CyTOFmerge: integrating mass cytometry data across multiple panels

Abstract Motivation High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel. Results To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection. Availability and implementation Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge). Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

ShinySOM: graphical SOM-based analysis of single-cell cytometry data

Bioinformatics ◽

10.1093/bioinformatics/btaa091 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3288-3289

Author(s):

Miroslav Kratochvíl ◽

David Bednárek ◽

Tomáš Sieger ◽

Karel Fišer ◽

Jiří Vondrášek

Keyword(s):

Single Cell ◽

High Throughput ◽

Statistical Information ◽

Supplementary Information ◽

High Dimensional ◽

Supplementary Data ◽

Mass Cytometry ◽

High Throughput Analysis ◽

Self Organizing Maps ◽

User Friendly

Abstract Summary ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes. Availability and implementation ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text

Dissecting CLL through high-dimensional single-cell technologies

Blood ◽

10.1182/blood-2018-09-835389 ◽

2019 ◽

Vol 133 (13) ◽

pp. 1446-1456

Author(s):

Satyen H. Gohil ◽

Catherine J. Wu

Keyword(s):

Chronic Lymphocytic Leukemia ◽

Single Cell ◽

Cancer Cells ◽

Lymphocytic Leukemia ◽

Response To Treatment ◽

High Dimensional ◽

Single Cell Level ◽

Disease Course ◽

Cell Level ◽

New Techniques

Abstract We now have the potential to undertake detailed analysis of the inner workings of thousands of cancer cells, one cell at a time, through the emergence of a range of techniques that probe the genome, transcriptome, and proteome combined with the development of bioinformatics pipelines that enable their interpretation. This provides an unprecedented opportunity to better understand the heterogeneity of chronic lymphocytic leukemia and how mutations, activation states, and protein expression at the single-cell level have an impact on disease course, response to treatment, and outcomes. Herein, we review the emerging application of these new techniques to chronic lymphocytic leukemia and examine the insights already attained through this transformative technology.

Download Full-text

A Mass-Ratiometry-Based CD45 Barcoding Method for Mass Cytometry Detection

SLAS TECHNOLOGY Translating Life Sciences Innovation ◽

10.1177/2472630319834057 ◽

2019 ◽

Vol 24 (4) ◽

pp. 408-419

Author(s):

Hongu Meng ◽

Antony Warden ◽

Lulu Zhang ◽

Ting Zhang ◽

Yiyang Li ◽

...

Keyword(s):

Data Acquisition ◽

Sample Size ◽

Single Cell ◽

Large Sample Size ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level ◽

Large Sample ◽

Antibody Staining ◽

Instrument Sensitivity

Mass cytometry (CyTOF) is a critical cell profiling tool in acquiring multiparameter proteome data at the single-cell level. A major challenge in CyTOF analysis is sample-to-sample variance arising from the pipetting process, staining variation, and instrument sensitivity. To reduce such variations, cell barcoding strategies that enable the combination of individual samples prior to antibody staining and data acquisition on CyTOF are often utilized. The most prevalent barcoding strategy is based on a binary scheme that cross-examines the existence or nonexistence of certain mass signals; however, it is limited by low barcoding efficiency and high cost, especially for large sample size. Herein, we present a novel barcoding method for CyTOF application based on mass ratiometry. Different mass tags with specific fixed ratios are used to label CD45 antibody to achieve sample barcoding. The presented method exponentially increases the number of possible barcoded samples with the same amount of mass tags compared with conventional methods. It also reduces the overall time for the labeling process to 40 min and avoids the need for expensive commercial barcoding buffer reagents. Moreover, unlike the conventional barcoding process, this strategy does not pre-permeabilize cells before the barcoding procedure, which offers additional benefits in preserving surface biomarker signals.

Download Full-text

Single Cell Level Quantification of Nanoparticle–Cell Interactions Using Mass Cytometry

Analytical Chemistry ◽

10.1021/acs.analchem.7b01006 ◽

2017 ◽

Vol 89 (16) ◽

pp. 8228-8232 ◽

Cited By ~ 16

Author(s):

Angela Ivask ◽

Andrew J. Mitchell ◽

Christopher M. Hope ◽

Simon C. Barry ◽

Enzo Lombi ◽

...

Keyword(s):

Single Cell ◽

Cell Interactions ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level

Download Full-text

0680 - Mass cytometry for detection of silver at the bacterial single cell level

10.26226/morressier.5b5199c0b1b87b000ecf0333 ◽

2018 ◽

Author(s):

Yuting Guo

Keyword(s):

Single Cell ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level

Download Full-text

Mass Cytometry Study on the Heterogeneity in Cellular Association and Cytotoxicity of Silver Nanoparticles in Human Immune Cells

10.1101/617332 ◽

2019 ◽

Author(s):

My Kieu Ha ◽

Jang-Sik Choi ◽

Zayakhuu Gerelkhuu ◽

Sook Jin Kwon ◽

Jaewoo Song ◽

...

