Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.</p> <p>Results</p> <p>In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.</p> <p>Conclusion</p> <p>In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.</p

Small chromosomal regions position themselves autonomously according to their chromatin class

The spatial arrangement of chromatin is linked to the regulation of nuclear processes. One striking aspect of nuclear organization is the spatial segregation of heterochromatic and euchromatic domains. The mechanisms of this chromatin segregation are still poorly understood. In this work, we investigated the link between the primary genomic sequence and chromatin domains. We analyzed the spatial intranuclear arrangement of a human artificial chromosome (HAC) in a xenospecific mouse background in comparison to an orthologous region of native mouse chromosome. The two orthologous regions include segments that can be assigned to three major chromatin classes according to their gene abundance and repeat repertoire: (1) gene-rich and SINE-rich euchromatin; (2) gene-poor and LINE/LTR-rich heterochromatin; and (3) genedepleted and satellite DNA-containing constitutive heterochromatin. We show, using fluorescence in situ hybridization (FISH) and 4C-seq technologies, that chromatin segments ranging from 0.6 to 3 Mb cluster with segments of the same chromatin class. As a consequence, the chromatin segments acquire corresponding positions in the nucleus irrespective of their chromosomal context, thereby strongly suggesting that this is their autonomous property. Interactions with the nuclear lamina, although largely retained in the HAC, reveal less autonomy. Taken together, our results suggest that building of a functional nucleus is largely a self-organizing process based on mutual recognition of chromosome segments belonging to the major chromatin classes

Mapping of lamin A- and progerin-interacting genome regions.

Mutations in the A-type lamins A and C, two major components of the nuclear lamina, cause a large group of phenotypically diverse diseases collectively referred to as laminopathies. These conditions often involve defects in chromatin organization. However, it is unclear whether A-type lamins interact with chromatin in vivo and whether aberrant chromatin–lamin interactions contribute to disease. Here, we have used an unbiased approach to comparatively map genome-wide interactions of gene promoters with lamin A and progerin, the mutated lamin A isoform responsible for the premature aging disorder Hutchinson–Gilford progeria syndrome (HGPS) in mouse cardiac myoytes and embryonic fibroblasts. We find that lamin A-associated genes are predominantly transcriptionally silent and that loss of lamin association leads to the relocation of peripherally localized genes, but not necessarily to their activation. We demonstrate that progerin induces global changes in chromatin organization by enhancing interactions with a specific subset of genes in addition to the identified lamin A-associated genes. These observations demonstrate disease-related changes in higher order genome organization in HGPS and provide novel insights into the role of lamin–chromatin interactions in chromatin organization. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00412-012-0376-7) contains supplementary material, which is available to authorized users

Nuclear p120-catenin regulates the anoikis resistance of mouse lobular breast cancer cells through Kaiso-dependent Wnt11 expression

E-cadherin inactivation underpins the progression of invasive lobular breast carcinoma (ILC). In ILC, p120-catenin (p120) translocates to the cytosol where it controls anchorage independence through the Rho-Rock signaling pathway, a key mechanism driving tumor growth and metastasis. We now demonstrate that anchorage-independent ILC cells show an increase in nuclear p120, which results in relief of transcriptional repression by Kaiso. To identify the Kaiso target genes that control anchorage independence we performed genome-wide mRNA profiling on anoikis-resistant mouse ILC cells, and identified 29 candidate target genes, including the established Kaiso target Wnt11. Our data indicate that anchorage-independent upregulation of Wnt11 in ILC cells is controlled by nuclear p120 through inhibition of Kaiso-mediated transcriptional repression. Finally, we show that Wnt11 promotes activation of RhoA, which causes ILC anoikis resistance. Our findings thereby establish a mechanistic link between E-cadherin loss and subsequent control of Rho-driven anoikis resistance through p120- and Kaiso-dependent expression of Wnt11

Nuclear p120-catenin regulates the anoikis resistance of mouse lobular breast cancer cells through Kaiso-dependent Wnt11 expression

E-cadherin inactivation underpins the progression of invasive lobular breast carcinoma (ILC). In ILC, p120-catenin (p120) translocates to the cytosol where it controls anchorage independence through the Rho-Rock signaling pathway, a key mechanism driving tumor growth and metastasis. We now demonstrate that anchorage-independent ILC cells show an increase in nuclear p120, which results in relief of transcriptional repression by Kaiso. To identify the Kaiso target genes that control anchorage independence we performed genome-wide mRNA profiling on anoikis-resistant mouse ILC cells, and identified 29 candidate target genes, including the established Kaiso target Wnt11. Our data indicate that anchorage-independent upregulation of Wnt11 in ILC cells is controlled by nuclear p120 through inhibition of Kaiso-mediated transcriptional repression. Finally, we show that Wnt11 promotes activation of RhoA, which causes ILC anoikis resistance. Our findings thereby establish a mechanistic link between E-cadherin loss and subsequent control of Rho-driven anoikis resistance through p120- and Kaiso-dependent expression of Wnt11

Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions

The architecture of human chromosomes in interphase nuclei is still largely unknown. Microscopy studies have indicated that specific regions of chromosomes are located in close proximity to the nuclear lamina (NL)(1-3). This has led to the idea that certain genomic elements may be attached to the NL, which may contribute to the spatial organization of chromosomes inside the nucleus. However, sequences in the human genome that interact with the NL in vivo have not been identified. Here we construct a high-resolution map of the interaction sites of the entire genome with NL components in human fibroblasts. This map shows that genome - lamina interactions occur through more than 1,300 sharply defined large domains 0.1 - 10 megabases in size. These lamina-associated domains ( LADs) are typified by low gene- expression levels, indicating that LADs represent a repressive chromatin environment. The borders of LADs are demarcated by the insulator protein CTCF, by promoters that are oriented away from LADs, or by CpG islands, suggesting possible mechanisms of LAD confinement. Taken together, these results demonstrate that the human genome is divided into large, discrete domains that are units of chromosome organization within the nucleus

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data-0

<p><b>Copyright information:</b></p><p>Taken from "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/88</p><p>BMC Bioinformatics 2008;9():88-88.</p><p>Published online 7 Feb 2008</p><p>PMCID:PMC2258289.</p><p></p>lemented in the MASDA R-package. Both objectives used for the comparison are estimated for each dataset , normalisation method and configuration of baseline correction and peak detection parameters. For each tuple (, , ), this results in a score (, ) for variance and (, ), (, ), and (, ) for classification performance

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data-3

<p><b>Copyright information:</b></p><p>Taken from "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/88</p><p>BMC Bioinformatics 2008;9():88-88.</p><p>Published online 7 Feb 2008</p><p>PMCID:PMC2258289.</p><p></p>rmalisation method is represented by a different symbol, global variants being annotated with an additional circle. The axes represent p-values of the statistical analysis described earlier, for classification performance and variance reduction on the x-axis and y-axis, respectively. The point (1, 1) indicates the position of unnormalised data. The region in which a method lies represents its performance for each objective with respect to using no normalisation

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data-2

<p><b>Copyright information:</b></p><p>Taken from "Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data"</p><p>http://www.biomedcentral.com/1471-2105/9/88</p><p>BMC Bioinformatics 2008;9():88-88.</p><p>Published online 7 Feb 2008</p><p>PMCID:PMC2258289.</p><p></p>in the figure titles. Black p-values indicate the significance with which "black" methods outperform the "red" methods and red p-values the opposite. Lines between elements indicate a pairing of variables for the statistical tests used. The following groupings are used: . global vs. local estimations of normalisation parameters, . non-zero vs. zero offsets and . scaling using the central location vs. the dispersion of data