Deconvolution of cell types and states in spatial multiomics utilizing TACIT DOI Creative Commons
Khoa Le Anh Huynh, Katarzyna M. Tyc, Bruno Fernandes Matuck

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 21, 2025

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays role, it is difficult to generalize due variability at the level of cells, neighborhoods, niches in health disease. To address this, we develop TACIT, an unsupervised algorithm annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding distinguish positive cells from background, focusing on relevant markers identify ambiguous multiomic assays. Using five datasets (5,000,000 cells; 51 types) three (brain, intestine, gland), outperforms existing methods accuracy scalability. Integrating TACIT-identified reveals new phenotypes two inflammatory gland diseases. Finally, combined transcriptomics proteomics, discover under- overrepresented immune regions interest, suggesting multimodality essential translating biology clinical applications.

Language: Английский

Organization of the human intestine at single-cell resolution DOI Creative Commons
John W. Hickey, Winston R. Becker,

Stephanie Nevins

et al.

Nature, Journal Year: 2023, Volume and Issue: 619(7970), P. 572 - 584

Published: July 19, 2023

Abstract The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health 1 . intesting has length of over nine metres, along which there are differences structure function 2 localization individual cell types, type development trajectories detailed transcriptional programs probably drive these function. Here, to better understand differences, we evaluated the organization single cells using multiplexed imaging single-nucleus RNA open chromatin assays across eight different intestinal sites from donors. Through systematic analyses, find compositions differ substantially regions demonstrate complexity epithelial subtypes, same types organized into distinct neighbourhoods communities, highlighting immunological niches present intestine. We also map gene regulatory suggestive differentiation cascade, associate disease heritability specific types. These results describe composition, regulation for this organ, serve as an important reference understanding human biology disease.

Language: Английский

Citations

155
Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP) DOI Open Access
Sanjay Jain, Liming Pei, Jeffrey M. Spraggins

et al.

Nature Cell Biology, Journal Year: 2023, Volume and Issue: 25(8), P. 1089 - 1100

Published: July 19, 2023

Language: Английский

Citations

89
Spatial transcriptomics: Technologies, applications and experimental considerations DOI Creative Commons
Ye Wang, Bin Liu, Gexin Zhao

et al.

Genomics, Journal Year: 2023, Volume and Issue: 115(5), P. 110671 - 110671

Published: June 21, 2023

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in given type, one needs understand how much, when where the is expressed. Classic bulk RNA sequencing popular single destroy structural fail provide spatial information. However, location expression or complex tissue provides key clues comprehend neighboring genes cells cross talk, transduce signals work together as team complete job. functional requirement for content has been driving force rapid development transcriptomics technologies past few years. Here, we present overview current with special focus on commercially available currently being commercialized technologies, highlight applications by category discuss experimental considerations first experiment.

Language: Английский

Citations

80
Spatial omics technologies at multimodal and single cell/subcellular level DOI Creative Commons
Jiwoon Park, Junbum Kim,

Tyler Lewy

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Dec. 13, 2022

Abstract Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within tissue interest. These assays can identify specific compartments or regions in with differential transcript protein abundance, delineate their interactions, complement other methods defining phenotypes. A variety spatial methodologies are being developed commercialized; however, these techniques differ resolution, multiplexing capability, scale/throughput, coverage. Here, we review the current prospective landscape single cell to subcellular resolution analysis tools provide comprehensive picture for both research clinical applications.

Language: Английский

Citations

76
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering DOI Creative Commons
Candace C. Liu, Noah F. Greenwald,

Alex Kong

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Aug. 1, 2023

Abstract While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization how it relates to disease processes, studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside cells, such as extracellular matrix. Here, we describe pipeline, Pixie, achieves robust quantitative annotation pixel-level features using unsupervised clustering show its application across variety biological contexts platforms. Furthermore, current cell phenotyping strategies rely be labor intensive require large amounts manual cluster adjustments. We demonstrate pixel clusters lie within cells used improve annotations. comprehensively evaluate pre-processing steps parameter choices optimize performance quantify reproducibility our method. Importantly, Pixie is open source easily customizable through user-friendly interface.

Language: Английский

Citations

50
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics DOI
Gunsagar S. Gulati,

Jeremy Philip D’Silva,

Yunhe Liu

et al.

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 26(1), P. 11 - 31

Published: Aug. 21, 2024

Language: Английский

Citations

33
Multiplex protein imaging in tumour biology DOI
Natalie de Souza, Shan Zhao, Bernd Bodenmiller

et al.

Nature reviews. Cancer, Journal Year: 2024, Volume and Issue: 24(3), P. 171 - 191

Published: Feb. 5, 2024

Language: Английский

Citations

30
Spatial transcriptomics in health and disease DOI
Sanjay Jain, Michael T. Eadon

Nature Reviews Nephrology, Journal Year: 2024, Volume and Issue: 20(10), P. 659 - 671

Published: May 8, 2024

Language: Английский

Citations

20
MAPS: pathologist-level cell type annotation from tissue images through machine learning DOI Creative Commons
Muhammad Shaban, Yunhao Bai,

Huaying Qiu

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 2, 2024

Highly multiplexed protein imaging is emerging as a potent technique for analyzing distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive necessitate iterative expert input, thereby constraining scalability practicality extensive datasets. We introduce MAPS (Machine learning Analysis of Proteomics Spatial biology), machine approach facilitating rapid precise type identification with human-level accuracy from data. Validated on multiple in-house publicly available MIBI CODEX datasets, outperforms current techniques terms speed accuracy, achieving pathologist-level precision even typically challenging types, including tumor immune origin. By democratizing rapidly deployable scalable annotation, holds significant potential to expedite advances tissue biology disease comprehension.

Language: Английский

Citations

19
How to build the virtual cell with artificial intelligence: Priorities and opportunities DOI Creative Commons
Charlotte Bunne, Yusuf Roohani, Yanay Rosen

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(25), P. 7045 - 7063

Published: Dec. 1, 2024

Cells are essential to understanding health and disease, yet traditional models fall short of modeling simulating their function behavior. Advances in AI omics offer groundbreaking opportunities create an virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent simulate the behavior molecules, cells, tissues across diverse states. This Perspective provides vision on design how collaborative efforts build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, guiding experimental studies, offering new for cellular functions fostering interdisciplinary collaborations open science.

Language: Английский

Citations

16