Scientist Stories: Musa Mhlanga, Inflammation and Metabolism

Let's get things underway. Um, first of all, has everybody here been in the meeting that has been occurring so far, like yesterday, the day before?

Yes.

Or maybe a better way of asking this question is who has not been— who didn't attend the meeting yesterday? Okay, so if nobody— if everybody— Mariana was not able to be around yesterday. Okay.

Can we introduce ourselves because we are only 10 people? I think we can do this as well, right?

Yeah. Yeah, we have people, more people joining all the time, it looks like. So, sure. So let's do introductions. So do you want to start, Helder?

Yes, I'm Helder Nakai. I'm from University of São Paulo and my lab works with systems biology. So we try to understand infectious disease. So therefore inflammation is a major part of what we always see when we are analyzing the data.

Helder, you want to say a little bit about, like, just maybe something about the HCA and what you see?

Yes, HCA for me is like something that I always dreamed. It's like the reason why I did biology in the first place was because of the Human Genome Project. So the Human Cell Atlas is like my Human Genome Project for adults. So I'm really glad to join and be part of this. And I hope you guys also enjoy because it's a very ambitious goal. But I think this is the state of the art of molecular biology. So I think that we have to make these connections between ourselves to be able to answer a lot of questions we cannot answer any other way.

Great, great. So I'll go next. My name is Musa Musanga. I'm a professor at the Radwood University Medical Center. My lab works on single-cell epigenomics and biophysics, especially on gene regulation and inflammation. As it relates to the Human Cell Atlas, I have been working in the Human Cell Atlas since it started in 2016 at our kickoff meeting in London. I co-chair the equity group of the Human Cell Atlas with Alex Shalek and Partha Majumdar. And I'm also a member of the organizing committee of the Human Cell Atlas. It was really a pleasure for us to visit Brazil about a year ago, almost to the day, to do a road show. And I've been to Brazil several times. I think it has such outstanding scientists, and I think having the Latin America HCA meeting is really bringing all of this together and extending this to the entire continent. Today, I hope that we can cover a lot of these really interesting ideas around how to build upon this field of inflammation and single-cell biology. So I'm just going to pick the next person based on whose avatar is next to mine. So I'm sorry if you randomly end up ended up next to me, but the next person is Pedro. Pedro, maybe 30 seconds. Right, so I'm Pedro. I'm from University of Campinas. I work with metabolic regulation of adipose tissue macrophages and other tissue-resident macrophages. And together with Marcelo Mori and Mariana, we got the CZI for single-cell inflammation. So it's a pleasure to be here and discuss a little bit of what we do with all of you guys. Congratulations, Pedro, and to your whole team. The next person I have is Luiz. You want to go ahead, Luiz?

Yeah.

Good morning, everyone. I'm Luiz Leiria. I'm a professor at the University of São Paulo. And my main scientific interest is in the brown fat-mediated thermogenesis and mechanism regulating thermogenesis, and how brown fat connects with other organs through secretion of brown fat secreted factors, especially regarding the brown fat and liver crosstalk. And we are very interested in going more in depth in the single-cell sequencing of these metabolic organs Yeah, especially brown fat and liver. Great, great. The next person I have is Silvia.

Yes, hello, good morning. I'm Silvia Sokoyan. I am a physician scientist for the National Scientific and Technical Research Council from Argentina and also it's a university-based institute. My main interest is in liver diseases, particularly in nonalcoholic fatty liver disease. I've been working in the field almost for 25 years, and my group is interested in understanding the disease pathogenesis. We don't have specifically the technology and infrastructure for single-cell analysis, but we were doing many omics techniques. Specifically based on tissue samples from our biobank. So we are very interested in having the chance to participate in these projects. Thank you.

Welcome, Silvia. Marcelo?

Good morning, everyone. I'm Marcelo Mori from the University of Campinas, and my main interests are the biology of aging. So, and I study aging in many aspects, but particularly I'm interested in the interface between metabolism and aging. And like Pedro mentioned, we got this grant from CZI to study adipose tissue macrophages. Adipose tissue is my favorite tissue, so we're interested in you know, sorting out different subpopulations of macrophages in adipose tissue and how it connects to metabolic diseases.

Great. Congratulations again, Marcelo. Next, we have Mariana. Oh, by the way, please say— I know many people from Brazil, but it was great that Silvia is from Argentina. Please say which country you're from. Okay, well, hello everybody. I'm Mariana Massioni. I'm from Argentina.

I am a professor of immunology in the National University Center of Argentina. I'm interested in type 1 interferon biology, in tumor immunology, and also in human conditions such as Down syndrome. And I'm really curious We are not doing any single-cell analysis or anything like that, but my— some of my students have done collaborations in the US and Europe, and I'm really interested, and I wish we could develop this area here. And so that's why I'm here.

Great, thank you so much, Mariana. The next person I have is Jennifer.

Hi, I'm Jenny Rood. I'm a science writer who works with the HCA, so I actually don't do my own research. I sort of help people communicate it, but I've been working with the Human Cell Atlas team since early 2017. And I'm based at the Broad Institute in the US. And I'm really here just to learn from you and to sort of listen in on your discussion and help in any way I can. From the Human Cell Atlas side.

Great. Thanks all for joining us, Jen. I think the next person I have is Camila, but Camila is a translator. Is that right, Camila?

No, I'm not a translator.

Oh, I'm sorry. Lucio told me about that. So sorry, sorry.

Excuse me.

Oh, go ahead, Camila.

Maybe it was a misunderstanding.

So I understand.

Yeah, no problem. I'm a postdoc from the University of São Paulo. In 2018, I had this amazing opportunity of optimizing the ATAC-seq single nuclei protocol to study in postmortem brains, like cryopreserved postmortem brains. So during the afternoon, we are going to have the showcase for the technology, and you'll see my video there. Like showing like how to perform the experiments and the benchwork. So it was a great opportunity. And in my postdoc project, I have been working with psychiatric disorders, more specifically bipolar disorder. And we know that bipolar disorder— we know that the pathophysiology of the disease involves inflammatory alterations, especially in the periphery that we can see in the patients. But we still We still don't know how it happens in the brain, so I'm kind of interested in doing like this bridge between the brain and the periphery regarding the inflammatory factors and how this works. And this is like my research interest on single-cell analysis.

Thank you so much, Camilla. The next person we have is Ken. I'm sorry, Ken, that's all I can tell from your name. You're on mute, Ken. On mute.

No problem. My full name is Ken Golub, and I'm immunology researcher at Secomargo Cancer Center in São Paulo, Brazil. We focus our research on looking at immunological mechanisms of failure to respond to immunotherapy treatment for patients treated with checkpoint inhibitors. Principally non-small cell lung cancer and melanoma. And we also study the peripheral and local immune response in head and neck cancers using a lot of high-dimensional flow cytometry. This week, we just installed a BD Rhapsody single-cell platform in the lab, and we'll be doing studies with that machine, combining the functional potential of T cell subpopulations with T cell receptor CDR3 usage. So we want to look at targeted focusing of T cell repertoire in patients that either respond to therapy or not, and those patients that have really severe immunopathology. A subset of patients that are treated with checkmate develop a severe autoimmune reaction, and we want to track those cells at the single cell level.

Wow, it's really nice to hear that, you know, you're getting all kinds of infrastructure like this in São Paulo. We have a few more people who've joined pretty late, so we're gonna have to skip through these pretty quickly now. Norbert, you're joining us from San Francisco. Norbert, you want to introduce yourself? Hello, Norbert Tavares. I'm a program manager with the Chan Zuckerberg Initiative. I used to be a microbiologist. No, damn it, I'm still a microbiologist. I just don't do any bench work anymore.

That's it.

Great. We also have Jose who's joining us from Boston. Jose, you want to introduce yourself?

