LabOps Leadership_Matt Meiners: Audio automatically transcribed by Sonix

LabOps Leadership_Matt Meiners: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.

Kerri Anderson:
By building a platform to share challenges, thoughts from leaders, and network together, the LabOps Leadership Podcast is elevating LabOps professionals as well as the industry as a whole.

Samantha Black:
With the intent of unlocking the power of LabOps, we deliver unique insights to execute the mission at hand, to standardize LabOps, and empower LabOps leaders.

Kerri Anderson:
I’m Kerri Anderson.

Samantha Black:
And I’m Samantha Black. Welcome to the LabOps Leadership Podcast.

Samantha Black:
Great, well today we’re so excited to be here with Matt Meiners, who is Senior Director of Production at EpiCypher. Thanks for joining us today, Matt.

Matt Meiners:
Very happy to be here. Thank you for having me.

Samantha Black:
Awesome, so let’s just kick it off. Can you just tell us a little bit about your background and how you got to where you are today?

Matt Meiners:
Yeah, absolutely, so I did my undergraduate work at East Carolina University. I’m a North Carolina native. I’m a, after finishing there, I moved to the University of North Carolina where I did my PhD work in Steve Madsen’s lab. It’s a lab that looked at enzymes that are involved in DNA damage and repair, so I was trained as a protein biochemist, had lots of interaction with DNA binding and DNA metabolizing proteins, how to purify them, how they work, how to treat them, make sure that they stay functional. And when I finished my PhD, like so many PhDs nowadays, career path was a bit murky, didn’t know what I wanted to do or where I wanted to go, and received some good advice from all the different groups and programs that were available at UNC. And one of those is a group called the Tech Teams or the Tech Transfer Teams from the Office of Technology Development and Tech Transfer, and they kind of link PhD students with professors who have commercializable ideas were looking to do things around IP or patent, so it’s actually working with one of those companies that had started up through that program. When I heard this disembodied voice lamenting not being able to find a protein biochemist who wanted to help make proteins for this other startup company, so I kind of poked my head in and raised my hand and said, I’m a protein biochemist. Lo and behold, that individual was from EpiCypher and was looking to make a hire, and I had an interview the next day, and a job a few days after that. I was a research scientist at the bench at EpiCypher, working on our line of recombinant nucleosomes. At the time, there were three people at the company who was, my presidents, my now vice president of product innovation, and myself. We had about two linear feet of bench space in our incubator, and we grew the company from there. And I hired a few more people, we brought on the rest of the management and executive suite, we built up a company that I think is one of the leading experts in the field of chromatin biology and epigenetics, and I’ve got to watch us grow from where we started to where we are now, and it’s been incredibly exciting and very rewarding.

Samantha Black:
That has got to be one of the coolest things, watching and being involved in a company starting from basically ground zero, up to a fully functioning company, all of the bells and whistles that you can really call yourself a fully operational company, and so, and along with that, like the science, watching everything develop. That’s got to be such a unique experience. Do you want to talk a little bit more about some of the challenges that were along the way, maybe specifically around operations and scaling that up to where you are today? Because it seems like you’ve been involved in that process very deeply.

