Denoising for single-cell resolution highly multiplexed imaging: Deep learning safeguards cell spatial heterogeneity analysis DOI
Yinjie Zhang, Jing Zhao,

Yaquan Liu

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118282 - 118282

Published: April 1, 2025

Language: Английский

Grow AI virtual cells: three data pillars and closed-loop learning DOI Creative Commons
Liujia Qian, Zhen Dong, Tiannan Guo

et al.

Cell Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Language: Английский

Citations

0
Proteomics in decoding cancer: A review DOI

Elaheh Gheybi,

Parisa Hosseinzadeh,

Vahid Tayebi-Khorrami

et al.

Clinica Chimica Acta, Journal Year: 2025, Volume and Issue: unknown, P. 120302 - 120302

Published: April 1, 2025

Language: Английский

Citations

0
Deep Visual Proteomics maps proteotoxicity in a genetic liver disease DOI Creative Commons
Florian A. Rosenberger, Sophia C. Mädler, Katrine Thorhauge

et al.

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Abstract Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood 1–3 . We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) single-cell analysis 4,5 , we probe intact patient biopsies resolve molecular events during hepatocyte stress pseudotime across fibrosis stages. achieve proteome depth of up 4,300 proteins from one-third a single cell formalin-fixed, paraffin-embedded This dataset reveals potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our data show accumulation is largely cell-intrinsic, minimal propagation between hepatocytes. integrated proteomic artificial intelligence-guided image-based phenotyping several disease stages, revealing late-stage phenotype characterized globular aggregates distinct signatures, notably elevated TNFSF10 (also known as TRAIL) amounts. may represent critical stage. study offers new insights into pathogenesis introduces powerful methodology for high-resolution, situ complex tissues. approach holds potential unravel mechanisms various disorders, setting standard understanding at level

Language: Английский

Citations

0
Denoising for single-cell resolution highly multiplexed imaging: Deep learning safeguards cell spatial heterogeneity analysis DOI
Yinjie Zhang, Jing Zhao,

Yaquan Liu

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118282 - 118282

Published: April 1, 2025

Language: Английский

Citations

0