Edward S. Boyden’s research while affiliated with Howard Hughes Medical Institute and other places

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Publications (534)


Ultrastructural membrane expansion microscopy (umExM) concept and workflow
umExM is a modified form of expansion microscopy with a custom-designed amphiphilic membrane labeling probe (termed pGk13a). a Chemical structure of pGk13a. The probe does not contain any fluorophore but has an azide to bind a fluorophore later. b umExM workflow. Blue-colored fine text highlight key differences from ExM¹ and proExM⁵, whereas black fine text highlight the same steps as ExM and proExM. b. i A specimen is perfused and chemically fixed with 4% paraformaldehyde (PFA) + 0.5% calcium chloride (CaCl2) at 4 °C for 24 hours. The brain is sliced on a vibratome to 100 μm thickness at 0-4 °C. b.ii The specimen is treated with pGk13a (structure is depicted in (a)) at 4 °C overnight (unless otherwise noted, overnight means >16 hours). b. iii The specimen is treated with acrylic acid N-hydroxysuccinimide ester (AX) at 4 °C overnight. b. iv The specimen is embedded in an expandable hydrogel (made with N,N’-Diallyl-L-tartardiamide (DATD) crosslinker⁴) at 4 °C for at least 24 hours. b. v The sample (specimen-embedded hydrogel) is chemically softened with enzymatic cleavage of proteins (i.e., non-specific cleavage with proteinase K) at room temperature (~24 °C) overnight. The probe is not digested during proteinase K treatment since it is composed of D-amino acids. b. vi Then, the sample is treated with 1x phosphate-buffered saline (PBS) to partially expand it. The pGk13a, that is anchored to the gel matrix, is fluorescently labeled via click-chemistry (i.e., DBCO-fluorophore) at room temperature, overnight. b. vii The sample is expanded with water at room temperature for 1.5 hours (exchanging water every 30 minutes).
Resolution and distortion of umExM
a Representative (n = 3 cells from one culture) single z-plane structured illumination microscopy (SIM) image of a pre-expanded HEK293 cell expressing mitochondrial matrix-targeted green fluorescent protein (GFP, shown in orange). b Single z-plane confocal image of the same HEK293 cell as in (a), after undergoing the umExM protocol, showing expression of mitochondrial matrix-targeted GFP in the same field of view as shown in (a). GFP, green color. c Single z-plane confocal image of the same umExM-expanded fixed HEK293 cell as in (a), showing pGk13a staining of the membrane in the same field of view as shown in (a). pGk13a, gray color. d Root-mean-square (RMS) length measurement error vs. measurement length, comparing pre-expansion SIM and post-expansion confocal images of cells with mitochondrial matrix-targeted GFP (blue line, mean; shaded area, standard deviation; n = 3 cells). e As in (d) but with post-expansion images showing pGk13a staining of the membrane. f Boxplot showing measured expansion factor as described (n = 4 pairs of landmark points; from 3 fixed brain slices from two mice; median, middle line; 1st quartile, lower box boundary; 3rd quartile, upper box boundary; error bars are the 95% confidence interval; black points, individual data points; used throughout this manuscript unless otherwise noted). g Boxplot showing resolution of post-expansion confocal images (60x, 1.27NA objective) of umExM-processed mouse brain tissue slices showing pGk13a staining of the membrane (n = 5 fixed brain slices from two mice). Scale bars are provided in biological units throughout all figures (i.e., physical size divided by expansion factor): (a–c) 5 μm. Source data are provided as a Source Data file.
Ultrastructure preservation and continuous labeling of membrane with umExM
a Representative (n = 5 fixed brain slices from two mice) single z-plane confocal image of expanded Thy1-YFP mouse brain tissue (hippocampus, dentate gyrus) showing pGk13a staining of the membrane. pGk13a staining of the membrane visualized in inverted gray color throughout this figure (dark signals on light background) except for (l). b Magnified view of black boxed region in (a). c As in (a) but imaging of the third ventricle. d As in (a) but imaging of mouse somatosensory cortex layer 6 (L6). e Magnified view of black boxed region in (d). f Representative (n = 2 fixed brain slices from two mice) single z-plane confocal image of expanded Thy1-YFP mouse brain tissue (hippocampus dentate gyrus), that underwent umExM protocol and anti-GFP labeling (here labeling YFP), showing YFP (magenta) and pGk13a staining of the membrane (inverted gray). g Diameter of unmyelinated axons (n = 17 axons from three fixed brain slices from two mice). h As in (f), but imaging of somatosensory cortex L6 that was used for measuring the diameter of myelinated axons. (i) Diameter of myelinated axons (n = 21 axons from two fixed brain slices from two mice). j As in (f) but imaging of the third ventricle that was used for measuring the diameter of cilia. k Diameter of cilia (n = 19 cilia