Matt Meiners:
Absolutely. First thing is, it was impossible to try and do it alone. So even though we were small, we all shared a lot of responsibilities, especially with regards to planning and executing, and the long-term strategy versus the short-term tactics was really kind of something we worked on collaboratively in order to build it. But we started off very much like any small lab does, we had paper notebooks, and we were printing out images of gels or HPLC chromatographs and taping them into the notebook to keep track of things. And we quickly realized that as we were growing, that was not tenable going forward, and so we started to develop by bringing small bits of infrastructure here and there to really support the growth of the company. We brought in an electronic lab notebook, so not only could protocols be immortalized and saved, but we brought in the ability to share data in a very nice, easy, searchable way. We also used it as our operational database to store everything from our … samples to our protein preps to keep lab records and track how different manufacturing improvements are made from batch to batch. That was an aspect that is near and dear to my heart, seeing something that we took from the inception, the innovation, moving it through development and through pilot scale, all the way up to full commercial manufacturing, to not only be able to provide this material to our customers, but to use it in-house for our own research and development to see what are the limits of the products that we make, what other applications can they be used for, but then looking back and seeing how those improvements were made and looking at what variables are still left to be optimized, what else can we tweak to ensure that we are being as efficient as we can be and that we’re making the best quality products we can? Having those historical records that survive in other places and paper notebooks I think was incredible. Being able to do everything from bringing in more infrastructure with regards to cold storage and temperature monitoring, it’s something you, when you’re coming out of academia, you kind of don’t have to think about, you know. If there ever is a freezer failure, typically there is a department that can bring by an emergency freezer to free to move your stuff over to. But when you’re on your own, and you’re building something from the ground up, you have to plan for every eventuality, you kind of have to reinvent the wheel in some spaces. And we learned from our stumblings until we built something I think is really great and useful. I like to say that, even though I’ve been there since … all my ideas weren’t the best ideas. So we have lots of people who have come up with great process improvements, coming up with solutions that I never would have thought of, recognizing problems I never would have thought of, and then implementing those solutions and making sure that we’re able to run the company as efficiently as we can.

Kerri Anderson:
So being part of the original team, you know, being the first bench scientist and kind of seeing the company progress, at what point were you like, okay, we need help, we need to bring in a lab manager, we need someone on operations? Because that’s something I think varies in every company, and so I’d be curious, when did you bring that person in?

Matt Meiners:
So I think when we first started, we were two bread lines to even know that we needed a person, you know, we were all just working on science. When we moved from our incubator to our first true standalone lab, we started to recognize that different areas of the company, different operations responsibilities, could probably be responsible for their own operations. That did not survive long as … thought process, we knew that there had to be someone who had to quarterback not only the long-term planning of what we want to do, but how to implement that, and at first, that person was me. I liked the idea of running the lab and making sure the improvements were where we wanted to be. My training was as a scientist, not as someone who wanted to be or knew how to be involved in operations, so I was trying to balance the science with that, that side, and that did not last long either. Like we knew pretty early on, we needed someone who was full-time committed to operation and lab management, and when we recognize that, we moved quickly. We took someone, her name is Keli Rodriguez, she’s our senior lab manager, and she is excellent, who was trained as a research associate. So she knew our science inside and out, and she’s the best technical set of hands we had. So not only was she a capable scientist, is she a scientist, but she understood where the current pinch points were and what our needs were to grow to be able to continue to do the science that we had been training her on, and she took it over and is now better than I am. I had restricted imagination at running those lab operations. I think that I can help Keli by helping sure, she has what she needs in certain respects to, especially on the production and manufacturing side, her insights, her knowledge needs from what our team needs. She delivers on solutions and urges. And so having someone now that can fulfill those roles with minimal or the necessary inputs from my team and from the other teams that we have here, just allows these solutions to, from my perspective, appear, and that kind of capability that we have by giving freedom to the people who are incredibly capable and able to do these operational aspects, allows us to focus on the science and really grow within those other areas of the company. So as early as possible is the way we would have wanted to put that in, but I think we learned from some potholes that we stumbled into, but we ultimately landed in that place that I am very, very happy, with some very good people to do the work.

Samantha Black:
So I guess I’m wondering, and also for our listeners, go listen to Andrea’s episode, it’s great. She explains the science in a wonderful, very easy-to-understand way, but I’m going to jump forward here because we’ve already heard that. But you’re a commercial business, and so you’re selling to other businesses, whereas, you know, a lot of the other operations folks we talk to are just strictly in R&D. So I’m wondering if you can touch a little bit on maybe how operations is like super essential when you’re in a commercial B2B setting versus strictly R&D?

