All right, folks, it's good to see everyone. Thank you so much for coming today. My name is Steven Moss. I'm a senior program officer and the board on life sciences, the national academies. And I just want to welcome you to the workshop that we have that we have here today. As you're aware, this is a part of a larger study related to RNA modifications, and we're really excited to get the opportunity to learn a lot more about this area, building on some of the important work that's been happening. I'm going to start with the NIH NSF and other organizations. So I'm going to start with some introductory remarks and then I'll pretty immediately pass it off to Brenda Bass, who is one of the co chairs of the study committee who will give us some further opening remarks. There we go. Yes, I was keeping background. To start, we have a land acknowledgement related to the National Academies. We do acknowledge the National Academies is physically housed on the traditional land of the Nakhachchuk, Anacostin and Piscataway peoples past and present. We honor with gratitude the land itself and the people who have steward it through generations. We honor and respect the enduring relationship that exists between these peoples and the nations in this land. We thank them for their resilience in protecting this land and aspire to uphold our responsibilities to their example. So I want to talk a little bit about the National Academies to start. As we kind of mentioned, we have two, we have this is part of a larger activity. We do several types of activities. One is consensus studies, which is what this larger activity is a part of. This is gathering groups of groups of experts to put together reports. And then we also do a lot of workshops, forums, roundtables, other types of convening activities. So today we're having a workshop to help learn, help the community get together and learn more about this area and form some of the parts of the consensus study. And so at this particular juncture, the two activities are connected. Sometimes they are separate. It all depends on kind of how we're operating, but we're excited to kind of move forward in this area. As I've said, about five times already, we are, we are part of a larger consensus study. This is just a clip from the website itself. I want to thank the Warren Alpert Foundation and the National Institutes of Health who are our sponsors of the study itself and the workshop today. We have a lot of background and previous reports and workshops that have similar topic areas and have helped to feed into the study that we're doing here today. The first that's a good mention, a good thing to mention is that the National Academies came out with a report about mapping and sequencing the human genome before the human genome project started. And then there has of course been a ton of work sets in subsequent to that. And I'm highlighting only four other four reports here toward precision medicine, which was a big effort related to omics and understanding the basic science needed to move into a realm of precision medicine. And then we had a precision medicine round table that was formed that did a lot of workshops and other areas of extra other areas of interest. And then more recently back in 2020 actually in this room right before the pandemic started, there was a workshop on next steps for functional genomics, where we talked a lot about multi omics and different areas that were emerging funded by NSF. So I want to quickly acknowledge the study committee that is putting together the consensus report, and it was also responsible for playing helping to plan this workshop, chaired by Brenda Bass and talk to our TJ Ha. We're really glad to have these folks helping us out. And then I also want to thank and acknowledge the staff, the National Academy staff who has been helping out with this project. Most of them are here today, and they're, they've been really instrumental in making sure that everything runs smoothly as this project goes along. A couple housekeeping items. We encourage you to be an active participant in today's discussion. You know, take advantage of your breaks take advantage of question and answer. We really encourage that there's an operative there's two ways to ask questions. In person, there is a microphone. So please, if you are interested in doing that, we encourage it. There's also Slido, which you can use on any of your devices. This is for virtual and in person folks, the way Slido will work is that once you are logged on and you type in your question. We'll have the opportunity folks who are moderating the Q&A panel will have the opportunity to see all the questions. You're actively on Slido, you can actually up and down not down vote you can up vote questions so that popular questions come to the top and are easier to ask. So I'm going to stay here for one more second while I see people using their phones. The QR code. If you need this information it's also in the agenda. The QR code isn't in the agenda but you should pretty easily be able to access the Slido by just going to the website and then typing in the code that's available so again, if you missed this, and you want to go back to it it's on the top of your agenda. And I'm going to go forward now. Here are our housekeeping items technical issues. My colleague Nam, who's up at the front helping with technical issues as we speak. It will be available and also for those who are online. Please email or message them. There's also other folks who can help you online. Other housekeeping comments and ideas made during the workshop should be attributed to individual speakers and not their organizations, unless otherwise stated. This includes the National Academy so thought shared during the course of the workshop should not be interpreted as the opinion of the National Academy of Science, Engineering or Medicine, and I'll specify or the committee who's putting together this study. The workshop will be recorded and will be posted after the workshop is complete. And there will be a summary available. Oh sorry I forgot to take that part out but there will be a summary available there is no graphic recording for this workshop. I forgot to edit that harassment and bullying will not be tolerated. Please be respectful of all panelists speakers and fellow participants. And with that, I will pass it over to Brenda. Thanks Steven, and Steven and all the staff here are just wonderful and have been very helpful to us and I, I'm really excited to see all of you in this room and the people on that are attending virtually. Thank you so many of you and I know that you have a lot of expertise that are going is going to be helpful to us so please don't hold back. Tell us what you're thinking and your opinions. So let's see, can I, do I have control over this somehow. Okay, I'm going to stand over here. Can I see this. What was that. Oh, I have. Oh, but then I might need my glasses. So, I, so I actually changed a few words in this first sentence on this slide, and the committee is charged with trying to map and sequence modifications. I think that that is sort of only part of it and that the ultimate task is to develop ways to an evaluate whether this should be done to start at one end of an RNA, a single transcript sequence the whole thing, and get all of the modifications in that single transcript. We know now that we can map and sequence short regions we can find in an ensemble of RNA where the modifications are, but that doesn't tell you who the partners are in one single transcript. So that is the ultimate goal. And we need to understand is there a scientific need for this. So where are we right now. What are the current methods. What are their limitations. What are the state of the current RNA databases where are we going to store all this all of this information information. What are the challenges related to using the outcome of such a study for scientific clinical and public health needs. What are the computational needs. What are the data ecosystems that we need to consider. What about policy related to this task. How will it affect workforce. How in the world would you set up the infrastructure to do such a huge project and you probably you saw on that last slide that that some people make an analogy to this project to the human genome project, and where when we started the human genome project it they had none of the techniques that now allow us to sit down at our desk and actually pull up our favorite gene and know its sequence. So someday should we have this for RNA and its modifications. And of course we envisioned that such a task would involve new technologies. So in. So this I am not going to go through this because I have essentially said all this. So today what you're going to hear, we're going to start off with a talk just to set the stage. What are modifications where do we find them what are some of the techniques, what are their functions and overview talk. And then we're going to have during the day a panel on direct sequencing technologies which we can't there every now and then. And then this development of all of the nucleotide standards is is a really, really important thing to consider. What we're talking about here are modified nucleotides in the context of a certain sequence, and maybe a repository that you might put these standard. So everybody can use these standards. So they understand what the signal would be, for example, in a direct sequencing experiment. The standards we're going to learn about today. I were we're also going to hear a little bit from people who have been involved in such large scale collaborations, and there's a lot of challenges to getting something like this and organizing how it's going to be done and we're going to try to learn from some of these people who have done this already. So what should be, what should we be really concerned about. Again, should we do this, what are going to be the pitfalls, if it's decided to, if we embark on this project. Tomorrow we're going to have we're going to continue with some talks on different RNA applications and their impact packs related to biotechnology and disease. We're going to have some sessions that are talking about methodologies that currently are really not applied to finding and sequencing RNA and its modifications, but maybe they, they should be used so we'll consider that. And I think now I turn it over to my wonderful co-chair, TJ Hawke. No. Say that again. Oh, okay. Lydia, please come up. Did we switch because that's okay. It's good to tell me. Is Lydia. Oh, Lydia's virtual. Good morning. Good morning, Brenda. Good morning, everyone. So I hope you can hear me okay. My name is Lydia Contreras. I'm a professor here at the University of Texas in Austin and chemical engineering. My lab works on regulatory RNAs, particularly in stress responses. We have been really interested in mechanisms of RNA oxidation, as well as an understanding RNA protein recognition of modified RNAs. So today, it is my pleasure to introduce our first speaker for our workshop, Dr. Fred Tyson. So Dr. Fred Tyson has really been a true champ for this area in general. He is currently a program director in genes, the genes environment and health branch at NIH, particularly at the Environmental Health Science Institute. His portfolio includes research on how the environment impacts the epigenome and most recently, of course, the epitranscriptome, lung cancer, tobacco exposures and electronic nicotine delivery systems. Dr. Fred Tyson leads the NIH S target epigenomics and frame epitranscriptomics programs, and it is the POC for the diversity supplement program. He has worked on several trans NIH initiatives, including the centers for population health and health disparities. The road epigenome mapping consortium and currently serves on four NIH common fund working groups. In May of May 24 and of May of last year, Dr. Tyson and an executive committee, along with several lead faculty in this area, put together a workshop that was entitled capturing RNA sequence and transcript diversity from technology innovation to clinical application. This was mostly led by the Human Genome Research Institute and, of course, the Institute of Environmental Health Sciences. I was fortunate to be part of this workshop, and it covered a number of topics that we have heard some of which which Brenda already alluded to. But I remember there was a lot of discussion about even defining our nomics and what does the term mean. There was a lot of discussions of applications, direct sequencing technologies, limitations, what is needed to move forward, and major themes that stick still to my head where themes around the lack of standards, data management and sharing in the field, and the biological impact of our name modifications. So even if we get to the point where we can sequence and map them, what does it mean for signal for trafficking for intermolecular interactions, particularly associated with proteins, etc. Yeah. So, again, welcome, Dr. Tyson. Thank you for being here and for all that you already contributing to supporting research in this area. We look forward to your talk. Thank you for that introduction, Lydia. See if I can move this one with mine here. Good morning all and thank you for the opportunity to give an overview of the workshop, proceeding this one that was convened by the National Human Genome Research Institute in the National Institute on Environmental Health Sciences in May of 2022. I'd like to get some of you that are on this committee participated in that workshop and we had representation from from leading RNA scientists coming from multiple sectors, including academia, government and industry that actively participated. Okay, so the workshop really had four primary objectives, and that were to they were to identify technologies that we need to characterize RNAs, determine infrastructural needs and what steps are going to be needed to facilitate the rapid adoption of these technologies once they come online by the scientific community. And then we also wanted to identify how we might best incorporate a public outreach component, as well as workforce development in this area. Next please. So several program and review staff members from both NHGRI and NIEHS participated in multiple aspects that are related to convening this three day workshop, and we were led by an executive committee. And I want to point out that the members of the executive committee were Blanton Talbot, Pete Deedon, Brent Gravely and I want to especially acknowledge Vivian Chung who has really been a mover and kind of a force of nature behind and in front of the scenes, and trying to get this this effort, moving forward here and in other venues. Let's see. So these committee members certainly have made many important contributions and continue to make important contributions to both RNA biology and chemistry. So I'd like to put into context why the leadership of both NHGRI and NIEHS supported our workshop as well as this this one in the larger consensus report that was mentioned earlier. The NHGRI director, Dr. Eric Green addressed aspects of the NHGRI 2020 strategic vision and how they've gone beyond these those broad brush strokes and have begun laying out goals for genome structure and function science in the following areas. First, we want to talk about direct sequencing. He identified need and opportunity for direct sequencing technologies as an area the NHGRI has actively encouraged for almost a decade through their long standing nucleic acid sequencing technology development efforts. He also pointed out that we currently lack methods to routinely detect or quantify the vast majority of modifications in sequence context or to simply determine each modification while directly sequencing. Moreover, we simply have an incomplete understanding of the diversity of RNA. Technology