Single Cell Detection of the p53 Protein by Mass Cytometry

Purpose: The p53 protein and its post-translational modifications are distinctly expressed in various normal cell types and malignant cells and are usually detected by immunohistochemistry and flow cytometry in contemporary diagnostics. Here, we describe an approach for simultaneous multiparameter detection of p53, its post-translational modifications and p53 pathway-related signaling proteins in single cells using mass cytometry. Method: We conjugated p53-specific antibodies to metal tags for detection by mass cytometry, allowing the detection of proteins and their post-translational modifications in single cells. We provide an overview of the antibody validation process using relevant biological controls, including cell lines treated in vitro with a stimulus (irradiation) known to induce changes in the expression level of p53. Finally, we present the potential of the method through investigation of primary samples from leukemia patients with distinct TP53 mutational status. Results: The p53 protein can be detected in cell lines and in primary samples by mass cytometry. By combining antibodies for p53-related signaling proteins with a surface marker panel, we show that mass cytometry can be used to decipher the single cell p53 signaling pathway in heterogeneous patient samples. Conclusion: Single cell profiling by mass cytometry allows the investigation of the p53 functionality through examination of relevant downstream signaling proteins in normal and malignant cells. Our work illustrates a novel approach for single cell profiling of p53.

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McSNAC: A software to approximate first-order signaling networks from mass cytometry data

10.1101/2021.12.02.470955 ◽

2021 ◽

Author(s):

Darren Wethington ◽

Sayak Mukherjee ◽

Jayajit Das

Keyword(s):

Single Cell ◽

In Silico ◽

Single Cells ◽

Signaling Network ◽

Signaling Networks ◽

Enzymatic Reactions ◽

Signaling Proteins ◽

Protein Species ◽

Mass Cytometry ◽

First Order

AbstractMass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful to dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. With a proper computational analysis, timestamped CyTOF data of signaling proteins could help develop predictive and mechanistic models for signaling kinetics. These models would be useful for predicting the effects of perturbations in cells, or comparing signaling networks across cell groups. We propose our Mass cytometry Signaling Network Analysis Code, or McSNAC, a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption breaks down often as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of the protein species that are involved in signaling. We carry out a series of in silico experiments here to show that 1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; 2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from timestamped CyTOF data.

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Compartmap enables inference of higher-order chromatin structure in individual cells from scRNA-seq and scATAC-seq

10.1101/2021.05.17.444465 ◽

2021 ◽

Author(s):

Benjamin K Johnson ◽

Jean-Philippe Fortin ◽

Kasper D. Hansen ◽

Hui Shen ◽

Timothy J. Triche

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Cell Lines ◽

Chromatin Structure ◽

Single Cells ◽

Higher Order ◽

Chromatin Domains ◽

Structural Alterations ◽

Chromatin Architecture ◽

Single Cell Profiling

Single-cell profiling of chromatin structure remains a challenge due to cost, throughput, and resolution. We introduce compartmap to reconstruct higher-order chromatin domains in individual cells from transcriptomic (RNAseq) and epigenomic (ATACseq) assays. In cell lines and primary human samples, compartmap infers higher-order chromatin structure comparable to specialized chromatin capture methods, and identifies clinically relevant structural alterations in single cells. This provides a common lens to integrate transcriptional and epigenomic results, linking higher-order chromatin architecture to gene regulation and to clinically relevant phenotypes in individual cells.

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Multiplexed profiling of single-cell extracellular vesicles secretion

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1814348116 ◽

2019 ◽

Vol 116 (13) ◽

pp. 5979-5984 ◽

Cited By ~ 22

Author(s):

Yahui Ji ◽

Dongyuan Qi ◽

Linmei Li ◽

Haoran Su ◽

Xiaojie Li ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Extracellular Vesicles ◽

Single Cell Analysis ◽

Comprehensive Evaluation ◽

Single Cells ◽

Deep Understanding ◽

Principal Component ◽

Cell Heterogeneity ◽

Indispensable Tool

Extracellular vesicles (EVs) are important intercellular mediators regulating health and diseases. Conventional methods for EV surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EV secretion. Herein, by using spatially patterned antibody barcodes, we realized multiplexed profiling of single-cell EV secretion from more than 1,000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to a deep understanding of previously undifferentiated single-cell heterogeneity underlying EV secretion. Notably, we observed that the decrement of certain EV phenotypes (e.g.,CD63+EV) was associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EV secretion and cytokines secretion simultaneously from the same single cells to investigate the multidimensional spectrum of cellular communications, from which we resolved tiered functional subgroups with distinct secretion profiles by visualized clustering and principal component analysis. In particular, we found that different cell subgroups dominated EV secretion and cytokine secretion. The technology introduced here enables a comprehensive evaluation of EV secretion heterogeneity at single-cell level, which may become an indispensable tool to complement current single-cell analysis and EV research.