Matt Meiners:
Absolutely. I will say that the team … manages, the production team, and it’s the foundation team of the company. We’re involved in every aspect from our work stemming from the inception and innovation steps, all the way up through the final commercial release. So it’s a great hybrid method where one foot in the R&D realm, one foot in the commercial manufacturing realm, and having that standard that we have to adhere to, for the purpose of creating robust, reproducible products that will be provided routinely to our customers, holding ourselves to that level of clarity and documentation on the R&D side is no small feat. You know, R&D, I remember those days, you’re just trying to get it to work. You don’t care what the scale is, you just need that product to be produced, and you can show that you’ve done it and you go back and improve on it from there. And sometimes in the scientific community, you just, you try what you need to try to get it done. And then doing that for commercial manufacturing, everything has to be documented. Having the infrastructure to understand, you know, everything from what lots of chemical or we’re using to manufacture things to, we had immortalized procedure that lays out the following steps in order to produce the material, but we changed it at this one manufacturing lot. How do you feel about reconciling this change …? If there is a problem, how do you document how you found out what the problem was? And how do you change it to make sure problems like that don’t happen again? Or worst case, what if you have a product that no longer meets your specifications and you have to either pull the product or contact customers who maybe got a product that no longer meets with your quality metrics? What do you go about doing with that? And the short answer is documentation is the most important step. You know, always knowing not only what you’ve used, but when you use it, you touched it. What does it look like and how does it deviate? Having that record allows you to avoid making the same mistakes twice, and it’s challenging. Manufacturing science is all about doing the exact same way you’ve done before, but making it better. But then those recruitments, ensuring that the finished product is indistinguishable from the last 8 or 9 times you’ve made it as well. So it’s a different application of the same scientific skill set that R&D scientists have to do, and the difference in the attention to detail that you have to have for it is incredibly challenging, but I also think it’s incredibly rewarding.

Kerri Anderson:
It sounds like you have to be very detail-oriented, as you mentioned, and organized in order to do that. Do you have any tips for our listeners on how to get to that point or any programs you use that you think have been incredible and you would recommend?

Matt Meiners:
So we have an electronic lab notebook system called Lab Guru. We like it a lot. And one of the things I will say is the amount of time you spend at the beginning to input your chemical stocks, your buffer stocks, your materials, and then allowing you to update those over time, pull those into your experiments, cite different changes in the vendor or the CAT number or the lock material you have, doing that at the beginning and allowing that bolus of knowledge that you have to be able to be passed out to other experiments over the lifetime of what you hold on to that material and then just be able to, once you have those initial records, update those records as new materials, used as new material comes in, saves you so much time in the long run. It does seem like quite an investment to go through, especially when you’re an already operating lab, and you need to go back and document. We have a thousand different plasmids that we have used for different projects, to have someone go in and, from scratch, document what those 1000 plasmids are and where are they stored. But the amount of time and effort that it saves, the amount of headache that it saves in the event that something might go missing is priceless in the long run. So I would say if you don’t have a system in place, a subscription-based electronic lab notebook is not tenable, an Excel sheet is a good start. And even something like share it on Dropbox or share it on Google Drive so you can track changes as people move or touch or reduce volumes. Just having that clarity enables so much more collaboration and team-based science and it’s kind of the name of the game. We like to produce material, hand it off to another team, have it tested in one application, and off to a separate team for a different application, rely on the expertise of those different groups to be as quick and as nimble as we can in our scientific endeavors, and it’s an investment that pays for itself. It’s immediately recognized that pays for itself.

Samantha Black:
So that’s really interesting that you say that because I feel like this is something that a lot of labs are still struggling with, it’s like their digitalization journey, like think it’s a struggle because they’re so used to doing it one way, and because I feel like there’s an upfront investment in time, and energy, and effort to get there. Kerri, you’re probably way more of an expert in this than I am, but, you know, operations folks are advocating for this because it really works under everything else that’s going on to support it, and the scientists and maybe other people on the team, all they see is that upfront investment in getting it started. So maybe just, how did you convince your scientists and the people who are doing the work to change, and how did you get people on board to do that?

