Go with the Flow

Hi, everybody. Welcome to the webinar today for harmonizing flow cytometry analysis and orchestration. My name is Sean Burke. I work as a VP of commercial operations at Dotmatics Flow Cytometry, spanning our products of OMIQ, FCS Express, and EasyPanel. I also serve as the president of De Novo Software with our product FCS Express. I'm excited to be here today, to make an introduction to my colleague, Rafael, for this webinar. As many of the folks on this call know, flow cytometry produces some of the richest single cell data in the life sciences. But too often, that value gets lost when data files and results are scattered across different files, different formats, folders, locations, assays, departments. What we're gonna try and do in today's session is focus and follow on the flow of data across its entire life cycle. We're gonna do so in the context of some of our Dotmatics products, but, keep in mind a lot of what we're gonna be talking about with Luma today. It... It's open and operates under the FAIR exchange principles, but you're gonna learn a little bit about how EasyPanel interacts with this workflow. You're gonna learn a little bit about how automated data capture at the instrument with Luma Lab Connect works. Then we're gonna spend a lot of time talking about how our tools like FCS Express and OMIQ can interact with lab orchestration and workflows that feed into this tool that we call Luma. Before we get there, though, we'll also spend a little bit of time talking about data as it gets to GraphPad Prism. When we come in to talk about Luma, we'll discuss how that tool can help you capture, structure, and link everything together into a traceable record so your whole team can build upon that. That means, teams working in different locations, working on different assays, working in different experiment modes. Today, our presenter is Rafael Gomez Amaro. Rafael is an application scientist for flow cytometry lab automation solutions at Dotmatics. What that means is, Rafael brings a wealth of experience for, FCS Express and OMIQ and EasyPanel. Part of his role is to, figure out how all that comes together and make sense of that, for within the Dotmatics, solutions, including Luma. Rafael brings more than a decade of experience in the field. Before joining Dotmatics, he served as a scientific adviser at BD Waters focusing on imaging and spectral flow cytometry. He also provides... Provided scientific advisory and high dimensional biology covering high parameter flow, cell sorting, and single cell multi omics. We, at the FCS Express team, were lucky enough to work with Rafael even earlier than that as a technical application specialist when he was at DeNovo Software before Dotmatics acquired DeNovo Software and some of our other companies. He knows these tools from the inside out. Whether you're running day to day gating, leading core facilities, working in bioinformatics and biopharma, or thinking about how flow data fits into your broader research infrastructure, you're in great hands today with Rafael. I'd like to welcome him. Thanks for joining us today for Rafael, and, we'll let you take away the webinar. Thank you, Sean, for that kind introduction. My name is Rafael Gomez Amaro. I am a flow cytometry automation solutions specialist at Dotmatics with, quite a bit of experience, not only on the instrument side, but also on the analysis side. A lot of us know the challenge today in flow cytometry, especially if you are a scientist working within a large organization like pharma or biotech. You see day to day that managing flow cytometry is hard. It's a challenging task to tackle. We have fragmented tools. We have manual steps and interventions that are required to go from our raw data to those insights. a lot of our processes have limited or no end to end traceability. Some of the challenges that we face today are siloed file stores. We have our flow cytometry data scattered across multiple different systems, and we have no unified view of our different files, our metadata, or across our studies, those results. We do significant manual data wrangling, and so teams will often spend hours retrieving and copying and reformatting different data files. That leads to not only errors in transcription, but also analysis bottlenecks and delays, not only when we're collaborating with our internal colleagues, but also externally with clients. Because of this fragmentation, we see limited traceability of our data, our results. We can have incomplete audit trails or even missing version control. When we transfer data to colleagues, that traceability is often lost. Scientists will lack the full context of their data because of that loss of traceability. Insights can be lost along the way of our flow cytometry analysis. We also encounter bottlenecks at scale, and this is important. Many of the tools that we work with are on the desktop, and they often struggle with larger volumes of data. We also have difficulty accessing, searching, monitoring, and sharing data across instruments and across workflows, across our entire organization and different teams. It's a manual process. We can see that fragmentation even clearer when we think about our day to day workflow in the lab, not just at an organizational level, but at the scientist level. In the panel design struggles and the difficulty in planning for a new experiment, Annotating those samples and templates with metadata and what goes into what well and when, collecting and moving those raw data files, analyzing them, exporting our results to other softwares, which can require temporary spreadsheets to wrangle your data together. Then finally, you run a secondary analysis, and you have to store and share all that information. Unfortunately, at each one of those steps in our experiments, we see a breakpoint, a manual intervention, and that leads to hard to trace handoffs of data between people and between softwares. What that all represents is time wasted, errors that accumulate, and that puts your pipelines at risk because we know that errors don't