Human Skeletal Muscle Epigenetic Clock Explained with Dr. Sarah Voisin

Maintaining muscle mass is crucial for healthy aging, as it is closely linked to overall physical function and quality of life. As we age, our bodies naturally experience a decline in muscle mass and strength, known as sarcopenia. This loss of muscle mass can lead to a range of negative health outcomes, including decreased mobility, increased risk of falls and fractures, and decreased metabolic rate. Additionally, loss of muscle mass can contribute to chronic conditions such as obesity, diabetes, and cardiovascular disease.

By developing an epigenetic clock for skeletal muscle, Dr. Voisin and her colleagues have identified specific methylation patterns that are associated with muscle aging. This research not only sheds light on the biological mechanisms behind sarcopenia, but may also provide new targets for interventions aimed at preserving muscle mass and function in older adults.

In this week’s Everything Epigenetics podcast, Dr. Sarah Voisin and I focus on her 2020 paper which describes her development of a human muscle-specific epigenetic clock that predicts age with better accuracy than the pan-tissue clock. Yes - you heard that right… better accuracy than Dr. Steve Horvath’s 2013 clock.

Dr. Voisin and I also chat about the importance of skeletal muscle and how this relates to epigenetics and aging, the power of machine learning, and how identifying which methylation positions change as we age may give us insight into the underlying reason as to WHY we age rather than just HOW. She is now focused on creating an atas of epigenetics for all human tissues at the cellular level by combining 75,000 DNA methylation profiles across 18 tissues.

In this episode of Everything Epigenetics, you’ll learn about: 

• How Dr. Voisin got her start in statistics and biology 
• The importance of skeletal muscle tissue and how this relates to Epigenetics and Aging
• When to start exercising and moving your body
• The importance of weight lifting 
• How often we should be moving our body 
• Why Dr. Voisin decided to develop this type of Epigenetic Clock
• The limitations of the Horvath 2013 Clock as it relates to skeletal muscle 
• The complications of data mining
• The importance of collaboration and data sharing  
• How Dr. Voisin created her muscle-specific Epigenetic Clock 
• The power of machine learning
• How the muscle clock outperforms Dr. Steve Horvath’s 2013 pan-tissue Clock
• Dr. Voisin’s epigenetic wide association studies (EWAS) she performed
• Differentiated methylated positions (DMPs) in this study
• Differentiated methylation regions (DMRs) in this study
• The utility/application of the skeletal muscle Epigenetic Clock
• Dr. Voisin’s next big project (I’m so excited about her next project!!!) 
• MEAT (muscle epigenetic age test)

Where to find Dr. Voisin:
Email: sarah.voisin@vu.edu.au
Twitter: https://www.vu.edu.au/research/sarah-voisin
GitHub account:https://github.com/sarah-voisin

Transcript

hannah_went:
today's episode we talk with dr sarah voicean welcome to the everything epigenetics podcast dr roys and i'm extremely excited to have you

sarah_voisin:
thank you very much for inviting me i'm really really pleased to be here

hannah_went:
yeah i'm very interested in your journey in particular i know you're using you know statistics and bioingphramatics and i really want to understand what piqued your interest in that i don't think i've had any one from atesisor bio statitians on the show quite yet so you're really using these skills in the epigenetic space can you tell me a little bit about your story and how you got where you are today

sarah_voisin:
yeah absolutely i think that my interest in statistics and mindformatics stems from my love for math and logic that i think i developed from an early age by playing a lot of video games growing up and in middle and high school i discovered the world of genetics and that fascinated me enormously and i understood that i could study biology to try to understand human beings because they fascinate me and i think that moving towards bioinfarmatics and statistics was just the perfect marriage of those two passions of mine