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Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

10.1101/459438 ◽

2018 ◽

Author(s):

Yahui Ji ◽

Dongyuan Qi ◽

Linmei Li ◽

Haoran Su ◽

Xiaojie Li ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Extracellular Vesicles ◽

Single Cells ◽

Cell Communication ◽

Deep Understanding ◽

Surface Markers ◽

Cell Heterogeneity ◽

Health And Disease ◽

Cytokines Secretion

AbstractExtracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.SignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling can’t tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

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Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa016 ◽

2020 ◽

Vol 2 (2) ◽

Cited By ~ 6

Author(s):

Noemi Andor ◽

Billy T Lau ◽

Claudia Catalanotti ◽

Anuja Sathe ◽

Matthew Kubit ◽

...

Keyword(s):

Gastric Cancer ◽

Single Cell ◽

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Gastric Cancer Cell ◽

Phenotypic Diversity ◽

Single Cells ◽

Cancer Cell Lines ◽

Gastric Cancer Cell Lines

Abstract Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

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SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

BMC Bioinformatics ◽

10.1186/s12859-020-03858-y ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Vivekananda Sarangi ◽

Alexandre Jourdon ◽

Taejeong Bae ◽

Arijit Panda ◽

Flora Vaccarino ◽

...

Keyword(s):

Single Cell ◽

Multiple Displacement Amplification ◽

Single Cells ◽

Sequencing Data ◽

High Coverage ◽

Amplification Bias ◽

Single Cell Profiling ◽

High Concordance ◽

Human Neuronal Cells

Abstract Background The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

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Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

Genome Medicine ◽

10.1186/s13073-021-01001-x ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Coral Fustero-Torre ◽

María José Jiménez-Santos ◽

Santiago García-Martín ◽

Carlos Carretero-Puche ◽

Luis García-Jimeno ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Tumour Cell ◽

Enrichment Score ◽

Cell Populations ◽

Drug Selection ◽

Malignant Cells ◽

Rna Seq ◽

Computational Methodology ◽

Cell Subpopulations

AbstractWe present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell.

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Single-cell detection of Nonsense-Mediated mRNA decay

10.21203/rs.3.rs-304645/v1 ◽

2021 ◽

Author(s):

Robert Singer ◽

Hanae Sato

Keyword(s):

Single Cell ◽

Mrna Decay ◽

Single Cells ◽

Mrna Degradation ◽

Cell Detection ◽

Globin Genes ◽

Reporter System ◽

Single Cell Detection ◽

Translational Readthrough ◽

Nonsense Mediated Mrna Decay

Abstract Nonsense-mediated mRNA decay (NMD) is an mRNA degradation pathway that eliminates transcripts containing premature termination codons (PTCs). Half-lives of the mRNAs containing PTCs demonstrates that a small percent escape surveillance and do not degrade. It is not known whether this escape represents variable mRNA degradation within cells or, alternatively cells within the population are resistant. Here we demonstrate a single-cell approach with a bi-directional reporter, which expresses two b-globin genes with or without a PTC in the same cell, to characterize the efficiency of NMD in individual cells. We found a broad range of NMD efficiency in the population; some cells degraded essentially all of the mRNAs, while others escaped NMD almost completely. Characterization of NMD efficiency together with NMD regulators in single cells showed cell-to-cell variability of NMD reflects the differential level of surveillance factors, SMG1 and phosphorylated UPF1. A single-cell fluorescent reporter system that enabled detection of NMD using flow cytometry revealed that this escape occurred either by translational readthrough at the PTC or by failure of mRNA degradation after successful translation termination at the PTC.

