EMPIAR-11469 - Cryo-ET tilt series from mouse islets lift-out sample [multiple data sets in TIFF format]

EMPIAR-11469

Cryo-ET tilt series from mouse islets lift-out sample [multiple data sets in TIFF format]

Publication:

A practical multicellular sample preparation pipeline broadens the application of in situ cryo-electron tomography

Wu Y, Qin C, Du W, Guo Z, Chen L, Guo Q

Journal of structural biology 215 (2023)

PMID: 37201639

DOI: 10.1016/j.jsb.2023.107971

Related EMDB entry:

EMD-35655 EMD-35653 EMD-35652 EMD-35654

Deposited:

2023-03-15

Released:

2023-06-02

Last modified:

2023-06-13

Imageset size:

16.04 GB

Imageset DOI:

10.6019/EMPIAR-11469

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multi-frame micrographs of mouse islets lift-out sample(TOMO_001~003)

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

Image size:

(3838, 3710)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(3.826, 3.826)

Details:

---

Files:

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2. Unaligned multi-frame micrographs of mouse islets lift-out sample(TOMO_004)

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

Image size:

(11520, 8184)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.32, 1.32)

Details:

---

Files:

Archives and downloads the selected files into an uncompressed zip file.

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

It is possible to download files by specifying the file list with rsync command, etc.

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)