iGEM 2024 — Team uflorida

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## Wet Lab Notebook

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In our lab, we utilized Notion as a digital lab notebook, organizing it into two key sections: the iPSC Log and the Differentiation Log.

* **iPSC Log:** This section was dedicated to documenting all findings related to our iPSC culturing. We recorded details about cell passaging, confluency levels, and any observations related to cell behavior or morphology. This allowed us to track the optimization of our culture methods and ensure consistency in our iPSC experiments.
* **Differentiation Log:** In this section, we noted our observations and results from differentiation experiments. We documented the protocols used, any modifications made, and the outcomes of each differentiation attempt. This log was essential for evaluating the effectiveness of different differentiation strategies and understanding how they influenced cell fate.

Week 1 (June 2-8)

We initiated our first iPSC plate, performing daily media changes while monitoring the cells to ensure proper growth and health. During thawing, we slightly over-pipetted, but the cells successfully clumped by the following day. By the end of the week, we observed strong colony formation.

Week 2 (June 9-15)

After passaging our first iPSC plate, we observed fibroblast-like differentiation the following day. ROCK inhibitor (ROCKi) was used during passaging, but we learned that it should only be applied during thawing or when there is a high risk of differentiation. We selected one well of iPSCs that exhibited the least amount of spontaneous differentiation to initiate the organoid differentiation protocol. ReLeSR was used to select undifferentiated cells for organoid formation. We performed aggregate formation (Day 0) and proceeded to Phase I on Day 1, learning the preparation of differentiation media and the collection of organoids. The cell aggregates formed were less than 100 µm in diameter, with uneven edges which improved by the end of phase I.

Week 3 (June 16-22)

We received an additional iPSC plate from Dr. Pan’s lab, expanding and passaging it twice successfully, correcting previous issues with ROCKi use. In parallel, we continued the differentiation of our first organoid plate, successfully completing Phase I and proceeding through Phases II and III. Colonies began forming round structures with distinct edges, with Well 6 showing faster growth than the others.

Week 4 (June 23-29)

We continued to culture and passage our iPSC plates to build up a stock for future organoid differentiation experiments. In addition, we completed Phase III of our first organoid differentiation and moved to Phase IV. We learned to use a Keyence microscope for imaging, capturing vascularization sprouting in the organoids. However, the organoids were smaller than expected, and we hoped they would grow further in Phase IV.

Week 5 (June 30-July 6)

Phase IV of the organoid differentiation was completed, and we imaged the Day 18 organoids using the Keyence microscope. This confirmed that we successfully generated organoids. We encountered challenges during organoid isolation due to the hydrogel being more watery than anticipated. We identified low pH as the cause and made adjustments for future protocols.

Additionally, we started a new round of aggregate formation but observed that no significant clumps formed upon resuspension in Phase I media on Day 0, prompting us to reconsider our approach.

Week 6 (July 7-13)

A second attempt at aggregate formation resulted in the visualization of only a few aggregates (approximately 10 across 4 wells), along with small cell clumps (10-15 cells). Due to the small aggregate size, we determined they would not be viable for further differentiation. A check 12 hours later revealed further dissociation, and no cells gravitated. We continued culturing two iPSC plates, which showed large, healthy colonies with minimal spontaneous differentiation.

Week 7 (July 14-20)

We thawed a new iPSC plate to maintain a stock for future differentiation experiments. This plate showed a higher level of spontaneous differentiation, so we changed the media after approximately 20 hours to reduce the concentration of ROCKi, suspecting it may have been the cause.

Week 8 (July 21-27)

We initiated a new round of organoid differentiation, this time preparing a much larger batch, filling 54 wells in 24-well ULA plates. Many round aggregates formed, although slightly smaller than expected. The iPSC plate from the previous week showed significant improvement in morphology, with less spontaneous differentiation. We continued culturing and passaged the cells when they reached 80% confluency.

Week 9 (July 28-August 3)

One of our differentiation plates became contaminated with coccus bacteria, with wells turning yellow and cloudy. We bleached the plate and sterilized the incubator. A subsequent iPSC thaw was unsuccessful, resulting in low confluency and no colony formation. Media changes were continued in case of survival.

Week 10 (August 4-10)

A new iPSC thaw was attempted, but this plate also became contaminated with coccus bacteria.

Week 11 (August 11-17)

We received a healthy iPSC plate from the Center for Cellular Reprogramming (CCR) and began expanding and culturing it. The cells displayed smooth colonies with no spontaneous differentiation or contamination. We initiated aggregate formation using the CCR plate, which resulted in appropriately sized clumps that appeared promising for organoid formation.

