A High-Throughput Method for Bulgeless Liquid Cell Imaging in the Transmission Electron Microscope
Notice bibliographique
Résumé
Liquid cell electron microscopy (LCEM) aims to image nanomaterials and biospecimens in their native liquid phase environment. To achieve this, liquid cells (LCs) in which the observed process is occurring must conform to strict requirements such as the establishment of hermetic sealing of liquid between two ultrathin windows and well-defined sample placement conditions. Precise nanofabrication of these LCs is therefore required, the details of which are outlined extensively in reviews [1]. The challenge of window membrane bulging has long remained unresolved [2, 3]. The bulging of these window membranes causes inhomogeneity across the viewing area (ViA). Since LC samples are prepared in the atmosphere of the laboratory environment and subsequently imaged in the high vacuum of the electron microscope, external bulging of the thin window membranes occurs. As a result, researchers have been led to collect their data in regions where this bulging is minimized (i.e., the window’s edge), and resolution is maximized. To address the challenge of window membrane bulging in LCEM, we have created a set of tools to prepare LCs for high-throughput imaging. In Figure 1, the primary components of this design are shown to include a LCEM holder, unique LCs, and a loading stage for the preparation of the LC assemblies. The process in brief consists of assembling two nanofluidic cell chips within the loading stage in the absence of air. Advanced details of both the design and methodology are available elsewhere [4]. Following this protocol, electron energy loss spectroscopy (EELS) results indicated that variation in the liquid layer thickness is on the order of 10’s of nanometres [4]. Gold nanorods (Figure 2a) were successfully imaged in the center of the ViA to readily obtain one nanometre resolution for mobile samples. This system has also demonstrated lattice resolution in the center of the ViA [4]. In addition, unstained dioleoyl-phosphatidylcholine (DOPC) liposomes (Figure 2b) were recorded with sufficient contrast. Note, that this specimen typically requires the use of contrast agents for imaging in standard transmission electron microscopy (TEM) [5, 6]. Therefore, soft materials are typically imaged through cryo-electron microscopy (cryo-EM) while the implementation of LCEM for this purpose is quickly growing. However, the preparation of these samples via many available LCEM approaches is often cumbersome, requiring a significant level of expertise, time, and the lack of a guarantee for uniform liquid layer thickness across the ViA of the LC. With our high-throughput approach, we can discern the thickness of an isolated DOPC lipid bilayer (Figure 2c) in a matter of minutes rather than hours, marking a vast improvement over both cryo-EM and conventional LCEM methods [7]. Further developments to this technology will aim at enhancing the maximum resolution capabilities by introducing features such as even thinner windows as well as other membrane materials [8]. The liquid cell electron microscopy (LCEM) imaging tool kit used to perform high-throughput sample preparation. (a) An image of the nanofluidic cell holder inside of the loading stage for sample preparation. (b) A nanofluidic cell chip designed to limit window bulging inside of the loading stage, in preparation for sample assembly. Transmission electron micrographs of gold nanorods and dioleoyl-phosphatidylcholine (DOPC) liposomes located at the centre of the viewing area (ViA). (a) Gold nanorods imaged at 200 kV in ultrapure water, a line profile (below (a)) indicates that the image yields approximately 1 nm of resolution. (b) A collection of DOPC liposomes imaged at 200 kV in a N-2-hydroxyethylpiperazine-N-2-ethane sulfonic acid (HEPES) / NaCl buffer mixture. (c) An isolated DOPC liposome from the same sample as (b) with an apparent double bilayer imaged.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».