Lun Wang

(1/2)Here is our TRU-FACT preprint: Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolution. biorxiv.org/content/10.648… Please let us know your comments and send email to trufact.info@gmail.com for workshop trainings. Let's make science easier!

The parallel revolutions in spatial biology and in vivo microscopy have transformed our ability to record cellular dynamics and profile molecular signatures—yet 𝗹𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗼 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗲𝘅𝗮𝗰𝘁 𝘀𝗮𝗺𝗲 𝗰𝗲𝗹𝗹𝘀 has remained a major bottleneck. We're excited to highlight 𝗧𝗥𝗨-𝗙𝗔𝗖𝗧 (𝗧𝗼𝘁𝗮𝗹 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗨𝗻𝗱𝗲𝗿 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗰𝘁𝗶𝘃𝗶𝘁𝘆, 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗼𝗺𝗶𝗰𝘀), a landmark study published as a preprint, "𝘔𝘶𝘭𝘵𝘪𝘮𝘰𝘥𝘢𝘭 𝘢𝘭𝘪𝘨𝘯𝘮𝘦𝘯𝘵𝘴 𝘰𝘧 𝘪𝘯 𝘷𝘪𝘷𝘰 𝘪𝘮𝘢𝘨𝘪𝘯𝘨 𝘢𝘯𝘥 𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘣𝘪𝘰𝘭𝘰𝘨𝘺 𝘥𝘢𝘵𝘢𝘴𝘦𝘵𝘴 𝘢𝘵 𝘤𝘦𝘭𝘭𝘶𝘭𝘢𝘳 𝘳𝘦𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯" on bioRxiv, which finally bridges this divide. Congratulations to the primary authors, Dr. Lun Wang, Dr. Xiqian Jiang, and Dr. Xiaochen Sun, along with PI Dr. Mark J. Schnitzer and the team at Stanford University. 𝗧𝗵𝗲 𝗧𝗥𝗨-𝗙𝗔𝗖𝗧 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: ☑️ 𝗔𝗹𝗶𝗴𝗻: An optomechanical strategy physically preserves the orientation of in vivo imaging planes, ensuring postmortem tissue sections are cut precisely parallel—critical for reliable cell matching across modalities. ☑️ 𝗙𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁: The Soma-print algorithm registers cells by their unique geometric relationship to neighbors, delivering robust matching even with dense populations, thin sections, or distorted tissue. ☑️ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲: A statistical framework that provides for each cell an a posteriori probability of correct registration, enabling true multimodal integration—molecular identity, connectivity, and function—for the same individual cells. Validated across 13 mice which gave 10,522 matched cells, the pipeline works with both low-plex (HCR-FISH) and high-plex (MERFISH, 500-gene) spatial biology methods. The TRANSVISTA 𝗺𝗶𝗻𝗶𝗮𝘁𝘂𝗿𝗲 𝘁𝘄𝗼-𝗽𝗵𝗼𝘁𝗼𝗻 𝗺𝗶𝗰𝗿𝗼𝘀𝗰𝗼𝗽𝗲 enabled the complete TRU-FACT workflow in 𝗳𝗿𝗲𝗲𝗹𝘆 𝗯𝗲𝗵𝗮𝘃𝗶𝗻𝗴 𝗺𝗶𝗰𝗲, seamlessly connecting naturalistic behavior, cellular activity, and molecular identity in the exact same cells. At TRANSVISTA (transvista.com) , we're proud to support tools that expand what's possible in freely moving brain and behavior research. Congratulations to the authors for creating a unified framework that opens new possibilities for 𝗹𝗶𝗻𝗸𝗶𝗻𝗴 𝗻𝗮𝘁𝘂𝗿𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗮𝗻𝗱 𝗰𝗲𝗹𝗹𝘂𝗹𝗮𝗿 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀 𝘁𝗼 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆. Read Article Here: doi.org/10.64898/2026.… Contact the authors: TRUFACT.info@gmail.com (They'll be hosting workshops in the coming months for groups interested in applying this method.) Contact TRANSVISTA: info@tvscope.cn #Neuroscience #TwoPhotonImaging #SpatialTranscriptomics #CalciumImaging #FreelyMovingMice #BrainResearch #MultimodalIntegration #TRANSVISTA

Excited that our TRU-FACT preprint is now live! TRU-FACT links what individual neurons do in vivo, with who they are molecularly, and who they connect to. As a neurologist, I’m excited by its potential for understanding and treating circuit disorders. bit.ly/42kictR

For years, neuroscientists faced a choice: record what a neuron does in a living animal, or read its molecular identity postmortem. Same cells, both readouts, at scale? Out of reach. TRU-FACT closes that gap. 🧵

Fantastic work from Schnitzer lab. Congrats to all!

@PlasmoLab Right! This is really something we need to crack the neural code and establish structure-function relationships for the neural networks, at precision and scale!

(1/2)Here is our TRU-FACT preprint: Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolution. biorxiv.org/content/10.648… Please let us know your comments and send email to trufact.info@gmail.com for workshop trainings. Let's make science easier!

Check our TRU-FACT videos! Now you can reliably match hundreds to thousands of neurons from in vivo to ex vivo, and perform spatial transcriptomics on the exact same cells throughout its journey! BioRxiv: biorxiv.org/content/10.648…

Such an amazing technique developed by my friends @LunWangm, @Xiaochen_Sun_ , @GChattree, and others from the Schnitzer lab! I watched the struggle to optimize this over the years and am genuinely so impressed with the results.

@bariskurtkaya @JuhlinLeo Thanks Baris, you explained very well! With TRU-FACT breakthroughs, we are making multimodal alignments a really routine thing that every lab can do it, without limitations like sparse cells(~10%) only or small gene panels or no p-values or small scale. Pls join our workshops!

@JuhlinLeo TRU-FACT extends to high-plex (MERFISH-class), dense excitatory populations, thin (~10 μm) sections, and deep brain & freely-moving prep — plus per-cell p-values on each match. I think @LunWangm can say more! (2/2)

TRU-FACT is finally out on bioRxiv! Aligning in vivo imaging and spatial biology data at cellular resolution has long felt like a “wish list” capacity, so it’s exciting to see it come together. 🧠 biorxiv.org/content/10.648…

@Yuxi_Ke Many thanks to your great efforts which pave the road to TRU-FACT!

Huge congratulations to Lun, Xiqian, Xiaochen, and everyone who carried it across the finish line.

@A_KwanSun Thank you so much for your supports, the great engineers, and the amazing TRANSVISTA!

Breakthrough in multimodal neuroscience from Mark Schnitzer's team! By bridging in vivo dynamics and molecular profiles, TRU-FACT sets a new bar for cellular resolution. Our TRANSVISTA SN100 was proud to facilitate the free-behavioral recordings in this study. Congrats to Lun!

For decades, two revolutions in neuroscience ran in parallel: - 🧠 In vivo imaging — watch neurons fire in living animals - 🧬 Spatial transcriptomics — read cell's molecular identity Meet TRU-FACT - a graph-based method that matches cells between these datasets at scale 🧵

@Xiaochen_Sun_ Our TRU-FACT movie is here:

Excited to share my co-first-authored work on registering in vivo Ca2+ imaging data to ex vivo transcriptomic and projection datasets, enabling the mapping of each cell’s functional coding to its genetic cell type: biorxiv.org/content/10.648….

(2/2)Check our TRU-FACT movie here: drive.google.com/file/d/10vZJTP… or download it from here: biorxiv.org/content/10.648…