Meta-Analysis of Immune Rejection Outcomes After Hypoimmunogenic Gene Editing in Laboratory-Grown Tissues
Advisor: Monica Isquith
The global shortage of transplantable organs continues to constrain the treatment of organ failure, prompting advances in laboratory-grown tissues through regenerative medicine. Immune rejection remains a significant obstacle to the long-term clinical application of engineered tissues derived from pluripotent stem cells. Hypoimmunogenic gene editing has emerged as a promising approach to reduce immune recognition by altering antigen presentation pathways and introducing immune-evasive molecules. Although many preclinical studies report decreased immune cell infiltration and enhanced graft survival following these modifications, results are heterogeneous across tissue models and experimental conditions. This study presents a systematic review and meta-analysis of preclinical transplantation studies that compare immune rejection outcomes in hypoimmunogenic gene-edited tissues with unedited controls. By quantitatively synthesizing current evidence, this research seeks to clarify the effectiveness and limitations of hypoimmunogenic gene editing as an immune-avoidance strategy in engineered tissues.
Project Blog
Post 1: Intro Blog: Potential Solution to Immune Rejection of Lab-grown Organs
Modern medicine has made remarkable progress in treating disease, yet one major challenge remains unresolved: the shortage of transplantable organs. In the United States alone, more than 103,000 people are currently on the national transplant waiting list, and another patient is added approximately every eight minutes. The number of people who need lifesaving organ transplants continues to grow faster than the number of available donor organs, leaving many patients waiting months or even years for treatment. This persistent gap between supply and demand has pushed scientists to explore new solutions, including the possibility of growing tissues and organs in the laboratory through regenerative medicine.
One promising approach to this issue involves using pluripotent stem cells to generate functional tissues that could eventually replace damaged organs. In theory, these engineered tissues could provide an alternative to donor transplantation and dramatically expand treatment options for patients with organ failure. However, a major scientific obstacle still prevents this idea from becoming a practical medical solution: immune rejection. The human immune system is designed to detect and eliminate foreign biological material, and even tissues engineered from human cells can trigger these defenses. To address this challenge, researchers have begun exploring gene-editing strategies that modify stem cells so they are less recognizable to the immune system. By altering genes involved in antigen presentation or introducing immune-evasive signals, scientists aim to create “hypoimmunogenic” cells that are less likely to provoke immune rejection after transplantation.
My research project focuses on evaluating how effective these gene-editing strategies actually are. Rather than examining a single experiment, this project uses a meta-analysis to evaluate results from multiple studies investigating hypoimmunogenic gene editing in lab-grown tissues. By systematically synthesizing findings across the scientific literature, the analysis aims to determine whether these gene-editing strategies consistently reduce immune rejection and to identify both their reliability and remaining limitations. This question is important because immune rejection represents one of the largest barriers preventing regenerative medicine from becoming a practical medical solution. If scientists can successfully engineer tissues that avoid immune detection, the implications could be transformative. Lab-grown organs could reduce reliance on donor transplants, shorten waiting lists, and expand treatment options for patients suffering from organ failure. Understanding how immune-evasive gene editing works across different tissue types can also guide future research by identifying the most promising strategies for further development. Studying this challenge therefore contributes not only to scientific understanding but also to the long-term goal of making regenerative medicine a realistic and accessible approach to treating serious disease.
Week 2: Learning the Foundations of Meta-Analysis
This week, I began the research phase of my senior project by focusing on an important first step: learning how to properly conduct a meta-analysis. Because this is my first time working with this method, I first learned about the methodological framework that will guide my project before reviewing research papers on immune rejection in engineered tissues.
To begin learning about this framework, I watched an introductory lecture on meta-analysis methods. This was where I was first introduced to the PRISMA guidelines, which are widely used in systematic reviews and meta-analyses.
One of the key standards used in meta-analysis research is the PRISMA guideline (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). PRISMA provides a structured framework that researchers follow to ensure transparency and reproducibility when reviewing large numbers of studies. It outlines how researchers should search for sources, define inclusion criteria, screen studies, and report their results. Because meta-analyses synthesize results from many independent experiments, following a clear standard such as PRISMA helps prevent bias and supports conclusions that are based on systematic evidence rather than selective reporting.
After gaining an overview from the video, I turned to two articles published in The BMJ that explain the PRISMA guidelines and the reasoning behind them. These articles describe how researchers should document each stage of the review process, including how studies are identified, screened, and ultimately included in the final analysis. They also emphasize the importance of transparency so that other researchers can replicate the review process if necessary. Learning about these standards helped me understand that conducting a meta-analysis involves careful methodological planning rather than simply collecting a large number of sources.
Through this process, I learned that a meta-analysis is much more structured than I initially expected. It requires clearly defined criteria for selecting studies, careful documentation of how sources are screened, and transparent reporting of each step in the process. I also learned that following established standards, such as PRISMA, helps ensure that the analysis remains systematic rather than subjective. This week’s work helped me better understand how researchers organize and synthesize large amounts of scientific literature, which will be essential as I begin collecting and evaluating studies for my own senior project.
