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Many enhancers play a crucial role in regulating gene expression by assembling regulatory factor (RF) clusters, also referred to as condensates. This process is essential for facilitating enhancer communication and establishing cellular identity. However, how DNA sequence and transcription factor (TF) binding instruct the formation of such high RF environments is still poorly understood. To address this, we developed a novel approach leveraging enhancer-centric chromatin accessibility quantitative trait loci (caQTLs) to nominate RF clusters genome-wide. By analyzing TF binding signatures within the context of caQTLs, we discovered a new class of TFs that specifically contributes to establishing cooperative environments. These “context-only” TFs bind promiscuously with cell type-specific pioneers, recruit coactivators, and, like super enhancers, render downstream gene expression sensitive to condensate-disrupting molecules. We further demonstrate that joint context-only and pioneer TF binding explains enhancer compatibility and provides a mechanistic rationale for how a loose TF syntax can still confer regulatory specificity.

Adipose tissue plasticity is orchestrated by molecularly and functionally diverse cells within the stromal vascular fraction (SVF). While several mouse and human adipose SVF cellular subpopulations have now been identified, we still lack an understanding of the cellular and functional variability of adipose stem and progenitor cell (ASPC) populations across human fat depots. To address this, we performed single-cell and bulk RNA-seq analyses of >30 Lin–SVF samples across four human adipose depots, revealing two ubiquitous hASPC subpopulations with distinct proliferative and adipogenic properties but also depot- and BMI-dependent proportions. Furthermore, we identified an omental-specific, highIGFBP2-expressing stromal population that transitions between mesothelial and mesenchymal cell states and inhibits hASPC adipogenesis through IGFBP2 secretion. Our analyses highlight the molecular and cellular uniqueness of different adipose niches while our discovery of an anti-adipogenic IGFBP2+ omental-specific population provides a new rationale for the biomedically relevant, limited adipogenic capacity of omental hASPCs.

Despite its popularity, chromatin immunoprecipitation followed by sequencing (ChIP-seq) remains a tedious (>2 d), manually intensive, low-sensitivity and low-throughput approach. Here, we combine principles of microengineering, surface chemistry, and molecular biology to address the major limitations of standard ChIP-seq. The resulting technology, FloChIP, automates and miniaturizes ChIP in a beadless fashion while facilitating the downstream library preparation process through on-chip chromatin tagmentation. FloChIP is fast (6 to 500 cells), is scalable and parallelized, and supports antibody- or sample-multiplexed ChIP on both histone marks and transcription factors. In addition, FloChIP’s interconnected design allows for straightforward chromatin reimmunoprecipitation, which allows this technology to also act as a microfluidic sequential ChIP-seq system. Finally, we ran FloChIP for the transcription factor MEF2A in 32 distinct human lymphoblastoid cell lines, providing insights into the main factors driving collaborative DNA binding of MEF2A and into its role in B cell-specific gene regulation. Together, our results validate FloChIP as a flexible and reproducible automated solution for individual or sequential ChIP-seq.

Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy, thus allowing to couple a cell’s ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell’s trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach., Nature, 608 (7924), ISSN:0028-0836, ISSN:1476-4687

Gut-draining mesenteric lymph nodes (mLN) provide the framework and microenvironment to shape intestinal adaptive immune responses. We previously delineated transcriptional signatures in LN stromal cells (SC), pointing to tissue-specific variability in composition and immuno-modulatory function of SCs. Here, we dissect the tissue-specific epigenomic DNA accessibility and CpG methylation landscape of LN non-endothelial SCs and identify a microbiota-independent core epigenomic signature of LN SCs. By combined analysis of transcription factor (TF) binding sites together with the gene expression profiles of non-endothelial SCs, we delineated TFs poising skin-draining peripheral LN (pLN) SCs for pro-inflammatory responses. Furthermore, using scRNA-seq, we dissected the developmental trajectory of mLN SCs derived from postnatal to aged mice, identifying two distinct putative progenitors, namely CD34+SC and fibroblastic reticular stromal cell (FRC) progenitors, which both feed the rapid postnatal LN expansion. Finally, we identified Irf3 as a key differentiation TF inferred from the epigenomic signature of mLN SCs that is dynamically expressed along the differentiation trajectories of FRCs, and validated Irf3 as a regulator of Cxcl9+ FRC differentiation. Together, our data constitute a comprehensive transcriptional and epigenomic map of mLN development and dissect location-specific, microbiota-independent properties of mLN non-endothelial SCs. As such, our findings represent a valuable resource to identify core transcriptional regulators that impinge on the developing mLN early in life, thereby shaping long-lasting intestinal adaptive immune responses.