Yeah, so my name is Jose Ordovas Montañez. I'm at Boston Children's Hospital now, immunologist by training turned single-cell biologist. I've had the pleasure of meeting a few people here and hoping to learn from everyone else as well. So Thanks, Musa and Helder. We've met virtually. Norbert, we've also had the chance to meet virtually. What my relatively new lab sets out to study is how inflammation shapes barrier tissues and the potential for different cells to not only adapt but also remember distinct inflammatory states beyond immune cells. So considering cells like epithelial cells and stromal cells within that framework. So happy to be here.

Thanks, José. I'm happy to have you here. Are you doing the Spanish translation on this as well?

Uh, I'll— I'm taking notes. I'm taking notes as like a meeting summary sort of thing, but, uh, I'm— I, I won't be able to come back for the, the breakout afterwards, or the, the return afterwards, I guess. So I'll, I'll update Christine.

Great. Um, last, we have 4 more people that I need to introduce, so Ludmila is next. Ludmila Ferreira, you're on mute.

My name is Ludmila.

Yeah, Ludmila Ferreira. I think we're having a bit of trouble hearing you, Ludmila. Let me just see. It seems that you're still on mute. Ludmila, we'll— maybe we'll come back to you. We'll go on to Elena.

Can you hear me?

Hello, Ludmila? Okay, let's— we can hear you now and we can see you. We can hear and see you now. It's my internet. Go ahead.

I'm from Belo Horizonte. Monte, Brazil. I'm from University of Federal University of Minas Gerais, and my lab research is related with cardiovascular disease, infectious disease like Chagas disease, and we also do a lot of system biology approach to integrate data from omics all kinds of omics data. And I have been working with microRNAs since 2011. So, I'm really interested in joining this group and see what we can do and help with this great opportunity to collaborate and learn about single-cell sequencing, and that's it.

Great. Um, we'll go on to Elena now.

Hi, my name is Elena. I'm from São Paulo, University of São Paulo, Brazil, and I am a psychiatrist, and I've been working with EST microarray, RNA-seq. So all my life I've been working with differential expression, and now I really want to learn about a single cell and how to work with these and this kind of— and I've been very interested in working with the interaction between the placenta and the brain. So I'm here to learn with you and to share some, you know, possibilities.

Wonderful. Last two people, we have Yemi. I'm sorry, I may be pronouncing your name wrong.

Good morning, everyone. Hi, everyone. Can you hear me?

Yes, we can hear you.

My name is Yemi. I am postdoctoral researcher at Pontificia Universidad Javeriana in Bogotá, Colombia. My research work is focused in to evaluate the role of telomerase in the gene regulation in astrocyte cells stimulated with an inflammatory molecule. I'm here to learn and to networking with other people. Thank you for this opportunity.

Thank you for joining us. And the last person we have is Isaiah. Hi, hello, can you hear me? Yes, we can hear you, Isaiah.

Okay.

Not sure if you can see me. We can see you too. Okay.

I'm not seeing my camera right now.

That's fine. Well, I am a researcher in São Paulo. I know a few guys here in this room. And I recently worked with olfactory epithelium and with this outbreak of coronavirus, I became really interested in and start to look at single cells. There's a lot of stuff to be discovered, and I'm learning a lot. And recently we had the chance to collaborate with physicians and get maybe access to human material, and that's why I believe I can learn a lot. And I was I'm really, really sorry about this dog problem here. I was really thrilled with the single nuclei studies that we can use frozen material, so I'm here to learn and try to interact with researchers. Wonderful. Okay, well, it's really nice that we have people from different countries and different parts of Brazil. And I guess Brazil, since it's got such a large population, will probably dominate numerically anyway. So there's a slide that I've put up here that's sort of just to guide our discussion. There's this slide and another slide, just two of these slides where there's a few points that we need to cover. And just so everybody understands the format, Um, once we have, um, gone through both of these slides and we will take careful notes, um, we will go back to a main report session, uh, where this will be collated with the other breakout groups and, and sort of will bring forward the main points that were the products of this discussion. So, um, it really works best if everybody kind of— maybe there's a— I think there's a raise your hand function in Zoom if you don't know how to do that. If not, you know, if that's difficult for everyone, we can just, you know, talk whenever. But if everybody starts talking at the same time, I may have to be forced to mute somebody. So, well, Helder, would you want to take us through the points on how best to collaborate with the Human Cell Atlas?

Yes.

Yes. So, hi guys, I put in the chat what I could hear from you about your interests and expertise. And I'm pretty sure we can definitely find some common interests so we can like build projects together, you know, like papers, because that is exactly what this session is about. It's really like trying to switch from the, yeah, let's work together to really like, okay, what can we do? To make these things, to start things up. So the goal when you ask Human Cell Atlas to make this session was really like to have a small group of people that could start WhatsApp group, like changing emails, exchanging emails, and writing grants and papers together. Yesterday I had a call with Pedro and and Marcelo, and they gave me some very interesting ideas where they can start to see the vibe here in this smaller group. And I would like to ask them to share the ideas. So if you guys think this is interesting, we can go further and include more people, and then in the report session, we can say that we are willing to to recruit more collaborators because that could be a nice start. But that is only for like us to understand what kind of stuff we could do in the Human Cell Atlas context. So Marcel and Pedro, do you want to say something?

Sure. I mean, we got the CZI funding for studying adipose tissue macrophages from obese and lean people, right? And I mean, One of the questions we had is what are the types of macrophages? What are the differences and the similarities between macrophages from different tissues? And we have a lot of datasets available. So the idea was, is kind of to make an atlas of the resident macrophage population in different tissues. And using public data, for example. And the idea is to use, for example, the adipose tissue macrophages from lean and obese people, from male and female, to kind of be as a proof of concept about regarding these macrophage atlas. So we know that macrophages have different origins. They can come from bone marrow, from yolk sac, from fetal liver. The environment where they live is really important. But what are they, just the same populations, or they have the same origin, or these macrophages actually are really tissue-specific? So I mean, the literature, of course, have a lot of data describing a little bit of these macrophages but using a panel of restricted markers. So we don't have a big picture of what they are inside of tissues. So that's kind of the idea. Marcelo can complement, can say a little bit more about that.

Yeah, so just for you guys to know a little bit more about what our grant is about. So So we got funded by CZI to study adipose tissue macrophages and try to sort out different cell populations within this tissue. And we're going to compare different adipose tissue depots and also obese versus lean people, males versus females, and people from different ethnic backgrounds. But in order to do that in a, you know, in an affordable way. We cannot do single-cell sequencing in all these people, so we want to select a small group of people, and then we want to see, identify the subpopulations, identify surface markers in this subpopulation so we can use high-dimensional flow cytometry to to identify these subpopulations in multiple tissues and in multiple people and increase our coverage. But while we're doing that, so one thing that struck us, and also it was very interesting that we got together here, is that we don't really have an atlas for tissue-resident macrophages in humans or even in other organisms. And we have tons, like Pedro said, we have tons of data available for healthy individuals and multiple tissue single-cell data. That we can use perhaps to identify this macrophage subpopulations using conventional markers. And then we try to sort out different subpopulations within these tissues that can define the repertoire of macrophages we might have in our organism, right? So based on that, we could then design specific experiments where we can compare the frequency or the level of activation of these macrophages in different conditions. For example, comparing a disease state and healthy state. And that, I think, could be really beneficial. And this Considering that macrophages are pretty much everywhere, I think it's really interesting and could be used as a way to, you know, link the Latin American community within a goal to collaborate with HTA but with a particular focus, which is basically identify this atlas of human macrophages, tissue-resident macrophages. And I think what is really interesting is that we have funding for, you know, even for a proof of concept that this is this can be used to, you know, compare disease versus healthy state. We can use our own project to compare obese versus lean to, you know, as a proof of concept that this can be used in other tissues and other disease states. So that was the idea.