Week 12 (August 18-24)

We continued expanding and freezing cells from the CCR plate to build up our iPSC stock. We observed a higher yield of aggregate collection via centrifugation and adapted this method for future protocols. However, despite entering Phase I, the aggregates did not increase significantly in size, and many dissociated, preventing transition to Phase II.

Week 13 (August 25-31)

We successfully initiated a new round of organoid differentiation using high-confluency iPSCs. Aggregates formed well, with a mix of large and small colonies being maintained. Phase I yielded a high number of promising aggregates through gravitation.

Week 14 (September 1-7)

One plate became contaminated, leading us to sterilize our workspace. We continued culturing the remaining plate and froze additional iPSC wells to build up stock. One well was used for aggregate formation, and Phase II of differentiation proceeded successfully, with most aggregates gravitating. Approximately 20-30 organoids were resuspended in Phase II media. However, a significant portion of the cells died before Phase III.

Week 15 (September 8-14)

A passage was unsuccessful, resulting in extremely low confluency and signs of iPSC cell death. Media remained pink and largely unused.

Week 16 (September 15-21)

We began writing reports and planning contributions to the Wet Lab wiki.

Week 17 (September 22-29)

A new iPSC thaw was contaminated. We continued work on lab write-ups.

Week 18 (September 29-Oct 2)

A new iPSC thaw was initiated, and while the cells formed large colonies, they appeared weak. This plate was also contaminated, likely due to incubator contamination. We initiated a new thaw using a different iPSC line to address this issue.

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## Wet Lab

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In our project, we adopted a three-aim approach that enhances the engineering process by breaking down complex projects into manageable objectives, fostering clarity and focus. Each aim allowed the team to concentrate on specific tasks, facilitating iterative improvement through testing and refinement.

* **Aim 1:** Growing and Differentiating Bone Marrow Organoids from iPSCs
* **Aim 2:** Validating the Bone Marrow Organoids
* **Aim 3:** Inducing Sepsis-like Infection

We used the Design, Build, Test, Learn (DBTL) framework to improve aggregation formation in our differentiation protocol for bone marrow organoid production.

DBTL Example, Aim 1

#### Design:

The goal was to optimize the iPSC culture and the aggregate formation process. Initially, we designed a protocol that involved passaging iPSC colonies into aggregates when they reached around 60% confluency, aiming to promote the formation of small, manageable colonies for aggregation in ultra-low attachment (ULA) plates.

#### Build:

We implemented this design by culturing the iPSCs and passaging them into ULA plates at 60% confluency. The intent was to facilitate the direct transfer of small colonies, anticipating that they would form robust aggregates under these conditions.

#### Test:

Upon testing, we observed that the colonies disintegrated significantly during the aggregation process, resulting in aggregates that were too small and fragile. These issues impeded further experimental phases, as we could not reliably collect the aggregates for downstream applications. Multiple attempts were made to validate this approach, all yielding similar results.

#### Learn:

Based on our observations, we learned that a higher confluency before passaging was necessary for stable aggregate formation. Adjusting the confluency to 80-90% before transferring to ULA plates resulted in significantly larger and stronger aggregates, leading to more reliable organoid production. Additionally, using cut P1000 tips gave us more control during the passaging process and helped prevent colony breakage, ensuring that the cell clusters remained intact when transferred to the ULA plates. This insight prompted us to revise our protocol, improving the overall success of the aggregation step and subsequent phases of the project.

## Dry Lab

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The Dry Lab team utilized a four-aim approach when developing our project:

* **Aim 1:** Develop and integrate a mathematical model for iPSC cell growth and achieve a steady-state
* **Aim 2:** Build on this base by adding equations for the differentiation process of iPSCs into HPSCs and its derivatives
* **Aim 3:** Implement a pathogenic function into the model, and achieve the following outcomes:

  1. Infection, followed by a full recovery and a return to the original steady-state

  2. Aseptic death/death as a direct result of the pathogen
* **Aim 4:** Add cytokine functions and achieve an outcome approximating septic death

Each of these aims required multiple DBTL cycles to achieve. Below are a few examples of design cycles we went through, and specifically problems we ran into, during the development of our model

DBTL Example, Aim 1

#### Design:

Our first step in the design process for Aim 1 was defining the time-dependent equation for HSPC growth (see 'Model' page for more details on the development of our equations).

