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Travis Daugherty

Head of Customer Experience

New publication: A human lung alveolus-on-a-chip model of acute radiation-induced lung injury

November 20, 2023

Research SpotlightsShare

We’re proud to highlight a new paper citing Pluto published in Nature Communications by Dr. Queeny Dasgupta and Dr. Donald Ingber at the Wyss Institute at Harvard University.

Roughly 60% of cancer patients receive radiation therapy1, but it has a toxic side effect: acute radiation induced lung injury (RILI). RILI can occur following exposure to high doses of ionizing radiation and causes DNA damage, accumulation of reactive oxygen species, and clinical symptoms including shortness of breath, fever, and chest pain.

There are currently no specific diagnostic tests or imaging approaches can definitively correlate findings to clinical symptoms of acute radiation induced lung injury.2

At the Wyss Institute at Harvard, the Donald Ingber lab is pioneering organ-on-a-chip (Organ Chip) microfluidic culture technology to model acute RILI in the human lung with the ultimate goal of better understanding the condition and identifying potential avenues for treatment.

Modeling acute radiation-induced lung injury with Organ Chips

Travis Daugherty, Head of Customer Experience at Pluto, had the pleasure of sitting down with Dr. Queeny Dasgupta to chat about the team's recent publication, as well as to get her perspective on scientific collaboration in industry and academia. Dr. Dasgupta shares her thoughts on using Pluto to support the discovery and publication process, and the broader impact of the budding Organ Chip field.

Travis: Thanks for spending some time with us! To start out, how long were you at the Wyss Institute? I‘d also love to understand more about the team you worked on.

Queeny: I worked at the Wyss Institute for about 3 years in a lab focused primarily on organ-on-chip projects. I was on the Lung-on-chip team, so I most closely worked with lung biologists, and a lot of engineers - mechanical and biomedical engineers who worked on different projects, different parts of the lung. The wider group was a lot of different expertise - people from sensors to different organ systems. We had a target identification team as well, with whom we’d work to identify gene and drug targets based on our transcriptomic data. Overall, it's a very interdisciplinary team.

Travis: That’s interesting – a lot of cooks in the kitchen! I’m curious what that dynamic looks like, is that fluid as far as involving different teams or was there a more defined strategy in working with all these separate groups?

Queeny: Each project has a main post-doc who is the scientific lead, like I was for the radiation in the lung part. Other than that, it would be mostly research associates working with you.

Working with other teams, yes, there were a lot of meetings and a lot of scheduling around people’s schedules. We also had a lot of drug screening projects going on, so that would involve understanding what they see in the cells and then testing those hypotheses on our platform, which comprised a lot of collaboration and coming together, sometimes trying to understand from all these different systems - what do these mean, and if there are differences, why are there differences? A lot of going back to understanding, fundamentally, what’s different on the Chip versus on the other model systems.

Travis: To a non-scientist, what would you describe as the most important take-away from research?


The most important thing is the fact that we have a model now for radiation injuring the lung. Typically, when humans are exposed to radiation, there are other organs systems which get more quickly affected like the gut or bone marrow, whereas in the lung the development of the disease is a little bit slower. However, damage to the lung is irreversible, so once the lung gets affected there's no recovering from it.

Currently there are no diagnostic tests to actually look at lung injury. Previously, clinicians might suspect lung injury if there were symptoms like wheezing, coughing, decreased lung function in general, for example. But that's only after the onset of symptoms, which is about 1 to 3 weeks after exposure, so we have no way to identify that short time frame and that window of what's going on after radiation exposure. So it seems this model helps to capture that, and we know what is really happening at the molecular level at the early time points. Now, if there are certain things that require intervention at the early time point we know what markers are going up and what needs to be taken care of.

Travis: That’s amazing! From reading your LinkedIn post – this was obviously a huge labor of love getting to this point, and a huge amount of effort that went into this - is this research that you're continuing or has this been handed off at the Wyss?

Queeny: I was funded by an FDA grant for almost 6 years, and then the lab applied for more funding for radiation injury from BARDA. The good news is that the Wyss did get that funding for looking at radiation further, so they will continue to study radiation injury in the lung and 3 other organ systems, actually. It's great, the fact that the data went into grant and made way for more funding.

