Deconvolving cell cycle expression data with complementary information

Bar‐Joseph, Ziv; Farkash, S.; Gifford, David K.; Simon, Itamar; Rosenfeld, Roni

doi:10.1093/bioinformatics/bth915

Cited by 49 publications

(59 citation statements)

References 16 publications

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“…A few studies have attempted to deconvolve time-series microarray data to survey either transcript levels (13,14) or peak expression timing (15) during the cell cycle in budding yeast. These approaches modeled variability in cell-cycle progression rate, but ignored the significant synchrony loss caused by asymmetric cell division.…”

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confidence: 99%

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Guo¹,

Bernard²,

Orlando

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cellcycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.O ne of the most fundamental processes in biology is the cell cycle, the intricate progression of events necessary for a cell's division. To better understand how cell-cycle events are regulated, studies in many organisms have monitored the dynamics of various molecular species (e.g., transcript levels, protein levels, nucleosome positions) throughout the cell cycle. Ideally, the dynamics of these species would be studied within individual cells traversing the cell cycle.Unfortunately, current technology enables accurate, genomewide quantification of many molecular species only in populations of cells. To provide insight into the dynamics of cell-cycle processes, the cells in such a population should be as synchronized as possible as they progress through the cell division cycle. To effect this synchrony, cells are arrested or selected at one stage of the cell cycle and then released to progress through subsequent division cycles. Molecular species can then be measured in the population at various time points after release (1-5).Measurements of cell populations would not be substantially different from average measurements of individual cells if the cells in the population were always perfectly synchronized. However, perfect cell synchrony is neither attainable at synchronization nor maintainable after release. Cells exhibit variability even at the time of release, and synchrony deteriorates further over time because individual cells progress through the cell cycle at different rates. Moreover, asymmetric cell division is a major source of synchrony loss in many kinds of cells and especially in budding yeast (6-10). After yeast cell division, newborn daughter cells are smaller than their mothers, and the cycle period of daughters is significantly longer than that of mothers. This is most likely due to mechanisms-not yet we...

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confidence: 99%

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Guo¹,

Bernard²,

Orlando

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…Although arrest methods were effective for characterizing cycling genes in a number of species (3-7), they did not lead to complete synchronization, even for yeast cells (8)(9)(10). A number of methods were introduced for resynchronizing yeast cells by either matching the profiles for the first and second cycle for each gene (9) or by combining expression and bud count information to reconstruct the expression profile (8).…”

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confidence: 99%

“…A number of methods were introduced for resynchronizing yeast cells by either matching the profiles for the first and second cycle for each gene (9) or by combining expression and bud count information to reconstruct the expression profile (8). These methods were shown to improve (the already good) yeast cell cycle expression data.…”

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confidence: 99%

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Bar‐Joseph

Siegfried

Brandeis

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

149

191

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Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into ''single-cell'' expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation.deconvolution ͉ expression profile T ight regulation of the cell cycle is necessary for the proper growth and development of all organisms. Dysregulation of cell cycle controls leads to proliferative diseases, most notably cancer. One approach to understanding basic cell cycle processes and their deregulation in cancer has been genome-wide characterization of the cell cycle transcriptional program (1). In these microarray experiments, the RNA levels of every gene is measured in a synchronized cell population at multiple time points. Synchronization is achieved by releasing cells from a cell cycle arrest. This approach was carried out initially to characterize the yeast cell cycle, and, subsequently, it was applied to examine the cell cycle in multiple organisms (reviewed in ref. 2).Although arrest methods were effective for characterizing cycling genes in a number of species (3-7), they did not lead to complete synchronization, even for yeast cells (8)(9)(10). A number of methods were introduced for resynchronizing yeast cells by either matching the profiles for the first and second cycle for each gene (9) or by combining expression and bud count information to reconstruct the expression profile (8). These methods were shown to improve (the already good) yeast cell cycle expression data. However, these methods cannot be directly applied to mammalian cells because of two major differences between yeast and mammalian cells: (i) normal diploid mammalian cells lose their synchronization relatively soon after release of growth arrest (11) and (ii) only 50-70% of wild-type mammalian cells reenter the cell cycle after release from arrest (12). The large percentage of arrested cells and loss of synchronization means that expression values represent a mixed population of cells, which introduces high background noise that confounds differentiation between genuine c...

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“…Microarray mRNA expression data were taken from the literature [1,[43][44][45] (23 different conditions, 310 profiles). Genes were clustered separately for each study or group of conditions (i.e., cell cycle, stress-related, metabolism) using only genes that changed significantly (standard deviation of log 2 -fold change .…”

Section: Methodsmentioning

confidence: 99%

Integrated Assessment and Prediction of Transcription Factor Binding

Beyer¹,

Workman²,

Hollunder³

et al. 2005

PLoS Comp Biol

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Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF-target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which ''standard conditions'' are ill defined.

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Deconvolving cell cycle expression data with complementary information

Abstract: Matlab implementation can be downloaded from the supporting website http://www.cs.cmu.edu/~zivbj/decon/decon.html

Cited by 49 publications

References 16 publications

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Integrated Assessment and Prediction of Transcription Factor Binding

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