#### Build:

Once we had the equation, we set to integrate it into our NetLogo model. This included defining all necessary variables and setting up the environment. During this process, we decided that the system should start with an uneven seeding density. This more closely mimics real wet lab experiments, as cell colonies are not uniformily distributed across a cell plate. Integrating this into our model would in theory provide a more realistic simulation.

#### Test:

Upon integrating this setup function into our model, we ran our simulation 10 separate times. Every single run of the model, however, produced something similar to this (window view, code):

Here we see that some patches of cells (those appearing brown) grew logarithmically up to their carrying capacity, exactly as expected. Some patches though (those appearing black) did not grow at all and had a final cell count of 0.

#### Learn:

After troubleshooting the problem and reviewing our code line-by-line, we concluded that the function we created for randomly seeding the patches did not iterate enough times to ensure each patch has a low probability of having '0' initial cells. If a patch starts with 0 initial cells, there will be no growth in that particular patch since the growth rate is dependent on the current cell population. In the errant code, each patch had approximately a 10% chance of starting with 0 initial cells, meaning about 10% of patches would not have any initial cell density. To fix this, we made the function take 10 times as many steps as it initially took. This reduced the probability of a patch having 0 initial cell density to essentially 0%. The corrected code produced the following results:

DBTL Example, Aim 2

#### Design:

Our first step in the design process for Aim 2 was defining the growth equations for each cell type that differentiated from the stem cells (see 'Model' page for more details on the development of our equations).

#### Build:

After creating our series of equations, we integrated them into our model. This involved defining variables for all the cell-to-cell interconversion rates, as well as each cell-specific death rate.

#### Test:

After integration, we tested our new functions by running the model multiple times. We noticed, however, that the populations of active and suppressive leukocytes were not matching the desired trends. When no pathogen is present, there are supposed to be no active leukocytes. This was not the case though. In fact, despite the lack of any pathogenic vector, the active and suppressive leukocyte cell counts were the highest of any cell-type.

#### Learn:

After reviewing our code, we found a foundational error in our system of equations. We had initially assumed that the active and suppressive leukocytes should be dependant on the population of stem cells and the population of leukocytes. This, however, was not the case. Instead, we determined that the active and suppressive leukocyte populations should be determined by the pathogen density and the leukocyte density. Once this change was implemented into the model, there were no longer any active or suppressive leukocytes without a pathogen present to elicit an immune response.

DBTL Example, Aim 3

#### Design:

Our first step in the design process for Aim 3 was defining the equations for pathogen growth (see 'Model' page for more details on the development of our equations).

#### Build:

It took many design-cycles to complete Aim 3. However, after defining the 'pathogen' class of agents, the 'infect' function, and the 'infection-intensity' variable, we had completed the first step of integration and were ready to test.

#### Test:

After these functions were defined, we ran a series of simulations to test our new functions. We found that the infection induced the expected trends in cell-levels, but for some reason the plot that graphed pathogen count over time stayed constant at 0. This indicated that there were never any pathogens in the system, which could not be true since we see the effects of our pathogen on the cell counts.

#### Learn:

After re-reading every line of code involving our pathogen functions, we found the source of our confusion. In our differentiation function, we first calculated the growth of pathogens, then the growth of active leukocytes, then the death of pathogens by way of active leukocytes. This order of operations, however, was faulty. The growth of active leukocytes should be dependent on the pathogen density in the previous timestep, not the subsequent timestep. This caused any growth of pathogen to be immediately cancelled out in the following line by the growth of active leukocytes. Simply changing the order of these lines to the proper sequence fixed the issues we had and produced the expected results:

DBTL Example, Aim 4

#### Design:

Our first step in the design process for Aim 4 was defining the growth equations for each cytokine type (pro- and anti- inflammatory). Please see 'Model' page for more details on the development of our equations.

#### Build:

Once we created the system of equations, we implemented them into our code. We created a class for each cytokine type and defined variables to track their concentrations over time.

