Admittedly, subsequent demonstration of the in vivo anti-tumor efficacy of Synergy is also crucial. |

![Cycle-3](https://static.igem.wiki/teams/5968/engineering/16.webp)

Figure 16. Design diagram of Synergy.
(A: Design diagram of Synergy; B: Structural design diagram of the plasmid used in Synergy)

![Cycle-3](https://static.igem.wiki/teams/5968/engineering/17.webp)

Figure 17. Immunofluorescence staining for F4/80 and P65 after co-culture of Syn-M with AML12 cells on coverslips
(Macrophage : AML12 cell ratio = 1:2, co-culture time 24h).

![Cycle-3](https://static.igem.wiki/teams/5968/engineering/18.webp)

Figure 18. Immunofluorescence staining for F480 and SIRPα after co-culture of Syn-M with AML12 cells on coverslips
(Macrophage : AML12 cell ratio = 1:2, co-culture time 24h).

Cycle-4

Delivery Method Optimization

|  |  |
| --- | --- |
| Design | We recognized that autologous macrophage-based therapies, involving ex vivo modification and reinfusion, have several limitations, including high production costs, stringent cell storage and transportation requirements, and limited sources of recipient-autologous macrophages. We needed to consider how to optimize the delivery method to better address these limitations and lower the treatment threshold. |
| Build | Through literature research and expert consultation **[[HP]](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27human-practices%27%29%7D%7D)**, we found that Lipid Nanoparticle (LNP) delivery systems offer advantages such as excellent biocompatibility, high modifiability, and simplified storage and transportation conditions, which could compensate for several shortcomings of the autologous macrophage reinfusion strategy. Based on this, we planned to utilize LNP as a delivery vehicle to encapsulate the Syn-M related plasmids, leveraging LNP for efficient and stable in vivo delivery and gene editing. |
| Test | We utilized the ionizable property of DLin-MC3-DMA and the hepatocellular carcinoma-associated macrophage targeting capability of DSPE-PEG-M2pep (Figure 19) to enhance the editing efficacy of the LNP. The LNP contains components for the in situ editing of tumor-associated macrophages (TAMs) into Syn-M, fulfilling our design objective. Furthermore, using techniques like transmission electron microscopy (TEM), we verified that the liposomes were spherical, of appropriate size (~85 nm), with uniform particle size distribution, and essentially electrically neutral (Figure 20), meeting the preliminary design requirements. |
| Learn | Our current design for Synergy@LNP is at the initial stage, and we plan to conduct subsequent experimental validation after the competition. |

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/20.webp)

Figure 19. Design diagram of LNP for in situ editing of TAMs into Syn-M at the liver cancer Site.
(Image adapted from: https://www.malvernpanalytical.com.cn/industries/biologics/lipid-nanoparticles.)

![Cycle-2](https://static.igem.wiki/teams/5968/engineering/21.webp)

Figure 20. A: Size, PDI, Zeta Potential (A) and representative transmission electron microscopy image (B) of Syn-M@LNP.

References

[1]Pan K, Farrukh H, Chittepu VCSR, Xu H, Pan CX, Zhu Z. CAR race to cancer immunotherapy: from CAR T, CAR NK to CAR macrophage therapy. J Exp Clin Cancer Res. 2022;41(1):119.

[2]Lei A, Yu H, Lu S, et al. A second-generation M1-polarized CAR macrophage with antitumor efficacy. Nat Immunol. 2024;25(1):102-116.
[3]Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human
proteome. Science. 2015;347(6220):1260419.
[4]Karlsson M, Zhang C, Méar L, et al. A single-cell type transcriptomics map of human tissues. Sci Adv. 2021;7(31):eabh2169.
[5]Song L, Zhang Q, Sui H, Gao C, Jiang Z. Recent advances
in synthetic Notch receptors for biomedical application. Am J Physiol Cell Physiol. 2025;328(5):C1473-C1486.
[6]Morsut L, Roybal KT, Xiong X, et al. Engineering Customized Cell Sensing and Response Behaviors Using Synthetic Notch
Receptors. Cell. 2016;164(4):780-791.
[7]Togawa N, Juge N, Miyaji T, Hiasa M, Omote H, Moriyama Y. Wide expression of type I Na+-phosphate cotransporter 3 (NPT3/SLC17A2), a membrane potential-driven organic anion transporter. Am J
Physiol Cell Physiol. 2015;309(2):C71-C80.
[8]Zhu I, Liu R, Garcia JM, et al. Modular design of synthetic receptors for programmed gene regulation in cell therapies. Cell. 2022;185(8):1431-1443.e16.
[9]曹硕,孙许涛,吴思雨,毛彩云,宋运佳.RelA/p65磷酸化：驱动癌症进展的关键修饰[J].生命的化学,2025,45(03):381-393.