So Marcelo, just not to interrupt you, too abruptly, but one of the things I think is important is I think this is a really good idea, and it really consolidates your consortium that was funded by the CZI Inflammatory Core. One of the ways I think we want to— one of the questions we want to answer here in this section is the shared interest with other working groups or other committees. And I think because— I think Pedro mentioned you have many different types of samples available and you want to do this sort of high-dimensional sorting. Is it— do you have samples that are from other, let's call it, tissue atlases that could also contribute to this? And Maybe that's a good way to sort of check off this first question we have here that's up on the screen about just sharing interest with other working committees so we can also cross information into other areas, really get synergy for these projects.

Yes, I think this has everything to do with the other HCA working groups and committees because As I put in the chat, what they said here, that what kind of people we need to do this little project. Of course, we will need the people from standards that's gonna say what is the pipeline that are used to transform the FASTQ into the gene counts. We'll need the bioethical people that's gonna help like people to decide which samples we can use. The people that already have the data that we just need to to find out from the different working groups based on tissues, how can we extract the macrophage data, you know, to put there. And then from that, I'm pretty sure that everyone that is here, like the people that work with inflammatory neurological conditions, bipolar disorders, autism, or Down syndrome, or tumor, everything is related to you can place macrophages on this. So I'm pretty sure here there is a common interest if you look deep into. And in the HTCA, I also think that this can be like an interest for the HTCA as a consortium. But of course, you have the expertise in telling us how can we like talk to the HTCA in terms of this become like a branch project.

You're asking, you think I have the expertise?

Yes, yes, my God.

I don't know if I have the expertise, but we all have opinions. So, I think in that note, one thing maybe if we opened up to other people here, for example, from Argentina, there's a couple of people in this breakout room. What is your general interest in what's been raised so far, and would you be happy to contribute to these efforts? Do they fall into your scientific areas of interest? Silvia, for example, I remember you're from Argentina.

Yes, Musa, thank you. Yes, I find this Summary, very, very interesting. In fact, we might be interested in replicating, for instance, the signals that you find in adipose tissue in the liver. We have a collection of liver samples for patients and also some controls, but I think we are not able to do right now, the single-cell experiment, because we don't have the infrastructure. And I think there's, as far as I know, there's no place in public institutions in Argentina that has a facility running now. So we might show this initiative by replicating things that are already validated. That sounds very interesting idea because we don't need for this replication things fresh for it and we can go to other collection of RNA or already isolated in the biobank and see what happened with this specific signals. So we can validate definitely if the liver disease is much better with the main idea of macrophages leading the pathogenesis of metabolic diseases.

So building upon that, I mean, obviously, again, I think liver is nice. 80% or so of the macrophages in the body are resident in the liver.

Do you—

do we also want to think about other inflammation-driven diseases or healthy atlases that can be made from perhaps some tissues that are not necessarily primary inflammatory tissues? For example, Jose mentioned epithelial issues or barrier issues. There's also endothelial tissues implicated in atherosclerosis, et cetera, where you have these types of chronic and low-grade inflammation. Within your group, are there interests in working with these tissues? Are there repositories of these samples available within your community here that you could introduce into this sort of a project? That was a general question, so somebody wants to pick up on that. For example, Ken mentioned something very specific about T cells and T cell therapy. That's something that hasn't really come up yet in the discussion so far besides Ken, but it's just Showing that there's quite a large scope of interest here around information, and we'd like to kind of capture that in our discussion right now.

Yeah, I think one thing I wanted to discuss a little bit with the group as another way of encouraging interaction and also get ideas is for— is with respect to integration of different types of single-cell datasets. So we do high-dimensional flow, which is a single-cell analysis. You can think, in most cases, we're looking at protein expression or either signaling molecules or effector molecules, but we have single-cell datasets that are based on flow cytometry. And we can have typical single-cell RNA-seq, and we may have more approaches of spatial semi-single cell, depending on the system that somebody is using, spatial transcriptome at a single cell or near single cell level. And one of the big issues that we have is going to be integrating different data types. So we have either flow cytometry-based datasets, which can be very high dimensional in terms of the number of components that you're looking at at any one given time. And then we'll have transcriptome, and then we can have single-cell RNA-seq, all from the same patient, sometimes from different sites. And then that's joined together with the clinical data of either response to therapy or development of severe adverse events, for example. So I think tools along integration of different datasets and different types of data And the implications of that would be extremely useful, I think, for lots of people. I think hearing a little bit about what type of research is being done, I think most people will have different types of data integration, and that brings some special problems, right, that maybe people have solutions for, and there could probably be many different solutions, and it would be nice to have a a discussion and a place where we can find out about how to approach those sorts of problems.

That was one thing.

Could I ask a quick question kind of on that? So maybe we can use the raise hand function in two rounds. You know, I think in the introduction we got an area of the biological interest, and it's clear there's many cross-cutting biological and disease interests across across the group. Then there's also the technical aspects that I think everyone brings to the table. So maybe— and I would struggle myself to classify cleanly into one group or another— but maybe we could go around and just raise a hand on if we consider ourselves to be interested in more the generation of the data or the analysis of the data. So would people be considered data generators and their groups are interested in generating and contributing data? Or if people are interested in the analysis of existing datasets from the community, how to access them, and how to maybe analyze data that the generators could be interested in providing, fully understanding that some of us may live in both of those camps. But, you know, maybe just to try to figure out where those interests and also bottlenecks lie. So maybe we can go with data generators first. So if people could raise their hands and we could get a sense of who's really interested in the data generation side or their group would be interested in leading efforts there. So you can just use the raise hand function under the participants.

I don't know how to use the raise hand function.

You can just raise your hand, Helena.

Or you can raise your hand like this too.

I can see two blue hands now, but there's like, so this is a data generation? Yeah. Is that the poll right now? Yeah. Okay, so we should probably count those. Sorry, I need some time to count. 'Cause 1, 2, 3, 4, 5, 6. Got 6 hands for data generation. Okay, and then how many— Did you note that?

Yeah, and so how, and then so people put their hands down and then how many analyzers do we have? So people who are principally interested in analysis. Helena was quick there. She was like, she was the first one up. She was waiting there.

So this is analysis of single-cell datasets, right?

Yeah.

Yeah. Yeah. So if you, if you have experience with that and you would like to be on that side, raise your hand. So we've held her, we still have Elena, we have Marcello. That's 3. Ludmila, you put your hand down. You're on mute. I can't hear you. We can't hear you, Ludmila. We still can't hear you. Maybe just type what you'd like to say in the chat and And we'll memorialize it there.

Yeah, the only thing, I think we're fairly evenly divided, I think, between the principal interests, but the reason why I wanted to try to ask that was just because of some of the themes that I think I heard in especially the latter sessions and yesterday, in yesterday's discussion and presentations of really some fantastic analytical efforts But maybe from the folks that are more on the analysis side, if I or others could learn what are some of the challenges that you're currently facing in either accessing or interpreting single-cell data? What are some of the things that you wish would accompany those datasets in order for you to be able to work more effectively with them? So our group is currently, and Norbert is aware of some of these efforts, Our group is currently working on putting together a data release and also kind of preprint that accompanies some larger efforts surrounding COVID-19 and profiling of nasopharyngeal swabs, et cetera. And we've kind of been working internally within our own group to figure out, OK, if I were then a user of this data, what is everything that I would want to know about it? For those folks who are more pulling data from public resources, you know, what are things where you're like, man, I wish that that was, you know, present with the data, or how could I work better with it? You know, do you oftentimes just pull the data and work with it independently? Do you always reach out to the authors? You know, have you successfully established collaborations with some individuals in this way? These kinds of things, just to spark some discussion from the analytical side, I think, be interesting.

