Publications by authors named "O I Ornatsky"

In the field of cell-based therapeutics, there is a great need for high-quality, robust, and validated measurements for cell characterization. Flow cytometry has emerged as a critically important platform due to its high-throughput capability and its ability to simultaneously measure multiple parameters in the same sample. However, to assure the confidence in measurement, well characterized biological reference materials are needed for standardizing clinical assays and harmonizing flow cytometric results between laboratories.

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Age-related clonal hematopoiesis is a major risk factor for myeloid malignancy and myeloid skewing is a hallmark of aging. However, while it is known that non-cell-autonomous components of the microenvironment can also influence this risk, there have been few studies of how the spatial architecture of human bone marrow (BM) changes with aging. Here, we show that BM adiposity increases with age, which correlates with increased density of maturing myeloid cells and CD34+ hematopoietic stem/progenitor cells (HSPCs) and an increased proportion of HSPCs adjacent to adipocytes.

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The synthesis of a polylysine polymer functionalized with the previously reported astonishingly inert [In()] chelate was performed. A biotin end group allowed the conjugation to biotinylated beads by the intermediary of a fluorescein isothiocyanate/neutravidin receptor. High quality imaging mass cytometry trials, based on In detection were performed to highlight the behavior of the material.

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Mass cytometry (MC) is a bioanalytical technique that uses metal-tagged antibodies (Abs) for high-dimensional single-cell immunoassays. Currently, this technology can measure over 40 parameters simultaneously on individual cells using metal-chelating polymer (MCP) based reagents. However, MC can in principle detect up to 135 parameters with the development of new elemental mass tags.

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Mass cytometry is an emerging technology capable of 40 or more correlated measurements on a single cell. The complexity and volume of data generated by this platform have accelerated the creation of novel methods for high-dimensional data analysis and visualization. A key step in any high-level data analysis is the removal of unwanted events, a process often referred to as data cleanup.

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