innovation and development are needed to enable full characterization and quantification of all RNAs transcribed from the genome. Now genomics research really requires physical standards including synthetic RNA with base modifications. NHGRI has been and will continue to encourage research efforts and development of resources in this area of strong need. Now this was the first mention of a theme that was continuously mentioned and throughout our workshop and that was standards. So going forward, our analysis will be a component of the of the NHGRI's multiomic approaches to disease and risk. And at the cellular and molecular level there's an enormous opportunity to study aspects of functional genomics. That is how elements of the genome contribute to biological processes. The NHGRI vision fully embraces the importance of developing technologies and applying them to comprehensive RNA sequencing and characterization. As part of what's needed to build a robust foundation for genomics. And in short, it would be, it should be routine that we consider on a genome wide scale, the diversity, fate and function of RNA molecules, including splice and transcription variants and modified bases. Now Rick Wojcik, the NIHES director followed up with describing why the capacity to directly sequence RNA with a base modification context is critical to the field of environmental health sciences. Environmental health science investigators that are supported by NIHES are increasingly appreciating the potential impact of environmental toxicants and exposures that can compromise the deposition, recognition and removal of RNA modifications and how this may be mechanistically associated with adverse health outcomes. As a result, there's a growing number of grants in our portfolio that we're supporting at the look at the impact of toxicants on epitranscriptomic processes. In 2018, five years ago, we were not supporting any grants in the area of epitranscriptomics, but currently we're almost up to 35 active grants in this area. We're supporting R01s, R21s, R03s and F31 as well as R35s that are looking at environmental impacts on epitranscriptomics. The NIHES portfolio is looking at modifications, readers, writers and erasers of epitranscriptomic marks. We're using a diversity of exposures and employing state-of-the-art technologies to address questions about where toxicants, the epitranscriptome and human health intersect. Being able to sequence RNA with a modified base context will certainly significantly advance the field.  The workshop, as Lydia told us, was entitled Capturing RNA Sequence and Transcript Diversity from Technology Innovation to Clinical Application. We had 298 attendees over a three-day period and it featured six scientific sessions. The keynote presentation came from Anna Pyle and that really laid the foundation for the meeting in terms of delivering a state-of-the-science address. She stressed the importance of studying the diversity of RNA sequences and modifications to advance the understanding of the role of RNA in health and disease. She provided an overview of the biological and technical challenges in the field, new strategies being developed, important unmet needs and potential future directions that we might go in. This was then followed by five scientific sessions with plenary talks with each followed by three concurrently held breakout sessions. And I'll spend the rest of the time summarizing the highlights of these sessions. I want to point out that Jensen Nam talked about structure and biological implications. Jeanne Lee talked about aronomics and applications with small molecules as therapeutics. And Juanunu talked about technologies for direct sequencing. Chris Burge addressed in the plenary talk, infrastructural needs. And Brent Gravely talked about how we might get the community to embrace the technologies once they're online and get more of them disseminated. Okay, if I can have the next slide, please. Okay, so technology development really underlies many biomedical discoveries and applications and advances in RNA science will require technological innovation, development and standardization. New methods and technologies are needed for full-length and direct RNA analysis to comprehensively characterize the diversity of transcripts. And we need to look at isoforms as well as modifications in sequence context. So how can this be accomplished? Well, we talked about investment in mass spectrometry capabilities through modifications of existing instrumentation as well as the development of new devices that are really based on our RNA. And this is really identified as a real critical opportunity. We also discussed focused enzymatic research efforts to develop useful molecular tools that overcome rate limiting analytical barriers to molecular analysis in RNA biology and would be helpful with sequencing approaches. Additionally, the group suggested that technology innovation and development efforts need to be enabled to generate long synthetic RNAs with specific modifications which would allow multiple advances in the field. This is another discussion of standards. And we also need high throughput and subcellular methods for detecting localizing and analyzing interactions between RNAs, DNAs and RNA binding proteins as well as small molecules. We also made efforts to identify what optimal biological standards and controlled centralized resources are most needed to advance the field. And certainly a gold standard of reference synthetic as well as cellular RNA spanning the range of known modifications will enable large scale efforts to develop and advance technologies. This would include common sources of well characterized RNAs both synthetic and cellular with specific exon chains and modifications for technology development and spike in controls that would enable comparisons across approaches. The field will also benefit from a centralized resource with bank tissues cell lines and an example or virus sample for testing of new modifications and methods. Participants also encourage the use of consortium or coordination center approach to stimulate collaborations for both wet and dry lab advances in direct RNA analysis. As we're talking about wish list or what we really need a centralized mass spec facility resource for RNA sequencing and modifications to assemble and calibrate work on a pilot project using direct RNA sequencing methods would also benefit the field. Okay, for opportunities and computational resources indicate that we need the development of a comprehensive centralized interoperable standardized and searchable RNA database that includes different RNA types and both are both typical and disease tissue and that would really again help support significant advances. There were calls for nomenclature file formats pipelines and software to be standardized updated and developed. The field would benefit from more efficient data processing and development of machine learning or AI based tools as well as a streamlined workflows to enable RNA analysis efforts. The determine at the field would benefit from the creation of publicly available and appropriate training sets, including large data sets and those generated from in vivo transcribed RNA. And very importantly we talked about developing RNA secondary structure prediction algorithms that consider chemical modifications. So robust discussions regarding how sequencing with a modification context can advance our understanding of how environmental stresses or exposures can compromise functional modified transcripts. The participants thought it important in the context of identification of specific enzymes reagents or tools that can induce or excuse me insert stress induced modifications to understand the biological impact of distinct environmental exposures. They also thought it important to develop tools to study specific RNA modifications and a variety of different cell types to connect these changes to environmentally induced or associated pathologies. Development of the capacity to map temporal and spatial dynamics of RNA modifications inside a cell and tissue in relation to functional responses to stress or physiological conditions would also advance this field. And finally we also talked about the development of methods to interrogate the impact of stress on RNA functions, like signaling RNA protein interactions and trafficking. Now I'll reach to the public is is is we think a needed component to increase our understanding of the importance and impact of RNA research on public health and medicine and I think the pandemic we're coming out of that is is a clear illustration of how the public outreach could inform folks better know what we're dealing with. And outreach components should be required of any large NIH funded consortium and center projects addressing this additional funding for diversity career awards and administrative supplements was recommended, as well as support of undergraduate and graduate level training, particularly at minority serving institutions to improve the diversity of the workforce in the next generation of scientists who will be tackling these issues. There must be interdisciplinary and incorporate both virtual and hands on instruction in both wet and dry lab methods. Training opportunities should be easily accessible, high quality, low or or no cost with online training videos on the use of technology as well as data analysis. And these would be considerable drivers and getting the community to adopt or embrace these technologies. Now we know that such a resource would require a home as well as a long time commitment and probably resources to maintain and keep the training modules going. But the training really should be available to all professional levels. Next slide please. Okay, there are several challenges and opportunities that that exist when discussing how direct sequencing can be directly tied to clinical applications. Many RNAs fold into structures that can be effectively targeted with small molecules strategies are needed to identify them. Validate and optimize small molecules that target functional structural units within the transcriptome. RNA sequence can predict small molecule binding sites that will allow like inviting to the functional structural units. Existing RNA taking on targeted small molecules currently use a range of different mechanisms to exert their effects and these include direct splicing with cellular proteins inhibition of translation of undruggable proteins and the deactivation of functional structures and non coding RNAs. So I think I'm kind of running out of time so what I'm going to do is just then go to this this last slide which really kind of highlights that we identified several different opportunities within that workshop that can be addressed as we move forward with this field. If you want to see a full report there's website that has the full beating report it's got an executive summary. There's a there's a full agenda there's speaker bios many other things are on that report. And I encourage you to go visit that and again I realize I've gone over my time I apologize for that and thank you. Thank you Dr Tyson so I'm wondering if there are questions in the room. There are questions in the room as of yet, but I'm monitoring that, but how much more time do we have for questions about 10 minutes. Okay. So, maybe I can ask someone to get us something to get us started Dr Tyson so when we had this workshop. I remember a big topic of conversation was this idea of standard. So should we be sort of consolidating the number of systems the cell types the types of mods. Should we be sort of constraining this problem a little bit so that we can be comparing results across labs and across different efforts. So we just wanted I wonder if you can recap some of that and maybe some of your own thoughts there. I thought that one of the things that we talked about really was if we are going to support large scale efforts that we want to have some centralized types of resources I think one of the things that we know certainly from from doing the epigenome work is when you get different labs doing different things and people are using different protocols. I think we had a lot of variability that hopefully if we had some centralized resources to kind of pilot some of these activities that would reduce that type of variability. I think we need to have standardized nomenclatures, as well as pipelines again to reduce variability. And those types of things again are going to require a kind of a coordinated approach. When you talk about standards there there are a number of different types of standards we talked about standards for both, you know, the transcripts, both long and short ones that have modifications on them. Standardized technologies for doing things and I think to, to kind of be able to reduce variability and interpret results consistently. That would be a lot of calls for standardizations I think we're going to need to kind of just define, you know, what is the low hanging food where we're the standard that we can more immediately kind of implement over the next few years. Lydia we have a question in the room from Kate Meyer. I think this meeting did a great job of identifying, you know, some of the major needs in terms of standards and bioinformatics resources training other infrastructure needs. I wonder if you can comment a little bit on some of the new initiatives that have maybe resulted from this meeting to help support some of those goals. That's, could I comment on them. I can say something but an actual comment for a record no. There are some preliminary efforts to try to see if we might be able to develop something but we can't really. If we were actually working on an initiative I couldn't tell you that. But I can tell you we are in discussions. And that will have to suffice for now. Maybe turn off the zoom and all the recording and then you can. No, no, no, no, no, no. I love how Kate you tried. I do too, but you know. Other questions in the room. There is one here. Do you hear me, Lydia. Yes. Okay. So, I guess I'm trying to envision, you know, if something like this project goes forward. Do you think about, you know, NIH is contribution but how it might intersect with contributions from private organizations. I guess our, how this can proceed at the level we've been discussing, almost clearly mandates a private public partnership I don't see how we can do it I mean I don't think we have the resources at NIH to solely, you know, support this. And I've had some conversations about what we think the figure would be to actually support this as fully as we'd like to see it supported, and I can't even make my mouth warm. Yeah, good answer. And then, and then if you were going to think about private contributions are the things that you think NIH would be much more suited for and that other things would rely on private contributions. I do think there are things that NIH can actually really foster and do but there are some things that are going to require some resources that we're not going to have access to. As we talked about standards for instance, when you think about doing these standards with these modified chemical bases there are very few places in the world that can do this. In fact, I think I can think of two and they're not here. So, yeah, we're going to need to really kind of cast a broad net net and identify what are the things that NIH can support what are the things that private entities can support and, and we can't be restricted to just doing this work here in the US because I was just saying, in terms of these types of standards on these nuclear side bases, they just, you know with modifications on the side bases. We don't have that capability right here right now, but there are people that do. Thank you. I have questions also from in the