from two fixed brain slices from two mice). l (left) Representative (n = 4 slices of fixed brains from three mice) volume rendering of epithelial cells in the third ventricle from mouse brain tissue, showing pGk13a staining of the membrane. pGk13a staining of the membrane visualized in gray color. (right) Magnified view of yellow boxed region in (left). Yellow arrows indicate putative extracellular vesicles. Serial image sections that were used for the 3D rendering are in Supplementary Fig. 15. m Single z-plane confocal image of expanded mouse brain tissue (third ventricle) processed by umExM, showing pGk5b staining (gray), focusing on the plasma membrane of cilia (i.e., ciliary membrane). n Transverse profile of cilia in the yellow dotted boxed region in (m) after averaging down the long axis of the box and then normalizing to the peak of pGk13a signal. o Boxplot showing the percent continuity of the membrane label (n = 5 separate cilia from two fixed brain slices from one mouse), where we define a gap as a region larger than the resolution of the images (~60 nm, from Fig. 2g), over which the pGk13a signal was two standard deviations below the mean of the intensity of pGk13a along the ciliary membrane. a 5 μm, b 2 μm, c 5 μm, d 5 μm, e 5 μm, f 0.25 μm h, j 1 μm, (l, left) (x); 13.57 μm (y); and 7.5 μm (z) (l, right) 3.76 μm (x); 3.76 μm (y); 1.5 μm (z) (m) 2 μm. Source data are provided as a Source Data file.
UmExM with antibody staining and RNA fluorescence in situ hybridization (FISH)
a Representative (n = 5 slices of fixed brain from two mice) single z-plane confocal image of expanded mouse brain tissue (hippocampus, CA3) after umExM processing with a pre-expansion antibody staining protocol (Supplementary Fig. 20), showing immunostaining with an antibody against the synaptic vesicle protein SV2A. b Magnified view of the yellow box in (a). c Single z-plane confocal image of the specimen of (a), showing pGk13a staining of the same field of view as in (a). pGk13a staining of the membrane visualized in inverted gray color throughout this figure. d Magnified view of the yellow box in (c). e Overlay of (a) and (c). f Magnified view of the yellow box in (e). g Representative (n = 5 slices of fixed brain from two mice) single z-plane confocal image of expanded mouse brain tissue (hippocampus, CA1) after umExM processing with a post-expansion antibody staining protocol (Supplementary Fig. 21), showing immunostaining against the post-synaptic density protein PSD-95. h Magnified view of the yellow box in (g). i Single z-plane confocal image of the specimen of (g), showing pGk13a staining of the same field of view as in (g). j Magnified view of the yellow box in (i). k Overlay of (g) and (i). l magnified view of the yellow box in (c). The examples of PSD95 signals that were aligned with pGk13a signals were pinpointed with yellow arrows. m Representative (n = 3 slices of fixed brain from one mouse) single z-plane confocal image of expanded mouse brain tissue (hippocampus, CA1) after umExM processing with a FISH protocol (Supplementary Fig. 23), showing HCR-FISH targeting ACTB. n Single z-plane confocal image of the specimen of (j), showing pGk13a staining of the same field of view as in (j). o Overlay of (m) and (n). Scale bars: (a–c, g, h, j) 5 μm, (d–f, j–l) 1 μm, (m–o) 20 μm.
Segmentation ability of umExM
a.i Single z-plane confocal image of expanded Thy1-YFP mouse brain tissue after umExM processing, showing pGk13a staining of the membrane. a.ii Single z-plane image showing manual segmentation of the cell body in (a.i). a.iii Overlay of (a.i) and (a.ii). (a.iv) Single z-plane confocal image of the specimen of (a.i), showing GFP signal of the same field of view as in (a.i). (a.v) single z-plane image showing manual segmentation of the cell body from (a.iv). (a.vi) overlay of (a.iv) and (a.v). b As in (a), but for segmenting dendrites. c (left) Single z-plane confocal image of expanded Thy1-YFP mouse brain tissue showing pGk13a staining of the membrane. (c.i) Magnified view of the yellow box on the left. c.ii single z-plane image showing manual segmentation of the myelinated axon in (c.i). c.iii overlay of (c.i) and (c.ii). c.iv Single z-plane confocal image of the specimen of (c.i), showing GFP signal of the same field of view as in (c.i). (c.v) Single z-plane image showing manual segmentation of the myelinated axon in (c.iv). (c.vi) Overlay of (c.iv) and (c.v). d As in (c), but for segmenting unmyelinated axons. (e) Rand score of pGk13a signal-guided segmentation of cell body, dendrites, myelinated axon and unmyelinated axons, using anti-GFP signal-guided segmentation as a “ground truth.” (n = 3 cell bodies and n = 3 dendrites from two fixed brain slices from two mice, and n = 5 myelinated axons and n = 5 unmyelinated axons from two fixed brain slices from two mice). Scale bars: (a.i–vi) 5 μm, (b.i–vi) 5 μm, (c) (left) 2 μm; (i–vi) 0.5 μm, (d) (left) 2 μm; (i–vi) 0.5 μm. Source data are provided as a Source Data file.

