1 00:00:05,160 --> 00:00:08,120 Welcome to the Proteomics and Proximity podcast, 2 00:00:08,120 --> 00:00:10,520 where your co-hosts Dale Yuzuki, 3 00:00:10,520 --> 00:00:13,920 Cindy Lawley and Sarantis Chlamydas from Olink Proteomics 4 00:00:13,920 --> 00:00:17,280 talk about the intersection of proteomics with genomics 5 00:00:17,280 --> 00:00:18,720 for drug target discovery, 6 00:00:18,720 --> 00:00:22,760 the application of proteomics to reveal disease biomarkers and current 7 00:00:22,760 --> 00:00:26,880 trends in using proteomics to unlock biological mechanisms. 8 00:00:27,160 --> 00:00:30,600 Here we have your hosts, Dale, Cindy and Sarantis. 9 00:00:30,600 --> 00:00:33,440 Welcome to proteomics and proximity. 10 00:00:33,440 --> 00:00:36,680 I am Dale Yuzuki, your host with my two co-hosts 11 00:00:36,960 --> 00:00:40,000 Sarantis and Cindy say Hi! Hey there. 12 00:00:40,360 --> 00:00:41,400 Oh, hello there. 13 00:00:41,400 --> 00:00:42,840 Nice to see you. 14 00:00:42,840 --> 00:00:44,880 For our inaugural episode. 15 00:00:44,880 --> 00:00:46,800 Yes, the very first episode. 16 00:00:46,800 --> 00:00:48,360 We'd like to go ahead and have you 17 00:00:48,360 --> 00:00:52,320 the audience know a little bit more about us and Sarantis, 18 00:00:52,440 --> 00:00:56,400 would you mind going first and telling us a little bit about your background. 19 00:00:57,280 --> 00:00:58,840 Yes, thank you. 20 00:00:58,840 --> 00:01:00,520 Thank you, Dale. Yes, yes. Tell us. 21 00:01:01,920 --> 00:01:03,080 I just 22 00:01:03,280 --> 00:01:07,840 start my bachelor and my PhD in Italy, in south Italy, 23 00:01:08,480 --> 00:01:11,120 studying Drosophila genetics and epigenetics. 24 00:01:11,680 --> 00:01:13,760 Oh, that's great. I yeah. 25 00:01:14,440 --> 00:01:16,120 You're a real geneticist. 26 00:01:16,120 --> 00:01:18,920 He's a real geneticist. 27 00:01:18,920 --> 00:01:22,600 And then I moved to Max Planck Institute for epigenetics in Germany, 28 00:01:23,160 --> 00:01:27,080 where I joined the Lab of Dr. Asifa Akhtar , the director of the institute 29 00:01:27,480 --> 00:01:32,040 working on chromatin gene regulation and nuclear functions. 30 00:01:32,360 --> 00:01:34,200 And so wait a minute, wait a minute. 31 00:01:34,200 --> 00:01:36,960 Max Planck has a center for epigenetics. 32 00:01:37,440 --> 00:01:39,560 Yes, it was a center for immunobiology and epigenetics. 33 00:01:39,560 --> 00:01:41,520 And I think one of 34 00:01:41,520 --> 00:01:44,640 the first worldwide nominated epigenetics institutes actually 35 00:01:45,120 --> 00:01:47,120 And I was really happy and it was really lucky 36 00:01:47,160 --> 00:01:49,520 because I got to work with Thomas Jenuwein 37 00:01:49,520 --> 00:01:55,280 Also was able to work with the famous SU(VAR)3-9, the code, 38 00:01:55,560 --> 00:01:59,520 the epigenetic codes and they was really, really nice times 39 00:01:59,520 --> 00:02:03,000 when epigenetics was emerging in the gene regulation field 40 00:02:03,720 --> 00:02:08,640 and after staying for quite a long time, scientists. 41 00:02:08,640 --> 00:02:09,880 How long's a long time? 42 00:02:09,880 --> 00:02:12,160 I More more a little bit more than seven years. 43 00:02:12,160 --> 00:02:14,000 I say, okay, actually. 44 00:02:14,000 --> 00:02:16,080 And then I join industry 45 00:02:16,960 --> 00:02:19,400 and Activ Motif for doing 46 00:02:19,400 --> 00:02:22,480 consultant for Epigenetics Project 47 00:02:23,040 --> 00:02:27,360 and since December I am part of the great Olink team. 48 00:02:27,680 --> 00:02:31,080 as the scientific director for multi-omics where I try to match 49 00:02:31,080 --> 00:02:35,840 the proteomics and preach the importance of proteomics in multi-omics work. 50 00:02:35,960 --> 00:02:37,360 And I'm really proud and happy 51 00:02:37,360 --> 00:02:41,400 to see how people they like proteomics and how and what's expanding to. 52 00:02:41,960 --> 00:02:43,920 And the affairs of the heart 53 00:02:43,920 --> 00:02:46,840 Yeah, right Sarantis. 54 00:02:48,400 --> 00:02:49,840 And this is true, this is true 55 00:02:49,840 --> 00:02:52,000 The affairs of the heart, referring to Cindy. 56 00:02:52,760 --> 00:02:55,240 Too, that he cardiology, 57 00:02:56,080 --> 00:02:58,600 cardio, metabolic, those 58 00:02:58,920 --> 00:03:02,360 that sort of broad umbrella of of disease 59 00:03:02,480 --> 00:03:05,360 types that that I think Sarantis has learned a lot about. 60 00:03:06,160 --> 00:03:08,360 So Sarantis what led you to industry? 61 00:03:08,400 --> 00:03:11,520 What made you decide to move from academics? 62 00:03:12,240 --> 00:03:13,680 That's great. So that's a great question. 63 00:03:13,680 --> 00:03:18,840 I guess I’m always asking myself… why has this happened? That is to say 64 00:03:19,400 --> 00:03:24,960 I think the motivation and the thing and let’s say the general question 65 00:03:24,960 --> 00:03:28,080 to learn more and to get to know about novel technologies. 66 00:03:28,520 --> 00:03:33,320 I think at the point in the academia have reached my top level, 67 00:03:33,320 --> 00:03:36,960 my top level and I wanted to explore more fields. 68 00:03:36,960 --> 00:03:39,640 I want to explore technologies and how this can be applied 69 00:03:39,960 --> 00:03:42,640 to the day by day basis to the disease areas. 70 00:03:43,080 --> 00:03:43,480 Right. 71 00:03:43,480 --> 00:03:45,400 And I think Olink offers me 72 00:03:45,400 --> 00:03:49,000 this possibility to apply my basic knowledge in science 73 00:03:49,400 --> 00:03:53,280 to application to translational medicine and to biomarker discovery. 74 00:03:53,280 --> 00:03:57,440 And that I can tell you it's a great journey already. 75 00:03:57,440 --> 00:04:00,560 Well, we're certainly luckily lucky to have you, and that's for sure. 76 00:04:00,720 --> 00:04:02,160 Very lucky with you. 77 00:04:02,160 --> 00:04:04,880 Was that a hard transition to capital? 78 00:04:04,880 --> 00:04:06,400 Was that a hard transition? 79 00:04:06,400 --> 00:04:07,960 No, actually not. 80 00:04:07,960 --> 00:04:09,120 Not at all. 81 00:04:09,120 --> 00:04:12,960 Because, I mean, in industry we are doing science 82 00:04:12,960 --> 00:04:16,760 and we are doing the high profile science and we are dealing with high technologies 83 00:04:16,920 --> 00:04:19,440 and novel technologies. And it was really smooth. 84 00:04:19,440 --> 00:04:23,080 I never actually I feel like I never left academia because I’m always reading papers 85 00:04:23,360 --> 00:04:24,480 I try to be updated 86 00:04:24,480 --> 00:04:28,000 for the novel technology and discussion with scientists and discussion about projects 87 00:04:28,440 --> 00:04:32,000 and I'm bringing the value to the scientific life 88 00:04:32,040 --> 00:04:34,200 and that that's that's really amazing. It's really exciting. 