#### Test:

Following this integration process, we tested our new functions. The cell culture procedure worked exactly as expected. As the HSPC density rose, so too did the concentration of cytokines. The differentiation procedure, however, did not produce the desired results. During differentiation, the cumulative density of the cells should increase. However, this should not significantly impact the concentration (molarity) of the cytokines. Those should remain essentially constant throughout the differentiation process. Instead, the following results were obtained:

#### Learn:

Reevaluating our system of equations, we decided to modify the cytokine dependency so that it only changed with the stem cell density. This resulted in the maintenance of the cytokine equillibrium that was produced in the cell culture phase of the simulation:

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## Wet Lab Experiments

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The goal of our Wet Lab project was to design a functional, physiologically-relevant organoid capable of precisely mimicking the human hematopoietic niche. In order to engineer such an organoid, we followed a protocol involving a four-phase differentiation of human induced pluripotent stem cells iPSCs). Differentiating iPSCs into bone marrow organoids requires mesodermal induction (Phase I), iPSC commitment to stromal and hematopoietic commitment (Phase II), formation of a vascular structure (Phase III), and establishment of the organoids’ individuality (Phase IV).

**Phase I** is crucial as it forms the very basic characteristics of the hematopoietic niche that will eventually give rise to an organoid. In human physiology, the mesoderm generates HSCs, blood cells, and immune cells during embryonic development and mesodermal progenitors give rise to various lineages in the human bone marrow structure. Directing iPSCs toward mesodermal lineages is critical for the further generation of hematopoietic cells as well as a complete hematopoietic structure. Mesodermal induction involves exposing the iPSC aggregates to a cocktail of developmental signals that promote mesodermal formation.

**Phase II** takes the mesodermal aggregates and exposes them to various hematopoietic, stromal, and vascular growth factors to induce differentiation and guide the structure toward hematopoietic fates. At this point in time, the organoid has mesodermal, hematopoietic, and stromal populations involved in function and support.

**Phase III** involves embedding the hematopoietic organoids into a hydrogel structure alongside vascular growth factors to support the formation of a vascular network. This phase allows the organoids to closely mimic bone marrow remodeling as the vascular network is key for nutrient supply, formation of the perivascular network, the site of cell trafficking, and more. This phase is the longest and most involved, as the generation of the vascular network allows for modeling of cell-cell interactions in 3D, endothelial and perivascular support, and paracrine signaling.

**Phase IV** establishes the organoids as individual structures that do not rely on other organoids for signaling and are capable of self-generation in a nutrient-dense environment. Organoids are physically removed from the hydrogel and placed in maintenance media.

## Protocols (iPSC Culture, Bone Marrow Organoid)

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#### iPSC Culture Protocol:

#### Bone Marrow Organoid Protocol:

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## Dry Lab Results

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##### We identified 7 key simulation events using our model:

* Cell Culture Process
* Differentiation Process
* Light Infection
* Moderate Infection
* Heavy Infection
* Consecutive Infections
* Immediate Infection
* Future Directions

Cell Culture Process

Our first simulated result is a model of the iPSC cell culture process. This process involves artificially seeding the cell-environment, then allowing it to grow up to its carrying capacity. During our simulation we noted a logistic growth curve, which matches with the natural growth pattern of cell culture methods.

![](https://static.igem.wiki/teams/5469/abm-results/cells-culture.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-culture.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-culture.png)

Differentiation Process

Our second simulated result is a model of the HSPC differentiation process. We first let the cell culture procedure run to completion (time 0 to 653), then we ran the differentiation simulation (time 653 to ~5500). Here, we again noted logarithmic growth of each cell-type up to its equilibrium point. In the differentiation process though, the equilibrium point is determined by the HSPC population and the differentiation rate of it into each component cell type.

![](https://static.igem.wiki/teams/5469/abm-results/cells-differentiated.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-differentiated.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-differentiated.png)

Light Infection

Our third simulation runs a light infection through the system (infection-intensity = 20). During this run, we noted that the HSPC population initially decreased a small amount (due to the pathogen), before rising above its initial equilibrium due inflationary effects. Peaking around 100 timesteps after the initial infection, the HSPC population then declined back to an equilibrium point. This new equilibrium point, however, was still higher than the initial equilibrium point, indicating that the infection has a long-term impact on the system beyond the initial pathogenic injury. Also found after the infection are elevated pro-inflammatory cytokine levels.

![](https://static.igem.wiki/teams/5469/abm-results/cells-lightinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/leukocytes-lightinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-lightinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-lightinfection.png)

Moderate Infection

The fourth simulation we ran was a moderate infection of the system (infection-intensity = 100). These results yielded a similar outcome to the light-infection simulation, but this time there was a much more noticeable drop in cell-density. This is a direct result of the more intense infection killing more cells. After the initial decline, the system fluctuated around an equilibrium point before finally leveling off at said equilibrium point.