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Dense, continuous membrane labeling and expansion microscopy visualization of ultrastructure in tissues
  • Article
  • Full-text available

February 2025

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9 Reads

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1 Citation

Tay Won Shin

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Hao Wang

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Chi Zhang

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[...]

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Edward S. Boyden

Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines an innovative membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validate the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supports the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrate the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yields ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

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Figure 1. Multiparameter-directed evolution of voltage sensor Archon3 in mammalian cells. (a) Screening workflow for multiparameter optimization of genetically encoded voltage sensors based on Archon3 in HEK293FT cells. CaPhos, calcium phosphate transfection. (b) Expression of the selected 7 variants in cultured hippocampal neurons. (c) Representative fluorescence optical traces showing spontaneous activity of neurons expressing different voltage sensor variants. (d) Brightness, ΔF/F, and SNR for the selected 7 variants. Data are presented as means ± S.D. n = 1~15 neurons from 1~3 cultures. (e) Photobleaching curves of variants #3, #4, and #7 under continuous illumination (n = 9, 8, and 15 neurons from 1~3 cultures, respectively). λem = 664 long pass at 1.5 W/mm 2 . (f) Structural model of variant #3 (monArch) as predicted by AlphaFold3. The chromophore is colored in cyan. The mutation sites are highlighted in red (compared with Archon1). The two mutation residues Gly225 and Cys99, which are within 4 Å from the chromophore, are highlighted in blue.
Engineering of Genetically Encoded Bright Near-Infrared Fluorescent Voltage Indicator

February 2025

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8 Reads

International Journal of Molecular Sciences

Genetically encoded voltage indicators (GEVIs) allow for the cell-type-specific real-time imaging of neuronal membrane potential dynamics, which is essential to understanding neuronal information processing at both cellular and circuit levels. Among GEVIs, near-infrared-shifted GEVIs offer faster kinetics, better tissue penetration, and compatibility with optogenetic tools, enabling all-optical electrophysiology in complex biological contexts. In our previous work, we employed the directed molecular evolution of microbial rhodopsin Archaerhodopsin-3 (Arch-3) in mammalian cells to develop a voltage sensor called Archon1. Archon1 demonstrated excellent membrane localization, signal-to-noise ratio (SNR), sensitivity, kinetics, and photostability, and full compatibility with optogenetic tools. However, Archon1 suffers from low brightness and requires high illumination intensities, which leads to tissue heating and phototoxicity during prolonged imaging. In this study, we aim to improve the brightness of this voltage sensor. We performed random mutation on a bright Archon derivative and identified a novel variant, monArch, which exhibits satisfactory voltage sensitivity (4~5% ΔF/FAP) and a 9-fold increase in basal brightness compared with Archon1. However, it is hindered by suboptimal membrane localization and compromised voltage sensitivity. These challenges underscore the need for continued optimization to achieve an optimal balance of brightness, stability, and functionality in rhodopsin-based voltage sensors.


A neural correlate of individual odor preference in Drosophila

January 2025

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1 Read

Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3,062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.


A neural correlate of individual odor preference in Drosophila

December 2024

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1 Read

Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3,062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.