89 00:04:34,440 --> 00:04:38,040 I haven't seen any change in my daily life, that's for sure. 90 00:04:38,440 --> 00:04:41,240 I see. And officially, your title at Olink is? 91 00:04:42,080 --> 00:04:43,880 Scientific Affairs for Multiomics 92 00:04:43,920 --> 00:04:48,640 nd I’m also taking care of cardiometabolic disease areas 93 00:04:49,480 --> 00:04:50,160 Yes, I see. 94 00:04:50,160 --> 00:04:53,200 But this is scientific affairs which is a unique discipline 95 00:04:53,400 --> 00:04:55,480 within commercial activities. 96 00:04:55,480 --> 00:04:56,760 Yeah, yeah, yeah. 97 00:04:56,760 --> 00:05:02,160 I mean I breach commercial, R&D, marketing 98 00:05:02,720 --> 00:05:07,040 My goal is to preach about the use of proteomics to the multi-omics work 99 00:05:07,080 --> 00:05:11,640 and to match the basic research with translational research. 100 00:05:11,640 --> 00:05:14,080 So that's yeah, that's my goal actually. Yeah. 101 00:05:14,400 --> 00:05:18,120 And getting back to your work at Max Planck as well as Active Motif 102 00:05:18,120 --> 00:05:23,240 was the main method of epigenetics looking at five methyl cytosine 103 00:05:23,280 --> 00:05:26,640 or were you looking at also histone modifications and the whole? 104 00:05:28,280 --> 00:05:29,160 I mean, we are 105 00:05:29,160 --> 00:05:33,600 we are focusing mainly on histone modification, transcription factor binding 106 00:05:33,600 --> 00:05:35,800 Of course we have done studies on methylation 107 00:05:36,360 --> 00:05:39,440 but our main focus is on histone modification, histone code” 108 00:05:39,640 --> 00:05:42,440 accessibility of the chromatin and how these may interfere 109 00:05:42,480 --> 00:05:45,240 with the gene expression and how this regulates the gene expression. 110 00:05:45,240 --> 00:05:47,280 This was the main focus. 111 00:05:47,280 --> 00:05:49,400 So it's a really nice connection to the 112 00:05:50,080 --> 00:05:52,360 to the mechanistic sites, you know, of the nucleus. 113 00:05:52,360 --> 00:05:52,680 Yeah. 114 00:05:52,680 --> 00:05:56,440 You can see accessibility of the chromatin and connect it to the gene expression. 115 00:05:56,720 --> 00:05:59,960 And now of course abrogates and now coming from the protein side 116 00:05:59,960 --> 00:06:02,240 You get the real phenotype right and. 117 00:06:02,240 --> 00:06:06,000 You get what all of those upstream aspects have affected. 118 00:06:06,000 --> 00:06:10,440 Right, which is exactly what I what I'm attracted to around proteomics as well. 119 00:06:10,440 --> 00:06:11,040 That's great. 120 00:06:12,040 --> 00:06:15,040 And in all that work around gene expression, 121 00:06:15,360 --> 00:06:19,280 was there concern that the relationship 122 00:06:19,320 --> 00:06:22,560 between RNA presence or absence 123 00:06:22,560 --> 00:06:26,880 with all that complex epigenetics upstream of that, that connection 124 00:06:26,880 --> 00:06:30,720 between the presence of RNA and the actual protein, 125 00:06:31,200 --> 00:06:37,600 was that ever a concern of researchers in terms of that linkage? 126 00:06:37,600 --> 00:06:41,040 No, I haven't see the concern, and I don't think that's a concern 127 00:06:41,040 --> 00:06:44,440 because it’s, of course it’s a question 128 00:06:44,440 --> 00:06:47,560 The scientific basis depends upon the question that you have 129 00:06:47,560 --> 00:06:50,120 Right. But there's a complementarity. 130 00:06:50,120 --> 00:06:51,080 There's a complementary 131 00:06:51,080 --> 00:06:53,920 because you learn different things from proteomics will have different things 132 00:06:54,120 --> 00:06:56,320 for transcriptomics they have quite an overlap. 133 00:06:56,320 --> 00:07:00,040 Not really nice correlation, not really high correlation to be correct. 134 00:07:00,320 --> 00:07:02,160 But this is normal because this is biology. 135 00:07:02,160 --> 00:07:05,760 There is a lot of steps from regulation to translation 136 00:07:05,960 --> 00:07:08,480 to translation to translation with protein translation. 137 00:07:08,880 --> 00:07:12,720 There's transcription regulation for epigenetics of transcriptomics, 138 00:07:13,320 --> 00:07:15,920 but it's also the beauty of science in order to get a better 139 00:07:15,960 --> 00:07:19,200 vision and connect better genetics to phenotypes, 140 00:07:19,280 --> 00:07:20,320 I think you have to apply 141 00:07:20,320 --> 00:07:24,160 both transcriptomics proteomics and actually you have to apply multi-omics 142 00:07:24,160 --> 00:07:24,880 approaches, right. 143 00:07:24,880 --> 00:07:26,960 That's that's the really beauty of science. 144 00:07:26,960 --> 00:07:29,880 Well, and we've seen we've seen this transition, 145 00:07:30,280 --> 00:07:34,960 of course, from bulk RNA as as the technology has evolved from bulk 146 00:07:34,960 --> 00:07:39,120 RNA sequencing to single cell sequencing, which I think is enormously 147 00:07:41,240 --> 00:07:43,800 helpful in understanding mechanistic biology. 148 00:07:43,800 --> 00:07:48,240 So I think, yeah, RNA is, is getting at real time 149 00:07:48,240 --> 00:07:51,600 biology and I think proteomics is as well. 150 00:07:51,600 --> 00:07:53,160 Yeah, that's so interesting, that background. 151 00:07:53,160 --> 00:07:57,800 Single cell is where the entire field is going. 152 00:07:58,160 --> 00:08:02,440 And though, I mean there's so much interesting biology to reveal. 153 00:08:02,840 --> 00:08:03,840 Thank you, Sarantis. 154 00:08:03,840 --> 00:08:07,040 I know your background in epigenetics gives 155 00:08:07,040 --> 00:08:11,040 a unique perspective on this particular podcast. 156 00:08:11,040 --> 00:08:11,600 Appreciate it. 157 00:08:11,600 --> 00:08:14,400 Really does. And Cindy, what about yourself? 158 00:08:15,000 --> 00:08:16,920 So background. 159 00:08:16,920 --> 00:08:18,600 Sure. So I yeah, 160 00:08:18,600 --> 00:08:22,440 I did my undergrad in bio psychology, so I thought I'd be in neurology. 161 00:08:22,560 --> 00:08:24,520 I thought I'd work in that field. 162 00:08:24,520 --> 00:08:26,080 I think I, 163 00:08:26,400 --> 00:08:32,960 I somewhere in my, in my final year, maybe my junior year, I realized that, 164 00:08:33,000 --> 00:08:38,280 that the tools were were hard to implement that there it was just really hard to 165 00:08:39,360 --> 00:08:42,600 to to get at what's happening in the brain. 166 00:08:43,160 --> 00:08:46,640 And and so I just started looking around for, for 167 00:08:47,240 --> 00:08:50,760 what was it about biology that was so fascinating to me. 168 00:08:50,760 --> 00:08:53,840 And so I, I actually have a similar background to you. 169 00:08:53,840 --> 00:08:55,640 I taught high school for a little bit 170 00:08:55,640 --> 00:08:59,680 while I figured that out, and then I went back for evolutionary biology. 171 00:08:59,680 --> 00:09:06,400 So I became… gained clarity I should say around my fascination 172 00:09:06,400 --> 00:09:11,360 with understanding how we reconstruct what's happened in the past to understand 173 00:09:12,440 --> 00:09:16,240 systems today and ultimately did my Ph.D. 174 00:09:16,280 --> 00:09:19,440 in a in a biological system in the ocean. 