SUMMARY The extent to which the three dimensional organization of the genome contributes to chromosomal translocations is an important question in cancer genomics. We now have generated a high resolution Hi-C spatial organization map of the G1-arrested mouse pro-B cell genome and mapped translocations from target DNA double strand breaks (DSBs) within it via high throughput genome-wide translocation sequencing. RAG endonuclease-cleaved antigen-receptor loci are dominant translocation partners for target DSBs regardless of genomic position, reflecting high frequency DSBs at these loci and their co-localization in a fraction of cells. To directly assess spatial proximity contributions, we normalized genomic DSBs via ionizing-radiation. Under these conditions, translocations were highly enriched in cis along single chromosomes containing target DSBs and within other chromosomes and sub-chromosomal domains in a manner directly related to pre-existing spatial proximity. Our studies reveal the power of combining two high-throughput genomic methods to address long-standing questions in cancer biology.

Summary Hematopoietic stem cells (HSCs) originate within the aorta-gonado-mesonephros (AGM) region of the midgestation embryo, but the cell type responsible for their emergence is unknown since critical hematopoietic factors are expressed in both the AGM endothelium and its underlying mesenchyme. Here we employ a temporally restricted genetic tracing strategy to selectively label the endothelium, and separately its underlying mesenchyme, during AGM development. Lineage tracing endothelium, via an inducible VE-cadherin Cre line, reveals that the endothelium is capable of HSC emergence. The endothelial progeny migrate to the fetal liver, and later to the bone marrow, are capable of expansion, self-renewal, and multi-lineage hematopoietic differentiation. HSC capacity is exclusively endothelial, as ex vivo analyses demonstrate lack of VE-cadherin Cre induction in circulating and fetal liver hematopoietic populations. Moreover, AGM mesenchyme, as selectively traced via a myocardin Cre line, is incapable of hematopoiesis. Our genetic tracing strategy therefore reveals an endothelial origin of HSCs.

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

Summary Maintenance of single layered endothelium, squamous endothelial cell shape, and formation of a patent vascular lumen all require defined endothelial cell polarity. Loss of β1 integrin (Itgb1) in nascent endothelium leads to disruption of arterial endothelial cell polarity and lumen formation. The loss of polarity is manifested as cuboidal shaped endothelial cells, dysregulated levels and mis-localization of normally polarized cell-cell adhesion molecules, as well as decreased expression of the polarity gene Par3 (pard3). β1 integrin and Par3 are both localized to the endothelial layer, with preferential expression of Par3 in arterial endothelium. Luminal occlusion is also exclusively noted in arteries, and is partially rescued by replacement of Par3 protein in β1 deficient vessels. Combined, our findings demonstrate that β1 integrin functions upstream of Par3 as part of a molecular cascade required for endothelial cell polarity and lumen formation.

In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.

Normalization is critically important for the proper interpretation of matrix-assisted laser desorption/ionization (MALDI) imaging datasets. The effects of the commonly used normalization techniques based on total ion count (TIC) or vector norm normalization are significant, and they are frequently beneficial. In certain cases, however, these normalization algorithms may produce misleading results and possibly lead to wrong conclusions, e.g. regarding to potential biomarker distributions. This is typical for tissues in which signals of prominent abundance are present in confined areas, such as insulin in the pancreas or β-amyloid peptides in the brain. In this work, we investigated whether normalization can be improved if dominant signals are excluded from the calculation. Because manual interaction with the data (e.g., defining the abundant signals) is not desired for routine analysis, we investigated two alternatives: normalization on the spectra noise level or on the median of signal intensities in the spectrum. Normalization on the median and the noise level was found to be significantly more robust against artifact generation compared to normalization on the TIC. Therefore, we propose to include these normalization methods in the standard “toolbox” of MALDI imaging for reliable results under conditions of automation.Electronic supplementary materialThe online version of this article (doi:10.1007/s00216-011-4929-z) contains supplementary material, which is available to authorized users.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) has emerged as a key technology for label-free bioanalysis of the spatial distribution of biomolecules, pharmaceuticals and other xenobiotics in tissue sections. Recent advances in instrumentation, sample preparation, multimodal workflows, quantification, analytical standardization and 'big data' processing have led to widespread utilization of MALDI MSI in pharmaceutical research. These developments have led to applications of the technology in drug discovery beyond drug disposition analysis, most notably in pharmacodynamic biomarker research and in toxicology.

The importance of cell types in understanding brain function is widely appreciated but only a tiny fraction of neuronal diversity has been catalogued. Here, we exploit recent progress in genetic definition of cell types in an objective structural approach to neuronal classification. The approach is based on highly accurate quantification of dendritic arbor position relative to neurites of other cells. We test the method on a population of 363 mouse retinal ganglion cells. For each cell, we determine the spatial distribution of the dendritic arbors, or “arbor density” with reference to arbors of an abundant, well-defined interneuronal type. The arbor densities are sorted into a number of clusters that is set by comparison with several molecularly defined cell types. The algorithm reproduces the genetic classes that are pure types, and detects six newly clustered cell types that await genetic definition.