High-throughput expansion microscopy enables scalable super-resolution imaging

November 2024

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18 Reads

eLife

Expansion microscopy (ExM) enables nanoscale imaging using a standard confocal microscope through the physical, isotropic expansion of fixed immunolabeled specimens. ExM is widely employed to image proteins, nucleic acids, and lipid membranes in single cells; however, current methods limit the number of samples that can be processed simultaneously. We developed High-throughput Expansion Microscopy (HiExM), a robust platform that enables expansion microscopy of cells cultured in a standard 96-well plate. Our method enables ~4.2 x expansion of cells within individual wells, across multiple wells, and between plates. We also demonstrate that HiExM can be combined with high-throughput confocal imaging platforms to greatly improve the ease and scalability of image acquisition. As an example, we analyzed the effects of doxorubicin, a known cardiotoxic agent, on human cardiomyocytes (CMs) as measured by the Hoechst signal across the nucleus. We show a dose-dependent effect on nuclear DNA that is not observed in unexpanded CMs, suggesting that HiExM improves the detection of cellular phenotypes in response to drug treatment. Our method broadens the application of ExM as a tool for scalable super-resolution imaging in biological research applications.


Figure 2. Dual-patching throughput and quality metrics. (A) An image of a brain slice with a box highlighting the brain region used for experiments: the somatosensory and visual cortices. (B) Histograms of patch clamp metrics: time to achieve simultaneous recording (n=44), distance between neurons during paired recordings (n=44), and amount of time to achieve gigaseal after a neuron is detected by the pipette (n=71), and (C) Membrane capacitance, time constant (tau), input resistance, resting membrane potential, and access resistance of all cells recorded during patch-walking experiments (n=71).
Figure 3. Connectivity matrix and recordings using patch-walking. (A) Matrix of voltage and current traces from seven neurons in one acute brain slice recorded using the patch-walking algorithm for the robot. Left column shows the firing pattern of the recorded neurons. Cells are numbered such that the number represents the cell and the letter represents the manipulator (a or b). Scale bars: Horizontal 200ms for firing pattern and connection screening. Vertical 40 mV for action potentials, 50 pA for postsynaptic traces. (B) Patch-walking scheme of all neurons from the experiment matrix in (A). The curved lines between neurons represent probed connections in the matrix in (A). (C) The probed connection from the connectivity matrix in (A). The stimulus was sent to cell 1 (black) and the response from cell 2 (red) was recorded and averaged over three sweeps.
Figure 4 continued on next page
Patch-walking, a coordinated multi-pipette patch clamp for efficiently finding synaptic connections

November 2024

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47 Reads

eLife

Significant technical challenges exist when measuring synaptic connections between neurons in living brain tissue. The patch clamping technique, when used to probe for synaptic connections, is manually laborious and time-consuming. To improve its efficiency, we pursued another approach: instead of retracting all patch clamping electrodes after each recording attempt, we cleaned just one of them and reused it to obtain another recording while maintaining the others. With one new patch clamp recording attempt, many new connections can be probed. By placing one pipette in front of the others in this way, one can ‘walk’ across the mouse brain slice, termed ‘patch-walking.’ We performed 136 patch clamp attempts for two pipettes, achieving 71 successful whole cell recordings (52.2%). Of these, we probed 29 pairs (i.e. 58 bidirectional probed connections) averaging 91 μm intersomatic distance, finding three connections. Patch-walking yields 80–92% more probed connections, for experiments with 10–100 cells than the traditional synaptic connection searching method.


Calcium Fluorescence Neural Activity Datasets Metadata. Metadata for calcium fluo- rescence neural activity datasets collected from various sources. Each dataset includes information about the experimental conditions, the number of worms, the average number of labeled and recorded neurons, and the range of labeled neurons observed.
Attributes in Graph Tensor Format. Attributes and their descriptions in the standardized graph tensor format. Includes neuron identities, edge connectivity types, synaptic weights, and spatial coordinates.
Column Data Format for Connectome Dataset. Example of tabular data structure used in connectome datasets. Columns describe the origin and target neurons, connection type (gap junction or chemical synapse), number of connections, and associated neurotransmitters. The example shown is the first and last few rows of the OpenWorm [21] connectome source file.
Homogenized C. elegans\textit{C. elegans} Neural Activity and Connectivity Data