hannah_went:
definitely no super interesting background and because i can i can really resonate with the biology space or the science world but i think then we really need more more of the interpretations of what that actually means on more of a map medical level so your skill set is definitely valued especially in our epi genetic space here as we're using predictors of all sorts of different things as we're applying them to the den methalatian data so what i why i wanted to have you on this this podcast is you you have an amazing paper that was published you know a couple of years back titled an epi genetic clock for human skeletal muscle and i after reading your paper i encourage anyone that is listening to give that that paper read um so so i want to first start discussing the why behind this paper can you give some back and regarding the importance of skeletal muscle tissue and aging why do we care in the first place

sarah_voisin:
so it's really important to understand that muscle tissue is the tissue that allows us to perform daily tasks and activities that give us independence and life enjoyment you know you re you're able to walk to live to run to hike to dance and to do all of these things because you have muscles and as we age unfortunately we lose considerable muscle mass and muscle strength and therefore we lose a great deal quality of life because muscle deteriorate as we age so it's very important to nurture muscle during aging

hannah_went:
yeah absolutely

sarah_voisin:
oh

hannah_went:
and would you encourage them to you know our listeners i know a big question that i get is when should i start weight lifting or even moving my body a little bit more it doesn't have to be heavy weight lifting right you could be moving boxes or different things of the sort when should i start that or what's what's really the age how how should i know they really want to work out plan from from start to finish what would be your recommendation based off of that question

sarah_voisin:
so i think that the recommendation i would give is you start now like whether

hannah_went:
yeah

sarah_voisin:
you are young or old and as early as you can it's a bit like playing an instrument the earlier you start the better it is and you need to keep doing it throughout your life i see the nurturing of your muscles through exercise as a hygiene sort of routine just like you brush your teeth i think you should exercise and move your body in this same way

hannah_went:
yeah

sarah_voisin:
and weight lifting in particular is very important like we underestimate weight the importance of weight lifting for men and women alike at all ages

hannah_went:
yeah that's

sarah_voisin:
i don't know

hannah_went:
that's

sarah_voisin:
yeah

hannah_went:
interesting because you know i've i've become an avid weight lifter you know in the past couple of years i've always been super athletic i played three different sports in high school played a little bit of of club socker in college as well and have really always been active i would say but more cardio right you think cardi is going to burn the most calories and i think that's a flaw in our thinking we've been raised in what we know about exercise today so i've since shifted to this this weight lifting model and it's great it's almost like a weight lifting high like when people say they get their runners high right and this morning i definitely didn't want to go to the gym and work out but since i've been in this habit it's almost like i could go on auto pilot and put my body through it and i'm so glad i remembered i was speaking with you today and i was like i can't not lift weights on the day that that i'm interviewing dr voisin so

sarah_voisin:
oh

hannah_went:
how how often should should people lift weights i know there's a really big argument in the space as well that we should have these rest days and i don't think humans you know are meant to rest all the time i think even in our rest days we need to walk or do some stretching and be be active but what do you think in terms of how many times per week or how long

sarah_voisin:
so i am in no means an expert on this so take my advice with a grand of soul because i am not a registered you know personal trainer or anything like this this is just second hand knowledge that i gathered through my reading but i think that the recommendation currently is weight lifting twice a week or something similar of course you can do more if you like it's based on personal preference as well but definitely at least twice a week and as you said only cardio is going to give you only limited i mean it's going to give you great benefits but limited and in particular in the

hannah_went:
yeah

sarah_voisin:
in view of injuries a lot of people underestimate how good weight lifting is to avoid injury especially if you practice another sport that might be at higher level so i would say that long any form of activity that allows you to move your body and in particular with weight lifting try to go as heavy as you can but obviously you know do not push yourself to the point of breaking that's the

hannah_went:
right

sarah_voisin:
general comment

hannah_went:
definitely that's that's what holds me back i would say oh you know this feels pretty heavy i could probably go a little bit heavier but you know i don't i don't want to because vaxwhyinzeo because i'm tired so you know we