stay contained. They don't stay silent. They travel downstream. They can often multiply, and they can surface at the worst possible moments. That can lead to months lost for a single error, Risks to your regulatory submissions growing over time and folks being able, if they have a better handle on this, to stay ahead and move faster to those, submissions if you're working in pharma. What is Luma? Well, in much the same way that you have a physical lab, you also have a digital lab. You have data files. You have LIMS, ELN systems, sample management systems, systems that are web based or on the desktop, systems that have different database back ends or different storage mechanisms. While your physical lab can often be in one place, the digital lab is usually disjoint. It's made up of that web of disparate systems. With Luma, we aim to tackle that problem, to connect and orchestrate all of your digital lab, your digital data, with Luma serving as a scientific intelligence platform for your entire organization. What Luma does is it centralizes your data, and it makes it accessible and ready for anything, including modeling, charting, querying that data, and using AI for getting those real deep insights. How does Luma work? From a high level, Luma is a cloud native platform. All of your data is stored in the cloud, and you interact with Luma through a browser. But that platform is driven by Databricks. What it does is it allows our products and others, Luma is an open system, to write data into the system and pull that data back out. That's gonna include OMIQ and FCS Express, which we'll show some demos today of. Luma is much at the center of our Dotmatics family of products. with Luma, the workflow is that, first, we automatically capture all of your raw data from your instruments and your software using Luma Lab Connect. This is our instrument and data management piece. That data doesn't have to be limited to flow cytometry. We have customers using Luma for mass spec, sequencing chromatography, chemistry studies, and more. Luma Lab Connect is able to take all of those raw data flows from your different instruments and softwares, and it can centralize it. It can structure it, and it can make it accessible to other products, Things like Luma, which can then connect all of that raw data, and we can use Luma to model it and to link all of that information, connect it to the rest of your science. What that does is turn these scattered cytometry workflows that we're so used to, data lakes in our organizations into a rich ecosystem with data that's findable, that is accessible, that is interoperable, and can be reused. Data in Luma aligns with FAIR data principles. That foundation of structured FAIR data, well, that's going to enable you to do better science, to automate your data workflows, and enable you to properly leverage AI across all of your scientific data with a single source of truth that you can trust and is accessible across the whole organization because all of the context, the data, it's all there. It's all traceable. It's all accessible with Luma. At the end of the day, what we want to bring you with Dotmatics and Luma is that we don't want you to have to dig through file shares or LIMS to find your FCS files and sample metadata. We don't want you to have to go through intermediary spreadsheets to get data from products like OMIQ and FCS Express into Prism or other softwares. We want you to focus your time to not spend time building and validating those panel designs. We have tools like EasyPanel. With Luma, our Dotmatics products, we have this more seamless system that can orchestrate that entire process from design and preparation all the way through execution, analysis, and data visualization and more with your flow cytometry workflows. That allows you to save time and money and make more confident scientific decisions with your data. Some of the things that we're going to show today, are gonna highlight examples of quality control, traceability of your instrument data, and monitoring of that instrument data, those QC results, speaking to data quality. This is something that spans across flow cytometry. It's not just folks in pharma and biotech. Clinical folks, folks in academia also have these data quality concerns, and so we'll show you how Luma can help with that. We'll also talk a little bit about cross domain data integration. Although we won't have an explicit example, you'll see how, flow cytometry data touches across all scientific disciplines. You may have a need to connect your flow data with antibody data or sequencing or chemistry data, and we'll show you how it's possible to do that with Luma. Then finally, we're gonna close out by showing an example that most of us are running into these days where we have these higher volumes of data, higher throughput applications. In order to get a result, we have to work across applications, and there are unique data processing challenges for that. We'll show how Luma can help us with those problems as well. For the first example that we're gonna be talking about today, again, we're gonna be looking at quality control, traceability, monitoring instrument data, and data quality, and we're gonna be doing that using FCS Express. So, for those of you who are not familiar with FCS Express and you're wondering why would you use FCS Express for those kinds of problems, FCS Express is a capable flow cytometry data analysis software. What it allows you to do is it allows you to do your flow analysis, but also generate all of the statistics, those results, in a presentation ready format that you can share with your collaborators easily, but all in the same software. You don't have to jump out to a PowerPoint or an Excel to do those statistics. We can do immunophenotyping, specialized analysis, like DNA cell cycle proliferation. We can integrate with other products like Prism to do your statistical analysis, but we can also export out all of your figures and make these customizable reports that can go into PowerPoint or you pull out those steps to Excel so we