I'm often asked how to learn about #AgingBiology, which I hope this thread can address.
Will first list recommended introductory resources, then contextualize and interpret.
There's exciting progress! But also many fanciful ideas, so you can't take everything at face value.
Will mostly skip the Why/philosophy of #AntiAging, since those asking for resources are likely already bought in.
If interested I wrote a Why/Why not post back in 2016: martinborchjensen.com/hypotheses/agi…, and most of the websites I'll list have a version of this discussion too.
The most comprehensive #Aging primer is @ArtirKel's FAQ: nintil.com/longevity.
It does a great job of introducing the field, and distilling the science for non-biologists. Bonus points for contextualizing how biology is different from engineering.
@LauraDeming's FAQ has a great list of interventions that extend #Lifespan in mice (@justsaysinmice go ahead), and their clinical stage: ldeming.com/longevityfaq.
Note that mice are not humans! And some of these studies haven't been independently replicated. Still, exciting data!
@LifespanIO also has a great overview of clinical trials for drugs that likely target #AgingMechanisms: lifespan.io/road-maps/the-…
Note that, in my opinion, many of these are going to fail. Some because fanciful drugs/poor execution, some because clinical trials fail.
@KarlPfleger has the best overview of #Biotech companies working on #AgingBiology (explicitly or de facto): agingbiotech.info/companies/.
He includes publicly available information on company stage, product, key people, and summary statistics across companies.
OK, let's add nuance.
Most importantly, understand that #Biology is full of nonlinear effects.
Text- & #PopSci books may have you thinking biology works as in left pic below. The reality (of one pathway) is 10x more complex than the right pic, incl. lots of circular feedback.
Non-linear interactions means that results are often context-specific. A protein will kill one cell type and protect another. Nothing is "better" or "worse", but shift a system in a way that may be desired.
#Aging is a complex system to tune, not a single pathway to turn down.
You'll quickly run into the Hallmarks of Aging. Know that unlike the Hallmarks of Cancer that inspired them, they are each neither essential nor sufficient for an aged state. Consider them an overview of what's predominantly being studied in the field, not a definition of #Aging.
Even the word "aging" is used with different meanings. Neither 'aging means senescent cells' nor 'aging means frailty' is wrong, but a solution to one of these doesn't necessarily apply to the other. Notice the context to determine which meaning applies.
Arlan Richardson showing that JAX-housed mice, like humans, have undergone a dramatic improvement in #lifespan this century ... by reducing deaths from pathogens. #MindYourModels
Recommends looking at lifespan data as the best indicator of husbandry quality at different institutions/sources. Mean survival should be at least 27-30 months.
Example: 2003 Igf1r study showing 33% lifespan extension (in het ♀️s), but mean lifespan of controls was only 19mos. Lifespan effect largely disappeared when replicated in cohorts with longer control lifespan.
This was my go-to question for fly lifespan studies as a postdoc.
Very excited (enough to get up at 5am) about this Temporal Single Cell Analysis organized by @singlecellomics. First talk by the amazing Caroline Uhlers, recently snagged from MIT by @ETH_en#SCOGtempSC#SingleCell
Livetweets here. Apologies in advance to any sophisticated 'ML on scSeq/spatial' presenters whose work I misinterpret/misrepresent, still a novice to that field.
Very nice talk by Caroline. Two parts: 1) mapping RNAseq and images to the same latent space, to enable timecourse measurements (w images) of (inferred) RNA state. Seems like WIP but cool.
I've been hunting for a delicious decaf coffee, and @elamadej gifted me this @Timelesscoffee (thanks!). At first it looks just high-end artisanal, but then things get a bit strange...
An apostle, sure. #CoffeeIsMyReligion and such things. A bit unusual but nice graphic design.
Well, this is a bit beyond the usual. Why seven fingers? The eye presumably the esoteric mysteries I'll learn after drinking this? But still, this is #BayArea and I've certainly seen cultier startups than this.
Livetweets from the 7th Annual Aging Research and Drug Discovery conference #ARDD2020. I can't attend every talk to coverage will be intermittent. Apologies to any speakers left out!
Christian Riedel from @karolinskainst presenting aging clocks. There are a lot of these, but excited to see him (A) making a human clock predicting time to death, not just age, and (B) deconvoluting both their human and model org clocks into FUNCTIONAL parameters. Sorely needed.
Now haut.ai from Estonia arguing that hand photos are more robust than faces for AI-based aging biomarkers, and that we need more explicit skin tone features for broadly applicable tools.
PS, Estonia is probably the world leader in digital health/EHRs.
@marissa_schafer, Xu Zhang, @NKLeBRASSEUR & team dive into the details of SASP, the Senescence Associated Secretory Phenotype first discovered in the Campisi lab @BuckInstitute.
SASP is a prime suspect for how #Senescent cells cause #Inflammation, #Cancer and #Fibrosis. But SASP is a mix of many secreted proteins, so the @MayoClinic looked closely at 24 factors.
1st, they show which factors are secreted by different cell types (in culture).
Next, and more exciting, they measured which SASP factors increase with age in human blood. #ChronicInflammation is an important mechanism of aging, but really defining #ChronicInflammation is hard and often not even attempted. So this detailed analysis is great.
Just kidding, you guessed it: disease model mice got better. Props to the authors for A) inducing #Senescence in 3 different ways, B) using 2 models of #NASH/liver #Fibrosis, and C) validating their senescence observations in human samples of #Cancer and #Atherosclerosis.
The linchpin of the paper was identifying a specific membrane marker on #Senescent cells, uPAR. They used bulk #Transcriptomics to identify candidates, then narrowed down with #Proteomic data. Go #Omics!
They didn't explore whether uPAR is causative for the #Senescent phenotype.