{% endblock %}


{% extends "layout.html" %} {% block title %}Results{% endblock %} {% block page\_content %}

* [Overview](#overview)
* [INPUT Module](#input-module)
  + [Screening of Liver-Specific Membrane Protein SLC17A2](#input-1)
  + [Preparation of Anti-SLC17A2 scFv](#input-2)
  + [Optimization and Functional Validation of the Syn-M based on Anti-SLC17A2 scFv](#input-3)
* [OUTPUT Module](#output-module)
  + [Transcriptomic analysis](#output-1)
  + [Infiltration kinetics analysis](#output-2)
  + [Functional validation of P65-related effects](#output-3)
  + [Functional validation of SIRPα-related effects.](#output-4)
* [Delivery Method Optimization](#delivery-method-optimization)

Results

Overview

Current therapies for liver cancer have poor efficacy. In recent years, the emerging CAR-T cell therapy has demonstrated remarkable success in treating hematological malignancies, but its limited ability to infiltrate solid tumors results in suboptimal
performance in liver cancer treatment. To address the issue of poor infiltration, our team initially considered adopting CAR-M cell therapy, leveraging the macrophage's natural capacity to infiltrate solid tumors. However, CAR-M therapy still
faces two major challenges: antigen heterogeneity in liver cancer and the immunosuppressive tumor microenvironment. After brainstorming and consulting relevant experts, the team decided to introduce the Syn-Notch system into the macrophage
chassis to overcome these challenges. The SynNotch system consists of an extracellular antigen recognition domain, a Notch transmembrane domain, and an intracellular transcription factor (e.g., Gal4-VP64) (Figure 1A). Upon recognition of a
primary target antigen (INPUT), it releases the transcription factor into the nucleus, initiating the expression of user-defined downstream genes (OUTPUT). This system allows for the incorporation of desired extracellular antibodies while
offering programmable output with complex logic gates.

Therefore, our overall design aim to use liver-specific (rather than liver cancer-specific) activation (Input) to address antigen heterogeneity, and subsequently maintain the anti-cancer M1 polarized phenotype (Output) to tackle the immunosuppressive
tumor microenvironment (Figure 1B). In the following sections, we will present our project design and experimental results in a progressively detailed manner.

![results-1](https://static.igem.wiki/teams/5968/results/1.webp)

Figure 1. Introduction to the SynNotch system (A) and our overall design concept (B).

Image adapted from: Cell. 2016 Feb; Cell, 164(4):780-791.

INPUT Module-Achieving Liver Microenvironment-Specific Activation

To achieve the preliminary input design, we needed to screen for a protein meeting the following criteria:

| Criteria | Rationale |
| --- | --- |
| Hepatocyte Localization | Hepatocytes are the primary cell type in the liver, essential for achieving liver-specific rather than hepatocellular carcinoma -specific activation. |
| High Specificity Expression | Ensures specificity and sensitivity of the input signal. |
| Membrane Localization | Requirement of the SynNotch system. |

The goal was to identify the target protein, obtain its single-chain variable fragment (scFv), and use it to construct the signal INPUT module of our SynNotch system.

Therefore, we conducted the following phases: (I) Screening of liver-specific membrane protein SLC17A2; (II) Preparation of Anti-SLC17A2 scFv; (III) Optimization and functional validation of the Syn-M based on Anti-SLC17A2 scFv.

### Screening of Liver-Specific Membrane Protein SLC17A2

#### 1. Protein screening

##### Background:

We need to screen for liver-specific proteins that meet the three criteria: high/exclusive expression in the liver, a hepatocyte marker protein, and membrane localization. This is the cornerstone for constructing the signal INPUT module of our
SynNotch system.

##### Results:

We categorized all genes into five groups using the Protein Atlas to identify proteins highly expressed in the liver. Subsequently, we filtered these proteins using our in-house software based on the criteria of hepatocyte-specific expression
and cell membrane localization (for more details, please refer to our **[Software page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27software%27%29%7D%7D)**).