November 2024

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13 Reads

There is renewed interest in modeling and understanding the nervous system of the nematode Caenorhabditis elegans\textit{Caenorhabditis elegans} (C. elegans\textit{C. elegans}). This is particularly interesting as this model system provides a path to bridge the gap between structure and function, from nervous system connectivity to physiology. However, the many existing physiology datasets, both recording and stimulation, as well as connectome datasets, are in distinct formats, requiring extra processing steps before modeling or other analysis can commence. Here we present a homogenized dataset of neural activity, including during stimulation, compiled from 11 neuroimaging experiments and from 10 connectome reconstructions. The physiology datasets, collected under varying experimental protocols, all measure neural activity via calcium fluorescence in labeled subsets of the worm's 300 neurons. Our preprocessing pipeline standardizes these datasets by consistently ordering labeled neurons and resampling traces to a common sampling frequency. The resulting dataset includes neural recordings from approximately 900 worms and 250 uniquely labeled neurons. The connectome datasets, collected from electron microscopy (EM) reconstructions, all contain the entire nervous system of the worm, preprocessed into a graph of connections across the neurons. Using our collection of datasets is facilitated through easy data sharing on HuggingFace. We believe that our joint dataset of physiology and connectivity will facilitate modeling, for example in terms of recurrent neural network or transformer architectures, making it easier to check how well models generalize across animals and labs.