chat. Fred, I'm going to read a couple of them to see what your thoughts on so one that I'm really curious about is in reflecting about the workshop. Did you think it achieved what you hope for. And if not, how else would you have restructured what else would you have liked to learn. I think the workshop really achieved what we wanted to do we want to bring together a good representation of some of the thought leaders in RNA biology chemistry together we certainly didn't get everybody but we got a lot of folks in there in the room. I think we were able to identify the priorities from the slides that Dr. Bass put up earlier. I think we covered many of those topics, but certainly they could be copied more exhaustively. I think maybe one of the things that we didn't do was really probably identify the low hanging fruit in terms of what can we do in the next five years. Certainly, again, I think we have some direction on some things as Kate asked, are we thinking about things yes we are thinking about things so we've, we've kind of identified some areas that we think we can begin to approach right now but I don't think. I think, I think we did a good job of identifying priorities, I think we could do better in terms of narrowing those down in terms of what we can attack right now. There's another question here that I'm also really curious about so based on the workshop, do you envision NIH as the lead in an RNA modifications effort. If not, who should, or if so, who can you work with who are the lead partners that you envision in this effort. I speak without authority. But I believe NIH should lead this NIH cannot do it alone but I believe NIH should do it. And I see members from NHGRI so I'm not going to, well I am going to but I'm not going to kind of put anybody on the spot but certainly they represent a natural leading place to get some of this started but they can't do this alone either. As I indicated earlier, these, these type, this type of work has great importance to environmental health sciences as well so I think that NIHS is also should be a lead on here but I think all of NIH I think you know that I don't see people here from IAD that I, they're here I don't know them but anyhow, I think this would be something that would be of great importance to that institute as well. I think most NIH institutes if not all really should be interested in this work because, again, it will impact, I think so much of human disease and health that all of the NIH Institute should be partnered in this to do this. In terms of who else, I don't think it would be appropriate for me to identify other partners in the private sector that probably could be engaged in this but they know who they are and and I suspect many of you all know who these folks should be. And I don't think it would be appropriate for me to kind of say well these folks should take all this these folks should tackle that other other people probably should should identify those components. So, a related question Fred, if NIH is the lead then should RNA be incorporated into NHGRI or should a new institute be created for these efforts. That's a great question Lydia, but if we look at the reality of where we are right now. I don't think, I don't think separate RNA Institute would be feasible right now. Certainly, I think NHGRI and Jennifer you can you can certainly correct me if I'm wrong but I think they've already integrated this into their program and it has been I think for the last 10 years or so it's not longer. So, again, is it being done exhaustively. No, but again nobody can do it exhaustively and I think that's why it really kind of requires a trans NIH effort to do this. I would think a common fund would be a great place to do this. Unfortunately, this year the common fund didn't agree with us. But that doesn't mean that that's dead in the water. There are certainly other places that they can address but I don't know if it's appropriate for me to do that but there are some other places that maybe engage this as well. Do you want to say anything about that. Okay, we won't say anything about that. But there are other agencies that could take a leading role in this activity as well. In the video we have one more question in the room and then we should maybe keep going but that's rough. So, do you foresee this and other initiatives like the studies could or should go beyond mapping of the modifications to something more like a structures aren't structures which would have more therapeutic value. So do you foresee this initiative could or should go beyond mapping modifications to say solving the RNA structures. Oh, absolutely that I think is a major component because certainly the RNA structures dictate a lot of the function as well as identify the binding sites that you may be able to get your your small molecules to target that can be used for therapeutics or diagnostics I certainly think that that the structure is huge because that again may identify therapeutic targets. So, absolutely yes I mean I think any initiatives that will come out will certainly address structural elements. Thank you. And maybe have one last question so that we can end on this one Fred. So, since the last workshop has there been any new developments in technology that show promise as disruptive towards sequencing. I know that actually one had developed some things are published something that came out right a long time at the same time as the workshop, where they're, I'm not sure how many he's able to get but he has some new technologies that are available. Jennifer Strasburg would probably address that better than I can because they have within the program at NHGRI different funding initiatives and grants that are supporting this as well as some work that they're doing with the NSF that may be able to talk about some really developing the top technologies that are becoming available. So, yes, I imagine in the interest of time we should probably move but but Tal is here and he could certainly address these things. Again, thank you all. Thank you so much Fred for starting us often. Thank you for your contrast for asking very good question. Our next speaker is Dr Kristen Koot Moo. Please come come forward Kristen and we decided that Kristen should give a talk pretty early on in our workshop because she works on modifications she thinks about their functions. And she also thinks about techniques to characterize and map these modifications so Kristen is an assistant professor at the University of Michigan, and she is the sehan a gay. Oh, I said it right assistant professor so she has a chair so Kristen please come forward and looking forward to your top. Thank you so much Brenda for the lovely introduction and I, and for everyone to introducing this topic. Going forward, let me set my timer so I don't ramble on too long. What's going on. Yeah, this screen is flashing I thought that screen was. Well, thank you for the invitation to be here I'm excited to really help kick off this workshop on RNA modifications one of my favorite topics cells face the daunting task of having to maintain the right number of proteins in the right place at the right time. One general strategy they use to do this is to modify all three major classes of biomolecules in the central dogma in order to control their structure function and stability. Beyond people in this room of scientists as a whole tend to think and talk a lot about the sorts of modifications that get put on to DNA, or onto proteins, things like methylation and phosphorylation. However, it turns out that that central molecule in the central dogma RNA can also be highly modified. And there are over 150 different varieties of modifications that can be put on to these molecules. And we've known about the existence of modifications in RNA really since the dawn of RNA research in the 1950s when some of the most abundant modifications in RNAs were first discovered. And for a very long time, we've thought that these modifications were primarily the purview of non coding RNAs. And just as a reminder and non coding RNA is a catch all for 98 to 99% of the RNAs that exists in a cell. These RNAs are simply RNAs that do not act as templates for protein synthesis. In contrast, it's long been thought that protein coding mRNAs contain many fewer modifications with the only one that we really knew where existed, commonly being the m seven cap found on the five prime end of eukaryotic mRNAs. However, the last 15 years or so have really flipped this long standing dogma on its head with the advent of deep sequencing technologies, allowing scientists to develop techniques to pick their favorite of those 150 modifications and go in and develop tools in order to directly sequence and identify map where that modification can sit at all sites across the transcriptome. These deep sequencing studies have given us maps of roughly 15 different types of RNA modifications and indicated that there are over 10,000 sites of RNA modifications in both non coding RNAs and protein coding mRNAs. These modifications have really ignited the imagination of scientists worldwide because they have the potential to impact every step in the life cycle of an RNA. After it's been made. They can change RNA structure and dynamics, splicing and maturation are RNA stability RNA protein interactions and even how the RNA itself functions. I'm consistent with this perturbations in non coding RNAs and redistrict redistribution of mRNA modifications are linked to a wide variety of diseases and negative outcomes for human health, including neurological diseases, cancers mitochondrial and vascular diseases. This shouldn't be surprising, given that these modifications are linked to really core biological processes including development circadian rhythm maintenance protein homeostasis immune response, and so on. Together, 50 years of enzyme knockout work coupled with genetics coupled with recent mRNA modification maps suggest that RNA modifications are important in some way for biology and this work has given us a really beautiful birds I view of the RNA modification landscapes where we know modifications exist and we know that they can redistribute in response to cellular stress cellular stress or other things that perturbed cellular biology. However, we don't really know what these individual modified sites do. In terms of thinking about mRNA, this has led to two sort of hypotheses in the RNA biology field about what mRNA modifications in particular might be doing and I'm showing these hypotheses in their, their extreme here in order to make a point. The first hypothesis is that this new found class of modifications in mRNAs don't do much, and that they're just biological noise resulting from the off target action of non coding RNA molecules and there's a whole lot of consequence to them. The second hypothesis is that they do everything, and that they are a major regulatory mechanism for pretty much all genes and are at the center of gene control. In reality, I think that the real answer is going to end up being somewhere in between, and a part of what I would like to put forward for us to discuss in this workshop is how we're going to get between these two different models in order to figure out which of those many sites are actually really contributing to biology. And I like to think of this as sort of a kin as to protein post translational modifications which we know are everywhere and are very important for controlling the action of some proteins, but also don't always matter that doesn't mean that we shouldn't look into them and wonder what it is they might be doing. So in order to do this, we need to move down from our birds eye view to what I like to refer to as the Google Street view of RNA modifications where we zoom in on one modification at a time and ask what's there, how much is there. How did it get there, and what is it actually doing in order to perhaps change interactions between a given RNA molecule and components of the cellular machinery. And I think that this really leaves us with four grand challenges for the RNA modification field. Our first challenge is to establish the chemical diversity of modifications in both non coding and protein coding mRNAs. Our second challenge is to figure out where those modifications reside and how they're added enzymatically. Also, how those enzymes are regulated and controlled. We also need to not only know what's there and where it is, but how much of it is there, because when you're trying to read through 10,000 plus sites of RNA modification to figure out which ones are biologically doing something. It's really useful to know if that modification is incorporated 2% of the time, or 90% of the time this is really a key piece of information. This is a key piece of information, not only for mRNA modifications, but also for non coding modifications and I just like to give a small shout out to T RNAs, which we thought for decades were stoichiometrically modified, but clearly are not. And so this is something that we need to be looking at in general across RNA species. And most near and dear to my heart is that we need to figure out what the function of individual sites actually are because, to my mind, mapping is a wonderful, beautiful thing but it is not a be all end all purpose. The ultimate purpose of these mapping studies needs to be figuring out what the actual function of individual sites are so that we can know how RNA modifications contribute to biology. So what I'm trying to take as has been discussed pretty, pretty extensively already some large scale collaborations between scientists from different fields in biology, chemists and engineers, collaborating heavily with both academic industrial partners and government agencies in order to achieve these really sort of lofty goals that I've stepped forward. The workshop on my modifications is going to address each one of these points Brenda laid this out nicely so I won't belabor it but there will be sessions today where we are going to learn more about quantitatively mapping modifications, and also about how those modifications might be impacting biology, as well as how to collaborate effectively. Okay, so I'm now going to switch from generally introducing the idea of RNA modifications to talking a little bit about the research in in my lab. I'm using this just as an example of the sorts of things that I think are going on in the RNA community it is certainly not everything that is happening out there but I just wanted to give you some perspective as to the sorts of things that we're thinking about. The research in my lab is really set up in order to try and come up with a bio with a with a biochemical framework for quantitatively understanding the biological role of RNA modifications on the molecular level. In order to do this we have four areas of interest. Our first area of interest is to develop and implement mass spectrometry tools in order to identify quantify and map modifications. Secondly, we're interested in how those modifications get put in by RNA modifying enzymes. Many of the enzymes that modify non coding RNAs are now being revealed to also have mRNA targets and I think that, although these enzymes have been studied for decades it's important to sort of revisit what it is we think we know about the enzymes because they're clearly targeting a much larger set of substrates that are much more diverse than the substrates that were initially identified. I'm also interested in trying to understand the consequences of both tRNA and RNA modifications on protein synthesis. And lastly, RNA viruses is something that hasn't come up yet today but RNA viruses just like all of the other RNAs in the cells contain lots of RNA modifications so I'm also interested in trying to look into what the molecular level consequences of those modifications are on viral gene expression and