175 00:09:19,600 --> 00:09:22,600 So worked for fisheries ended up working 176 00:09:22,600 --> 00:09:26,440 for fisheries for ten years like look at using genetics as tools. 177 00:09:26,440 --> 00:09:30,160 So I make the comment about Sarantis being a pure geneticist, right? 178 00:09:30,160 --> 00:09:34,560 Because calling me a geneticist when I'm really just using genetics as a tool, 179 00:09:36,240 --> 00:09:38,520 you know, I've, I've thought about that a lot, 180 00:09:38,520 --> 00:09:41,960 but I guess, I guess we're all we're all pushing the field forward. 181 00:09:41,960 --> 00:09:42,360 Right. 182 00:09:42,360 --> 00:09:45,280 So wait, when you talk about fisheries, you're talking 183 00:09:45,280 --> 00:09:48,240 about places like in Maine or down in Florida. 184 00:09:48,720 --> 00:09:50,440 Gloucester. Right. 185 00:09:50,440 --> 00:09:53,040 Or or the coast of California or. 186 00:09:53,360 --> 00:09:56,040 Yeah, Newfoundland or certainly Iceland. 187 00:09:56,520 --> 00:09:57,680 Norway. Yeah. 188 00:09:57,680 --> 00:10:02,640 Some of your work took you or your research took you to far flung places. 189 00:10:03,200 --> 00:10:06,080 I yeah, I was I was really lucky. 190 00:10:06,200 --> 00:10:07,680 I was really fortunate. 191 00:10:07,680 --> 00:10:10,720 Well, were you then involved in some of the 192 00:10:11,040 --> 00:10:14,520 grittier, dirtier aspects of the fisheries industry? 193 00:10:14,520 --> 00:10:16,600 By that I mean I can just imagine. 194 00:10:16,600 --> 00:10:17,640 Did I go on boats? 195 00:10:17,640 --> 00:10:19,000 Yeah, yeah. 196 00:10:19,000 --> 00:10:21,360 Specifically, that's what I think. 197 00:10:21,360 --> 00:10:24,200 I think I you know, I remember after my after 198 00:10:24,240 --> 00:10:27,640 my presentation for my dissertation, somebody said to me, 199 00:10:28,800 --> 00:10:30,360 so you just did one cruise? 200 00:10:30,360 --> 00:10:32,360 And I was like, Oh, I made a mistake. 201 00:10:32,360 --> 00:10:35,520 I really should have emphasized how many cruises. 202 00:10:35,520 --> 00:10:38,080 I think there was eight cruises that that 203 00:10:39,360 --> 00:10:41,480 that contributed in some way 204 00:10:41,880 --> 00:10:45,600 to the, to the final, you know, dissertation. 205 00:10:45,960 --> 00:10:48,280 They would call it a cruise literally. 206 00:10:49,040 --> 00:10:49,960 I guess. 207 00:10:49,960 --> 00:10:52,520 Yeah. My association with cruises. 208 00:10:52,760 --> 00:10:53,520 Yeah. 209 00:10:53,520 --> 00:10:55,240 In fact we had cruise directors. 210 00:10:55,240 --> 00:10:55,840 Right. 211 00:10:56,040 --> 00:10:59,360 See I think about the Love Boat or something, you know, so when you're a kid 212 00:10:59,360 --> 00:11:03,240 But then you are collecting samples, are you collecting samples then? 213 00:11:03,240 --> 00:11:03,640 And you said. 214 00:11:03,640 --> 00:11:06,120 That's right. On tour, but you have it done. 215 00:11:06,960 --> 00:11:10,360 So both adult samples and some cruises that that was the focus. 216 00:11:10,360 --> 00:11:14,920 And so there was actually a lot of scuba so working off of those NOAA ships, 217 00:11:16,120 --> 00:11:17,760 but also larval samples. 218 00:11:17,760 --> 00:11:21,840 So it turns out with some of these fish that live that are bottom dwellers, 219 00:11:22,240 --> 00:11:27,120 that you can't just collect them as larvae along the bottom. 220 00:11:27,120 --> 00:11:28,320 They're actually pelagic. 221 00:11:28,320 --> 00:11:32,520 They actually move through the water column as they develop and then eventually 222 00:11:32,520 --> 00:11:36,600 settle into places like kelp beds, depending upon the species. 223 00:11:37,080 --> 00:11:39,360 And so we needed to understand their life history. 224 00:11:39,600 --> 00:11:42,640 You know, it turns out that, you know, people may not think about this. 225 00:11:42,640 --> 00:11:45,920 I didn't before I worked for the fisheries, but 226 00:11:45,920 --> 00:11:48,000 the burden of demonstrating 227 00:11:48,000 --> 00:11:51,760 that we're overfishing is on the managers of the fishery. 228 00:11:52,080 --> 00:11:56,280 And so collecting the information to demonstrate that is is essential, 229 00:11:56,520 --> 00:11:59,720 especially if you want buy in from the fishermen 230 00:12:00,040 --> 00:12:04,560 whose livelihood depends upon being able to have access to those fishery sites. 231 00:12:04,760 --> 00:12:07,800 So a big part of my dissertation was looking at marine 232 00:12:07,800 --> 00:12:10,720 protected areas and characterizing the scale at which 233 00:12:11,160 --> 00:12:16,520 they could help reseed the fishery outside the protected area. 234 00:12:16,720 --> 00:12:18,840 Yeah, that’s cool. 235 00:12:18,840 --> 00:12:21,000 Wow. You've been places. 236 00:12:21,000 --> 00:12:22,920 You've seen things. 237 00:12:22,920 --> 00:12:24,800 So have you. 238 00:12:24,800 --> 00:12:26,040 You both have, right? 239 00:12:26,040 --> 00:12:29,880 I mean, I think I just have to emphasize that Sarantis wins 240 00:12:29,880 --> 00:12:32,760 the prize for knowing the most languages in this in this group. 241 00:12:33,120 --> 00:12:34,400 How so. 242 00:12:34,560 --> 00:12:36,160 Yeah. Sarantes. How many languages do 243 00:12:36,160 --> 00:12:36,840 you know? 244 00:12:36,840 --> 00:12:39,960 Only three for the moment then Germany, Italy and Germany. 245 00:12:39,960 --> 00:12:40,360 A little bit. 246 00:12:40,360 --> 00:12:43,200 And I apologize again for my German friends. 247 00:12:43,200 --> 00:12:46,320 So I never managed to. 248 00:12:46,320 --> 00:12:51,000 I think, conversational and in German at the very least. 249 00:12:51,000 --> 00:12:51,960 But yeah. 250 00:12:51,960 --> 00:12:52,440 Yeah. 251 00:12:52,440 --> 00:12:56,280 Well I’ve to give Sarantis the most languages prize 252 00:12:56,280 --> 00:12:58,040 There we go. There we go. 253 00:12:58,040 --> 00:13:00,040 And I get the most cruises prize maybe. 254 00:13:00,520 --> 00:13:01,360 There you go. 255 00:13:01,360 --> 00:13:03,600 That's for definitely. That's for sure. 256 00:13:04,800 --> 00:13:07,200 Then how did you end up in industry, Cindy? 257 00:13:08,760 --> 00:13:11,040 You know, I transition to industry. 258 00:13:11,040 --> 00:13:12,720 It was a big surprise to me. 259 00:13:12,720 --> 00:13:15,400 I had been 260 00:13:15,400 --> 00:13:17,840 suggested for a position at this little company 261 00:13:17,880 --> 00:13:21,320 that I thought was probably, you know, it had a lawsuit against it. 262 00:13:21,320 --> 00:13:25,280 It was I had a friend who worked there, this little tiny startup. 263 00:13:25,280 --> 00:13:28,080 I thought it was tiny, about 150 people. 264 00:13:28,080 --> 00:13:31,360 I guess that's mid-sized these days. But 265 00:13:31,360 --> 00:13:34,280 and so I she said this job would be perfect for you. 266 00:13:34,280 --> 00:13:36,920 And I said, I'm really looking for something an academic. 