Schematic of multiExR procedure
a Expansion revealing (ExR), a technology for decrowding of proteins through isotropic protein separation. ai Coronal section of mouse brain before staining or expansion. aii Anchoring and first gelation step. The specimen is embedded in a swellable hydrogel (gray wavy lines), mechanically softened via detergent and heat treatment, and expanded in water. aiii Re-embedding and second swellable gel formation. The fully expanded first gel is re-embedded in a charge-neutral gel (not shown), followed by the formation of a second swellable hydrogel (light gray wavy lines). aiv Final up to 20x expansion with the addition of water, followed by a recommended re-embedding step to preserve gel strength for multi-round imaging (blue wavy lines). av, Post-expansion primary antibody staining (Y-shaped proteins). avi Post-expansion staining with fluorescent secondary antibodies to visualize decrowded biomolecules. b Multiplexed ExR procedure. bi Free-floating gels are stained with conventional primary and secondary antibodies, and the images are collected. bii After imaging, primary and secondary antibodies are stripped using detergent and heat-based denaturation while endogenous proteins are preserved by physical anchoring in hydrogel networks. biii Gels are re-incubated with a new round of primary and secondary antibodies, and the same field of view is imaged again. biv A 3 or 4-channel z-stack is obtained on a confocal microscope. One or more of the four channels serves as the reference channel. After imaging, the antibody stripping and staining processes are repeated for up to 10 rounds. c Registration of multi-round images using the reference channel. The multi-round images are registered using one or a combination of the methods (i-a and i-c, or i-b and i-c) in this toolbox (see Supplementary Fig. 1 and “Methods” section for more details). i-a a feature-based affine registration algorithm8,9. i-b an intensity-based affine registration algorithm¹⁰ iteratively refining the estimation from the coarse scale of the image pairs to the fine scale. i-c, a point-based registration algorithm¹¹, designed specifically to further align fine structures. cii Registered multiExR images are obtained after applying calculated warps to all channels from later rounds, creating multi-channel image volumes. Schematic created with BioRender.com. Bolded, green text highlights technical innovations of the multiExR procedure.
Validation of multiExR technology by staining, stripping, and re-staining the same set of primary and secondary antibodies across multiple rounds in the mouse somatosensory cortex
a Example field of view (max intensity projection) of registered validation dataset images in round 1, stripping after round 1, round 2, stripping after round 2, and round 3. Pixel intensities are adjusted to the same minimum and maximum values for staining and stripping rounds. b Zoom in of boxed region of (a). Scale bar, 5 μm in biological units (i.e., real size divided by expansion factor). c Mean number of objects detected in a field of view (see “Methods” section) after 7 staining rounds and the first 3 stripping rounds (n = 7 fields of view from one mouse for staining rounds, where the first 3 stripping rounds were imaged but stripping was performed between all rounds). d Mean number of puncta detected in manually-identified synaptic regions of interest (ROIs) after 7 staining rounds (the same n = 7 fields of view from one mouse, mean is taken over 51-53 ROIs per field of view). e Mean volume of puncta detected in manually-identified synaptic ROIs after 7 staining rounds (the same n = 7 fields of view from one mouse, mean is taken over 51-53 ROIs per field of view). Error bars in c–e represent standard error of the mean across the fields of view. f Estimated population distribution (violin plot of density, with a dashed line at the median and dotted lines at the quartiles) of the registration error in a representative field of view (different from panels (a, b), as it was more representative of registration error). The 95% confidence interval for each round pair is [0.01467, 0.01578] for rounds 1–2, [0.02271, 0.02430] for rounds 1–3, [0.02443, 0.02635] for rounds 1–4, [0.02337, 0.02516] for rounds 1–5, [0.02491, 0.02881] for rounds 1–6, and [0.02657, 0.02855] for rounds 1–7 (see “Methods” section, n = 1000 randomly sampled subvolumes from one field of view from one mouse). Source data are provided as a Source Data file.
23-plex nanoscale characterization of amyloid beta pathology and synapse loss in Alzheimer’s model mouse somatosensory cortex
a, b 6-channel and composite maximum intensity projections of Aβ and synaptic proteins in representative fields of view and zoom-ins (lower panels) from WT (a) and 5xFAD (b), obtained using multiExR. Scale bar, 2 µm (upper panels), (i) and (ii) 500 nm. c Violin plots of the population distribution of registration error for these fields of view. d Total volume in intensity-thresholded regions (see Methods) for D54D2, 12F4, and 6E10 Aβ species in WT and 5xFAD registered fields of view (statistical significance determined using a linear mixed effects model without multiple comparisons correction, n = 17 fields of view from two WT and two 5xFAD animals, error bars are mean ± standard error of the mean. e Total volume of objects detected after intensity thresholding and size filtration in WT and 5xFAD registered fields of view (statistical significance determined using a linear mixed effects model without multiple comparisons correction, the same n = 17 fields of view from two WT and two 5xFAD animals, error bars are mean ± standard error of the mean) for various synaptic proteins (see Supplementary Table 7 for full statistics). WT wild type, 5xFAD 5x familial Alzheimer’s disease model mice. Source data are provided as a Source Data file.
Analysis of nanoscale colocalization of synaptic proteins and amyloid-beta in Alzheimer’s model mouse brain
a Example 5-channel and composite maximum intensity projections of a 5xFAD field of view, cropped to show Aβ nanoclusters. (Scale bar, 1 μm). b Bar plots of total volume of select proteins within Aβ nanocluster ROIs (n = 71 ROIs from 9 fields of view from 2 5xFAD animals; Supplementary Table 8 for full statistics, error bars indicate mean ± standard error of the mean). c Bar plots of the fraction of volume of D54D2 occupied by AMPA receptor (error bars are mean ± standard error of the mean, statistical significance determined by Tukey’s multiple comparisons test following one-way ANOVA, p < 0.0001 for all asterisked comparisons except p = 0.0047 for GluA3 vs. GluA4, n = 44 nanocluster ROIs from 8 fields of view from 2 5xFAD animals; Supplementary Table 9(i) for full statistics). d Scatterplot of GluA2 (yellow circles) and GluA4 (blue triangles) volume vs. D54D2 volume within Aβ nanocluster ROIs. Lines indicate the best-fit lines from simple linear regressions, and the shaded regions indicate the 95% confidence interval on the best-fit line (n = 71 ROIs from 9 fields of view from 2 5xFAD animals; Supplementary Table 9(ii) for full statistics). e Scatter plot of GluA2 volume vs. GluA4 volume within Aβ nanocluster ROIs. Black line indicates the best-fit line from a simple linear regression, and the shaded region indicates the 95% confidence interval on the best-fit line (the same n = 71 ROIs from 9 fields of view from 2 5xFAD animals; Supplementary Table 9(iii) for full statistics). f Maximum intensity projections for selected channels of the ROIs circled in black in the plot in c. Scale bar, 50 nm. ****p < 0.0001, ***p < 0.001 **p < 0.01, ns, not significant. WT, wild type. 5xFAD, 5x familial Alzheimer’s disease model mice. The 5xFAD data are from the same animals and fields of view as Fig. 3. Source data are provided as a Source Data file.
20-plex nanoscale characterization of synapses in the mouse somatosensory cortex
a Example composite 5-channel maximum intensity projection a field of view showing synaptic proteins in mouse somatosensory cortex obtained using multiExR (from one of two mice from one batch of experiments). Scale bar, 2 μm in biological units. i–ii Single-channel and composite maximum intensity projections of synaptic proteins in the boxed regions from (a). Line-headed arrows indicate colocalized postsynaptic scaffold proteins; triangle-headed arrows indicate sandwich-like structures between pre- and postsynaptic scaffold proteins; red arrows indicate gephyrin with excitatory synaptic proteins nearby; blue arrows indicate colocalized AMPA receptors with transmembrane AMPA receptor regulatory proteins (Tarp gamma-2, Stargazin)). Scale bar, 500 nm in biological units. iii-iv Single-channel and composite maximum intensity projections of synaptic proteins forming sandwich-like structures from (i)-(ii). Scale bar, 100 nm in biological units.
Multiplexed expansion revealing for imaging multiprotein nanostructures in healthy and diseased brain

