⚡️GREG_CORDER⚡️

Associate Professor @Penn • Dept Psychiatry + Dept Neuroscience + Dept Anesthesiology | Pain • Placebo • Poppies • Psychedelics

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

🧠⚡️💊New @Nature publication !

Mimicking opioid analgesia in cortical pain circuits

We built a brain-behavior framework to decode spontaneous chronic pain in mice—and to biologically mimic morphine with a synthetic opioid gene therapy

https://t.co/YqvBzdEqEL

@PennMedicine

[nature @Nature]

Researchers identify the neurons involved in the emotional distress associated with pain

https://t.co/4QTPlVpG6S

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@FlanBrain @katybraden 🔥🔥🔥🍾🍾🍾🙌🏼🙌🏼🙌🏼

[Cheshire @Thatsregrettab1]

https://t.co/n3vfEH4SdB

cc: @addictedtoigno1

[Jerzy Szablowski @JerzySzablowski]

qPCR allowed us to measure transcripts, but just once, destructively, and only in post-mortem tissues. Here, we show we can record transcript level history in vivo and recover this information with a blood test to make a "noninvasive qPCR". https://t.co/fe6yjsrdGr

[Takaki Komiyama @takaki_komiyama]

Our new paper is out in Neuron!

https://t.co/FhP19Jg3du

How does the brain decide how much of the past to use when making decisions? In rapidly changing environments, recent experiences matter more; in stable environments, longer histories are useful.

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@PiantadosiSean @UCIrvine @UCIneurobio ❤️🙌🏼🤘🏽🍾😎🔥

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

Mullet time 🤘🏽🍺

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

🦩🐷💕🍬🌸

[Matt Fillingim @FillingimMatt]

Super excited to share our new preprint!
https://t.co/fs1kRLV9fH

We built global reference curves of pain from 6.1M people in 118 countries 🌍 to find out how pain unfolds across the lifespan, and how individual- and country-level risk factors shape these trajectories.

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@JerzySzablowski 🔥🍾🔥🍾🔥🍾🔥🍾

[OpTic Hitch @hitchariide]

13:28 per mile pace for TWO HUNDRED AND FIFTY MILES 🤯🤯🤯🤯🤯🤯🤯🤯

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@LaurenSlosky 🔥🔥🔥

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@ladenardo @ScottWilke5 @michaelgongwer 🔥🔥🔥🧲

that fiber angle tho ! 📐

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@IshmailSaboor This was fun race ! never did broad street before

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

7 weeks post meniscus injury .. I’ll take it 🙏🏼

Broad Street 10 Miler 🦅

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@JMZiermann 9:30 is awesome! 👏🏼 😎

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

@AmarSahay_ @JerzySzablowski 🙌🏼

[Chris Chavez @ChrisChavez]

OMG.

🇰🇪 Sabastian Sawe becomes the first man ever to break 2 hours in a marathon (legal conditions) in 1:59:30 at the London Marathon!

Yomif Kejelcha 🇪🇹 runs 1:59:41 in his DEBUT.

Jacob Kiplimo 🇺🇬 takes third in 2:00:28

All under the previous WR.

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

check out this piece for @SfNtweets on my grad student @neuro_melody ❤️

https://t.co/i1B2LL01fF

[Will Renthal @willrenthal]

Pleased to share our latest work by Lily He, Parth Bhatia, Shams Bhuiyan et al. combining human and mouse DRG multi-omics with in vivo AAV screening to identify enhancers that bias gene expression toward distinct classes of nociceptors. @NIH_NINDS & HEAL

https://t.co/cSjix4nU3f

[⚡️GREG_CORDER⚡️ @FlyBottleEscape]

Really beautiful + rigorous work from the Liston and Levitz Labs combining circuit, molecular, and behavioral approaches to dissect the role of μ-opioid receptors (MOR/OPRM1) in ketamine’s antidepressant effects