Okay, so maybe what we can do just to follow up on your question, Jose, is there's a sec— there's the second page of these slides, there's data sharing. Okay, a part that I've just thrown up there, and, um, or I'm trying to throw up there. And anyway, and yeah, you can see There's ethics, goal setting, and data sharing. So maybe we can come back to that. People can think about what Jose just asked, and we can discuss that when we talk about data sharing. Got it. Can I kind of get a sense of what the regional interests and characteristics that are maybe unique to the Latin American community Now, these may be the same ones that exist in other communities, but it's just good to— for us to be able to register them in this kind of a setting. It seems like, you know, Ken has raised the one of treating different types of small cell lung carcinomas, etc. That's one research interest that Evidently is in Brazil and people have. What are some of the other interests and maybe characteristics that are very regional that any of you would like to raise here? So they could be specific to certain diseases, certain atlases that you think would be unique from a community like yours.

Can I speak? Sure. I'm sorry, I don't think that it's specific for us here, but I think that's very important for us to work with the exposition to stress and violence. And we know that during gestation or during early childhood or your life, and the effects of this exposition are associated with inflammation.

So you're saying exposure to difficult, you know, urban or, you know, violence, things like this?

Yes, we know that this can Turns out, as the Doha concept, that you can have a lot of disorders, and most of them are associated with inflammation.

So we have— so that could be one. We could call it, you know, social effects. That could include violence. It could include, you know, difficult urban living conditions, maybe in poor housing or things like this. And indeed, the Human Cell Atlas wants to sample across multiple economic strata, so this is really important to capture.

In terms of São Paulo, I would add the ethnic differences because we have a very mixed population.

So do you mean like for São Paulo, for Brazil in general?

For Brazil in general, yeah.

That's definitely very, very important, and I think Norbert may have a few words to say about that. He had some to say yesterday, and that's for sure. That's definitely an interest of me personally and CCI in general. So one interesting thing that I have— one caveat, one concern that I have when we talk about— at least I'm giving you the perspective from the United States, the way we bucket things ethnically. And so we have the Hispanic bucket, and if anybody can tell me what the Hispanic bucket is, I would be happy to learn, but it's kind of a meaningless word, right? Because if we look at Mexico, if we look at Brazil, Brazil is almost 50% European descent, and so is Mexico. And so that category of Hispanic then really means European. It doesn't actually cover Native individuals or Afro-Latinos. So I would guard against, let's survey the ethnic group in Latin America, and then we just come back with, say, Argentina is almost 90% European descent, and so we have the Hispanic We've touched Hispanic, but we haven't actually touched Hispanic. So if we're going to talk about ethnic variety in Latin America, we have to actually mean ethnic, various ethnic populations and not just Europeans that happen to speak Spanish or Portuguese. But I think it's a very interesting area if we can do it correctly and ethically, and it's something I would love to see more of and well supported as well. I also really like the idea of social stresses. So there's a group at Howard, there's a couple groups in the United States that look at the epigenetic changes that happen to mainly African Americans with result to stress and chronic stress and chronic violence. But I don't think anybody's looking at it at the single cell level, so that I think would be very interesting to look at. I don't know if it's the appropriate moment to say, but hi everyone. I'm working with viral infections like dengue, Zika, also here in Brazil, São Paulo. So I have interest to move. We have already funding for generating data, but initially I would work with bulk RNA sequence, but now With this opportunity to join you, I will change for single cell and also here working with Elde together, so I hope I can also contribute. So I think we've had two really, really interesting points. The first one's brought up by Norbert around being able to capture at a more granular level the ethnic diversity of Latin America. I think that's basically the stratifications have to be sort of come along with the data. Otherwise we can, the curation of the data can give us kind of false information because of the origin of the Latin American people is so diverse. And, you know, there's large components that are from Europe or from our native indigenous and/or from Africa. So there's that aspect. There's also, I think, a new one that's been introduced by Otavio, which is what I would call neglected tropical diseases. So Zika, dengue, leishmania, Chikungunya, infectious disease samples from people, and these are highly inflammatory diseases, would be, I think, a more or less unique regional interest. And it's also interesting, I think it's also catalyzed by the fact that some of these diseases like Leishmania are starting to emerge in other parts of the world. And so the atlases that are created in Brazil could be, could be very useful well outside the South American continent. Luiz, you got something to say? Yeah, no, I think this is a really nice point that you guys brought. We work with metabolic disease and we know that the ethnics have has a huge influence on the outcomes for different populations, different ethnical origins. And as soon as we start to generate our own data here in Brazil, the single-cell data for whatever, for metabolism, for cardiovascular disease, for inflammation, and I presume that there is a big chance here that we find contrasting data in comparison to data generated in humans in Europe or in the United States. So I think that will be a really rich data set that we will generate, that we can generate here in Latin America, because there is a huge chance to our data contrast with them, with the ones in North America and in Europe. And especially for us, we work with cardiometabolic disease, and we know changing odds to develop cardiovascular disease in some populations in comparison to others. And I think the social thing also will impact a lot. And we have to consider this when we compare our data with previously generated datasets. One of the issues with this kind of when you start to sample across socioeconomic status, you know, that the way you have to control in those samples is not that straightforward, right? It's a whole new dimension of sampling that needs to be done and needs to be very carefully controlled and And you know, some of these studies can be quite difficult to do because of the many conflations in the data. So it's just something to consider quite carefully when doing this sort of, when incorporating the socioeconomic violence factors that have been brought up, which I think are very, very important. It's just we have to construct those in a way I think that most of us as scientists are not really used to doing. Marcelo?

No, it's— I just want to mention that I think it, you know, when we think about stress and ethnicity and all the diversity, We have to consider as well what's going to be the object of the study, right? So I think if we're going to choose to study diversity, ethnicity, we need to define 1, 2, or 3 tissues, or 1, 2, or 3 different cell types so we can compare, right? Otherwise, we're gonna have to study in different populations, different cell types, and different conditions, and that's good, different protocols like Ken mentioned, and that's gonna be a nightmare. So I think trying to connect the ideas that were raised here, if we could select one cell type or one tissue, and study the diversity in different tissues and different populations with different diseases. I think that would be a great effort, a team effort, so that we can connect our different expertise to tackle one single problem.

So, Marcelo, I think that's a really good point. I mean, a lot of people just take blood for that, but maybe one important element that's missing in our community is we're all really molecular scientists in the main, and it could be good to engage with social scientists who could help to assemble a cohort of people that would be well-controlled and adequately powered so that then the kind of hard science could take over and raise the issues you raise, Marcello, become even more important. So I completely agree. And I think if you want to pursue that kind of a study here in South America, in Latin America, you should probably engage with anthropologists and social scientists to assemble the right cohorts to do that. So Helena, you're— your question is just— I'm gonna choose Jennifer for the next question just because she hasn't spoken yet, and then after Jennifer we'll do Jose, and then after Jose it'll be Ken.

Jennifer. Yeah, I just wanted to say to Marcelo's point about, you know, picking a few organs to— or a few tissues to focus on for this sort of cross comparison looking at these factors, it would be— while we're on the topic of, you know, how to collaborate with the HCA, there is actually many of our biological network working groups for the immune system, say, or for various tissues throughout the body. We also have a working group that's specifically focused on genetic diversity, and as Musa said, they're mainly looking at you know, PBMCs and the blood. But I think it would be great for you guys to connect with them and say, look, this is something unique we can offer here in Latin America that we have, you know, very different sort of ethnic mix going on than, you know, I think a lot of the genetic diversity work to date has been sort of focused on Asia and Africa. So it would be great to include Latin America in that as well, since you have a very different sort of history. And that also you could sort of expand in thinking about, you know, sort of maybe different tissue directions as well, and different questions you want to look at that are unique to— as Helena pointed out, there are things that may be unique for certain countries in Latin America. So I just wanted to say that that might be another connection you might want to think about making is with the genetic diversity working group.

Okay.

