A Multi-Omics Approach For Characterizing Clinical-Scale iPSC Batches

A conversation with Merlinda-Loriane Sewavi, Syntheia Biosystems

There’s an unavoidable pinch-point when scaling induced pluripotent stem cell (iPSC) manufacturing from flask to bioreactor scales. Two-dimensional analytical methods like cell counting, imaging, and flow cytometry can’t effectively characterize diverse cell populations or batch-to-batch variations.

Beyond that, leveling up to advanced 3D analytical methods presents its own issues, not for lack of technology but because the 2D and 3D camps don’t talk to each other.

Merlinda-Loriane Sewavi, a researcher and the founder and lead consultant at Syntheia Biosystems, proposes a modular multi-omics approach built on qPCR, and she explored where the friction is happening earlier this year at the Cambridge Health Institute’s Bioprocessing Summit in Boston. We tracked her down after the event for the highlights. Here’s what she told us. The text transcript is edited for clarity.

Help us to understand what you are speaking about when you talk about 2D and 3D processes. Why is it important to think about this right now? What's the difference between the two of them?

Sewavi: You would think that we're talking about a whole new language of assays, but it's really more applying what we've known to work consistently in 2D and adjusting these first principle-based assays to 3D. So, we're pushing a new frontier entirely where we're no longer just thinking about working in a different dimension as the novelty but trying to make that actually the standard for preclinical modeling and translational applications. So, when we think about these assays like qPCR, flow cytometry, and spatial transcriptomics sequencing in general, it's more about how we make sure we take this new standard in 3D and match it to what's already been established in 2D to give us that base level of understanding that we actually use in clinical applications. So, 2D assays are our baseline. Our map shows us what has been done, what has been well understood, trusted, and well established, and then we try and fit 3D to the second standard in parallel and show the future moving forward.

So, does this just give us a more robust picture of what's actually going on? Where did 2D analytics fall short? What kind of gaps do 3D analytics fill in?

Sewavi: I would say 3D analytics are largely undiscovered. We need to push forward many things in the field as a whole to really understand what's going on as a baseline.

Two-dimensional analytics are robust and very well established under large umbrellas, depending on the application. But because of the novelty and the lack of scalability as it stands right now, there really is a gap in the field for what 3D analytics will need across the field, again, depending on the application.

In my experience, I think because we are using stem cells that can be differentiated to a different lineages and different cell types, we need those genetic based 2D based assays to allow us to see how close we are actually moving to what we've already been able to establish in regular 2D environments.

It's really more leveraging an assay that we understand in a format we understand and being able to try and adjust that to a format we don't understand so we can actually look underneath the hood and really see what's going on.

You mentioned a couple of different assays just a minute ago. Can you tell us about the full scope of your -omics stack?

Sewavi: When we're looking at 2D to 3D or just 3D-omics in general, I think that the first critical assays that really need to be established are qPCR, flow cytometry, bulk sequencing, single-cell sequencing, and spatial transcriptomics. I'll give a quick rundown of each:

• qPCR is nonnegotiable simply because the protocols that work well in 2D don't translate well in 3D. Applying well-established genetic assays like qPCR to 3D systems, we can begin to impose structure on the complexity and identify where and why protocols break down. These foundational tools are key to making sense of the messiness and advancing the field.
• Flow cytometry is a really great, turnkey, go/no-go purity assay. It's kind of like your report card. It allows you to see how well your run went and what to do if it's going to get pushed through for more QC or if it needs to be taken back to the drawing board to understand how to move your protocol forward.
• For bulk sequencing, we are looking at pathways at large. We're starting with a blank slate and trying to force cells through a specific pathway to see, as we approach the final product, what other genes are activated on their way through. Are they harmless changes that we simply need to know about, or will they cause real problems as we advance to clinical studies?
• Single-cell sequencing adds resolution, especially for human translation. We can compare individual cells to actual human cells, helping identify off-target populations and refine models for greater accuracy and relevance. For off-target populations, we need to prevent them altogether or decrease the population while increasing the population we do want. These are questions that we won't be able to look at from just surface level assays. It's really more about being able to take these 3D constructs and reduce them into principles that can be understood so that we can understand everything further
• For spatial transcriptomics as a whole, but especially for cardiomyocyte production, we don't understand batch-to-batch variation or whether higher purity correlates with specific markers like cardiac troponin or atrial genes. These relationships haven’t been clearly defined.
  Most people have not even begun to conceptualize using transcriptomics for QC, but they can really help move protocols forward. Having gone through many institutions that have used these really pivotal clinically-related assays to push forward and bring truth, I'm confident we need more work in this area. It has a lot of potential.

What are the steps to get from where we are now — optimizing 2D processes — to a future in which we're using 3D assays in GMP environments for clinical production?

It's not far off, but we need a lot more elbow grease. We need to map out where we are so we know how big the leap to the next step will be.

During my work at Stanford, I was able to see what it looks like when you want to clinically manufacture things, what burn rates look like, how that alters your protocols, and how that alters what you're going to use reproducibly and sustainably. I can tell you these silos don't talk.

Until we bridge them together, we're not going to be able to have lineage-specific, clinically relevant models that actually scale well and don't burn through resources without reaching reproducibility. That's the first thing.

Once we have alignment, we can focus on scaling. In regular process development, you're punching holes in your process, and you patch them and scale up.

We don't know what it looks like to scale up with clinically relevant cell models. We don't know what it looks like to actually do that reproducibly. We don't have a QC system that's well-established and agreed upon.

We know from other well-established processes that it can be done. I think that once we bridge that, once we have interdisciplinary scientists who can take assays, do the work, and understand how to scale it, we can really get this going quickly.

About The Expert:

Merlinda-Loriane Sewavi, M.S., is a bioprocess and molecular systems engineer specializing in 3D tissue systems, multi-omics quality control, and scalable biomanufacturing. She is the founder of Syntheia Biosystems, a consulting firm that supports companies with platform audits, scientific counsel, and validation strategy development. Previously, she was a researcher at Stanford University School of Engineering, where she led work on cell-type mapping and sequencing-grade RNA validation to support GMP-scale biomanufacturing across iPSC lineages. She has a master's degree in chemical biology from the University of Michigan.