sarah_voisin:
m

hannah_went:
always need to push ourselves a little bit more to to our limits because you could probably lift a lot more than than you can it's it's really just more of that mental block to so um you know i really encourage anyone who who may be be interested or on the fence we're guarding weight lifting to just at least start you know start with some cans of soup and you're in your kitchen where you know two and a half pound weights because we're going to lose that muscle mass as we ate so it's it's never too too late to start you begin now and try and keep some of that muscle

sarah_voisin:
yes and

hannah_went:
so with this

sarah_voisin:
also sorry i just want to get one point across because i hear this a lot from people even

hannah_went:
m

sarah_voisin:
all the individuals can lift weights and it is not dangerous for them to lift it a lot of people are worried that they're going to injure them and it is safe i just want to state that out loud

hannah_went:
definitely definitely um so yeah again you know i really want to encourage my parents to even lift weights to be a little bit more active too because it really really starts to decline as you become older chronologically um so dr voisin i want to hear a little bit about the background of this paper what started it i'm always super interested to hear how you came about to do this question and how you started this paper can you get us a little bit of back about that

sarah_voisin:
yeah absolutely so um this paper is basically building an opportunity clock which is a predictor of chronological age based on skill to muscle denitic profiles and the reason why i decided to build this clock is because there were there was at the time a clock that was available for all tissues that was known as the hoof a pan tissue clock was developed in two thousand and thirteen but i tried to apply this clock to my data in muscle the data that i was handling at the time and i noticed that this clock performed rather poorly in skillet muscle compared the other tissues that were available out there such as blood adipose tissue brain et cetera and i didn't really understand why and i dug into the paper and when i looked deeper i realized that when horat developed his clock in twenty thirteen there were virtually no dinmathalation data sets in muscle available at the time so when he developed his clock he didn't put any muscle data to deff like the clock which means that the clock performed rather poorly in this tissue because you have to know that emiginatic patterns are rather tissue specific and there are some changing changes that are happening with g that are restricted to certain tissues and so m i by the time i became interested in this in two thousand and nineteen many data sets had become a lebel

hannah_went:
hm

sarah_voisin:
allowing me to build a muscle specific clock that could predict an individual's chronological age based based on their denmathilation parents in muscle

hannah_went:
perfect yeah and i think you know some of our listeners will be familiar with that two thousand thirteen horror bath pan tissue or multi tissue clock if if that clock should work well in all tissues why don't you think it was representated well then with the skeletal muscle were there were there muscle were there other excuse me tissue or organ groups that weren't very well represented in the clock as well

sarah_voisin:
i do not know i haven't looked specifically at which tissues

hannah_went:
ah

sarah_voisin:
were not really well represented aside from muscle because i was just handling that type of data there might be some very specific to such as i don't know ovaries or some

hannah_went:
m

sarah_voisin:
things that are

hannah_went:
m

sarah_voisin:
pretty rare or i mean like hard for example heart is also that's difficult to get in humans so i'm not

hannah_went:
m

sarah_voisin:
too sure but they probably other tissues

hannah_went:
absolutely and

sarah_voisin:
once

hannah_went:
and how did you get you know the samples that you were working with with that skelatile muscle

sarah_voisin:
so that was one of the one of the interesting parts of this paper at least and also in my journey as well as a researcher because it involved of data mining so i felt

hannah_went:
m

sarah_voisin:
like i was sherlock holmes trying to and all the cues that was out there to be able to gather all the data that i that i could to build this clock which meant looking first of all in online repository so all the online data bases that are now overflowing with molecular data such as the gin expression omnibus platform the gap data base of gen types and fine types are express and all those great platforms where people just down their data

hannah_went:
yeah

sarah_voisin:
and i just dug into it and i and i found a lot of data said that i could use but it was not sufficient to build a really good clock so i also read out to my network of collaborators around the world from europe from the us from australia because i knew that they had some data at some point so sitting somewhere that i could potentially use and is one of the things that i love about academia and science people are so collaborative and so open and they allowed me to use their data very generously allowing this big effort which you know yeah led to the clock the muscle