#### 2. Identify the extracellular domain of the candidate protein

##### Background:

After identifying SLC17A2 with its high liver-specific expression, and considering our foundational SynNotch system, we needed to further investigate whether this protein has a transmembrane structure and possesses an ideal extracellular domain
for antibody development.

##### Results:

For this purpose, we performed TMHMM-2.0 prediction for transmembrane helix structure and AlphaFold structure prediction for murine SLC17A2. TMHMM-2.0 prediction indicated that murine SLC17A2 has 10 transmembrane helices. AlphaFold structure prediction
revealed it possesses an ideal extracellular structure, with amino acids 48-84 being relatively conserved, representing an ideal region for antibody development (for more details, please refer to our**[Model page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%7D%7D)**).

##### Conclusion:

Combining software screening and structural predictions, we identified SLC17A2 as a suitable target for constructing the corresponding SynNotch extracellular domain to achieve liver microenvironment-specific activation.

### Preparation of Anti-SLC17A2 scFv

##### Background:

Based on the prior screening phase identifying the liver-highly-specific target protein SLC17A2, we planned to prepare its single-chain variable fragment (scFv) as the extracellular domain of the SynNotch system to achieve liver-specific activation
of macrophages.

##### Results:

Initially, we planned to generate high-affinity scFvs against SLC17A2 using genetic engineering techniques. However, the process of scFv development involves multiple technical challenges requiring advanced experimental skills and extensive experience,
which exceeded the technical capabilities of an undergraduate team within the project timeframe. To ensure antibody quality and project timeline, we decided to outsource the preliminary scFv preparation work to a professional biotechnology
company.

We provided the 48-84 amino acid sequence to Xi'an Haina Biotechnology Co., Ltd. for the preparation of Anti-SLC17A2 scFv. The company delivered three antibody clones (No.1, No.2, No.3). We sequenced these antibodies and tested their efficacy
(Figures 2). Immunofluorescence results indicated that Clone No.3 performed significantly better than the other two (Figures 3).

![result-2](https://static.igem.wiki/teams/5968/results/2.webp)

Figure 2. Sequencing results of the three Anti-SLC17A2 scFv clones. (The red parts are antigen complementary determining region (CDR).)

![result-3](https://static.igem.wiki/teams/5968/results/3.webp)

Figure 3. Immunofluorescence staining with Anti-SLC17A2 scFvs on mouse liver cancer cell line Hepa1-6 and mouse lung cancer cell line LLC after cell climbing. (A, B and C correspond to Anti-SLC17A2 scFvs No.1, No.2 and No.3 delivered by Xi'an
Haina Biotechnology Co., Ltd., all conjugated with Cy-3.)

##### Conclusion:

Building upon the assistance of the professional biotechnology company, we further confirmed the optimal efficacy of Clone No.3 antibody, successfully obtaining the most suitable scFv for the SynNotch system extracellular domain.

### Optimization and Functional Validation of the Syn-M based on Anti-SLC17A2 scFv

#### 1. Syn-M-INPUT Test 1 System

##### Background:

The SynNotch system consists of a ligand-binding domain (LBD), extracellular domain (ECD), transmembrane domain (TMD), juxtamembrane domain (JMD), and transcription factor (TF). Using the acquired Anti-SLC17A2 scFv, we designed the Syn-M-INPUT
Test 1 system to verify whether the desired liver-specific activation could be achieved. In this system, the SLC17A2 scFv serves as the ligand-binding domain (LBD), GV as the intracellular transcription factor (TF), and the remaining domains
retain the Notch1 structure (Figure 4).

##### Results:

To verify the feasibility of the Syn-M-INPUT Test 1 system, we performed immunofluorescence experiments. Fluorescence microscopy results showed that this system partially self-activated even in the absence of the SLC17A2 signal from hepatocyte
surfaces (Figure 5).

![result-4](https://static.igem.wiki/teams/5968/results/4.webp)

Figure 4. Design diagram of the Syn-M-INPUT Test 1 System. (A: Design diagram of the Syn-M-INPUT Test 1 System; B: Structural design diagram of the plasmid used in the Syn-M-INPUT Test 1 system; C: Lentivirus packaging and cell infection schematic
diagram)

![result-5](https://static.igem.wiki/teams/5968/results/5.webp)

Figure 5. Basal activation of the Syn-M-INPUT Test 1 System in the absence of hepatocyte SLC17A2 signal.

