EMPIAR-13156 - FIB-SEM imaging of control HeLa cells, cells expressing WT STARD3, and cells expressing the STARD3 FA/YA mutant [multiple data sets in TIFF format]

EMPIAR-13156

FIB-SEM imaging of control HeLa cells, cells expressing WT STARD3, and cells expressing the STARD3 FA/YA mutant [multiple data sets in TIFF format]

Publication:

STARD3 regulates lysosome positioning and contacts via a GSK3-controlled phosphorylation switch

Eichler J, Wendling C, Huver S, Zouiouich M, Hanss V, Cardinal A, Fimbel V, Birck C, McEwen AG, Knorr C, Fromental-Ramain C, Boutry M, Chenard MP, Drin G, Tomasetto C, Alpy F

The EMBO journal (2026)

PMID: 41741634

DOI: 10.1038/s44318-026-00705-3

Related EMDB entry:

Deposited:

2025-12-04

Released:

2026-02-26

Last modified:

2026-03-02

Imageset size:

10.29 GB

Imageset DOI:

10.6019/EMPIAR-13156

Experimental metadata:

Download xml json

Contains:

reconstructed volumes

Image Sets

1. HeLa/Ctrl

Category:

reconstructed volumes

Image format:

TIFF

No. of images or tilt series:

994

Image size:

(2768, 1188)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(80.0, 80.0)

Details:

---

Files:

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2. HeLa/STARD3

Category:

reconstructed volumes

Image format:

TIFF

No. of images or tilt series:

810

Image size:

(2418, 2748)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(50.0, 50.0)

Details:

---

Files:

This entry cannot be downloaded from PDBj.

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3. HeLa/STARD3 FA/YA

Category:

reconstructed volumes

Image format:

TIFF

No. of images or tilt series:

600

Image size:

(2516, 1092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(80.0, 80.0)

Details:

---

Files:

This entry cannot be downloaded from PDBj.

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

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

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Citations

AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM.

Ramírez-Aportela E, Zarrabeitia OL, Fonseca YC, Ceska T, Subramaniam S, Carazo JM, Sorzano COS. (2026)

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Yu C, Xu Z, Zeng Q, Wan X, El-Messiry H, Zhang F, Han R. (2026)

Helical reconstruction of amyloids in cryoSPARC.

Schaefer JH, O'Neill RT, Donnelly JP, Powers ET, Kelly JW, Lander GC. (2026)

An unattended image-processing pipeline for on-the-fly quality assessment and 3D exploration in cryo-EM.

Marchán Torres D, Conesa P, Garcia A, Iceta M, Broche L, Gragera M, Linares R, Kwong HS, Chichón FJ, Svensson O, Arranz R, Effantin G, Kandiah E, Carazo JM, Sorzano COS. (2026)

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Poudel B, Gyawali R, Dhakal A, Cheng J, Xu D. (2026)

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)

Unbend, correction of local beam-induced sample motion in cryo-EM images using a 3D spline model.

Kong L, Zottig X, Elferich J, Grigorieff N. (2026)

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

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

CryoWriter: a robotic solution for improved Cryo-EM grid preparation.

Kv C, Ekundayo B, Di Fabrizio M, Mohammed I, Radecke J, Stahlberg H, Kube M. (2026)