![](https://static.igem.wiki/teams/5469/abm-results/cells-moderateinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/leukocytes-moderateinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-moderateinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-moderateinfection.png)

Heavy Infection

Our fifth simulation infects our system with an intense pathogenic injury (infection-intensity = 150). Here, the immune system is able to overcome the pathogenic infection, leading to the entire cell colony quickly dying. Our simulation automatically stopped when the HSPC population reached 0 because no differentiation can occur after that point, meaning all the differentiated cell populations would necessarily go to 0 shortly after.

![](https://static.igem.wiki/teams/5469/abm-results/cells-heavyinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/leukocytes-heavyinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-heavyinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-heavyinfection.png)

Consecutive Infection

In our 6th simulation we tested the impact of two consecutive infections on the system. After culturing and differentiating the system, we added a moderate pathogen insult (infection-intensity = 100) and allowed the system to fight off the infection and recover to equilibrium. We then added another identical infection event (infection-intensity = 100) and allowed the system to recover. Things to note during this simulation are the elevated immune response indicators during the second infection. Despite the same pathogen population, the second infection event resulted in higher pro-inflammatory cytokine levels and higher active leukocyte levels. This is an interesting result considering the fact that sepsis is a result of an overreaction of the immune system. While the system was still able to recover during our simulation, this is a path we plan to explore further in the future.

![](https://static.igem.wiki/teams/5469/abm-results/cells-multipleinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/leukocytes-multipleinfections.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-multipleinfections.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-multipleinfections.png)

Immediate Infection

In our 7th simulation we infected the system with a moderate infection (infection-intensity = 100) shortly after the cell culture process, approximately 200 timesteps after the beginning of the differentiation process. This was primarily an experimental simulation, a test to see how our model reacts to extreme stimuli. We noted that the HSPC population decline was more extreme than any of the moderate infection events shown earlier. This is likely due to the fact that the immune system was not fully developed when the infection started. This caused a lagging response to the infection. The system, however, was eventually able to catch-up, fight off the infection, and return to equilibrium.

![](https://static.igem.wiki/teams/5469/abm-results/cells-immediteinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/leukocytes-immediateinfection.png)

![](https://static.igem.wiki/teams/5469/abm-results/cytokines-immediateindection.png)

![](https://static.igem.wiki/teams/5469/abm-results/pathogen-immediateinfection.png)

Future Directions

In its totality, our model provides unique insight into the evolution of the bone marrow organoid system and its evolution in response to environmental stimuli. In the future we aim to further refine our system of equations by fitting it to Wet Lab data produced by our group. This will increase both its accuracy and predictive power, making it a much more applicable tool to those seeking to understand infectious responses in our organoids. We also hope to refine our system of equations further and achieve simulated septic death as a result of infection. As mentioned in the “Consecutive Infections” section above, our current model produces results that trend towards an overreaction of the immune system, but we have yet to find the conditions under which a full septic response would occur.

## Wet Lab Results

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##### Project Achievements:

* Establishing improved iPSC protocols
* Instituting improved aggregation and passaging techniques
* Creating organoids at hypoxic and normoxic oxygen conditions
* Defining consequences to abnormal hydrogel pH in organoid embedding
* Constructing organoids with expected differentiation morphology
* Plans for further organoid validation and production optimization

iPSC Conditions

Establishing the iPSC line was a significant challenge that required considerable time and effort before we could initiate differentiation. We undertook a systematic approach to address several challenges we encountered, including contamination, overpipetting, and spontaneous differentiation Each setback delayed our progress, making it imperative to meticulously monitor and optimize our protocols to establish a reliable iPSC line, outlined in the iPSC guide.

To address the issue of spontaneous differentiation of our iPSCs, we focused on fine-tuning the concentration of the ROCK inhibitor (ROCKi) in our culture conditions. Initially, we were using a concentration of 10 µM/ml, which, while effective in promoting cell survival and reducing apoptosis, was also contributing to unwanted differentiation into fibroblast-like cells. After careful consideration and experimentation, we decided to reduce the ROCKi concentration to 7 µM/ml. This adjustment was based on our observations that lower concentrations could still effectively prevent cell death without promoting differentiation. Following this modification, we monitored the cell morphology and growth patterns closely. The results were promising: the reduced ROCKi concentration helped maintain the pluripotent state of the iPSCs, significantly minimizing spontaneous differentiation. The cells exhibited more uniform morphology typical of undifferentiated iPSCs, and we noted an increase in their overall viability and proliferation rates. This refinement proved to be a key improvement in our iPSC culture protocol, allowing us to create a more stable environment for maintaining pluripotency while effectively mitigating differentiation.

