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.

Ion channels are among the most important proteins in biology, regulating the activity of excitable cells and changing in diseases. Ideally it would be possible to actuate endogenous ion channels, in a temporally precise and reversible manner, and without requiring chemical cofactors. Here we present a modular protein architecture for fully genetically encoded, light-modulated control of ligands that modulate ion channels of a targeted cell. Our reagent, which we call a lumitoxin, combines a photoswitch and an ion channel-blocking peptide toxin. Illumination causes the photoswitch to unfold, lowering the toxin’s local concentration near the cell surface, and enabling the ion channel to function. We explore lumitoxin modularity by showing operation with peptide toxins that target different voltage-dependent K+ channels. The lumitoxin architecture may represent a new kind of modular protein-engineering strategy for designing light-activated proteins, and thus may enable development of novel tools for modulating cellular physiology.

Light-sheet imaging of cleared and expanded samples creates terabyte-sized datasets that consist of many unaligned three-dimensional image tiles, which must be reconstructed before analysis. We developed the BigStitcher software to address this challenge. BigStitcher enables interactive visualization, fast and precise alignment, spatially resolved quality estimation, real-time fusion and deconvolution of dual-illumination, multitile, multiview datasets. The software also compensates for optical effects, thereby improving accuracy and enabling subsequent biological analysis.

Cells regulate function by synthesizing and degrading proteins. This turnover ranges from minutes to weeks, as it varies across proteins, cellular compartments, cell types, and tissues. Current methods for tracking protein turnover lack the spatial and temporal resolution needed to investigate these processes, especially in the intact brain, which presents unique challenges. We describe a pulse-chase method (DELTA) for measuring protein turnover with high spatial and temporal resolution throughout the body, including the brain. DELTA relies on rapid covalent capture by HaloTag of fluorophores that were optimized for bioavailability in vivo. The nuclear protein MeCP2 showed brain region- and cell type-specific turnover. The synaptic protein PSD95 was destabilized in specific brain regions by behavioral enrichment. A novel variant of expansion microscopy further facilitated turnover measurements at individual synapses. DELTA enables studies of adaptive and maladaptive plasticity in brain-wide neural circuits.

The inherent limitations of fluorescence microscopy, notably the restricted number of color channels, have long constrained comprehensive spatial analysis in biological specimens. Here, we introduce cycleHCR technology that leverages multicycle DNA barcoding and Hybridization Chain Reaction (HCR) to surpass the conventional color barrier. cycleHCR facilitates high-specificity, single-shot imaging per target for RNA and protein species within thick specimens, mitigating the molecular crowding issues encountered with other imaging-based spatial omics techniques. We demonstrate whole-mount transcriptomics imaging of 254 genes within an E6.5\~7.0 mouse embryo, achieving precise three-dimensional gene expression and cell fate mapping across a specimen depth of \~ 310 µm. Utilizing expansion microscopy alongside protein cycleHCR, we unveil the complex network of 10 subcellular structures in primary mouse embryonic fibroblasts. Furthermore, in mouse hippocampal slice, we image 8 protein targets and profile the transcriptome of 120 genes, uncovering complex gene expression gradients and cell-type specific nuclear structural variances. cycleHCR provides a unifying framework for multiplex RNA and protein imaging, offering a quantitative solution for elucidating spatial regulations in deep tissue contexts for research and potentially diagnostic applications.

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 μm) 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 spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis 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.

In optical microscopy, fine structural details are resolved by using refraction to magnify images of a specimen. We discovered that by synthesizing a swellable polymer network within a specimen, it can be physically expanded, resulting in physical magnification. By covalently anchoring specific labels located within the specimen directly to the polymer network, labels spaced closer than the optical diffraction limit can be isotropically separated and optically resolved, a process we call expansion microscopy (ExM). Thus, this process can be used to perform scalable superresolution microscopy with diffraction-limited microscopes. We demonstrate ExM with apparent ~70-nanometer lateral resolution in both cultured cells and brain tissue, performing three-color superresolution imaging of ~107 cubic micrometers of the mouse hippocampus with a conventional confocal microscope.

Expansion microscopy (ExM) is a recently developed technique that enables nanoscale-resolution imaging of preserved cells and tissues on conventional diffraction-limited microscopes via isotropic physical expansion of the specimens before imaging. In ExM, biomolecules and/or fluorescent labels in the specimen are linked to a dense, expandable polymer matrix synthesized evenly throughout the specimen, which undergoes 3-dimensional expansion by ∼4.5 fold linearly when immersed in water. Since our first report, versions of ExM optimized for visualization of proteins, RNA, and other biomolecules have emerged. Here we describe best-practice, step-by-step ExM protocols for performing analysis of proteins (protein retention ExM, or proExM) as well as RNAs (expansion fluorescence in situ hybridization, or ExFISH), using chemicals and hardware found in a typical biology lab. Furthermore, a detailed protocol for handling and mounting expanded samples and for imaging them with confocal and light-sheet microscopes is provided. © 2018 by John Wiley & Sons, Inc.

Expansion microscopy (ExM) is a physical form of magnification that increases the effective resolving power of any microscope. Here, we describe the fundamental principles of ExM, as well as how recently developed ExM variants build upon and apply those principles. We examine applications of ExM in cell and developmental biology for the study of nanoscale structures as well as ExM's potential for scalable mapping of nanoscale structures across large sample volumes. Finally, we explore how the unique anchoring and hydrogel embedding properties enable postexpansion molecular interrogation in a purified chemical environment. ExM promises to play an important role complementary to emerging live-cell imaging techniques, because of its relative ease of adoption and modification and its compatibility with tissue specimens up to at least 200 μm thick. Expected final online publication date for the , Volume 35 is October 7, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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 µm) 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.