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.
Catherine Kaczorowki argues for large, genetically diverse cohorts for studying neurodegeneration in mice, on the basis of varying age of onset in human risk mutant carriers (presumably due to other influences):
Crossed the standard 5xFAD #Alzheimer's mouse with the outbred BXD cohort, which gives +- AD transgenic with genetically matched (but diverse) littermates as controls:
There are still big challenges in measuring cognitive phenotypes in mice, but the work certainly looks like an improvement in models. Notably, B6 genetic background seemed protective.
Full paper here: pubmed.ncbi.nlm.nih.gov/30595332/, by @sneuner_ in @KaczorowskiLab.
David Allison follows: By exquisitely controlling environment and genetics to detect small effects, results may not reproduce in real life or even at other study sites.
That's why @Gordian_Bio goes straight to diverse, spontaneous disease models, to better predict human outcomes
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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.
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.