hannah_went:
yeah

sarah_voisin:
clock

hannah_went:
that's that's amazing and i think that's one of the things you know that's that's great with all the technology that that we have today as we're able to collaborate and come together and you know i'm sure you will be able to return the favorite for all people who sent you information to use for your skeleton muscle clock as well right because we could only get so much of that data from the online data mining and then having some some collaborators around the world always always helpful too um so

sarah_voisin:
yeah

hannah_went:
you know diving deeper a little bit into that point when people discuss epi genetic clocks they get really excited because they always go to this idea of biological aging and how there they may be agin on a cellular level and that's super exciting in the field right we know if you're your biological age in any sense whether it's a first generation second generation or third generation clock is above your chronological you are at risk for almost every single chronic disease and death as well but what i don't think people understand is really how these epi genetic age clocks are created there's a lot of work a lot of time that goes into this and an effort put into the development of again these epi genetic clocks are these predictors so i want to spend some some time really diving deeply into this process how did you create this muscle clock can you describe the process so i think you just went over the first which was actually getting that data so you have the data what do you do with it from there

sarah_voisin:
so once i got my hands on the data the first thing that i need to check was that i had sufficient sample size because you need a substantial amount of data to build a clock and the reason for this is because human molecular data is usually very noisy you have a lot of you have a signal that you're trying to capture but a lot of other factors can inflow it's your denmethlation levels and make them vary to a degree that is masking the signal that you're trying to detect so i needed at least a thousand samples so you know i managed to get there but it was it was a challenging

hannah_went:
yeah

sarah_voisin:
second

hannah_went:
m

sarah_voisin:
i think that was very important for me to check was that each cohort or each data said that i had my hands on displayed a broad age range to be able to detect changes in genimathilation that i associate with age i need individuals in my data sets to vary in age i cannot build a clock if my data set only contains people who are fifty years old because they don't vary in age though data is not going to vary at all so

hannah_went:
m

sarah_voisin:
it was very important to check that the age was variable between data sets and inside data sets as well um and then i just decided to go with the method that horat had implemented in his two thousand and thirteen paper which was to use machine learning algorithm called elastic so what you have to know is that there are new developments in the field of epitunity clocks especially worked by morgan levin and she

hannah_went:
m

sarah_voisin:
developed i think a better way to use ethulation data to the clock that does not use necessarily elastic nets but i am not entirely familiar familiar with it but i just know that these perform better now

hannah_went:
hm

sarah_voisin:
with regard to elastic net it is it is actually i mean i was really scared to use it at first because i didn't know

hannah_went:
yeah

sarah_voisin:
anything about christian learning but thanks to steve horvath who actually put his code completely open access i could re use his code an to dig into it and understand exactly what it did and elastic net is a really beautiful piece of algorithm that actually tries to find the best combination

hannah_went:
yes

sarah_voisin:
of features in your data in in this case the features are dnmmethalation sides called the c p g sides

hannah_went:
m

sarah_voisin:
so it tries to find the

hannah_went:
m

sarah_voisin:
best combination of those sides that can predict the out of interest as accurately as possible so you can ask scholastic net to predict chronlogical age you can you can ask the algorithm to predict anything you want you can predict sex you can anything um and each feature is actually assigned a specific weight and then all the

hannah_went:
m

sarah_voisin:
features are summed up together they are lineally combined into one ticular measure that predicts with high precision somebody's age based on the dinmethulation level

hannah_went:
yeah that's a beautiful example like i said i don't think we've had anyone on the show yet really dig into elastic net regression modeling or how these are actually created and um that's that really just you know definition by book how we're diving into this data so you you go through this elastic net regression modeling and and what do you find how many c p gs actually make up the skeleton clock and remember c p g is going to be that side of scene phosspateand guaning so you have your two nucleotids with that phosspate bond holding it together and that's going a be the location of methalation in that geno correct me if i'm wrong on on any of that doctor voice but how yeah c p gs did you find and give us some insight there