Matt Meiners:
Science is never easy. Scientists are resistant to change, and we like to cite data mostly in the past, and we’ve done it this way, and it’s worked this way, and we want to continue to do that. Why fix a system that isn’t broken? Kind of have our blinders on without realizing the system may not be broken, but certainly can work better, and we’re all about driving improvements forward. So by setting up even just a small database, and say you’re a lab that has a lot of immunohistochemistry or Western blots or something, set up a small antibody database, and that allows you to track, we’re getting low on this stock of antibody, or we’re changing lots from this vendor, so we’re going to have to maybe re-titrate the antibody to know how it would work in these experiments, so you don’t go into an experiment expecting data of a certain quality, have the data be subpar or uninterpretable, and start fumbling as to what could have possibly changed and where do I need to start my troubleshooting process. Having that documentation saves time, time for even the academic lab. Time is money. You know, you only have a certain amount of time to have your postdocs or your grad students or even your undergraduates in that lab doing that research, and you want to have them be as efficient as possible. And if they spend time troubleshooting, they’re not generating your data. People aren’t publishing papers, people aren’t getting grants. And a small amount of upfront time investment produces data where you can say, hey, we’ve been able to produce X number more successful experiments in the same timeframe as we did in the same timeframe in the last three months, and you can track that in the paper. You know, how many times did you say I had to repeat the experiment from this one because the Western didn’t produce it and I tried to develop it, or the staining was too bright to be able to distinguish the structures? So a small amount of data, especially to a scientist, goes a long way.

Kerri Anderson:
Yeah, that was a great question, Sam, and I want to kind of build off of that question. So you were at the company at the start, so I mean, you were in with the executive team and you knew all of them, so you might have had an easier time with this. But do you have any advice on how to get buy-in from the executive team to implement some of these systems?

Matt Meiners:
Yeah, I do have a bit of a unique story where I was here before some of the executive team was here. I got to train them on how we do our science, and I realized it won’t always be the case, but I’d like to think most executive teams, especially at scientific companies, are at their heart scientists, they are data-driven. They may not have the degree, but they understand what they’re doing, what they’re trying to accomplish, and they’re going to respond. If you have an idea, especially if it’s an idea that’s backed up by data, your interpretation of data, having someone that you can go to encourage you to advocate for you to be heard out by the executive team is great, but if the company is small and there’s no one there that can do that, it’s that activation energy. It’s a lot of getting yourself built up to be able to share this data, but ultimately, I think everyone, be they in business or in science, responds to data, and you can say having this database saved me four hours a week for the last three months, that’s a saving of X number of dollars in labor hours. You know, even when you say, that’s for one database, I want to make ten databases. So how much time will that potentially save? And that’s just for one person. How much can that save across my entire team, across the entire company? So a good idea is more than an idea, it’s an idea plus an implementation to solve a specific problem. So with all your ducks in a row, all your data lined up in a quick, easily digestible presentation, I think is the best path forward to get buy-in from the executives, but ultimately, a conversation is a good starting point. So start planting those seeds early about how we need some improvements, need some changes, have these ideas, I’m going to show you later, and then come back with the data, and then don’t let that be the end of the conversation. Continue to follow up. Squeaky wheel gets that grease. So just continue to, and even for someone who’s been here for a while, sometimes I will have ideas that don’t get listened to the first time, or the second time, or the third time, but if I know it’s a good idea, I’m not going to let it go.

Samantha Black:
That’s super interesting, and I think you come to the table with a solution, right, instead of just a problem, and I think that’s generally a good piece of advice for anybody. But I’m wondering, like, how do you guys, do you talk as a team and you look at the data together? Because I feel like that is a big barrier, too. So we have all this data, the lab is generating data all the time for you, but I feel like sometimes it’s hard to know what to focus on, what to look at. There’s so many places that you could focus on, so do you guys just sit down, and as a team and say, Well, we’re having this problem, so let’s zoom in on this area? Or do you kind of look at a wide set of data and say, oh, we see this like kind of wondering what your general approach there from like a management perspective and also like an efficiency perspective?

