Publications by authors named "Davide Risso"

54 Publications

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles.

Bioinformatics 2021 Feb 9. Epub 2021 Feb 9.

Dept. of Science and Technology, Università degli Studi del Sannio, Benevento, Italy.

Motivation: Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear.

Results: Here, we present an Asymmetric Winsorization per Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications.

Availability: The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst\_analysis.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab091DOI Listing
February 2021

mbkmeans: Fast clustering for single cell data using mini-batch k-means.

PLoS Comput Biol 2021 Jan 26;17(1):e1008625. Epub 2021 Jan 26.

Department of Statistical Sciences, University of Padova, Padova, Italy.

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.
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http://dx.doi.org/10.1371/journal.pcbi.1008625DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864438PMC
January 2021

A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain.

Nat Commun 2021 01 19;12(1):463. Epub 2021 Jan 19.

Brain and Mind Research Institute and Center for Neurogenetics, Weill Cornell Medicine, New York, NY, USA.

Splicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 ( www.isoformAtlas.com ). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.
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http://dx.doi.org/10.1038/s41467-020-20343-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815907PMC
January 2021

Alteration, Reduction and Taste Loss: Main Causes and Potential Implications on Dietary Habits.

Nutrients 2020 Oct 27;12(11). Epub 2020 Oct 27.

University of Gastronomic Sciences, Piazza Vittorio Emanuele 9, Bra, 12042 Pollenzo, CN, Italy.

Our sense of taste arises from the sensory information generated after compounds in the oral cavity and oropharynx activate taste receptor cells situated on taste buds. This produces the perception of sweet, bitter, salty, sour, or umami stimuli, depending on the chemical nature of the tastant. Taste impairments (dysgeusia) are alterations of this normal gustatory functioning that may result in complete taste losses (ageusia), partial reductions (hypogeusia), or over-acuteness of the sense of taste (hypergeusia). Taste impairments are not life-threatening conditions, but they can cause sufficient discomfort and lead to appetite loss and changes in eating habits, with possible effects on health. Determinants of such alterations are multiple and consist of both genetic and environmental factors, including aging, exposure to chemicals, drugs, trauma, high alcohol consumption, cigarette smoking, poor oral health, malnutrition, and viral upper respiratory infections including influenza. Disturbances or loss of smell, taste, and chemesthesis have also emerged as predominant neurological symptoms of infection by the recent Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus strain 2 (SARS-CoV-2), as well as by previous both endemic and pandemic coronaviruses such as Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and SARS-CoV. This review is focused on the main causes of alteration, reduction, and loss of taste and their potential repercussion on dietary habits and health, with a special focus on the recently developed hypotheses regarding the mechanisms through which SARS-CoV-2 might alter taste perception.
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http://dx.doi.org/10.3390/nu12113284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693910PMC
October 2020

Editorial: Multi-omic Data Integration in Oncology.

Front Oncol 2020 15;10:1768. Epub 2020 Sep 15.

Department of Biology, University of Padua, Padua, Italy.

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http://dx.doi.org/10.3389/fonc.2020.01768DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522593PMC
September 2020

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia.

Sci Adv 2020 07 24;6(31). Epub 2020 Jul 24.

Harvard Medical School Department of Neurobiology, Boston MA 02115 USA.

Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.
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http://dx.doi.org/10.1126/sciadv.abc5801DOI Listing
July 2020

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data.

Genome Biol 2020 08 3;21(1):191. Epub 2020 Aug 3.

Department of Biotechnology, University of Verona, Verona, Italy.

Background: The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.

Results: We compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, power, and correct identification of differentially abundant genera. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.

Conclusions: The multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.
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http://dx.doi.org/10.1186/s13059-020-02104-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398076PMC
August 2020

Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition.

Nature 2020 01 8;577(7790):421-425. Epub 2020 Jan 8.

Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

KRAS GTPases are activated in one-third of cancers, and KRAS(G12C) is one of the most common activating alterations in lung adenocarcinoma. KRAS(G12C) inhibitors are in phase-I clinical trials and early data show partial responses in nearly half of patients with lung cancer. How cancer cells bypass inhibition to prevent maximal response to therapy is not understood. Because KRAS(G12C) cycles between an active and inactive conformation, and the inhibitors bind only to the latter, we tested whether isogenic cell populations respond in a non-uniform manner by studying the effect of treatment at a single-cell resolution. Here we report that, shortly after treatment, some cancer cells are sequestered in a quiescent state with low KRAS activity, whereas others bypass this effect to resume proliferation. This rapid divergent response occurs because some quiescent cells produce new KRAS(G12C) in response to suppressed mitogen-activated protein kinase output. New KRAS(G12C) is maintained in its active, drug-insensitive state by epidermal growth factor receptor and aurora kinase signalling. Cells without these adaptive changes-or cells in which these changes are pharmacologically inhibited-remain sensitive to drug treatment, because new KRAS(G12C) is either not available or exists in its inactive, drug-sensitive state. The direct targeting of KRAS oncoproteins has been a longstanding objective in precision oncology. Our study uncovers a flexible non-uniform fitness mechanism that enables groups of cells within a population to rapidly bypass the effect of treatment. This adaptive process must be overcome if we are to achieve complete and durable responses in the clinic.
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http://dx.doi.org/10.1038/s41586-019-1884-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308074PMC
January 2020

