Evidence map›Paper›PMID 40060527›Full record

ArticlebioRxiv : the preprint server for biology2025

Characterization of energy filtering slit widths for MicroED data collection.

Max T B Clabbers, Johan Hattne, Michael W Martynowycz, Tamir Gonen

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Max T B ClabbersHoward Hughes Medical Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0002-5466-6508
Johan HattneHoward Hughes Medical Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0002-8936-0912
Michael W MartynowyczDepartment of Biological Chemistry, University of California, Los Angeles, CA 90095.ORCID 0000-0003-0055-230X
Tamir GonenHoward Hughes Medical Institute, University of California, Los Angeles, CA 90095.ORCID 0000-0002-9254-4069

Funding

TRD4: Design and Engineering of MicroED hardware for nanocrystallization and time resolved studiesP41GM136508 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GONEN, TAMIR · 2020 to 2024
$7.6M
NIGMS NIH HHS P41 GM136508
6 · The paper itself

Abstract

A favorable signal-to-noise ratio is essential for obtaining high-quality diffraction data in macromolecular electron crystallography. Inelastic scattering contributes significantly to the noise, reducing contrast between diffraction peaks and background, which complicates peak detection and compromises the accuracy of intensity integration. Energy filtering mitigates these challenges and enhances diffraction data quality by removing the inelastically scattered electrons, leading to reduced background noise and sharper Bragg peaks. Previously, we reported a substantial improvement in MicroED data quality and resolution with energy filtering. Here, we systematically evaluate the impact of different energy filter slit widths for optimal MicroED data collection. Data from proteinase K lamellae were collected using the 5, 10, and 20 eV energy filter slit widths. Our results show that the narrowest slit widths result in a stronger diffraction signal with lower background noise, improving the precision of the intensity measurements which resulted in better structural models. Our findings provide insights into the optimization of energy filter slit settings that, when paired with direct electron detection, enhance MicroED data collection strategies in MicroED by improving the signal-to-noise ratio, supporting higher quality data and ultimately enabling more precise structure determination.

Identifiers

PMID40060527
PMCPMC11888265

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.