Yeah, so I'll follow up. So to kind of, you know, play off of one of the points, and I think one of the strengths of the potential that exists in Latin America, of course, is to understand these so-called neglected tropical diseases that have plagued that area, and that we up here, up until maybe more recently, have only learned really through textbooks or kind of reading things, although some of them have started to infiltrate further into other countries. But I was wondering, almost flipping that around, there's a variety of different diseases that are associated with that more traditional westernized lifestyle that have really set on and are fully ingrained in certain societies that might be the main generators of single-cell data currently. And so I was wondering if we could almost flip that and maybe identify opportunities to study inflammatory diseases that are starting off and earlier in their development on a population scale within certain countries. Because I think right now, in thinking about how we might study one patient, we would really want to capture the entire temporal choral diversity of that disease. And oftentimes we're only really studying the end state of maybe 50 or 60 years of societal or environmental changes that have led to that. So I was wondering if maybe there would be opportunity to brainstorm some things. You know, here we're plagued by things like food allergy, which has some inflammatory component, for example. You know, where is food allergy currently? You know, are there epidemics of that? Are there certain epidemics which seem to just be starting but are at an earlier stage than what we're experiencing currently in, you know, US or Europe. So, you know, kind of thinking about some of those elements.

Are you also referring to things like type 2 diabetes, Jose, or—

Yeah, no, in general. In general, I mean, I think basically any disease you can basically tie back to inflammation, but really considering what the current prevalence of that disease is within a population and are we earlier in the curve, right? And are there opportunities to learn from that earlier part of the curve than where the disease currently might be in the US, for example. So yeah, totally.

I mean, it sounds to me like from maybe about an hour ago, Luis and Marcelo described how they work a lot in metabolic diseases and cardiovascular diseases. So there seems to be some capture of that there, but, you know, I've added it to the meeting notes as something that, you know, maybe kind of to presage the greater outbreak of food allergies, etc., that may or may not be occurring in Latin America.

Ken?

Yeah, so my comment was with relation to the ethnic diversity question and in general in Brazil and I think for all these datasets. I mean, what would be obviously ideal would be to have samples of genomic DNA stored for each of the patients or each of the samples that gave origin to a single-cell dataset. So that even people that don't have funds today to do a GWAS or an exome on those patients, we would have that resource available to go back and map the genetic diversity of each individual, especially in Brazil. Because if we think of metadata and think, okay, we'll just generate a nice metadata dataset that says whether people are white or Black or Asian or indigenous or other characteristics, ethnic characteristics. We know that in Brazil those can be fairly, uh, they can be very, very doubtful in their ability to predict the DNA content in that person, especially in the state like Minas Gerais where you have a lot of admixed populations where external phenotype will really not reflect well in many people their actual DNA content for in terms of ethnic origin. So I realize that it's probably impractical to think that everybody will be able to do an exome or a GWAS study on the same samples that they're using for their single cell, but they could stock DNA for future studies where you could have a large consortium say, okay, let's, you know, we have 1,000 different samples across Brazil that have been used in single cell. We can fund now GWAS on all those 1,000 samples or an exome on all those of those 1,000 to go back and truly map the ethnic diversity of those samples. I think in a place like Finland, it's not a big deal, right? You're going to have your Caucasian population, and if you say someone's white or Caucasian, then their content will likely be white or Caucasian. But in Brazil, we have serious issues with that classification. So we may think that we have in our metagenomic data identification of ethnic background of someone, but it could either mislead us or be inaccurate depending on the population we're working with.

I think so, Ken. What you're basically saying is that— maybe I can collapse what you're saying to something way more simple, which is that there probably should be some SNP array studies that should accompany the single-cell studies that are done in Brazil when you're trying to stratify for ethnicity. Yes, so maybe lots of— otherwise, otherwise, uh, um, you know, self-identification of ethnicity is, is— yeah, it's dubious, probably. Yeah, yeah, I wanted to be kinder, but I guess dubious is the right word.

So that's just because, I mean, it depends a lot, I guess, on where your population's from. Um, but the example I did of Minas Gerais, there's quite a few studies comparing, uh, particularly from one group in particular, Seja Pena's group, uh, comparing phenotypic, uh, definition of, of race and the actual genetic makeup of those individuals. And there's very little correlation in this state. But as you go further south, uh, in Brazil and then further north for African descendancy, uh, in, in places like Bahia, then you can have skin color phenotypic characteristics maybe have a little more power for distinguishing the true genetic background of somebody, and it kind of just shifts more European in the south and more African descendants in the north. But it's a super interesting point that in our studies where in some of the cohorts we will have exome on everybody because we're looking at neoantigens and other, you know, scientific questions, but we'll use that same exome to do the ethnic background. But I understand that we won't be able to do that a lot of our studies, but we can just freeze down, you know, some leukocytes or some DNA for future studies if it's possible. That could be something everybody could do and be aware of.

I mean, there are ethics questions that are related to that, but, you know, we also have groups within the HCA that, that kind of, um, well, yeah, and we can put this in the TCL, in the, in the—

sorry, I was translating the Brazilian, uh, in the, the TCLA, uh, the, the, um, we can add that into the, uh, informed consent, uh, for sure.

Oh, okay.

Yeah, yeah, that, that the pay— the, the person who's donating the blood, uh, agrees for use of the of their DNA for these purposes.

So I think it will be— this is a good point to switch gears because I think we've covered a lot of these interests and we've basically, I think, proposed many different topics that cover inflammation that could be— could nucleate a group like this one in the HCA Latin America. And that brings us to kind of one of the key issues, which is financial resources and other types of resources like infrastructure that could be available to do these projects. Helder, do you— this is kind of like out of my wheelhouse.

Yes, yes.

Because I don't know what the landscape is for financial resources and infrastructure?

Well, I think that the landscape is basically the same, right? It's always hard to get the grants, but of course, most of the people that I know that are here are very successful in getting the grants. So, but of course, you may probably not have grants towards single-cell experiments, but I think that's How grant funding works is like you do some work there and then you have enough preliminary data to ask for more grants. So we do have FAPESP, and FAPESP, which is the major funding agency in Brazil, like, and most of people from Brazil here are from São Paulo. So, and I know that they are very much interested in funding single-cell stuff. So, we— Lucio is already negotiating with them, like some calls for that. So I think there will be like some open calls soon for— from Brazil. And I'm already collaborating with some people in Argentina, for example, when they have grants, they want to apply for grants there or NIH. So what I think that this breakout session would be like, because everyone here is already experiencing in getting grants is just finding the— what can we propose, right? And we can get the samples from this group, we can get the analysis from this other group, and then we can make a point.

I mean, I miss— are your constraints— are the constraints in this group— maybe I should be more proactive— are they only funding? Are there technical constraints? Would it Would it be helpful for people to come and do exchanges?

Yeah, so until last year, let me say, until last year we were not aware of, for example, of single cell machines here like 10x. Maybe Canada was the only one that was getting the stuff. But this year I already know like a lot of places where we are going to have the machines. So at least in Brazil, I can say that we are going to be able to generate data soon. Of course, because the dollar's value skyrocketed, so for importing the reagents is going to be like a very big problem from the people that already have funds. So this is a huge constraint because we are going to have to negotiate with them about that. So I think the constraints is the same as all the other countries in Latin America. The dollar is very high. The machines is already, is still getting here. So we are not fully prepared to generate new data, but I already know that several groups are able to do analysis with publicly available data. So we are like preparing ourselves when the data comes. And also to ask nice questions. So I think that while we are not able to generate data because these constraints about funding and infrastructure, we can definitely work together to, to prepare for the data analysis plan and to raise the interesting questions, you know. And I think that that's why this breakout session happened, because We have to be ready when the data comes.

So one of the important reasons I'm trying to enlarge this is because, I mean, Jonah's on the call, and maybe Jonah, you can say more about this, but Jonah, for those of you don't know, works with Norbert, and they're both at the CZI. And Jonah visited Brazil last year as part of the the roadshow the HCA had to introduce the community to the, to the HCA broadly in Latin America and Brazil. Jonah, do you want to talk about like training and workshops and things around that nature that CZI is interested in doing to sort of help with any challenges and in terms of technical resources? Sure.

