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1.
Cell Rep ; 29(1): 202-211.e6, 2019 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-31577949

RESUMO

Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies.

2.
Nat Methods ; 16(10): 987-990, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31501547

RESUMO

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-µm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

3.
Cell Metab ; 29(3): 755-768.e5, 2019 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-30713109

RESUMO

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing ß cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of ß cell loss and insulitis. Our analyses revealed that ß cell destruction is preceded by a ß cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments.

5.
Cell Rep ; 23(4): 1205-1219, 2018 Apr 24.
Artigo em Inglês | MEDLINE | ID: mdl-29694896

RESUMO

Aging is associated with tissue-level changes in cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that change with age in a manner consistent with increased susceptibility to breast cancer.

6.
Cell Syst ; 6(1): 25-36.e5, 2018 Jan 24.
Artigo em Inglês | MEDLINE | ID: mdl-29289569

RESUMO

To build comprehensive models of cellular states and interactions in normal and diseased tissue, genetic and proteomic information must be extracted with single-cell and spatial resolution. Here, we extended imaging mass cytometry to enable multiplexed detection of mRNA and proteins in tissues. Three mRNA target species were detected by RNAscope-based metal in situ hybridization with simultaneous antibody detection of 16 proteins. Analysis of 70 breast cancer samples showed that HER2 and CK19 mRNA and protein levels are moderately correlated on the single-cell level, but that only HER2, and not CK19, has strong mRNA-to-protein correlation on the cell population level. The chemoattractant CXCL10 was expressed in stromal cell clusters, and the frequency of CXCL10-expressing cells correlated with T cell presence. Our flexible and expandable method will allow an increase in the information content retrieved from patient samples for biomedical purposes, enable detailed studies of tumor biology, and serve as a tool to bridge comprehensive genomic and proteomic tissue analysis.

7.
Nat Methods ; 14(9): 873-876, 2017 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-28783155

RESUMO

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.


Assuntos
Comunicação Celular/fisiologia , Citometria de Fluxo/métodos , Imagem Molecular/métodos , Proteoma/metabolismo , Software , Análise Serial de Tecidos/métodos , Algoritmos , Interpretação de Imagem Assistida por Computador/métodos , Interface Usuário-Computador
8.
Nat Biotechnol ; 35(2): 164-172, 2017 02.
Artigo em Inglês | MEDLINE | ID: mdl-28092656

RESUMO

Signaling networks are key regulators of cellular function. Although the concentrations of signaling proteins are perturbed in disease states, such as cancer, and are modulated by drug therapies, our understanding of how such changes shape the properties of signaling networks is limited. Here we couple mass-cytometry-based single-cell analysis with overexpression of tagged signaling proteins to study the dependence of signaling relationships and dynamics on protein node abundance. Focusing on the epidermal growth factor receptor (EGFR) signaling network in HEK293T cells, we analyze 20 signaling proteins during a 1-h EGF stimulation time course using a panel of 35 antibodies. Data analysis with BP-R2, a measure that quantifies complex signaling relationships, reveals abundance-dependent network states and identifies novel signaling relationships. Further, we show that upstream signaling proteins have abundance-dependent effects on downstream signaling dynamics. Our approach elucidates the influence of node abundance on signal transduction networks and will further our understanding of signaling in health and disease.


Assuntos
Fator de Crescimento Epidérmico/metabolismo , Receptores ErbB/metabolismo , Citometria de Fluxo/métodos , Regulação da Expressão Gênica/fisiologia , Modelos Biológicos , Proteoma/metabolismo , Simulação por Computador , Perfilação da Expressão Gênica/métodos , Células HEK293 , Humanos , Transdução de Sinais
9.
Cytometry A ; 87(10): 936-42, 2015 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-26147066

RESUMO

The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images.


Assuntos
Neoplasias da Mama/diagnóstico , Diagnóstico por Imagem/métodos , Citometria de Fluxo/métodos , Análise de Célula Única , Neoplasias da Mama/patologia , Feminino , Humanos , Imuno-Histoquímica
10.
Nat Methods ; 11(4): 417-22, 2014 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-24584193

RESUMO

Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.


Assuntos
Neoplasias da Mama/metabolismo , Citometria por Imagem/métodos , Proteínas de Neoplasias/metabolismo , Linhagem Celular , Células Epiteliais/citologia , Células Epiteliais/metabolismo , Feminino , Regulação Neoplásica da Expressão Gênica/fisiologia , Humanos , Proteínas de Neoplasias/genética
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