can connect with other softwares. We support the entire spectrum of flow cytometry from conventional and spectral cytometry to mass cytometry to image cytometry. Those workflows are supported in FCS Express. Then finally, for folks who are doing the more advanced high dimensional analysis, we have those tools, dimensionality reduction, clustering, and we can also connect to your Python and our analysis pipelines using FCS Express. You can do things on the cutting edge with FCS Express as well. Now one of the unique things about FCS Express is two different products, an RUO product, which most folks are familiar, but we also have an IVD clinical edition, which is listed with the FDA as a medical device. What that software brings, this is a unique situation for a flow cytometry analysis software, is it brings you a lot of tools that can speak to the requirements for data integrity. You think about, 21 CFR part 11 compliance needs, audit trails, e-signatures, user permissions, secure login environments. All of those things, FCS Express can provide. We even have a third option. It's this validation ready package that has all the RUO softwares and a lot of the great things about that clinical edition. regardless of where you are in your pipeline, early R&D, analytical development, development, or doing clinical things late stage in trials and whatnot, FCS Express can support you in your flow cytometry analysis. Diving right into this first example, what we're gonna be talking about again is something that we see lots of folks encountering these days, where they're taking data from many different cytometers across the different sites and many different experiment runs. What they're finding challenging, and this is affecting pharma, this is affecting clinical labs, this affects biotech, academia, everyone in flow, is it's hard to trace back those results that you have from an individual experiment, an individual sample, all the way back to those instrument quality control runs or instrument monitoring data, metadata, or other critical QC results for your instruments or assays. What we want to do is take a result that we got maybe three months ago and say, hey. Can I look for my instrument or my assay that I read? Can I say with confidence that result on that day was collected on my instrument when it was running to specifications? The problem with that is that collecting that data, those QC results is a manual and tedious process. It's not easily searchable, especially if you're working in, between different teams. You're not the owner of the cytometers or other folks are doing that at different sites and locations. That leads to decisions and context that is missing when you're evaluating if you wanna move forward, with a specific model... Molecule or specific result. That's leading us into these siloed information spaces where data is missing and we have that loss of context. In the example I'm gonna show you in FCS Express, we're gonna take data... QC data from our cytometers. We're gonna analyze, collate that information quickly in FCS Express to generate a QC style report, and we're gonna send that information into Luma. We'll go through the journey of how that data travels there where visualize and share and make that data accessible. What I'm gonna do is I'm gonna open up a layout a in FCS Express. In this layout if you've never seen FCS Express before, FCS Express looks and feels and works a lot like Microsoft Office PowerPoint. We have our plots with our flow data. You know, you can see that we can format them. We have these different spreadsheets, that are pulling data from these plots, and we're visualizing them down on these charts. the central thesis of FCS Express is you can do all these different kinds of analyses without having to go outside of the software, build these presentation, review reports. But when you update the information all of this data, all of my stats and my charts, you can see their links. As I move my gate around or as I load a new data files, those results will update. What we're looking at again, is a QC experiment. Presumably, you'll be looking at one of these measures for, like, a mean FITC or a mean PE, and we're collecting that information and tracking it over time to monitor instrument or, say, assay performance. Presumably, you would use this layout for is you would, come in and say, well, I ran an experiment on October 1, and so I want to now go down and see, for the result that I got on that date, was my instrument, running to specifications? You'd be able to confirm that. But the problem, again, is that collecting all of this data, finding it, getting it all into one place, and having access to the results that you need, that is a manual, tedious, time consuming process. What we've done in FCS Express to help tackle this challenge is we have now made, an integration with Luma Lab Connect, where we'll be able to use our existing batch processing, feature to export all of these results, these spreadsheets, statistics, charts, and other visualizations, even our FCS files directly into Luma where it's gonna centralize this data and, again, make it accessible using those Luma dashboards so that folks can find and search this information. The way that we would get this data out is we would set up these, batch actions, then we click run, and that would go directly into Luma Lab Connect, which would store it, centralize it, and, again, make it accessible, with Luma. What I wanna do is go through the journey that this data takes as it exits FCS Express and goes into Luma Lab Connect and Luma. What I'm gonna bring up is Luma Lab Connect. Again, we're accessing all of this data from the browser. Within Luma Lab Connect, again, we have all of our data centralized. There are two ways to get data into Lab Connect. We can either directly connect it to an instrument so it can monitor. When new data files come out, it'll automatically ingest that, and we'll have access to that data. We can also connect with Luma Lab Connect programmatically via an API call. That's how we're pulling data