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      Stabilizing Antibody Cocktails for Mass Cytometry

      1 , 1 , 1 , 1 , 1 , 1
      Cytometry Part A
      Wiley

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          An Immune Atlas of Clear Cell Renal Cell Carcinoma

          Summary Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.
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            Is Open Access

            Stereotypic Immune System Development in Newborn Children

            Summary Epidemiological data suggest that early life exposures are key determinants of immune-mediated disease later in life. Young children are also particularly susceptible to infections, warranting more analyses of immune system development early in life. Such analyses mostly have been performed in mouse models or human cord blood samples, but these cannot account for the complex environmental exposures influencing human newborns after birth. Here, we performed longitudinal analyses in 100 newborn children, sampled up to 4 times during their first 3 months of life. From 100 μL of blood, we analyze the development of 58 immune cell populations by mass cytometry and 267 plasma proteins by immunoassays, uncovering drastic changes not predictable from cord blood measurements but following a stereotypic pattern. Preterm and term children differ at birth but converge onto a shared trajectory, seemingly driven by microbial interactions and hampered by early gut bacterial dysbiosis.
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              Normalization of mass cytometry data with bead standards.

              Mass cytometry uses atomic mass spectrometry combined with isotopically pure reporter elements to currently measure as many as 40 parameters per single cell. As with any quantitative technology, there is a fundamental need for quality assurance and normalization protocols. In the case of mass cytometry, the signal variation over time due to changes in instrument performance combined with intervals between scheduled maintenance must be accounted for and then normalized. Here, samples were mixed with polystyrene beads embedded with metal lanthanides, allowing monitoring of mass cytometry instrument performance over multiple days of data acquisition. The protocol described here includes simultaneous measurements of beads and cells on the mass cytometer, subsequent extraction of the bead-based signature, and the application of an algorithm enabling correction of both short- and long-term signal fluctuations. The variation in the intensity of the beads that remains after normalization may also be used to determine data quality. Application of the algorithm to a one-month longitudinal analysis of a human peripheral blood sample reduced the range of median signal fluctuation from 4.9-fold to 1.3-fold. Copyright © 2013 International Society for Advancement of Cytometry.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Cytometry Part A
                Cytometry
                Wiley
                1552-4922
                1552-4930
                May 06 2019
                May 06 2019
                Affiliations
                [1 ]German Rheumatism Research Center Berlin (DRFZ)a Leibniz Institute Berlin Germany
                Article
                10.1002/cyto.a.23781
                31058420
                f0228a88-8a4e-4f80-ad44-532cd881ad93
                © 2019

                http://doi.wiley.com/10.1002/tdm_license_1.1

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