Reproducible, high-dimensional imaging in archival human tissue by multiplexed ion beam imaging by time-of-flight (MIBI-TOF) DOI Creative Commons
Candace C. Liu, Marc Bossé,

Alex Kong

et al.

Laboratory Investigation, Journal Year: 2022, Volume and Issue: 102(7), P. 762 - 770

Published: March 30, 2022

Language: Английский

Immune evasion and provocation by Mycobacterium tuberculosis DOI Open Access
Pallavi Chandra, Steven J. Grigsby, Jennifer A. Philips

et al.

Nature Reviews Microbiology, Journal Year: 2022, Volume and Issue: 20(12), P. 750 - 766

Published: July 25, 2022

Language: Английский

Citations

312

The dawn of spatial omics DOI
Dario Bressan, Giorgia Battistoni, Gregory J. Hannon

et al.

Science, Journal Year: 2023, Volume and Issue: 381(6657)

Published: Aug. 3, 2023

Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas biology and eventually revolutionize pathology by measuring physical tissue structure molecular characteristics at same time. Although field came age past 5 years, it still suffers from some growing pains: barriers entry, robustness, unclear best practices for experimental design analysis, lack standardization. In this Review, we present systematic catalog different families spatial technologies; highlight their principles, power, limitations; give perspective suggestions on biggest challenges lay ahead incredibly powerful-but hard navigate-landscape.

Language: Английский

Citations

256

Spatial profiling technologies illuminate the tumor microenvironment DOI Creative Commons
Ofer Elhanani, Raz Ben-Uri, Leeat Keren

et al.

Cancer Cell, Journal Year: 2023, Volume and Issue: 41(3), P. 404 - 420

Published: Feb. 16, 2023

Language: Английский

Citations

210

Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control DOI Creative Commons
Hannah P. Gideon,

Travis K. Hughes,

Constantine N. Tzouanas

et al.

Immunity, Journal Year: 2022, Volume and Issue: 55(5), P. 827 - 846.e10

Published: April 27, 2022

Mycobacterium tuberculosis lung infection results in a complex multicellular structure: the granuloma. In some granulomas, immune activity promotes bacterial clearance, but others, bacteria persist and grow. We identified correlates of control cynomolgus macaque granulomas by co-registering longitudinal positron emission tomography computed imaging, single-cell RNA sequencing, measures clearance. Bacterial persistence occurred enriched for mast, endothelial, fibroblast, plasma cells, signaling amongst themselves via type 2 immunity wound-healing pathways. Granulomas that drove were characterized cellular ecosystems 1-type 17, stem-like, cytotoxic T cells engaged pro-inflammatory networks involving diverse cell populations. arose later displayed functional characteristics restrictive more capable killing Mtb. Our define underlying (lack of) granuloma resolution highlight host targets can be leveraged to develop new vaccine therapeutic strategies TB.

Language: Английский

Citations

180

Spatial biology of cancer evolution DOI
Zaira Seferbekova, Artem Lomakin, Lucy Yates

et al.

Nature Reviews Genetics, Journal Year: 2022, Volume and Issue: 24(5), P. 295 - 313

Published: Dec. 9, 2022

Language: Английский

Citations

119

A spatially resolved timeline of the human maternal–fetal interface DOI Creative Commons
Shirley Greenbaum, Inna Averbukh, Erin Soon

et al.

Nature, Journal Year: 2023, Volume and Issue: 619(7970), P. 595 - 605

Published: July 19, 2023

Abstract Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with maternal decidua promote a tissue microenvironment conducive artery remodelling (SAR) 1–3 . However, it remains matter of debate regarding which immune stromal cells participate these interactions this evolves respect gestational age. Here we used multiomics approach, combining strengths spatial proteomics transcriptomics, construct spatiotemporal atlas human maternal–fetal interface half pregnancy. We multiplexed ion beam imaging by time-of-flight 37-plex antibody panel analyse around 500,000 588 arteries within intact from 66 individuals between 6 20 weeks gestation, integrating dataset co-registered transcriptomics profiles. Gestational age substantially influenced frequency cells, tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 IDO-1 becoming increasingly enriched colocalized at later time points. By contrast, SAR progression preferentially correlated EVT invasion was transcriptionally defined 78 gene ontology pathways exhibiting distinct monotonic biphasic trends. Last, developed an integrated model whereby is accompanied upregulation pro-angiogenic, immunoregulatory programmes that vascular endothelium while avoiding activation cells.

Language: Английский

Citations

83

Immune cell interactions in tuberculosis DOI Creative Commons
JoAnne L. Flynn, John Chan

Cell, Journal Year: 2022, Volume and Issue: 185(25), P. 4682 - 4702

Published: Dec. 1, 2022

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

51

Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments DOI Creative Commons
Yuzhou Feng, Tianpei Yang, John Zhu

et al.

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

Published: May 15, 2023

Abstract Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and underlying biology clinical features, but there is significant lag development downstream analysis methods benchmarking tools. Here we present SPIAT (spatial image tissues), a spatial-platform agnostic toolkit with suite spatial algorithms, spaSim simulator), simulator data. includes multiple colocalization, neighborhood heterogeneity metrics to characterize patterns cells. Ten are benchmarked using simulated data generated spaSim. We show how can uncover cancer immune subtypes correlated prognosis cell dysfunction diabetes. Our results suggest as useful tools for quantifying patterns, identifying validating correlates outcomes supporting method development.

Language: Английский

Citations

50

CellSighter: a neural network to classify cells in highly multiplexed images DOI Creative Commons
Yael Amitay, Yuval Bussi,

Ben Feinstein

et al.

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

Published: July 18, 2023

Abstract Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states tissues. However, classification, the task identifying type individual cells, remains challenging, labor-intensive, limiting throughput. Here, we present CellSighter, a deep-learning based pipeline accelerate classification multiplexed images. Given small training set expert-labeled images, CellSighter outputs label probabilities for all cells new achieves over 80% accuracy major across platforms, which approaches inter-observer concordance. Ablation studies simulations show that is able generalize its data learn features protein expression levels, as well spatial such subcellular patterns. CellSighter’s design reduces overfitting, it can be trained with only thousands or even hundreds labeled examples. also prediction confidence, allowing downstream experts control results. Altogether, drastically hands-on time while improving consistency datasets.

Language: Английский

Citations

49