1 00:00:05,625 --> 00:00:06,375 Welcome to the 2 00:00:06,375 --> 00:00:09,500 Proteomics in Proximity podcast, where your co-host 3 00:00:09,625 --> 00:00:12,916 Cindy Lawley and Sarantis Chlamydas from Olink Proteomics. 4 00:00:13,250 --> 00:00:16,875 Talk about the intersection of proteomics with genomics for drug target 5 00:00:16,875 --> 00:00:20,875 discovery, the application of proteomics to reveal disease biomarkers, 6 00:00:21,250 --> 00:00:25,875 and current trends in using proteomics to unlock biological mechanisms. 7 00:00:26,250 --> 00:00:29,250 Here we have your host, Cindy, and Sarantis 8 00:00:30,125 --> 00:00:33,500 What if your chronological age only told part of your story? 9 00:00:34,125 --> 00:00:36,916 What if your brain, your lungs, your ovaries? 10 00:00:36,916 --> 00:00:40,541 What if they were all aging at different rates in your body, 11 00:00:41,250 --> 00:00:45,166 and that you could know that you could find that out from a simple blood test? 12 00:00:46,541 --> 00:00:50,166 Today's guest, Tony Wyss-Coray from Stanford University. 13 00:00:50,541 --> 00:00:53,125 We'll talk about just that. 14 00:00:53,125 --> 00:00:55,750 He's one of the pioneers who's building 15 00:00:55,750 --> 00:00:59,541 organ aging clocks out of simple Olink. 16 00:00:59,541 --> 00:01:01,125 Protein measurements. 17 00:01:01,125 --> 00:01:02,541 Tony and his team at Teal 18 00:01:02,541 --> 00:01:06,041 Rise are revealing remarkable insights into biological age, 19 00:01:06,500 --> 00:01:11,666 organ health, and even future disease risk years before symptoms 20 00:01:12,666 --> 00:01:13,166 at all. 21 00:01:13,166 --> 00:01:17,375 Of course, we're delighted to have our customers get more out of the data, 22 00:01:17,375 --> 00:01:20,375 so we're partnering with a team at Teal Rise to ensure 23 00:01:20,541 --> 00:01:23,166 customers do just that. 24 00:01:23,166 --> 00:01:26,875 In this episode, we talk about young blood vampires, 25 00:01:27,375 --> 00:01:31,000 immune cells, organ aging clocks and so much more. 26 00:01:31,541 --> 00:01:34,000 I hope you enjoy it. 27 00:01:34,000 --> 00:01:34,750 Hello everyone. 28 00:01:34,750 --> 00:01:37,750 Welcome back to Proteomics in Proximity. 29 00:01:38,125 --> 00:01:40,750 We're very excited to have, guest. 30 00:01:40,750 --> 00:01:43,666 We haven't had before here to talk about 31 00:01:43,666 --> 00:01:46,916 aging and some amazing innovations happening in aging. 32 00:01:47,166 --> 00:01:47,875 Sarantis, 33 00:01:47,875 --> 00:01:50,625 do you want to introduce our our guest? 34 00:01:50,625 --> 00:01:51,041 Thank you. 35 00:01:51,041 --> 00:01:53,500 Thank you Cindy. Welcome everybody. 36 00:01:53,500 --> 00:01:57,041 We are excited and honored to have with us a leader in the field of aging research 37 00:01:57,041 --> 00:01:59,125 professional, Tony Wyss-Coray. 38 00:01:59,125 --> 00:02:01,791 Tony is a professor of neurology at the Stanford University School 39 00:02:01,791 --> 00:02:06,750 of Medicine, director of the and Benny Knight Initiative for Brain Resilience, 40 00:02:06,916 --> 00:02:10,875 where he leads biomedical research on aging and age related diseases. 41 00:02:11,500 --> 00:02:15,125 His work focuses on blood proteomics and circulating factors 42 00:02:15,375 --> 00:02:19,125 and how they influence healthspan and lifespan, with a goal, of course, 43 00:02:19,250 --> 00:02:22,875 of developing interventions to help us live longer, healthier. 44 00:02:23,416 --> 00:02:25,250 Tony is a pleasure to have you with us. 45 00:02:25,250 --> 00:02:28,250 To start, we would love to hear a little bit more about your journey. 46 00:02:28,500 --> 00:02:31,916 What led you to aging research and what continues to inspire your work 47 00:02:31,916 --> 00:02:33,166 every day in the lab? 48 00:02:33,166 --> 00:02:33,500 Thank you. 49 00:02:33,500 --> 00:02:35,125 And it's hot, hot, hot right now. 50 00:02:35,125 --> 00:02:37,166 This aging stuff is on fire. 51 00:02:37,166 --> 00:02:38,416 It's so exciting. 52 00:02:38,416 --> 00:02:41,000 Yeah. Thank you. Thank you so much for having me. 53 00:02:42,250 --> 00:02:43,541 Yeah, I was when I 54 00:02:43,541 --> 00:02:47,375 started out in research, I started actually immunology. 55 00:02:47,375 --> 00:02:51,250 I really had no interest in aging research, to be honest. 56 00:02:51,791 --> 00:02:56,500 But, I got into neurodegenerative research and studied 57 00:02:56,500 --> 00:03:01,125 how the immune system affects the brain, and particularly in Alzheimer's disease. 58 00:03:01,875 --> 00:03:04,875 And, I realized 59 00:03:04,875 --> 00:03:09,000 that age is really the key risk factor for this disease. 60 00:03:09,000 --> 00:03:12,625 And we, did more than 20 years ago, 61 00:03:12,625 --> 00:03:15,625 some very simple experiment, 62 00:03:16,416 --> 00:03:19,500 where we asked patients, 63 00:03:20,416 --> 00:03:23,416 for blood samples, and, 64 00:03:23,750 --> 00:03:28,250 could we see changes in immune, factors in the blood 65 00:03:28,250 --> 00:03:31,666 of these patients with the disease compared to those without? 66 00:03:32,250 --> 00:03:36,625 We saw some differences, but we realized that in the healthy 67 00:03:36,625 --> 00:03:40,500 controls, the changes are actually often bigger. 68 00:03:40,500 --> 00:03:46,000 And so there were a lot of changes in the composition of proteins that we measured. 69 00:03:46,000 --> 00:03:51,000 And at the time, it was actually a filter area where we measured 120 proteins. 70 00:03:51,750 --> 00:03:54,000 And these changes in healthy people are often 71 00:03:54,000 --> 00:03:57,000 bigger than between disease and control. 72 00:03:57,125 --> 00:03:59,750 And that really got me into aging research. 73 00:03:59,750 --> 00:04:04,375 Meaning that over time, the longitudinal aspect 74 00:04:04,750 --> 00:04:09,375 showed changes despite them not having any apparent disease, 75 00:04:09,750 --> 00:04:12,916 but that there were changes that happened longitudinally that were 76 00:04:13,125 --> 00:04:18,375 because so much of the big population health projects are one sample, right? 77 00:04:18,375 --> 00:04:22,500 The baseline sample, like in the UK Biobank data, which went into the paper 78 00:04:22,500 --> 00:04:26,500 that we certainly want to talk about the organ aging paper today, 79 00:04:26,750 --> 00:04:28,166 but those are baseline samples. 80 00:04:28,166 --> 00:04:32,625 And so this was really about longitudinal sampling way ahead of its time 81 00:04:32,625 --> 00:04:34,875 I'll say 120 proteins. It's a lot. 82 00:04:34,875 --> 00:04:37,625 At the time it actually wasn't longitudinal. 83 00:04:37,625 --> 00:04:39,000 It was still cross-sectional. 84 00:04:39,000 --> 00:04:42,416 But what we saw is that people who were younger 85 00:04:42,916 --> 00:04:46,375 had different concentrations of proteins than those who were older. 86 00:04:46,625 --> 00:04:49,791 So this was cross-sectional, but 87 00:04:49,875 --> 00:04:53,041 it just showed that, you know, age 88 00:04:53,416 --> 00:04:57,166 seems to be associated with changes in concentrations of proteins. 89 00:04:57,166 --> 00:05:00,625 And, you know, now this makes total sense. 90 00:05:01,791 --> 00:05:03,875 And we know, of course, 91 00:05:03,875 --> 00:05:08,750 you know, most molecules change in concentrations with age 92 00:05:08,750 --> 00:05:13,500 and in organisms and certainly function changes with age. 93 00:05:13,500 --> 00:05:16,000 We look different as we get older. 94 00:05:16,000 --> 00:05:20,750 So the fact that we can capture this in the blood, provided us 95 00:05:20,750 --> 00:05:26,666 with an opportunity to start asking, are these changes cause or effect? 96 00:05:27,375 --> 00:05:30,250 And we can talk a little bit about what that led to? 97 00:05:30,250 --> 00:05:33,625 Yeah, definitely want to talk about causality for sure. 98 00:05:33,750 --> 00:05:37,375 And I'm guessing that this is what drives you later on to parabiosis experiments. 99 00:05:37,375 --> 00:05:37,625 Right. 100 00:05:37,625 --> 00:05:41,125 Because it was a big actually discussions. 101 00:05:41,125 --> 00:05:44,375 They make a lot of, impact in the community 102 00:05:44,375 --> 00:05:48,125 also of these parabiosis experiments parabiosis 103 00:05:48,125 --> 00:05:51,125 Yeah. Let's explain. So exactly. 104 00:05:51,416 --> 00:05:53,875 So Sarantis is actually absolutely right. 105 00:05:53,875 --> 00:05:55,500 So this was really the 106 00:05:56,666 --> 00:05:57,875 motivation for 107 00:05:57,875 --> 00:06:01,000 us to get, into, into this model. 108 00:06:01,000 --> 00:06:05,625 And so basically what this is, is it's a model where you can, 109 00:06:06,541 --> 00:06:09,375 pair animals, 110 00:06:09,375 --> 00:06:11,875 of, you know, different composition 111 00:06:11,875 --> 00:06:15,416 or different age or different geneticbackgrounds. 112 00:06:15,916 --> 00:06:18,500 And in this case, and it's called parabiosis 113 00:06:18,500 --> 00:06:21,500 So it means living next to each other. 