Travis: Excellent building block for sure! Were there any particular ways Pluto helped you answer some of the scientific questions you had set out to answer?

Queeny: Oh yes, for sure –

All my transcriptomic data (RNA-seq) data was analyzed using Pluto, and starting with the volcano plots - you see figures 3 and 4 in my paper - are all figures generated from the Pluto platform directly.

These helped me really analyze and visually see what my data looks like, and without having to code. I would just upload the data on the platform and then look at what's going up, what's going down, what's the overall situation.

It helped me run a lot of different analyses actually - tSNE and UMAPs, and doing the GSE analysis for sure, understanding what pathways were getting affected. A lot of those results made it into the paper. Another thing that helped: sometimes the journal wants the figure to be a certain way - I would reach out to the Pluto team and say “this is what I need and this has to be the font size and this has to be the color” things like that. The team was very responsive and got back to me immediately with the figures that I wanted, so yeah that was really great back and forth. And a lot of time the journal doesn’t give you a lot of time, so I was glad Pluto got back to me so quickly." (laughter)

Travis: I love hearing that - it was special for us to have played even a small part in the overall process! So, you recently switched roles – I’d love to hear about where you’re at now what you are working on currently

Queeny: Yeah, I went to Systemic Bio. It’s a 3-D bio printing company which is focused on 3-D organ on -chip systems that are vascularized and we focus on drug screening. I joined the position in April, and basically we build models - currently the way our system works is that we partner with Pharma companies and if they have certain specific drugs in mind, we would custom design our model based on the inputs we get from them, and then we test screen drugs from these companies.

Travis: I'm curious now that you have experience in both Academia and now industry, are there any major similarities or differences between the two that you’ve experienced?

Queeny: Yes, my work involves a lot of bench work, doing experiments, so in that way it hasn't changed a lot. In terms of my role, I still work in the organ-on-chip field at large.

But I think the main difference between the academic position and this is, in academia you have a more central role towards getting your project to fruition. But in industry, it's more like a team, where everybody has a defined role and everybody contributes a certain thing into the overall project and to the company. So I guess the team side is definitely more prominent in industry as compared to academia. In academia you have teams - the lab was really big - but I think your responsibility towards the project is much more than in industry – a lot of things get taken care of by other teams in industry.

Travis: Considering the work you’ve done, the work you are doing now and into the future, anything that you are particularly excited about?

Queeny: Yes! I’m actually very excited about the whole organ-on-chip field really. I think there's a lot of potential there, a lot of different problems that can be answered and really, improving the whole drug discovery and drug development process. I'm really excited for that and the FDA modernization being passed, and there being so much room to do organ-on-chip research with it being accepted. I'm really excited to see what the future holds for this field.

Travis: Any parting thoughts you’d liked to share?

Queeny: There were really a lot of challenges in the project, but I think overall it was a very exciting one. I really enjoyed my time and learned a lot in the process, and thanks to Pluto and to all the other people who contributed. When we started the contract at the Wyss I think my project was one of the first ones that you started working on with Pluto, so I think I got a lot of special attention because of that (laughter).

Travis: My role here is to keep that special attention going for our customers! But you were definitely a very early adapter and a pioneer of our platform in sense! Queeny I really appreciate your time, this was awesome, thank you!

Queeny: Thank you so much, very nice meeting you finally!

Explore a key result

Note: the plot above shows live data from Pluto for this experiment. Click the button in the upper right corner to view the methods. Learn how to create your own public plot share links with Pluto here.

The Pluto team congratulates Dr. Dasgupta and the Donald Ingber Lab on these novel and clinically relevant findings! Be sure to check out the publication in Nature Communications.


1 Society, A., Cancer treatment & survivorship facts & figures 2019–2021. American Cancer Society Atlanta. 2019.

2 Dasgupta, Q. et al. A human lung alveolus-on-a-chip model of acute radiation-induced lung injury. Nature Communications 14, 6506. 2023. https://doi.org/10.1038/s41467-023-42171-z