scholarly journals MATISSE: An analysis protocol for combining imaging mass cytometry with fluorescence microscopy to generate single-cell data

2022 ◽  
Vol 3 (1) ◽  
pp. 101034
Author(s):  
Daniëlle Krijgsman ◽  
Neeraj Sinha ◽  
Matthijs J.D. Baars ◽  
Stephanie van Dam ◽  
Mojtaba Amini ◽  
...  
2018 ◽  
Author(s):  
Tyler J. Burns ◽  
Garry P. Nolan ◽  
Nikolay Samusik

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.


2020 ◽  
Author(s):  
Giovana Ravizzoni Onzi ◽  
Juliano Luiz Faccioni ◽  
Alvaro G. Alvarado ◽  
Paula Andreghetto Bracco ◽  
Harley I. Kornblum ◽  
...  

Outliers are often ignored or even removed from data analysis. In cancer, however, single outlier cells can be of major importance, since they have uncommon characteristics that may confer capacity to invade, metastasize, or resist to therapy. Here we present the Single-Cell OUTlier analysis (SCOUT), a resource for single-cell data analysis focusing on outlier cells, and the SCOUT Selector (SCOUTS), an application to systematically apply SCOUT on a dataset over a wide range of biological markers. Using publicly available datasets of cancer samples obtained from mass cytometry and single-cell RNA-seq platforms, outlier cells for the expression of proteins or RNAs were identified and compared to their non-outlier counterparts among different samples. Our results show that analyzing single-cell data using SCOUT can uncover key information not easily observed in the analysis of the whole population.


2018 ◽  
Author(s):  
Subarna Palit ◽  
Fabian J. Theis ◽  
Christina E. Zielinski

AbstractRecent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3060-3060
Author(s):  
Rebecca S. Larue ◽  
Klara E Noble ◽  
Conner Hansen ◽  
Ngoc Ha ◽  
David A Largaespada ◽  
...  