##### Conclusion:

Syn-M-INPUT Test 1 System did not meet the design goal of liver-specific activation and required further optimization.

#### 2. Syn-M-INPUT Test 2 System

##### Background:

Through a literature review, we decided to adopt a systematic modular engineering approach to modify the extracellular domain (ECD) of the existing Syn-M-INPUT Test 1 system, aiming to reduce its basal self-activation level.

##### Results:

We replaced the extracellular region sequence of Notch1 with the extracellular sequence of the CD8α hinge, while retaining the transmembrane domain and cleavage site of mouse Notch1, to design the Syn-M-INPUT Test 2 system (Figure 6). The optimized
SynNotch receptor (Syn-M-INPUT Test 2 system) is 810 base pairs shorter than the original full-length design (Syn-M-INPUT Test 1 system). To verify the specificity of the optimized system, we performed immunofluorescence and flow cytometry
assays on the Syn-M-INPUT Test 2 system.

![result-6](https://static.igem.wiki/teams/5968/results/6.webp)

Figure 6. Design diagram of the Syn-M-INPUT Test 2 System.

Both immunofluorescence and flow cytometry results indicated that the Syn-M-INPUT Test 2 system did not self-activate in the absence of the SLC17A2 signal from hepatocyte surfaces. Upon receiving the correct protein signal, the Syn-M-INPUT Test
2 system exhibited a strong fluorescence signal, indicating that it could be normally and specifically activated, thus meeting the design goal (Figure 7).

![result-7](https://static.igem.wiki/teams/5968/results/7.webp)

Figure 7. Fluorescence microscopy detection and flow cytometry assays after co-culture of the Syn-M-INPUT Test 2 System with mCherry-AML12 Cells. (A: Schematic diagram of the experimental workflow; B: Fluorescence microscope images of each group; C: Flow
cytometry assays of each group. Ctrl refers to unedited wild-type macrophages. Syn-M-INPUT Test 2: AML12 cell ratio = 1:2, co-culture time = 24h.)

##### Conclusion:

We replaced the ECD module component with a higher-performing element (the optimized CD8α hinge). Due to its efficient, high-fidelity activation characteristics and compact sequence length, this optimized CD8α hinge SynNotch receptor was selected
for subsequent development and engineering of the macrophage chassis.

OUTPUT Module-Constructing a P65-SIRPα shRNA Bicistronic System

In the previous section, through multiple rounds of iteration and optimization, we successfully achieved liver-specific activation of Syn-M-INPUT Test 2 system, thereby addressing the challenge of hepatocellular carcinoma antigen heterogeneity.
To address the other major challenge, the immunosuppressive tumor microenvironment, we needed to carefully design the OUTPUT signal following Syn-M-INPUT Test 2 system's liver-specific activation.

Literature review indicated that P65 is a key anti-cancer pro-inflammatory target, and the SIRPα-CD47 pathway is a crucial axis for tumor cell immune evasion. Therefore, we plan to design the output module to simultaneously overexpress P65 and
SIRPα shRNA, thereby achieving upregulation of P65 and downregulation of SIRPα. Since mRNA and shRNA are transcribed by RNA Polymerase II (Pol II) and Pol III respectively, we designed a bicistronic system. The SIRPα shRNA was flanked by 5'
and 3' miRNA flanking sequences, enabling it to utilize Pol II transcription linkage sites, thus allowing synchronous transcription of P65 and SIRPα shRNA.

Our final design schematic is as follows (Figure 8): the INPUT module achieves liver-specific activation based on Anti-SLC17A2 scFv, and the OUTPUT module simultaneously expresses P65 and SIRPα shRNA to function synergistically against the immunosuppressive
microenvironment. We named this system "Synergy " (also be referred to as Syn-M when describing experimental results).

![result-8](https://static.igem.wiki/teams/5968/results/8.webp)

Figure 8. Design diagram of Synergy. (A: Design diagram of Synergy; B: Structural design diagram of the plasmid used in Synergy; C: Lentivirus packaging and cell infection schematic diagram)

Therefore, after constructing Synergy based on the above design, we carried out the following phases to validate its functions: (I) Transcriptomic analysis; (II) Infiltration kinetics analysis; (III) Functional validation of P65-related effects;
(IV) Functional validation of SIRPα-related effects.