Matt Meiners:
We are big proponents of the 80/20 rule. You know, 80% of the problems come from 20% of the material that you’re working with, or the alternative to that is if you can improve that 20% of it, they’ll solve 80% problems downstream. So we tend to tailor our focus on where we have the most bandwidth with regards to what projects are we working on right now. We also tend to look at where are we spending the most time in the lab? Are we having 4 people working on the same 2 or 3 projects? Great, let’s focus our attention on those 2 or 3 projects, see how we can improve those. Once that project is done we go to the next one. What did we learn on that project that can be implemented onto the next …? Data is made all the time and it’s easy to kind of get lost in the deluge of, we have all this information, what can we do with it? I think with experience you can start to tease apart where to ask the questions, and the questions don’t necessarily need to be, how do we make this better? It could be something like the teams in the lab and they find that steps one, two, and three of protocol are particularly onerous or troublesome. How can we improve that? It may not even have a huge impact on the yields or the quality of the final product, but it makes things better for the team in labs. So sometimes you can be driven by the final readout, how efficient was the experiment? Other times, you can be driven by the quality of the experience when you’re doing the experiment, or by the overall time that it takes to be done. You can make small improvements along the way, they add up, make larger improvements. Wish I had a secret recipe for, oh yes, look at the data and this one will jump off the page. Actually, you know, that’s the one you got to focus on, but unfortunately, even I haven’t figured that out yet in my time here, so I wish I had to, but I would say, when you start to spend more time trying to solve the problem than what the problem is creating, I think you know that you’re probably not being particularly useful in your time there. But as you continue to work on these projects, I think a lot of times the biggest pinch points, the biggest pain points, they come to you from the people who are doing the experiments. They’re, the people in the lab know where the inefficiencies lie because they’re the ones who are doing the data generation. But I think it’s imperative they have to listen to the team, listen to the people in the lab. They’ll tell you where some of the biggest problems are, and fostering that environment where they feel empowered to come to you and say that, I think is incredibly important.

Kerri Anderson:
I love that. I think in Ops, you know, we’re here to help solve problems, but that means we have to listen to the team and figure out what those problems are. This is a conversation that could go on all day about just, I love data. I love using it to solve problems, It’s really interesting to me. But I’m curious, you know, you’ve been at EpiCypher for eight years now. What’s your biggest lesson you’ve learned?

Matt Meiners:
Biggest lesson? I like to focus on the things that I know the most about. I think every scientist is comfortable with things have been trained on the things that they feel comfortable that they’re an expert. The vast majority of my time, I think, would be better served learning new things, being adept, because I was trained as a biochemist, I work for the chromatin biology and epigenetics company now. Being able to learn genetics and learn how my application, biochemistry, to the field of chromatin biology and epigenetics can be useful is paramount. Having that additional opportunity to learn, I think is the thing that I really enjoy focusing on the most, and I think it is the thing that I would recommend people focusing on. Just take the time to expand that skill set. You know, you’re an expert on what you’ve already brought to the table, become an expert on some different things, and then when you brought those components together, how can you blend those expertise together to make improvements?

Samantha Black:
That’s awesome, and that’s like such a PhD thing to say. I feel like if I’m not learning something new, I feel like really antsy in my skin. I’m like, oh, like I need to be doing something else, so I completely relate to that. But I think it’s exciting because that attitude is going to bring you guys to the next, the next thing, and the next thing. So I think it’s really a cool perspective to have, and I think they’re lucky to have you. So I am just wondering, you know, we haven’t talked much about EpiCypher because we already had Andrea on, but I just wanted to give you a chance to maybe share like what’s in the future for the company and like what are some of the exciting things that, you know, you see the field going and the company as a whole. What direction do you think you guys are going and what’s exciting there?