Keyword(s):

Silver Nanoparticles ◽

Adverse Effects ◽

Single Cell ◽

Dose Response ◽

Immune Cells ◽

Intracellular Signaling ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level ◽

Human Immune

AbstractThere have been many reports about the adverse effects of nanoparticles (NPs) on the environment and human health. Conventional toxicity assessments of NPs frequently assume uniform distribution of monodisperse NPs in homogeneous cell populations, and provide information on the relationships between the administered dose of NPs and cellular responses averaged for a large number of cells. They may have limitations in describing the wide heterogeneity of cell-NP interactions, caused by cell-to-cell and NP-to-NP variances. To achieve more detailed insight into the heterogeneity of cell-NP interactions, it is essential to understand the cellular association and adverse effects of NPs at single-cell level. In this study, we applied mass cytometry to investigate the interactions between silver nanoparticles (AgNPs) and primary human immune cells. High dimensionality of mass cytometry allowed us to identify various immune cell types and observe the cellular association and toxicity of AgNPs in each population. Our findings showed that AgNPs had higher affinity with phagocytic cells like monocytes and dendritic cells and caused more severe toxic effects than with T cells, B cells and NK cells. Multi-element detection capability of mass cytometry also enabled us to simultaneously monitor cellular AgNP dose and intracellular signaling of individual cells, and subsequently investigate the dose-response relationships of each immune population at single-cell level, which are often hidden in conventional toxicity assays at bulk-cell level. Our study will assist future development of single-cell dose-response models for various NPs and will provide key information for the safe use of nanomaterials for biomedical applications.

Download Full-text

Mass Cytometry Enabling Absolute and Fast Quantification of Silver Nanoparticle Uptake at the Single Cell Level

Analytical Chemistry ◽

10.1021/acs.analchem.9b01870 ◽

2019 ◽

Vol 91 (18) ◽

pp. 11514-11519 ◽

Cited By ~ 7

Author(s):

Ana López-Serrano Oliver ◽

Andrea Haase ◽

Anette Peddinghaus ◽

Doreen Wittke ◽

Norbert Jakubowski ◽

...

Keyword(s):

Single Cell ◽

Silver Nanoparticle ◽

Single Cell Level ◽

Mass Cytometry ◽

Nanoparticle Uptake ◽

Cell Level

Download Full-text

TMIC-06. MYELOID POPULATIONS AND THE EFFECT OF NEOADJUVANT PD-1 INHIBITION IN THE GLIOBLASTOMA MICROENVIRONMENT: A SURFACEOMIC AND TRANSCRIPTOMIC DISSECTION AT THE SINGLE-CELL LEVEL

Neuro-Oncology ◽

10.1093/neuonc/noz175.1040 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi248-vi248

Author(s):

Aaron Mochizuki ◽

Alexander Lee ◽

Joey Orpilla ◽

Jenny Kienzler ◽

Mildred Galvez ◽

...

Keyword(s):

Brain Tumor ◽

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

Myeloid Cells ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level ◽

Single Cell Rna Sequencing

Abstract INTRODUCTION Glioblastoma (GBM) is the most common malignant brain tumor in adults and is associated with a dismal prognosis. Neoadjuvant anti-PD-1 blockade has demonstrated efficacy in melanoma, non-small cell lung cancer and recurrent GBM; however, responses vary. While T cells have garnered considerable attention in the context of immunotherapy, the role of myeloid cells in the GBM microenvironment remains controversial. METHODS We isolated CD45+ immune populations from patients who underwent brain tumor resection at UCLA. We hypothesized that myeloid cells in glioblastoma contribute to T cell dysfunction; however, this immune suppression can be mitigated by neoadjuvant PD-1 inhibition. To test this, we utilized mass cytometry and single-cell RNA sequencing to characterize these immune populations. RESULTS Mass cytometry profiling of tumor infiltrating lymphocytes from patients with GBM demonstrated a preponderance of CD11b+ myeloid populations (75% versus 25% CD3+). At the transcriptomic level, myeloid cells in newly diagnosed GBMs exhibited decreased expression of CCL4 (loge fold change -1.18, Bonferroni-adjusted P = 1.62x10-254) and its ligands compared to anaplastic astrocytoma. In ranked gene set enrichment analysis, patients who received neoadjuvant pembrolizumab demonstrated enrichment in TNFα-, NFκB- and lipid metabolism-related gene sets by bootstrapped Kolmogorov-Smirnov test (Benjamini-Hochberg adjusted P = 4.74x10-3, 1.45x10-2 and 2.48x10-3, respectively) in tumor-associated myeloid populations. Additionally, single-cell trajectory analysis demonstrated increased CCL4 and decreased ISG15 with neoadjuvant checkpoint inhibition. CONCLUSIONS Here, we utilize mass cytometry and single-cell RNA sequencing to demonstrate the predominance and transcriptomic features of myeloid populations in GBM. Myeloid cells in patients who receive neoadjuvant PD-1 blockade re-express increased levels NFκB, TNFα and CCL4, a cytokine crucial for the recruitment of dendritic cells to the tumor for antigen-specific T cell activation. By delving into the GBM microenvironment at the single-cell level, we hope to better delineate the role of myeloid populations in this uniformly fatal tumor.