evolution. Today I'm going to really just touch on these three main areas of interest starting off with mass spec. The reason I chose to start off with mass spectrometries we're going to talk a lot today about direct sequencing by nanopore but I notice that mass spec was underrepresented so I'm going to highlight some of the aspects of my program that focus on using this and developing this sort of technology. The work conducted in the studies I'm going to be talking about today has really been the brainchild of a single student in my lab Joshua Jones who's absolutely excellent. And we're working together with my analytical chemistry collaborator Dr Bob Kennedy because I'm a mechanistic entomologist by training who just had some crazy ideas about things I wanted to do with mass spec and Josh was brave enough to make them happen. So before I go into what my lab has done I just want to generally frame for the audience what mass spec is capable of doing. So mass spectrometry can tell us a lot about RNA sequence and RNA modifications and there it can do so really in two different levels. At the first level, you can chop down RNAs in a sample and analyze the nucleosides that are present. This sort of approach has been used widely for a very long time in order to discover RNA modifications. In order to identify which modifications are in a particular sample and this really is the gold standard way of quantifying RNA modifications. However, mass spectrometry can also be used to directly sequence RNAs which is actually a much more difficult thing to do. Right now, sort of the state of the art in the field is that we can purify short RNAs for example short t RNAs can be purified individually and robustly sequenced and you can also we're starting to be able to sequence semi complex mixtures. So for example, mixtures of our RNA partial degradations. And I'm going to talk about work in both of these realms now. We started to give everybody an idea as to what the sort of nucleoside workflow looks like and the sort of information we can get out of this type of studies. When we're trying to analyze what modifications are present in a sample and how much is there. We take an RNA sample we digested down into nucleosides we then separate those nucleosides out by liquid chromatography and shoot each one of those nucleosides on to a mass spec. This allows us to simultaneously monitor all of the signals arising from both modified and unmodified nucleosides at in a single assay. And the technology that my lab has developed which really is just a forwarding of technology developed by the limbaugh lab and others really standing on the shoulder of giants. We're now able to simultaneously quantify 51 ribonucleosides with the highest sensitivity for ribonucleosides, and this is done in the absence of mass spec contaminating ion pairing reagents. And just in general the pros of this sort of approach are that they are direct you're directly looking at the RNA, their high throughput, they're extremely sensitive, they're quantitative. There are a number of internal sample quality controls because by looking at 51 nucleosides in parallel. If I'm looking at an mRNA sample, for example, it's very obvious if there are tRNA contaminations if I start to see big funky tRNA modifications pop up in my sample. The major drawback to this particular technique is there's no sequence context and I'm not going to lie that is a major drawback but I just want to emphasize that right now this is still the gold standard method for quantifying modified nucleosides in a sample. My lab has also become more interested in trying to directly sequence RNA by mass spectrometry and I'm going to tell you two brief stories now about directly sequencing total tRNAs as well as viral RNAs. So the limitation in sequencing RNAs is that it's hard to do top down sequencing because you can't just shoot long RNAs onto a mass spec and expect them to fly and be able to sequence them. However, it's difficult to chop down RNAs into reasonable pieces that can be mapped back to the transcriptome because there are no RNAs that are akin to proteases. Proteomics researchers have these great tools for being able to chop up proteins into reasonable sized pieces that they can then use for analysis. So RNAs that are commercially available right now don't do this. They are incredibly promiscuous and they just chew things down into tiny bits. And so this makes it really difficult to chop up RNAs in a complex mixture and be able to analyze them in a way that we can get any information out. This has been done once by a previous group that developed orthogonal nucleases in order to be able to do this, but these nucleases are not commercially available, and they are really good nucleases so they kill cells. Trust me, we've tried. When you try to make them because they just chew up all of the RNA in the cell. And so we wanted to see if we could come up with a more robust way using commercial enzymes to directly sequence total RNAs. And so we did this. This is an example of why having perspectives from multiple areas in science can be really useful for pushing things forward. We did this simply by using our RNA knowledge.  However, my analytical graduate student, Josh Jones has been sitting in on all of these RNA meetings for, you know, five years now and he comes into my office one day and he says, you know, we've been trying to make all of these enzymes in order to get larger pieces of RNA and it's just a pain in the butt. Why don't we just fold the RNAs before we chew them up with single stranded nucleases that should really give us discrete sizes that we could then go on the sequence. This seemed like a really straightforward idea. And we tried it and it works quite robustly so now we get much longer sequences, just simply by folding the RNAs before we degrade them and we can then subject to these two mass spectrometry. And sequence them directly using CID and doing this if we were to compare the coverage we got from the fully digested tRNA and from the digestion of the folded tRNA you can see we've improved our sequence coverage significantly and this is true not only for tRNA. And I have shown here but for tRNAs broadly across the transcriptome where we can now using this method directly sequence total yeast tRNAs up to with up to 98% sequencing coverage and most tRNAs having 60 to 90% sequencing coverage and if you were just to compare here the standard tRNAs and RNAs t1 digestion on unfolded tRNAs to our current method which as the bars in blue you can see we've dramatically increased our ability to directly sequence tRNAs. Additionally, we've been interested in trying to sequence even more complex mixtures and so we've moved up to started to start directly sequencing viral RNAs. Professor Blake wouldn't have to at Montana State University in order to obtain viral RNA samples and we're then applying these samples to direct sequencing not only by mass spectrometry, but also by nanopore sequencing so we can directly compare the two. To resubject MS2 bacteriophage first to nucleoside analysis and then to direct sequencing by both LCMS and nanopore we see by both mass spectrometry techniques the inclusion of a single pseudo uredine modification. This is in great contrast to what our nanopore sequencing suggested which was over 300 modification sites many of which were from what modifications that our nucleoside analysis indicate don't even exist in our sample. This lesson has led us to propose that we should be really thinking carefully about integrating the LCMS and nanopore platforms in order to better develop algorithms for directly sequencing RNA modification by nanopore. Specifically, I would suggest that we consider at this venture where we are in terms of the state of technology using mass spec to identify which nucleosides are in a sample that we're analyzing so that we're at least picking the right program, nanopore program to use. Secondly, we should be critically thinking about the data that we get for nanopore thinking to the number of sites that I can measure by nucleoside analysis correspond to a reasonable number of sites given by these algorithms. We can also use nanopore to verify sequence context in well expressed genes I think that one of the take home messages for for me or from me I hope you get is that we should trust but verify with all of these RNA sequences we should be looking at sequences by orthogonal methods and making sure that we know really what's there. And lastly, mass spec is still a great tool for providing foundational data sets on short abundant RNAs like tRNAs that can then be used to ultimately enhance the algorithms in nanopore technologies which I think are eventually going to be the way to sequence RNAs. Okay, now we're going to move on to talk a little bit about the consequences of modifications that it is we're discovering and we're studying. I wanted to focus on the consequences on translation. Because translation is a cellular process near and dear to my heart it also is a process that uses a lot of modified RNAs including modified tRNAs, and we now know modified mRNAs. So there have been a number of studies by many people including honey czar and Jodi Puglisi and many many others that have established sort of two separate classes of modifications in mRNAs and how the ribosome deals with those two classes of modifications. The first class of modifications I like to think of as modifications that basically cause the ribosome to have a hard stop. When the ribosome encounters these modifications it can't go on. Many of these modifications are incorporated by RNA, sorry by RNA damage processes. When the ribosome stops at one of these modifications this causes the ribosomes to collide, eventually signaling for the degradation of the nascent polypeptide chain and the aberrant mRNA. The sorts of modifications that I want to focus on talking about now are a second class of mRNA modifications which have been shown to not cause the ribosome to form a hard stop but instead cause the ribosome to simply slow down. These sorts of modifications don't result in ribosome colliding and therefore the ribosome makes it all the way through transcripts containing these modifications in cells, going on there, makes it all the way through these modifications in cells, and there's the focus of what I'm going to talk about today. Something is wrong with this slide. I was supposed to say there's supposed to be an image of Inocene right there which somehow didn't translate. So, we know that in addition to slowing the ribosome down which could of course influence protein production, RNA modifications can alter also alter translational accuracy there's a large body of literature demonstrating how this can happen with the RNA modification in a scene as well as with damaged bases. I'm going to talk to you a little bit today about some of our work looking at ureidine modifications particularly focusing for this crowd on the pseudo ureidine modification. The pseudo ureidine modification is of particular interest in terms of trying to figure out its biological function because it's one of the two most abundant modifications found in protein coding mRNAs it's in fact almost nearly as abundant as that N6 methyl adenosine or M6 a modification we tend to hear so much about pseudo ureidine is primarily found in coding regions this suggests that it is a modification that the ribosome is going to be seeing regulatory in cells. And they can also be incorporated with quite high occupancy meaning that many of the pseudo you sites in mRNAs are in fact, conserved, and they can also be incorporated at high levels meaning 60 to, you know, 98%. Additionally, we know that the insertion of pseudo ureidine varies across the transcriptome with stress and development. I'm really interested in pseudo ureidine because this particular modification has been studied for decades in the context of non coding RNAs, where Paul agris Eric Westhoff and others have demonstrated that pseudo ureidine, in addition to base pairing with the adenosine can also base pair with other nucleosides suggesting the possibility that this might allow the ribosome to sometimes prefer to take in and bind a non cognate tRNA leading to the insertion of alternative amino acids. So we started to get at this correct question directly we used to fully reconstituted in vitro translation system in which we have all of the components for translation purified and we can add them back together in discrete amounts and really control our reaction, adding or subtracting one modification in this particular reaction we have a unmodified or modified phenylalanine codon here in the empty ribosome A site and we've added total charged tRNAs and just looked at an unbiased manner to see if the ribosome only makes the expected product or also does some level of alternative amino acid selection. Of course it did do some level low level of alternative amino acid selection or I wouldn't be talking to you about this. We went on to characterize some of the alternative amino acids we saw get put in kinetically and to really do transient kinetics and get an idea of how much of these amino acids could be inserted and how robustly on modified codons. In this particular scheme we're reacting ribosomes that contain an unmodified or modified phenylalanine codon with an isoleucine tRNA so we're looking at misincorporation on the ribosome. When we do this, we see that isoleucine misincorporation can really be either enhanced or limited in a very context dependent manner, depending on where that pseudo urodine is located within the mRNA. As a mechanistic enzymologist when you see funny things happening in your test tube you always want to go on and make sure that you actually see the same thing happening in cells. So we worked with Dr. Bejoy to Roy who was at NEB and is now at Moderna in order to do this. Dr. Roy and her team generated Luciferase mRNAs that contained either no modifications or were fully substituted with pseudo urodine. They transmitted these RNAs into cells, purified out the resulting protein products and did an unbiased mass spec looking at what amino acid was present at every single position. In the peptide analysis of the products of these two different mRNAs are shown here. So up here is the Luciferase peptide that flew the best and for which they have the best sequencing depth. All of the codons that do not contain a U or all of the amino acids that are generated from codons that don't contain a U are not bolded. Those from codons that contain a U are bolded and those where we actually observe substitutions are shown in red. The take home message from this is that we do see substitutions at a higher level on the mRNAs that contain pseudo urodine. However, we do not see them at all use sites suggesting that there is context dependence just like our in vitro assays demonstrated and subsequent work has come out the last year showing that this occurs not only in our system but that it can happen in other people's hands as well, which has been very reassuring. As a chemist I now that we know that there's some sort of context dependence the next natural question is why. So we worked with Dr Aaron Frank, who was at the University of Michigan and is now head of chemical biology at a raucous therapeutics in order to do some MD simulations to try and understand this we also concurrently did quite a bit of RNA melting studies. Aaron and his team modeled an isoleucine tRNA bound to a phenylalanine codon in the context of a crystal structure we'd collected with Dr Yuri Polinikov containing a pseudo urodine mRNA. Aaron's MD simulations much to my astonishment absolutely recapitulated what it was we had seen in vitro where he demonstrated that by inserting a pseudo urodine at the first position and a phenylalanine codon, you have a stronger base