sarah_voisin:
so i mean if i'm really honest i don't remember the number of c p g sides in the clock because actually the right come i mean the c p g that are selected to build the clock are in my opinion there's nothing special the club could have selected a different set of cpgs all together and you know it could have arrived at a prediction of age that would be just as accurate or slightly sycurits ever so slightly and it could be a completely different set of c p g so there's nothing special about this particular hundred or two hundred c p g sites that

hannah_went:
h

sarah_voisin:
are

hannah_went:
m

sarah_voisin:
containing the clock because you have to know it's the clock itself it's a very artificial process it's a machine learning is artificial intelligence and by definition artificial it doesn't give you i don't think much insight into the tire biology of the aging muscle it is purely a program to predict age so

hannah_went:
and

sarah_voisin:
the number of sides don't really matter to me to be honest

hannah_went:
and i and that is such a great answer because a lot of times people think if a clock has a higher number of c p gs then it's a better clock and that is just not correct right i know off the top of had the two thousand thirteen horabotone has three hndrdanfifty three dr gregory hannamone has seventy one um you know i think fino age s around the five hundred area which is drmorganlvin second generation clock so they're all varying there's some clocks that only have three c p gs right so so they're all varying and i think that's a very big misconception in the space is that people think with more positions it's better but like you said that's machine learning that is just predictor it's a prediction outcome we don't really necessarily know on the biological level what those those cpgs are meaning or what that method ation is actually telling us um there can be prediction based predictions based off of that and there are some groups out there some really great research groups who are looking more at the causation of aging in what positions might act really be causing us to age so that's a completely different question and completely different interview to have but really appreciate the honesty and the feed back there regarding the number of sites and your muscle clock is going to out perform the pan tissue clock correct and reporting now that that age so can you explain this further how does that perform better than dr

sarah_voisin:
m

hannah_went:
as two thousand thirteen clock

sarah_voisin:
so it performs better just because if you use a particular sample of muscle from a person who is at say fifty years of age

hannah_went:
h

sarah_voisin:
the muscle

hannah_went:
m

sarah_voisin:
clock that that we developed actually together with tevehorvat is just going to predict that person's age with better accuracy so there is there is a smaller error in the prediction of the age so the person might be predicted to be fifty two years old with meet clock so the muscle clock versus

hannah_went:
yah

sarah_voisin:
something like sixty with the horbopantissue clock so it's just a more accurate but there's nothing

hannah_went:
for a

sarah_voisin:
magical about it safe

hannah_went:
perfect and it's it's a first generation clock correct so it's it's

sarah_voisin:
correct

hannah_went:
using

sarah_voisin:
absolutely

hannah_went:
to predict the chronological age within using that that skeleton muscle so the second part of your study i found this super interesting as well so if you're you're reading doctor voice in study and you move on to more of the second half you you also performed an epic geno wide association study or what people call an a study of age and paper let's start with describing what an as study is

sarah_voisin:
yes absolutely so was actually is it's very similar to a gas that you might

hannah_went:
m

sarah_voisin:
be familiar with it's actually a hypothesis hypothesis free approach that is used to identity i the dmethilation law sit so the ipigunity close in this case that are statistically associated with particular trait of interest and in our case it was aged chronological age so the way that this this works is that it tries for every single c p g side that is in your data it tries just to line your model to associate the methulation level at this side where h adjusting for other potential confounders such as sex and disease and whatever you want and

hannah_went:
ye

sarah_voisin:
then it does particular type of correction to avoid as positive findings and then you end up with a list of dnmthilation lowside that are called d m ps that