out of FCS Express or out of OMIQ and Prism. You'll see later on, directly into Lab Connect. Once we have that data in Lab Connect, you'll be able to search for it. You can see I have a little table with different instruments and folders listed. These are all waiting for a new data to arrive in Luma Lab Connect. But what Luma Lab Connect does is beyond making this data findable and accessible within Luma Lab Connect all centralized, it also makes our data more interoperable. What I'm gonna show you quickly is what happens when you export one of those data files. In this example, this layout or XML file directly into Luma Lab Connect. What we do is we parse this data, and pull out key pieces of information that are associated with that data file. We have over 500 different, parsers for different file types from instruments and software. But the idea is this parse representation is what powers that interoperability of data, within Luma. If you want to learn more about how this works, please reach out to us. Happy to discuss how the parsing can work, and what we can parse as far as file types go. But the idea is that once we have that raw data that's linked, the parsed representation, take that data into a Luma dashboard, where connect all that information and make it accessible, visualize, query it. This dashboard this is showing that QC experiment we saw earlier. I can search for my QC runs by, the file ID but I can as easily make this searchable by the cytometer or the site or the date that the analysis was run. I have a pivot table to the right that has all of that information from those FCS files from that analysis, different keywords, and other things. Then I have all of the associated files in this analysis directly linked. I have direct traceability right back to that raw data. These results are audible. I can go in and see, for these results, where did that data come from, and find and access that information from this Luma instance. Of course, we have the readouts for, that QC analysis so I can, again, look on October 2 and look to see if my instrument was performing to specification or my assay was performing to specification with this dashboard. This is shareable across teams. Moving on, for the next example, we're going to be using OMIQ for data analysis. OMIQ is a cloud platform for classical and advanced flow cytometry data analysis. All of your data, you're stored in the cloud. All of the compute is done in the cloud. You access OMIQ through a web browser. And, having everything in the cloud is a key benefit, especially for some of the examples we're gonna see later on when we talk about higher dimensional analysis or high throughput analysis where you have lots of different data files. Being able to break away from the limitations of our work desktop or our personal laptop to do data analysis, and having that cloud compute power, that frees us and helps us to compress the time to analysis. That's a big benefit with OMIQ being in the cloud. Now your data is stored securely in the cloud. It's private to you until you decide to share it, and then it's easy for you to share your data and your workflows with any other OMIQ user. We also have seamless integrations with other Dotmatics products like Prism, you'll see that later on, Luma Lab Connect, and also, automations to help improve the efficiency cut down and the time it takes you to get from idea to insight using OMIQ. Only has a lot of benefits, but we can compress that timeline even further with our Dotmatics flow cytometry solution that you'll see today, where we can... With all of that data connectivity from Luma and Luma Lab Connect, we can help to automate a lot of the parts of our workflows, like importing our data directly from Luma Lab Connect into no more having to search through file shares to find your data files and email them back and forth. You can pull that data in from that centralized source from Luma Lab Connect into OMIQ. Then once we have that data in OMIQ, we can perform our analysis. We can push that data out into other products, or we can export it directly into Luma Lab Connect where those results from OMIQ are going to be centralized and made accessible for us to combine with our other scientific data using Luma to query and visualize and do all those things that we wanna do, like AI, across our scientific data and ecosystem. What I wanna do is take a few moments to go through the workflow in a little bit more detail. The first step of your flow workflows is typically the importing of your cytometry data and other datasets. We are flexible in how you can import that data into OMIQ. You can do it manually, but you can also semi automate that by, using OMIQ's API to, pull data in. You can also easily, get data from collaborators, from other OMIQ users. But for the purpose of the day, we're gonna show off how you can automate the import of that data using that OMIQ and Luma Lab Connect integration. OMIQ was built with data science principles in mind, and you're gonna see a number of benefits for that. One of the ways that you'll see that data science principles upfront is that we treat metadata inside of OMIQ as a first class citizen. What that means is that you can take your data and you can group it, sort it, filter it, and perform different kinds of analysis from high dimensional analysis to gating using that metadata. That unlocks tremendous functionality, and it can help streamline your analysis across OMIQ and all of our Dotmatics products. As far as data science principles go, our analysis is performed in workflows. Workflows are a visible and explicit record of all the different steps in your analysis you can know, all the things that you've done in your flow workflows, whether that was six months ago, or whether you shared something with a collaborator and they made some changes three months ago. You can track all of those changes. These workflows are templatable. They're versionable, so we do support standardized and reproducible analysis as well. I do wanna note that, despite talking about cloud compute and