Gut-draining mesenteric lymph nodes (mLN) provide the framework and microenvironment to shape intestinal adaptive immune responses. We previously delineated transcriptional signatures in LN stromal cells (SC), pointing to tissue-specific variability in composition and immuno-modulatory function of SCs.Here, we dissect the tissue-specific epigenomic DNA accessibility and CpG methylation landscape of LN non-endothelial SCs and identify a microbiota-independent core epigenomic signature of LN SCs. By combined analysis of transcription factor (TF) binding sites together with the gene expression profiles of non-endothelial SCs, we delineated TFs poising skin-draining peripheral LN (pLN) SCs for pro-inflammatory responses. Furthermore, using scRNA-seq, we dissected the developmental trajectory of mLN SCs derived from postnatal to aged mice, identifying two distinct putative progenitors, namely CD34+SC and fibroblastic reticular stromal cell (FRC) progenitors, which both feed the rapid postnatal LN expansion. Finally, we identifiedIrf3as a key differentiation TF inferred from the epigenomic signature of mLN SCs that is dynamically expressed along the differentiation trajectories of FRCs, and validatedIrf3as a regulator of Cxcl9+FRC differentiation.Together, our data constitute a comprehensive transcriptional and epigenomic map of mLN development and dissect location-specific, microbiota-independent properties of mLN non-endothelial SCs. As such, our findings represent a valuable resource to identify core transcriptional regulators that impinge on the developing mLN early in life, thereby shaping long-lasting intestinal adaptive immune responses.

Gut-draining mesenteric lymph nodes (LN) provide the framework to shape intestinal adaptive immune responses. Based on the transcriptional signatures established by our previous work, the composition and immunomodulatory function of LN stromal cells (SC) vary according to location. Here, we describe the single-cell composition and development of the SC compartment within mesenteric LNs derived from postnatal to aged mice. We identify CD34

Background The evolution of embryological development has long been characterized by deep conservation. In animal development, the phylotypic stage in mid-embryogenesis is more conserved than either early or late stages among species within the same phylum. Hypotheses to explain this hourglass pattern have focused on purifying the selection of gene regulation. Here, we propose an alternative—genes are regulated in different ways at different stages and have different intrinsic capacities to respond to perturbations on gene expression. Results To eliminate the influence of natural selection, we quantified the expression variability of isogenetic single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that the expression variability is lower at the phylotypic stage, supporting that the underlying regulatory architecture in this stage is more robust to stochastic variation on gene expression. We present evidence that the phylotypic stage is also robust to genetic variations on gene expression. Moreover, chromatin regulation appears to play a key role in the variation and evolution of gene expression. Conclusions We suggest that a phylum-level pattern of embryonic conservation can be explained by the intrinsic difference of gene regulatory mechanisms in different stages.

The human conductive respiratory tract spans a long anatomical distance and represents an important barrier to constrain invading respiratory pathogens. The disparate ambient temperatures found in the upper and lower respiratory tract have been demonstrated to influence the replication kinetics of common cold viruses as well as the associated host responses. Here, we employed the human airway epithelial cell (hAEC) culture model to investigate the impact of ambient temperatures found in the upper and lower respiratory tract, 33°C and 37°C, respectively, on the viral replication kinetics and host innate immune response dynamics during SARS-CoV-2 and SARS-CoV infections. Strikingly, SARS-CoV-2, in contrast to SARS-CoV, replicated more efficiently at temperatures encountered in the upper respiratory tract, and displayed higher sensitivity to type I and type III IFNs than SARS-CoV. Time-resolved transcriptome analysis highlighted a temperature-dependent induction of IFN-mediated antiviral response, whose amplitude inversely correlated with the replication kinetic efficiencies of both SARS-CoV-2 and SARS-CoV at temperatures found in the upper and lower respiratory tract. Altogether, these data reflect clinical features of SARS-CoV-2 and SARS-CoV and subsequently, their associated human-to-human transmission efficiencies. They provide crucial insights of the profound impact of ambient temperatures on viral replication and associated pivotal virus - host interaction dynamics. This knowledge can be exploited for the development of novel intervention strategies against SARS-CoV-2.