114 00:06:21,541 --> 00:06:25,125 But essentially when you use this model, the, 115 00:06:25,125 --> 00:06:29,791 the circulation of two mice in this case is shared. 116 00:06:30,250 --> 00:06:32,791 And so we paired young with old mice. 117 00:06:32,791 --> 00:06:36,625 And we did this actually in collaboration with Tom rando, who, 118 00:06:38,041 --> 00:06:41,375 you know, in a seminal paper with the Con Boys, 119 00:06:41,875 --> 00:06:46,625 showed that he can change the age of muscle stem cells. 120 00:06:46,750 --> 00:06:51,750 So use an old mouse, which has very low stem cell activity in a muscle. 121 00:06:51,750 --> 00:06:54,791 He paired it with a young mouse and could show that 122 00:06:54,791 --> 00:06:57,875 this regenerated and rejuvenated the old muscle. 123 00:06:58,666 --> 00:07:03,500 And so he had this model and he actually recruited me to Stanford. 124 00:07:03,500 --> 00:07:06,500 So we asked what is the effect on the brain? 125 00:07:06,500 --> 00:07:10,666 Because we saw these changes in blood composition with age. 126 00:07:10,666 --> 00:07:14,291 And again, this allowed us to ask, is this cause or effect? 127 00:07:14,291 --> 00:07:20,750 Are the change a reflection of the aging organism, or do they even influence it? 128 00:07:20,791 --> 00:07:22,625 Turns out they do both. 129 00:07:22,625 --> 00:07:27,250 And this is really the basis of now the diagnostic potential of this, 130 00:07:28,666 --> 00:07:31,500 and the implications for biology in general. 131 00:07:31,500 --> 00:07:36,000 But what we did is we basically used, the same model 132 00:07:36,000 --> 00:07:39,000 that Tom had used and asked, 133 00:07:39,916 --> 00:07:43,000 is it the brain of a of an old mouse? 134 00:07:44,375 --> 00:07:48,125 Affected by exposure to a young circulation? 135 00:07:48,125 --> 00:07:50,125 So young blood factors. 136 00:07:50,125 --> 00:07:51,541 And that was indeed the case. 137 00:07:51,541 --> 00:07:54,541 So we could rejuvenate the old brain. 138 00:07:54,750 --> 00:07:59,125 These mice, had less inflammation, more stem cell activity. 139 00:07:59,541 --> 00:08:03,125 But then what we also showed for the first time, actually, 140 00:08:03,125 --> 00:08:08,875 if this was Saul Villeda, who has now his own lab at UCSF, he showed that he can 141 00:08:08,875 --> 00:08:13,875 simply take blood from young animals and repeatedly infuse it into old. 142 00:08:14,625 --> 00:08:17,375 So he did this every three days for three weeks. 143 00:08:17,375 --> 00:08:21,666 He infused small amounts of just the liquid part 144 00:08:21,666 --> 00:08:27,125 of the blood, actually, not the cells, just the plasma, and could show 145 00:08:27,125 --> 00:08:31,166 that this reproduced pretty much the effects of this para bias model. 146 00:08:31,875 --> 00:08:36,000 And, most importantly it improved function of these mice. 147 00:08:36,000 --> 00:08:40,000 So when mice get old, they get cognitively impaired, 148 00:08:41,041 --> 00:08:44,125 they have difficulty navigating a maze. 149 00:08:44,625 --> 00:08:49,000 And when they're exposed to young plasma, they can navigate it better. 150 00:08:49,000 --> 00:08:51,375 And their function improves. 151 00:08:51,375 --> 00:08:55,375 And so that proved to us that there are factors in the blood 152 00:08:55,875 --> 00:09:00,625 that can not just that, not just reflect the aging 153 00:09:00,625 --> 00:09:04,250 of the organism, that they can actually change the age of an organism. 154 00:09:04,750 --> 00:09:06,000 But this is amazing. 155 00:09:06,000 --> 00:09:07,250 I mean, 156 00:09:07,250 --> 00:09:09,500 from the way that I understand this, like these factors, 157 00:09:09,500 --> 00:09:12,541 they target specific cells in brain, right? 158 00:09:12,541 --> 00:09:15,750 Because at the end, the brain seems that this is the the final, 159 00:09:16,125 --> 00:09:18,791 actually hit on these on these factors. 160 00:09:18,791 --> 00:09:22,750 Do you know what type of cells are you targeting with these factors? 161 00:09:22,875 --> 00:09:23,666 Are we aware 162 00:09:23,666 --> 00:09:26,750 I'm guessing more of them than most of them are stem cells now, right. 163 00:09:27,250 --> 00:09:28,750 Yeah, that's a great question. 164 00:09:28,750 --> 00:09:34,000 So initially you can imagine when when we first reported these results 165 00:09:34,000 --> 00:09:35,875 and also when Tom reported these results, 166 00:09:35,875 --> 00:09:38,125 people thought that's too good to be true. 167 00:09:38,125 --> 00:09:40,666 How could young blood just have these effects? 168 00:09:40,666 --> 00:09:42,500 That's folklore. 169 00:09:42,500 --> 00:09:42,791 Right? 170 00:09:42,791 --> 00:09:46,250 People thought, you know, give me Young Blood and Dracula. 171 00:09:46,250 --> 00:09:48,916 Exactly. Little Dracula stuff. 172 00:09:48,916 --> 00:09:53,291 But then with the advance of single cell, genomics, 173 00:09:53,291 --> 00:09:56,791 where we can take cells basically of every, 174 00:09:57,666 --> 00:09:59,916 organ tissue in the body, 175 00:10:01,000 --> 00:10:02,416 of a mouse, 176 00:10:02,416 --> 00:10:06,500 we can study how every cell is affected by these, factors. 177 00:10:06,500 --> 00:10:09,750 And so because we saw these effects, 178 00:10:10,875 --> 00:10:14,916 I was really interested in, in harnessing the, 179 00:10:15,500 --> 00:10:19,666 the transcriptomic approaches and worked with Steve Craig, 180 00:10:19,666 --> 00:10:23,500 who was one of the pioneers in developing these, technologies. 181 00:10:24,250 --> 00:10:29,125 First genetic diagnosis, first genetic diagnosis made by Stephen Quake on himself. 182 00:10:29,125 --> 00:10:30,000 Yeah, yeah, yeah. 183 00:10:30,000 --> 00:10:30,625 That's it. 184 00:10:30,625 --> 00:10:31,000 Yeah. 185 00:10:31,000 --> 00:10:35,875 So we, we built this atlas, we called Tabula Muris, 186 00:10:35,875 --> 00:10:39,791 where we profiled the cells of all major organs. 187 00:10:40,250 --> 00:10:45,166 And then we used this approach to ask in a very unbiased way, 188 00:10:45,375 --> 00:10:50,666 how is, how are the cells in the parabiosis model affected, 189 00:10:51,000 --> 00:10:55,125 which cells, as you asked Sarantis, which cells are most affected? 190 00:10:56,250 --> 00:10:58,875 Globally, throughout the organism, 191 00:10:58,875 --> 00:11:03,750 we find that, stem cells are a major target, 192 00:11:04,000 --> 00:11:06,916 especially hematopoietic stem cells, 193 00:11:06,916 --> 00:11:09,750 but also, hepatocytes, for example. 194 00:11:09,750 --> 00:11:12,541 And as you know, hepatocytes produce, 195 00:11:12,541 --> 00:11:15,541 the majority of proteins, at least in quantity, 196 00:11:16,375 --> 00:11:21,500 in the blood, like albumin and coagulation factors, component factors and so forth. 197 00:11:22,666 --> 00:11:25,250 But what was really also exciting 198 00:11:25,250 --> 00:11:30,916 is that almost every cell responds to, this hetero chronicity. 199 00:11:31,125 --> 00:11:36,041 So whether you give an old animal young blood or a young animal old blood, 200 00:11:36,625 --> 00:11:41,500 which accelerates aging, most cells show transcriptional changes. 201 00:11:41,875 --> 00:11:44,000 And you can then ask what are key pathways. 202 00:11:44,000 --> 00:11:46,250 So one of the key pathways are mitochondria. 203 00:11:46,250 --> 00:11:49,791 For example, there's a general, reduction 204 00:11:49,791 --> 00:11:52,791 in inflammation, 205 00:11:53,875 --> 00:11:57,416 from young plasma to to old animals. 206 00:11:57,875 --> 00:12:00,625 And I think with respect to the brain, we don't know 207 00:12:00,625 --> 00:12:03,625 exactly how these factors get into the brain, 208 00:12:04,000 --> 00:12:08,125 but there is sort of, reduction in inflammation in the vasculature. 209 00:12:08,291 --> 00:12:11,500 So for that, factors don't even have to go into the brain. 210 00:12:12,291 --> 00:12:16,125 And that may be one prominent way to get benefits. 211 00:12:16,666 --> 00:12:21,250 But we also know now from other studies that Andrew Yang, in my lab, 212 00:12:21,625 --> 00:12:24,375 you know, is at the class on institutes, 213 00:12:24,375 --> 00:12:29,625 these simply labeled, proteins, in the plasma and injected them into mice 214 00:12:29,625 --> 00:12:34,291 and sees that they're actually broadly taken up into brain tissue, 215 00:12:34,791 --> 00:12:39,291 you know, unexpectedly, you know, people always think there's this barrier. 216 00:12:39,291 --> 00:12:41,625 Nothing goes and this is totally not true. 217 00:12:42,791 --> 00:12:45,791 So we're now following up on different cell types. 218 00:12:46,125 --> 00:12:49,166 Neurons take up proteins from the circulation, 219 00:12:49,166 --> 00:12:53,125 but we pursue a very specific type of microglia 220 00:12:53,125 --> 00:12:56,625 that are specialized in taking up proteins from circulation. 221 00:12:56,875 --> 00:13:00,625 So I think there's a whole biology of how this might actually work. 222 00:13:02,000 --> 00:13:03,625 But, yeah. 223 00:13:03,625 --> 00:13:07,750 But I think the key message was really that factors in the blood 224 00:13:08,291 --> 00:13:11,000 can modulate aging. 