high dimensional things, OMIQ is a software for everyone when it comes to flow cytometry. OMIQ can help speed up your classical analysis. We have all the tools that you would come to expect for any software to perform conventional or spectral flow cytometry, whether that's a small panel, a large panel, a few files, or tens of... You know, thousands of files. We can do a mixing and compensation in OMIQ. We can scale your data. We can do manual gating. We can, create figures and reports that you can share with colleagues. You can even export those statistics out of OMIQ, and then do those analysis in other softwares. But if you wanted to layer a high dimensional analysis on top of the classical analysis, you can absolutely do that in OMIQ. We have more than 30 natively integrated machine learning algorithms that are accessible to you inside of OMIQ. Things like, normalization algorithms to, like, batch normalization to clean your FCS data to do dimensionality reduction, clustering, trajectory inference, differential expression, and more. All of that's available inside of OMIQ. Then we have different kinds of automations, like auto gating. You'll see an example of it today. Auto scaling and integrations with other products like Prism to take your raw data to those lovely Prism statistical analyses and results and charts and graphs and things we can do there, in the span of minutes. We'll see an example of that today. We can also integrate with EasyPanel when we're thinking about panel design. I do wanna take a moment. While we're not gonna see EasyPanel and a demo, I wanna take a moment to talk about EasyPanel. This is a panel design tool at Dotmatics that allows you to design a panel with your specific requirements. What EasyPanel will do is it will take, whatever cytometer you're using. It will take the markers for your panel, the fluorochromes you need from what vendor, from what clone. It'll bring all those requirements, in EasyPanel, and it can automatically generate a optimized flow cytometry panel for you to then go and use in your experiments. We can do that in the span of a few minutes instead of the many hours or days it takes to design a panel, manually. One of the great integrations with EasyPanel and... Is our ability to perform spectral signature validation with OMIQ. You can compare the spectral signatures that, you're getting on your cytometer, using this validate with EasyPanel button. You can pull your single stain control data out, and then we'll compare those signatures to what, EasyPanel used to design your panel, and you can see if there are any issues on your cytometer or discrepancies that you may need to address during your panel validation efforts. That's a tool within OMIQ. But once you have done all of that, you're performing your data analysis, it's all finished, you can easily then share your datasets, your workflows, your reports, and more using OMIQ. Course, we can do that manually. But for the purposes of today, we're gonna be talking about how we can automate the export of that data directly into Luma Lab Connect and make that centralized and accessible for us to use within Luma. With that introduction let's dive into an example in OMIQ where, we're gonna be talking about a situation that speaks to where many of us have moved to in flow cytometry, where, folks are requiring tools to process and distill results from high volumes of data. You can think high throughput applications, or maybe you can think of a high parameter panel with lots of populations you're tracking. But ultimately, to do these kinds of analysis, you usually need to involve multiple different kinds of software to get the information that you need. Those will have different storage back ends. You'll need different application tool sets, different file formats that you'll need to access data from. You can see a representation of programs, applications, and data that you might need to access in a typical flow workflow on the left. We see we might be using OMIQ for data analysis, Prism for statistical analysis. But you see we have an Excel spreadsheet in there. We might need to use to pass data between other software to wrangle data from other storage or database back ends to share it with those programs. We may need to, do other kinds of analysis in Excel and share those results in a PowerPoint presentation, different figures, or other kinds of information. What I want to show by using OMIQ is that we can perform these kinds of analyses where we have high volumes of data, high throughput, high parameter, in OMIQ. We can also share our data with our other dot products through its integration with Prism, Luma Lab Connect, and others. With all of our data in Luma Lab Connect centralized and accessible, use tools like Luma to collect, distill, and visualize all those different data and results coming from different sources all in one place, and how that can help us to tackle this issue where things are working across softwares and applications to get our results. This is OMIQ. Again, OMIQ is accessed from the browser. All of our data is stored up in the cloud. What we're seeing is a single experiment inside of OMIQ there. But if I had other datasets that I wanted to use for my analysis, I could, go ahead and, search for those inside of OMIQ and pull that in. In this single experiment within OMIQ, what we can see is a number of different files that I have already pulled into this analysis. What we're able to do now is rather than manually upload these data, I can now access with Luma Lab Connect all the data that it is automatically ingested, centralized. I can now access and search that data, and I can pull that directly into my flow cytometry experiments in OMIQ. I can go to this file actions tab and click on import from Luma Lab Connect. I can search all of those instrument folders, to find my data for my experiment, and then I can pull those different data files in, by, using that little search feature. With that, I can automatically import my different data files. I've already imported a few files from Luma Lab