### Transcriptomic analysis

##### Background:

We co-cultured Synergy with AML12, a normal mouse liver cell line, to activate it, and then commissioned Beijing Biomarker Biotechnology Co., Ltd. to perform transcriptome sequencing to analyze the differences between it and unedited macrophages.

##### Results:

Pathway enrichment analysis indicated that inflammatory-related pathways such as TNF, TGF, and NFκB in activated Synergy showed significant changes. Further analysis of relevant factors revealed that the levels of factors associated with pro-inflammation,
phagocytosis, T cell activation, and neutrophil regulation were all significantly elevated in activated Synergy (for more details, please refer to our **[Model page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%7D%7D)**).

##### Conclusion:

Compared to unedited macrophages, activated Synergy exhibits distinct pro-inflammatory and anti-tumor properties.

### Infiltration kinetics analysis

##### Background:

We aim to determine whether the liver cancer infiltration capacity of activated Synergy differs from that of unedited macrophages. However, such a complex experiment is clearly difficult to complete in vitro within a short timeframe. Therefore,
we performed a full-process kinetic simulation of macrophage infiltration into liver cancer.

##### Results:

Using a multi-field coupled grid model and a gradient-based migration algorithm, we achieved dynamic simulation of macrophage behavior within the liver cancer microenvironment. The simulation results show that macrophages can migrate from vascular
regions toward tumor areas along chemotactic gradients. Upon SynNotch activation, the immediate behavioral switch (P65↑, SIRPα↓) altered their migration trajectories, validating the feasibility of the engineered design (for more details, please
refer to our**[Model page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%7D%7D)**).

##### Conclusion:

Compared to unedited macrophages, activated Synergy exhibits superior liver cancer infiltration capability, which may be attributed to its sustained maintenance of the pro-inflammatory, anti-tumor M1 phenotype.

### Functional validation of P65-related effects

##### Background:

We co-cultured Synergy with the normal mouse hepatocyte cell line AML12 to activate it, followed by a series of experiments, including immunofluorescence, ELISA, and qRT-PCR, to verify whether the P65-related functions aligned with our intended
design.

##### Results:

By co-staining the macrophage marker F4/80 and P65 via immunofluorescence, Figure 9 shows a significantly enhanced red fluorescence intensity in activated Syn-M, confirming the overexpression of the P65 gene in this system. ELISA kits were used
to measure the concentrations of TNF-α, IL-6, and IFN-γ in the cell supernatant. The results indicated that, compared to unedited macrophages, the levels of TNF-α, IL-6, and IL-1β were all elevated in the Syn-M supernatant (Figure 10). After
co-culturing Syn-M with AML12 for 24 hours, F4/80-positive macrophages were isolated using magnetic beads, and qRT-PCR was performed to assess the expression of P65-related factors. The results showed significantly increased expression of
inflammatory cytokines, including iNOS, IL-12, TNF-α, and IL-1β (Figure 11).

![result-9](https://static.igem.wiki/teams/5968/results/9.webp)

Figure 9. Immunofluorescence staining for F4/80 and P65 after co-culture of Syn-M with AML12 cells. (A: Schematic diagram of the experimental workflow; B: Fluorescence microscope images of each group, Ctrl refers to unedited wild-type macrophages. Syne-M:
AML12 cell ratio = 1:2, co-culture time = 24h.)

![result-10](https://static.igem.wiki/teams/5968/results/10.webp)

Figure 10. ELISA kit detection of TNF-α, IL-6, and IFN-γ concentrations after co-culture of Syn-M with AML12 cells. (A: Schematic diagram of the experimental workflow; B: ELISA kit detection of TNF-α, IL-6, and IFN-γ concentrations of each group, Ctrl
refers to unedited wild-type macrophages. Syn-M: AML12 cell ratio = 1:2, co-culture time = 24h.)

![result-11](https://static.igem.wiki/teams/5968/results/11.webp)

Figure 11. qRT-PCR detection of expression levels of cytokines after co-culture of Syn-M with AML12 cells. (A: Schematic diagram of the experimental workflow; B: qRT-PCR detection of expression levels of cytokines of each group, Ctrl refers to unedited
wild-type macrophages. Syn-M: AML12 cell ratio = 1:2, co-culture time = 24h.)

### Conclusion:

These findings demonstrate that activated Syn-M not only effectively overexpresses P65 but also simultaneously enhances the expression of downstream inflammatory factors of P65, thereby boosting its pro-inflammatory and anti-tumor capabilities.

