Novel sequencing technologies permit the rapid production of large sequence data sets. These technologies are likely to revolutionize genetics and biomedical research, but a thorough characterization of the ultra-short read output is necessary. We generated and analyzed two Illumina 1G ultra-short read data sets, i.e. 2.8 million 27mer reads from a Beta vulgaris genomic clone and 12.3 million 36mers from the Helicobacter acinonychis genome. We found that error rates range from 0.3% at the beginning of reads to 3.8% at the end of reads. Wrong base calls are frequently preceded by base G. Base substitution error frequencies vary by 10- to 11-fold, with A > C transversion being among the most frequent and C > G transversions among the least frequent substitution errors. Insertions and deletions of single bases occur at very low rates. When simulating re-sequencing we found a 20-fold sequencing coverage to be sufficient to compensate errors by correct reads. The read coverage of the sequenced regions is biased; the highest read density was found in intervals with elevated GC content. High Solexa quality scores are over-optimistic and low scores underestimate the data quality. Our results show different types of biases and ways to detect them. Such biases have implications on the use and interpretation of Solexa data, for de novo sequencing, re-sequencing, the identification of single nucleotide polymorphisms and DNA methylation sites, as well as for transcriptome analysis.

Blood of both humans and mice contains 2 main monocyte subsets. Here, we investigated the extent of their similarity using a microarray approach. Approximately 270 genes in humans and 550 genes in mice were differentially expressed between subsets by 2-fold or more. More than 130 of these gene expression differences were conserved between mouse and human monocyte subsets. We confirmed numerous of these differences at the cell surface protein level. Despite overall conservation, some molecules were conversely expressed between the 2 species' subsets, including CD36, CD9, and TREM-1. Other differences included a prominent peroxisome proliferatoractivated receptor ␥ (PPAR␥) signature in mouse monocytes, which is absent in humans, and strikingly opposed patterns of receptors involved in uptake of apoptotic cells and other phagocytic cargo between human and mouse monocyte subsets. Thus, whereas human and mouse monocyte subsets are far more broadly conserved than currently recognized, important differences between the species deserve consideration when models of human disease are studied in mice. (Blood. 2010;115:e10-e19) IntroductionSubpopulations of blood monocytes exist in humans, mice, and other species. 1-4 CD14 ϩϩ CD16 Ϫ and CD14 ϩ CD16 ϩ cell surface protein signatures 2 identify and distinguish the 2 major human monocyte subsets. In wild-type mice, monocytes can be identified as CD115 ϩ (c-fms/macrophage colony-stimulating factor [M-CSF] receptor), CD11b ϩ , F4/80 int blood cells, with monocyte subsets distinguished as Ly-6C ϩ and Ly-6C lo cells. [4][5][6][7] Ly-6C is frequently identified by flow cytometry using the Gr-1 antibody, which recognizes an epitope of both Ly-6C and Ly-6G. 8 It should be noted that monocytes express only Ly-6C. 4,9 It has been proposed that CD14 ϩϩ CD16 Ϫ human monocytes (here called CD16 Ϫ monocytes), which comprise approximately 95% of human blood monocytes, are counterparts to CD115 ϩ Ly-6C ϩ mouse monocytes (here called Ly-6C ϩ monocytes), which comprise approximately 50% of circulating mouse monocytes. 2,3,6,7,10 Moreover, CD14 ϩ CD16 ϩ human monocytes (here called CD16 ϩ monocytes) and CD115 ϩ Ly-6C lo mouse monocytes (here called Ly-6C lo monocytes) appear to bear similarity. 2,3,6,7,10 These proposed similarities arise from evidence that differential expression patterns of certain molecules between the 2 major subsets are shared in humans and mice. Namely, chemokine receptors CCR1 and CCR2 are more highly expressed on CD16 Ϫ human and Ly-6C ϩ mouse monocytes at the mRNA or protein level, 3,11,12 whereas CX 3 CR1 is elevated on CD16 ϩ human and Ly-6C lo mouse monocytes. 3 In addition, CD11a (␣ L integrin; Itgal), CD62L, and CD43 are conserved in their differential expression between monocyte subsets in humans and mice. [2][3][4]10,13 Further, CD16, the signature marker for distinguishing human monocyte subsets, was recently observed on the surface of mouse Ly-6C lo , but not Ly-6C ϩ , monocytes. 14 CD11c (␣ x integrin; Itgax) is more highly expressed on CD16 ϩ human monocytes...

The blood–brain barrier (BBB) and the environment of the central nervous system (CNS) guard the nervous tissue from peripheral immune cells. In the autoimmune disease multiple sclerosis, myelin-reactive T-cell blasts are thought to transgress the BBB and create a pro-inflammatory environment in the CNS, thereby making possible a second autoimmune attack that starts from the leptomeningeal vessels and progresses into the parenchyma. Using a Lewis rat model of experimental autoimmune encephalomyelitis, we show here that contrary to the expectations of this concept, T-cell blasts do not efficiently enter the CNS and are not required to prepare the BBB for immune-cell recruitment. Instead, intravenously transferred T-cell blasts gain the capacity to enter the CNS after residing transiently within the lung tissues. Inside the lung tissues, they move along and within the airways to bronchus-associated lymphoid tissues and lung-draining mediastinal lymph nodes before they enter the blood circulation from where they reach the CNS. Effector T cells transferred directly into the airways showed a similar migratory pattern and retained their full pathogenicity. On their way the T cells fundamentally reprogrammed their gene-expression profile, characterized by downregulation of their activation program and upregulation of cellular locomotion molecules together with chemokine and adhesion receptors. The adhesion receptors include ninjurin 1, which participates in T-cell intravascular crawling on cerebral blood vessels. We detected that the lung constitutes a niche not only for activated T cells but also for resting myelin-reactive memory T cells. After local stimulation in the lung, these cells strongly proliferate and, after assuming migratory properties, enter the CNS and induce paralytic disease. The lung could therefore contribute to the activation of potentially autoaggressive T cells and their transition to a migratory mode as a prerequisite to entering their target tissues and inducing autoimmune disease.

Glioblastoma multiforme (GBM) is paradigmatic for the investigation of cancer stem cells (CSC) in solid tumors. Growing evidence suggests that different types of CSC lead to the formation of GBM. This has prompted the present comparison of gene expression profiles between 17 GBM CSC lines and their different putative founder cells. Using a newly derived 24-gene signature, we can now distinguish two subgroups of GBM: Type I CSC lines display "proneural" signature genes and resemble fetal neural stem cell (fNSC) lines, whereas type II CSC lines show "mesenchymal" transcriptional profiles similar to adult NSC (aNSC) lines. Phenotypically, type I CSC lines are CD133 positive and grow as neurospheres. Type II CSC lines, in contrast, display (semi-)adherent growth and lack CD133 expression. Molecular differences between type I and type II CSC lines include the expression of extracellular matrix molecules and the transcriptional activity of the WNT and the transforming growth factor-β/bone morphogenetic protein signaling pathways. Importantly, these characteristics were not affected by induced adherence on laminin. Comparing CSC lines with their putative cells of origin, we observed greatly increased proliferation and impaired differentiation capacity in both types of CSC lines but no cancer-associated activation of otherwise silent signaling pathways. Thus, our data suggest that the heterogeneous tumor entity GBM may derive from cells that have preserved or acquired properties of either fNSC or aNSC but lost the corresponding differentiation potential. Moreover, we propose a gene signature that enables the subclassification of GBM according to their putative cells of origin. Cancer Res; 70(5); 2030-40. ©2010 AACR.