A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response.

Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response.

Glatiramer Acetate modulates ion channels expression and calcium homeostasis in B cell of patients with relapsing-remitting multiple sclerosis.

To investigate the effects of Glatiramer Acetate (GA) on B cells by an integrated computational and experimental approach. GA is an immunomodulatory drug approved for the treatment of multiple sclerosis (MS). GA effect on B cells is yet to be fully elucidated. We compared transcriptional profiles of B cells from treatment-naïve relapsing remitting MS patients, treated or not with GA for 6 hours in vitro, and of B cells before and after six months of GA administration in vivo. Microarrays were analyzed with two different computational approaches, one for functional analysis of pathways (Gene Set Enrichment Analysis) and one for the identification of new drug targets (Mode-of-action by Network Analysis). GA modulates the expression of genes involved in immune response and apoptosis. A differential expression of genes encoding ion channels, mostly regulating Ca2+ homeostasis in endoplasmic reticulum (ER) was also observed. Microfluorimetric analysis confirmed this finding, showing a specific GA effect on ER Ca2+ concentration. Our findings unveils a GA regulatory effect on the immune response by influencing B cell phenotype and function. In particular, our results highlight a new functional role for GA in modulating Ca2+ homeostasis in these cells.

PML-RARA-associated cooperating mutations belong to a transcriptional network that is deregulated in myeloid leukemias.

It has been shown that individual acute myeloid leukemia (AML) patients are characterized by one of few initiating DNA mutations and 5-10 cooperating mutations not yet defined among hundreds identified by massive sequencing of AML genomes. We report an in vivo insertional-mutagenesis screen for genes cooperating with one AML initiating mutations (PML-RARA, oncogene of acute promyelocytic leukemia, APL), which allowed identification of hundreds of genetic cooperators. The cooperators are mutated at low frequency in APL or AML patients but are always abnormally expressed in a cohort of 182 APLs and AMLs analyzed. These deregulations appear non-randomly distributed and present in all samples, regardless of their associated genomic mutations. Reverse-engineering approaches showed that these cooperators belong to a single transcriptional gene network, enriched in genes mutated in AMLs, where perturbation of single genes modifies expression of others. Their gene-ontology analysis showed enrichment of genes directly involved in cell proliferation control. Therefore, the pool of PML-RARA cooperating mutations appears large and heterogeneous, but functionally equivalent and deregulated in the majority of APLs and AMLs. Our data suggest that the high heterogeneity of DNA mutations in APLs and AMLs can be reduced to patterns of gene expression deregulation of a single 'mutated' gene network.

An experimental approach to identify dynamical models of transcriptional regulation in living cells.

We describe an innovative experimental approach, and a proof of principle investigation, for the application of System Identification techniques to derive quantitative dynamical models of transcriptional regulation in living cells. Specifically, we constructed an experimental platform for System Identification based on a microfluidic device, a time-lapse microscope, and a set of automated syringes all controlled by a computer. The platform allows delivering a time-varying concentration of any molecule of interest to the cells trapped in the microfluidics device (input) and real-time monitoring of a fluorescent reporter protein (output) at a high sampling rate. We tested this platform on the GAL1 promoter in the yeast Saccharomyces cerevisiae driving expression of a green fluorescent protein (Gfp) fused to the GAL1 gene. We demonstrated that the System Identification platform enables accurate measurements of the input (sugars concentrations in the medium) and output (Gfp fluorescence intensity) signals, thus making it possible to apply System Identification techniques to obtain a quantitative dynamical model of the promoter. We explored and compared linear and nonlinear model structures in order to select the most appropriate to derive a quantitative model of the promoter dynamics. Our platform can be used to quickly obtain quantitative models of eukaryotic promoters, currently a complex and time-consuming process.

Systems and Synthetic biology: tackling genetic networks and complex diseases.

In the era of post-genomic research two new disciplines, Systems and Synthetic biology, act in a complementary way to shed light on the ever-increasing amount of data produced by novel high-throughput techniques. Systems biology aims at developing a formal understanding of biological processes through the development of quantitative mathematical models (bottom-up approach) and of 'reverse engineering' (top-down approach), whose aim is to infer the interactions among genes and proteins from experimental observations (gene regulatory networks). Synthetic biology on the other hand uses mathematical models to design novel biological 'circuits' (synthetic networks) able to perform specific tasks (for example, periodic expression of a gene of interest), or able to change the behavior of a biological process in a desired way (for example, modify metabolism to produce a specific compound of interest). The use of a pioneering approach that combines biology and engineering, to describe and/or invent new behaviors, could represent a valuable resource for studying complex diseases and design novel therapies. The identification of regulatory networks will help in identifying hundreds of genes that are responsible for most genetic diseases and that could serve as a starting point for therapeutic intervention. Here we present some of the main genetics and medical applications of these two emerging fields.

Inference of gene networks from temporal gene expression profiles.

Genes interact with each other in complex networks that enable the processing of information and the metabolism of nutrients inside the cell. A novel inference algorithm based on linear ordinary differential equations is proposed. The algorithm can infer the local network of gene-gene interactions surrounding a gene of interest from time-series gene expression profiles. The performance of the algorithm has been tested on in silico simulated gene expression data and on a nine gene subnetwork part of the DNA-damage response pathway (SOS pathway) in the bacteria Escherichia coli. This approach can infer regulatory interactions even when only a small number of measurements is available.

Linear matrix inequalities approach to reconstruction of biological networks.

