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This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructed regulons (i.e., transcription factors and their target genes) assess the activity of these discovered regulons in individual cells and use these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon’s target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in

Summary Background In Drosophila, male courtship behavior is regulated in large part by the gene fruitless (fru). fru encodes a set of putative transcription factors that promote male sexual behavior by controlling the development of sexually dimorphic neuronal circuitry. Little is known about how Fru proteins function at the level of transcriptional regulation or the role that isoform diversity plays in the formation of a male-specific nervous system. Results To characterize the roles of sex-specific Fru isoforms in specifying male behavior, we generated novel isoform-specific mutants and used a genomic approach to identify direct Fru isoform targets during development. We demonstrate that all Fru isoforms directly target genes involved in the development of the nervous system, with individual isoforms exhibiting unique binding specificities. We observe that fru behavioral phenotypes are specified by either a single isoform or a combination of isoforms. Finally, we illustrate the utility of these data for the identification of novel sexually dimorphic genomic enhancers and novel downstream regulators of male sexual behavior. Conclusions These findings suggest that Fru isoform diversity facilitates both redundancy and specificity in gene expression, and that the regulation of neuronal developmental genes may be the most ancient and conserved role of fru in the specification of a male-specific nervous system., Highlights • Isoform-specific fru mutants reveal both functional redundancy and specificity • Fru isoform-specific genomic occupancy is characterized in the Drosophila nervous system • All Fru isoforms directly target neuronal morphogenesis genes • Isoform-specific motifs are associated with specific Fru isoform occupancy, Neville et al. characterize the roles of sex-specific Fruitless isoforms in specifying male behavior in Drosophila by generating novel isoform-specific mutants, along with using a genomic approach to identify direct Fruitless isoform targets during development.

Basal cell carcinoma, the most frequent human skin cancer, arises from activating hedgehog (HH) pathway mutations; however, little is known about the temporal changes that occur in tumour-initiating cells from the first oncogenic hit to the development of invasive cancer. Using an inducible mouse model enabling the expression of a constitutively active Smoothened mutant (SmoM2) in the adult epidermis, we carried out transcriptional profiling of SmoM2-expressing cells at different times during cancer initiation. We found that tumour-initiating cells are massively reprogrammed into a fate resembling that of embryonic hair follicle progenitors (EHFPs). Wnt/ β-catenin signalling was very rapidly activated following SmoM2 expression in adult epidermis and coincided with the expression of EHFP markers. Deletion of β-catenin in adult SmoM2-expressing cells prevents EHFP reprogramming and tumour initiation. Finally, human basal cell carcinomas also express genes of the Wnt signalling and EHFP signatures.

The human sensitivity to interaural temporal differences in the acoustic waveforms to the two ears shows remarkable acuity but is also very sluggish. Fast changes in binaural parameters are not detectable, and this inability contrasts sharply with the excellent temporal resolution of the monaural auditory system. We studied the response of binaural neurons in the inferior colliculus of the cat to sinusoidal changes in the interaural correlation of broadband noise. Responses to the same waveforms were also obtained from auditory nerve fibers and further analyzed with a coincidence analysis. Overall, the auditory nerve and inferior colliculus showed a similar ability to code changes in interaural correlation. This ability extended to modulation frequencies an order of magnitude higher than the highest frequencies detected binaurally in humans. We conclude that binaural sluggishness is not caused by a lack of temporal encoding of fast binaural changes at the level of the midbrain. We hypothesize that there is no neural substrate at the level of the midbrain or higher to read out this temporal code and that this constitutes a low-pass “sluggishness” filter.