225 00:13:11,000 --> 00:13:13,291 And so let's define hepatocytes. Right. 226 00:13:13,291 --> 00:13:16,291 These are the functional cells in the liver. 227 00:13:16,875 --> 00:13:20,541 And as you said they they are incredibly 228 00:13:20,541 --> 00:13:23,541 important in producing proteins that show up in the blood. 229 00:13:23,916 --> 00:13:27,375 So I think that that leads us down a path of 230 00:13:27,791 --> 00:13:30,916 proteomics that you've explored since then. 231 00:13:31,250 --> 00:13:35,000 Can you tell us about how you got to where we are today? 232 00:13:35,291 --> 00:13:39,125 And in particular, I'm, I'm pretty excited to talk about the origin organ 233 00:13:39,125 --> 00:13:44,000 aging work that, that your team published in Nature Medicine. 234 00:13:44,750 --> 00:13:49,375 So this is plasma proteomics links brain and immune system. 235 00:13:49,375 --> 00:13:53,500 Your, your, your foundation in immunology, which is, 236 00:13:55,375 --> 00:13:57,166 makes complete sense that 237 00:13:57,166 --> 00:14:01,000 that the immune system is key to this, and healthspan and longevity. 238 00:14:01,000 --> 00:14:04,666 So maybe it's useful to define healthspan as opposed to lifespan 239 00:14:05,250 --> 00:14:09,541 and then talk a little bit about the path that led you to, work 240 00:14:09,541 --> 00:14:13,625 with the UK Biobank data and build the organ aging clocks that we, 241 00:14:14,416 --> 00:14:17,541 we're excited to see leverage today. 242 00:14:18,000 --> 00:14:19,000 Right? Yeah. 243 00:14:19,000 --> 00:14:22,666 So maybe just the definition of health and and lifespan. 244 00:14:22,666 --> 00:14:29,250 So, healthspan is, is described as the, the time of your life 245 00:14:29,375 --> 00:14:32,291 where you're generally healthy 246 00:14:32,291 --> 00:14:35,291 and you don't have any major diseases. 247 00:14:35,500 --> 00:14:39,416 So now I think the field sort of describes 248 00:14:39,416 --> 00:14:43,000 that we live, roughly 80 years of age. 249 00:14:44,250 --> 00:14:47,250 You know, in, in sort of, 250 00:14:47,291 --> 00:14:49,541 economically developed countries 251 00:14:49,541 --> 00:14:52,541 with good health care systems. 252 00:14:53,166 --> 00:14:56,666 And of those 80 years, roughly 50, 253 00:14:56,666 --> 00:15:01,666 55 years where were totally healthy. The average population. 254 00:15:01,666 --> 00:15:01,916 Right? 255 00:15:01,916 --> 00:15:06,375 So there's of course, always people who get sick at different ages of, 256 00:15:07,750 --> 00:15:10,375 or different times of the, of their life. 257 00:15:10,375 --> 00:15:14,291 But, the 55 years would be the health span. 258 00:15:14,625 --> 00:15:18,625 And then we're getting more and more diseases as we get older. 259 00:15:19,166 --> 00:15:22,166 And some people call this sick span. 260 00:15:22,541 --> 00:15:25,375 But the goal, really, of, 261 00:15:25,375 --> 00:15:29,250 efforts in this field of, aging research 262 00:15:29,250 --> 00:15:34,166 is to extend the health span so that you lis healthy until you die. 263 00:15:34,166 --> 00:15:37,166 Ideally. The sled ride to the bottom. 264 00:15:37,250 --> 00:15:39,291 Yeah. Like, just I just want it to. 265 00:15:39,291 --> 00:15:39,625 Yeah. 266 00:15:39,625 --> 00:15:42,541 You fall asleep and or you say, I'm tired. 267 00:15:42,541 --> 00:15:44,250 I think I've seen it. 268 00:15:44,250 --> 00:15:46,625 You know, you're you're. 80 and you're done 90. 269 00:15:46,625 --> 00:15:49,000 Years old and and that would be it. 270 00:15:49,000 --> 00:15:53,000 I mean, that would be a nice, way to, to die 271 00:15:53,000 --> 00:15:56,000 I think, rather than suffering from 272 00:15:56,500 --> 00:16:01,125 multiple degenerative diseases and, I think the worst for me, 273 00:16:01,375 --> 00:16:04,166 but for many other people, would be to lose 274 00:16:04,166 --> 00:16:07,500 your cognitive abilities and don't even know, 275 00:16:08,375 --> 00:16:11,375 you know, who your children are, who your 276 00:16:11,541 --> 00:16:14,625 your loved ones are, things like that. Yeah. 277 00:16:15,041 --> 00:16:16,291 Yeah, those big ones. 278 00:16:16,291 --> 00:16:17,625 Cardiovascular disease, 279 00:16:17,625 --> 00:16:21,166 cancer, Alzheimer's disease or neurodegenerative disease. 280 00:16:21,166 --> 00:16:21,750 Exactly. 281 00:16:21,750 --> 00:16:26,416 Those are the biggest risk for getting those living long enough to to get those. 282 00:16:26,500 --> 00:16:28,416 Yeah. That's true. Yeah, yeah. 283 00:16:28,416 --> 00:16:31,416 But maybe getting back to how did we get here. 284 00:16:32,000 --> 00:16:35,416 So having having shown that, 285 00:16:37,500 --> 00:16:40,125 the blood is really sort of 286 00:16:40,125 --> 00:16:43,375 able to affect the aging 287 00:16:43,375 --> 00:16:47,000 or even the physiological state of cells in the body. 288 00:16:48,875 --> 00:16:51,750 Of course, we wanted to know where do these factors come from? 289 00:16:51,750 --> 00:16:55,250 That was one of the motivations to do the single cell transcriptomics 290 00:16:55,250 --> 00:16:59,625 and ask, well, which cells are potentially producing factors, 291 00:17:00,250 --> 00:17:03,375 but also which cells are targets of these factors. 292 00:17:04,000 --> 00:17:08,000 And so the question was then more generally, 293 00:17:08,000 --> 00:17:11,000 if we look at the changes in the blood, 294 00:17:11,916 --> 00:17:14,791 can we ask where these factors come from? 295 00:17:14,791 --> 00:17:15,875 And that's that. 296 00:17:15,875 --> 00:17:18,875 Tell us something about the state 297 00:17:19,416 --> 00:17:22,875 or the physiology of a given cell or organ. 298 00:17:24,291 --> 00:17:27,666 And that's really a very trivial question if you think about it. 299 00:17:28,500 --> 00:17:31,875 And we pursued this for decades. 300 00:17:32,500 --> 00:17:36,500 You know, in clinical chemistry, if you go to the doctor, 301 00:17:36,916 --> 00:17:39,750 they take a blood sample and they measure 302 00:17:39,750 --> 00:17:42,750 a number of different analytes. 303 00:17:42,875 --> 00:17:44,291 They measure lipids. 304 00:17:44,291 --> 00:17:47,541 They measure also a few proteins, very few, actually, 305 00:17:48,000 --> 00:17:53,250 but one, you know, very prominent, to come back to the liver protein 306 00:17:53,250 --> 00:17:57,666 that is measured in our transaminases, liver transaminases 307 00:17:58,125 --> 00:18:00,625 They tell you if the liver is damaged or not. 308 00:18:00,625 --> 00:18:04,625 So if they go up, that means your liver is not really functioning well. 309 00:18:05,125 --> 00:18:09,041 But we've used this mostly in medicine 310 00:18:09,541 --> 00:18:13,791 to record abnormality, to record pathology. 311 00:18:14,000 --> 00:18:17,000 So people go to the doctor when they feel sick. 312 00:18:17,291 --> 00:18:20,291 And then the doctor says, yeah, I can see your liver. 313 00:18:20,750 --> 00:18:24,625 Your liver values are too high or you know, there's something wrong 314 00:18:24,625 --> 00:18:25,625 with your heart 315 00:18:25,625 --> 00:18:29,500 based again, on on a couple of proteins that are measured from the heart. 316 00:18:31,750 --> 00:18:33,500 We measure, 317 00:18:33,500 --> 00:18:38,125 of course, some lipids, cholesterol, that's a prognostic factor. 318 00:18:38,125 --> 00:18:41,125 But we have very few factors that really look in the future. 319 00:18:41,750 --> 00:18:46,416 So we asked, you know, can we get information from, 320 00:18:46,875 --> 00:18:49,125 the brain in the circulation. 321 00:18:49,125 --> 00:18:54,250 So are there proteins in the blood that might be derived from the brain? 322 00:18:54,916 --> 00:18:57,291 And what was really sort of a revelation 323 00:18:57,291 --> 00:19:00,291 for us if we once we started to use 324 00:19:00,750 --> 00:19:04,500 these multiplex platforms like the one, 325 00:19:05,166 --> 00:19:09,500 you are, developing or have been developing a link, 326 00:19:10,666 --> 00:19:12,625 what we see is that there's 327 00:19:12,625 --> 00:19:15,166 lots of proteins in the blood 328 00:19:15,166 --> 00:19:19,125 that are not made as secreted proteins. 329 00:19:19,500 --> 00:19:24,250 Remember I said we started out by looking at immune factors in the blood 330 00:19:24,250 --> 00:19:29,291 because I thought, well, the immune system sort of communicates across the body. 331 00:19:29,666 --> 00:19:32,666 So we may find some cytokines and chemokines 332 00:19:33,125 --> 00:19:36,375 that tell us about the state of the organism, 333 00:19:36,625 --> 00:19:40,500 but it turns out you can literally measure almost every protein 334 00:19:40,500 --> 00:19:42,750 that the body produces in the blood. 335 00:19:42,750 --> 00:19:47,666 If your assay is sensitive enough and one of the proofs 336 00:19:47,666 --> 00:19:51,500 for that statement is that we can now measure a-beta 337 00:19:52,000 --> 00:19:56,625 or tau with the key pathological hallmarks of Alzheimer's disease in the blood. 