hannah_went:
m

sarah_voisin:
are statistically significantly associated with eight

hannah_went:
perfect and what is the d m p stand for again

sarah_voisin:
differentially metholated position

hannah_went:
position okay and is that the same as the d m r the differentiated methalated region

sarah_voisin:
so a d

hannah_went:
yeah

sarah_voisin:
m r actually it's a contiguous stretch of d n a that

hannah_went:
oh

sarah_voisin:
harbor multiple d m p so a d m

hannah_went:
hm

sarah_voisin:
r is composed of multiple d m p

hannah_went:
perfect thank you for that that clarification there and what did you find in your study

sarah_voisin:
so we found actually a balanced a number of dnmethalation side of d m p s that increased in methlation with age or decreased in methalation with age and we also found that those loci that change with age in the gen are not randomly distributed they are enriched in specific regions of the genus that perform specific functions such as endhancers into genie regions and promoters et cetera so i won't go maybe into all the details of it but there is there is a very specific distribution where this change is happened with age in the gum it's not just randomly distributed which gives an insight as to what is the upstream reason why the cpgenum changes in the first place during aging is the actual cause of those changes so i think this is the interesting part of it

hannah_went:
sure yeah let's go into the details no i want to i want to hear them so

sarah_voisin:
a

hannah_went:
what if you don't mind what you know what sides were they more related to you know skeletal muscle and an aging or kind of what were those groups s

sarah_voisin:
yes so when

hannah_went:
ah

sarah_voisin:
looking first at the function of the gens that displayed a difference in methilation during aging i found that those gens were

hannah_went:
yeah

sarah_voisin:
particularly

hannah_went:
ye

sarah_voisin:
there were i would i will use the term enriched so

hannah_went:
m

sarah_voisin:
what i mean by that is that many of those gents that changed with age in in muscle at the epitenity level were involved in skillitl muscle structure and function such as miozeentroponine and all those kinds of proteins that make up the muscle itself

hannah_went:
hm

sarah_voisin:
so it's just confirmed to me that the signal that i was detecting during aging might be functionally involved in the degradation of muscle during aging and especially the decline in muscles length and muscle mass during aging

hannah_went:
sure thank you for for that description again i would want to use the muscle clock on on myself and i know you mentioned the meat package which will talk about how people can access that at at the end here um so that leads me to what's the utility or application of your clock you know someone's tested their their methalation and they have four fifty k they have their iedabtfiles or they have the eight fifty k or some of their all data can they use it or you know where do you think this is you're going to see your your clock use most widely

sarah_voisin:
yes so i think that the first development that i see using first i

hannah_went:
ah

sarah_voisin:
mean it's a first generation clock so it can

hannah_went:
m

sarah_voisin:
give you unfortunately a little insight into the biological aging

hannah_went:
yes

sarah_voisin:
of your so because because i asked my algorithm to predict age with with

hannah_went:
oh

sarah_voisin:
as much as curacy accuracy as possible it selected those mathulation sides that change with age and nothing else like it didn't select the mathulation sides that change with exercise or diet or anything else that counters the degradation of your muscle as you so it's the first

hannah_went:
sure

sarah_voisin:
generation clock so you need we need to take this with a grain of salt but i think that the

hannah_went:
oh

sarah_voisin:
application of this clock might be in

hannah_went:
right

sarah_voisin:
future program experiments that are currently being tested in mice

hannah_went:
m

sarah_voisin:
and that i see could be applied to human muscle culture to try to for example treat those muscles with always m factor o re programming factors or maybe with molecules which has n d to try

hannah_went:
m

sarah_voisin:
to understand whether those particular molecules that are known to have ent aging properties at least in animal models or in other cells could potentially rejuvenate the opiginatic age of skillitol muscle and to a degree and what that actually means for the function of the muscle etcetera the second

hannah_went:
perfect

sarah_voisin:
yeah i was going to say the second potential application but that's like far fetched is in forensic