Many cells in the inferior colliculus (IC) are sensitive to interaural time differences (ITDs), in the form of an oscillatory dependency of average firing rate on ITD. We studied the degree of damping in such binaural responses, recording from neurons in the inferior colliculus of pentobarbital-anesthetized cats to binaural broadband noise and tones. Noise-delay functions and composite curves were characterized by computing the difference between responses to correlated and anticorrelated stimuli. We use a new metric, based on the envelope of this difference, to quantify damping. There is a clear relationship between damping and characteristic frequency (CF), but even neurons of the same CF can differ in their damping. For individual cells, damping can be stronger to tones or to noise; at the population level the two are positively correlated and are scarcely affected by SPL. The frequencies that dominate ITD sensitivity are near the CF in response to noise, but are often below CF in response to tones. These findings qualify conclusions from earlier reports but overall they support the conclusion that, at a population level, basic aspects of binaural responses to wideband noise are consistent with summed responses to pure tones.

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org., Author Summary Gene regulatory networks control developmental, homeostatic, and disease processes by governing precise levels and spatio-temporal patterns of gene expression. Determining their topology can provide mechanistic insight into these processes. Gene regulatory networks consist of interactions between transcription factors and their direct target genes. Each regulatory interaction represents the binding of the transcription factor to a specific DNA binding site near its target gene. Here we present a computational method, called iRegulon, to identify master regulators and direct target genes in a human gene signature, i.e. a set of co-expressed genes. iRegulon relies on the analysis of the regulatory sequences around each gene in the gene set to detect enriched TF motifs or ChIP-seq peaks, using databases of nearly 10.000 TF motifs and 1000 ChIP-seq data sets or “tracks”. Next, it associates enriched motifs and tracks with candidate transcription factors and determines the optimal subset of direct target genes. We validate iRegulon on ENCODE data, and use it in combination with RNA-seq and ChIP-seq data to map a p53 downstream network with new predicted co-factors and targets. iRegulon is available as a Cytoscape plugin, supporting human, mouse, and Drosophila genes, and provides access to hundreds of cancer-related TF-target subnetworks or “regulons”.

A defining feature of auditory systems across animal divisions is the ability to sort different frequency components of a sound into separate neural frequency channels. Narrowband filtering in the auditory periphery is of obvious advantage for the representation of sound spectrum and manifests itself pervasively in human psychophysical studies as the critical band. Peripheral filtering also alters coding of the temporal waveform, so that temporal responses in the auditory periphery reflect both the stimulus waveform and peripheral filtering. Temporal coding is essential for the measurement of the time delay between waveforms at the two ears—a critical component of sound localization. A number of human psychophysical studies have shown a wider effective critical bandwidth with binaural stimuli than with monaural stimuli, although other studies found no difference. Here we directly compare binaural and monaural bandwidths (BWs) in the anesthetized cat. We measure monaural BW in the auditory nerve (AN) and binaural BW in the inferior colliculus (IC) using spectrally manipulated broadband noise and response metrics that reflect spike timing. The stimulus was a pair of noise tokens that were interaurally in phase for all frequencies below a certain flip frequency (fflip) and that had an interaural phase difference of π above fflip. The response was measured as a function of fflip and, using a separate stimulus protocol, as a function of interaural correlation. We find that both AN and IC filter BW depend on characteristic frequency, but that there is no difference in mean BW between the AN and IC.

The field of regulatory genomics today is characterized by the generation of high-throughput data sets that capture genome-wide transcription factor (TF) binding, histone modifications, or DNAseI hypersensitive regions across many cell types and conditions. In this context, a critical question is how to make optimal use of these publicly available datasets when studying transcriptional regulation. Here, we address this question in Drosophila melanogaster for which a large number of high-throughput regulatory datasets are available. We developed i-cisTarget (where the ‘i’ stands for integrative), for the first time enabling the discovery of different types of enriched ‘regulatory features’ in a set of co-regulated sequences in one analysis, being either TF motifs or ‘in vivo’ chromatin features, or combinations thereof. We have validated our approach on 15 co-expressed gene sets, 21 ChIP data sets, 628 curated gene sets and multiple individual case studies, and show that meaningful regulatory features can be confidently discovered; that bona fide enhancers can be identified, both by in vivo events and by TF motifs; and that combinations of in vivo events and TF motifs further increase the performance of enhancer prediction.