Week 3: Developing My Search Strategy for Meta-Analysis
This week, I moved from learning about meta-analysis methods to actively applying them in my research project. After gaining an understanding of the PRISMA guidelines and the structure of systematic reviews, I began developing a search strategy to identify relevant studies. I quickly realized that this process was more complex than I initially expected, because I needed to balance finding a sufficient number of studies with maintaining a clear focus on my research question across different databases.
One of the main challenges I encountered was creating a search string that accurately reflected my topic without being too broad or too narrow. My research examines how hypoimmunogenic gene editing influences immune rejection in lab-grown tissues derived from pluripotent stem cells for transplantation. When I tried to include every part of this question in a single search string, the results were very limited. In some cases, PubMed returned fewer than 10 papers, suggesting that my search was too specific and might have excluded relevant studies. However, broadening the search introduced a different problem. Using less specific terms or searching in databases like PubMed Central produced thousands of results. Many of these were not directly related to my research. To address these issues, I began refining my approach by concentrating on the core concepts of my research rather than trying to capture every detail in the initial search. I prioritized key elements such as induced pluripotent stem cells (iPSCs), transplantation or graft contexts, immune rejection outcomes, and gene-editing techniques. I also adjusted my use of Boolean operators, relying less on broad “OR” terms and using more precise “AND” combinations to keep the results relevant.
After obtaining a comprehensive set of articles from multiple databases, I began developing my inclusion and exclusion criteria for screening. These criteria further narrow down to the articles that help me answer my research question. For example, I set out to include studies on gene-edited pluripotent stem cell–derived tissues that report measurable immune rejection outcomes in transplantation or graft settings. In contrast, I will exclude studies that focus only on basic stem cell biology, do not involve gene editing, or do not report immune-related outcomes. Setting out these guidelines beforehand helps me speed up the screening process, as I start with a clear sense of what I do and don’t want in the articles.
Week 6: Completion of Study Screening and Transition to a Systematic Review
Over the past few weeks, I have been developing my search strategy to complete the two rounds of screening for the studies I have collected. After refining my database searches and establishing clear inclusion and exclusion criteria, I applied these standards to evaluate each article based on its title and abstract. This process allowed me to significantly narrow the initial pool of studies and identify the ones most relevant to my research question.
Through this screening phase, I began to notice an important issue with my dataset. While I was able to find studies related to pluripotent stem cell–derived tissues, gene editing, and immune response, very few papers addressed all components of my research question simultaneously. In particular, there was a lack of consistency in how immune rejection was measured and reported across studies. Some papers focused on molecular markers such as HLA expression, while others examined immune cell activation or general inflammatory responses. Additionally, many studies were conducted in different experimental systems, including organoids, animal models, or in vitro assays, making direct comparison difficult.
As I continued the full-text screening of the remaining articles, it became clear that this variability posed a significant challenge to conducting a meta-analysis. A meta-analysis requires a relatively consistent set of quantitative outcomes across studies to combine and statistically analyze the results. However, the studies I identified used different methodologies, measured different outcomes, and often lacked comparable data formats. Because of this, combining their results into a single statistical analysis would be neither reliable nor meaningful.
Based on these observations, I realized that continuing with a meta-analysis would not be the most appropriate method for my research. For now, I am converting my project into a systematic review, in which I will qualitatively synthesize how different studies approach hypoimmunogenic gene editing and evaluate their effectiveness in reducing immune rejection across various models.
Week 7: Analysis of HLA Gene Editing Study by Ji et al. (2023)
This week, I decided to move forward with doing a systematic review. With this shift, I started reading and analyzing individual research papers in depth and trying to extract their results.
To start this process, I worked through one of the main papers I selected this week, “Genome editing HLA alleles for a pilot immunocompatible hESC line in a Chinese hESC bank for cell therapies” by Ji et al. (2023). In this study, the researchers aimed to reduce immune rejection in stem cell–derived tissues by editing specific HLA genes. They created a modified stem cell line by removing certain HLA components that trigger immune responses while keeping one key HLA type to maintain some level of immune recognition. They then tested these edited cells using a series of experiments, including co-culture with immune cells and testing in animal models. They found that the edited cells triggered much weaker T-cell responses while still avoiding increased attack from natural killer (NK) cells.
This paper provides evidence that gene editing can successfully reduce certain immune responses, particularly those driven by T cells. At the same time, it shows that these edits must be carefully designed to avoid unintended consequences, such as activating other parts of the immune system. It helps answer how effective gene-editing strategies are in reducing immune rejection and what factors influence that effectiveness. It also shows that targeting multiple genes and balancing immune pathways can lead to better outcomes compared to simpler approaches.