Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases.

Genome-wide association studies have identified many loci associated with hair and skin disease, but identification of causal variants requires deciphering of gene-regulatory networks in relevant cell types. We generated matched single-cell chromatin profiles and transcriptomes from scalp tissue from healthy controls and patients with alopecia areata, identifying diverse cell types of the hair follicle niche. By interrogating these datasets at multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than previous approaches and show that aggregate enhancer accessibility for highly regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema and other complex traits. AGA genome-wide association studies signals are enriched in dermal papilla regulatory regions, supporting the role of these cells as drivers of AGA pathogenesis. Finally, we train machine learning models to nominate single-nucleotide polymorphisms that affect gene expression through disruption of transcription factor binding, predicting candidate functional single-nucleotide polymorphism for AGA and eczema.

NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells.

In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation.

High-throughput biochemical profiling reveals functional adaptation of a bacterial Argonaute.

Argonautes are nucleic acid-guided proteins that perform numerous cellular functions across all domains of life. Little is known about how distinct evolutionary pressures have shaped each Argonaute's biophysical properties. We applied high-throughput biochemistry to characterize how Thermus thermophilus Argonaute (TtAgo), a DNA-guided DNA endonuclease, finds, binds, and cleaves its targets. We found that TtAgo uses biophysical adaptations similar to those of eukaryotic Argonautes for rapid association but requires more extensive complementarity to achieve high-affinity target binding. Using these data, we constructed models for TtAgo association rates and equilibrium binding affinities that estimate the nucleic acid- and protein-mediated components of the target interaction energies. Finally, we showed that TtAgo cleavage rates vary widely based on the DNA guide, suggesting that only a subset of guides cleaves targets on physiologically relevant timescales.

High-Throughput Analysis Reveals Rules for Target RNA Binding and Cleavage by AGO2.

Argonaute proteins loaded with microRNAs (miRNAs) or small interfering RNAs (siRNAs) form the RNA-induced silencing complex (RISC), which represses target RNA expression. Predicting the biological targets, specificity, and efficiency of both miRNAs and siRNAs has been hamstrung by an incomplete understanding of the sequence determinants of RISC binding and cleavage. We applied high-throughput methods to measure the association kinetics, equilibrium binding energies, and single-turnover cleavage rates of mouse AGO2 RISC. We find that RISC readily tolerates insertions of up to 7 nt in its target opposite the central region of the guide. Our data uncover specific guide:target mismatches that enhance the rate of target cleavage, suggesting novel siRNA design strategies. Using these data, we derive quantitative models for RISC binding and target cleavage and show that our in vitro measurements and models predict knockdown in an engineered cellular system.

Development of a full-length human protein production pipeline.

There are many proteomic applications that require large collections of purified protein, but parallel production of large numbers of different proteins remains a very challenging task. To help meet the needs of the scientific community, we have developed a human protein production pipeline. Using high-throughput (HT) methods, we transferred the genes of 31 full-length proteins into three expression vectors, and expressed the collection as N-terminal HaloTag fusion proteins in Escherichia coli and two commercial cell-free (CF) systems, wheat germ extract (WGE) and HeLa cell extract (HCE). Expression was assessed by labeling the fusion proteins specifically and covalently with a fluorescent HaloTag ligand and detecting its fluorescence on a LabChip(®) GX microfluidic capillary gel electrophoresis instrument. This automated, HT assay provided both qualitative and quantitative assessment of recombinant protein. E. coli was only capable of expressing 20% of the test collection in the supernatant fraction with ≥20 µg yields, whereas CF systems had ≥83% success rates. We purified expressed proteins using an automated HaloTag purification method. We purified 20, 33, and 42% of the test collection from E. coli, WGE, and HCE, respectively, with yields ≥1 µg and ≥90% purity. Based on these observations, we have developed a triage strategy for producing full-length human proteins in these three expression systems.

Plasma cytokine profiling in sibling pairs discordant for autism spectrum disorder.

OBJECTIVE: Converging lines of evidence point to the existence of immune dysfunction in autism spectrum disorder (ASD), which could directly affect several key neurodevelopmental processes. Previous studies have shown higher cytokine levels in patients with autism compared with matched controls or subjects with other developmental disorders. In the current study, we used plasma-cytokine profiling for 25 discordant sibling pairs to evaluate whether these alterations occur within families with ASD. METHODS: Plasma-cytokine profiling was conducted using an array-based multiplex sandwich ELISA for simultaneous quantitative measurement of 40 unique targets. We also analyzed the correlations between cytokine levels and clinically relevant quantitative traits (Vineland Adaptive Behavior Scale in Autism (VABS) composite score, Social Responsiveness Scale (SRS) total T score, head circumference, and full intelligence quotient (IQ)). In addition, because of the high phenotypic heterogeneity of ASD, we defined four subgroups of subjects (those who were non-verbal, those with gastrointestinal issues, those with regressive autism, and those with a history of allergies), which encompass common and/or recurrent endophenotypes in ASD, and tested the cytokine levels in each group. RESULTS: None of the measured parameters showed significant differences between children with ASD and their related typically developing siblings. However, specific target levels did correlate with quantitative clinical traits, and these were significantly different when the ASD subgroups were analyzed. It is notable that these differences seem to be attributable to a predisposing immunogenetic background, as no other significant differences were noticed between discordant sibling pairs. Interleukin-1ß appears to be the cytokine most involved in quantitative traits and clinical subgroups of ASD. CONCLUSIONS: In the present study, we found a lack of significant differences in plasma-cytokine levels between children with ASD and in their related non-autistic siblings. Thus, our results support the evidence that the immune profiles of children with autism do not differ from their typically developing siblings. However, the significant association of cytokine levels with the quantitative traits and the clinical subgroups analyzed suggests that altered immune responses may affect core feature of ASD.