267 00:13:37,560 --> 00:13:41,480 And I had an eye on a couple of postdoc positions 268 00:13:42,040 --> 00:13:45,320 along the coast and of the U.S. 269 00:13:45,320 --> 00:13:47,160 on the West Coast here. 270 00:13:47,160 --> 00:13:50,120 And and I said, well, I'll go interview just 271 00:13:50,640 --> 00:13:52,520 for the practice and I'll learn a little bit. 272 00:13:52,520 --> 00:13:55,160 And I was so blown away by the technology 273 00:13:55,160 --> 00:13:58,720 and just the ability to support technology. 274 00:13:59,160 --> 00:14:03,640 That is a rising tide that lifts all boats like that just blew my mind 275 00:14:03,640 --> 00:14:08,360 and it felt like an opportunity to learn so much about so many different fields. 276 00:14:08,760 --> 00:14:12,800 And I think have after having been, you know, what it's like in a Ph.D., 277 00:14:13,120 --> 00:14:16,240 after having had your nose to the grindstone in a system 278 00:14:16,240 --> 00:14:19,720 and learning it to the extent you need to to be credible. 279 00:14:19,720 --> 00:14:24,520 And in getting that Ph.D., this was just so different and so 280 00:14:26,160 --> 00:14:27,720 it was just so awe-inspiring 281 00:14:27,720 --> 00:14:29,920 I was really excited about the technology. 282 00:14:30,080 --> 00:14:31,960 Cindy, what was the company? 283 00:14:31,960 --> 00:14:35,080 Well, you were there. 284 00:14:35,080 --> 00:14:36,120 So I asked. 285 00:14:36,120 --> 00:14:38,400 You couldn't tell me what the name of the company is. 286 00:14:38,400 --> 00:14:39,480 It was Illumina. 287 00:14:39,480 --> 00:14:42,400 It was Illumina. And I ended up staying for 14 years. 288 00:14:42,400 --> 00:14:45,720 So it was it was such a good such a good time. 289 00:14:45,960 --> 00:14:49,360 So I joined in the very early days and. 290 00:14:49,440 --> 00:14:51,480 And for 2004. 291 00:14:51,480 --> 00:14:53,120 That's right. 292 00:14:53,120 --> 00:14:55,080 Yeah. I remember trying to negotiate. 293 00:14:55,080 --> 00:14:56,520 You know, you're coming out of a PhD 294 00:14:56,520 --> 00:15:00,320 in the industry like you got no no where to negotiate from. 295 00:15:00,320 --> 00:15:00,800 Right. 296 00:15:01,080 --> 00:15:03,840 So I was trying to negotiate a little extra time 297 00:15:03,840 --> 00:15:06,960 before I started in the position and I got two weeks 298 00:15:06,960 --> 00:15:09,960 from the time I defended my dissertation to the time that I, 299 00:15:10,480 --> 00:15:13,280 I remember my mother and I went to travel in New Mexico. 300 00:15:13,280 --> 00:15:14,640 It was a fantastic trip. 301 00:15:14,640 --> 00:15:17,840 But yeah, I started, you know, almost right away. 302 00:15:17,840 --> 00:15:19,800 It was it was quite. 303 00:15:19,800 --> 00:15:22,480 Quite a New Mexico was how you spent those two weeks. 304 00:15:22,480 --> 00:15:24,000 Right, right. Sorry. Yeah, yeah. 305 00:15:24,000 --> 00:15:24,480 Okay. 306 00:15:24,480 --> 00:15:27,000 And yeah, but I. Was born in New Mexico, so. 307 00:15:27,240 --> 00:15:28,040 Oh, okay. 308 00:15:29,320 --> 00:15:32,640 I'm guessing that there were a lot of DNA sequencing at the time. 309 00:15:32,640 --> 00:15:34,920 What was the most weird species that you were 310 00:15:35,040 --> 00:15:36,880 You were sequencing at the time? What was the most wierd projects 311 00:15:36,880 --> 00:15:39,720 that you had? One of your first let's say. 312 00:15:40,200 --> 00:15:42,320 One of the first projects? Yeah. 313 00:15:42,320 --> 00:15:46,400 So I'll tell you, one of my first projects was working with Decode Genetics. 314 00:15:47,160 --> 00:15:50,480 And so I remember and I, you know, I had no idea 315 00:15:50,480 --> 00:15:53,280 the impact they had on, 316 00:15:53,800 --> 00:15:56,960 you know, steering or anticipating, I should say, 317 00:15:58,200 --> 00:16:00,920 you know, where the field might go and various. 318 00:16:01,320 --> 00:16:06,600 Sarantis, to clarify, right this is before NGS, so at the time Illumina 319 00:16:06,600 --> 00:16:09,200 was just offering genotyping. 320 00:16:09,320 --> 00:16:10,480 That's right so it was. 321 00:16:10,480 --> 00:16:14,160 Yeah so 2003 Illumina launched an 1152 322 00:16:14,160 --> 00:16:16,920 plex, Golden Gate genotyping platform. 323 00:16:17,280 --> 00:16:21,840 And then Illumina was able to sew-up a lot of the HapMap projects 324 00:16:21,960 --> 00:16:23,160 that were going on. 325 00:16:23,160 --> 00:16:25,400 And this was again after the genome project 326 00:16:25,400 --> 00:16:29,080 right to characterize variation across populations 327 00:16:29,400 --> 00:16:33,360 And Cindy, your first role was it as a Project Manager? 328 00:16:33,360 --> 00:16:36,240 Yeah, I was a project manager within the scientific 329 00:16:37,280 --> 00:16:39,600 team that delivered data to customers 330 00:16:39,600 --> 00:16:43,120 that were sort of testing out the technology in order to determine 331 00:16:43,120 --> 00:16:47,160 whether they wanted to invest in a BeadLab or a BeadArray Reader 332 00:16:47,160 --> 00:16:51,120 Now, the bead lab was the big genome center, you know, offering. 333 00:16:51,520 --> 00:16:56,560 It was a million dollars. $1,000,000, a LIMS liquid-handling automation system. 334 00:16:56,880 --> 00:16:57,480 Yeah. 335 00:16:57,480 --> 00:17:01,680 Yeah, it was, you know, an I had not worked at that scale before. 336 00:17:01,680 --> 00:17:04,960 So seeing the number of samples and the number of variants 337 00:17:05,280 --> 00:17:10,360 within the genome that were query able at that point was just mind boggling to me. 338 00:17:10,720 --> 00:17:13,680 And the person that interviewed me was 339 00:17:14,880 --> 00:17:18,480 just such a, such a a great people person. 340 00:17:18,480 --> 00:17:22,280 He just listened to all my, you know, objections or all my, 341 00:17:22,280 --> 00:17:26,160 you know, you know, questions about the technology was so patient. 342 00:17:26,160 --> 00:17:28,360 And I thought, you know, I could work for this person. 343 00:17:28,360 --> 00:17:29,760 Who was it by the way. 344 00:17:29,760 --> 00:17:31,560 John Stuelpnagel. Oh Okay 345 00:17:31,560 --> 00:17:33,080 One of the founders of Illumina. 346 00:17:33,080 --> 00:17:36,160 That's right. He was he was very, 347 00:17:37,400 --> 00:17:39,800 you know, just a great a great leader. 348 00:17:39,920 --> 00:17:42,480 Just a great person to learn from. 349 00:17:42,600 --> 00:17:45,760 And for those of you who may not be familiar with the name, 350 00:17:45,960 --> 00:17:49,600 he is the principal behind many, many companies 351 00:17:49,600 --> 00:17:50,640 After Illumina. 352 00:17:50,640 --> 00:17:51,880 Successful companies. 353 00:17:51,880 --> 00:17:53,400 Many successful companies. 354 00:17:53,400 --> 00:17:54,840 Yeah, right. Yeah. 355 00:17:55,000 --> 00:17:58,840 And what's I find fascinating, right, is at that time 356 00:17:59,160 --> 00:18:02,480 1152 Plex blew people's minds. 