pair between the phenylalanine codon in the isoleucine. He saw the opposite at the second position where we also saw isoleucine being discriminated against suggesting that it's really fundamental properties of these RNAs that are driving the phenomenon that we're seeing. And this leaves us with a sort of overall summary of what we think pseudo urodine might be doing during translation elongation that is without pseudo urodine there the ribosome makes a lot of protein, mostly the right thing. When pseudo urodine is there the ribosome can make a little bit less protein sometimes and it can also make a small number a low level number of peptide products containing single mutations. We then went on and said okay now that we know pseudo urodine might be doing something how is it getting where it's supposed to be in order to do this we're studying pseudo urodine synthase enzymes or these enzymes have been linked to intellectual disabilities and cancers. Our work I'm going to summarize here in one slide that basically suggests that these enzymes are much less specific than has been long thought, and let us to a model for pusses selecting their RNA targets, which is that under unstressed growth. Pusses reside mostly in the nucleus, where they have access to a limited number of substrates and the substrates they do have access to are bound with RNA binding proteins or with ribosomes under stress. We know that puss seven can relocalize at least under heat shock out of the nucleus and into the cytoplasm where it can now see a much wider variety of substrates, and it doesn't fact modify more substrates under these heat shock conditions. We've gone on to follow up on these studies identifying a number of different conditions under which puss seven relocalizes under stress. We've also tagged the puss seven enzyme, forcing it to be either only in the nucleus or only in the cytoplasm with an nls or ns tag, and look to see how that impacts cell viability. And we do see that having either an nls or an ns tag under different stress conditions, changes cellular fitness and response to stress. This is really in line with a sort of an emerging idea in RNA modifications both across mRNAs and tRNAs that many of these RNA modifying enzymes relocalize in response to stress and that this might be doing something important in terms of stress response. So again, welcome to the RNA workshop meeting. Thank you for listening to all I've had to say and I'm excited to talk to everybody today about trying to figure out which sites do what when and where, and thank you so much for your time. That was great, Kristen. I'm going to try to start us off and it relates to your point that we've got extremes of what modifications might do they might be the key to life, they might not do anything. And I agree, the answer might be intermediate. And, you know, if we're embarking on trying to sequence all RNAs with their modification, you know, we probably want to prioritize in some way and I'm, I'm, there's no right answer to this but I'm thinking about your, your assays of pseudo uroding in message and how it could trigger a different amino acid insertion. Can you envision a screening, something to say okay, these modifications are the most important we should focus on these and, you know, you were putting them in and not a natural context but I would be very curious if you have any ideas on that. Yeah, I think this absolutely has to be done. This is the sort of thing that we're thinking about how to do moving forward it's actually pretty tricky with the proteomics to be able to come up with how to approach such a problem I think our approach currently has been more to try to build up some tools. So, I didn't show it here but we've done a bunch of different you modifications where you basically walk the modification around the base and put it in different contexts, and we're trying to figure out what context, cause the biggest perturbation in rate and the biggest perturbation in amino acid addition and then go back into the transcriptome and look and say okay where are sites where it's in this context and start with those because I think we need a priority list.  Thank you. You're welcome. Kathy. Yeah. So I'm just wondering that quantitatively how much of the pseudo user that in the coding sequence in terms of the given site occupancy will lead to a functional impact. And what's your plan to tease apart past MR and the tRNA targets in terms of its function. And that is, those are all fantastic questions. I think that we only recently even have an idea of what the stoichiometry is right Chuan had a paper, a bid seek paper out earlier this year that measured stoichiometry is transcriptome and in that paper they saw a lot. They saw translational read through with pseudo year Dean at stop code on mRNAs and they were able to actually verify this by, by Western blotting. And the stoichiometry of the pseudo year dings that were at those sites did not necessarily directly correlate which have with how much read through they saw so I think it's going to be a tricky question, unfortunately, none of this is easy. I wish that I can say it was and then in terms of teasing out the role of mRNA versus tRNA. I don't know, I think in vitro is really going to be the way that has to be done, because you really can't just knock out one of these enzymes and look in vivo and say that's what's going on. So, I was thinking back to your, you had the one modification that you saw, using mass spec versus the 300 position by nanopore. So I'm assuming that one was was determined those are the 51 you could look at. Yeah. Okay, so, and then those are the most common ones. I mean to be very well, we even those include some weird ones. My question was, so there's 300 by nanopore you believe that you had 299 just totally erroneous ones by nanopore do you think that those are actually modifications that aren't those 51. No. So you think it's 299. I do. Yeah. And we are working. So I want to say I think, first of all, I want to be clear that I think nanopore is going to be the future of this field I think long term, absolutely that is where we have to go. But just like Fred was talking about this morning standards are going to be absolutely necessary for the development of rigorous platforms so that we really know what it is we're looking at and nanopores great for looking at the RNAs where we already know what modifications are there. It can say yes or no we see something there. But in terms of being able to call modifications de novo, it's not there yet and that's going to need some some more implementation. I want great, great talk. So the, um, towards the end of your talk you alluded to the contribution of intracellular cell transport dynamics to modification say oh I think it's a big deal. And I know that correctly so people usually address situations of stress. As you know my lab will be pushing the idea that before we can understand stress, we need to actually reconcile with the fact that even transport set homeostasis. And it is and it is those changes where there are competing rates between rates of transport and the rate of modification by particular modification enzymes that that we have something to say so my question is, among all the challenges that we have listed, right. How can you comment on the challenge of even establishing what homeostasis look like with so many competing rates with current or future technologies. It's a big challenge, big my comment. I think we're going to need to do a lot of imaging and I think it's also going to be end up being pretty cell type, specific. So it's not a small question in that we could establish this in tech 293 cells I imagine if you go into neuronal cells, something's going to look a little bit different and it's, it's going to be hard. Right so this is not like sequencing DNA, where each cell contains the same DNA. So this is a much more complex problem. Oh, sure. So we have some questions from online or the virtual attendance, do you see any favorite ones there. I just will go through them from top to bottom. Lydia asked pseudo use been studied for many years. How can we accelerate studies uncovering the biological function of other mods, and that remain uncharacterized. What types of assays and technologies could be helpful so I would say pseudo use been studied for many years but we have not yet had the technology to study pseudo you well until about. I don't know the last six months or so. And so there's still definitely a lot left in that space to begin with types of assays or technologies. I think, coupling what we're looking at in the RNAs with actual proteomics and with whole RNA direct sequencing by furthering nanopore studies will be most most useful. John Moss is asking about a fifth grand challenge or suggesting a fifth grand challenge, which is looking at the interaction of different types of modifications or crosstalk. This is a great question. I have a story I didn't talk about today where we actually see crosstalk in the installation of tRNA modifications. This is for sure going to be a thing and it's quite interesting. I'm going to answer one more question than than than many has a question. Jeff keeps wants to know about stoichiometry. So he's asking what percentage of a modification of an RNA species is modified at a given position I think this is absolutely a key question again as we're trying to weed through thousands upon thousands of sites, knowing the stoichiometry at individual sites is absolutely essential. And so we again when we're thinking about mapping modifications we don't just mean mapping we really need quantifying those modifications as well. Yes, Jeff, I think it absolutely matters. Many. Yeah, sorry. Great talk. I have two small questions. One is regarding that transcript for Luciferase with those size exclusive like they're everywhere or the particular locations. Yeah, they're everywhere. They're everywhere. Okay, so for that particular transcript. Okay. And then the second question is, I was just wondering about the significance of the, the subs that you got for the phenylalanine tRNA. Before there was still like what point 5% of proteins point 05 yeah and that's totally normal that that's completely in the realm of when translation makes a mistake. And then after it was like 1% one. Yeah. Okay, yeah, that's significant difference. But I mean it's still I'm not going to say you're still a ton of it, but biology is fantastic at taking whatever is around and available and usurping it for its needs at specific times so I imagine that there might be one or two sites at which this happens much more robustly and it's important in a scene for example does this. Are there anything we have. Did you go through these. No, I didn't. We are going to about to have a break is that right so. Yeah, I think we should go on for a break. Thank you so much. And that was great start. And we're going to take a break until quarter of the hour. So it would be about, I don't have any ideas at 945 1045. Yeah, 1045 and for those of you who are in person. There's a coffee downstairs there's no more left here but there's coffee available if you need it. I'm an associate professor of bioinformatics and data science at the Luddie School of Informatics Computing and Engineering at Indiana University recently changed our university name from IUPU I are going to be changing very soon to IUI and also our school had an engineering my lab is broadly interested in single molecule methods for mapping RNA modifications structures and interactions. So that's our major interest. And today I'm going to be moderating this session on direct sequencing technologies. Our first speaker today is Eva Eva no Nova. She leads a epitranscriptomics and RNA dynamics lab at the Center for genomic regulation in Barcelona, Spain. She works on the use of use and development of direct seek direct RNA sequencing to study RNA modifications. So this session will have four speakers each taking about 15 minutes of presentation and followed by 30 minutes of Q&A. Without further ado, Eva, she's online. We move to the next speaker while. Okay, so you suggest. Oh, we are good. Perfect. Thank you. Hello. You hear me. We can hear you. Oh, no, yes. Okay, good. Good. Sorry, sorry for the issues. Okay, let me just now share my presentation. Okay, do you see the full screen. Okay, perfect. So yeah, so thank you very much for having me here. My name is Eva, and I'm group leader at the Center for genomic regulation. And then basically our lab is called the epitranscriptomics and RNA dynamics lab, because we're basically interested in what RNA modifications do because RNA modifications as others already know, but very briefly are dynamic features that expand the lexicon. And basically in our laboratory, we have been using different technologies to study them. A lot of the efforts are related to direct RNA sequencing and it is what I will be basically talking about. And then we also use other technologies also to study them, like Illumina or also mass spectrometry. And this is because we're interested in understanding their biological function both at the molecular level as well as more at the development as well as their role in disease and in intergenerational inheritance. So briefly, the epitranscriptome comes in more than 170 different flavors. And here it's already a plot that we made in the lab already now some years ago there's an updated version that has been just published. But basically what you can see from this plot is that there's a huge variety of modifications. There's many that have been associated based on the literature with the diverse human diseases the ones labeled in red here. And then like back then, these were the ones that we could actually study in a transcriptome white fashion the ones that are circled in green. And this is basically because most methods actually relied either on antibodies or chemicals that had to be selective for the modification of interest. So very briefly. You need an antibody or a chemical that will selectively react with the modification of interest. And then, then you can couple this to library preparation and finally to you can get the peaks that will tell you where the modification is you may have single nucleotide resolution using certain techniques. And then you can couple it to reverse transcription that if there's a bulky modification, it will lead to a drop off or if the chemical affects the modification in a way that also causes a drop off you will see this kind of signature. So in this sense, back already now some years ago, direct learning sequencing appeared as a promising alternative to actually study any modifications in a transcriptome white fashion. I don't need to introduce the technology to the public but pretty briefly it's it's a technology that allows to sequence native RNA molecules. And basically as the molecule goes through the poor, it causes a disruption in the current intensity, which can be converted into the nucleotides. One of the major advantages are that it will have no PCR bias it can attack all modifications. You don't need to customize your protocols to actually, you know, for each modification which isn't especially important because then you don't need to pre bias your decisions of which modification you think is especially important for your biological system of interest, because you actually do single molecule resolution you can actually get quantitative results in principle. Um, and then you can also get eyes up from specific RNA modification information and principle, as well as study codependencies and so on and so forth. So the advantages in theory are clear, but then I would say that there's some major challenges that remain to actually make this real solution. Firstly, and most importantly, there's actually no base color for RNA modification so the Nanopore has