338 00:19:57,250 --> 00:20:00,250 If you told anybody 20, 30 years ago, 339 00:20:00,625 --> 00:20:04,375 they would have said, that's not possible, that won't be too noisy. 340 00:20:04,375 --> 00:20:09,000 We will not be able to pick this up because there is no relationship. 341 00:20:10,750 --> 00:20:13,750 But it will also not be, measurable. 342 00:20:14,291 --> 00:20:17,250 And now we, you know, we can measure transcription factors, 343 00:20:17,250 --> 00:20:19,000 we can measure kinases. 344 00:20:19,000 --> 00:20:22,166 Any protein seems to make it 345 00:20:22,166 --> 00:20:25,166 somehow into the blood that, 346 00:20:26,625 --> 00:20:28,625 could then potentially tell us something 347 00:20:28,625 --> 00:20:31,625 about where does that protein come from? 348 00:20:31,750 --> 00:20:36,250 And, if we know that, does it tell us something about the cell, 349 00:20:36,250 --> 00:20:40,125 where it comes from or the organ, the organ that it comes from? 350 00:20:41,250 --> 00:20:43,250 Yeah. So so plasma. 351 00:20:43,250 --> 00:20:44,375 I've thought about this a lot, 352 00:20:44,375 --> 00:20:48,500 and I'm curious because I don't I don't necessarily think of plasma as 353 00:20:48,791 --> 00:20:52,375 or even the organ aging clock which, which we'll talk about in this paper 354 00:20:53,125 --> 00:20:56,000 as, reflection of the tissue state 355 00:20:56,000 --> 00:20:59,000 as much as a reflection of, 356 00:20:59,125 --> 00:21:02,125 you know, secretion, leakage, 357 00:21:02,166 --> 00:21:07,000 turnover of cells, signaling between cells, maybe between organs. 358 00:21:08,375 --> 00:21:11,875 But but maybe because of all of those activities 359 00:21:11,875 --> 00:21:15,166 and the way they change with age, then maybe 360 00:21:15,500 --> 00:21:18,500 plasma does give you 361 00:21:18,625 --> 00:21:20,500 tissue state. Exactly. 362 00:21:20,500 --> 00:21:23,000 Yeah. I think, 363 00:21:23,000 --> 00:21:28,250 you know, intuitively, we think about the proteins in the blood, 364 00:21:28,250 --> 00:21:32,875 maybe like you just stated, right, that they're a reflection of some 365 00:21:33,666 --> 00:21:38,791 either secretion or damage, but it seems not to be the case 366 00:21:39,000 --> 00:21:42,750 because in a young, healthy organism, 367 00:21:43,250 --> 00:21:46,250 you find any type of proteins, 368 00:21:47,125 --> 00:21:50,541 again, cytosolic proteins that were never meant 369 00:21:50,541 --> 00:21:53,625 to be secreted, synaptic proteins. 370 00:21:55,166 --> 00:21:58,166 And I think they're a reflection of cellular, 371 00:21:59,791 --> 00:22:01,250 activity. 372 00:22:01,250 --> 00:22:04,500 And many of these proteins are maybe the majority, certainly 373 00:22:04,500 --> 00:22:07,750 of intracellular proteins are released 374 00:22:07,750 --> 00:22:11,000 through some vasicular mechanism. 375 00:22:11,000 --> 00:22:12,500 Exosomes. 376 00:22:12,500 --> 00:22:16,125 Maybe there's others that we haven't really characterized that described well. 377 00:22:16,500 --> 00:22:19,875 But exosomes is, you know, a key part of this. 378 00:22:19,875 --> 00:22:24,125 And that's part of, a communication system that people start to discover 379 00:22:24,125 --> 00:22:25,750 and describe. 380 00:22:25,750 --> 00:22:29,916 So, you know, immune cells, in particular, macrophage 381 00:22:29,916 --> 00:22:33,875 type cells, they release tons of these exosomes. 382 00:22:33,875 --> 00:22:37,375 And maybe it's a part of communication with, with the rest 383 00:22:37,375 --> 00:22:40,375 of the body cancer cells, of course also. 384 00:22:40,666 --> 00:22:44,000 But it turns out every cell can release exosomes 385 00:22:44,666 --> 00:22:47,666 and people that now describe markers 386 00:22:48,041 --> 00:22:52,500 in these exosomes that tell them the exosome comes from a neuron 387 00:22:53,000 --> 00:22:57,791 or the exosome comes from a macrophage, or it comes from a liver or a tumor. 388 00:22:57,875 --> 00:23:00,250 So or a cancer. 389 00:23:00,250 --> 00:23:03,791 When we when we then apply the plasma proteomics 390 00:23:04,541 --> 00:23:08,500 and measure the concentration of proteins with your tool. 391 00:23:09,625 --> 00:23:10,000 You know, 392 00:23:10,000 --> 00:23:14,000 we, we look at all the proteins that are present in the blood, 393 00:23:14,500 --> 00:23:17,291 whether they're in a vescicle or not. 394 00:23:17,291 --> 00:23:21,916 And so we really have all this information wherever it comes from. 395 00:23:22,375 --> 00:23:24,875 Some proteins are secreted. 396 00:23:24,875 --> 00:23:28,000 They're meant to be secreted as a, you know, 397 00:23:28,500 --> 00:23:31,500 an endocrine, communication. 398 00:23:32,000 --> 00:23:35,750 But and others are shared from the surface. 399 00:23:35,916 --> 00:23:40,041 And, if a protein is shed, it may also have a physiological function. 400 00:23:40,750 --> 00:23:43,250 And yet others come through these, you know, 401 00:23:43,250 --> 00:23:46,250 access exosomal mechanisms and other parts. 402 00:23:46,375 --> 00:23:49,375 And of course, some come from damaged cells. 403 00:23:49,750 --> 00:23:56,291 But I think it's it's really a combination of, anything that happens in the body. 404 00:23:56,291 --> 00:23:58,250 And I really, 405 00:23:58,250 --> 00:24:01,250 state now, in all my talks, the blood, 406 00:24:01,250 --> 00:24:04,375 is an endophenotype of organ physiology. 407 00:24:04,750 --> 00:24:05,416 That's great. 408 00:24:05,416 --> 00:24:07,875 That's amazing. Yeah, that's a good point, Tony. 409 00:24:07,875 --> 00:24:09,375 I mean, stay on this. 410 00:24:09,375 --> 00:24:11,250 And I want to follow up on that question. 411 00:24:11,250 --> 00:24:15,000 Now, having technology that Olink democratized proteomics 412 00:24:15,000 --> 00:24:18,875 and we can identify more, much more proteomics and low abundant proteins, 413 00:24:19,375 --> 00:24:23,500 now we are able to predict right to have biomarkers with predictive power. 414 00:24:23,750 --> 00:24:25,875 You mentioned p tau right now. 415 00:24:25,875 --> 00:24:27,250 It's already for late stages. 416 00:24:27,250 --> 00:24:29,750 I mean, already for your sick. 417 00:24:29,750 --> 00:24:34,000 I think now what you bring in the field and how you, you are the pioneer in 418 00:24:34,000 --> 00:24:38,000 the field is like having predictive biomarkers at the level of organ. 419 00:24:38,416 --> 00:24:42,166 And that's really super powerful for precision medicine. 420 00:24:42,875 --> 00:24:43,416 Yeah. 421 00:24:43,416 --> 00:24:45,166 How how would you see this moving forward? 422 00:24:45,166 --> 00:24:49,000 Yeah, I think I think it has the potential really to, 423 00:24:50,166 --> 00:24:52,250 change the way we, 424 00:24:52,250 --> 00:24:56,041 we do medicine and apply sort of this concept of, 425 00:24:56,791 --> 00:25:01,166 you know, blood pressure and cholesterol, which are predictive markers 426 00:25:01,875 --> 00:25:04,541 to, to many more organs by simply 427 00:25:04,541 --> 00:25:07,875 looking at molecules that are derived from these organs. 428 00:25:08,500 --> 00:25:12,125 And if they change with age and physiology. 429 00:25:12,625 --> 00:25:16,000 And I really see the state of an organ 430 00:25:16,000 --> 00:25:19,166 reflected in the age related changes. 431 00:25:19,791 --> 00:25:22,000 So in other words, 432 00:25:22,000 --> 00:25:26,166 if the function of an organ changes it, 433 00:25:26,666 --> 00:25:30,750 it is because the molecular composition of the organ changes 434 00:25:31,291 --> 00:25:34,250 and we can measure that in the blood. 435 00:25:34,250 --> 00:25:37,250 So in other words, we can measure how, 436 00:25:38,916 --> 00:25:41,041 your liver changes with age, 437 00:25:41,041 --> 00:25:45,500 how your heart changes the age, how your brain changes with age. 438 00:25:45,500 --> 00:25:49,000 And if it shows an accelerated aging, 439 00:25:49,375 --> 00:25:52,750 it is more likely to develop what we call disease. 440 00:25:52,750 --> 00:25:55,750 Now, disease is a human construct, right. 441 00:25:56,250 --> 00:26:00,291 And if Vadim Gladyshev, you know his analysis of, 442 00:26:01,250 --> 00:26:03,125 the same data we analyze, his 443 00:26:03,125 --> 00:26:06,875 conclusion is that all these diseases that we give 444 00:26:06,875 --> 00:26:11,166 specific names are a reflection of accelerated organ age. 445 00:26:12,125 --> 00:26:16,625 A very bold statement, but I think you might be right for many of them. 446 00:26:16,625 --> 00:26:18,500 Of course, if you have to pass, 447 00:26:18,500 --> 00:26:22,250 a pathogen that causes a disease, then that would be exempt. 448 00:26:22,750 --> 00:26:26,625 But these chronic diseases of dysfunction of an organ 449 00:26:27,375 --> 00:26:31,666 like Alzheimer, they're a reflection maybe of, 450 00:26:32,041 --> 00:26:36,125 the aging of that organ and the failure of that function. 451 00:26:36,666 --> 00:26:39,625 We give a name, we call it a disease. 