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Scattome: A Single-Cell Analysis of Targeted Transcriptome Program to Predict Drug Sensitivity of Single Cells within Human Myeloma Tumors

Blood ◽

10.1182/blood.v126.23.4249.4249 ◽

2015 ◽

Vol 126 (23) ◽

pp. 4249-4249

Author(s):

Amit Kumar Mitra ◽

Ujjal Mukherjee ◽

Taylor Harding ◽

Holly Stessman ◽

Ying Li ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Lines ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Single Cell Analysis ◽

Single Cells ◽

Pcr Analysis ◽

Cell Analysis

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

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Modelling Single Cell B-Cell Receptor Signaling Reveals Enhanced Activity in Primary CLL Cells Compared to Non-Malignant Cells While Fundamental Network Circuit Topology Remains Stable Even with Novel Therapeutic Inhibitors

Blood ◽

10.1182/blood-2019-127837 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4275-4275

Author(s):

Daniel Mertens ◽

Christine Wolf ◽

Carsten Maus ◽

Michael Persicke ◽

Katharina Filarsky ◽

...

Keyword(s):

B Cell ◽

Single Cell ◽

Network Topology ◽

Research Funding ◽

Single Cells ◽

Cell Receptor ◽

B Cell Receptor ◽

Malignant Cells ◽

Network Learning ◽

Bcr Signaling

B-cell receptor (BCR) signalling is central for the pathomechanism of chronic lymphocytic leukemia (CLL). Novel inhibitors of BCR signalling have recently substantially improved treatment of CLL, and a better characterization of the molecular circuitry of leukemic BCR signalling will allow a more refined targeting of this Achilles heel. In order to model malignant and non-malignant BCR signalling, we quantified after stimulation 5 components of BCR signaling (ZAP70/SYK, BTK, PLCy2, AKT, ERK1/2) in single cells from primary human leukemic and non-malignant tissue via phospho-specific flow cytometry over 6 time points. We stimulated cells from 11 patients and non-malignant CD19 negative enriched B-cells from 5 healthy donors by crosslinking the BCR with anti-IgM and/or anti-CD19 and synchronous inhibition of phosphatases with H2O2. As expected, we found more phosphorylation of all BCR signalling components after stimulation in malignant vs non-malignant cells and in IGHV non-mutated CLL cells compared to IGHV mutated CLL cells. Intriguingly, inhibition of phosphatases with H2O2 led to higher phosphorylation of BCR components in CLL cells with mutated IGHV genes compared to CLL cells with non-mutated IGHV genes, suggesting a stronger dampening of signalling activity in mutated IGHV CLL by phosphatases. In order to characterize the signalling circuitry, we modelled the connectivity of the cascade components by correlating signal intensities across single cells of the cell populations of single samples (Figure 1). Surprisingly, upon stimulation no substantial differences in network topology were observed between malignant and non-malignant cells. To additionally test for changes in network topology, we challenged the BCR signaling cascade with inhibitors for BTK (ibrutinib), PI3K (idelalisib). Ibrutinib and idelalisib acted complementary, but not synergistic, and were similarly effective in IGHV mutated and non-mutated CLL. Effects of idelalisib were the same on malignant and non-malignant cells, whereas ibrutinib was mostly active on CLL cells, not on non-malignant B-cells. Upon stimulation with combinations of IgM and CD19 crosslinking augmented with H2O2, phosphorylation of PLCy2 could not be significantly inhibited by idelalisib or ibrutinib on a timescale of 28mins. We therefore aimed to identify central activating nodes of the BCR signalling cascade using targeted inhibitors. In fact, we found that inhibition of LYN with dasatinib and inhibition of SYK with entospletinib could substantially reduce phosphorylation of PLCy2, BTK and ERK but not AKT after all combinations of BCR stimulation. This suggests additional signalling cascades modulating AKT and a strong impact of SYK/LYN activity on the regulation of PLCy2. In summary, our findings underline the importance of single cell analysis of the dynamic circuitry of B-cell receptor signalling to understand development of resistance mechanisms and potential vulnerabilities. Figure 1: Workflow scheme of the Bayesian network learning and averaging approach. After discretizing the continuous single cell data, an optimal network is derived from each of R bootstrap samples. The Bayesian network learning strategy uses the BDe scoring function and a greedy hill-climbing algorithm to find the network model that represents the resampled data best. An average arc strength for each connection between nodes is derived from the number of occurrences of the respective connection in the set of R best scoring networks. Further averaging among networks derived from different data sets was applied for identifying conditional, temporal, and group-specific differences. Figure 1 Disclosures Döhner: Novartis: Consultancy, Honoraria, Research Funding; Astex: Consultancy, Honoraria; Bristol Myers Swuibb: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Arog: Research Funding; Seattle Genetics: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Pfizer: Research Funding; Celgene Corporation: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria. Stilgenbauer:GSK: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pharmacyclics: Other: Travel support; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Hoffmann La-Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau.

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