To address contamination issues in our iPSC cultures, we implemented a two-pronged strategy: wrapping plates in Parafilm and placing them in sealed containers. Initially, we recognized that exposure to the environment and improper sealing were significant contributors to contamination. To combat this, we began wrapping each culture plate tightly with Parafilm. This created a barrier that minimized exposure to airborne contaminants while still allowing for some gas exchange necessary for cell growth. The flexible nature of Parafilm also provided an effective seal that could accommodate slight changes in plate size without compromising the integrity of the seal. In addition to wrapping plates, we further enhanced our contamination prevention measures by placing the wrapped plates inside sterile containers. It is not common practice to wrap cell culture plates in Parafilm to prevent contamination. Parafilm can be an effective tool for preventing contamination of cells in a culture plate by providing an additional layer of protection against airborne contaminants and environmental exposure. When sealing the edges of culture plates or dishes, Parafilm creates a barrier that helps minimize the risk of dust, microbes, and other potential contaminants entering the culture environment. Placing culture plates in sterile containers inside the incubator is an effective method for preventing contamination and maintaining the integrity of the cell cultures. This practice offers a controlled environment that minimizes exposure to airborne contaminants and reduces the risk of microbial invasion. By using sterile containers, such as plastic boxes or dedicated cell culture carriers, researchers can create a barrier that protects the plates from dust, moisture, and other potential sources of contamination. Additionally, this approach allows for better organization and tracking of samples within the incubator, ensuring that they remain undisturbed and free from cross-contamination. By combining these methods, we significantly reduced the incidence of contamination in our cultures. The wrapped plates and sealed containers ensured a more controlled environment, promoting healthier iPSC growth and maintaining the integrity of our experiments.

To tackle the issue of overpipetting, we implemented the unconventional approach of using cut P1000 tips, which is not commonly practiced in standard iPSC protocols for passaging and seeding cells. This modification allowed for a larger opening and more control, which was crucial for handling our iPSC cultures more gently. Previously, using standard pipette tips often led to excessive shear stress on cell clumps and colonies, resulting in their breakdown and loss of integrity. By cutting the tips, we created a wider bore that facilitated smoother transfer of cell aggregates while minimizing mechanical disruption. This adjustment enabled us to maintain the structural integrity of the colonies during pipetting, promoting better cell viability and overall health. In addition, we trained our team on proper pipetting techniques, emphasizing the importance of gentle aspiration and dispensation. The combination of the modified tips and careful handling not only improved the quality of our iPSC cultures but also enhanced consistency across experiments.

Passaging and Aggregate Formation

We determined that the ideal confluence for passaging iPSCs is between 60% and 80%. We noticed that when cultures reached 100% confluence, there was a significant increase in cell death and detachment, indicating that overcrowding was negatively impacting cell viability and overall health. Conversely, we also tested passaging at lower confluences, specifically below 60%. In these cases, the cells often exhibited weaker growth and poorer colony formation, suggesting that they were not yet ready for passaging, which compromised their robustness and pluripotent potential. To establish a consistent protocol, we typically performed passaging at a 1:6 split ratio within this optimal confluence range. This ratio allowed for sufficient space for the cells to thrive while preventing overcrowding.

Through our experiments, we found that higher confluences of 80% to 90% were optimal for aggregate formation in iPSCs. While literature typically recommends a confluence of around 60%, we observed that when we pipetted cells at higher confluences into ultra-low attachment (ULA) plates, the aggregates formed more robustly. At 80% to 90% confluence, the cells were densely packed, which helped maintain their structural integrity during pipetting. This density minimized the breakdown of cell clumps and allowed for the formation of nicely sized organoids. In contrast, cells at lower confluences often resulted in smaller, less cohesive aggregates that did not develop as effectively. This finding was significant because it demonstrated that while traditional guidelines suggest a lower confluence, our specific conditions and goals for organoid formation benefitted from a higher density.

Organoid Oxygen Conditions

![](https://static.igem.wiki/teams/5469/wet-lab-oxygen-conditions.png)

The recommended oxygen conditions for in vivo bone marrow organoid formation are critical for mimicking the physiological environment of the bone marrow. Bone marrow typically experiences a low oxygen tension, around 1-5% O₂, which supports hematopoietic stem cell maintenance and function. In vitro, replicating these hypoxic conditions is essential for promoting the growth and differentiation of hematopoietic cells within organoids. Low oxygen levels enhance cell survival, maintain stemness, and facilitate the appropriate cellular interactions necessary for organoid integrity.