released some base color models for our for DNA modifications but this is not yet the case for RNA modifications. Also like in general there's a large input requirements. So the default library preparation requires 500 nanograms of collie plus material. But there's no some protocols that have been released that in principle can start with 15 nanograms. But there's other issues with that which I mean beyond the scope of what I would like to talk about today. But yeah basically, in any case is still large amount of inputs. In principle, the library prep is actually only limited to poly A plus RNA. And of course you can then maybe consider polyadenylated in vitro but that also has some consequences and issues. And it's in principle also limited to long RNA so it's not that the technology in theory cannot sequence short molecule but the truth is that it is capturing them poorly and when you try to map them there's issues there's these calling issues. And so on and so forth. So this kind of sets up a series of limitations that need to be overcome to actually make this a real alternative to study modifications in a transcript from white fashion. So today, due to the scope of this workshop I will basically just talk about challenge one. We've been working on the four different challenges and I'm happy to answer questions regarding any of the four. But the presentation will be focused only on challenge one which is detecting modifications. So briefly, the theory is that whenever you find a modification you should have a shift in the current intensity at the position where there's a modification of interest. So there's the theory which is very nice. But then in order to actually get this to work you first need to align or resquiggle, which, because basically the nanopore signal, it's not actually, even though there's like, it's supposedly translocated at an average speed. This is like as any biological system it's not perfect so there's actually like an over-stretching in some regions and under-stretching and then you need to kind of do this resquiggling. You can use dynamic time warping or different approaches, but basically this resquiggling process is also dependent on the base calling accuracy. So when we were actually doing the very first approaches trying to use, you know, these resquiggling to detect modifications, we were actually using very old base callers, so albacore, which is now deprecated. And then it basically was a disaster back then. So this is 2017. So then actually what we realized is that modifications also causes kind of base calling errors. So we could somehow overcome the issue of detecting modifications of the resquiggling by looking into these systematic base calling errors. And that's when we actually developed a Pinano where basically we generated some synthetic sequences that had unmodified NTPs and then we also generated ones that were completely modified. It is important to know that because initially we wanted to try to train a base caller. We actually generated the synthetic sequences with covering all the possible 5MERS with and without the modifications. That proved to not work and I'm happy to discuss why. But what we also found is that, you know, if we just extract the features, both at the current information and the base calling information and do some machine learning. In this case, we finally ended up using SVMs. You can then classify the KMERS on whether they are modified or unmodified. And this was set up for M6A but then later on we show that it also works for different modifications. So this base calling error signature is a simple proxy to actually detect modifications in a simple manner in a transcript in white fashion. And it works for many diverse modifications. So here I'm just illustrating some additional examples. I'm going to insert in here to primal methyl and importantly here when you remove this no RNA guiding that specific modification, the error signature is completely lost. Meaning that this is not just some base calling accuracy issue, but actually this signal is driven by the modification because when you remove this no RNA guiding this modification. It is lost in a very selective manner. And the same happens with the other modification types. It's important noticing that sometimes these errors are not just like a single position but actually a spread signal across, you know, the Pfeiffer region that actually contains the modification which makes a bit more complex to detect certain modification types. So okay this is nice so we can detect them at single site but what about in single read so I can we go quantitative. So then for this we kind of have to revisit the issue of risk wiggling this problem needs to be solved but then, thanks to the improvements in base calling and base calling the algorithm that basically you know boopy was actually doing a much better job this problem became reasonable. And basically we can now sorry we can now resquiggle the reads and then see for example shifts in the current intensity and positions that that basically have the modification. And here that is just a control showing that in other cases you don't see this shift even though it also has the modification. So basically using this kind of approach, one can envision that you can predict the stoichiometry of modification, we propose this software here but others also have now developed their own. But basically the idea is that using specific features you can use signal intensity. You can also use dwell time other people and we have, we actually propose to use a different feature called trace as well. You can basically predict the stoichiometry of how many reads are modified relative to those that are modified. And here's the final overall estimates and in the knockout you can see that basically we don't predict them to be modified. And in the wild type they are actually highly modified in agreement with mass spectrometry. So something that is also important we're mentioning here is that actually as I was briefly mentioning before the feature that you use in my opinion do matter to you know to actually how well you predict your estimation so here we were doing some comparisons of you know signal intensity or feeding different combinations and finally we went up with the one that was predicting better this stoichiometry based on the mass spec standards that we had built, which were validated by mass spectrometry to actually make sure that they had the amounts that we were expecting in terms of incorporation of pseudo uredin. Okay so then if we can detect modification individual reads can we look into modification dynamics so we looked at this some time ago but then like the long story and a short answer is yes that you can use it to detect modification dynamics you can go from absent to present kind of scenarios where basically for example here in this example heat shock conditions you can detect the modification and then the normal you can't. But then you can also see scenarios in which basically you know you have a low stoichiometry of modification in the normal condition, and then upon heat shock you increase the modification abundance. And then this is just to also show that not only in these kind of snow RNAs that we were looking here but also even in mRNAs this also works. So now there's many tools to predict modifications that are now out there. And basically in general they rely on either base calling errors as I introduced or they also rely on this on the current intensity or raw signal information right. So if I kind of but then this is really like not what you know what we have for DNA it's still far from being the standard because there's still not base calling the novel RNA modification which I think is ultimately the goal that we should aim for. So basically like just to kind of recap of where all this fits, like here we have the sequencing of the RNA strand. There's some data acquisition, you know using the minnow software, which basically records the fast fives for each individual read that is sequence. And then in the base calling process these fast fives are converted into fast queue here using goofy but other softwares could have been used in the old times albacore. We also consider using bonito although the RNA version is not available. And the important thing is here that you have a base calling model that is being fed to the base calling algorithm. And then finally you can then map your rates. So then basically at the end as I was saying you the tech modifications in three different ways using base calling errors is one way using alterations and current intensity is another way that has been proposed. You would use a modification aware base calling model that would be the third version so you would need here a bit different base calling model that is not just for the canonical basis so it doesn't just predict four letters but it would predict more letters. So then for example here from the wild type you would predict in which individual read you have a modification and in the knockout you should see a loss or practically a complete loss of your modification. You can even have estimation of modification probability for each base and read and position in your reads. So then the question is, can we base call modifications. So as I was saying for this what you need is a model that will predict not four letters but actually you know for example in the case of m6 say at least five letter and for this you need to train a modification aware model. So that's what we've been working a lot on and we started again with m6 say precisely because we thought it would be the an easy scenario for which we could have some training data. So, so here the key thing is that for this base calling model you need to train it, and you need a training set. And basically this is a bit what we have been trying to do so here there's just some couple of snapshots of real data, where actually we have tried to base call to train a m6 say base calling model. And here you can see you know that an individual reads, you know for this position that is a previously described on six say you have some reason are predicted to be modified others that are not predicted to be modified. And then basically you can validate this in vitro and some synthetic sequences where the modification is not predicted or. And then finally you can also even go in vivo and see that in the knockout is less modified and this position in the wild type is more modified. So what we're finding globally is that with this model, we can see very strong differences globally in the distribution of wild type and knockout. When we look into this with the base calling model so we think that there is actually quite some hope for RNA based calling models. But then, like generating this training sets for other modifications is going to be a bit more challenging so basically it's not just about having ways to train the data. But actually it's about also having the right data to train the model and this is also an important limitation that I wanted to highlight. And with this, oops, oops, sorry, I want to thank the people actually that did the work that I've been presenting here today, as well as the collaborators and funding and take you all for your attention. And I hope I wasn't too fast or too slow and everybody follow. Thank you. Well, we'll take questions towards the end. Our next speaker is the next speaker is Ben Garcia Ben Garcia is the head of the Department of biochemistry and molecular biophysics at Washington University in St. Where is lab dollars mass spec technologies related to chemical modifications of proteins and RNA. Ben. All right, thank you so much for the introduction and opportunity to be here and share a little bit about how we're using mass metrometry to sequence and characterize RNA. Let me go ahead and try to share this. Everybody. Everybody see this. Okay, good. Okay. And so today when I'm hoping to discuss with everyone is just kind of give a crash course update on some of the mass spec based approaches that we've been using to detect modifications quantify modifications and even sequence RNA using mass metrometry. So there's a lot of challenges with trying to analyze RNA by mass metrometry based approaches. But there's a lot to gain here as well. Mass metrometry is a fairly unbiased approach. And since we're measuring the mass of molecules we have the opportunity to detect many different types of modifications, as well as novel modifications. Ben. Yeah, or not in the slideshow mode, could you. Oh, well, it's not in slideshow mode. Let me hold on a sec hold on a sec sorry. Sometimes my computer does not like it when I'm hooked up to my dual screen. How does that look. That looks good. Okay, yeah, sorry. And so I'm just a pointer. Okay. And so, while there's a lot of challenges by mass. You know, in using mass metrometry. To sequence RNA, a lot to gain as well, because mass metrometry is is an unbiased approach that allows you to detect the masses of molecules and therefore we can detect, you know, theoretically any modification that might be present including multiple modifications. And then we could also kind of do this in a sequence specific manner by sequencing the RNA as well. However, there are issues with this approach in that RNA is difficult to separate by liquid chromatography which is often kind of used before coupling to the mass spectrometer RNA compared to other molecules is quite unstable in and out of the mass spectrometer. Even seeing larger RNA transcripts is quite difficult and most people in the mass spec field, you know, focus on metabolites and proteins. And so there's just not as as many resources, especially computational for characterization of RNA by mass spectrometry but my lab has been very interested in this problem for the last maybe three four years. And so we've developed a lot of approaches to try to enhance the analysis of RNA by mass spectrometry.  We've developed a derivatization approach where you drivetize RNA that's been chewed down to one oligonucleoside or mononucleoside unit, and we drivetize it without a methane which is deuterated and I'll show you why this is important later. This is a very strict efficient strategy, very clean reaction, it's per methylation. It's been used before on many other molecules but what this does is it really enhance the analysis of RNA mononucleosides and typically, if you run RNA on a liquid chromatography column C 18 based you know, the, you know, the nucleosides all kind of be looped very close together kind of, you know, in a very small area on all up in the front of the gradient. However, with the derivatization you can see that the the nucleosides now spread out they're more hydrophobic. And so we get better separations and that's that's better for quantification. But this also helps us identify different types of modifications on the RNA as well. And so when you derivatize with the iodo methane. What this does is it will react with any electron acceptor such as hydroxyl group or a mean groups. And so you can see here we've driven ties the three hydroxyl groups and the, and you know the two hydrogens on the mean group are replaced as well. And this derivation of adenosine. But the nice thing is when we start derivatizing these different isomeric modifications such as a six methyl a or two primal methyl a. You can see that the different positions are actually modified here and so we have an endogenous modification methylation here. And in this case we have the dodges methylation down here in the two prime. And so when we fragment these molecules together or detect them or fragment them together on they give us signatures that allow us to differentiate these isobaric modifications that at the MS one level or just the intact level they look the same. But here that actually give us separation and with this we've been able to develop this derivation approach to tech somewhere on 60 to 80 modifications for most cell types.  