EMPIAR-13031 - Systematic membrane thickness variation across cellular organelles revealed by cryo-ET [multiple data sets in MRC format]

EMPIAR-13031

Systematic membrane thickness variation across cellular organelles revealed by cryo-ET [multiple data sets in MRC format]

Publication:

Systematic membrane thickness variation across cellular organelles revealed by cryo-ET

Glushkova D, Boehm S, Beck M

Journal of Cell Biology (---)

Related EMDB entry:

EMD-55310 EMD-55308

Deposited:

2025-10-06

Released:

2025-10-29

Last modified:

2025-10-29

Imageset size:

97.95 GB

Imageset DOI:

10.6019/EMPIAR-13031

Experimental metadata:

Download xml json

Contains:

reconstructed volumes
tilt series

Image Sets

1. cryoCARE-denoised bin4 tomograms of HEK293 cells

Category:

reconstructed volumes

Image format:

MRC

No. of images or tilt series:

Image size:

(1024, 1024)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(7.884, 7.884)

Details:

CryoCARE denoised bin4 tomograms of HEK293 cells. Denoising was performed on even and odd frames. Tomograms were reconstructed using weighted back-projection (WBP) implemented in AreTomo2.

Files:

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2. Processed tilt series with alignment files for HEK293 cells

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(4096, 4096)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(1.971, 1.971)

Details:

Aligned, sorted, and cleaned tilt series (prior to and after dose-filtering), organized in separate folders. Each tilt series folder includes acquisition metadata (.mrc.mdoc), tilt angles (.rawtlt), and GCTF CTF estimation output (.star) files. An AreTomo subfolder within each tilt series contains AreTomo2 alignment (.aln) and IMOD transform (.xf) files used for weighted back-projection reconstruction.

Files:

選択したファイルを無圧縮zipファイルにアーカイブ化してダウンロードします。

rsync、aspera、globus 等の利用をおすすめします。

選択したファイルのリストをダウンロードします。

rsyncコマンド等でファイルリストを指定してダウンロードすることが可能です。

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

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Citations

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)