Thank you for the generous comment - we really appreciate it!!

Thank you Hyejung - that means a lot to us!!!

Thank you so much - really appreciate your kind words!!!

We sincerely thank #NIMH #NIGMS & @lieberinstitute.bsky.social for supporting this work and are deeply grateful to the donors and their families, whose generosity made this research possible.

Again, we’re very excited to share this work with the community and look forward what comes next 🤩 (9/9)

We’re also grateful to @martinowk.bsky.social, @stephaniehicks.bsky.social, @kr-maynard.bsky.social, #StephaniePage for their mentorship, supervision, and guidance, and to @lcolladotor.bsky.social , #ShizhongHan, and @nikosdaskalakis.bsky.social and their teams for their close collaboration 🙏 (8/9)

Special thanks 👏 to @findycang.bsky.social and @madhavitippani.bsky.social for leading the cell- and microenvironment-level analyses, to #SvitlanaBach and team for the meticulous dissections of dlPFC samples🧠, and to #RyanMiller for developing the interactive visualization tools🖥️ (7/9)

This was truly a collaborative effort across multiple labs and institutes. I’m deeply grateful to have been part of leading this effort alongside co-first author and lead data scientist @boyiguo.bsky.social and an incredible team 🙌🙌🙌 (6/9)

Our data are also available through an interactive browser🖥️: (research.libd.org/spatialDLPFC...) Feel free to explore and look up your gene of interest in our spatial dataset🔎(5/9)

By integrating GWAS data🧬, we also connect spatial gene expression landscape to genetic risk architecture and find evidence for disrupted “neuron to non-neuronal” interactions that may contribute to cortical dysfunction in schizophrenia (4/9)

With this 🗺️, we provide spatial context for disease-associated genes, including genes involved in trophic-inhibitory signaling and stress- and immune-related pathways (3/9)

Using Visium + Xenium profiling, we generated an integrated spatial atlas 🗺️ resolving schizophrenia-associated transcriptional changes across cortical layers, cell types, and distinct microenvironments, including neuropil-, perineuronal net-, and neurovascular-enriched niches (2/9)

Special thanks to @findycang.bsky.social
and @madhavitippani.bsky.social for leading the cell- and microenvironment-level analyses, to #SvitlanaBach and team for the meticulous dissections of dlPFC samples🧠; and to #RyanMiller for developing the interactive visualization tools🖥️ 👏(7/9)

This was truly a collaborative effort across multiple labs and institutes. I’m deeply grateful to have been part of leading this effort alongside co-first author and lead data scientist
@boyiguo.bsky.social and an incredible team🙌 (6/9)

Our data are also available through an interactive browser🖥️: (research.libd.org/spatialDLPFC...) Feel free to explore and look up your gene of interest in our spatial dataset🔎! (5/9)

By integrating GWAS data🧬, we also connect spatial gene expression landscape to genetic risk architecture and find evidence for disrupted “neuron to non-neuronal” interactions that may contribute to cortical dysfunction in schizophrenia (4/9)

With this 🗺️, we provide spatial context for disease-associated genes, including genes involved in trophic-inhibitory signaling and stress- and immune-related pathways (3/9)

Using Visium + Xenium platforms, we generated an integrated spatial atlas 🗺️ resolving schizophrenia-associated transcriptional changes across cortical layers, cell types, and distinct microenvironments, including neuropil-, perineuronal net-, and neurovascular-enriched niches (2/9)