Connect into this experiment already. Once we have our files in OMIQ, annotate our, analysis with metadata. This is important, if we're going to be pulling data out into other softwares like Prism, where that metadata will be used to perform different kinds of analyses. We can do this manually, or we can do it programmatically with an OMIQ API call. Once I have my files in metadata I can then do my analysis within my workflows. We can, start from a workflow that is already existing, so I can pivot from an existing analysis, but I can also utilize templates. Again, thinking about these high volume of data applications, high throughput, high parameter, we're probably not going to want to recreate all those gates every time that full analysis. We can, make use of these templates for that standardized reproducible, workflows. Here, I have a demo workflow all set up, so I'm gonna jump right into that. You can see in this analysis tab that I have, my analysis, all the steps in this workflow, already present. I've already built this up for this demonstration. Each of these are individual tasks that have done something to our data. If I wanted to add a new task, I could click that little green arrow, and I could add one of these different tasks that represent different kinds of analyses that we can perform on our data inside of OMIQ. I wanna note that we do have, features that can support 21 CFR part 11 compliance. We have e-signatures and audit trails and permissions that are inside of OMIQ. But we also have this collaborators tab, again, to share our datasets and... With other OMIQ users and define what it is that they'll have access to, whether they can see the data analysis or whether they can actually, collaborate in that analysis with me. Next, I'm going to show you two different features in OMIQ that can help, to support, automation of our workflows. these features, the first is an automated gating feature. It's called adaptive gate. It can help with, bringing the time down that we spend in analysis for manual gating. The other feature that I'm gonna show is our integration with Prism and how we can go from raw data to statistical results, within the span of about a minute. But we're gonna start with the automated gating demonstration. For the manual gating, we do that in the gating task. I'm gonna open this up to the left. Then to view our results, we use figure tasks, and so I'm gonna open that on the right. In the gating task, what we have is a gating hierarchy I've already created, and then I have a plot which I can then use to go and make those manual gate adjustments across my different files and different populations. What we can see on the right hand, this figure task, right, and this is the workflow for manual gating is that I would go and I would look at all of my different files. I would look at my gates, and I would start to make adjustments. I have some nonsensical gating. Looks like this live gate is in the wrong position. I can make a manual adjustment in my gating task, and we'll see that all the data, now updates with that new gate position. With manual gating, what we would do is continue with that review these different files and plots, and we would do that file by file, gate by gate, plot by plot. I would look for you can see where these, CD8 gates, my CD19, my CD3 gate, all these gates look like they are not quite in the right position for many of these files. Rather than having to manually adjust these, which can take, many tens of minutes with many files or even many hours, depending on the volume of data you have, we can make use of this new automation inside of OMIQ, our Adaptigate feature. The way that Adaptigate, works is that, it is a generalized machine learning algorithm. We've trained it on lots and lots of flow data, and what it can do is it can perform that manual gate adjustment task for us. Since it's a computer, it could do it much faster than a human can. What I would do is I click in adaptive gate, and then I would select the gates that I would want to perform that gating task on. I would tell adaptive gate a example file for my gates, seem to be well placed. This file seven looks like a good example, so we use that. Then I would select the file or files that I'd want to have, adapt to gate, adjust those gates for. Then And I'd click on adaptive gate, and it would go and it would automatically adjust those gate positions for me. Because we're in a demo, I'm gonna show you the finished result. We're gonna go into this, Adaptigate complete the result and figure. You can see now that all of, my gates are now in a much better position across all my different files and populations. But if I do see, an example where something's not quite right, I can... This is file nine. I can go and do normal manual gating to make that adjustment. All of these gates with the DAPI are done on a per file basis, so I can easily make that change, and that's not gonna impact any of my other gate positions for my files. That's a automation when we're thinking about working with lots of data, these higher throughput, higher volume applications to be more efficient, get to our results quicker. The other, feature I wanted to show is our integration with at Prism. With the Prism integration, rather than having to perform our normal export statistics function where we're gonna export our stats, from OMIQ into a CSV file, and then maybe we would have to go, and wrangle that data and annotate it and even multiple spreadsheet versions to get it ready to go directly into Prism. What we can instead do is we can make use of our Prism integration where we can directly export those tests as a Prism file. I'm gonna show you an example where I have an export stats set up, or I selected my files and then the statistics, whether it's MFIs or whether that's, gate statistics that I'm gonna pull out, count ratios, custom statistics. We pull that out into our export. But we're also gonna pull out, metadata. This metadata is gonna go along into another application. You'll see that, and that'll be critical for performing