357 00:18:02,480 --> 00:18:05,680 as far as how many genotypes you can get at the time 358 00:18:06,560 --> 00:18:09,400 because people are used to like mass 359 00:18:09,400 --> 00:18:11,400 spec methods, right? From 360 00:18:12,480 --> 00:18:14,920 what is that San Diego company that whose name I 361 00:18:14,920 --> 00:18:15,920 can't remember. 362 00:18:15,920 --> 00:18:16,760 Sequenom. 363 00:18:16,760 --> 00:18:17,040 Right. 364 00:18:17,040 --> 00:18:22,120 Yeah, it was a handful, maybe ten or 15 SNPs at a time for a sample 365 00:18:22,480 --> 00:18:26,000 and you go from 15 at a time to over a thousand. 366 00:18:26,440 --> 00:18:30,120 And then of course the first HumanOne Genotyping BeadChip 367 00:18:30,120 --> 00:18:34,720 which you know, I was involved in the development of that was 108,000. 368 00:18:35,080 --> 00:18:37,800 So you go from right one 1100 369 00:18:37,800 --> 00:18:42,040 to 108,000 to the first HapMap chip that was over.. 370 00:18:42,040 --> 00:18:44,280 Remember, we had a we had the gene chip in between. 371 00:18:44,280 --> 00:18:46,480 We had a 10,000 chip in between. 372 00:18:47,040 --> 00:18:49,280 Oh I don't. Remember. Was gene centric. Yeah. 373 00:18:49,440 --> 00:18:51,760 Yeah. Okay. And there was the HapMap chip. But it didn’t it” 374 00:18:51,840 --> 00:18:52,600 Yeah. Yeah. 375 00:18:52,600 --> 00:18:56,160 But then really quickly that 300 the HapMap 300 376 00:18:56,520 --> 00:18:59,040 that was tag-based SNP selection you know 377 00:18:59,160 --> 00:19:03,960 intentional SNP selection to collection additional information 378 00:19:03,960 --> 00:19:08,680 beyond just the ones you're querying because you have some understanding of, 379 00:19:09,080 --> 00:19:11,280 of genomic diversity within the population 380 00:19:12,080 --> 00:19:14,960 that quickly overshadowed that gene centric chip. 381 00:19:14,960 --> 00:19:17,960 But I thought that gene centric chip with maybe it was 100,000, 382 00:19:17,960 --> 00:19:21,120 maybe that's the one you're thinking of to the. 383 00:19:21,480 --> 00:19:24,720 Yeah, the what's interesting is the parallels to today. 384 00:19:24,800 --> 00:19:25,360 Right. 385 00:19:25,360 --> 00:19:29,520 Where Olink’s competitors on the low-plex side 386 00:19:29,520 --> 00:19:32,640 They do four plex, they do ten plex, they do 20 plex. 387 00:19:33,000 --> 00:19:38,960 And now Olink comes out with a 96-plex and then panels of 96 388 00:19:39,440 --> 00:19:43,360 and then 1536 and then now 3000. 389 00:19:43,840 --> 00:19:44,200 Okay. 390 00:19:44,200 --> 00:19:48,720 May not have ramped as quickly due to the inherent challenges right 391 00:19:48,720 --> 00:19:51,040 of various complexity of proteins. 392 00:19:51,040 --> 00:19:53,120 They have a dynamic range, those little buggers. 393 00:19:53,120 --> 00:19:54,480 Yeah. Yeah. 394 00:19:54,480 --> 00:19:57,480 And it's fascinating to think well we're on a similar 395 00:19:57,600 --> 00:20:01,320 multiplexing track here and right. 396 00:20:01,320 --> 00:20:04,080 and history is prologue. 397 00:20:04,480 --> 00:20:07,680 It really does give us ideas of where the future is. 398 00:20:07,880 --> 00:20:08,200 Yeah. 399 00:20:08,200 --> 00:20:10,840 And I think we can think about it quite a quite 400 00:20:11,480 --> 00:20:14,440 similar to genotyping right targeted 401 00:20:16,000 --> 00:20:17,600 locations in the genome. 402 00:20:17,600 --> 00:20:21,080 It's, it's a great way to get a general view of the whole genome. 403 00:20:21,080 --> 00:20:24,320 It's sort of like a satellite view of the genome before sequencing 404 00:20:24,320 --> 00:20:28,560 technologies evolved to be so accessible and so affordable. 405 00:20:28,920 --> 00:20:33,360 And I think we're we're going to hopefully we see these technologies 406 00:20:33,360 --> 00:20:37,440 on the on the horizon that may offer 407 00:20:37,440 --> 00:20:41,520 a future of next generation proteomics 408 00:20:42,760 --> 00:20:45,280 or maybe next next generation proteomics 409 00:20:46,120 --> 00:20:48,840 that maybe one day we'll be able to sequence the proteome. 410 00:20:49,320 --> 00:20:52,560 I think the that mass spec is beautiful because you can see everything, 411 00:20:52,560 --> 00:20:55,160 but you're limited by how much you can push through. 412 00:20:55,160 --> 00:20:58,280 So those low, abundant proteins are really, really challenging 413 00:20:58,280 --> 00:20:59,120 with that technology. 414 00:20:59,120 --> 00:21:04,080 So I think that's where we're we're nicely complementary to existing methods. 415 00:21:04,400 --> 00:21:06,760 I mean, here it is. Cindy, 416 00:21:06,760 --> 00:21:10,120 How was the transition for you, how to do you see genomics through proteomics? 417 00:21:10,120 --> 00:21:12,400 How was these transitions actually for you? 418 00:21:12,400 --> 00:21:13,880 Yeah good question so 419 00:21:16,000 --> 00:21:18,040 so you know that that 420 00:21:18,040 --> 00:21:19,560 I got I was pretty clear 421 00:21:19,560 --> 00:21:23,080 about what was motivating to me about the genotyping technology 422 00:21:23,080 --> 00:21:26,400 and then ultimately the sequencing technology you know, I stuck around, 423 00:21:27,320 --> 00:21:29,840 you know, holding that tiger by the tail while, 424 00:21:29,840 --> 00:21:32,920 you know, it blew into the Illumina that it is today. 425 00:21:33,800 --> 00:21:36,600 And I, I really wanted 426 00:21:36,600 --> 00:21:39,000 a way to understand the 427 00:21:39,960 --> 00:21:42,960 the impact genetics is having on more real time health. 428 00:21:43,360 --> 00:21:46,440 And so I actually had a stint at a company called Metabolon, 429 00:21:46,760 --> 00:21:51,280 looking at metabolomics now talk about complexity of of biological pathways. 430 00:21:51,280 --> 00:21:51,880 Right. 431 00:21:52,000 --> 00:21:56,120 And, and I was there until I saw the launch of the NGS readout 432 00:21:56,160 --> 00:22:00,880 on the proteomics at Olink and that NGS readout, 433 00:22:00,880 --> 00:22:05,120 I was an AHA for me and I thought, well, I can, I can help with that. 434 00:22:05,120 --> 00:22:07,240 And I'm excited about that. 435 00:22:07,280 --> 00:22:10,400 You know, specificity, you know, the quality of the assay, which I think 436 00:22:10,960 --> 00:22:15,600 I think is, is exciting because you want to be able to make discoveries 437 00:22:15,880 --> 00:22:20,520 and then drill in to those discoveries and focus in on on individual targets. 438 00:22:20,520 --> 00:22:20,960 Right. 439 00:22:20,960 --> 00:22:23,400 So that's the that was the attraction. 440 00:22:23,400 --> 00:22:26,160 And I actually approached Olink and said, 441 00:22:27,360 --> 00:22:29,760 you should hire me. 442 00:22:30,400 --> 00:22:31,640 Just like that. 443 00:22:31,640 --> 00:22:33,240 So cheeky, right? 444 00:22:33,240 --> 00:22:38,720 Never, in… a million years would I expect myself to have done that 445 00:22:38,720 --> 00:22:42,600 But yeah, they were, they were very amenable. 