@article {69385, title = {A Bayesian Solution to Count the Number of Molecules within a Diffraction Limited Spot}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 22}, abstract = {

We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed at each timepoint. This problem occurs regularly in light microscopy of objects that are smaller than the diffraction limit, where one wishes to count the number of fluorescently labelled subunits. Our proposed solution directly models the photo-physics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model. Given a trace of intensity over time, our model jointly estimates the parameters of the intensity distribution per emitter, their blinking rates, as well as a posterior distribution of the total number of fluorescent emitters. We show that our model is consistently more accurate and increases the range of countable subunits by a factor of two compared to current state-of-the-art methods, which count based on autocorrelation and blinking frequency, Further-more, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability, and therefore can inform experimental conditions that will maximize counting accuracy.

}, doi = {10.1101/2024.04.18.590066}, url = {https://www.biorxiv.org/content/early/2024/04/22/2024.04.18.590066}, author = {Alexander Hillsley and Johannes Stein and Paul W. Tillberg and David L. Stern and Jan Funke} } @article {68469, title = {Multimodal mapping of cell types and projections in the central nucleus of the amygdala}, journal = {eLife}, year = {2023}, month = {2023 Jan 20}, abstract = {

The central nucleus of the amygdala (CEA) is a brain region that integrates external and internal sensory information and executes innate and adaptive behaviors through distinct output pathways. Despite its complex functions, the diversity of molecularly defined neuronal types in the CEA and their contributions to major axonal projection targets have not been examined systematically. Here, we performed single-cell RNA-sequencing (scRNA-Seq) to classify molecularly defined cell types in the CEA and identified marker-genes to map the location of these neuronal types using\ expansion\ assisted iterative fluorescence in\ situ\ hybridization (EASI-FISH). We developed new methods to integrate EASI-FISH with 5-plex retrograde axonal labeling to determine the spatial, morphological, and connectivity properties of \~{}30,000 molecularly defined CEA neurons. Our study revealed spatio-molecular organization of the CEA, with medial and lateral CEA associated with distinct cell families. We also found a long-range axon projection network from the CEA, where target regions receive inputs from multiple molecularly defined cell types. Axon collateralization was found primarily among projections to hindbrain targets, which are distinct from forebrain projections. This resource reports marker-gene combinations for molecularly defined cell types and axon-projection types, which will be useful for selective interrogation of these neuronal populations to study their contributions to the diverse functions of the CEA.

}, doi = {10.7554/eLife.84262}, url = {https://doi.org/10.7554/eLife.84262}, author = {Yuhan Wang and Sabine Krabbe and Mark Eddison and Fredrick E. Henry and Greg Fleishman and Andrew L. Lemire and Lihua Wang and Wyatt Korff and Paul W. Tillberg and Andreas L{\"u}thi and Scott M. Sternson} } @article {67849, title = {Rapid reconstruction of neural circuits using tissue expansion and lattice light sheet microscopy}, journal = {eLife}, year = {2022}, month = {2022 Oct 26}, abstract = {

Electron microscopy (EM) allows for the reconstruction of dense neuronal connectomes but suffers from low throughput, limiting its application to small numbers of reference specimens. We developed a protocol and analysis pipeline using tissue expansion and lattice light-sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many samples with single synapse resolution and molecular contrast. We validate this approach in\ Drosophila, demonstrating that it yields synaptic counts similar to those obtained by EM, can be used to compare counts across sex and experience, and to correlate structural connectivity with functional connectivity. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.

}, doi = {10.7554/eLife.81248}, url = {https://doi.org/10.7554/eLife.81248}, author = {Joshua L. Lillvis and Hideo Otsuna and Xiaoyu Ding and Igor Pisarev and Takashi Kawase and Jennifer Colonell and Konrad Rokicki and Cristian Goina and Ruixuan Gao and Amy Hu and Kaiyu Wang and John Bogovic and Daniel E. Milkie and Edward S. Boyden and Stephan Saalfeld and Paul W. Tillberg and Barry J. Dickson} } @article {66029, title = {Visualizing cellular and tissue ultrastructure using Ten-fold Robust Expansion Microscopy (TREx)}, journal = {eLife}, year = {2022}, month = {2022 Feb 18}, abstract = {

Expansion microscopy (ExM) is a powerful technique to overcome the diffraction limit of light microscopy that can be applied in both tissues and cells. In ExM, samples are embedded in a swellable polymer gel to physically expand the sample and isotropically increase resolution in x, y and z. The maximum resolution increase is limited by the expansion factor of the polymer gel, which is four-fold for the original ExM protocol. Variations on the original ExM method have been reported that allow for greater expansion factors, for example using iterative expansion, but at the cost of ease of adoption or versatility. Here, we systematically explore the ExM recipe space and present a novel method termed Ten-fold Robust Expansion Microscopy (TREx) that, like the original ExM method, requires no specialized equipment or procedures to carry out. We demonstrate that TREx gels expand ten-fold, can be handled easily, and can be applied to both thick tissue sections and cells enabling high-resolution subcellular imaging in a single expansion step. We show that applying TREx on antibody-stained samples can be combined with off-the-shelf small molecule stains for both total protein and membranes to provide ultrastructural context to subcellular protein localization.

}, doi = {https://doi.org/10.1101/2021.02.03.428837}, author = {Hugo G.J. Damstra and Boaz Mohar and Mark Eddison and Anna Akhmanova and Lukas C. Kapitein and Paul W. Tillberg} } @article {67243, title = {Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization}, journal = {bioRxiv}, year = {2021}, month = {2021 Mar 8}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed\ Expansion-Assisted Iterative\ Fluorescence\ In\ Situ\ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 {\textmu}m) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, doi = {10.1101/2021.03.08.434304}, url = {https://doi.org/10.1101/2021.03.08.434304}, author = {Yuhan Wang and Mark Eddison and Greg Fleishman and Martin Weigert and Shengjin Xu and Frederick E. Henry and Tim Wang and Andrew L. Lemire and Uwe Schmidt and Hui Yang and Konrad Rokicki and Cristian Goina and Karel Svoboda and Eugene W. Myers and Stephan Saalfeld and Wyatt Korff and Scott M. Sternson and Paul W. Tillberg} }