A dataset of >700 tissue-specific transcriptomes of D. melanogaster reveals population-level molecular circadian clock variation., Natural genetic variation affects circadian rhythms across the evolutionary tree, but the underlying molecular mechanisms are poorly understood. We investigated population-level, molecular circadian clock variation by generating >700 tissue-specific transcriptomes of Drosophila melanogaster (w1118) and 141 Drosophila Genetic Reference Panel (DGRP) lines. This comprehensive circadian gene expression atlas contains >1700 cycling genes including previously unknown central circadian clock components and tissue-specific regulators. Furthermore, >30% of DGRP lines exhibited aberrant circadian gene expression, revealing abundant genetic variation–mediated, intertissue circadian expression desynchrony. Genetic analysis of one line with the strongest deviating circadian expression uncovered a novel cry mutation that, as shown by protein structural modeling and brain immunohistochemistry, disrupts the light-driven flavin adenine dinucleotide cofactor photoreduction, providing in vivo support for the importance of this conserved photoentrainment mechanism. Together, our study revealed pervasive tissue-specific circadian expression variation with genetic variants acting upon tissue-specific regulatory networks to generate local gene expression oscillations.

Objective To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. Materials and Methods Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology “N-of-1-pathways.” The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided. Results Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). Discussion Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes. Conclusion The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway–level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

Size of organs/organisms is a polygenic trait. Many of the growth‐regulatory genes constitute conserved growth signaling pathways. However, how these multiple genes are orchestrated at the systems level to attain the natural variation in size including sexual size dimorphism is mostly unknown. Here we take a multi‐layered systems omics approach to study size variation in the Drosophila wing. We show that expression levels of many critical growth regulators such as Wnt and TGFβ pathway components significantly differ between sexes but not between lines exhibiting size differences within each sex, suggesting a primary role of these regulators in sexual size dimorphism. Only a few growth genes including a receptor of steroid hormone ecdysone exhibit association with between‐line size differences. In contrast, we find that between‐line size variation is largely regulated by genes with a diverse range of cellular functions, most of which have never been implicated in growth. In addition, we show that expression quantitative trait loci (eQTLs) linked to these novel growth regulators accurately predict population‐wide, between‐line wing size variation. In summary, our study unveils differential gene regulatory systems that control wing size variation between and within sexes., The study investigates transcriptomic variation that reflects a natural genetic diversity in fly wings. Through a multi‐layered systems omics approach, regulatory systems that control two distinct aspects of wing size variation are determined.

Despite its popularity, chromatin immunoprecipitation followed by sequencing (ChIP-seq) remains a tedious (>2d), manually intensive, low-sensitivity and low-throughput approach. Here, we combine principles of microengineering, surface chemistry and molecular biology to address the major limitations of standard ChIP-seq. The resulting approach, FloChIP, automates and miniaturizes ChIP in a beadless fashion while facilitating the downstream library preparation process through on-chip chromatin tagmentation. FloChIP is fast (6 to 500 cells), is high-throughput (up to 64 parallel, antibody- or sample-multiplexed experiments) and is compatible with both histone mark and transcription factor ChIP. In addition, FloChIP’s interconnected design allows for straightforward chromatin re-immunoprecipitation, thus constituting the first example of a microfluidic sequential ChIP-seq system. Finally, we demonstrate FloChIP’s high-throughput capacity by performing ChIP-seq of the transcription factor MEF2A in 32 distinct human lymphoblastoid cell lines, providing novel insights into the main factors driving collaborative DNA binding of MEF2A and into its role in B-cell-specific gene regulation. Together, our results validate FloChIP as a flexible and reproducible automated solution for individual or sequential ChIP-seq.

The evolution of embryological development has long been characterized by deep conservation. Both morphological and transcriptomic surveys have proposed a “hourglass” model of Evo-Devo1,2. A stage in mid-embryonic development, the phylotypic stage, is highly conserved among species within the same phylum3–7. However, the reason for this phylotypic stage is still elusive. Here we hypothesize that the phylotypic stage might be characterized by selection for robustness to noise and environmental perturbations. This could lead to mutational robustness, thus evolutionary conservation of expression and the hourglass pattern. To test this, we quantified expression variability of single embryo transcriptomes throughout flyDrosophila melanogasterembryogenesis. We found that indeed expression variability is lower at extended germband, the phylotypic stage. We explain this pattern by stronger histone modification mediated transcriptional noise control at this stage. In addition, we find evidence that histone modifications can also contribute to mutational robustness in regulatory elements. Thus, the robustness to noise does indeed contributes to robustness of gene expression to genetic variations, and to the conserved phylotypic stage.