446 00:22:42,600 --> 00:22:44,760 I had a great experience with that. 447 00:22:44,760 --> 00:22:48,800 And I will say working for a Swedish company with a U.S. 448 00:22:49,800 --> 00:22:53,160 representation is, is a really nice 449 00:22:53,440 --> 00:22:56,040 it blends some nice qualities. 450 00:22:56,920 --> 00:22:58,200 It's remarkable. 451 00:22:58,200 --> 00:23:02,680 Before I joined Illumina in 2003 I was at QIAGEN 452 00:23:02,680 --> 00:23:06,480 a German company, for four years representing their commercial efforts. 453 00:23:06,480 --> 00:23:10,920 And this is, of course, to 1999 to 2003, where 454 00:23:11,520 --> 00:23:15,080 I saw through the whole genome project from the lens of a sample 455 00:23:15,080 --> 00:23:18,440 prep provider for the Human Genome Project. 456 00:23:18,440 --> 00:23:22,760 Of course, at that time Affymetrix had just grown like gangbusters. 457 00:23:22,760 --> 00:23:28,000 And all this interest in whole genome transcription expression analysis via 458 00:23:28,000 --> 00:23:32,120 microarrays and coming back to a company 459 00:23:32,320 --> 00:23:34,840 that's from Northern Europe 460 00:23:35,400 --> 00:23:38,600 and it just the precision in 461 00:23:39,000 --> 00:23:41,480 and QIAGEN was wonderful because the engineering mentality” 462 00:23:41,520 --> 00:23:45,480 is very much exhibited, right, the precision of their, 463 00:23:46,200 --> 00:23:49,480 of the assay development and of the product development 464 00:23:49,840 --> 00:23:53,280 and at Olink I see shade of that as far as very disciplined 465 00:23:53,280 --> 00:23:57,480 approach to accuracy, to the quality of their product. 466 00:23:57,880 --> 00:24:00,240 There is a lot of care taken, right. 467 00:24:00,240 --> 00:24:04,760 And it's a great cross-functional collaboration here at the company. 468 00:24:04,760 --> 00:24:08,520 And I think we all know that the more contribution 469 00:24:08,520 --> 00:24:13,200 you get from diverse opinions, the better your products going to be, provided 470 00:24:13,200 --> 00:24:16,760 you also have that discipline to ensure that you've got the specificity. 471 00:24:16,760 --> 00:24:19,800 So anyway, it's a it's a fun, fun ride. 472 00:24:19,960 --> 00:24:24,000 Sarantis, I'm curious what you're so what you're most excited about 473 00:24:24,400 --> 00:24:25,560 in the coming year 474 00:24:26,560 --> 00:24:28,320 at Olink, in your role. 475 00:24:28,320 --> 00:24:32,040 I am really excited to see that data integration” 476 00:24:32,240 --> 00:24:35,760 of really multi-omics data coming true, you know because we hear a lot of 477 00:24:36,120 --> 00:24:39,080 multi-omics we hear a lot of this buzzword multiomics, 478 00:24:39,880 --> 00:24:43,440 but we are not still there, you know, the different regions 479 00:24:43,440 --> 00:24:47,520 and I think Olink offers the perfect tool being an NGS-based assay 480 00:24:47,520 --> 00:24:50,560 mainly to create this, 481 00:24:50,560 --> 00:24:54,000 multi-omics approach in life, to bring it to life 482 00:24:54,240 --> 00:24:58,600 And I'm really excited to see projects coming really multi-omics projects 483 00:24:58,600 --> 00:25:01,280 that they have epigenetics, they have transcriptomics, 484 00:25:01,280 --> 00:25:02,240 they have proteomics, 485 00:25:02,240 --> 00:25:05,560 they have genomics, that it will be really the future of of science. 486 00:25:06,480 --> 00:25:09,040 What do you think has held that back so far? 487 00:25:09,040 --> 00:25:11,040 Like why do you think now is the time? Right. 488 00:25:11,040 --> 00:25:14,200 I have this this sense that we're in an inflection point. 489 00:25:14,200 --> 00:25:14,400 Right? 490 00:25:14,400 --> 00:25:18,600 There's this there's this energy for sure when I go to conferences. 491 00:25:18,720 --> 00:25:20,280 Sure. And so I'm curious. 492 00:25:20,280 --> 00:25:23,320 You're your perspective on that Sarantis. 493 00:25:23,320 --> 00:25:25,720 Now, I'm really happy to have your feedback on that 494 00:25:25,720 --> 00:25:27,320 because it's really an open discussion. 495 00:25:27,320 --> 00:25:29,520 I would really like to know from your side, 496 00:25:30,120 --> 00:25:34,680 say this, I see that I think that people in regards of proteomics 497 00:25:35,160 --> 00:25:37,760 the making, let’s say the Mass Spec assay was must because 498 00:25:37,800 --> 00:25:40,840 it was really difficult to break into the multi-omics work. 499 00:25:40,840 --> 00:25:44,640 I think having an NGS-based approach to this makes things easier 500 00:25:45,120 --> 00:25:48,000 I suspect big data, 501 00:25:48,440 --> 00:25:52,840 you know, having expensive experiments, the sequencing is is a quite 502 00:25:52,840 --> 00:25:56,160 is not an easy experiment to do you need bioformatics tool, 503 00:25:56,240 --> 00:25:58,240 we need bioformatic analysis 504 00:25:58,240 --> 00:26:01,200 you need the specific platforms that they can integrate this data 505 00:26:01,200 --> 00:26:05,520 that they are not very well advanced. Let’s say their data analysis. 506 00:26:05,520 --> 00:26:06,440 Yeah. 507 00:26:06,440 --> 00:26:09,440 They are not so well advanced in the sense that they are really 508 00:26:09,680 --> 00:26:12,560 not easy, accessible to everybody in this respect. 509 00:26:12,560 --> 00:26:14,560 Of course they are advanced, but not the science 510 00:26:14,560 --> 00:26:17,160 that will be accessible to everyone, it has to be easy for everybody 511 00:26:17,200 --> 00:26:21,200 and they are not there for the multi-omics approach but we hope soon 512 00:26:21,200 --> 00:26:23,280 Yeah. What do you think from your side? 513 00:26:23,280 --> 00:26:26,960 Yeah, I was just going to say, I think I think you touched right on what I 514 00:26:27,080 --> 00:26:30,960 when I come across a lot is this analysis piece and having 515 00:26:31,080 --> 00:26:34,880 having tools to make those analyzes happen where you're integrating these data 516 00:26:34,920 --> 00:26:37,760 because some of the platforms allow the data to sit by side by side. 517 00:26:37,760 --> 00:26:42,320 But, but actually having scripts and tools that are that are bringing those data 518 00:26:42,320 --> 00:26:45,600 together in a multi-omics analysis is not trivial. 519 00:26:45,600 --> 00:26:47,880 But I think maybe part of the as you were talking, 520 00:26:47,880 --> 00:26:51,720 I was thinking maybe maybe part of this sense of urgency and excitement 521 00:26:51,720 --> 00:26:56,480 is, is all that's happened in the UK Biobank in the last five years. 522 00:26:56,480 --> 00:26:57,080 Right. 523 00:26:57,080 --> 00:27:00,000 The exome sequencing, the whole genome sequencing. 524 00:27:00,000 --> 00:27:02,720 Now the proteomics that's coming out of the UK Biobank 525 00:27:03,000 --> 00:27:07,080 and making those data accessible means that there is a playground 526 00:27:07,440 --> 00:27:10,680 for people to advance their skills, to integrate those tools. 