The general problem of reconstructing a biological interaction network from temporal evolution data is tackled via an approach based on dynamical linear systems identification theory. A novel algorithm, based on linear matrix inequalities, is devised to infer the interaction network. This approach allows to directly taking into account, within the optimisation procedure, the a priori available knowledge of the biological system. The effectiveness of the proposed algorithm is statistically validated, by means of numerical tests, demonstrating how the a priori knowledge positively affects the reconstruction performance. A further validation is performed through an in silico biological experiment, exploiting the well-assessed cell-cycle model of fission yeast developed by Novak and Tyson.

A multi-step approach to time series analysis and gene expression clustering.

MOTIVATION: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. RESULTS: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. AVAILABILITY: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The transcriptional landscape of the mammalian genome.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

Identification of quadratic nonlinear models oriented to genetic network analysis.

The goal of this paper is to provide a novel procedure for the identification of nonlinear models which exhibit a quadratic dependence on the state variables. These models turn out to be very useful for the description of a large class of biochemical processes with particular reference to the genetic networks regulating the cell cycle. The proposed approach is validated through extensive computer simulations on randomly generated systems.

Robust identification of large genetic networks.

Temporal and spatial gene expression, together with the concentration of proteins and metabolites, is tightly controlled in the cell. This is possible thanks to complex regulatory networks between these different elements. The identification of these networks would be extremely valuable. We developed a novel algorithm to identify a large genetic network, as a set of linear differential equations, starting from measurements of gene expression at steady state following transcriptional perturbations. Experimentally, it is possible to overexpress each of the genes in the network using an episomal expression plasmid and measure the change in mRNA concentration of all the genes, following the perturbation. Computationally, we reduced the identification problem to a multiple linear regression, assuming that the network is sparse. We implemented a heuristic search method in order to apply the algorithm to large networks. The algorithm can correctly identify the network, even in the presence of large noise in the data, and can be used to predict the genes that directly mediate the action of a compound. Our novel approach is experimentally feasible and it is readily applicable to large genetic networks.

Effect of lead exclusion for the manual measurement of QT dispersion.

To investigate the effect of different lead exclusion criteria for the manual measurement of QT dispersion (QTd). Simultaneous 12-lead ECGs from three groups of 25 subjects were studied; healthy normal subjects, subjects with a myocardial infarction, and subjects with arrhythmias. Leads were excluded with (1) small absolute T wave amplitudes, (2) small relative T wave amplitudes, and (3) small and/or large relative QT measurements. QTd was calculated as the QT range and assessed for its ability to differentiate between the normal and pathological groups. With exclusion of no leads or low absolute amplitude T waves (< 50 microV) significant differences were observed only between normal and myocardial infarct groups (P < 0.05). Significant differences between normal and both pathological groups were observed when excluding the lead with the smallest amplitude T wave or shortest QT (P < 0.05), or when two leads of either type were excluded (P < 0.005). There was good agreement between leads excluded by amplitude or QT (P < 0.01). Lead exclusion for QTd is important. Exclusion of the two smallest amplitude or two shortest QT leads from each subject produced the greatest differences between the normal and pathological groups.

Dispersion of QT intervals: a measure of dispersion of repolarization or simply a projection effect?

QT interval dispersion may provide little information about repolarization dispersion. Some clinical measurements demonstrate an association between high QT interval dispersion and high morbidity and mortality, but what is being measured is not clear. This study was designed to help resolve this dilemma. We compared the association between different clinical measures of QT interval dispersion and the ECG lead amplitudes derived from a heart vector model of repolarization with no repolarization dispersion whatsoever. We compared our clinical QT interval dispersion data obtained from 25 subjects without cardiac disease with similar data from published studies, and correlated these QT dispersion results with the distribution of lead amplitudes derived from the projection of the heart vector onto the body surface during repolarization. Published results were available for mean relative QT intervals and mean differences from the maximum QT interval. The leads were derived from Uijen and Dower lead vector data. Using the Uijen lead vector data, the correlation between measurements of dispersion and derived lead amplitudes ranged from 0.78 to 0.99 for limb leads, and using the Dower values ranged from 0.81 to 0.94 for the precordial leads. These results show a clear association between the measured QT interval dispersion and the variation in ECG lead amplitudes derived from a simple heart vector model of repolarization with no regional information. Therefore, measured QT dispersion is related mostly to a projection effect and is not a true measure of repolarization dispersion. Our existing interpretation of QT dispersion must be reexamined, and other measurements that provide true repolarization dispersion data investigated.

Computer model for study of cardiac repolarization.

INTRODUCTION: We propose a new and simple method to model repolarization in the left ventricle and the corresponding T waves on the surface ECG. METHODS AND RESULTS: We modeled the cardiac cell action potentials (APs) in the left ventricle (LV) with differences in only the duration of the plateau phase. Using published experimental data on the epicardial and endocardial repolarization sequences, for each point on the left ventricular surface we set a different AP repolarization starting time, determined by the duration of the plateau phase. The surface source model was used to compute potentials on the surface of the torso, generated by repolarization of the LV. Both the torso and the LV had homogeneous and isotropic conductivity. We simulated T waves on the 12-lead ECG and compared our results with measured T waves from five normal subjects. The orientation and shape in each lead were reproduced. In each lead we computed the root mean square error between simulated and measured T waves. The average error across the 12 leads was small, with a mean value of 0.11 mV across all the subjects. CONCLUSION: Repolarization of the LV can be modeled independently of the depolarization sequence and AP duration gradients. This method is an easy and powerful tool to describe the ECG features of repolarization.