During our experiments, we discovered that we were able to successfully produce organoids in Phase I under varying oxygen conditions, specifically at 3.5% O₂ and even in normoxic conditions at 20% O₂. While the recommended oxygen level for this phase is typically around 5% O₂, our findings indicated that the organoids could still thrive at the lower level of 3.5% O₂. This was particularly encouraging, as it suggested that slightly reducing oxygen tension might still support the necessary cellular processes for organoid formation and maintenance.

The surprising success we observed in the 20% O₂ condition starkly contrasts with the prevailing literature, which often emphasizes the necessity of hypoxic environments for optimal organoid growth and development.The organoids demonstrated viability and some degree of functional characteristics, indicating that they could adapt to higher oxygen levels than previously thought. This flexibility opens up new possibilities for culturing strategies, allowing for greater accessibility and scalability in organoid production.

Hydrogel pH

We found that the optimal pH for hydrogels used in organoid formation typically ranges from 7.0 to 7.4, which closely mimics the physiological conditions of the human body. Maintaining this pH range is crucial for optimal cell viability, maintaining stem cell pluripotency, and promoting the appropriate signaling pathways for organoid development. When we used a hydrogel with a slightly lower pH, we encountered significant issues: the hydrogel failed to solidify properly. As a result, we were unable to create stable structures for the organoids, making it impossible to harvest them effectively. This experience underscored the importance of maintaining the optimal pH range of 7.0 to 7.4 in hydrogel formulations, as even minor deviations can adversely affect gelation and overall organoid development. This lesson reinforced our commitment to carefully monitor pH levels in future experiments to ensure successful organoid formation and collection.

Organoid Differentiation Observations

![](https://static.igem.wiki/teams/5469/wet-lab-differentiation.png)

In Phase I of bone marrow organoid differentiation, we observed that the aggregates began to grow larger as the iPSCs proliferated and clustered. This enlargement was primarily driven by rapid cell division, resulting in an increased number of cells within the aggregates. As the cells aggregated, they likely established stronger cell-to-cell interactions, promoting collective behavior that facilitated the coalescence of smaller aggregates into larger structures. The culture conditions, including media composition and oxygen levels, further supported the expansion by ensuring adequate nutrient and oxygen supply. Careful handling and optimized pipetting techniques minimized mechanical disruption, allowing the aggregates to grow without breaking apart. Collectively, these factors enabled the formation of larger, more cohesive aggregates, setting the stage for more complex development and differentiation in the subsequent phases of bone marrow organoid formation.

In Phase II of bone marrow organoid development, we began to observe the emergence of more clearly defined borders within the aggregates. This marked a significant step in the organization of the organoids, as distinct boundaries suggested initial cell sorting and spatial organization. As the iPSCs differentiated we could visually see the perimeter cells losing their original morphology and turning into specific lineages, contributing to a more structured architecture. In this phase we expect to see a formation of a heterogeneous cell population, with different types of cells occupying specific regions within the organoid. This early compartmentalization is crucial for mimicking the natural organization of bone marrow, where various cell types, including hematopoietic stem cells and stromal cells, coexist in defined niches. Moreover, during this phase, we noted changes in cell morphology. Cells at the periphery began to exhibit characteristics associated with early differentiation, while those at the center retained a more undifferentiated state. This gradient of differentiation is vital for developing a functional organoid, as it reflects the dynamic microenvironment found in native bone marrow.

In Phase III of bone marrow organoid development, vascularization is a critical process that significantly enhances the functionality and complexity of the organoids. This phase involves the formation of a vascular network within the organoid structure, which is essential for nutrient and oxygen delivery, as well as waste removal. During this stage, endothelial cells typically differentiate and proliferate, forming capillary-like structures that integrate with the surrounding stromal cells. This vascular network not only supports the survival and proliferation of hematopoietic stem cells but also facilitates hematopoiesis by creating a microenvironment similar to native bone marrow. The interplay between endothelial cells and other cell types within the organoid is crucial. Signaling molecules and growth factors, such as vascular endothelial growth factor (VEGF), play a key role in promoting angiogenesis and ensuring that the vascularized structure is functional. We observed substantial multi-layered sprouting throughout the entirety of phase III, with development beginning as early as day 6 (day 2 of phase III). The vascularization exhibited offshoots and intricate interweaving patterns, closely resembling those seen in human tissue. Successful vascularization leads to improved organoid viability, enhanced cell function, and a more accurate representation of bone marrow physiology, making Phase III a vital step in creating effective and reliable bone marrow organoids for research and therapeutic applications.