hannah_went:
hm yeah

sarah_voisin:
because because a piece of muscle found on a i'm seeing you could look at the genmtulation level of this piece of muscle and determine the age of the person that to whom this piece of muscle belong but i think this is far fetch because blood is more readily available more useful in this context

hannah_went:
definitely talking about the second application that's very interesting because that's really how i heard of or a part of how i heard of dnmethalation testing and its utility you know back in twenty eleven in twenty thirteen when these for generation clocks really came out as you could use blood from a crime scene or even even muscle nou and you know they even were really used at the beginning for people to seek asylum see if they were old enough to seek asylum so they are really great at predicting chronological age and those first generation clocks still have utility for those purposes if you accurately want to predict you know that specific interest in come which would be that chronological age going back

sarah_voisin:
oh

hannah_went:
to your your first application um so for for a study there you could do and let me know if this is what you are imagining but you could do a um you could you could start with mice right could take muscle tissue and you could test their their age based on that muscle and then you could intervene with something like n d or i think you were talking about the reprogramming factors right like the yamanaca factors correct and then do

sarah_voisin:
correct

hannah_went:
wait a certain amount of time and then re test afterwards to see how their chronological age based on their skeleton muscle is being effected would that be something you you were thinking along the lines of

sarah_voisin:
absolutely it's exactly what i was thinking about knowing that the clock that i built is specific for humans so i would

hannah_went:
yeah

sarah_voisin:
not go through my ale it would be directly

hannah_went:
it

sarah_voisin:
applied to human muscle ceres but yes this is exactly the line of thinking

hannah_went:
and human is perfect

sarah_voisin:
ah

hannah_went:
and can you just explain a little bit more and again forgive me if this is maybe not your specialty but can you describe just the yamanaca factors for our listeners i think that may be a little bit of a foreign um um subject for them or even just some of the re programming factors

sarah_voisin:
so the i know little about it but i just know that these factors these are four proteins for transcription factors whose combination was actually and to turn a differentiated

hannah_went:
oh

sarah_voisin:
adult cell back into a baby stage of a sort

hannah_went:
yeah

sarah_voisin:
of a

hannah_went:
yeah

sarah_voisin:
differentiated cell which

hannah_went:
yeah

sarah_voisin:
which actually got yasnamanaka who found those factors the noble price because this this discovery is has many applications for organ transplant for trying to rigivinate tissues and in the aging field in particular so this the combination of these four proteins turns back the if you will to a baby stage with a caveat that there is one one of the four i know that tends to turn the tissue in censers and i know that one of them has been removed lately in experiments to try to rejuvenate the tissue without turning it into a terratoma but once again i'm not super familiar with that

hannah_went:
no understood i appreciate that answer that just gives our audience at least a picture or some insight and i know that's really what alto slabs is working on now a lot of those cellular programming options to see how we can turn age of those cells back to stage zero or those those baby cells so it'll be interesting to see what they come up with in the years to come so drfoyson

sarah_voisin:
my

hannah_went:
what's next for you what have you currently been studying looking at or tell us your interest tell us what we can expect in the next coming years from you

sarah_voisin:
so i have actually a big project that i'm working on at the moment that is probably going to take me another two years to complete but once it's completed i'm going to be very happy with it because

hannah_went:
yeah

sarah_voisin:
it's

hannah_went:
yeah

sarah_voisin:
it is absolutely a mammoth task my my goal is to build an atlas of apigentic aging across all human tissues at the celtiresolution by combining something like seventy five thousand ethilation

hannah_went:
yeah

sarah_voisin:
profiles across eighteen tissues and

hannah_went:
oh wow

sarah_voisin:
i want to build that huge atlas so people can understand exactly what type of denemathulation changes happen in which cell type in which organs and further down the line what i want to do as well is to investigate sex differences in the aging profile because we know that men and women do not age similarly by the same rate and i want to understand whether this it's true at the oppitunitic level and in which tissues and whether this actually explains why for example women developed more alzimers while man parkinson and whether it has some