Download Full-text

SCHNEL: Scalable clustering of high dimensional single-cell data

10.1101/2020.03.30.015925 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tamim Abdelaal ◽

Paul de Raadt ◽

Boudewijn P.F. Lelieveldt ◽

Marcel J.T. Reinders ◽

Ahmed Mahfouz

Keyword(s):

Single Cell ◽

Graph Clustering ◽

Original Data ◽

Large Datasets ◽

Supplementary Information ◽

High Dimensional ◽

Sequencing Data ◽

Novel Approach ◽

Cellular Markers ◽

Cell Data

AbstractMotivationSingle cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.ResultsWe developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable timeframes. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.Availability and ImplementationImplementation is available on GitHub (https://github.com/paulderaadt/HSNE-clustering)[email protected] informationSupplementary data are available at Bioinformatics online.

Download Full-text

NRASG12V-Mediated Self-Renewal Signaling in Self-Renewing AML Stem Cells.

Blood ◽

10.1182/blood-2018-99-119448 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2626-2626

Author(s):

Marie Lue Antony ◽

Klara Noble-Orcutt ◽

Karen Sachs ◽

Anna Khoroshilov ◽

Daniel Chang ◽

...

Keyword(s):

Stem Cells ◽

Single Cell ◽

Molecular Mechanisms ◽

Signaling Molecules ◽

The Self ◽

Single Cell Level ◽

Mass Cytometry ◽

Cell Level ◽

Self Renewal ◽

Signaling Activation

Abstract In acute myeloid leukemia (AML) standard therapies often induce complete remission, but patients frequently relapse and die of the disease. Leukemia stem cells (LSCs) have self-renewal potential and ability to recapitulate the disease. Our goal is to define the molecular mechanisms that allow AML to relapse. We have previously shown that activated NRAS (NRASG12V) facilitates self-renewal in the LSC-enriched subpopulation in a mouse model of AML (Mll-AF9/NRASG12V, Sachs et al. Blood 2014). We subsequently utilized single-cell RNA sequencing of the LSCs from this model to define and validate the only subset of the LSC-enriched population that can efficiently transplant leukemia in mice. We hypothesize that NRASG12V exerts a unique signaling profile that directs self-renewal in this subset of LSCs. Understanding these pathways at the single-cell level would enable us to design rational therapeutics that would prevent relapse in AML. We used mass cytometry (CyTOF2) to define the signaling activation state of LSC subsets in our AML model. Similar to flow cytometry, mass cytometry provides quantitative measurements of cell-surface and intracellular proteins at the single-cell level. In addition, it can simultaneously and accurately measure over 40 proteins, allowing us to quantitate a panel of intracellular signaling molecules in well-defined immunophenotypic leukemia subpopulations. We previously reported that the LSC-enriched population in this leukemia model is Mac1LowKit+Sca1+ (MKS) and subsequently showed that the self-renewing subset within the MKS population is MKSCD36LowCD69High. In contrast, the MKSCD36HighCD69Low population is incapable of transplanting leukemia in mice. The MKS cells displayed elevated levels of activated signaling molecules relative to the non-MKS population. Comparing the MKS subsets to each other, we found that the self-renewing MKSCD36LowCD69High population displayed significantly higher levels of several signaling molecules including Myc, NF-kB, and β-catenin relative to MKSCD36HighCD69Low cells (which lack self-renewal capacity). We reasoned that self-renewal might be mediated through these signaling molecules uniquely elevated in MKSCD36LowCD69High cells. Next, we sought to define the global signaling activation network within individual MKS subsets to determine if the signaling cascades and dependencies vary between these populations. We used Bayesian network modeling (Sachs K et al. Science 2005) to compare the statistical relationships between these signaling molecules, at the single-cell level. Signaling molecules that impact the levels of downstream effectors can be inferred using this approach. Using this method, we found that the signaling activation network does not significantly vary between MKS subsets. These observations suggest that self-renewal may be driven by alteration in the levels of signaling intermediates rather than alternate signal transduction architecture. We previously found that NRASG12V-mediated signals drive self-renewal in this AML model (Sachs Z. et al. Blood 2014). We used this model to ask which of these self-renewal-associated signaling molecules might be NRASG12V-regulated. We abolished NRASG12V transgene expression in these mice and harvested leukemia cells 72 hours later (per our standard lab protocol). Using this approach, found that self-renewal-associated signaling molecules, including NF-kB and β-catenin, are significantly reduced after NRASG12V-withdrawal indicating that NRASG12V -dependent signaling likely leads to the increase in these signaling molecules. In conclusion, we used mass cytometry analysis to identify the LSC self-renewal-associated signaling state in a murine model of AML and show that NRASG12V activates this signaling program. These data can be used to rationally design therapeutics such as small molecule inhibitors to target self-renewal-specific signaling and prevent relapse in AML. Disclosures No relevant conflicts of interest to declare.

Download Full-text