In order to know what a Community Scientist may need to contribute to a research meeting, it is helpful to understand the nature of the content discussed, and the dynamics of, team research meetings. In this series, you will first watch an introduction to help lay the groundwork for the meeting. Then, you will see a behind-the-scenes view of a research meeting during which data, decision making, and progress to date are reviewed by the team.
Learning Objectives
- Explain how research meetings foster collaboration
- Propose how Community Scientists can potentially contribute to similar meetings
Background and Context
For these types of research meetings, a Community Scientist would typically have prior knowledge of the topic or disease. As this meeting skips that step, some background information about the disease discussed in the video is below. This information will provide context as you watch the video:
- Kaposi’s Sarcoma (KS) is a type of cancer triggered by immunodeficiency in those with the Kaposi’s sarcoma-associated herpesvirus (KSHV).
- Because all KS tumors are caused by KSHV, there is no healthy “control” sample of a tumor without the virus to compare to, and it will be tough to figure out which treatments may have an impact.
- KSHV can be dormant, or latent, in the body for a long time while prompting cell growth that will lead to cancer.
Part 1: Introduction
Note: The video refers to participants and/or the program as “Citizen Scientist,” which was the previous name of the Community Scientist Program.
Part 2: Meeting
Extended Video Captions- 2.3: Renne Lab Meeting, Part 2
[Slide: Renne Lab Meeting: Part Two]
Slide 1
[Video meeting: Six participants]
Dr Renne: Thank you all for participating in this meeting today where we really want to ask and discuss a very important question which is: Which gene changes and altered pathways cause Kaposi’s Sarcoma? And here, you see the classical picture of [unclear] KS. [image: Kaposi’s Sarcoma]
Slide 2
Dr Renne: So, we know that Kaposi’s Sarcoma-associated Herpes Virus [acronym used on slide: KSHV] causes this cancer. We know this for a very long time because all KS tumors are positive for the virus. The goal in our lab, ultimately, will be to understand what the virus changes in these cells and whether we can therapeutically target these changes. So, ultimately, we want to treat the tumor. The challenge, however, is that there’s not very easy models to study this in vivo, so there’s no mouse models, for example, because the virus infects strictly only humans. So, that makes us in the lab restricted to what we call “in vitro” tissue culture [acronym used on slide: TC] models: So we grow cells in the lab and then we can infect them with the virus and then we can actually look at these genes by comparing uninfected and infected cells. We have done this for years and have very interesting results here but what we need to know is, [diagram: comparison of in vitro vs human samples] are these same genes also altered in the human tumors?
So, we are lucky now that we have access to a significant number of tumor samples but now when we plan the experiments, a problem popped up and this is: what do we compare? How do we compare the human tumor? To what do we compare it to? Because we cannot have a non-KSHV positive tumor. So that’s the issue and then the big question, of course, is all this work we have done over years on the left side, does it really inform us what’s going on in the human tumors? Before we open up the discussion, I want to show you one more little detail about why this is complicated.
Slide 3
[title: KS tumors have different cells and not all cells are KS positive]
Dr Renne: So, what you see on the left here is what works wonderful in the labs, very well controlled. These are uninfected cells and we can infect them and we use a fancy virus which we have labeled with the green marker so all the cells which are infected get green.
[text: Tissue culture cells are green due to labeling the virus with a Green Fluorescent Protein (GFP), which is a jellyfish-based protein that turns green when exposed to light. GFP helps researchers easily spot specific cells in lab samples.]
Dr Renne: And then, we compare what the cells express in genes. And you don’t need to know any details here, but what you can see is in green, there’s a lot of genes changed when we compare uninfected with infected cells, which go down, and a lot of genes which go up. [graph: comparison of gene expression in infected vs uninfected cells] So, the hypothesis clearly is right, that the virus does something but here’s the problem: So, we can take a lesion of a patient and do histology- [on-screen text: histology= the study of body tissue at the microscopic level] and this is a histological slide, what you can see here- and the dark grain over the tumor sample here is the epidermis, the skin, which you can see here is the cells that are actually really infected. [image: KS tumor under a microscope] So, there’s two problems which pop up right away. First, what do we compare it to? Second, not all of the cells in the tumors are actually infected- that’s another problem. And that’s what we want to discuss today, how to address this. And so here I’ve stopped sharing and would prefer questions so that we can discuss this from all our expertise and different skill sets. [Video meeting: Six participants]
Dr Renne: So, Dan, based on your expertise in bioinformatics, I would like to ask you first and pick your brain how we think you can address the comparison problem.
Dan: Yeah, so when you’re showing the histology slide, there’s a couple of different cell types I noticed. You have your epidermals- skin cells, you have the infiltrating endothelial blood vessel cells, and I’ve been looking through some of the online databases recently and I think we might be able to find some collections of data that other researchers have made public from those healthy cells. And so, if maybe we could use those healthy cell data sets as a comparison for our infected cells and compare them and maybe even see if we can piece out which cells are the blood vessel cells, which cells are the infected tumor cells. It requires some complicated analysis, but it could be a useful way.