452 00:26:39,625 --> 00:26:40,666 Now, we know, of course, there's 453 00:26:40,666 --> 00:26:44,625 many different diseases in the brain and there or the brain age, 454 00:26:44,625 --> 00:26:47,750 for example, is not a good predictor of Parkinson's disease. 455 00:26:48,416 --> 00:26:51,416 But I think that's just a question of resolution. 456 00:26:51,750 --> 00:26:55,125 As we get more and more proteins 457 00:26:55,125 --> 00:26:58,416 that are specific to different brain regions, for example, 458 00:26:58,791 --> 00:27:02,750 we may pick up a signal from the substantia nigra 459 00:27:02,750 --> 00:27:06,791 or from specific neurons that degenerate in Parkinson's disease. 460 00:27:07,500 --> 00:27:11,500 And that will give us information about Parkinson's disease. 461 00:27:11,500 --> 00:27:15,875 And in fact, we just showed this recently with measures 462 00:27:15,875 --> 00:27:20,000 that, estimate the age of cell types, not just organs, 463 00:27:21,000 --> 00:27:24,000 based on changes with age 464 00:27:24,166 --> 00:27:27,625 and the age of muscle, of muscle cells 465 00:27:28,125 --> 00:27:31,250 is a strong predictor of future risk for ALS, 466 00:27:32,750 --> 00:27:36,166 again, based on 50,000 people in the UK Biobank. 467 00:27:36,166 --> 00:27:39,166 So we have a blood sample from people, 468 00:27:40,500 --> 00:27:43,500 when they enter the study healthy 469 00:27:43,750 --> 00:27:46,500 between age 40 to 60, 470 00:27:46,500 --> 00:27:49,500 we measure their blood proteins 471 00:27:49,625 --> 00:27:51,000 with the Olink platform 472 00:27:51,000 --> 00:27:55,250 and we derive signatures of different cell types. 473 00:27:55,250 --> 00:27:58,750 We can derive about 40 different cell type signatures 474 00:27:59,375 --> 00:28:02,791 based on proteins that are cell type specific. 475 00:28:02,791 --> 00:28:04,791 And we can measure in the blood. 476 00:28:04,791 --> 00:28:09,375 And so we can estimate how old your skeletal myocytes are. 477 00:28:09,375 --> 00:28:11,125 Your muscle cells. 478 00:28:11,125 --> 00:28:14,791 And if they show an accelerated aging phenotype, 479 00:28:14,791 --> 00:28:19,125 you're three times more likely to develop ALS in the next 15 years. 480 00:28:19,416 --> 00:28:23,000 So this brings us to the paper where you leverage the UK 481 00:28:23,000 --> 00:28:26,250 Biobank population, 54,000 individuals, 482 00:28:27,041 --> 00:28:30,750 and developed organ aging estimates 483 00:28:31,125 --> 00:28:36,375 relative to known chronological age and demonstrated 484 00:28:36,375 --> 00:28:41,041 what you've demonstrated before that aging is heterogeneous, meaning it changes. 485 00:28:41,041 --> 00:28:45,625 It's different across different organs, maybe more heterogeneous in some people, 486 00:28:46,000 --> 00:28:49,000 over others, depending on on age, probably as well, 487 00:28:49,250 --> 00:28:52,000 but that some of these nodes are, 488 00:28:52,000 --> 00:28:56,291 are dominating outcomes of longevity. 489 00:28:56,291 --> 00:28:59,291 Healthspan likelihood of death. 490 00:28:59,375 --> 00:29:02,375 How would you how would you explain that? 491 00:29:02,625 --> 00:29:02,875 Yeah. 492 00:29:02,875 --> 00:29:06,750 So what what you're bringing up is a very interesting point 493 00:29:06,750 --> 00:29:10,500 that when we. So again, we built these models, 494 00:29:11,125 --> 00:29:16,375 we measured proteins that are derived from specific organs. 495 00:29:16,375 --> 00:29:20,875 We have about 11 organs where we have good enough signals. 496 00:29:22,250 --> 00:29:25,291 And we can measure proteins that are 497 00:29:26,541 --> 00:29:31,416 produced in the liver, but not in other organs or mostly in the liver. 498 00:29:32,125 --> 00:29:35,541 And then if we add 5 or 10 proteins together 499 00:29:35,541 --> 00:29:41,625 that are all enriched in the liver, and we can measure in the blood and 500 00:29:41,625 --> 00:29:47,291 they change with age, we can build a model that estimates the age of the liver. 501 00:29:47,875 --> 00:29:50,875 We can then apply this model to every person 502 00:29:51,291 --> 00:29:54,291 and ask how? 503 00:29:54,750 --> 00:29:58,750 How well does our model predict your age? 504 00:29:59,750 --> 00:30:03,125 And the deviation from that age, which we call an age 505 00:30:03,125 --> 00:30:06,291 gap, is a reflection of faster 506 00:30:06,291 --> 00:30:09,291 or slower aging of your liver. 507 00:30:09,291 --> 00:30:12,916 So if somebody has an accelerated aging of the liver, 508 00:30:12,916 --> 00:30:17,541 they're more likely to have cirrhosis later on and so forth. 509 00:30:17,541 --> 00:30:20,250 So we get these organ specific diseases. 510 00:30:20,250 --> 00:30:23,666 But then when we look at these 50,000 people and we ask, 511 00:30:25,125 --> 00:30:29,000 how many people have accelerated organ aging 512 00:30:30,125 --> 00:30:33,125 and then how many people have one 513 00:30:33,500 --> 00:30:36,250 accelerated organ age or so, 514 00:30:36,250 --> 00:30:40,750 one organ that is aging, two or 3 or 4? 515 00:30:41,250 --> 00:30:44,250 We find most of the people have only one organ, 516 00:30:44,916 --> 00:30:49,125 showing accelerated aging consistent with this concept 517 00:30:49,125 --> 00:30:52,500 that Mike Snyder came up, which he called ageotypes. 518 00:30:53,375 --> 00:30:58,291 So it seems like you have one organ that seems to get sort of out of control, 519 00:30:58,291 --> 00:31:01,500 and that may be the disease you develop later on. 520 00:31:02,000 --> 00:31:05,250 But then we do find people who have multiple organs age, 521 00:31:06,000 --> 00:31:10,250 organs older, and we can predict mortality. 522 00:31:10,250 --> 00:31:13,875 So we can then ask which organ is the strongest 523 00:31:13,875 --> 00:31:17,000 predictor of, of future death. 524 00:31:17,916 --> 00:31:21,625 And, it turns out that an old brain 525 00:31:21,625 --> 00:31:25,541 and an old immune system are the strongest risk factors. 526 00:31:26,375 --> 00:31:29,541 And this is because we've we collected those samples 527 00:31:30,000 --> 00:31:33,541 almost 20 years ago, and we know who died. 528 00:31:33,541 --> 00:31:35,916 We know who got what diseases we've got. 529 00:31:35,916 --> 00:31:38,916 Those data in those outcome data from the UK Biobank. 530 00:31:39,166 --> 00:31:42,250 And so the strongest predictors of death 531 00:31:43,500 --> 00:31:46,500 are brain and the immune system. 532 00:31:46,500 --> 00:31:48,750 Yeah. In this UK Biobank population. 533 00:31:48,750 --> 00:31:54,375 And what was even more surprising is if you have young brains 534 00:31:54,375 --> 00:31:57,875 and young immune systems, you live actually longer. 535 00:31:57,875 --> 00:32:03,208 It's not a traumatic, increase in longevity, but it's clearly significant 536 00:32:03,416 --> 00:32:06,416 And it's young relative to your logical age. 537 00:32:07,166 --> 00:32:10,458 And then you're looking at this across, you know, a normally distributed 538 00:32:10,458 --> 00:32:14,791 population, presumably, and looking at, you know, the mean of that. 539 00:32:14,916 --> 00:32:15,166 Yeah. 540 00:32:15,166 --> 00:32:18,416 So the way you do this is basically you can imagine like 541 00:32:18,541 --> 00:32:21,791 like anything height is always a good example, right? 542 00:32:21,791 --> 00:32:26,541 If you look at the population, you most people who are similar size. 543 00:32:27,916 --> 00:32:31,333 But then you have some people are very short and some are very tall. 544 00:32:31,791 --> 00:32:37,291 So similarly, if we predict the age of your of brains 545 00:32:37,291 --> 00:32:42,208 and 50,000 people, most people's brains will match their actual age. 546 00:32:42,208 --> 00:32:46,041 So if you're 50, your brain is predicted to be 50 years old. 547 00:32:46,458 --> 00:32:49,541 But then some people have a little bit younger, a little bit older. 548 00:32:49,708 --> 00:32:53,541 It's not normally distributed all the time. 549 00:32:53,541 --> 00:32:57,166 So you often get a skew towards older because 550 00:32:57,333 --> 00:33:02,291 and that makes maybe sense because our organs get older all the time. 551 00:33:03,666 --> 00:33:04,041 But we 552 00:33:04,041 --> 00:33:07,291 really look at the outliers at the very tail. 553 00:33:07,291 --> 00:33:10,916 So, you know, those with the very youngest and those with the very oldest. 554 00:33:11,291 --> 00:33:15,041 And that's usually only 1 to 2% of the population. 555 00:33:15,208 --> 00:33:18,541 You can make this cut arbitrarily anywhere you want. 556 00:33:18,541 --> 00:33:22,333 But we have decided to really look at this top 1 to 2% . 557 00:33:22,916 --> 00:33:25,916 And that's where you find the strongest, 558 00:33:27,166 --> 00:33:30,291 effects or risk predictions, right? 