### Functional validation of SIRPα-related effects.

##### Background:

We co-cultured Synergy with the normal mouse hepatocyte cell line AML12 to activate it, followed by a series of experiments, including immunofluorescence, and phagocytosis assay, to verify whether the SIRPα-related functions aligned with our intended
design.

##### Results:

By co-staining the macrophage marker F4/80 and SIRPα via immunofluorescence, Figure 12 shows a significantly reduced red fluorescence intensity in activated Syn-M, confirming the block expression of the SIRPα gene in this system. Figures 13 and
14 show the tumor cell phagocytosis capability of activated Syn-M. The flow cytometry results indicate that, compared to unedited macrophages, activated Syn-M can not only engulf more liver cancer cells (Hepa1-6) but also more lung cancer
cells (LLC).

![result-12](https://static.igem.wiki/teams/5968/results/12.webp)

Figure 12. Immunofluorescence staining for F4/80 and SIRPα after co-culture of Syn-M with AML12 cells. (A: Schematic diagram of the experimental workflow; B: Fluorescence microscope images of each group, Ctrl refers to unedited wild-type macrophages.
Syne-M: AML12 cell ratio = 1:2, co-culture time = 24h.)

![result-13](https://static.igem.wiki/teams/5968/results/13.webp)

Figure 13. After Syn-M was co-cultured with AML12 cells (Syne-M: AML12 cell ratio = 1:2, co-culture time = 24h), Syn-M was sorted by magnetic beads and co-cultured with CSFE-labeled hepatoma cells hepa1-6 (Syne-M: hepa1-6 cell ratio = 1:2, co-culture
time = 2h), followed by flow cytometry analysis.

![result-14](https://static.igem.wiki/teams/5968/results/14.webp)

Figure 14. After Syn-M was co-cultured with AML12 cells (Syne-M: AML12 cell ratio = 1:2, co-culture time = 24h), Syn-M was sorted by magnetic beads and co-cultured with CSFE-labeled lung cancer cell LLC (Syne-M: hepa1-6 cell ratio = 1:2, co-culture time
= 2h), followed by flow cytometry analysis.

##### Conclusion:

These findings indicate that activated Syn-M exhibits enhanced phagocytic capacity against tumor cells, and this enhancement is not restricted by different tumor antigens. In other words, tumors in the liver—whether heterogeneous hepatocellular
carcinomas or metastatic cancers from other origins (e.g., lung cancer liver metastases)—can all be phagocytosed by Syn-M.

Delivery Method Optimization

##### Background:

We recognized that autologous macrophage-based therapies, involving ex vivo modification and reinfusion, have several limitations, including high production costs, stringent cell storage and transportation requirements, and limited sources of
recipient-autologous macrophages. We needed to consider how to optimize the delivery method to better address these limitations and lower the treatment threshold. we found that Lipid Nanoparticle (LNP) delivery systems offer advantages such
as excellent biocompatibility, high modifiability, and simplified storage and transportation conditions, which could compensate for several shortcomings of the autologous macrophage reinfusion strategy. Based on this, we planned to utilize
LNP as a delivery vehicle to encapsulate the Syn-M related plasmids, leveraging LNP for efficient and stable in vivo delivery and gene editing.

##### Results:

We utilized the ionizable property of DLin-MC3-DMA and the hepatocellular carcinoma-associated macrophage targeting capability of DSPE-PEG-M2pep (Figure 15) to enhance the editing efficacy of the LNP. The LNP contains components for the in situ
editing of tumor-associated macrophages (TAMs) into Syn-M, fulfilling our design objective. Furthermore, using techniques like transmission electron microscopy (TEM), we verified that the liposomes were spherical, of appropriate size (~85
nm), with uniform particle size distribution, and essentially electrically neutral (Figure 16), meeting the preliminary design requirements.

![result-15](https://static.igem.wiki/teams/5968/results/15.webp)

Figure 15. Design diagram of LNP for in situ editing of TAMs into Syn-M at the liver cancer Site. (Image adapted from: https://www.malvernpanalytical.com.cn/industries/biologics/lipid-nanoparticles.)

![result-16](https://static.igem.wiki/teams/5968/results/16.webp)

Figure 16. Size, PDI, Zeta Potential (A) and representative transmission electron microscopy image (B) of Syn-M@LNP.

##### Conclusion:

Our current design for Syn-M@LNP is at the initial stage, and we plan to conduct subsequent experimental validation after the competition.

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