Dense 4D nanoscale reconstruction of living brain tissue

Philipp Velicky, Eder Miguel, Julia M Michalska, Julia Lyudchik, Donglai Wei, Zudi Lin, Jake F Watson, Jakob Troidl, Johanna Beyer, Yoav Ben-Simon, Christoph Sommer, Wiebke Jahr, Alban Cenameri, Johannes Broichhagen, Seth G N Grant, Peter Jonas, Gaia Novarino, Hanspeter Pfister, Bernd Bickel, Johann G Danzl*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain's complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.

Original languageEnglish
Pages (from-to)1256–1265
JournalNature Methods
Early online date10 Jul 2023
Publication statusPublished - Aug 2023


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