559 00:33:30,541 --> 00:33:33,291 Like we know if you smoke, your risk 560 00:33:33,291 --> 00:33:37,291 to get lung cancer increases by, you know, two fold. 561 00:33:37,666 --> 00:33:41,291 Here we find if you have an older brain 562 00:33:41,291 --> 00:33:44,791 you risk to develop Alzheimer's increases three fold. 563 00:33:45,291 --> 00:33:49,666 And if you compare those with the youngest brains and those with the oldest, 564 00:33:49,666 --> 00:33:53,416 there's a 12 fold difference in risk to develop Alzheimer's. Wow. 565 00:33:53,916 --> 00:33:55,791 And and what about protective. 566 00:33:55,791 --> 00:33:58,083 What what about the other side of the curve? 567 00:33:58,083 --> 00:34:01,041 The those folks that are outliers. 568 00:34:01,041 --> 00:34:02,041 On the younger side. 569 00:34:02,041 --> 00:34:02,916 Yeah. Yeah. 570 00:34:02,916 --> 00:34:06,958 So again what we find is those with young brains, 571 00:34:07,083 --> 00:34:11,916 they live longer and have very reduced risk to develop Alzheimer's disease. 572 00:34:12,791 --> 00:34:16,791 And and in a study that will be published, 573 00:34:17,791 --> 00:34:20,208 next month, I think 574 00:34:20,208 --> 00:34:25,166 in Nature Medicine, we made similar, predictions 575 00:34:25,166 --> 00:34:28,416 at the cellular level as I, as I indicated earlier. 576 00:34:28,916 --> 00:34:31,208 So we can 577 00:34:31,208 --> 00:34:34,041 build models that estimate the age 578 00:34:34,041 --> 00:34:38,916 of a cell in your brain or a cell in your muscle. 579 00:34:39,333 --> 00:34:42,541 So you get more resolution, basically not just a. 580 00:34:42,541 --> 00:34:43,541 Cell at the cell level. 581 00:34:43,541 --> 00:34:45,791 You get resolution of the cell level, actually. 582 00:34:45,791 --> 00:34:48,041 Yeah. Yeah. That's amazing. 583 00:34:48,041 --> 00:34:48,541 That's something. 584 00:34:48,541 --> 00:34:53,333 And so there we find a cell type called astrocytes. 585 00:34:53,333 --> 00:34:57,041 So this is a very important cell type in the brain 586 00:34:57,416 --> 00:35:03,083 that is involved in glucose transport from the blood to neurons. 587 00:35:03,083 --> 00:35:05,916 It's involved in, 588 00:35:05,916 --> 00:35:08,916 in modulating synapses, 589 00:35:09,833 --> 00:35:13,458 and, making them work and actually building them. 590 00:35:14,791 --> 00:35:17,708 And we can estimate the cell of the cell 591 00:35:17,708 --> 00:35:22,541 type in every person based on protein measurements in the blood. 592 00:35:23,416 --> 00:35:26,916 And what we saw is even a more dramatic risk 593 00:35:26,916 --> 00:35:30,041 association with Alzheimer's disease. 594 00:35:30,791 --> 00:35:32,291 Particularly it's interesting. 595 00:35:32,291 --> 00:35:38,291 In individuals who also have the genetic risk factors called APOE4. 596 00:35:38,708 --> 00:35:42,416 So that genetic factor is, a predictor 597 00:35:42,416 --> 00:35:47,791 of future Alzheimer's disease, similar to having an old brain. 598 00:35:47,916 --> 00:35:50,708 So it's about a three fold increase. 599 00:35:50,708 --> 00:35:53,041 And if you have two 600 00:35:53,041 --> 00:35:56,041 genetic copies of the E4, 601 00:35:56,041 --> 00:35:59,916 it's a 10 to 12 fold increased risk for Alzheimer's disease. 602 00:36:00,541 --> 00:36:05,041 But what we find if you're also unlucky and you have very old astrocytes, 603 00:36:05,333 --> 00:36:08,333 that increases by a further three fold. 604 00:36:08,416 --> 00:36:09,166 Wow, wow. 605 00:36:09,166 --> 00:36:15,208 So we find actually in the UK Biobank that almost 40% of the individuals 606 00:36:15,208 --> 00:36:20,333 who have the genetic risk factors and old astrocytes, 607 00:36:20,333 --> 00:36:23,416 almost 40% will have Alzheimer's 608 00:36:23,416 --> 00:36:26,416 disease over the next 15 or 17 years. 609 00:36:26,708 --> 00:36:28,666 Which of them do you think is more predictive? 610 00:36:28,666 --> 00:36:32,291 The genome genetics, proteomics, combinationcombination 611 00:36:32,416 --> 00:36:34,166 It goes I would like combination. 612 00:36:34,166 --> 00:36:35,291 I would like to highlight here 613 00:36:35,291 --> 00:36:38,291 the value of having proteomics data, because so far we're really focusing 614 00:36:38,291 --> 00:36:39,416 on genetics data. Right. 615 00:36:40,416 --> 00:36:42,541 How would you see this moving forward. 616 00:36:42,541 --> 00:36:45,833 And like a follow up question is to the clinical relevance. 617 00:36:45,833 --> 00:36:48,833 Would you see the clocks to be like, 618 00:36:49,333 --> 00:36:52,166 a nice tool to have in patient stratification 619 00:36:52,166 --> 00:36:55,541 for clinical trials, for clinical diagnostic in the future? 620 00:36:55,541 --> 00:36:56,958 Yes, absolutely. 621 00:36:56,958 --> 00:36:58,291 Is moving forward the field. 622 00:36:58,291 --> 00:37:02,791 Before we change from the astrocyte story and we can we can switch over to that. 623 00:37:02,791 --> 00:37:06,708 I just want to talk about the oligodendrocytes that you the pathways 624 00:37:06,708 --> 00:37:10,708 that you saw that were important in brain aging, 625 00:37:10,708 --> 00:37:13,708 which may be to some extent, a function of which proteins 626 00:37:14,166 --> 00:37:17,166 you were able to measure because it is a targeted assay. 627 00:37:17,291 --> 00:37:17,541 Yeah. 628 00:37:17,541 --> 00:37:21,333 But this these were proteins that weren't neurodegenerative proteins 629 00:37:21,333 --> 00:37:25,041 that I typically think of, you know, that show up and say, 630 00:37:25,208 --> 00:37:28,416 Parkinson's disease, for example, when you've got these neurons dying. 631 00:37:28,791 --> 00:37:30,208 They were myelin structure. 632 00:37:30,208 --> 00:37:32,958 They were the sheath on these. 633 00:37:32,958 --> 00:37:34,833 Can you just comment a bit about that. 634 00:37:34,833 --> 00:37:38,833 And then I absolutely want to talk about the genetics, the proteomics, you know, 635 00:37:38,833 --> 00:37:42,208 bringing these modalities together and that importance. 636 00:37:42,208 --> 00:37:43,666 Yeah it's super interesting. 637 00:37:43,666 --> 00:37:45,833 So when we make these models, 638 00:37:46,958 --> 00:37:49,041 the algorithm, 639 00:37:49,041 --> 00:37:52,583 first we make the assignment out of the thousands of proteins 640 00:37:52,583 --> 00:37:55,291 that are on your platform, we make the assignment. 641 00:37:55,291 --> 00:38:00,166 Where do they potentially come from based on gene expression data. 642 00:38:00,291 --> 00:38:04,416 So if a gene is only expressed in an in the brain, 643 00:38:04,708 --> 00:38:08,916 then we say, okay, this is a brain protein in the blood. 644 00:38:10,416 --> 00:38:14,791 And when we when we then ask it, 645 00:38:15,458 --> 00:38:19,291 when we don't develop a model that estimates the age of the brain, 646 00:38:19,666 --> 00:38:24,166 we know, of course, which proteins are making that signature, 647 00:38:24,166 --> 00:38:29,958 and we can ask, what is the biological, role of these proteins? 648 00:38:29,958 --> 00:38:31,708 What what is known about them? 649 00:38:31,708 --> 00:38:34,708 And as, as you just mentioned, 650 00:38:35,041 --> 00:38:37,791 the proteins that make up the brain age 651 00:38:37,791 --> 00:38:41,541 model are largely involved in synaptic, 652 00:38:42,541 --> 00:38:47,458 a synaptic structure that wraps around synapses and is, 653 00:38:47,916 --> 00:38:50,666 produced in part by oligodendrocytes, 654 00:38:50,666 --> 00:38:54,166 maybe also some other, cell types, but, 655 00:38:55,333 --> 00:38:56,291 it, it 656 00:38:56,291 --> 00:39:01,916 it reinforces this notion that maybe synapses are the first part 657 00:39:01,916 --> 00:39:06,291 that that declines as we get older and that get this function dysfunctional. 658 00:39:06,291 --> 00:39:06,833 So was point. 659 00:39:06,833 --> 00:39:09,041 And that's what we miss communication. 660 00:39:09,041 --> 00:39:12,208 And that's what we may be picking up long before 661 00:39:12,208 --> 00:39:15,291 you get amyloid plaques and tangles in the brain. 662 00:39:15,583 --> 00:39:16,291 Amazing. 663 00:39:16,291 --> 00:39:20,541 This is also consistent with parallel studies that we did 664 00:39:20,541 --> 00:39:26,666 in cerebrospinal fluid, where we looked at which proteins are strongest 665 00:39:26,666 --> 00:39:31,916 associated with cognitive function independent of amyloid pathology and tau. 666 00:39:32,291 --> 00:39:35,291 And there two we found plasma. 667 00:39:35,541 --> 00:39:39,666 We found synaptic proteins are the strongest predictors. 668 00:39:39,666 --> 00:39:42,666 And specifically this NP two. 669 00:39:42,958 --> 00:39:46,666 And why w h h three 670 00:39:46,916 --> 00:39:49,916 that we found are very strong predictors 671 00:39:50,208 --> 00:39:54,916 15 years into the future with a hazard ratio of 15 or so. 672 00:39:54,916 --> 00:40:00,083 So a 15 fold increased risk if you add, very high levels of that protein. 673 00:40:00,333 --> 00:40:01,916 Amazing. Yeah. 674 00:40:01,916 --> 00:40:05,916 So to come back for what that all mean and, 675 00:40:05,916 --> 00:40:09,041 and how do we integrate this with current knowledge? 676 00:40:09,583 --> 00:40:12,208 Genetics is incredibly powerful. Right. 