And so again, when we derivatize here's your dean you can see the different derivatizations and when we fragment we get this fragment with the ring getting the underization. But when we look at pseudo your dean, you can see here that, you know, we have two different types of our two sites on the on the ring that are now derivatized which we didn't have before so has a different mass and one of fragments has a different mass as well. So very easy to separate these are, you know, distinguished these even though by liquid chromatography they're difficult to separate. And so there's lots of different ways to look at RNA modifications by mass spectrometry a lot of people use targeted approaches, where you know the molecule that you want to study and you target it usually it's at the MS to level so this is at the fragment and so the target is known you typically do not take a MS once so you don't have the parent spectrum, but you'll quantify at the MS to level by isolation of the fragment detection and quantification of that fragment. However, if you're not, if you don't know what you're going to, you know, try to detect you can't detect unknown species. There's another approach called data dependent acquisition where something is known it will trigger an MS MS or a fragmentation spectrum and so typically you detect everything and the parent ions and so these are all different mononucleosides. And then you would select one of these and isolate it and fragment it and get a fragmentation spectrum to identify it. However, the problem here is that something that's low level, and maybe an unknown modification or species may not be selected for the second MS MS event will give you a fragmentation spectra so they go on detected. Lastly, there's a new approach in the mass spec field called data independent acquisition mass spectrometry. And here, you know, the idea is that we have MS one detection so we see everything just like we saw before, but instead of isolating one species to then fragment, we isolate a region of the mass spectrometry and then we fragment these things together. So the MS MS spectra are mix a composite of multiple species. And so you can actually create these little windows that you want to fragment everything that's found in these windows together and we've done this for our name to help windows that are specific to certain modifications and the advantage here is that if something is known, it will be abundant and you'll fragment it and you'll be able to pull it out of the fragmentation spectra. But even the low level species that normally would not be triggered for MS MS in this other approach here the DDA approach. These will be present there in these windows they might be at very low levels but we won't miss them they'll be present. And with power for algorithms, you can extract this data and detect and quantify these very low level species. And so we've been able to do this, you know, applying this type of approach which has been used quite a bit for quantitative proteomics and apply it to RNA analysis and so here's just an example of how here we have a mass of this one species, and there's several MS MS spectra here that confirm that it's real. So we can go back and remind the data but we can actually go back and remind the data for any potential modification or mass change that we would want to identify. And so in the last recent years or so, there's been a discovery that RNA can be glycosylated and so we can go back to our data sets and actually at mind these peaks that correspond to signature ions of different combinations of glycans on the RNA and other sites and then we can go back and kind of confirm these as well. So we've been actually been able to use this type of approach to detect novel modifications and new, you know, different types of branch forms of, you know, different glycan molecules on the RNA as well. You know, everything I kind of showed you before was just mononucleoside analysis and mass spectrometers are very good at sequencing peptides and proteins so can we sequence RNA as well by mass spectrometry and the answer is yes but it is very difficult. Part of the difficulty is that RNA doesn't behave well in the mass spectrometer so we've been kind of, you know, working on that as well. But the other real difficulty is that there are not very many approaches for the computational analysis of kind of this R and shotgun RNA types of analysis. And so we've developed the first infrastructure to kind of in, you know, adapted an open search algorithm that we now call a nucleic acid search engine that will take tandem mass spectra of a ligonucleotide spectra and match them to a database. And so this is kind of just a screenshot of what it kind of looks like so you can see here here's a small RNA transcript with a modification. Very similar composition but a different a ligonucleotide with a modification a different position, and so the program can pick these up quite well. We've been also, you know, adding more and more technology to the RNA toolbox. And so another area of interest in lab is to add iron mobility mass spectrometry, and this is a gas separation. And so we have liquid chromatography separation but now we want to include even more separation in the gas phase. And so this separates molecules in the gas phase based on their cross sections and shape. And so when we turn on this iron mobility we can get slight selectivity of RNA that are then separated from one another but this time in the mass spectrometer themselves and so this is what it kind of looks like when we turn on this fames and fame stands for field asymmetric iron mobility spectrometry. And so we have different voltages that we step through and NF is no fame so you can see with no fame everything kind of eludes out in one big peak and maybe there's a little tiny shoulder here. But when we step through different voltages we can get selectivity and start separating out different RNA species in the mass spectrometer which more or less corresponds to their size as well. And charge state distribution so we kind of get a charge and a size separation in the gas phase with this iron mobility. And because we are able to enrich for species that are really low level if we just throw everything in at the same time. What we see is an increase in their fragmentation spectra so here's a spectrum of two RNA oligonucleotides with fames off no fames or with the fames on you can see we much get we get much more fragments with the fames on so the sensitivity increases and if we get more fragments for the mass spectra we get more scoring species that we can identify. And so here's again here's our computational program this NACE nucleic acid search engine scoring here's with no fames. Here's with the fames on we see many more species and with much higher scoring distributions this allows us to dig deeper into the data and detect many more modifications I think that's just the tRNA digest, then we can with no fames and then you know just get much better sequence coverage and quantification of these species as well by mass spectrometry. In the last couple minutes I just wanted to share a little bit about some new chromatography that we're developing as well. We use a lot of C18 based chromatography but then we have to derivatize the ligand nucleotides and, you know, while that works. We've also in parallel been working on a different type of chromatography, polygraphic carbon chromatography and this has been used for a lot of hydrophilic species. What's interesting about this chromatography is that you can apply a charge to this chromatography and create kind of electrochemical field that will then retain different molecules such as RNA, and then with a polarity switch you can elute the RNA off and this is actually the basis for a lot of RNA biosensors and they use this kind of principles of adding kind of a voltage onto this polygraphic carbon material. So we do this type of electrochemical elution, but in an LC liquid chromatography kind of format. And so if we have a trapping column that has the graphite carbon, the idea is, you know, can we load and trap RNA, and then eluted out with a polarity switch change that we would put a voltage right here before it gets into the mass spectrometer. So in the proof of principle we've loaded RNA onto one of these pallet polygraphic carbons at a specific voltage. And when we start the HPLC gradient, we can see that there's very little RNA species that are eluted, but once we have a polarity switch, and have the same gradient of RNA species, you see everything kind of elutes out and this is kind of shown here in the heat map. You do get some RNA species at elute but once you have the polarity switch, you really elute them all out so you can trap any elutes RNA species into the mass spectrometer. And the last piece of data I'll show is just kind of a further characterization of this process so we have kind of our electrochemical elution RNA trapping and then we're going to, you know, increase the voltages and so we've been playing with just the stepwise increasing the voltage and you can see that we have different species that we trap any loop into the mass spectrometer and it really does trap any loop based on the RNA size. So you can, the lower voltages trap some lower RNAs and kind of step through and, you know, get selectivity of different RNAs that are eluted. So when we kind of combine the stepwise voltage with a continuous HPLC gradient, we get very nice separation of a lot of different RNAs more or less on size as well. It allows us to dig much deeper into the data sequence a lot more oligonucleotides, including many more modifications and so now we're hoping in the future to kind of combine this with the ion mobility is kind of like the ultimate platform for sequencing and characterization of RNA oligonucleotides and this just kind of shows that we get, you know, very very good sequencing, you know, through the mass spec data. So, yeah, really quickly I just kind of wanted to share, you know, some of the things that we're working on. There are a lot of challenges for RNA analysis by mass spectrometry but the upsides are enormous being able to detect a lot of different modifications including novel modifications and do it pretty quantitatively as well but that's going to involve a lot of improvements to liquid chromatography the fragmentation approaches. And even some out of the box thinking, you know, potentially, you know, ion mobility spectrometry and, and some other, you know, potential new chromatography such as this electrochemical elution chromatography to just try to help improve the dynamic range and the selectivity of the mass with a thermometer for sequencing RNA species and detecting the different post transcriptional modifications that are present. And we're continuing to build on a lot of these, you know, platforms that we've established with the goal of just sequencing longer and longer RNA transcripts right now we're kind of topping out at a probably like a 30 or 40 mer oligonucleotide but we'd like to go a lot a lot longer and detect even more modifications lower level ones as well. So with that just want to thank, you know, everyone for the opportunity to present in this workshop and looking forward to a good discussion. Thank you. Thank you Ben for an exciting talk. We'll move on with the next speaker. Our next speaker is a Shuo Huang is a professor at Nanjing University. His research interests are in single molecule sensing applications using engineered biological nanopores. Alright, so can you see the slides. Okay, so I'll just go ahead. So good morning, good afternoon and good night everyone. So I'm Shuo Huang, I'm a professor from Nanjing University. So first, I'm really delighted to have this opportunity to talk with everyone about our recent progress in nanopores sensing of ribonucleotides and their application modifications. So this is actually me, my expertise is only nanopore. And like this is today's outline. So I will just take a very short time to explain what nanopore is and how it can sequence DNA and RNA. And then particularly important, I will talk about this kind of new concept that people are not familiar with. It's called single and nanopore single molecule chemistry, and then how it could be helpful to identify different RNA and MPs. So, the network concept is very straightforward. Like, you have a core and then you have a pair of electrodes you can drive your analytes, electrically, or even by diffusion through the pool, you will get its characteristic events. So this is the first data that I produced when I was an independent PI. So this is a molecule passing through the pool and this is another. But normally a nanopore sensing scenario that produce this kind of signal doesn't give you too much information. So people are more interested to develop like more developed nanopores so that it can sequence DNA or RNA so that can provide more information. So the first nanopore in the world is biological nanopore. And the first biological nanopore is this half humanizing pore. It's very stable and robust, and it could be easily prepared by like prokaryotic expression, and later people develop more many, many more pores. And then eventually they form a very big family. So my expertise is with this kind of now called MSPA. And with MSPA actually we can do like so many things and has a very high dynamic range from like a small proton to like a tRNA. So you can do like a lot of stuff, but today our focus is RNA. So before I talk about RNA sequencing, I think just now people already talked about our nanopore sequencing. So in the MSP, now I used MSPA, I did some engineering of it, and then I produced a DNA sequencing signal in the beginning. And then this is what DNA sequencing signal look like from this nanopore. And if you use using like an Oxford nanopore device, you will produce pretty much the same thing. So I'd be very surprised if you have only four DNA bases, why do you produce like so many different combinations of signals? And the reason is because this nanopore doesn't have a sufficient spatial resolution to resolve each individual DNA or RNA base. Later, Oxford Nanopore announced this kind of RNA sequence, direct sequencing technology, but still it's still experiencing the same problem. You still can't decombolute like RNA one base at a time. So the signal you get is still like that. And if you zoom in into the detail, you will find it's still very complicated and it requires a lot of efforts of bioinformatics to decombolute its original sequence. So when you have like epigenetic modifications, this situation is getting even worse because your epigenetic modification will contribute to your nanopore signal and produce like unpredictable like event features. So now that's why we need like a new concept. So, back to 1997, my postdoc advisor, Hagen Bailey, started this kind of approach called nanopore single molecule chemistry. So normally people just treat a nanopore like that as a core, but actually he started a kind of concept to engineer this board so that it can have a reactive site inside the middle of the board. So this demonstration is like chemical binding between zinc ion and the amino acid side chains like that.  