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We present Vision , a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration. The increasing accessibility of single cell RNA sequencing demands tools that enable data visualization and interpretation. Here, the authors introduce Vision, a flexible annotation tool that operates directly on the manifold of cell-cell similarity and aids interpretation of cellular heterogeneity.

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool. Single cell RNA-sequencing can be a powerful approach to characterizing cell composition in a population of cells but is thought to be too expensive for population-scale analyses. Here, the authors show how lower coverage of more samples can increase the power to detect cell-type-specific eQTL.

T-cell genome engineering holds great promise for cell-based therapies for cancer, HIV, primary immune deficiencies, and autoimmune diseases, but genetic manipulation of human T cells has been challenging. Improved tools are needed to efficiently “knock out” genes and “knock in” targeted genome modifications to modulate T-cell function and correct disease-associated mutations. CRISPR/Cas9 technology is facilitating genome engineering in many cell types, but in human T cells its efficiency has been limited and it has not yet proven useful for targeted nucleotide replacements. Here we report efficient genome engineering in human CD4⁺ T cells using Cas9:single-guide RNA ribonucleoproteins (Cas9 RNPs). Cas9 RNPs allowed ablation of CXCR4, a coreceptor for HIV entry. Cas9 RNP electroporation caused up to ∼40% of cells to lose high-level cell-surface expression of CXCR4, and edited cells could be enriched by sorting based on low CXCR4 expression. Importantly, Cas9 RNPs paired with homology-directed repair template oligonucleotides generated a high frequency of targeted genome modifications in primary T cells. Targeted nucleotide replacement was achieved inCXCR4andPD-1(PDCD1), a regulator of T-cell exhaustion that is a validated target for tumor immunotherapy. Deep sequencing of a target site confirmed that Cas9 RNPs generated knock-in genome modifications with up to ∼20% efficiency, which accounted for up to approximately one-third of total editing events. These results establish Cas9 RNP technology for diverse experimental and therapeutic genome engineering applications in primary human T cells.

Vα24-invariant natural killer T cells (NKTs) have anti-tumor properties that can be enhanced by chimeric antigen receptors (CARs). Here we report updated interim results from the first-in-human phase 1 evaluation of autologous NKTs co-expressing a GD2-specific CAR with interleukin 15 (IL15) (GD2-CAR.15) in 12 children with neuroblastoma (NB). The primary objectives were safety and determination of maximum tolerated dose (MTD). The anti-tumor activity of GD2-CAR.15 NKTs was assessed as a secondary objective. Immune response evaluation was an additional objective. No dose-limiting toxicities occurred; one patient experienced grade 2 cytokine release syndrome that was resolved by tocilizumab. The MTD was not reached. The objective response rate was 25% (3/12), including two partial responses and one complete response. The frequency of CD62L + NKTs in products correlated with CAR-NKT expansion in patients and was higher in responders ( n  = 5; objective response or stable disease with reduction in tumor burden) than non-responders ( n  = 7). BTG1 (BTG anti-proliferation factor 1) expression was upregulated in peripheral GD2-CAR.15 NKTs and is a key driver of hyporesponsiveness in exhausted NKT and T cells. GD2-CAR.15 NKTs with BTG1 knockdown eliminated metastatic NB in a mouse model. We conclude that GD2-CAR.15 NKTs are safe and can mediate objective responses in patients with NB. Additionally, their anti-tumor activity may be enhanced by targeting BTG1. ClinicalTrials.gov registration: NCT03294954 . In updated results from a phase 1 trial of GD2-specific CAR-NKT cells in patients with neuroblastoma, no dose-limiting toxicities were observed across multiple dose levels; the maximum tolerated dose was not reached; and there was evidence of anti-tumor activity.

Organogenesis requires the complex interactions of multiple cell lineages that coordinate their expansion, differentiation, and maturation over time. Here, we profile the cell types within the epithelial and mesenchymal compartments of the murine pancreas across developmental time using a combination of single-cell RNA sequencing, immunofluorescence, in situ hybridization, and genetic lineage tracing. We identify previously underappreciated cellular heterogeneity of the developing mesenchyme and reconstruct potential lineage relationships among the pancreatic mesothelium and mesenchymal cell types. Within the epithelium, we find a previously undescribed endocrine progenitor population, as well as an analogous population in both human fetal tissue and human embryonic stem cells differentiating toward a pancreatic beta cell fate. Further, we identify candidate transcriptional regulators along the differentiation trajectory of this population toward the alpha or beta cell lineages. This work establishes a roadmap of pancreatic development and demonstrates the broad utility of this approach for understanding lineage dynamics in developing organs. Coordinated proliferation and differentiation of diverse cell populations drive pancreatic epithelial and mesenchymal development. Here, the authors profile cell type dynamics in the developing mouse pancreas using single-cell RNA sequencing, identifying mesenchymal subtypes and undescribed endocrine progenitors.

