@phdthesis{20206,
  abstract     = {The internal structure of biomolecules and their organization in higher-order arrangements are key factors governing the working principles of biological systems. Bioimaging has successfully revealed arrangements across relevant spatial scales. For example, cryo-electron tomography has become widely used for analyzing biomolecular structures in situ due to its comprehensive structural visualization of near-natively preserved samples, and its capability of sub-nm resolution via averaging. However, the identification of molecules within crowded cellular environments is often hindered by low contrast. Fluorescence microscopy, on the other hand, routinely visualizes specifically labeled targets at single-molecule contrast against essentially zero background. Moreover, it provides comparatively high throughput and is amenable to multiplexing. Due to this complementarity, combining datasets from both modalities acquired on the same region via correlative light and electron microscopy can reveal novel types of information. 
The spatial scale at which information can be extracted depends on imaging resolution and correlation accuracy. Since diffraction of light limits the resolution of conventional fluorescence microscopy to few hundreds of nanometers, reaching the full potential of correlative imaging requires super-resolution approaches. Performing imaging at cryogenic temperature preserves structures in a near-native state and minimizes distortions between the fluorescence and the electron microscopy datasets. Implementations of this concept have achieved correlation on the scale of cellular organelles or bacterial domains.
We have worked towards pushing correlative imaging to the single-molecule scale by improving cryo-super-resolution microscopy, and devising a refined image correlation workflow. As part of this project, I constructed a microscopy setup and adopted it for super-resolution fluorescence microscopy at room temperature and cryogenic conditions. I explored different cryo-stages and acquisition strategies. Specifically, I developed a new scheme for correcting sample drift, thus increasing mechanical stability during microscopy acquisitions.
},
  author       = {Vorlaufer, Jakob},
  issn         = {2663-337X},
  pages        = {107},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Construction of a cryo-super-resolution microscope to guide in situ structure analysis}},
  doi          = {10.15479/AT-ISTA-20206},
  year         = {2025},
}

@phdthesis{18681,
  author       = {Tavakoli, Mojtaba},
  isbn         = {978-3-99078-048-0},
  issn         = {2663-337X},
  pages        = {230},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Developing molecular and structural tools for studying brain architecture with super resolution expansion microscopy. LICONN: Molecularly-informed connectomics reconstruction with light microscopy}},
  doi          = {10.15479/at:ista:18681},
  year         = {2024},
}

@phdthesis{18674,
  abstract     = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue requires volumetric imaging at nanoscale spatial resolution. While light microscopy excels at visualizing specific molecules and individual cells, achieving dense, synapse-level circuit reconstruction has not been possible with any light microscopy technique. Thus, the goal of my work was to develop image and data analysis pipelines for brain tissue visualization and reconstruction with light microscopy. To achieve dense circuit reconstruction with single-synapse resolution, I developed both conventional and deep-learning-based synapse detection algorithms, as well as connectivity analysis pipelines that integrate synapse detection with volumetric segmentation of brain tissue.},
  author       = {Lyudchik, Julia},
  isbn         = { 978-3-99078-051-0},
  issn         = {2663-337X},
  pages        = {217},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Image analysis for brain tissue reconstruction with super-resolution light microscopy}},
  doi          = {10.15479/at:ista:18674},
  year         = {2024},
}

@phdthesis{18471,
  abstract     = {Spatial omics technologies are enriching our understanding of complex biological samples, by
allowing us to study their molecular composition while preserving the spatial relationships
between molecules in their native context. As the field continues to advance, there are
technical challenges that need to be addressed in order to take full advantage of the spatial
capabilities of these methods. In this work, I present two technical developments that I
established for multiplexed error robust FISH (MERFISH) throughout my PhD: (1) pushing the
spatial resolution limits to the nanoscale, and (2) adding rich tissue context to the mouse brain
transcriptome. To achieve nanoscale resolution with MERFISH in cultured cells, I combined it
with stimulated emission depletion (STED) and expansion microscopy (ExM) to achieve a
spatial resolution as low as ~20 nm, and explored the compatibility of MERFISH with singlemolecule localization microscopy (SMLM) techniques. To visualize targeted mRNAs in mouse
brain tissue, I applied the comprehensive analysis of tissues across scales (CATS) toolbox, which
provides an unbiased morphological readout by labeling the extracellular domain. I
successfully established this method, which we call CATS-MERFISH-ExM, to work with thick
mouse brain slices, being able to extract transcriptomics information with 3D tissue context.
CATS-MERFISH-ExM enabled us to identify cell types and further visualize the subcellular
distribution of transcripts in mouse brain tissue, shedding light on the neuropil-specific
transcriptome. This method provides integrated information on cellular structure and
transcriptomes in situ, and could potentially be applied with other modalities, opening new
avenues for scientific discovery. },
  author       = {Agudelo Duenas, Nathalie},
  isbn         = {978-3-99078-044-2},
  issn         = {2663-337X},
  pages        = {97},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Visualizing the neuronal transcriptional landscape with tissue context}},
  doi          = {10.15479/at:ista:18471},
  year         = {2024},
}

@phdthesis{12470,
  abstract     = {The brain is an exceptionally sophisticated organ consisting of billions of cells and trillions of 
connections that orchestrate our cognition and behavior. To decode its complex connectivity, it is 
pivotal to disentangle its intricate architecture spanning from cm-sized circuits down to tens of 
nm-small synapses.
To achieve this goal, I developed CATS – Comprehensive Analysis of nervous Tissue across 
Scales, a versatile toolbox for obtaining a holistic view of nervous tissue context with (superresolution) fluorescence microscopy. CATS combines comprehensive labeling of the extracellular
space, that is compatible with chemical fixation, with information on molecular markers, superresolved data acquisition and machine-learning based data analysis for segmentation and synapse 
identification.
I used CATS to analyze key features of nervous tissue connectivity, ranging from whole tissue 
architecture, neuronal in- and output-fields, down to synapse morphology.
Focusing on the hippocampal circuitry, I quantified synaptic transmission properties of mossy 
fiber boutons and analyzed the connectivity pattern of dentate gyrus granule cells with CA3 
pyramidal neurons. This shows that CATS is a viable tool to study hallmarks of neuronal 
connectivity with light microscopy.},
  author       = {Michalska, Julia M},
  isbn         = {978-3-99078-026-8},
  issn         = {2663-337X},
  pages        = {201},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{A versatile toolbox for the comprehensive analysis of nervous tissue organization with light microscopy}},
  doi          = {10.15479/at:ista:12470},
  year         = {2023},
}