[text: This would enable a comparison of similar types of cells to determine changes between the infected (KSHV+) cells vs cells without the virus, which may be as close as we can get]
Dr Renne: Okay so actually for that, I would like also to ask Dr Fredenburg, because she’s an experienced pathologist [text: Pathologists interpret and diagnose changes caused by disease in bodily tissues and fluids], to tell us a little bit more what other cell types possibly could be involved here.
Dr Fredenburg: I was thinking that some of the cell types that I can- just in my experience, looking at the, you know, different skin of Kaposi’s or Kaposi’s Sarcoma would be, maybe some immune cell infiltrates that we could investigate that might be there along with the infected endothelial cells, and we could maybe look at just lymph nodes that have immune cells in them and compare that to the population that’s in the cancer.
[text: Endothelial cells are the main cells inside the lining of blood vessels, lymph vessels, and the heart. These cells help regulate the passage of blood and other materials from the bloodstream to tissues in the body. These are the cells impacted by KS, and why the tumors are red.]
Dr Renne: So it would be maybe a good idea that you and Dan develop a list of different cell types, tissue types, where we could then screen the NCI databases for sequencing results.
Dr Fredenburg: Yeah that’s a great idea.
Dr Renne: So, Lauren- I know you have a technique which particularly can actually look only at infected cells. What do you think how we could apply this here?
Lauren: That’s correct. So, as you know, I’m interested in the smallest type of RNA, called MicroRNAs
[text: MicroRNA inhibits gene expression, the process during which instructions from DNA are carried out to make protein molecules such as enzymes, hemoglobin, collagen, or antibodies],
and these specifically are able to suppress the expression of various genes. And so, what my method focuses on is specifically extracting these MicroRNAs from cells. I’ve designed it so that, even if there are only a few cells present, this method should still work. And then once I’ve done that, this should give me some insight into what the MicroRNAs are targeting inside cells for suppression. And so, we’ll understand which genes are down regulated and which MicroRNAs are responsible.
[text: Down regulated is when the quantity of a cellular component is decreased in response to environmental factors or to compensate for imbalances in the body]
Dr Renne: That’s great, that’s great. I would also like to introduce Ritu and thank her for replacing Melissa today, who couldn’t make it. So, Ritu is a postdoc in the laboratory and she has developed techniques which particularly can enrich for viral genes and analysis like this, but-, Do you want to…?
Ritu: Yes, so maybe, I mean, you said about comparing the expression of genes and the in vitro models, so in the in vitro models as well as the clinical samples for KS, I mean, the expression of virus transcripts is very low so maybe we can first enrich those transcripts using the virus-specific base so that it’s not hindered by the expression of cellular genes, the host genes, and then we can compare the expression between the infected and uninfected.
[text: Tumors make only a few viral gene products. Enriching the transcript from these tumor tissues will allow for a better comparison.]
Dr Renne: That’s a powerful technique.
Ritu: Yeah.
Dr Renne: Yeah. And also, Yuan, you’re working on some of those viral genes. You should also maybe talk to Ritu a little bit about how you could focus on the transcript you’re studying with great detail.
Yuan: Yeah, so, I was working on some staining assays, either on cultured tissues or in cells or in tissues, to show the RNA abundance and distribution in cases we infected the tumors.
[text: The technique called a staining assay is a process that uses coloring to analyze the components of a material (such as cancer cells within a tumor) so they can be easily seen through a microscope]
And especially I was interested in long noncoding which lies across the latency region. Have you identified any long transcripts in the- on the same strand, on the antisense strand, of the latency region, Ritu?
Ritu: So, yes I have been reading the KSHV transcripts after enriching them. So, what I have found are a lot of new transcripts and many of those that start from the latency region, so maybe that can help you.
Dr Renne: So, I think we had a really good starting discussion today. So, what I see is that we actually can address this from very different points, but what I would suggest is that that we all go out and create more detail about what we just decided and put this together as a working model because one of the things is, we have to plan this very carefully because these samples are very valuable- we don’t have so many of those. And then we can adjust what we prioritize, what we do first with which of these samples. And so, I think that the key things are summarized today is that that we’re going to have a discussion between Lauren, Ritu, and Yuan to think about how we can focus on the viral genes and then between Kristianna and Dan about the different cell types and how we can address this comparison system and create comparative data sets from already-sequenced data in the database. So, I think this was very productive today and I want to thank you. Let’s all go to work and let’s have meeting- the next meeting on this in a week from now. Thank you very much.
All: Thank you.
[text: The working model the team will create serves as a plan of action to address this research question through multiple angles and techniques based on what each member of the team discovers through their work]
[Slide: Renne Lab Meeting: Part Two]