Human cord blood (CB)-derived CD133؉ cells carry characteristics of primitive hematopoietic cells and proffer an alternative for CD34 ؉ cells in hematopoietic stem cell (HSC) transplantation. To characterize the CD133 ؉ cell population on a genetic level, a global expression analysis of CD133 ؉ cells was performed using oligonucleotide microarrays. CD133؉ cells were purified from four fresh CB units by immunomagnetic selection. All four CD133؉ samples showed significant similarity in their gene expression pattern, whereas they differed clearly from the CD133 ؊ control samples. In all, 690 transcripts were differentially expressed between CD133؉ and CD133 ؊ cells. Of these, 393 were increased and 297 were decreased in CD133 ؉ cells. The highest overexpression was noted in genes associated with metabolism, cellular physiological processes, cell communication, and development. A set of 257 transcripts expressed solely in the CD133 ؉ cell population was identified. Colonyforming unit (CFU) assay was used to detect the clonal progeny of precursors present in the studied cell populations. The results demonstrate that CD133؉ cells express primitive markers and possess clonogenic progenitor capacity. This study provides a gene expression profile for human CD133 ؉ cells. It presents a set of genes that may be used to unravel the properties of the CD133 ؉ cell population, assumed to be highly enriched in HSCs. STEM CELLS 2006;24: 631-641

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643-2665, 2017. © 2017 Wiley Periodicals, Inc.

Background: There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.

CD34 and CD133 are the most commonly used markers to enrich hematopoietic stem cells (HSCs). Positively selected HSCs are increasingly used for autologous and allogeneic transplantation, yet the biological properties of CD34(+) and CD133(+) cells are largely unknown. In the present study, a genome-wide gene expression analysis of human cord blood (CB)-derived CD34(+) cells was performed. The CD34(+) gene expression profile was compared to an identically constructed CD133(+) gene expression profile to reveal the specific expression patterns and major differences of CD34(+) and CD133(+) cells. As expected, many genes were similarly expressed in the two cell populations, but cell-type-specific gene expression was also demonstrated. Self-organizing map analysis was used to identify transcripts having similar expression patterns, and the results were compared between CD34(+) and CD133(+) cells. Also, a prioritization algorithm was used to rank the genes best separating CD34(+) and CD133(+) cells from their CD34() and CD133() counterparts in CB. Our results show that CD133(+) cells have higher numbers of up-regulated genes than CD34(+) cells. Furthermore, the uniquely expressed genes in CD34(+) or CD133(+) cell populations were associated with different biological processes. CD34(+) cells overexpressed many transcripts associated with development and response to stress or external stimuli. In CD133(+) cells, the most significantly represented biological processes were establishment and maintenance of chromatin architecture, DNA metabolism, and cell cycle. The differences between the gene expression profiles of CD34(+) and CD133(+) cells indicate the more primitive nature of CD133(+) cells. These profiles suggest that CD34(+) and CD133(+) cells may have different roles in hematopoietic regeneration.

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