In Phase IV of bone marrow organoid development, we observed a notable transition where the organoids rounded out again, with the vascularization from Phase III integrating more seamlessly into the organoid structure. This rounding indicates a maturation process, where the organoids adopt a more compact morphology that enhances their stability. During this phase, the remnants of the vascular network established in Phase III began to interweave with the densely packed cells within the organoid. The presence of this integrated vascular network supports essential functions, such as nutrient delivery and waste removal, even as the overall vascular density may have decreased. Additionally, the rounded shape of the organoids reflects a balance between cell adhesion and structural integrity, allowing the organoids to maintain their functionality while accommodating the integrated vascular elements.

Future Directions

To validate an organoid model, cell types making up the real functional organ have to be identified. Still, even an organoid with all the proper cells may lack the correct structure for functionality. As such it is important to ensure the organoid has both the expected cells and overall structure to match its desired function. The identification of cell types will be accomplished using flow cytometry. After identifying extracellular or intracellular markers that are unique to each cell type, it is possible to create a panel of antibodies to sequentially determine the cells present in an organoid.

As an initial experiment, it is important to identify the broad cell groups required for bone marrow organoid functionality. These groups are stromal (structural) and hematopoietic (functional), and each cell in the organoid can be assigned to one of these groups. This is because in order to approximate a bone marrow space, the organoid should have heavy vascularization (presence of endothelial cells) as well as functional hematopoiesis (presence of progenitors and differentiated cells).

In the hematopoietic group, CD34+ cells can be identified as hematopoietic stem and progenitor cells (HSPCs) and are a critical component of the organoid that can differentiate into downstream effector cells. It is also expected that myelomonocytic, megakaryocyte, erythroid and mesenchymal stem cells are present within this hematopoietic compartment once fully matured. The stromal compartment of the organoid is expected to contain abundant endothelial (CD31+CD144+) cells needed for sufficient vascularization.

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When adding lipopolysaccharides (LPS), IIL-1β, and TNF-α to bone marrow organoids, we can effectively mimic an inflammatory response.LPS, IL-1, and TNF-α have been successfully used to mimic inflammation in studies of inflammatory bowel diseases (IBD), including irritable bowel syndrome (IBS) and Crohn's disease, within intestinal organoids. LPS, a component of the outer membrane of Gram-negative bacteria, is introduced directly into the culture medium. It activates Toll-like receptor 4 (TLR4) on the surface of immune cells and other cells within the organoids, initiating a cascade of signaling pathways. Upon binding of LPS to TLR4, key transcription factors, including NF-κB (nuclear factor kappa B), are activated. This activation triggers the release of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6. The secretion of these cytokines amplifies the inflammatory response within the bone marrow organoids. As a result of this, we can expect several responses within the organoids. There will likely be an increase in the recruitment of immune cells to the site of inflammation. Additionally, the presence of these pro-inflammatory cytokines can alter cell proliferation and differentiation patterns, promoting a shift toward an inflammatory phenotype called blebbing.

Organoid blebbing as a result of inflammation is an observable phenomenon where organoids exhibit abnormal protrusions or bulges, known as blebs, due to cellular stress or damage. These blebs are often a result of changes in the cytoskeleton, particularly the actin filaments, and are indicative of cellular damage or apoptosis (programmed cell death).

If we see these observations in our organoid model it indicates potential in using it to study sepsis. By effectively mimicking the inflammatory environment associated with sepsis, the model provides a platform for investigating the underlying mechanisms of the disease, helping to elucidate the complex interactions between the immune response and various tissues. This understanding is crucial for identifying biomarkers and therapeutic targets that can improve patient outcomes. Additionally, the organoid model allows for the evaluation of potential anti-inflammatory therapies, streamlining the drug development process and paving the way for novel treatments that could reduce morbidity and mortality rates associated with sepsis. The ability to create patient-specific organoids also opens avenues for personalized medicine, enabling tailored treatments based on individual inflammatory profiles. Overall, by demonstrating that our organoid model can effectively replicate the inflammatory processes associated with sepsis, we not only advance our understanding of this critical condition but also position our study as a valuable resource for innovative research approaches that could significantly impact clinical practice and patient care.

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