November 2024

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46 Reads

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1 Citation

Proteins work together in nanostructures in many physiological contexts and disease states. We recently developed expansion revealing (ExR), which expands proteins away from each other, in order to support better labeling with antibody tags and nanoscale imaging on conventional microscopes. Here, we report multiplexed expansion revealing (multiExR), which enables high-fidelity antibody visualization of >20 proteins in the same specimen, over serial rounds of staining and imaging. Across all datasets examined, multiExR exhibits a median round-to-round registration error of 39 nm, with a median registration error of 25 nm when the most stringent form of the protocol is used. We precisely map 23 proteins in the brain of 5xFAD Alzheimer’s model mice, and find reductions in synaptic protein cluster volume, and co-localization of specific AMPA receptor subunits with amyloid-beta nanoclusters. We visualize 20 synaptic proteins in specimens of mouse primary somatosensory cortex. multiExR may be of broad use in analyzing how different kinds of protein are organized amidst normal and pathological processes in biology.


A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features

October 2024

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124 Reads

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2 Citations

Nature Medicine

Although metastatic disease is the leading cause of cancer-related deaths, its tumor microenvironment remains poorly characterized due to technical and biospecimen limitations. In this study, we assembled a multi-modal spatial and cellular map of 67 tumor biopsies from 60 patients with metastatic breast cancer across diverse clinicopathological features and nine anatomic sites with detailed clinical annotations. We combined single-cell or single-nucleus RNA sequencing for all biopsies with a panel of four spatial expression assays (Slide-seq, MERFISH, ExSeq and CODEX) and H&E staining of consecutive serial sections from up to 15 of these biopsies. We leveraged the coupled measurements to provide reference points for the utility and integration of different experimental techniques and used them to assess variability in cell type composition and expression as well as emerging spatial expression characteristics across clinicopathological and methodological diversity. Finally, we assessed spatial expression and co-localization features of macrophage populations, characterized three distinct spatial phenotypes of epithelial-to-mesenchymal transition and identified expression programs associated with local T cell infiltration versus exclusion, showcasing the potential of clinically relevant discovery in such maps.


Citations (56)


... Our results establish a means of estimating this variable from immunofluorescence data: we demonstrate that synaptic contact area based on volume electron microscopy (the gold standard) correlates strongly with PSD-95 immunofluorescence. This supports synapse size estimation with other super-resolution immunofluorescence modalities, such as expansion microscopy, that are incompatible with electron microscopy 54,93,111,116 . ...

Reference:

Data-driven synapse classification reveals a logic of glutamate receptor composition
Multiplexed expansion revealing for imaging multiprotein nanostructures in healthy and diseased brain

... 18,19 A recent protocol achieves 20× single-step expansion through further optimizing gel composition and the polymerization environment. 20 Samples imaged with ExM can be stained with fluorescent antibodies both before or after the expansion step. 21 Isotropic expansion via hydration of a sample embedded in a hydrogel involves four major steps (Fig. 1). ...

Single-shot 20-fold expansion microscopy

Nature Methods

... of the final images. Nonetheless, SRRF has gained popularity in the super-resolution community [80][81][82][83][84][85][86][87][88][89][90] because it promises faster imaging by seemingly requiring fewer raw images to obtain a final super-resolved image. It remains to be seen whether a quantitative and exact model of SRRF image formation can be developed to assess the method's reliability and accuracy. ...