527 00:27:10,920 --> 00:27:15,040 And it's very cohort projects as well, you know, not just the UK 528 00:27:15,040 --> 00:27:18,240 Biobank, but it's certainly the buzz at ASHG every year. 529 00:27:18,560 --> 00:27:19,320 Sorry, go ahead. 530 00:27:19,320 --> 00:27:22,920 It is ‘the’ definition of big data 531 00:27:23,080 --> 00:27:23,880 Right. 532 00:27:23,880 --> 00:27:29,160 Is volume, its velocity and its variety. 533 00:27:29,160 --> 00:27:31,280 So we have a big data problem, right. 534 00:27:31,280 --> 00:27:33,760 Was we have whole genomes at scale. 535 00:27:34,120 --> 00:27:36,880 You've got whole transcriptomes 536 00:27:36,880 --> 00:27:40,360 at scale. We have whole epigenomes 537 00:27:40,800 --> 00:27:42,840 And now we are going to overlay proteomes? 538 00:27:42,840 --> 00:27:45,200 You know, these are different types of data 539 00:27:45,920 --> 00:27:48,960 in large scale, in multiple dimensions. 540 00:27:48,960 --> 00:27:49,400 Yeah. 541 00:27:49,400 --> 00:27:53,640 And I think I think that back to Sarantis comment about mass spec. 542 00:27:53,640 --> 00:27:57,480 I think that was the barrier maybe is that you know mass spec it's 543 00:27:57,480 --> 00:28:01,320 hard to get a lot of samples through under this sort of service 544 00:28:01,320 --> 00:28:05,360 wrapper conditions where you're you're controlling variability and then and then 545 00:28:05,440 --> 00:28:07,480 feeding it into what you're talking about Dale, 546 00:28:09,360 --> 00:28:11,560 integrated project with, you know, 547 00:28:11,560 --> 00:28:15,840 50,000, 100,000, ultimately, you know, half a million samples. 548 00:28:15,840 --> 00:28:20,160 It's and we can't talk about mass spec as a monolithic item, right. 549 00:28:20,400 --> 00:28:24,920 Because of all the infinite amount of varieties of. 550 00:28:25,320 --> 00:28:27,200 GC, LC, tandem. Yeah 551 00:28:27,200 --> 00:28:31,200 And timsTOF, I mean you just go down the line 552 00:28:31,200 --> 00:28:35,240 in terms of everything from sample prep all the way through the technology, 553 00:28:35,240 --> 00:28:39,000 all the way through the analysis, bottom up proteomics, 554 00:28:39,000 --> 00:28:41,400 top down proteomics and everything in between. 555 00:28:42,000 --> 00:28:46,760 Yeah, it gets really complicated from even the analytical chemistry side. 556 00:28:46,800 --> 00:28:47,400 Yeah. 557 00:28:47,400 --> 00:28:50,880 Is absolutely that monolitic is really a lot of advances. 558 00:28:50,920 --> 00:28:53,200 Also the single cell proteomics space, right? 559 00:28:53,200 --> 00:28:56,960 I mean the single cell proteomics protocols, they're designed to done 560 00:28:57,000 --> 00:28:57,640 by mass spec. 561 00:28:57,640 --> 00:29:00,080 Nowadays I think there are different questions 562 00:29:00,080 --> 00:29:02,080 and there are different scientific questions 563 00:29:02,080 --> 00:29:06,080 that I think get different answers from a Mass Spec and Olink approaches 564 00:29:06,080 --> 00:29:09,120 PEA approach, I think they can really nicely complement 565 00:29:09,320 --> 00:29:11,240 And that's, that's the beauty of science, right. 566 00:29:11,240 --> 00:29:12,240 That isn't out there. 567 00:29:12,240 --> 00:29:15,920 Really nicely complimenting can take really nice and integrated information. 568 00:29:15,920 --> 00:29:16,560 So that's. 569 00:29:17,000 --> 00:29:17,400 That's right. 570 00:29:17,400 --> 00:29:19,520 That's right. That tied right up. 571 00:29:19,520 --> 00:29:21,360 Oh, there's all this great stuff. 572 00:29:21,360 --> 00:29:23,280 Alas, we just have a few minutes left. 573 00:29:23,280 --> 00:29:25,360 Does anybody ever ask me about my background? 574 00:29:25,520 --> 00:29:28,600 I can ask before I wanted to ask you that question. 575 00:29:28,600 --> 00:29:30,960 QIAGEN was your first, it was your first?” 576 00:29:30,960 --> 00:29:33,080 That was my first. That's correct. 577 00:29:33,080 --> 00:29:36,960 Well, before that, I was a manager of a small laboratory in Santa Monica. 578 00:29:37,840 --> 00:29:42,080 I worked for a P.I., Dave Hoon, whom we worked on tumor immunology back” 579 00:29:42,120 --> 00:29:46,640 in the late nineties when nobody worked on tumor immunology except for 580 00:29:48,680 --> 00:29:50,800 Don Morten there at the 581 00:29:50,800 --> 00:29:56,080 John Wayne Cancer Institute in Santa Monica, and then Rosenberg here at the NCI though 582 00:29:56,120 --> 00:29:59,200 They were the only two working on tumor immunology 583 00:29:59,200 --> 00:30:03,640 And now how I look at it now, wow, you were a pioneer. 584 00:30:03,840 --> 00:30:04,680 Incredible. 585 00:30:04,680 --> 00:30:08,720 And then that time at QIAGEN was fascinating because I started 586 00:30:08,720 --> 00:30:12,960 with customer service – technical support, 1-800-DNA-PREP 587 00:30:13,520 --> 00:30:18,600 And the manager of that department is Kirk Malloy, who ended up joining Illumina 588 00:30:18,960 --> 00:30:21,640 at the end of 2002 589 00:30:21,960 --> 00:30:24,920 and invited me to interview, invited me to take a tour, 590 00:30:25,080 --> 00:30:28,560 and I’ll never forget the tour I had because he showed me the ‘Oligator’ 591 00:30:28,920 --> 00:30:32,320 the Vacuum Box and all of its 96-well glory. 592 00:30:32,520 --> 00:30:33,960 And this was, Sarantis, 593 00:30:33,960 --> 00:30:38,160 their secret sauce Illumina's the ability to make 594 00:30:38,280 --> 00:30:41,640 very inexpensive oligos at scale. 595 00:30:42,200 --> 00:30:44,920 And so they developed this method, 596 00:30:44,920 --> 00:30:48,120 and Cindy is putting up her little.... 597 00:30:48,200 --> 00:30:50,080 That's me! A piece of history. 598 00:30:51,240 --> 00:30:51,760 Illumina. 599 00:30:51,760 --> 00:30:53,800 Different haircut, you know. 600 00:30:53,800 --> 00:30:56,760 That led to the coincidence of the fact 601 00:30:56,760 --> 00:30:59,480 that we're side by side, we're right next to each other and that 602 00:31:00,840 --> 00:31:01,240 yeah. 603 00:31:01,240 --> 00:31:06,280 So those were kind of, like Cindy said, kind of scary days, right? 604 00:31:06,280 --> 00:31:08,800 Because it was a small company that had... 605 00:31:08,800 --> 00:31:11,920 We had a lawsuit, remember, there was a lawsuit over... 606 00:31:11,960 --> 00:31:14,840 There are several lawsuits. 607 00:31:14,840 --> 00:31:17,400 Actually, there was a lawsuit there was from another company. 608 00:31:17,400 --> 00:31:20,680 And I learned now that, you know, maybe that's a badge of honor sometimes. 609 00:31:20,680 --> 00:31:21,760 But I didn't know that then. 610 00:31:21,760 --> 00:31:24,840 I thought I thought it was going to be, you know, the death of us. 611 00:31:24,840 --> 00:31:26,200 You know, I had no idea. 612 00:31:26,200 --> 00:31:27,960 And then there was a there was another lawsuit. 