677 00:40:12,208 --> 00:40:14,541 But what we've also learned is that, 678 00:40:15,666 --> 00:40:17,916 there's very relatively few genes 679 00:40:17,916 --> 00:40:21,666 that are really predicting your risk to get a specific disease. 680 00:40:22,666 --> 00:40:25,416 April E4 is a strong exception. 681 00:40:25,416 --> 00:40:28,916 That link to Alzheimer's disease is almost 682 00:40:28,916 --> 00:40:31,916 unprecedented in any other disease. 683 00:40:32,958 --> 00:40:35,333 So we need something on top of genes. 684 00:40:35,333 --> 00:40:40,666 And that's where proteins are, because proteins capture where we live. 685 00:40:40,791 --> 00:40:43,791 They integrate many different genes. 686 00:40:44,291 --> 00:40:47,583 And there are that's why I call this the end of phenotype. 687 00:40:47,583 --> 00:40:51,791 They capture, our life experiences and, 688 00:40:52,416 --> 00:40:55,291 our daily, challenges. 689 00:40:55,291 --> 00:40:59,083 Now, some of these are very noisy, but the beauty of it is, 690 00:40:59,083 --> 00:41:03,208 if you measure a lot of proteins, you can pick the stable ones 691 00:41:03,208 --> 00:41:06,791 that provide information on disease risk. 692 00:41:06,791 --> 00:41:08,541 15 years later. 693 00:41:08,541 --> 00:41:12,291 And that's really what we can call the proof in the pudding. 694 00:41:12,291 --> 00:41:12,583 Right. 695 00:41:12,583 --> 00:41:18,208 Because we can say what your risk is in an unknown sample from a person. 696 00:41:18,208 --> 00:41:19,583 We've never seen before. 697 00:41:19,583 --> 00:41:23,416 We know nothing about this person except the concentration. 698 00:41:23,416 --> 00:41:26,166 No lifestyle factors, no clinical. 699 00:41:26,166 --> 00:41:28,041 We don't need. Any other knowledge. 700 00:41:28,041 --> 00:41:31,791 And that answers your question, Sarantis. 701 00:41:31,791 --> 00:41:32,083 Right. 702 00:41:32,083 --> 00:41:36,291 That means you can potentially translate that to the clinic 703 00:41:36,291 --> 00:41:41,041 and provide information to a patient about their future risk. 704 00:41:41,666 --> 00:41:45,791 And, you know, while that might not be helpful for some diseases 705 00:41:45,791 --> 00:41:49,541 at this point, I think you could change lifestyle. 706 00:41:49,541 --> 00:41:51,291 And for some there are drugs. 707 00:41:51,291 --> 00:41:57,416 And we you know, we started a company to really, take advantage of this 708 00:41:57,416 --> 00:42:00,416 and hopefully bring it to the consumer in the coming years. 709 00:42:00,916 --> 00:42:06,416 Vero Bio Sciences, that is trying to to harness this information 710 00:42:06,833 --> 00:42:09,916 where, the consumer can then, 711 00:42:10,833 --> 00:42:13,208 you know, have their blood measured. 712 00:42:13,208 --> 00:42:17,416 We give an indication of which organs show accelerated aging. 713 00:42:18,291 --> 00:42:21,291 And then with advice from a clinician, 714 00:42:21,541 --> 00:42:26,541 the clinician helps interpret the results and what you can do about it. 715 00:42:26,666 --> 00:42:31,041 Because of course, you don't want to just know that you have an old heart. 716 00:42:32,083 --> 00:42:34,291 You would like to know, what should I do about it? 717 00:42:34,291 --> 00:42:36,583 You have to regenerate. Yeah. 718 00:42:36,583 --> 00:42:39,791 And then the beauty is that you can follow up 719 00:42:39,791 --> 00:42:44,291 three months later and say, okay, did my intervention do something? 720 00:42:44,291 --> 00:42:44,916 Exactly. 721 00:42:44,916 --> 00:42:48,041 And this is really what we're working on now to prove that, 722 00:42:49,583 --> 00:42:53,291 these, these measures are functionally tied 723 00:42:53,291 --> 00:42:57,958 to, to, to to outcomes and, and specific organ. 724 00:42:58,583 --> 00:43:03,166 And so what would be needed to leverage organ aging clocks 725 00:43:03,541 --> 00:43:06,541 as a surrogate endpoint and a surrogate endpoint would be, 726 00:43:06,791 --> 00:43:12,291 you know, shifting your biological age of an organ or overall biological age 727 00:43:12,583 --> 00:43:16,291 would be an endpoint in a clinical trial to demonstrate effectiveness 728 00:43:16,291 --> 00:43:19,458 of an intervention, either a therapy or, you know, certainly we do 729 00:43:19,458 --> 00:43:25,291 clinical trials on some, some companies, support clinical trials on their vitamins. 730 00:43:25,291 --> 00:43:26,416 Exactly. 731 00:43:26,416 --> 00:43:27,541 Things like this. 732 00:43:27,541 --> 00:43:30,916 Yeah. So what what will be required is to, 733 00:43:32,208 --> 00:43:32,541 you know, 734 00:43:32,541 --> 00:43:36,291 keep validating these tools across the community. 735 00:43:36,291 --> 00:43:40,916 And it seems a lot of studies, you know, have very similar findings, 736 00:43:41,458 --> 00:43:45,416 and I've shown the value, but there's, of course, more work to be done. 737 00:43:46,791 --> 00:43:49,958 I think the, the platforms have to be stable, 738 00:43:49,958 --> 00:43:53,333 what with whatever, you know, from a technical perspective. 739 00:43:53,333 --> 00:43:57,291 And I think they they show that they can get 740 00:43:57,291 --> 00:44:00,291 the same results with repeated testing. 741 00:44:00,833 --> 00:44:03,416 And then what we also need to show is that 742 00:44:03,416 --> 00:44:06,416 if you measure these, 743 00:44:07,041 --> 00:44:10,041 if you make these estimates in a, in a person, 744 00:44:10,291 --> 00:44:14,916 you know, once a month, over a year, that they're relatively stable, right? 745 00:44:14,916 --> 00:44:18,416 That it doesn't change based on the breakfast you had. 746 00:44:18,916 --> 00:44:21,916 So again, we need to figure out what are the, 747 00:44:23,291 --> 00:44:27,833 what are the signatures that, that really have this predictive value on are stable. 748 00:44:27,833 --> 00:44:29,041 And and we're getting there. 749 00:44:29,041 --> 00:44:32,958 We have such a data set and it seems they're remarkably stable. 750 00:44:33,666 --> 00:44:36,291 But then they need to be tested in 751 00:44:36,291 --> 00:44:39,458 the, in, in the traditional, 752 00:44:40,708 --> 00:44:43,708 placebo controlled clinical studies, 753 00:44:44,666 --> 00:44:47,666 where you show that they track function 754 00:44:48,666 --> 00:44:51,541 and then you can start using them as a, 755 00:44:51,541 --> 00:44:55,541 as a secondary endpoint, maybe, and then as a primary end point. 756 00:44:55,541 --> 00:44:58,583 So it's a, a stepwise validation 757 00:44:59,166 --> 00:45:03,416 in the scientific community, then into, clinical trials. 758 00:45:04,458 --> 00:45:06,166 And they're also stepwise. 759 00:45:06,166 --> 00:45:08,041 But I think we can get there. 760 00:45:08,041 --> 00:45:09,208 In the biomarker world. 761 00:45:09,208 --> 00:45:13,291 Then not only will you envision a model based on proteomic signatures, 762 00:45:13,666 --> 00:45:15,458 because now I have the impression that we are moving 763 00:45:15,458 --> 00:45:18,958 from the epigenetic laws that initially Steve Horvath has launched 764 00:45:18,958 --> 00:45:22,208 with, like the methylation profile to something more functional 765 00:45:22,208 --> 00:45:26,083 to the to the biology, the proteomics, then the future will be bringing 766 00:45:26,166 --> 00:45:30,041 these proteomic signatures in the in a model that you can predict. 767 00:45:30,041 --> 00:45:32,291 This is how you see this moving forward. 768 00:45:32,291 --> 00:45:36,791 And we definitely want to talk about Teal Rise, another company that, 769 00:45:37,458 --> 00:45:41,666 that is leading the way and their expertise for building such models. 770 00:45:41,666 --> 00:45:41,916 Yeah. 771 00:45:41,916 --> 00:45:45,833 So the idea is that that you would be able to make models 772 00:45:45,833 --> 00:45:49,916 that, that predict function and that, track 773 00:45:49,916 --> 00:45:55,541 with efficacy of drugs and allow you then to use these models as surrogates. 774 00:45:55,541 --> 00:45:59,333 And this is what, another company that is really trying 775 00:45:59,333 --> 00:46:03,541 to provide these services that we started Teal, 776 00:46:04,333 --> 00:46:07,166 Teal Rise, is 777 00:46:07,166 --> 00:46:10,666 is partnering with, with Olink to provide that service. 778 00:46:12,166 --> 00:46:13,541 Not everyone has 779 00:46:13,541 --> 00:46:17,416 maybe the tools to, to develop these models. 780 00:46:17,833 --> 00:46:22,041 And teal has assembled a large, database 781 00:46:22,041 --> 00:46:25,041 that it uses to train the models 782 00:46:25,083 --> 00:46:28,708 and can then provide all these, services, to, 783 00:46:30,416 --> 00:46:31,708 to really across 784 00:46:31,708 --> 00:46:34,708 any type of disease and prediction, 785 00:46:34,708 --> 00:46:38,791 that, that you can imagine and see how, how valid they are. 786 00:46:39,083 --> 00:46:42,416 And I see this is you know, hastening us to that, 787 00:46:43,333 --> 00:46:48,541 that state Barrett, by which we do see these as surrogate endpoints. 