A highly selective, sensitive caffeic acid (CA) detection based on calcium oxide nanoparticles (CaO NPs) derived from extract of Moringa oleifera leaves decorated graphitic carbon nitride covalently grafted poly vinyl alcohol (CaO/g-C 3 N 4 /PVA) nanocomposite modified glassy carbon electrode (GCE) was studied. A facile sonochemical method was adapted to synthesis nanomaterials and characterized by HR-TEM (High resolution transmission electron microscopy), FT-IR (Fourier transform infrared spectroscopy), XRD (X-ray diffraction), FE-SEM (Field emission scanning electron microscopy), EDX (Energy dispersive X-ray analysis), Mapping and BET (Brunauer-Emmett-Teller) analysis, and electrochemical techniques. The nanocomposite modified GCE exhibited an excellent catalytic performance to the oxidation of CA under optimized conditions owing to better electron transfer efficiency, conductivity and high surface area of the electrode material. The present electrochemical sensor showed high selectivity towards the determination of 10 µM CA in the presence of 100-fold higher concentrations of interferents. The modified CA sensor exhibited a wide sensing linear range from 0.01 µM to 70 µM and the detection limit (LOD) was found to be 0.0024 µM (S/ N  = 3) in 0.1 M phosphate buffer saline (PBS) as a supporting electrolyte at pH 7.0. The fabricated CA sensor provides an excellent stability, reproducibility and selectivity for the determination of CA. The modified CA sensor was applied to real blood plasma samples and obtained good recovery (97.6-100.1%) results.

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter ).

The observation that disease-associated genetic variants typically reside outside of exons has inspired widespread investigation into the genetic basis of transcriptional regulation. While associations between the mRNA abundance of a gene and its proximal SNPs ( cis -eQTLs) are now readily identified, identification of high-quality distal associations ( trans -eQTLs) has been limited by a heavy multiple testing burden and the proneness to false-positive signals. To address these issues, we develop GBAT, a powerful gene-based pipeline that allows robust detection of high-quality trans -gene regulation signal.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Ubiquitin is essential for eukaryotic life and varies in only 3 amino acid positions between yeast and humans. However, recent deep sequencing studies indicate that ubiquitin is highly tolerant to single mutations. We hypothesized that this tolerance would be reduced by chemically induced physiologic perturbations. To test this hypothesis, a class of first year UCSF graduate students employed deep mutational scanning to determine the fitness landscape of all possible single residue mutations in the presence of five different small molecule perturbations. These perturbations uncover 'shared sensitized positions' localized to areas around the hydrophobic patch and the C-terminus. In addition, we identified perturbation specific effects such as a sensitization of His68 in HU and a tolerance to mutation at Lys63 in DTT. Our data show how chemical stresses can reduce buffering effects in the ubiquitin proteasome system. Finally, this study demonstrates the potential of lab-based interdisciplinary graduate curriculum. The ability of an organism to grow and reproduce, that is, it’s “fitness”, is determined by how its genes interact with the environment. Yeast is a model organism in which researchers can control the exact mutations present in the yeast’s genes (its genotype) and the conditions in which the yeast cells live (their environment). This allows researchers to measure how a yeast cell’s genotype and environment affect its fitness. Ubiquitin is a protein that many organisms depend on to manage cell stress by acting as a tag that targets other proteins for degradation. Essential proteins such as ubiquitin often remain unchanged by mutation over long periods of time. As a result, these proteins evolve very slowly. Like all proteins, ubiquitin is built from a chain of amino acid molecules linked together, and the ubiquitin proteins of yeast and humans are made of almost identical sequences of amino acids. Although ubiquitin has barely changed its sequence over evolution, previous studies have shown that – under normal growth conditions in the laboratory – most amino acids in ubiquitin can be mutated without any loss of cell fitness. This led Mavor et al. to hypothesize that treating the yeast cells with chemicals that cause cell stress might lead to amino acids in ubiquitin becoming more sensitive to mutation. To test this idea, a class of graduate students at the University of California, San Francisco grew yeast cells with different ubiquitin mutations together, and with different chemicals that induce cell stress, and measured their growth rates. Sequencing the ubiquitin gene in the thousands of tested yeast cells revealed that three of the chemicals cause a shared set of amino acids in ubiquitin to become more sensitive to mutation. This result suggests that these amino acids are important for the stress response, possibly by altering the ability of yeast cells to target certain proteins for degradation. Conversely, another chemical causes yeast to become more tolerant to changes in the ubiquitin sequence. The experiments also link changes in particular amino acids in ubiquitin to specific stress responses. Mavor et al. show that many of ubquitin’s amino acids are sensitive to mutation under different stress conditions, while others can be mutated to form different amino acids without effecting fitness. By testing the effects of other chemicals, future experiments could further characterize how the yeast’s genotype and environment interact.