So, I mean, first, good morning, good afternoon. I hope everyone's doing well. Yeah, so maybe the thing to start with is what we've done in the past, just to kind of give a sense for some different possibilities in different areas. And, you know, these are very much in collaboration and connection with folks throughout the Human Cell Atlas. So, one is what have been referred to as Jamborees. And these often are pretty large events, so pulls together students and postdocs and folks from individual labs to look at different computational challenges. I think probably like the shining star that came out of one of these is a package called emptydrops, which is doublet detection that's now integrated in most of the major workflows. But it looks at very specific challenges and then tries to come to community understanding and solving of those challenges. You know, I think if we look across all the different areas of discussion that people have identified here, whether it's, you know, maybe some challenges on, you know, if you're interested in calling or trying to understand different ethnicity or ancestral backgrounds from single-cell data, it could be differences and challenges in analyzing data associated with infectious disease, especially if, you know, we didn't talk about this, but the desire is to actually detect in single cells which cells are infected versus which cells are just simply affected at the transcriptional level. So there's a whole bunch of different things. So these are very much kind of working computational meetings that might be of interest as different needs arise. So that's kind of a cool different area. The other one is just around computational training in general. And this is something that we try to offer and thinking about offering more broadly to our grantees, but I think it would also be a really exciting possibility to think about offering to, you know, a larger region or community. So maybe HCA members in Latin America, and that is more kind of foundational computational training. So, you know, maybe there are some groups that are interested in doing data analysis, but, you know, they want to work in Python and they've only been exposed to R, or vice versa, or maybe haven't been exposed to either and just, you know, want some help and need some time to work with others, or maybe they've learned some of those basics but then need to learn how to extend it into some of the specific single-cell analysis toolchains. So, you know, doing like a couple-day tutorial in Seurat or ScanPy or Seavi or some of the other kind of major toolchains that folks use. So that I think could be a really interesting and cool way to again connect some of the groups that are developing those tools to some of the groups that are interested in using those tools. So I think all of those could be interesting to parts of this group and certainly something that would be really fun to talk about, think about doing together.

Thanks, Jonah. Helder, do you want to talk about any— I know you talked about Lucio talking to is it FAPESP about funding projects? FAPESP is only for the state of São Paulo, if I understand. So are there any like federal government or just— and people from Argentina and Colombia, please feel free to chime in, but any other government funding available that sort of outside the people of São Paulo here?

Yes, there are funding agencies at federal level, but the federal agency in Brazil has the same budgets as the São Paulo funding agency, you know? So it's a little bit better to make science within the states here. And what people were actually asking Norbert, but I I think he couldn't— maybe was not clear is that if CCI is going to open a call specifically for Latin America or this is not possible.

Anything's possible. The answer is we don't know. We're still—

I can— Go ahead. One thing to say, Helder, is I don't think we've ever actually had a call specifically for for one region, right? And that includes the US and elsewhere. But I think that to the extent that we would look to support the Human Soil Atlas or this community, it would likely be much more thematically as opposed to just one region. But that, of course, would hopefully encourage and allow for more groups to apply and connect across Latin America, or Latin America to connect to some other groups around the world. So, I don't know. I mean, I'd be curious to—

I'm not talking about the competitor, okay? But the Gates Foundation do have some calls specifically to regions where they associate with funding agencies that like— so each one pays a little bit of the grants. So I was just— people were actually wondering if CZI would do some joint calls, you know, like with funding agencies from Latin America and then open for specifically. Because I think that that's the equity part, I think, of the consortium, because as I said, like, we were like starting behind because of the infrastructure and all this, you know, but it's good to know that.

Yeah, I mean, I think Norbert was right that, you know, anything is possible, and certainly partnerships are really important. But yeah, I guess I'd be curious to hear from you whether the hope would be to have specific funding just for Latin America, or if the more helpful mechanisms would be ones in which scientists throughout Latin America can connect more broadly with other folks, right? Like, yeah. Is there one that would be more or less helpful in your mind? Maybe all of the above are helpful.

Maybe I'll say something real quick about that. We, the, you know, the Medical Research Council, OMRC, in the UK has a Newton Fund mechanism. And I've had one of those grants. They're large grants, 5 years, and that format where you have partner in a developed country and another— so you have 2 PIs. You have a PI in the developing country and a PI in the developed country. But each one has their own budget. And they're robust budgets. So that's key. The problem with some of the partnership studies or grant design is that the partner in the developed country gets large funds. And the partner in the developing country gets a very small budget. That's not so helpful. The more, especially under today's financial situation and the way the real is in other countries in Latin America. So partnership grants are really good when both parties can have a robust budget. I think that's kind of the point I would bring up. And so MRC Newton Fund is a really good model to look at. And the NIH also has the NIH, which I've had one of these as well, the NIH R01s for international infectious diseases. Those are directly given to the investigator in the developing country, but it's all developing countries based on their classification by the World Bank. So those are two good examples of ways you can help with funding directly to people outside of developed countries. I think that you're probably aware of those, but just in case, those are nice models, I think.

Okay, thank you so much for those suggestions, and I think the Newton Fund is something that perhaps CZI and other HCA-related funding agencies should consider. We in the equity group have been trying to boost capacity in many different ways. And we think these partnerships between labs in the way that you just described can be very catalytic in many ways. Because I think, I just want to do a show of hands, like Helder was saying before, everybody's waiting for the opportunity to generate data. But just as a little show of hands, how many of you here have generated single-cell data in your labs? So Camila, Camila, that was just— okay, so Camila raised her hand and Ken raised his hand.

Yeah, I generated, but it was optimization. It was not like a large dataset.

Yeah, so And this isn't to say that I don't think people can learn how to do this and do it well, but it's also a learning curve that's associated with this. And so I think some of the mechanisms that Ken described can collapse the time of that learning curve. And we always learn faster by copying.

Always.

We even pass exams by copying, so depending on who you are. So, you know, I think that just maybe, as Norbert said, everything is possible. Maybe these kind of things are possible too. And I don't think a seed network, by the way, substitutes for this. I think what Ken is describing is— I've seen these Newton funding projects before in South Africa, and they're really powerful in their ability to transfer knowledge and to be catalytic because the funding is so stable for such a long time. I'm going to switch over to the next slide because we've got about 20 minutes or so. So A discussion like this can't be had without talking a little bit about ethics. Who would like to raise some points about ethics? We've had a couple so far, but who would like to raise some points around this? Jose?

I think you and I, Musa, have discussed some of these things, and I think they echo also the equity in terms of funding mechanisms and how to really make sure that both sides are equitably supported. But one thing that, that really strikes me and is, I think, really important is to be respectful of all of the scientists and all of the effort that goes into generating the data, assembling clinical cohorts, etc. So to make sure that any partnerships that exist in terms of sample sharing and also credit for all aspects of the work don't fit the mold of groups coming in, taking samples, analyzing it, and then taking majority credit, for example, on publications. And so it really should be an equitable partnership throughout the entire process, both in terms of generation of ideas, the acquisition of funding, practical, experimentation and analysis, and then also putting together the manuscript. Because I think we all know of very many examples where that model falls apart at various stages. And that's something that I particularly care a lot about. And I want to make sure that if I enter into any types of collaborative partnerships with anyone here or others, that I'm making sure that I'm constantly reminding myself of those aspects. In terms of how to best move science forward.