hannah_went:
yes

sarah_voisin:
actual application to understand sex differe his inane related diseases oh

hannah_went:
wow i am excited for that and

sarah_voisin:
m

hannah_went:
i'll have to have you back on when you complete that project too so will the atlas will that be publicly available to researchers or anyone

sarah_voisin:
absolutely

hannah_went:
perfect

sarah_voisin:
so a big part of the project is to build an open access here's a friendly transparent website where people can browse anything they want a bit like the g t x portal clicking the tissue and understanding which side changes to a degree with age because this is only when we build an atlas of this kind that then we can test experiments in human tissues to know whether the reprogramming fact i can actually affect you know this tissue or that tissue so once we have the base the atlas then we can move on to understanding yea the effect of entiaging therapies

hannah_went:
and that's your your gift back to the community right again all those people share

sarah_voisin:
yeah

hannah_went:
their data with you regarding the skeleton muscle clock for you to build that so that's great i love when the community comes together and we're sharing all this information and data i'm particular interested in the male versus female information and what you'll find from that because there's always that that sex paradox where men typically age a little bit quicker than women and they die younger too recent article came out that shows and i give me as i don't remember which clock they actually used but it showed that men are typically about four years older biologically compared to women i think it was one of the first generation clocks so that would be super interesting at a cellular level to understand a little bit further i think a lot of the um you know you know question stem from that especially you know most of these interventional trials you'll see done on men for example so you know women i think a little bit under represented there and we can find out why and why we're developing diseases earlier too

sarah_voisin:
exactly

hannah_went:
perfect

sarah_voisin:
oh

hannah_went:
in my last question for you it's a curve ball i ask everyone this at the end of the podcast

sarah_voisin:
oh

hannah_went:
if you could be any animal in the world what would you be and why

sarah_voisin:
my god if i could be an animal

hannah_went:
yeah

sarah_voisin:
i think this is a random one but this is a question that i've thought about in the past and i would be a cat you have the

hannah_went:
really

sarah_voisin:
best of evengohyeah you have the best of everything you hunt as much as you want being fed part and i would love to be a cat i think

hannah_went:
i love that you've thought about that question because i've thought about the question too i haven't haven't given my answer out yet but really a handful of people have said cat cat is winning for the same reasons that you just said they have cats and they are sleeping or they're fed all the time in their pet do you have any cats at home then

sarah_voisin:
i used to

hannah_went:
kay

sarah_voisin:
actually yeah i used to yeah i love cats they am a spirit animal i think hm

hannah_went:
favorite animal well i really appreciate your time we've we've come to the end of this amazing podcast interview for listeners who want to connect with you where can they find you and can you talk about where they can find you our co as well

sarah_voisin:
yes so i do have a git hub account so with my name sarah is so you can find all the code that i applaud on my githabigularly as i move on to different projects i'm also i can be reached with my work email that is now at the university of copenhagen so i'm pretty sure you will share that email with the listeners

hannah_went:
absolutely i'll put everything in the show notes and why is it called the meat package what does that stand for i love

sarah_voisin:
oh

hannah_went:
it i think it's a great fit but what does it stand for again

sarah_voisin:
so meet stands for muscle epiginitic h test and to be fair i am not the one who found that acronym but it was one

hannah_went:
yes

sarah_voisin:
of my colleagues whom i presented the the clock to and you looked at me said you should call to meet and i'm like what are you talking about and that you gave me the acronm and i thought it was so sport on i had to i had to use it

hannah_went:
you said muscle epi genetic age and what is the t stand for

sarah_voisin:
yes

hannah_went:
test test perfect i love it well thank you so much for for joining us doctor voicing at the everything i genetics podcast and remember

sarah_voisin:
m

hannah_went:
you have control over your epi genetic so stay tuned next time learn more thank you so much

sarah_voisin:
thank you