The pioneer activity of transcription factors allows for opening of inaccessible regulatory elements and has been extensively studied in the context of cellular differentiation and reprogramming. In contrast, the function of pioneer activity in self-renewing cell divisions and across the cell cycle is poorly understood. Here we assessed the interplay between OCT4 and SOX2 in controlling chromatin accessibility of mouse embryonic stem cells. We found that OCT4 and SOX2 operate in a largely independent manner even at co-occupied sites, and that their cooperative binding is mostly mediated indirectly through regulation of chromatin accessibility. Controlled protein degradation strategies revealed that the uninterrupted presence of OCT4 is required for post-mitotic re-establishment and interphase maintenance of chromatin accessibility, and that highly OCT4-bound enhancers are particularly vulnerable to transient loss of OCT4 expression. Our study sheds light on the constant pioneer activity required to maintain the dynamic pluripotency regulatory landscape in an accessible state.

Genome-wide gene expression analyses by RNA sequencing (RNA-seq) have quickly become a standard in molecular biology because of the widespread availability of high throughput sequencing technologies. While powerful, RNA-seq still has several limitations, including the time and cost of library preparation, which makes it difficult to profile many samples simultaneously. To deal with these constraints, the single-cell transcriptomics field has implemented the early multiplexing principle, making the library preparation of hundreds of samples (cells) markedly more affordable. However, the current standard methods for bulk transcriptomics (such as TruSeq Stranded mRNA) remain expensive, and relatively little effort has been invested to develop cheaper, but equally robust methods. Here, we present a novel approach, Bulk RNA Barcoding and sequencing (BRB-seq), that combines the multiplexing-driven cost-effectiveness of a single-cell RNA-seq workflow with the performance of a bulk RNA-seq procedure. BRB-seq produces 3’ enriched cDNA libraries that exhibit similar gene expression quantification to TruSeq and that maintain this quality, also in terms of number of detected differentially expressed genes, even with low quality RNA samples. We show that BRB-seq is about 25 times less expensive than TruSeq, enabling the generation of ready to sequence libraries for up to 192 samples in a day with only 2 hours of hands-on time. We conclude that BRB-seq constitutes a powerful alternative to TruSeq as a standard bulk RNA-seq approach. Moreover, we anticipate that this novel method will eventually replace RT-qPCR-based gene expression screens given its capacity to generate genome-wide transcriptomic data at a cost that is comparable to profiling 4 genes using RT-qPCR.‘SoftwareWe developed a suite of open source tools (BRB-seqTools) to aid with processing BRB-seq data and generating count matrices that are used for further analyses. This suite can perform demultiplexing, generate count/UMI matrices and trim BRB-seq constructs and is freely available at http://github.com/DeplanckeLab/BRB-seqToolsHighlightsRapid (~2h hands on time) and low-cost approach to perform transcriptomics on hundreds of RNA samplesStrand specificity preservedPerformance: number of detected genes is equal to Illumina TruSeq Stranded mRNA at same sequencing depthHigh capacity: low cost allows increasing the number of biological replicatesProduces reliable data even with low quality RNA samples (down to RIN value = 2)Complete user-friendly sequencing data pre-processing and analysis pipeline allowing result acquisition in a day

Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome). Availability and implementation: http://www.lussierlab.net/publications/N-of-1-pathways. Contact: yves@email.arizona.edu or piegorsch@math.arizona.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Display Omitted N-of-1-pathways framework is an effective tool for interpretable dynamic personal transcriptome.Personal dysregulated pathways reflect dynamic transcriptome viral response ex-vivo and in vivo.We developed the Similarity Venn Diagram to show similarity between two qualitative measures.Using N-of-1-pathways serves as a foundational work for personalized "virograms". BackgroundUnderstanding individual patient host-response to viruses is key to designing optimal personalized therapy. Unsurprisingly, in vivo human experimentation to understand individualized dynamic response of the transcriptome to viruses are rarely studied because of the obvious limitations stemming from ethical considerations of the clinical risk. ObjectiveIn this rhinovirus study, we first hypothesized that ex vivo human cells response to virus can serve as a proxy for otherwise controversial in vivo human experimentation. We further hypothesized that the N-of-1-pathways framework, previously validated in cancer, can be effective in understanding the more subtle individual transcriptomic response to viral infection. MethodN-of-1-pathways computes a significance score for a given list of gene sets at the patient level, using merely the 'omics profiles of two paired samples as input. We extracted the peripheral blood mononuclear cells (PBMC) of four human subjects, aliquoted in two paired samples, one subjected to ex vivo rhinovirus infection. Their dysregulated genes and pathways were then compared to those of 9 human subjects prior and after intranasal inoculation in vivo with rhinovirus. Additionally, we developed the Similarity Venn Diagram, a novel visualization method that goes beyond conventional overlap to show the similarity between two sets of qualitative measures. ResultsWe evaluated the individual N-of-1-pathways results using two established cohort-based methods: GSEA and enrichment of differentially expressed genes. Similarity Venn Diagrams and individual patient ROC curves illustrate and quantify that the in vivo dysregulation is recapitulated ex vivo both at the gene and pathway level (p-values≤0.004). ConclusionWe established the first evidence that an interpretable dynamic transcriptome metric, conducted as an ex vivo assays for a single subject, has the potential to predict individualized response to infectious disease without the clinical risks otherwise associated to in vivo challenges. These results serve as a foundational work for personalized "virograms".

Background Filter feature selection methods compute molecular signatures by selecting subsets of genes in the ranking of a valuation function. The motivations of the valuation functions choice are almost always clearly stated, but those for selecting the genes according to their ranking are hardly ever explicit. Method We addressed the computation of molecular signatures by searching the optima of a bi-objective function whose solution space was the set of all possible molecular signatures, ie, the set of subsets of genes. The two objectives were the size of the signature-to be minimized–and the interclass distance induced by the signature-to be maximized–. Results We showed that: 1) the convex combination of the two objectives had exactly n optimal non empty signatures where n was the number of genes, 2) the n optimal signatures were nested, and 3) the optimal signature of size k was the subset of k top ranked genes that contributed the most to the interclass distance. We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy. Conclusions Defining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics. The complexity of the computation was very low because the optimal signatures were the sets of genes in the ranking of their valuation. Software can be freely downloaded from http://gardeux-vincent.eu/DeltaRanking.php

International audience; The emergence of high-dimensional data requires the design of new optimization methods. Indeed, conventional optimization methods require improvements, hybridization, or parameter tuning in order to operate in spaces of high dimensions. In this paper, we present a new adaptive variant of a pattern search algorithm to solve global optimization problems exhibiting such a character. The proposed method has no parameters visible to the user and the default settings, determined by almost no a priori experimentation, are highly robust on the tested datasets. The algorithm is evaluated and compared with 11 state-of-the-art methods on 20 benchmark functions of 1000 dimensions from the CEC’2010 competition. The results show that this approach obtains good performances compared to the other methods tested.

Background The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method ‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. Software http://lussierlab.org/publications/N-of-1-pathways

Motivation Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. Results We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. Availability and implementation The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). Methods We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. Results In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value Conclusion Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.

Objective Applying the science of networks to quantify the discriminatory impact of the ICD-9-CM to ICD-10-CM transition between clinical specialties. Materials and Methods Datasets were the Center for Medicaid and Medicare Services ICD-9-CM to ICD-10-CM mapping files, general equivalence mappings, and statewide Medicaid emergency department billing. Diagnoses were represented as nodes and their mappings as directional relationships. The complex network was synthesized as an aggregate of simpler motifs and tabulation per clinical specialty. Results We identified five mapping motif categories: identity, class-to-subclass, subclass-to-class, convoluted, and no mapping. Convoluted mappings indicate that multiple ICD-9-CM and ICD-10-CM codes share complex, entangled, and non-reciprocal mappings. The proportions of convoluted diagnoses mappings (36% overall) range from 5% (hematology) to 60% (obstetrics and injuries). In a case study of 24 008 patient visits in 217 emergency departments, 27% of the costs are associated with convoluted diagnoses, with ‘abdominal pain’ and ‘gastroenteritis’ accounting for approximately 3.5%. Discussion Previous qualitative studies report that administrators and clinicians are likely to be challenged in understanding and managing their practice because of the ICD-10-CM transition. We substantiate the complexity of this transition with a thorough quantitative summary per clinical specialty, a case study, and the tools to apply this methodology easily to any clinical practice in the form of a web portal and analytic tables. Conclusions Post-transition, successful management of frequent diseases with convoluted mapping network patterns is critical. The http://lussierlab.org/transition-to-ICD10CM web portal provides insight in linking onerous diseases to the ICD-10 transition.