Yeah, that's very important. I mean, I think what you're bringing up there is this idea that there's a lot of exploitation of countries like Brazil and other Latin American countries where they're just used to harvest samples. Essentially. And this is really counter to the thinking of the HCA. Are there any other points that people would like to bring up in terms of ethics? I know we've had a slight discussion of that when we talked about using SNP array data and conjoining it to single-cell datasets. In terms of tissue collection, I don't know what the regulations are in Brazil or in Argentina, Colombia around the shipping of DNA and the data transfer of DNA. What are the guidelines for that? Is it easy to send genetic material and genetic information across South America. Because one of the thoughts has been to set up a main center to bring infrastructure in Latin America into a more hub model so that, for example, Argentina doesn't have to build all the infrastructure that's present in São Paulo from scratch. They can collaborate more directly and have kind of local hubs of excellence that can really work regionally. But then the question is raised, well, can you even ship that sample from Buenos Aires to São Paulo legally, right? Are there major ethics violations involved in doing that? Is the data fully exchangeable? I think these are important questions that your community, and with the help of the HCA, of course, should work with and grapple with ahead of time before they become a reality. Because I think many of you have mentioned that you do have large tissue repositories, and I think you're very willing to generate data and make them available and make those data available. But the ethical framework for the transmission of those samples and the transmission of that data is something the community, I think, should do some work on establishing.

Well, I think that the ethical issues, again, like all the researchers that are here that I know, follow the international standards for ethics. So you have to get approved So you can deal with human samples, you know, so there is always the, the consent issues. But let me follow up on what Jose said. And I completely agree, you know, like, of course, you have to be ethical in like using someone else's data to publish and not giving the proper credits, you know, and that's, I think, it's a problem that is above HCA or any other thing. It's a ethical problem the same way we have issues with authorships. You know, everyone has plenty of examples of that, people that do not, should be there. It's the same thing with people using the data. Now, of course, there is always going to be some people that are going to use data without giving the proper credits. But I don't think that's a Latin America problem. I think that's a problem in anywhere. Like I can cite many other countries that do that. So I think that's, if the data is published in a paper, the reason why the journals ask them to put the raw data there is for them to be used, right? But of course, if the data is in the consortium, it's not published and someone else is using, of course they have to get authorization and do all this procedures that are, that they had to sign up. And I'm pretty sure like Latin American researchers are gonna comply with that because nobody will get the risk to be kicked out if they do like a wrong use of the, of the data. So in short words, I would say that the research, at least the ones that I know that are here, like They are aware of the ethical issues and data sharing policies are usually given by either the consortium or the journal that you want to publish, right? So, and also the funding agency now is requiring this here as well.

So it's going to be like mostly in line with what HCA would expect in terms of like So, I mean, I think that's a nice segue into data sharing, and I think it's good that we have Jose here because during this COVID-19 pandemic, Jose has been doing a lot of work on single-cell sequencing in COVID-19 patients. And, you know, outside the framework of a funding agency, outside the framework of— let's call it regulatory authorities, he's had to face some pretty challenging data sharing ideas. And maybe a little bit of a case study here from Jose is important. And then we'll do— I think, Luiz, you wanted to say something? Just to add one point to what Helder just said, FAPESP is requesting now a plan for data sharing. And you are, you know, you are requested to do that. And the reviewer will evaluate your capacity to— sorry, will evaluate your plan. So actually, we are being encouraged to share and to, of course, to show the plan to share. So that's really an important and requested thing. Here required. So, so thanks for that contribution, Lunes. Jose, do you want to tell us the data sharing story around your recent work on COVID-19 and just, just to underline some of the challenges, some of the successes, and how it helped to move the, our understanding of COVID-19 forward?

Yeah, and I'll tie it into actually what Bartha and Opera that UNESCO basically came out and said that it is a human right to not only benefit from scientific advances, which clearly we need to do better at, but it's also a human right to share in that process of discovery. And I think that using that as an underlying principle for how collaborative efforts around HCA, I don't think necessarily we will solve everything as Helder outlined, but I think that really providing a different way of doing science and working together with funders who also appreciate those complexities and the different pressures that exist on scientists, but allowing us to do what we really want to do. And so to provide some context to what Moussa said, we became really interested early on in the COVID pandemic, having worked in the nasal epithelium and in the upper airway in nonviral diseases. And so we had some datasets and collaborations with others that were basically sitting on large amounts of unpublished data, but that we could mine to understand what cells might be the potential targets. I thought that that was something that was very important to get out into the community. However, there you have to, of course, respect the fact that these were projects that took 3 or 4 years to laboriously collect collect this data from human cohorts, from non-human primate cohorts. And by basically throwing it out there in the open, now all of a sudden, that initial question, those initial hypotheses that that PhD student or postdoc and PI had worked so hard to do might be completely lost in terms of their ability to then say, ah, this is a novel data set that I'm publishing. However, hand in hand, you have this global pandemic. Here we're talking late February, where maybe some of us didn't just appreciate how global it would be. And how do you balance that kind of credit, authorship, primacy, novelty question in science with fundamental challenges that are facing society? And the best way of doing that is, of course, to share data, and especially human data, as openly as possible. But in order to do that better, I think we need to align a variety variety of different aspects, both in terms of how we attribute scientific credit and also how funding mechanisms happen, so on and so forth. But I've been really pleased to be a part of an effort together with CZI, Jonah, and Norbert, and others to support people precisely to do that, to generate data in advance of publication to be able to share it broadly. And so thinking about mechanisms like that, especially during a pandemic, But considering the fact that we've been dealing with inflammatory and metabolic diseases that are at pandemic proportion, so why don't we also apply that type of thinking and the new lessons that we've learned to those diseases? So, you know, I've been really inspired by some of those changes, but I think, you know, as with everyone here, I would like to see that change take hold even more rapidly.

So I think that's a really nice vignette about data sharing and just again to provoke some ways of thinking around how data should be shared. You could wind back a couple years ago to Brazil during the yellow fever outbreak or during the most recent Zika outbreak and think about how data sharing during that time, especially of datasets that people may have been sitting on for many years, could have been used. I think that's really important to bear in mind. Go ahead, Helder.

No, actually, when you mentioned Zika and Jose was talking about this terrible thing, it's exactly what happened here in the epidemics of Zika because we are actually not data sharing, we're like sample sharing. So we're like, oh my God, it's our patients, we are doing our research, but then someone make a collaboration with someone else and give the data and then the samples and then that's over. Like all the PhD work and everything, you know, like, yeah, so I fully agree with you, Jose, like that. But that what I was saying is that I think it's more about the minority that make bad use of samples or data than a community issue, right? Like I think we all should be aware of that. But you're right, we have to discuss this.

So, that was kind of just something to seed discussion. I'm pretty sure we're not going to get any data sharing regulations and GDPR for South America is not coming out of that discussion today. So, we can move on to goal setting in these last 15 minutes. And I think this is This is quite important because I think we've had a really, really well-organized and, you know, catalyzing meeting over these last two days. It's important to set some goals of how we, as a community in Latin America, would like to move forward in terms of goals. And, you know, I think we started off with, um, Luis and Marcelo and Pedro articulating some immediate interests and goals building upon what they have just had funded from their CZI grants. And are there ways that other groups outside of your grant can collaborate with you to consolidate this are there other immediate interests that are parallel to the ones you raised? So maybe we should deal with, with that bullet point first.

Well, I could definitely say if, uh, if the idea of looking at different macrophage population and or different PBMC or like, or PBMC in different ethnic backgrounds or different people. I think these two ideas could be complemented in a way that could interest many people here and many different countries in Latin America, I think. And here in Brazil, You know, as far as I understood, here we are building the infrastructure to run single-cell transcriptomics, and we could use that and also the CZI funding as a way to build from within this community. You know, like we could provide the the infrastructure to gather samples, data from all Latin America and try to expand and, and strengthen the network from within. That's how I see it. And, you know, we need to discuss, like Jennifer and Norbert mentioned, there are other initiatives that could have some overlap, but also ways that we could use to collaborate outside Latin America. And, you know, we could do our part in HCA and also help building community here.