613 00:31:27,960 --> 00:31:31,200 I can't you know, both of them ended up being, you know, just 614 00:31:31,280 --> 00:31:34,760 just indications of success, I think, when we look back. 615 00:31:34,760 --> 00:31:36,520 But, yeah, yeah, yeah. 616 00:31:36,520 --> 00:31:38,400 I think the secret sauce. Right. 617 00:31:38,400 --> 00:31:41,640 Was not just the right technology, really, 618 00:31:41,640 --> 00:31:44,320 it's the people behind the technology. 619 00:31:44,640 --> 00:31:49,360 It's also the creative thinking of people behind the technology in terms of 620 00:31:49,680 --> 00:31:54,320 what are the critical things to work on, what is the critical 621 00:31:54,320 --> 00:31:57,360 fundamentals of the business to make it successful? 622 00:31:57,760 --> 00:32:00,720 And I can say career wise, right, 623 00:32:00,720 --> 00:32:04,000 I've I've been in a lot of different genomics companies. 624 00:32:04,000 --> 00:32:10,200 It was 17 years from that time leaving QIAGEN, and then to come back to Olink 625 00:32:10,200 --> 00:32:15,040 yeah, the 17 years and the in between so much I've learned in the genomics realm. 626 00:32:15,600 --> 00:32:18,720 And then now to bring it back home to proteomics. 627 00:32:18,920 --> 00:32:24,520 Yeah, bring it back to very close to common disease, very close to 628 00:32:25,680 --> 00:32:27,880 rare disease, very close to 629 00:32:28,560 --> 00:32:34,600 population health, very close to wellness, very close to aging. 630 00:32:34,600 --> 00:32:39,080 Do you realize the advances we've made in cancer have been remarkable 631 00:32:39,120 --> 00:32:42,720 because of a focus on therapeutics and prevention? 632 00:32:43,480 --> 00:32:46,520 Can you imagine doing the same thing to longevity 633 00:32:46,800 --> 00:32:50,040 and aging to make similar advances? 634 00:32:50,040 --> 00:32:53,120 And we're on the edge. Of prevention, right? 635 00:32:53,240 --> 00:32:55,320 There's the potential. Yeah. Yes. 636 00:32:55,880 --> 00:33:00,320 It's remarkable times we're living in from the point of view of 637 00:33:01,320 --> 00:33:04,320 measuring proteins at the scale that we're talking about 638 00:33:04,560 --> 00:33:09,000 can have true impact on cardiovascular disease like matter. 639 00:33:09,000 --> 00:33:11,000 Matters of the heart. 640 00:33:11,280 --> 00:33:14,080 Just as well as right population 641 00:33:14,080 --> 00:33:18,360 health with the UK Biobank, like you mentioned, Cindy, what a amazing resource. 642 00:33:18,360 --> 00:33:19,800 I mean, yeah, what is it? 643 00:33:19,800 --> 00:33:24,640 500,000 healthy individuals measured from 2006 onward. 644 00:33:24,880 --> 00:33:26,400 Yeah. And not checking. In. 645 00:33:26,400 --> 00:33:26,760 Go ahead. 646 00:33:26,760 --> 00:33:29,000 Not to forget or to close the loop. 647 00:33:29,000 --> 00:33:31,760 You know, we're right back with Decode Genetics. Right. 648 00:33:31,760 --> 00:33:34,120 They're also very engaged. 649 00:33:34,120 --> 00:33:36,960 So what led you to industry, Dale? 650 00:33:36,960 --> 00:33:39,480 I think what it was was an opportunity, 651 00:33:39,920 --> 00:33:43,320 the opportunity to apply a lot of what I know as a scientist, 652 00:33:43,320 --> 00:33:48,600 a lot of what I value in terms of learning about science in a new way. 653 00:33:48,600 --> 00:33:51,600 And that is the combination of science with business. 654 00:33:51,600 --> 00:33:53,520 And it's fascinating, right? 655 00:33:53,520 --> 00:33:57,400 Some of my favorite classes as an undergraduate were in psychology, 656 00:33:58,200 --> 00:34:01,200 and now I get to use that every day in a marketing role 657 00:34:01,200 --> 00:34:05,400 because I'm thinking about the psychology of buying behavior, the psychology of, 658 00:34:05,600 --> 00:34:08,520 you know, what messages resonate, the psychology of, 659 00:34:08,800 --> 00:34:11,960 okay, if somebody's searching for solutions 660 00:34:12,320 --> 00:34:16,800 from a search engine, what terms do they use to find Olink Proteomics? 661 00:34:17,000 --> 00:34:20,400 I mean, yeah, it's yeah that's part of my day to day believed. 662 00:34:20,840 --> 00:34:23,040 Is understanding people's motivations. 663 00:34:23,040 --> 00:34:24,920 Yeah back to the people right. 664 00:34:24,920 --> 00:34:26,680 Back to the people. 665 00:34:26,680 --> 00:34:30,560 And I think what's fun about the a holding a podcast 666 00:34:30,560 --> 00:34:33,560 and this proteomics and proximity is that 667 00:34:33,560 --> 00:34:36,960 we get to talk as people about science. 668 00:34:37,280 --> 00:34:42,360 We will interview people as scientists, we will go ahead and talk about papers 669 00:34:42,360 --> 00:34:46,440 that people have written, their conclusions, their ideas. 670 00:34:46,840 --> 00:34:49,520 And so it's I think there's going to be a lot of fun 671 00:34:49,560 --> 00:34:52,520 to talk about sort of papers in the context of 672 00:34:52,560 --> 00:34:56,760 like a journal club, I think will also be really interesting to bring in guests. 673 00:34:56,760 --> 00:34:59,840 and certainly Sarantis and Cindy you have some ideas of who you’d like 674 00:34:59,880 --> 00:35:05,120 to bring on in this remote kind of personal and intimate environment. 675 00:35:05,560 --> 00:35:10,400 And then we can just the three of us talk a lot about fish 676 00:35:11,360 --> 00:35:14,280 or larvae… or what’s it 677 00:35:14,280 --> 00:35:18,360 like to scuba dive in Nova Scotia or wherever it was you were. 678 00:35:18,360 --> 00:35:18,640 Yeah. 679 00:35:18,840 --> 00:35:22,920 Or maybe what's it like to work for one of the leading institutes in Germany? 680 00:35:22,920 --> 00:35:27,760 They're on a true model organism from a true geneticist, no doubt. 681 00:35:27,760 --> 00:35:28,920 Right? Yeah. 682 00:35:28,920 --> 00:35:32,880 And if we can give some perspective to maybe graduate students who are 683 00:35:33,120 --> 00:35:34,960 who are finishing up their Ph.D. 684 00:35:34,960 --> 00:35:36,600 around ideas of what 685 00:35:36,600 --> 00:35:39,760 it's like to be in industry, I'd love to contribute to that as well. 686 00:35:39,760 --> 00:35:42,840 I'm pretty still pretty passionate about working with students. 687 00:35:43,000 --> 00:35:44,400 Great. Well. 688 00:35:44,400 --> 00:35:46,840 Again, people, right? People and people. 689 00:35:47,520 --> 00:35:54,280 Well, we got to get that in there. 690 00:35:54,520 --> 00:35:56,280 You got to get in there. 691 00:35:56,280 --> 00:35:56,880 All right. 692 00:35:56,880 --> 00:35:59,280 Well, until next time, take care audience. 693 00:35:59,280 --> 00:36:01,920 Thanks for joining. Thanks. 694 00:36:02,760 --> 00:36:05,480 Thanks. Bye. Thank you. Thank you. Thank 695 00:36:11,120 --> 00:36:11,840 Thank you for 696 00:36:11,840 --> 00:36:15,600 listening to the Proteomics in Proximity podcast brought to you by Olink Proteomics. 697 00:36:15,600 --> 00:36:19,560 To contact the hosts or for further information simply 698 00:36:19,560 --> 00:36:23,320 email info@olink.com.