those analysis in Prism. The real trick with this export is in this configuration tab. Rather than doing the default CSV, I would select Prism, and then I can export those stats. You'll see that OMIQ is going to process that task. Then once it is complete, I can go to my results tab, and then I can download the statistics as a Prism file. Then I'll open them up so you can see what the data look like with our OMIQ Prism integration. The way set that up is that, your data is going to be imported into Prism as a multivariable table, and then we could go and we can perform our analysis in Prism. I can use tools like extract and rearrange. Then I can, set up the analysis for, like, regression for EC50, or I can set up this table to do these statistical tests across groups. I set my, response variable, my grouping variable, which is our metadata from OMIQ, and then, I can quickly, have that data set up to then do my statistical testing in Prism and get those stats and graphical, displays in a span of a minute or so. Once we have all of our results that are ready inside of OMIQ, we can take all of those figures, all of those exports from Prism, and we can directly sync them into Luma using this sync to Luma button. That will push that data into Luma Lab Connect, where then we will be able to access it from a Luma dashboard. I'm gonna show you a Luma dashboard. This example is taking data from OMIQ and other places and connecting it all together. This is, an example from a high throughput screening campaign where we're looking at bispecific antibodies, and this is a plate based campaign. There's lots and lots of data experiments, done over time. In this dashboard what we can do is, for that campaign, we can search across the different experiments. This is an AML study. I have some summary details up at the top. But down at the bottom, I have all of these different tabs. I'm gonna go through what each of these are showing. Because this is a plate based campaign for the experiment that I pulled in, I'm pulling information in from multiple different sources to have my information from that experiment at a glance. I'm doing that down to the level of a well. I can pull individual details from a well. Here's well D10 where, I'll say get well information, and it'll be able to pull well information from there. Let's go to C10. It's gonna pull in the well information. It's pulling that in from a LIMS system, so other platforms or databases. It's pulling in information about my sample. It's pulling information about my compound. Again, a bispecific, and we can link out to other Luma dashboards direct to other information about that bispecific. Cross domain integration I can link out to the data about that antibody. I also have information about my panel for that specific well, I know what went in that well. I have information about my materials the different reagents, the lots, so I can trace that information. Again, I can access information across, any of the wells. We'll go to another well, P4 and it will access that information and pull it up for me, quickly. The idea is that not only do we have access to that flow data, those raw results, but we all have access to the different analysis we've done across applications. I have data that I pulled from OMIQ from those stat exports directly, accessible and visualized inside of this Luma dashboard. I'm looking at, across different treatment groups percentage of parent for CD33 positive populations as myeloid cells, AML example. Also have a T cells that I'm looking at, the different stats again, across those treatment groups. We can look at, our flow data results from OMIQ, but we can also pull in data from Prism. I can look at my statistical analyses on the different statistical tests, see, which one of these treatments, is giving a significant result. I even have those Prism plots down at the bottom. We also have other kinds of visualizations for plates where you can quickly use a plate heat map to look at hits or trends and also links to the raw data files. We have that traceability back to the raw result from our raw data from all of these results from my Prism, FCS files, and OMIQ files. We can also make reports but we can also importantly make use of AI. This whole platform is built on FAIR data principles. We have embedded, an AI chatbot is gonna enable us to interact and query this, data in my dashboard. I can ask it questions, and it will pull up those results for me whether it's in this specific experiment or whether it's across data available in this dashboard. I can point the AI, to pull out specific data and not waste time, looking at other things. It'll pull out, all the treatment group information that I'm asking for in a few moments. But the idea is we can embed AI into these dashboards with Luma. Today, in summary, what we've shown you today are several examples of how Luma and our Dotmatics flow cytometry, solution to connect your data workflows from end to end. With our instrument and data integration piece with Luma Lab Connect automatically importing, and centralizing, structuring all that data across all those different kinds of instruments, not just flow cytometry. We're able to do mass spec, and other things, chemistry data, antibody data. Luma Lab Connect centralizes that data, so it is searchable, findable, accessible, throughout our data, ecosystem throughout our organization. Using OMIQ and FCS Express, we can further automate our analysis and integrate those results with the wider Dotmatics ecosystem. I showed examples of that with Prism, but we can also do that with EasyPanel and Luma. Then using Luma, we can take all of that data in one place, model it, visualize it, combine it so that we can query it from those traditionally disparate outputs, LIMS, ELNs, softwares like OMIQ and FCS Express and Prism. Again, all of that data, in Luma conforms to data... Fair data principles, so it's AI ready and future proof. The last thing that I'm gonna talk about before we close out, and this is briefly take a moment is that beyond the AI chatbot that I showed