Publisher Correction: Orchestrating single-cell analysis with Bioconductor.

Nat Methods 2020 Feb;17(2):242

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41592-019-0700-8DOI Listing
February 2020

Orchestrating single-cell analysis with Bioconductor.

Nat Methods 2020 02 2;17(2):137-145. Epub 2019 Dec 2.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.
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http://dx.doi.org/10.1038/s41592-019-0654-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7358058PMC
February 2020

The Human-Specific BOLA2 Duplication Modifies Iron Homeostasis and Anemia Predisposition in Chromosome 16p11.2 Autism Individuals.

Am J Hum Genet 2019 11 24;105(5):947-958. Epub 2019 Oct 24.

Center for Integrative Genomics, University of Lausanne, Lausanne, 1015, Switzerland.

Human-specific duplications at chromosome 16p11.2 mediate recurrent pathogenic 600 kbp BP4-BP5 copy-number variations, which are among the most common genetic causes of autism. These copy-number polymorphic duplications are under positive selection and include three to eight copies of BOLA2, a gene involved in the maturation of cytosolic iron-sulfur proteins. To investigate the potential advantage provided by the rapid expansion of BOLA2, we assessed hematological traits and anemia prevalence in 379,385 controls and individuals who have lost or gained copies of BOLA2: 89 chromosome 16p11.2 BP4-BP5 deletion carriers and 56 reciprocal duplication carriers in the UK Biobank. We found that the 16p11.2 deletion is associated with anemia (18/89 carriers, 20%, p = 4e-7, OR = 5), particularly iron-deficiency anemia. We observed similar enrichments in two clinical 16p11.2 deletion cohorts, which included 6/63 (10%) and 7/20 (35%) unrelated individuals with anemia, microcytosis, low serum iron, or low blood hemoglobin. Upon stratification by BOLA2 copy number, our data showed an association between low BOLA2 dosage and the above phenotypes (8/15 individuals with three copies, 53%, p = 1e-4). In parallel, we analyzed hematological traits in mice carrying the 16p11.2 orthologous deletion or duplication, as well as Bola2 and Bola2 animals. The Bola2-deficient mice and the mice carrying the deletion showed early evidence of iron deficiency, including a mild decrease in hemoglobin, lower plasma iron, microcytosis, and an increased red blood cell zinc-protoporphyrin-to-heme ratio. Our results indicate that BOLA2 participates in iron homeostasis in vivo, and its expansion has a potential adaptive role in protecting against iron deficiency.
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http://dx.doi.org/10.1016/j.ajhg.2019.09.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849090PMC
November 2019

Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia.

Nature 2019 05 15;569(7757):576-580. Epub 2019 May 15.

New York Genome Center, New York, NY, USA.

Genetic and epigenetic intra-tumoral heterogeneity cooperate to shape the evolutionary course of cancer. Chronic lymphocytic leukaemia (CLL) is a highly informative model for cancer evolution as it undergoes substantial genetic diversification and evolution after therapy. The CLL epigenome is also an important disease-defining feature, and growing populations of cells in CLL diversify by stochastic changes in DNA methylation known as epimutations. However, previous studies using bulk sequencing methods to analyse the patterns of DNA methylation were unable to determine whether epimutations affect CLL populations homogeneously. Here, to measure the epimutation rate at single-cell resolution, we applied multiplexed single-cell reduced-representation bisulfite sequencing to B cells from healthy donors and patients with CLL. We observed that the common clonal origin of CLL results in a consistently increased epimutation rate, with low variability in the cell-to-cell epimutation rate. By contrast, variable epimutation rates across healthy B cells reflect diverse evolutionary ages across the trajectory of B cell differentiation, consistent with epimutations serving as a molecular clock. Heritable epimutation information allowed us to reconstruct lineages at high-resolution with single-cell data, and to apply this directly to patient samples. The CLL lineage tree shape revealed earlier branching and longer branch lengths than in normal B cells, reflecting rapid drift after the initial malignant transformation and a greater proliferative history. Integration of single-cell bisulfite sequencing analysis with single-cell transcriptomes and genotyping confirmed that genetic subclones mapped to distinct clades, as inferred solely on the basis of epimutation information. Finally, to examine potential lineage biases during therapy, we profiled serial samples during ibrutinib-associated lymphocytosis, and identified clades of cells that were preferentially expelled from the lymph node after treatment, marked by distinct transcriptional profiles. The single-cell integration of genetic, epigenetic and transcriptional information thus charts the lineage history of CLL and its evolution with therapy.
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http://dx.doi.org/10.1038/s41586-019-1198-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533116PMC
May 2019