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy P=0.046), bladder cancer (entropy P=0.039), and rheumatoid arthritis (PheWAS case–control P−4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

Swarm intelligence deals with the study of collectively intelligent behavior that emerges from a decentralized system of non-intelligent individual agents. The concept is widely used in the fields of simulation, optimization or robotics, but less known in the domain of generative art. This paper presents the swarm paradigm in the context of artistic creation, and more particularly explores the interest of enhancing swarm models with dynamics inspired from natural ecosystems. The authors introduce an energy budget to the agents of a swarm system, and show how mapping the energy level to visual information such as line width or color, combined with mechanisms such as resource chasing and consumption, enriches the search space of possible images. Moreover, the authors highlight that the approach allows the user to partially control the creation process of the drawings. The authors argue that the exploration of ecosystem dynamics in generative systems may open up novel artistic opportunities and shift the perspective from swarm art toward ecosystem art.

Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10−7. Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p

Background: Several prognostic models have been proposed and demonstrated to be predictive of survival outcomes in breast cancer. In the present article, we assessed whether three of these models are comparable at an individual level. Methods: We used a large data set (n=965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath.net and PREDICT, and we assessed concordance of these models in 10-year survival prediction. Results: Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0.74 (95% CI 0.71–0.77) for the Cancermath.net model and 0.72 (95% CI 0.69–0.75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the subgroup, with a predicted survival higher than 90% with a median score dispersion at 0.08 (range 0.06–0.10). Dispersion, however, reached 30% for the subgroups with a predicted survival between 10 and 50%. Conclusion: This study revealed that the three web-based predictors equally perform well at the population level, but exhibit a high degree of discordance in the intermediate and poor prognosis groups.

Background The current state of the art for measuring stromal response to targeted therapy requires burdensome and rate limiting quantitative histology. Transcriptome measures are increasingly affordable and provide an opportunity for developing a stromal versus cancer ratio in xenograft models. In these models, human cancer cells are transplanted into mouse host tissues (stroma) and together coevolve into a tumour microenvironment. However, profiling the mouse or human component separately remains problematic. Indeed, laser capture microdissection is labour intensive. Moreover, gene expression using commercial microarrays introduces significant and underreported cross-species hybridization errors that are commonly overlooked by biologists. Method We developed a customized dual-species array, H&M array, and performed cross-species and species-specific hybridization measurements. We validated a new methodology for establishing the stroma vs cancer ratio using transcriptomic data. Results In the biological validation of the H&M array, cross-species hybridization of human and mouse probes was significantly reduced (4.5 and 9.4 fold reduction, respectively; p < 2x10-16 for both, Mann-Whitney test). We confirmed the capability of the H&M array to determine the stromal to cancer cells ratio based on the estimation of cellularity index of mouse/human mRNA content in vitro. This new metrics enable to investigate more efficiently the stroma-cancer cell interactions (e.g. cellularity) bypassing labour intensive requirement and biases of laser capture microdissection. Conclusion These results provide the initial evidence of improved and cost-efficient analytics for the investigation of cancer cell microenvironment, using species-specificity arrays specifically designed for xenografts models.

Summary: Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches. There are often systematic but unpredictable biases from batch-to-batch, so proper randomization of biologically relevant traits across batches is crucial for distinguishing true biological differences from experimental artifacts. When a large number of traits are biologically relevant, as is common for clinical studies of patients with varying sex, age, genotype and medical background, proper randomization can be extremely difficult to prepare by hand, especially because traits may affect biological inferences, such as differential expression, in a combinatorial manner. Here we present ARTS (automated randomization of multiple traits for study design), which aids researchers in study design by automatically optimizing batch assignment for any number of samples, any number of traits and any batch size. Availability and implementation: ARTS is implemented in Perl and is available at github.com/mmaiensc/ARTS. ARTS is also available in the Galaxy Tool Shed, and can be used at the Galaxy installation hosted by the UIC Center for Research Informatics (CRI) at galaxy.cri.uic.edu. Contact: mmaiensc@uic.edu Supplementary information: Supplementary data are available at Bioinformatics online.