Michelle L. Nguyen

Radiographic Fibrosis Score Predicts Survival in Hypersensitivity Pneumonitis

BACKGROUND It is unknown if the radiographic fibrosis score predicts mortality in persistent hypersensitivity pneumonitis (HP) and if survival is similar to that observed in idiopathic pulmonary fibrosis (IPF) when adjusting for the extent of radiographic fibrosis. METHODS We reviewed records from 177 patients with HP and 224 patients with IPF whose diagnoses were established by multidisciplinary consensus. Two thoracic radiologists scored high-resolution CT (HRCT) scan lung images. Independent predictors of transplant-free survival were determined using a Cox proportional hazards analysis. Kaplan-Meier survival curves were constructed, stratified by disease as well as fibrosis score. RESULTS HRCT scan fibrosis score and radiographic reticulation independently predicted time to death or lung transplantation. Clinical predictors included a history of cigarette smoking, auscultatory crackles on lung examination, baseline FVC, and FEV1/FVC ratio. The majority of HP deaths occurred in patients with both radiographic reticulation and auscultatory crackles on examination, compared with patients with only one of these manifestations (P < .0001). Patients with IPF had worse survival than those with HP at any given degree of radiographic fibrosis (hazard ratio 2.31; P < .01). CONCLUSIONS Survival in patients with HP was superior to that of those with IPF with similar degrees of radiographic fibrosis. The combination of auscultatory crackles and radiographic reticulation identified patients with HP who had a particularly poor outcome.

Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements

The challenge of linking intergenic mutations to target genes has limited molecular understanding of human diseases. Here we show that H3K27ac HiChIP generates high-resolution contact maps of active enhancers and target genes in rare primary human T cell subtypes and coronary artery smooth muscle cells. Differentiation of naive T cells into T helper 17 cells or regulatory T cells creates subtype-specific enhancer–promoter interactions, specifically at regions of shared DNA accessibility. These data provide a principled means of assigning molecular functions to autoimmune and cardiovascular disease risk variants, linking hundreds of noncoding variants to putative gene targets. Target genes identified with HiChIP are further supported by CRISPR interference and activation at linked enhancers, by the presence of expression quantitative trait loci, and by allele-specific enhancer loops in patient-derived primary cells. The majority of disease-associated enhancers contact genes beyond the nearest gene in the linear genome, leading to a fourfold increase in the number of potential target genes for autoimmune and cardiovascular diseases.

Discovery of stimulation-responsive immune enhancers with CRISPR activation

The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell-type-specific transcriptional programs and responses to extracellular cues. Systematic mapping of functional enhancers and their biological contexts is required to understand the mechanisms by which variation in non-coding genetic sequences contributes to disease. Functional enhancers can be mapped by genomic sequence disruption, but this approach is limited to the subset of enhancers that are necessary in the particular cellular context being studied. We hypothesized that recruitment of a strong transcriptional activator to an enhancer would be sufficient to drive target gene expression, even if that enhancer was not currently active in the assayed cells. Here we describe a discovery platform that can identify stimulus-responsive enhancers for a target gene independent of stimulus exposure. We used tiled CRISPR activation (CRISPRa) to synthetically recruit a transcriptional activator to sites across large genomic regions (more than 100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA. We identified several CRISPRa-responsive elements with chromatin features of stimulus-responsive enhancers, including an IL2RA enhancer that harbours an autoimmunity risk variant. Using engineered mouse models, we found that sequence perturbation of the disease-associated Il2ra enhancer did not entirely block Il2ra expression, but rather delayed the timing of gene activation in response to specific extracellular signals. Enhancer deletion skewed polarization of naive T cells towards a pro-inflammatory T helper (TH17) cell state and away from a regulatory T cell state. This integrated approach identifies functional enhancers and reveals how non-coding variation associated with human immune dysfunction alters context-specific gene programs.

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-β and perform eQTL analysis on 23 pooled samples.

Drill Sergeant: Supporting Physical Construction Projects through an Ecosystem of Augmented Tools

Mapping techniques from software tutorials onto physical craft processes can assist novices in building multi-material assemblies. By providing in-situ step instructions and progress tracking, generating dynamic feedback on technique, and adapting tutorial content to a users specific context and preferences, an ecosystem of smart tools can guide users through complete project tutorials. We demonstrate how such techniques can be enabled by augmenting common workshop tools (drill/driver, saw, router) with measurement, state sensing and interactive feedback; and by sequencing instructions across multiple tools. We validate the benefits of a smart tool ecosystem through reflections on a series of author-created design examples and informal feedback from four fab lab users.

Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes

Droplet-based single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes from tens of thousands of cells. Multiplexing samples for single cell capture and library preparation in dscRNA-seq would enable cost-effective designs of differential expression and genetic studies while avoiding technical batch effects, but its implementation remains experimentally challenging. Here, we introduce an in-silico algorithm demuxlet that harnesses natural genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These capabilities enable simple experimental designs where cells from genetically diverse samples are multiplexed and captured at higher throughput than standard workflows. To demonstrate the performance of our method, we sequenced 3 multiplexed pools of peripheral blood mononuclear cells (PBMCs) from 8 lupus patients. Given genotyping data for each individual, demuxlet correctly recovered the sample identity of > 99% of singlets, and identified doublets enriched for multiple cell types and at rates consistent with previous estimates. We further demonstrate the utility of sample multiplexing by characterizing cell type-specific responses and interindividual variability in 2 pools of PBMCs from 8 additional lupus patients before and after cytokine stimulation. Demuxlet enables droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

Discovery of an autoimmunity-associated IL2RA enhancer by unbiased targeting of transcriptional activation

The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell type-specific transcriptional programs and responses to specific extracellular cues 1-3. In order to understand the mechanisms by which non-coding genetic variation contributes to disease, systematic mapping of functional enhancers and their biological contexts is required. Here, we develop an unbiased discovery platform that can identify enhancers for a target gene without prior knowledge of their native functional context. We used tiled CRISPR activation (CRISPRa) to synthetically recruit transcription factors to sites across large genomic regions (>100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA (interleukin-2 receptor alpha; CD25). We identified several CRISPRa responsive elements (CaREs) with stimulation-dependent enhancer activity, including an IL2RA enhancer that harbors an autoimmunity risk variant. Using engineered mouse models and genome editing of human primary T cells, we found that sequence perturbation of the disease-associated IL2RA enhancer does not block IL2RA expression, but rather delays the timing of gene activation in response to specific extracellular signals. This work develops an approach to rapidly identify functional enhancers within non-coding regions, decodes a key human autoimmunity association, and suggests a general mechanism by which genetic variation can cause immune dysfunction.

Landscape of stimulation-responsive chromatin across diverse human immune cells

The immune system is controlled by a balanced interplay among specialized cell types transitioning between resting and stimulated states. Despite its importance, the regulatory landscape of this system has not yet been fully characterized. To address this gap, we collected ATAC-seq and RNA-seq data under resting and stimulated conditions for 25 immune cell types from peripheral blood of four healthy individuals, and seven cell types from three fetal thymus samples. We found that stimulation caused widespread chromatin remodeling, including a large class of response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the critical importance of these cell states in autoimmunity. Use of allele-specific read-mapping identified thousands of variants that alter chromatin accessibility in particular conditions. Notably, variants associated with changes in stimulation-specific chromatin accessibility were not enriched for associations with gene expression regulation in whole blood – a tissue commonly used in eQTL studies. Thus, large-scale maps of variants associated with gene regulation lack a condition important for understanding autoimmunity. As a proof-of-principle we identified variant rs6927172, which links stimulated T cell-specific chromatin dysregulation in the TNFAIP3 locus to ulcerative colitis and rheumatoid arthritis. Overall, our results provide a broad resource of chromatin landscape dynamics and highlight the need for large-scale characterization of effects of genetic variation in stimulated cells.

Author Correction: Discovery of stimulation-responsive immune enhancers with CRISPR activation

In this Letter, analysis of steady-state regulatory T (Treg) cell percentages from Il2ra enhancer deletion (EDEL) and wild-type (WT) mice revealed no differences between them (Extended Data Fig. 9d). This analysis included two mice whose genotypes were incorrectly assigned. Even after correction of the genotypes, no significant differences in Treg cell percentages were seen when data across experimental cohorts were averaged (as was done in Extended Data Fig. 9d). However, if we normalize the corrected data to account for variation among experimental cohorts, a subtle decrease in EDEL Treg cell percentages is revealed and, using the corrected and normalized data, we have redrawn Extended Data Fig. 9d in Supplementary Fig. 1. The Supplementary Information to this Amendment contains the corrected and reanalysed Extended Data Fig. 9d. The sentence “This enhancer deletion (EDEL) strain also had no obvious T cell phenotypes at steady state (Extended Data Fig. 9).” should read: “This enhancer deletion (EDEL) strain had a small decrease in the percentage of Treg cells (Extended Data Fig. 9).”. This error does not affect any of the main figures in the Letter or the data from mice with the human autoimmune-associated single nucleotide polymorphism (SNP) knocked in or with a 12-base-pair deletion at the site (12DEL). In addition, we stated in the Methods that we observed consistent immunophenotypes of EDEL mice across three founders, but in fact, we observed consistent phenotypes in mice from two founders. This does not change any of our conclusions and the original Letter has not been corrected.

T-bet-ing on autoimmunity variants

1 Department of Microbiology and Immunology, University of California, San Francisco, California, United States of America, 2 Diabetes Center, University of California, San Francisco, California, United States of America, 3 Innovative Genomics Institute, University of California, Berkeley, California, United States of America, 4 Biomedical Sciences Graduate Program, University of California, San Francisco, California, United States of America, 5 Department of Medicine, University of California, San Francisco, California, United States of America, 6 UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, United States of America, 7 Chan Zuckerberg Biohub, San Francisco, California, United States of America