you that was embedded, with our Luma dashboard, we also have the capability to agentic AI. With Luma AI engine, instead of taking question after question, and putting that to the chatbot and then navigating those different responses, so we query response, query response, we can describe what we need and let Luma agent build a plan, work through it, and come back with something complete. It doesn't need to wait for the next question. It figures out how to get the answer and goes. Because Luma is an open platform, if your data scientists would rather bring their own preferred AI models to the task, all of Luma's scientific structured data is available to connect with AI tools and agents that are compatible with that model context protocol or MCP. You can use the things that your teams are already building on and already using with AI and with our Luma data. To summarize Luma is the scientific intelligence platform that is built to fit the way that you do science. It enables you to be ready to query anything, to combine data, visualize it, and use tools like AI to get to those insights that we're all looking for. We think of Luma as a discovery amplifier at Dotmatics, and so I'm excited, to hear from all of you about how we can enable your lab with Luma and further your science using our Dotmatics flow cytometry solutions. With that, I'm gonna thank you all, and I'm gonna open up the floor to questions. Thanks, Rafael, and thanks everybody for joining us on the webinar today. I did see a few questions come through, and, I've been kind of, consolidating them for you until we got to the end. One question, you might have addressed it, but it came up a few times. It's... Is Luma limited to Dotmatics software tools? No. Luma is an open platform. As long... I mentioned this. As long as the software or tools you're building are able to make an API call directly to Luma. Like, they can... Luma can talk to those different softwares, those different tools. They can interact, pull data. Absolutely, not limited to Dotmatics products. That's a common thing we have folks doing. I showed an example where we're using a LIMS that wasn't a Dotmatics LIMS to pull data in. Absolutely can use other products besides what we have at Dotmatics. Another question came up, as you were discussing some of the dashboards in Luma. Essentially, you showed a few different dashboards. Are those all premade? Are they default? Or, are you able to customize them? Or how does that function for, different groups who, utilize Luma? The Luma dashboards, the idea is that we take your raw data flows, that are coming out of Luma Lab Connect. Then in Luma, we're able to model those in an application, using whatever business or scientific logic you need to connect all that information together. Then we are able to then use a Luma experience to bring it all together and make it sort of, visualize that data and make it queryable. So, usually, those dashboards are going to be built for a specific purpose. We do have generic, dashboards for things that we've built for customers during their evaluation periods where we can connect things, if they have those systems in place. But usually, folks want a specific dashboard, and so we work with them to get to that point, that solves their problem with that fit for purpose dashboard or Luma experience in mind. It's a bit of both, but, oftentimes folks want what they want, and it's not like a cookie cutter thing. There's another question had to do with how does Luma, connect to and find cytometers? That's probably a question around Luma Lab Connect and, that. Yeah. Luma Lab Connect, the way that we typically set it up, so Lab Connect is a software, and we would set up an instance of that software on the computer that controls your instrument. It'll just, we often export FCS files to a specific folder, and it'll monitor that folder for new files to come in. Then it will be able to automatically then upload that data, the new data into the Luma Lab Connect. That's the way that we typically are connecting to instrument and cytometers. There was a question around, could you upload data files from a BD Accuri instrument? Could you upload data files that came out of FlowJo, Kaluza, other kind of, say, competitor tools from Dotmatics products? What I would encourage everybody to do is to visit the Dotmatics Luma website. We do list out all of these different data files where we have built parsers for them so that, it's up and ready to pull different kinds of information from those files. I would encourage you to look there to see if your specific file format or, from an instrument, we have that accessible. But for folks out there who have something that is not on that list, we are constantly adding the ability to parse that data in. You know, that may take a little bit of time, but that is something that, is absolutely... We work with customers to do those kinds of new file types that we haven't seen or worked with before in the past. There was a question that came in. It said, can you analyze images from BD FACSDiscover S8? Maybe that's something I can answer quickly as well. The FCS Express software has the ability to parse and analyze both images and raw data, FCS files from the S8 and A8. It also has the ability to work with the spectral components of those datasets. OMIQ can work with the numeric versions of those datasets. But as a data aggregator type tool, you can upload data files. You can upload images. Parsing that... Parsing the images out in Luma is a different question that we can answer as it comes up. But with some of the software tools that Dotmatics provides, like FCS Express and OMIQ, you're well covered with analysis of those. I'm gonna add to that and shamelessly plug a webinar I did two weeks ago that covers exactly how you can do, image analysis with FACSDiscover data in FCS Express. That's available on our YouTube channel. I don't see any other questions coming through at this point. But again, if you have questions, make sure to reach out through any of the channels. Thank you everybody for joining.