One of the central findings is that MORs are enriched in somatostatin (SST) interneurons in cortex, and that these cells play a key role in mediating ketamine’s behavioral effects

There is a lot to like here: the study is technically sophisticated, the circuit logic is compelling, and the SST-dependent mechanism is supported by multiple independent experiments

But stepping back — it’s also critical that we get the cell-type distribution of OPRM1 correct, because this has direct implications for how we think about:
• opioid analgesia
• opioid use disorder
• cortical circuit modulation
• and the design of next-generation MOR-targeting therapeutics

And on that specific point, I think there is a non-trivial dataset interpretation issue worth discussing

The key claim relies heavily on older SMART-seq datasets (ACA/ALM; ~5K cells) from the Allen Institute

These datasets were incredibly important at the time — but they were not designed as unbiased quantitative cellular censuses

A few technical considerations that matter a lot for a gene like Oprm1:

1) Sampling design (not a census)
These datasets rely on:
• targeted dissections (ACA/ALM only)
• FACS-based sorting
• heavy use of transgenic driver lines

This means the data reflect what was selected, not necessarily the true population distribution

2) Cell numbers and statistical power
~5,000 ACA cells total. Once subdivided across excitatory subclasses, power drops quickly

For low-abundance GPCR transcripts like Oprm1, this creates:
• dropout sensitivity
• threshold artifacts
• unstable “presence/absence” calls
• inflated apparent enrichment in small populations

3) SMART-seq recovery biases
Requires intact dissociated cells:
• large projection neurons underrepresented
• pyramidal neurons more fragile
• interneurons often more recoverable

4) Quantification + annotation (2018-era pipelines)
Older gene models + isoform collapsing + exon/intron handling can all affect detection of isoform-complex GPCRs like Oprm1

Now contrast that with newer datasets:
The Allen Brain Cell (ABC) Atlas includes ~4 million cells across the entire mouse brain, with:
• orders-of-magnitude larger sampling
• stable estimates across cortical subclasses
• improved taxonomy
• spatial integration

And importantly — across this dataset, OPRM1 signal is not restricted to SST interneurons

We see the same pattern across:
• our own single-nucleus RNA-seq (100k+ cells)
• Allen ABC Atlas (4 million cells)
• multiplexed FISH
• immunohistochemistry
• n=4 Oprm1-Cre mouse lines
• MORp viral promoter strategies

Across all of these orthogonal approaches, the result is highly consistent:
→ OPRM1/MOR is enriched in glutamatergic cortical populations, not exclusively confined to SST interneurons

So how do we reconcile this?
The most parsimonious explanation is not that either dataset is “wrong,” but that they are answering different questions under different constraints:
SMART-seq (2018):
→ high depth, small N, targeted sampling
→ vulnerable for sparse genes
ABC-scale (modern):
→ massive N, robust population estimates
→ better suited for cell-type distribution
For genes like Oprm1 — low abundance, heterogeneous, biologically critical —
scale + sampling design + cross-modal validation are decisive

The broader point:
As a field, we need to be careful about making strong claims about cell-type specificity of neuromodulatory receptors based on early-generation datasets — especially when:
• atlas-scale data now exist
• multiple orthogonal methods converge
• and the implications extend to therapeutics

None of this detracts from the importance of SST interneurons in MOR-dependent circuit function — that biology may be very real and important

But the global cortical distribution of OPRM1 appears broader, and heavily includes glutamatergic neurons

And that distinction matters for new appraoches for depression, pain and OUD

[BryanRoth @zenbrainest]

Mechanism-guided identification of antidepressant G protein-coupled receptor drug targets: Cell https://t.co/meI82uM4zk

[Anshul Kundaje @anshulkundaje]

"When scientists are absent from public conversations, misinformation fills the space". We have to discuss science openly in public. That means not just advertising & hyping & retweeting but also educating, discussing, criticizing, defending, arguing. All of it.