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@ARTICLE{Budnik2018-qh,
title = "{SCoPE-MS}: mass spectrometry of single mammalian cells
quantifies proteome heterogeneity during cell differentiation",
author = "Budnik, Bogdan and Levy, Ezra and Harmange, Guillaume and Slavov,
Nikolai",
abstract = "Some exciting biological questions require quantifying thousands
of proteins in single cells. To achieve this goal, we develop
Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and
validate its ability to identify distinct human cancer cell types
based on their proteomes. We use SCoPE-MS to quantify over a
thousand proteins in differentiating mouse embryonic stem cells.
The single-cell proteomes enable us to deconstruct cell
populations and infer protein abundance relationships. Comparison
between single-cell proteomes and transcriptomes indicates
coordinated mRNA and protein covariation, yet many genes exhibit
functionally concerted and distinct regulatory patterns at the
mRNA and the protein level.",
journal = "Genome Biol.",
volume = 19,
number = 1,
pages = "161",
month = oct,
year = 2018,
language = "en"
}
@ARTICLE{Zhu2018-bf,
title = "Nanodroplet processing platform for deep and quantitative
proteome profiling of 10-100 mammalian cells",
author = "Zhu, Ying and Piehowski, Paul D and Zhao, Rui and Chen, Jing and
Shen, Yufeng and Moore, Ronald J and Shukla, Anil K and Petyuk,
Vladislav A and Campbell-Thompson, Martha and Mathews, Clayton E
and Smith, Richard D and Qian, Wei-Jun and Kelly, Ryan T",
abstract = "Nanoscale or single-cell technologies are critical for biomedical
applications. However, current mass spectrometry (MS)-based
proteomic approaches require samples comprising a minimum of
thousands of cells to provide in-depth profiling. Here, we report
the development of a nanoPOTS (nanodroplet processing in one pot
for trace samples) platform for small cell population proteomics
analysis. NanoPOTS enhances the efficiency and recovery of sample
processing by downscaling processing volumes to 3000 proteins are
consistently identified from as few as 10 cells. Furthermore, we
demonstrate quantification of ~2400 proteins from single human
pancreatic islet thin sections from type 1 diabetic and control
donors, illustrating the application of nanoPOTS for spatially
resolved proteome measurements from clinical tissues.",
journal = "Nat. Commun.",
volume = 9,
number = 1,
pages = "882",
month = feb,
year = 2018,
language = "en"
}
@UNPUBLISHED{Huffman2019-ns,
title = "{DO-MS}: {Data-Driven} Optimization of Mass Spectrometry Methods",
author = "Huffman, Gray and Specht, Harrison and Chen, Albert Tian and
Slavov, Nikolai",
abstract = "The performance of ultrasensitive LC-MS/MS methods, such as
Single-Cell Proteomics by Mass Spectrometry (SCoPE-MS), depends
on multiple interdependent parameters. This interdependence makes
it challenging to specifically pinpoint bottlenecks in the
LC-MS/MS methods and approaches for resolving them. For example,
low signal at MS2 level can be due to poor LC separation,
ionization, apex targeting, ion transfer, or ion detection. We
sought to specifically diagnose such bottlenecks by interactively
visualizing data from all levels of bottom-up LC-MS/MS analysis.
Many search engines, such as MaxQuant, already provide such data,
and we developed an open source platform for their interactive
visualization and analysis: Data-driven Optimization of MS
(DO-MS). We found that in many cases DO-MS not only specifically
diagnosed bottlenecks but also enabled us to rationally optimize
them. For example, we used DO-MS to diagnose poor sampling of the
elution peak apex and to optimize it, which increased the
efficiency of delivering ions for MS2 analysis by 370 \%. DO-MS
is easy to install and use, and its GUI allows for interactive
data subsetting and high-quality figure generation. The modular
design of DO-MS facilitates customization and expansion. DO-MS is
available for download from GitHub:
https://github.com/SlavovLab/DO-MS",
journal = "bioRxiv",
pages = "512152",
month = jan,
year = 2019,
language = "en"
}
@UNPUBLISHED{Specht2019-jm,
title = "High-throughput single-cell proteomics quantifies the emergence
of macrophage heterogeneity",
author = "Specht, Harrison and Emmott, Edward and Koller, Toni and Slavov,
Nikolai",
abstract = "The fate and physiology of individual cells are controlled by
networks of proteins. Yet, our ability to quantitatively analyze
protein networks in single cells has remained limited. To
overcome this barrier, we developed SCoPE2. It integrates
concepts from Single-Cell ProtEomics by Mass Spectrometry
(SCoPE-MS) with automated and miniaturized sample preparation,
substantially lowering cost and hands-on time. SCoPE2 uses
data-driven analytics to optimize instrument parameters for
sampling more ion copies per protein, thus supporting
quantification with improved count statistics. These advances
enabled us to analyze the emergence of cellular heterogeneity as
homogeneous monocytes differentiated into macrophage-like cells
in the absence of polarizing cytokines. We used SCoPE2 to
quantify over 2,000 proteins in 356 single monocytes and
macrophages in about 85 hours of instrument time, and the
quantified proteins allowed us to discern single cells by cell
type. Furthermore, the data uncovered a continuous gradient of
proteome states for the macrophage-like cells, suggesting that
macrophage heterogeneity may emerge even in the absence of
polarizing cytokines. Our methodology lays the foundation for
quantitative analysis of protein networks at single-cell
resolution. ![Figure][1] [1]: pending:yes",
journal = "bioRxiv",
pages = "665307",
month = jun,
year = 2019,
language = "en"
}
@ARTICLE{Gatto2012-tb,
title = "{MSnbase-an} {R/Bioconductor} package for isobaric tagged mass
spectrometry data visualization, processing and quantitation",
author = "Gatto, Laurent and Lilley, Kathryn S",
abstract = "UNLABELLED: MSnbase is an R/Bioconductor package for the analysis
of quantitative proteomics experiments that use isobaric tagging.
It provides an exploratory data analysis framework for
reproducible research, allowing raw data import, quality control,
visualization, data processing and quantitation. MSnbase allows
direct integration of quantitative proteomics data with
additional facilities for statistical analysis provided by the
Bioconductor project. AVAILABILITY: MSnbase is implemented in R
(version $\geq$ 2.13.0) and available at the Bioconductor web
site (http://www.bioconductor.org/). Vignettes outlining typical
workflows, input/output capabilities and detailing underlying
infrastructure are included in the package.",
journal = "Bioinformatics",
volume = 28,
number = 2,
pages = "288--289",
month = jan,
year = 2012,
language = "en"
}