788 00:46:48,541 --> 00:46:53,333 We do see these as ways to confirm and corroborate interventions 789 00:46:53,333 --> 00:46:56,333 or even discount interventions. 790 00:46:56,666 --> 00:46:59,916 You talked in the paper, Hamilton, you know, first author, 791 00:47:00,416 --> 00:47:03,958 and your team talked a bit about ibuprofen, 792 00:47:03,958 --> 00:47:06,958 about vitamins, you know, looking at associations. 793 00:47:06,958 --> 00:47:10,833 I just want to clarify, we talked about causality earlier. 794 00:47:11,208 --> 00:47:14,458 Those associations are are helpful. 795 00:47:14,458 --> 00:47:17,458 They're one step toward understanding, 796 00:47:18,666 --> 00:47:19,291 causality. 797 00:47:19,291 --> 00:47:21,833 But causality is about finding 798 00:47:21,833 --> 00:47:24,166 what are the levers, what are the pathways? 799 00:47:24,166 --> 00:47:25,708 What are the cells. 800 00:47:25,708 --> 00:47:30,791 What are the, triggers that are actually moving us toward disease? 801 00:47:30,791 --> 00:47:37,291 And those are the magic that, that pharma can leverage if they're, 802 00:47:37,833 --> 00:47:41,333 if they meet the right criteria to build therapies 803 00:47:41,333 --> 00:47:45,583 to nudge someone out of a disease state, back to a healthy state. 804 00:47:45,583 --> 00:47:47,666 Right. So the vision here is precision medicine. 805 00:47:47,666 --> 00:47:50,791 Being able to treat the individual based upon 806 00:47:50,791 --> 00:47:54,833 what their needs are, to bring them safely back 807 00:47:54,833 --> 00:47:58,666 into a healthy state and extend their health span. 808 00:47:58,666 --> 00:48:00,083 Absolutely. Yeah. 809 00:48:00,083 --> 00:48:04,041 So as you said, we have been able in the UK 810 00:48:04,041 --> 00:48:07,041 Biobank to look at, 811 00:48:07,416 --> 00:48:10,416 people's lifestyles, people's, 812 00:48:10,666 --> 00:48:13,416 use of medications. 813 00:48:13,416 --> 00:48:17,791 But this is all retrospective and some of it is self-reported. 814 00:48:17,791 --> 00:48:19,416 So it's noisy. 815 00:48:19,416 --> 00:48:20,791 It looks very promising. 816 00:48:20,791 --> 00:48:21,958 It looks interesting. 817 00:48:23,333 --> 00:48:24,291 Like I often 818 00:48:24,291 --> 00:48:27,666 mentioned glucosamine this in our first study 819 00:48:27,666 --> 00:48:30,458 with a different platform on 5000 individuals. 820 00:48:30,458 --> 00:48:35,791 We had a signal on glucosamine use associated with younger organs. 821 00:48:36,291 --> 00:48:41,208 We felt this was not good enough to report yet 822 00:48:41,666 --> 00:48:45,666 because this was a relatively small number of individuals, 5000. 823 00:48:46,041 --> 00:48:50,166 So the exact same signal in 50,000 people with the Olink platform. 824 00:48:51,541 --> 00:48:53,708 And we reported it then, 825 00:48:53,708 --> 00:48:56,041 but it's still it's retrospective. 826 00:48:56,041 --> 00:49:00,666 People report I use glucosamine that is associated with some younger organs. 827 00:49:02,041 --> 00:49:03,916 So now the next step, as you 828 00:49:03,916 --> 00:49:09,041 said, is to do a clinical trial where you have people 829 00:49:09,041 --> 00:49:13,166 placebo controlled, they get the supplement or not. 830 00:49:13,541 --> 00:49:16,041 And you do these measurements of the clocks 831 00:49:16,041 --> 00:49:17,166 and you say, okay, does it. 832 00:49:17,166 --> 00:49:22,916 Actually track with the functional benefits, does it track with drug use, 833 00:49:24,041 --> 00:49:24,791 and so forth. 834 00:49:24,791 --> 00:49:26,291 And we're starting to do that. 835 00:49:26,291 --> 00:49:29,291 We have a collaboration with Heike A. Bischoff-Ferrari 836 00:49:29,291 --> 00:49:32,291 who ran the DO-HEALTH trial. 837 00:49:32,666 --> 00:49:35,291 This was a 3000 individual trial 838 00:49:35,291 --> 00:49:38,291 with vitamin D, 839 00:49:39,166 --> 00:49:42,166 exercise and omega-3 fatty acids. 840 00:49:42,208 --> 00:49:46,041 Was a four arm trial to see what control they had, some, 841 00:49:46,791 --> 00:49:49,791 functional outcomes. And, 842 00:49:50,416 --> 00:49:52,333 Steve Horvath showed that 843 00:49:52,333 --> 00:49:55,333 epigenetically some people got younger. 844 00:49:55,416 --> 00:49:57,958 And so we're now, running, 845 00:49:57,958 --> 00:50:01,791 with, Vero and Teal, we are running the proteomics 846 00:50:01,791 --> 00:50:06,166 to, to say, okay, is there a signal in the protein clocks? 847 00:50:06,166 --> 00:50:10,541 And indeed we, we see signals suggesting that, you know, 848 00:50:10,541 --> 00:50:13,916 you can pick up, functional outcomes 849 00:50:14,541 --> 00:50:17,541 and, you get also this, 850 00:50:18,166 --> 00:50:22,291 agreement between epigenetic and proteomic signatures. 851 00:50:23,041 --> 00:50:23,916 Amazing. 852 00:50:23,916 --> 00:50:29,666 So, so can you tell our audience where might they track or 853 00:50:29,666 --> 00:50:33,958 keep an eye on some of the interventions that you might be involved in 854 00:50:33,958 --> 00:50:37,541 if there are clinical trial calls for participants? 855 00:50:37,916 --> 00:50:41,291 I know Mike Snyder has has this, often, 856 00:50:41,791 --> 00:50:46,833 these QR codes to invite participants that are of a particular demographic. 857 00:50:47,166 --> 00:50:48,916 Are there opportunities 858 00:50:48,916 --> 00:50:52,666 for people to get involved and support the work that your team is doing? 859 00:50:52,833 --> 00:50:53,541 Yeah, certainly. 860 00:50:53,541 --> 00:50:56,041 They can, sign up on Vero 861 00:50:57,041 --> 00:50:59,791 for, for updates. 862 00:50:59,791 --> 00:51:03,333 And, we're still going to launch an IRB 863 00:51:03,333 --> 00:51:06,333 approved, beta trial, 864 00:51:06,833 --> 00:51:09,833 so people can sign up for that. 865 00:51:10,291 --> 00:51:12,041 To analyze data sets. 866 00:51:12,041 --> 00:51:15,708 They can, of course, reach out to you and, work, 867 00:51:16,166 --> 00:51:19,208 with Olink and, and, Teal Rise 868 00:51:20,791 --> 00:51:23,666 and otherwise. 869 00:51:23,666 --> 00:51:24,208 Yeah. 870 00:51:24,208 --> 00:51:27,791 Stay tuned with the publications that we're working on. 871 00:51:28,541 --> 00:51:29,791 Exciting, exciting. 872 00:51:29,791 --> 00:51:32,166 Well, so happy to have you. 873 00:51:32,166 --> 00:51:32,958 Thank you so much. 874 00:51:32,958 --> 00:51:38,291 We had a little bit of a hiatus in our, in our, our, podcast publishing. 875 00:51:38,458 --> 00:51:40,666 We had some months off. 876 00:51:40,666 --> 00:51:43,458 This is an exciting episode to be our first one back. 877 00:51:43,458 --> 00:51:45,708 So thank you so much for agreeing. 878 00:51:45,708 --> 00:51:46,791 Much on it. 879 00:51:46,791 --> 00:51:47,791 Yeah. Thank you. 880 00:51:47,791 --> 00:51:50,791 If we can stay all day asking you. 881 00:51:50,916 --> 00:51:51,583 Exactly. 882 00:51:51,583 --> 00:51:52,541 Exactly how, you know. 883 00:51:52,541 --> 00:51:55,541 I, I, we have to stop at the point because they have so many questions 884 00:51:55,541 --> 00:51:56,291 popping up all the time. 885 00:51:56,291 --> 00:51:59,041 You know, it's definitely we will catch up again soon. 886 00:51:59,041 --> 00:52:00,041 Definitely. 887 00:52:00,041 --> 00:52:02,458 Because there’s so many things we need to know from you. 888 00:52:02,458 --> 00:52:04,291 Thanks so much. Thank you so much. 889 00:52:06,166 --> 00:52:07,541 Well, that wraps up 890 00:52:07,541 --> 00:52:10,541 this episode of Proteomics in Proximity. 891 00:52:10,541 --> 00:52:14,666 Huge thanks to our guests and authors of such impactful publications. 892 00:52:15,041 --> 00:52:17,666 I also want to thank you for tuning in. 893 00:52:17,666 --> 00:52:19,916 Really appreciate you being here. 894 00:52:19,916 --> 00:52:22,416 If you enjoy the content of this episode, 895 00:52:22,416 --> 00:52:25,541 please think about sharing it with friends or colleagues 896 00:52:25,541 --> 00:52:27,833 you think might be interested in the content. 897 00:52:27,833 --> 00:52:31,833 In addition, if you'd be willing to head over to Apple or Spotify 898 00:52:31,833 --> 00:52:35,333 or wherever you digest your podcasts and give us a rating and review, 899 00:52:35,333 --> 00:52:37,416 this will help others find the podcast 900 00:52:37,416 --> 00:52:40,708 when they're searching for proteomics or precision medicine podcasts. 901 00:52:40,916 --> 00:52:44,541 And mostly I want to say we would love to hear from you. 902 00:52:44,708 --> 00:52:46,708 So we have a dedicated email address. 903 00:52:46,708 --> 00:52:49,708 Pip@olink.com. Please reach out. 904 00:52:49,708 --> 00:52:53,666 Let us know what you're interested in hearing, about what you care about, 905 00:52:53,666 --> 00:52:57,666 and any feedback on the episodes that we have already done so far. 906 00:52:57,958 --> 00:53:01,416 This is all about you, and so we're really keen 907 00:53:01,416 --> 00:53:04,416 to make sure that we're meeting what you'd like to hear about. 908 00:53:04,666 --> 00:53:06,791 Thank you so much, and we'll see you soon. 909 00:53:07,708 --> 00:53:13,666 In.