So corresponding to bonding and dissociation of this kind of metal ions. So at that time, people believe that actually it because you analyze very small so your signal is always very small. And people didn't proceed to further develop this technology. But actually, we actually discovered, pretty much by accidents that the network that can sequence DNA at the same time, it's a perfect network reactor. So this is the MSP in our war. And if you engineer this site to have like a single amino acid side chain that is reactive. So you can actually observe chemical reactions in a very precise manner. So for example, this is a resigning. You can actually put tetrachlorate into your system and then they will point to the resigning and eventually will oxidize the resigning into sulfoxide. So this kind of reaction actually takes three steps so you can observe each individual step like in atomic resolution manner with no problem. And to my surprise, these events look so big. It's like maybe a few tens pick ones or even larger, considering that your analyze is still very small. Why is that so big? It's because your MSP nanopore is actually focusing a lot of ionic current into like a tiny spot. So you amplify your signal so that you can get more information. So when this paper is published, a lot of people ask me the same question. So what is the significance of this work? So the significance is not to observe this reaction. It's because actually you have observed a pore that can report exceptional atomic resolution. So later, then further, I actually started like a different approach. I want to just detect very difficult to separate stuff like saccharides. You might be surprised like why are you talking about saccharides in our meeting? Well, later you will figure out why. But actually, these saccharides are very similar or are almost identically similar in the molecular weight. So sorbos, broktoes, galactose and Reynolds, they have exactly the same molecular weight. So I have to use like a special engineering technology to make this ball to behave even better. So we developed this kind of hetero-optima MSPA. So this is a hetero-optima. And because it's a hetero-optima, so we can actually modify this site with a soil modification site. So this is a phenoboron acid. And then the phenoboron acid will bind with almost any kind of saccharides and produce a unique signal. So this is for sorbos. Remember that, and then later you see like broktoes. They are quite different. And then later, galactose. Considering that it's producing like so much different distinct signals, you can actually put all every different kind of monoseccharides through this pool and you can get like extremely high-resolution data. So this is actually the first night. We got success. My students and I were very happy. So I recorded this moment using my cell phone. And you can actually see from the screen, these kind of events. These are based on chemical reactions between your analyte and your report. And if you do like proper treatment of your data, you can see that even for monoseccharides that have almost identical molecular weight, they can be almost 100% separated. There is no problem. And then later, we realized that we can use that to identify different RNA like NMPs. So in 2022, you probably see this kind of journal cover from Nature & Nello Technology. It's from our work. And then you also see this kind of research highlights and news interviews. We are also discussing the same thing. So in principle, we were using exactly the same technology that we used to detect different monoseccharides to do like RNA modification detection. So this is the same hydro-optimal MSPA. It's modified with phenolbrone acid. It's very easy and very efficient. And then later, this phenolbrone acid will react with this ribose part of the RNA like nucleoside monophosphate in a highly reversible manner. So basically, you are observing like binding and dissociation of like a nucleoside monophosphate to the pore and dissociation from the pore. And this is the raw data that we produced. So considering that you have like four different bases, G, C, A, and U, they will produce like four different distinct signals. And they have no overlap at all.  To demonstrate like we are definitely using exactly the same core to do this kind of discrimination job. We use the same core and we just keep adding different analyzing to your system. So the first one we have is C and then later we put U inside and you can see this separation and then A and G. So there's no problem at all for like discrimination of this four different RNA bases. Then if we have this kind of resolution, then why not just go ahead and do absolutely modifications. So these are the afternet modifications that we can get easily commercially or by synthetic like equivalent. And so all together we have tried like 14nm pace and their overall accuracy about 99.6%. So the more you include, there will be like a slightly drop of your accuracy because some of them might be like slightly overlapping. And the speed for detection in like minutes, you will get like this kind of accuracy and efficiency with no problem. So here I think it's very straightforward to just show you the data that makes that has excited us a lot. So this is the raw data without any treatment. Based on your naked eye, you can actually see how different they are. And especially for like M1A and M7G, they always behave like a very deep blockage comment. But for like other afternet modifications like M6A and M1A, they all can also be easily recognized by even your naked eye or a machine learning algorithm. So to prove that this kind of afternet modification can be discriminated from their canonical basis. You can actually do like this kind of side by side comparison using exactly the same now for so if we mix C and M5C. So you can see the separation and G and M7G and many others. So I'm pretty sure that you are convinced that you can use that to recognize different average net and MPs. So when we submit this manuscript to National Nanotechnology, like our referees are generally very friendly and very supportive. But they give us like a different task that we didn't do before. So they want us to show like how we can deal with natural RNA like the whole stuff has to be completely natural. So, and then we tried like something simple. This is an Easter tRNA phenylalanine. According to this map, you can see there are so many different types of modifications. So we can use enzyme to degrade that into different NNPs and put the whole mixture into your manifold sensor. Then this is the raw data that we have produced. This color coded dots corresponds to the data that we know or like we have previously recorded before. So these black dots we mark that as UT1 and UT2, UT3. So we have actually four like clusters of events that we have never seen before. And eventually we realize that it corresponds to the M2G, M22G, T and Y. So there are two that we can't detect because these two has modification on the ribose hydroxide group. So it can also do quantitative. So this is the measured value and the true value comparison. You can see that is producing like pretty like accurately the option of modification and space composition in a highly quantitative manner. And it's matching with your true value very well. So recently we also further developed this technology so that actually we can use exactly the same core to measure different NNPs and DPs and TPs. And even there are different combinations of FGNAC based like this kind of digitized. So this is the raw data that show this kind of simultaneous discrimination and the statistic in a scatter plot. So to summarize actually we are developing like a technology that can almost 100% accurately identify different NNPs and their FGNAC modifications. So we are not yet there with sequencing but I think we are pretty close to that. And also eventually I would like to thank my students and my members and also the founding bodies for their support and thank you. Also if you are interested in that of course we can give them to you like on this platform for almost for free. Thank you. Thank you so for another exciting talk. We'll move forward with our next speaker the last session. Marcus Stoiber is the principal machine learning researcher at Oxford Nanopore Technologies where he has been leading a team to develop tools and models to investigate and detect modified basis from raw Nanopore signal. And how we can use and he has been helping several researchers around with using the technology for understanding disease and genetic disorders. Thank you so much. I'm really excited to talk to everybody. Yeah, I'm really excited about all the work that I've been hearing about here and all the work that emphasis especially on all these different tasks that everybody's talking about. I think my talk will hopefully synergize quite well with all of those topics. Cool. Yeah, so I spent a lot of time in my six years in Nanopore working on DNA modifications we're going to go over where that's led us in the state of play of how we detect modified bases in Nanopore. And then some some the other recent RNA upgrades that are soon to come and where we're going with RNA modified based detection. As people know, got a little messed up here that you can directly detect detect modified bases from the ionic current. And so the current state of play is that we have really good direct detection of methylated bases and we have really good models for DNA bases of five mc five hmc. And a lot of people in the community are introducing models for lots of other things, everything you get with Nanopore comes along with this so unrestricted relink flexible approaches and real time sequencing. So the one of the big work that I've been working on for the past about two years is this remora algorithm. So this latches on to this remora fish as the fish attached to the shark ears that how we named it so the bigger fish is the base caller, and the modified bases latch on so the remora algorithm latches on to the base caller and performs a different task of modified bases actually separates it from the base calling task goes along with a big theme that we've heard a lot about is that it separates the samples you can use for training the base caller from training the modified bases, which is really important. And it takes very little time over the regular base colors and feeds off those outputs. So just as a high overview of how remora works you see in the top here is an example signal of a full read for our CPG model we go in and find where we have a CPG call within that and you pull out that little bit of signal so that importantly what goes into the model is what you see right Let's see you can get that so on the bottom here is the sequence and the top is the signal so it's just this little slice of this big read that goes into the model and that means that all that that little bit of signal is the only thing that we need to match in a training samples that's really important for introducing really complex samples into training modified bases in the 904 base caller, and we get these high accuracies from this, we also employ scaling to give us a little bit more accuracy here. So one of the big themes that we've heard so far and this is, these are all for our DNA, but I think a lot of this translates to RNA modified base training here is the data types are super important. So these are the main five data types that we use. So we have the synthetically created all ago nucleotide so that we've talked about here. And we also have synthetic randomers so here I'll go into this in a bit more but where you have random stretches of bases either side of a known modified base in a small context. And then we have the biologically derived obviously native enzymatic modified bases and second strand doping. They have different advantages and disadvantages for training and modified base detection. So the modified the biologically derived native, that's what we want, right, we want to be able to detect all this in native samples that's that's the goal, but they contain mixed modified bases and no concrete ground truth. I would even challenge before we said that in DNA we know the modified base of a cell even in a cell line, you get a single site in the genome that's 50% modified in a completely homogenous pool of cells so why is that right, even the simplest cell lines are still complex in DNA. Second strand doping again so a little bit complex we have modified bases in all contexts but we don't actually know where the individual modified bases are. So it's useful for some tasks of training and not for others. Finally enzymatic where we've so we've used this a lot for five mc and five hmc detection, where you just get every C in a CG converted to five mc or five hmc. So our analytics group has done a lot of work to get this reaction really highly accurate there. So this synthetically created these have been really important for us, the synthetically modified oligonucleotides are really important to show that we what our accuracy is the even the ground truths in alternative assays are not accurate enough for us to know what our accuracy is. So we had to use samples like this to show that we're have higher accuracy than by sulfite sequencing. Finally, this is sort of our holy grail of training samples is this random or the problem with a random or is that you don't actually know the canonical sequence. So we've used our duplex sequencing where you sequence both strands of this molecule can see up in the top so you get the random canonical sequence. And now you know where your modified bases within that so this is really an important sample for us for training data sets, and it's important around the remora that we can take a tiny slice out of this as our base callers are trained off of much larger chunks of three to 500 bases, and you just can't have good solid ground truths for three to 500 base pair chunks so we can get 50 60 base pair chunks with these randomers. All right, I'm going to go through the DNA models relatively quickly. I think of less interest to this group but hopefully of some interest. So the big one that we released in December was a five MC plus five HMC so two modified bases at once this is sort of where we're moving to detect multiple modified bases and this is obviously incredibly important in RNA, but the problem becomes harder right it's easier to tell two things apart. So we do take a little bit of a hidden accuracy but if you collapse that back down and look at it like you would by sulfite we still get the same accuracy out of that. And again you can see in our all printed oligonucleotide that we have real this is how we measure all of our accuracies that we quote are all from printed oligonucleotide so not from an enzymatic sample not from our training data these are completely held out of our training to get these accuracy numbers. We get the slightly lower accuracy but we get really good when you get these highly modified you can see in the top is the ground truth printed so these are sort of we wanted to show how good we are when we get a more real life sample. When you get HMC and 585 MC the M is is, and I'm in the weeds a bit here but M is five MC and H is five HMC. And you can see that even when they're densely connected to one another and the signal is obviously affected across we as we've heard from several people. And you can still detect them with really high accuracy over 90% accuracy on the single read single base level, not stacking up over read this is on the single read level that we're still over 90% accuracy and these really complex samples. And, and even beyond that so you can show the initial one that we released a year ago on the R9 for the kit 10, we were a slightly behind by sulfite so you can now with the R10 chemistry that we're really moving towards for DNA. We're a good chunk better than the accuracy we see from by sulfite so we took the same ground truths and sent them off for sequencing effort by sulfite and we were showing that we're better than than by sulfite so we're really excited about that. So again, this is sort of where we're looping in a little bit more into the RNA modified bases is getting these all context models so that the enzymatic samples were used to train where we could modify every C in a CPG to be fiv