I think Pedro, you were going to say something. It's more about— it's more of the same. So the idea is to contribute and to, to kind of initiate a network, a community here in Latin America where we can analyze data, get inputs, and see this, and see this, I mean, see this going forward, you know. So I think we have the tools, we have the people, it's just a matter of organization. Try to get a little bit of funds and have a good question, a good, a good play, a good question for us to begin this network. And maybe the microfauna might be a good thing to start because you don't even need a big number of samples. You don't need— because when you talk about different ethical groups, my concern is the number of samples you may need. Here in Brazil, it's going to be a lot. And I don't think we are going to be able to start with that. So that's a future plan, but I would not call that for short term to initiate the network, you know, because this network is going to begin and it's not going forward. You know, what's interesting, I think, is that you're going to have obviously a unique, I think, ethnic background, whatever way we look at it at these samples. And I think perhaps to edit some of what's been said before, my suggestion is to get SNP array data that comes along already with your study, simply because it would contribute enormously to understanding the genetic backgrounds that you are working with as it is. And my second point, which is probably a little bit more longer term, is you're completely right. You're going to need to assemble cohorts that are correctly powered, informed by SNP array data, so that you can actually sample well-designed cohorts. And that's an entire process in itself. So I think that's all self-contained. I just want to ask an important question here because neglected diseases did come up. Is that an immediate interest of this group? I know Octavio brought it up. A couple other people brought it up, but maybe missing your names, but is that an immediate interest of this group? Camila?

You mean neglected diseases?

Yes, so I think dengue was mentioned, Zika, and others. I'm sorry, I may have interrupted somebody who wanted to speak.

Sorry?

Go ahead, sorry. No, I was saying that I have been working with Chagas disease. It's a neglected disease caused by a parasite. So we have a lot of samples from heart, heart disease, The major problem with Chagas disease is the cardiomyopathy that the patient develops late in the disease. So we have a lot of samples that we have been collecting for years. So people who has Chagas disease had to do like a heart transplant. So we have all these heart transplant samples that were have been collected. And so one of the things that I was thinking when I took all the HCA data is to get some information about single-cell sequencing based on all these samples that we are— we already have. And also all the data that we have been generating about microRNA expression and regulation during the infection and the heart problem and all the cardiovascular problems that the patient develops. So I don't know, I mean, I'm not— maybe I'm not getting very well, like how we could put all the interests of every group together.

You know, I mean, like, I think you brought up some good suggestions already in terms of neglected diseases like Chagas. I don't know, Jonah, did you want to say something?

Sorry. Yeah, I was also just going to say that I find this area very interesting and compelling. And maybe I think that the work for the group is that if that is one of those areas that is a superpower of Latin America, to start to think a little bit about what are the big barriers, right? So I could imagine for different parasites, there has been some work to think about the technologies and what needs to be adapted and optimized within those technologies to either study certain organ systems that may be underrepresented or may need to have, you know, more regional or local contributions to atlases that would be important for understanding, for example, susceptibility to some of these diseases. But then also maybe to start thinking about the technology improvements for what's going to be required to actually, you know, if you want to study not only the reaction in the host but then also the parasite itself. And I think there's been, you know, very, very early work, but interesting work on how different parasitic life cycles evolve and can be captured and understood. So I don't know, it seems like there's a lot there. I'm rambling a little bit here, but simply kind of a nice thumbs up. And this seems like a very interesting area that this group has a lot of expertise and would probably have a lot of contributions.

So it's a cool area. So that's great, John. And I'm pretty sure like people here have access to samples like cardiomyocytes and brains from people that had microcephaly and Zika, you know. So I'm pretty sure it's going to be like very exciting tissues to look at single-cell level.

You know, and then—

that's a good question, actually.

Just very quickly, Halder, the other thing that I think is really important to consider there is making sure that there are the right references. In the normal atlas for those datasets. Oh, good point. So it's kind of a two— it's a two-part question. And that's where I think it gets really intriguing is, of course, you want to ask the questions about the disease. But my guess would be that if you went to then use the reference atlas as your control, if you will, there'd be some deficiencies. There'd be some things that need to be improved. And that could be a great contribution, whatever those are.

You're right. Well, usually I'm pretty sure the researcher will gonna get some control samples from the same country, right? Because of the ethnicity and all this. But you're right, like trying to have more references is always better as a control. So I have actually a practical question, Musa. How can we go forward after today? For example, how can we really like getting the emails and start like using something that will be—

Yes.

So I think, um, um, please can you, uh, each put your emails into the chat so, um, Helder can register them in the notes we've been taking for this, for this call. We've also, um, recorded your direct interest, so maybe if you add your email, just add your, let's call it your immediate interest to this. We just want to answer one last question about how do you think the HCA Latin America looks like in a year, 2 years, 5 years, 10 years from now? We'd really like to answer that question before we close out. So we have like 2 or 3 minutes to answer that. And we will take all the notes that we've taken from the discussion that we've had over the last 90 minutes back to the organizers of this meeting. And then the local HCA steering committee will then have this information and will probably be reaching out to many of you to be able to work more in these areas. So can we just do like 2, 3 minutes What do people think HCA Latin America looks like in a year from now? Are we seeing more countries? Are we seeing a common project? Are we seeing another group of investigators like Pedro and Luis and Marcelo getting another type of grant? What, what are the— what's the thinking over the next 1 to 5 year horizon on that?

Anybody? So I guess I'll speak up here real quick. I think the— what would be spectacular to see are Latin American-specific grant callings or developing country, developed country partnerships focused around this area. It is an extremely expensive methodology. I mentioned I got the BD Rhapsody installed in the lab this week. And we ran a couple samples already, but I already costed with the dollar at the rate it's at, we're looking at, you know, one whole transcriptome kit is around $30,000 for around 12 samples. And that's just one kit, not even counting on the CDR3 and the sequencing costs. So grants that can fund our research here and give us a little better competitive chance, of winning grants are really welcome. So I would hope that the Latin America community would have more access to funds and we'll have a good strong database with relevant protocols online and data online and all that kind of stuff.

Great.

That's a nice vision to have. Any others that people would like to contribute? Or what they'd like to see? 1 to 5 years? Maybe I'll call on somebody from Argentina.

Silvia. Yes, the main thing I can say is here in Latin America, we have brain. The only machine that is available is brain and ideas. We agree that funding and money, cost is the only limitation. But as you can see, we survived to all of this because we are active scientists. But I think this is a good start. We can put priorities in what all has been said And for instance, keeping this group active with ideas, let us know where calls for funding are active or new funding calls are coming in the next years is a good idea for us. And of course, I think that in the meanwhile, to generate some preliminary publications together or to be active, a good starting point is to start to reuse public data to support our interest. This is the way we can start. And in the meanwhile, perhaps small groups that can share specific interests can also share technology. I think it's something that we need to digest to see how to improve our grants, but this is something that we cannot do easily because if there's no money, we can't do anything. So this is something really, really important. Perhaps we can also be in touch with some groups from Europe because the European Commission is also often very open to add Latin American scientists to their consortium. But again, we do need infrastructure, so sometimes we need a lot of money to to be active in this, but still I think it's a good, very good starting point. Thank you, Musa, and thank you everyone.

Thank you everybody for attending. We're gonna have to close it now. It's really been our pleasure from the HCA, and also I think Helder can say it's been his pleasure from the HCA Brazil. Um, you want to say a few words to close out, Helder?

Well, no, actually, I'm really happy that, uh, you guys stay into here, like, and, and I, I guarantee you that we are going to follow up on that, okay? It's not going to be like just like filling up the time and then blah blah blah. We're definitely going to do something together, and I'm going to follow up with the email. So I'll see you there in the report, and thank you so much, Musa. For like helping moderating the session.

It's my pleasure, Helder, and you know I'm always here to help out with HCA Latin America, and I love being down there, and if there was no COVID, I'd be there for sure. Thank you, everybody. I'm gonna end our meeting now, and it's really been a pleasure to see you all.