High-quality ultrastructural preservation using cryofixation for 3D electron microscopy of genetically labeled tissues
Abstract
Electron microscopy (EM) offers unparalleled power to study cell substructures at the nanoscale. Cryofixation by high-pressure freezing offers optimal morphological preservation, as it captures cellular structures instantaneously in their near-native states. However, the applicability of cryofixation is limited by its incompatibilities with diaminobenzidine labeling using genetic EM tags and the high-contrast en bloc staining required for serial block-face scanning electron microscopy (SBEM). In addition, it is challenging to perform correlated light and electron microscopy (CLEM) with cryofixed samples. Consequently, these powerful methods cannot be applied to address questions requiring optimal morphological preservation. Here we developed an approach that overcomes these limitations; it enables genetically labeled, cryofixed samples to be characterized with SBEM and 3D CLEM. Our approach is broadly applicable, as demonstrated in cultured cells, Drosophila olfactory organ and mouse brain. This optimization exploits the potential of cryofixation, allowing quality ultrastructural preservation for diverse EM applications.
Data availability
A source data file has been provided for Figure 4 (Figure 4-source data 1). The SBEM volume of a Drosophila antenna presented in this study has been deposited to the Cell Image Library. The SBEM volume, the tdTomato confocal volume and the DRAQ5 confocal volume used for 3D CLEM in a mouse brain (corresponding to Figure 5) have also been deposited to the Cell Image Library. The video of 3D CLEM in a mouse brain expressing tdTomato that corresponds to Figure 5-video supplement 1 has been deposited to the Cell Image Library.
-
SBEM of Drosophila antenna (10x UAS-myc-APEX2-Orco; Or47b-GAL4)Publicly available at the Cell Image Library under accession number CIL:50452.
-
SBEM volume used for 3D CLEM in mouse brainPublicly available at the Cell Image Library under accession number CIL:50451.
-
tdTomato confocal volume used for 3D CLEM in a mouse brainPublicly available at the Cell Image Library under accession number CIL:50453.
-
DRAQ5 confocal volume used for 3D CLEM in a mouse brainPublicly available at the Cell Image Library under accession number CIL:50454.
-
Video of 3D CLEM in a mouse brain expressing tdTomatoPublicly available at the Cell Image Library under accession number CIL:50401.
Article and author information
Author details
Funding
National Institute on Deafness and Other Communication Disorders (R01DC015519)
- Chih-Ying Su
National Institute of General Medical Sciences (P41GM103412)
- Mark H Ellisman
Croucher Foundation
- Tin Ki Tsang
Kavli Foundation (2015-004)
- Chih-Ying Su
- Mark H Ellisman
Ray Thomas Edwards Foundation
- Chih-Ying Su
Frontiers of Innovation Scholars Program
- Tin Ki Tsang
National Institute of General Medical Sciences (R01GM086197)
- Daniela Boassa
Kavli Foundation (2016-038)
- Daniela Boassa
- Davide Dulcis
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2018, Tsang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 8,305
- views
-
- 1,280
- downloads
-
- 60
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.
-
- Neuroscience
A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.