Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples DOI Open Access
Nejla Ozirmak Lermi,

Marta Ayala,

Sharia Hernandez

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Abstract Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, strengths commercially available ST platforms have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma pleural mesothelioma tumor samples tissue microarrays to compare performance single cell CosMx, MERFISH, Xenium (uni/multi-modal) reference bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, hematoxylin eosin staining data for same samples. addition objective assessment automatic segmentation phenotyping, performed pixel-resolution manual evaluation phenotyping carry out pathologically meaningful comparison between platforms. Our study detailed intricate differences platforms, revealed importance parameters such age probe design determining quality, suggested reliable workflows accurate profiling molecular discovery.

Language: Английский

Spatial transcriptomic clocks reveal cell proximity effects in brain ageing DOI Creative Commons
Eric Sun, Olivia Y. Zhou, Max Hauptschein

et al.

Nature, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 18, 2024

Old age is associated with a decline in cognitive function and an increase neurodegenerative disease risk1. Brain ageing complex accompanied by many cellular changes2. Furthermore, the influence that aged cells have on neighbouring how this contributes to tissue unknown. More generally, tools systematically address question tissues not yet been developed. Here we generate spatially resolved single-cell transcriptomics brain atlas of 4.2 million from 20 distinct ages across adult lifespan two rejuvenating interventions—exercise partial reprogramming. We build spatial clocks, machine learning models trained atlas, identify cell-type-specific transcriptomic fingerprints ageing, rejuvenation disease, including for rare cell types. Using clocks deep learning, find T cells, which increasingly infiltrate age, marked pro-ageing proximity effect cells. Surprisingly, neural stem strong pro-rejuvenating also potential mediators their neighbours. These results suggest types can potent neighbours could be targeted counter ageing. Spatial represent useful tool studying cell–cell interactions contexts should allow scalable assessment efficacy interventions disease. A map mouse at different reveals signatures effects

Language: Английский

Citations

5
Unified integration of spatial transcriptomics across platforms DOI Creative Commons

Eldad Haber,

Ajinkya Deshpande, Jian Ma

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 5, 2025

Spatial transcriptomics (ST) has transformed our understanding of tissue architecture and cellular interactions, but integrating ST data across platforms remains challenging due to differences in gene panels, sparsity, technical variability. Here, we introduce LLOKI, a novel framework for imaging-based from diverse without requiring shared panels. LLOKI addresses integration through two key alignment tasks: feature technologies batch datasets. Feature constructs graph based on spatial proximity expression propagate features impute missing values. Optimal transport adjusts sparsity match scRNA-seq references, enabling single-cell foundation models such as scGPT generate unified features. Batch then refines scGPT-transformed embeddings, mitigating effects while preserving biological Evaluations mouse brain samples five different demonstrate that outperforms existing methods is effective cross-technology program identification slice alignment. Applying ovarian cancer datasets, identify an integrated indicative tumor-infiltrating T cells Together, provides robust cross-platform studies, with the potential scale large atlas deeper insights into organization environments.

Language: Английский

Citations

0
MerQuaCo: a computational tool for quality control in image-based spatial transcriptomics DOI Creative Commons
Naomi Martin, Paul Olsen,

Jacob Quon

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 7, 2024

Image-based spatial transcriptomics platforms are powerful tools often used to identify cell populations and describe gene expression in intact tissue. Spatial experiments return large, high-dimension datasets several open-source software packages available facilitate analysis visualization. results typically imperfect. For example, local variations transcript detection probability common. Software characterize imperfections their impact on downstream analyses lacking so the data quality is assessed manually, a laborious subjective process. Here we dataset of 641 fresh-frozen adult mouse brain sections collected using Vizgen MERSCOPE. Common included loss tissue from section, outside imaging volume due detachment coverslip, transcripts missing dropped images, varying through space, differences between experiments. We incidence each imperfection likely accuracy type labels. develop MerQuaCo, code that detects quantifies without user input, facilitating selection for further with existing packages. Together, our MerQuaCo rigorous, objective assessment results.

Language: Английский

Citations

2
Powerful microscopy technologies decode spatially organized cellular networks that drive response to immunotherapy in humans DOI
Jonathan H. Chen, Liad Elmelech,

Alexander L. Tang

et al.

Current Opinion in Immunology, Journal Year: 2024, Volume and Issue: 91, P. 102463 - 102463

Published: Sept. 14, 2024

Language: Английский

Citations

0
Single-cell spatial transcriptomics of fixed, paraffin-embedded biopsies reveals colitis-associated cell networks DOI Creative Commons
Elvira Mennillo, Madison L. Lotstein, Gyehyun Lee

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 11, 2024

Imaging-based, single-cell spatial transcriptomics (iSCST) using formalin-fixed, paraffin-embedded (FFPE) tissue could transform translational research by retaining all cell subsets and locations while enabling the analysis of archived specimens. We aimed to develop a robust framework for applying iSCST clinical FFPE mucosal biopsies from patients with inflammatory bowel disease (IBD).

Language: Английский

Citations

0
Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples DOI Open Access
Nejla Ozirmak Lermi,

Marta Ayala,

Sharia Hernandez

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Abstract Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, strengths commercially available ST platforms have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma pleural mesothelioma tumor samples tissue microarrays to compare performance single cell CosMx, MERFISH, Xenium (uni/multi-modal) reference bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, hematoxylin eosin staining data for same samples. addition objective assessment automatic segmentation phenotyping, performed pixel-resolution manual evaluation phenotyping carry out pathologically meaningful comparison between platforms. Our study detailed intricate differences platforms, revealed importance parameters such age probe design determining quality, suggested reliable workflows accurate profiling molecular discovery.

Language: Английский

Citations

0