Abstract Acute myeloid leukemia (AML) is a lethal malignancy because patients who initially respond to chemotherapy eventually relapse with treatment-resistant disease. Leukemia stem cells (LSCs) reestablish the disease by self-renewal: the ability of a stem cell to reproduce itself and give rise to progeny. LSC self-renewal is therefore critical to relapse. Most anticancer therapies are designed to inhibit proliferation. Yet, the mechanisms that direct hematopoietic stem cell (HSC) proliferation are distinct from the mechanisms that allow HSCs to self-renew (Li et al. Nature 2013). Consequently, targeting proliferation may explain the failure of traditional chemotherapy to eradicate this disease. To study leukemia self-renewal, we use a manipulatable, transgenic mouse model of AML with an Mll-AF9 fusion and a tetracycline repressible, activated NRAS (NRASG12V, Kim et al. Blood 2009). Doxycycline abolishes NRASG12V expression leading to leukemia remission. We demonstrated that expression of NRASG12V is required for self-renewal in this AML model and that NRASG12V -mediated signaling is distinct among leukemic subsets (Sachs et al. Blood 2014). We hypothesize that NRAS-activated pathways required for LSC self-renewal are limited to a subpopulation of cells with the LSC immunophenotype. Defining the mechanisms of self-renewal has been a challenge because cancer cells are highly heterogeneous and because disengaging proliferation from self-renewal can be difficult experimentally. To overcome these obstacles, we use single-cell technologies (single-cell, whole transcriptome, RNA sequencing and mass cytometry, CyTOF) to define the signaling and transcriptional profiles of individual cells. We performed single-cell RNA sequencing on unsorted leukemia cells and on a sorted, LSC-enriched population. The single-cell transcriptional profile of LSCs was distinct from the bulk population (Fig. 1A). The 100 most differentially expressed genes between these groups are involved in hematopoietic cell fate and differentiation, confirming the biological validity of this technique. Next, we sought to identify an NRASG12V -mediated self-renewing subpopulation among the LSCs. Unsupervised, two-dimensional, hierarchical clustering of LSC single-cell data identified three discrete subpopulations among the LSCs, each expressing a unique gene expression profile (Fig. 1B). Comparing the single-cell transcriptional profiles of NRASG12V -expressing LSCs to those of LSCs treated with doxycycline to extinguish NRASG12V ("RAS-On" and "RAS-Off" LSCs) revealed that two of the three LSC-expression profiles seen in RAS-on cells (Groups 1 and 3, Fig. 1B) are lost when NRASG12V is withdrawn (Fig. 1C). These data suggest that these two profiles (Groups 1 and 3) are NRASG12V -dependent, consistent with an earlier report that activated NRAS exerts bimodal effects on HSCs (Li et al., Nature 2013). Gene set enrichment analysis of these profiles, modified for single-cell data, revealed that Group 1 preferentially expresses genes associated with leukemia self-renewal. On the basis of this gene expression data, we identfied cell surface markers (CD36 and CD69) that delineate the two NRASG12V -responsive LSC-subpopulations (Groups 1 and 3). We sorted LSCs based on CD36 and CD69 status and found that CD36- CD69+ LSCs (consistent with Group 1 gene expression) harbor nearly all of the colony-forming capacity of the LSCs, forming an average of 13 colonies versus 0.33 colonies for CD36+CD69- LSCs (Group 3) and versus 0.11 colonies for non-LSCs (per 10,000 cells plated, p < 0.00001 for each comparison). We have previously shown that colony-forming capacity is an accurate surrogate for in vivo leukemia reconstituting ability and self-renewal in our model (Sachs, et al. Blood 2014). These experiments characterize the NRASG12V -mediated self-renewal transcriptional signature and suggest that single-cell RNA sequencing data may be an effective tool for delineating the self-renewing subpopluation among immunophenotypically-defined LSCs. Using mass cytometry to query the activation status of signaling pathways simulteneously with multiple immunophenotypic markers, we show that Ki67Low LSCs (the putative self-renewing LSCs) preferentially express increased levels of b-catenin and Myc. These data implicating AML self-renewal pathways can provide precise molecular targets for treating this deadly disease. Disclosures Largaespada: NeoClone Biotechnology, Inc.: Consultancy, Other: stockholder; Genentech Inc: Honoraria, Research Funding; Discovery Genomics Inc.: Consultancy, Other: stockholder; B-MoGen Biotechnologies Inc.: Consultancy, Other: stockholder; Orbimed Inc: Consultancy.


BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Matthijs J. D. Baars ◽  
Neeraj Sinha ◽  
Mojtaba Amini ◽  
Annelies Pieterman-Bos ◽  
Stephanie van Dam ◽  
...  

Abstract Background Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm2 area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art. Results MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods. Conclusion MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.


2019 ◽  
Author(s):  
Chao Zhang

AbstractVariational Autoencoder (VAE) is a generative model from the computer vision community; it learns a latent representation of images and generates new images in an unsupervised way. Recently, Vanilla VAE has been applied to single-cell data analysis, in the hope of harnessing the representation power of latent space to evade the “curse of dimensionality” of the original dataset. However, Vanilla VAE is suffering from the issue of less informative latent space, which raises a question concerning the reliability of Vanilla VAE latent space in representing the high-dimensional single-cell datasets. Therefore I set up such a study to examine this issue from the multiple perspectives.This paper confirms the issue of Vanilla VAE by comparing it with MMD-VAE, a variant of VAE which has claimed to have overcome this issue based on image data, across a series of single-cell RNAseq and mass cytometry datasets. The result indicates that MMD-VAE is superior to Vanilla VAE in retaining the information not only in the latent space but also the reconstruction space, which suggests that MMD-VAE be a better option for single-cell data analysis than Vanilla VAE.


2017 ◽  
Author(s):  
Smita Krishnaswamy ◽  
Nevena Zivanovic ◽  
Roshan Sharma ◽  
Dana Pe’er ◽  
Bernd Bodenmiller

AbstractCellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data. We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGFβ. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3DDREMI) to predict and validate the effect of drug perturbations on EMT.


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