Matt Meiners:
Right now, everything, everything’s exciting, everything’s growing. It’s one of the things I like the most about the team that I work with. You know, we produce the base core component. We make nucleosomes on this team, and those nucleosomes are used in every application, every experiment. We work with our synthetic biochemistry group to be able to find new ways to make nucleosomes sporting different post-translational modifications along the histone proteins that comprise that protein component of the nucleosome. We work with our applications team, our biochemistry team, and our genomics team to look at how these nucleosomes can be used in ways that enable that next step forward in the field of chromatin biology. I think that what we’ve done is an incredible amount of time investment and success with this, using these nucleosomes as internal controls for next-generation sequencing experiments like cut and run and cut and tack, knowing that your experiments worked before you even take them to the sequencer saves so much time and effort that it allows you to assess the quality of your data before you can crack open that kit to put your libraries into. And we know these work for histone …, but in what else can we use nucleosomes? What other kind of internal controls or other different platforms could we use these controls for? And we haven’t done thus far, we haven’t planned for or even thought about, and inherent modularity of chromes, where if you swap out one protein for another, you swap out some DNA for a different one, you make modifications, so you have to be native modifications. It can be different ones that you haven’t even thought about, What could you use as a targeting model? So to leverage the flexibility of these nucleosomes as control molecules, we’re still figuring out what the limit is of what we can use them for. And it’s an incredibly exciting time because essentially every idea is a good idea that it’s never been done before. So we like to try to straddle that line between being cutting edge and being on the bleeding edge, and we need to make sure that whatever we make actually is going to want to use it for something, it will actually serve some purpose. But considering we’re at the precipice of this field, every idea, there’s someone out there who likely would want to use it. You just need to figure out how many of each of them are and where should we start. Because when you’re staring at this wide open landscape and then you can go any path you’d like to, where do you take that first step?

Samantha Black:
It’s so exciting. I think it’s so cool. Well, I know that people are gonna have a ton of questions about this and want to talk to you about it, so for our listeners out there and anybody who may be interested in learning more, how can they find you, and where can they follow you, find out more about what you’re doing or connect with the company? Just give us what, your contact, or how you want people to reach out to you if they feel so inclined?

Matt Meiners:
Yeah, absolutely. So EpiCypher.com has all of our products, as all of our technical resources as well. So if you’re homebrewing a … or … reaction and you want to learn more about the technique and the technologies themselves, we have educational documents there. So even if you’re not getting the products from us, we would love for you to come and learn from our experience and benefit from what we’ve been able to do, our website EpiCypher.com. We have a LinkedIn presence, you can find me on LinkedIn. I routinely am on there checking, I’m always inviting people in my contact list to follow the company because we make most of our announcements through social media with regards to new products that are coming out. We have a particularly exciting release coming up later this month, we’re releasing a new product in our cut-and-tag line so people can check that out on our social media, feel free to reach out to me there as well. Always happy to talk to people, especially with regards to science. Even though we’re a commercial entity, we are a company of scientists for scientists. So if there are any ideas, if there are custom requests that people need with regards to, can this molecule be made? It would be so useful for my experiments. Talk to us, we’d be happy to see what we can and can’t do. We’re always looking to push an envelope as far as how can we make the field better, and we do that by talking to people in the field and figuring out what they…

Samantha Black:
Awesome, well, Matt, thank you so much for joining us. This has been a lovely conversation, very interesting, and wish you the best of luck, and we will catch up with you soon.

Matt Meiners:
Thank you. It’s a pleasure being here, and I really appreciate the opportunity to talk with people who have as much passion for the laboratory operations side.

Samantha Black:
Great, thanks!

Kerri Anderson:
Thank you for tuning in to this episode of the LabOps Leadership podcast. We hope you enjoyed today’s guest.

Samantha Black:
For show notes, resources, and more information about LabOps Unite, please visit us at LabOps.Community/Podcast. This show is powered by Elemental Machines.

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