One-step nanoscale expansion microscopy reveals individual protein shapes

Nature Biotechnology

... field to targeted cortical areas, improving focality and minimizing off-target effects (Datta et al., 2009;Dmochowski et al., 2011;Hogeveen et al., 2016;Tedla et al., 2023;Villamar et al., 2013). TI stimulation, introduced by Grossman et al., generates low-frequency electric fields at specific brain regions through interference patterns of highfrequency currents, enabling to constrain the interference part of the stimulation (i.e. the beat frequency) to deeper brain regions (Demchenko et al., 2024;Grossman et al., 2017Grossman et al., , 2018Luff et al., 2024). ...

Target engagement of the subgenual anterior cingulate cortex with transcranial temporal interference stimulation in major depressive disorder: a protocol for a randomized sham-controlled trial

... Expansion microscopy has gained significant traction within the broader research community; however, its application in virology remains limited, with particularly few studies exploring HIV-1-related phenomena [56,57]. Other studies have investigated general aspects of the broader virology field, such as the spatial distribution of virions and viral proteins in infected cells and tissues, as well as host cell alterations following infection [58][59][60][61]. Among the available expansion protocols, the U-ExM method, which offers a 4-5-fold expansion alongside a post-expansion labeling approach, has found broader application in fields such as parasitology, centriole biology, and neurobiology, where it has significantly improved the visualization of target structures [62][63][64][65][66]. Here, we aim to assess the effectiveness of the U-ExM protocol to probe processes during virus infection, with a particular focus on nuclear HIV-1-cell interactions at high resolution. ...

Inefficient tissue immune response against MPXV in an immunocompromised mpox patient

Journal of Medical Virology

... This influence can be mitigated by reducing the illumination radiation dose, leading to low photon budget (LPB) imaging conditions characterized by low illumination power relative to the measurement time (camera exposure time). LPB conditions can also arise when imaging samples in high speed scenario [25] or using exotic wavelengths that greatly fall outside the detector's optimal quantum efficiency range [26], [27]. ...

Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy

... field to targeted cortical areas, improving focality and minimizing off-target effects (Datta et al., 2009;Dmochowski et al., 2011;Hogeveen et al., 2016;Tedla et al., 2023;Villamar et al., 2013). TI stimulation, introduced by Grossman et al., generates low-frequency electric fields at specific brain regions through interference patterns of highfrequency currents, enabling to constrain the interference part of the stimulation (i.e. the beat frequency) to deeper brain regions (Demchenko et al., 2024;Grossman et al., 2017Grossman et al., , 2018Luff et al., 2024). ...

The neuron mixer and its impact on human brain dynamics
  • Citing Article
  • May 2024

Cell Reports

... Comparing neural data models Current efforts to understand scaling in neural data Simeon et al. (2024); Sato et al. (2024) will have their reach limited by the specificity of every neural dataset. A meta-challenge for the field is understanding how different parameters (species, brain area, modality, task) impact scaling properties. ...

Scaling Properties for Artificial Neural Network Models of a Small Nervous System
  • Citing Conference Paper
  • March 2024

... However, these studies disagree on the prevalence of exon or intron definition or cotranscriptional assembly. Single-molecule RNA FISH combined with expansion microscopy showed that splicing occurs generally posttranscriptionally, in a slowmoving zone surrounding the site of transcription (Coté et al. 2024). Nanopore sequencing of nascent RNA showed that <20% of introns are spliced before RNAPII has transcribed a further 1 kb (Drexler et al. 2020). ...

Post-transcriptional splicing can occur in a slow-moving zone around the gene

eLife

... Traditional patch clamping is not feasible for drug screening due to its aforementioned shortcomings, but APC has the potential to perform as an excellent platform. Many efforts have been put into increasing the throughput of APC while decreasing human labor, such as the patcherBot Pharma , which is an automated robotic patch-clamping system (Kolb et al., 2019;Perszyk et al., 2021), the Qube384 fixed 384-well APC system (i.e., plates with open chambers at the bottom of each well to form seals for patch clamping (Sophion Bioscience) (Seibertz et al., 2022), or APC with multiple pipettes (Yip et al., 2024). However, each of these methods has drawbacks, and the throughput is not satisfying. ...

Patch-walking: Coordinated multi-pipette patch clamp for efficiently finding synaptic connections