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Cell Syst 2019 04;8(4):315-328.e8

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, CA, USA. Electronic address:

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.
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http://dx.doi.org/10.1016/j.cels.2019.03.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544759PMC
April 2019

Shank3 modulates sleep and expression of circadian transcription factors.

Elife 2019 04 11;8. Epub 2019 Apr 11.

Department of Biomedical Sciences, Elson S. Floyd College of Medicine, Washington State University, Spokane, United States.

Autism Spectrum Disorder (ASD) is the most prevalent neurodevelopmental disorder in the United States and often co-presents with sleep problems. Sleep problems in ASD predict the severity of ASD core diagnostic symptoms and have a considerable impact on the quality of life of caregivers. Little is known, however, about the underlying molecular mechanisms of sleep problems in ASD. We investigated the role of , a high confidence ASD gene candidate, in sleep architecture and regulation. We show that mice lacking exon 21 of have problems falling asleep even when sleepy. Using RNA-seq we show that sleep deprivation increases the differences in prefrontal cortex gene expression between mutants and wild types, downregulating circadian transcription factors , , , , and . mutants also have trouble regulating wheel-running activity in constant darkness. Overall, our study shows that is an important modulator of sleep and clock gene expression.
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http://dx.doi.org/10.7554/eLife.42819DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488297PMC
April 2019

Complementary networks of cortical somatostatin interneurons enforce layer specific control.

Elife 2019 03 18;8. Epub 2019 Mar 18.

Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.

The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.
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http://dx.doi.org/10.7554/eLife.43696DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422636PMC
March 2019

An African-specific haplotype in MRGPRX4 is associated with menthol cigarette smoking.

PLoS Genet 2019 02 15;15(2):e1007916. Epub 2019 Feb 15.

National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland, United States of America.

In the U.S., more than 80% of African-American smokers use mentholated cigarettes, compared to less than 30% of Caucasian smokers. The reasons for these differences are not well understood. To determine if genetic variation contributes to mentholated cigarette smoking, we performed an exome-wide association analysis in a multiethnic population-based sample from Dallas, TX (N = 561). Findings were replicated in an independent cohort of African Americans from Washington, DC (N = 741). We identified a haplotype of MRGPRX4 (composed of rs7102322[G], encoding N245S, and rs61733596[G], T43T), that was associated with a 5-to-8 fold increase in the odds of menthol cigarette smoking. The variants are present solely in persons of African ancestry. Functional studies indicated that the variant G protein-coupled receptor encoded by MRGPRX4 displays reduced agonism in both arrestin-based and G protein-based assays, and alteration of agonism by menthol. These data indicate that genetic variation in MRGPRX4 contributes to inter-individual and inter-ethnic differences in the preference for mentholated cigarettes, and that the existence of genetic factors predisposing vulnerable populations to mentholated cigarette smoking can inform tobacco control and public health policies.
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http://dx.doi.org/10.1371/journal.pgen.1007916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6377114PMC
February 2019

Publisher Correction: A general and flexible method for signal extraction from single-cell RNA-seq data.

Nat Commun 2019 02 4;10(1):646. Epub 2019 Feb 4.

CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, 75006, Paris, France.

The original PDF version of this Article contained errors in two equations. In Eq. (1), all Γ symbols were inadvertently omitted. In the second equation in the subsection entitled '1. Dispersion optimization' within the Methods section 'ZINB-WaVE estimation procedure', all Ψ symbols were inadvertently omitted. These errors have been corrected in the PDF version of the Article; the HTML version was correct from the time of publication.
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http://dx.doi.org/10.1038/s41467-019-08614-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362193PMC
February 2019

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.

PLoS Comput Biol 2018 09 4;14(9):e1006378. Epub 2018 Sep 4.

Department of Statistics, University of California - Berkeley, Berkeley, California, United States of America.

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.
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http://dx.doi.org/10.1371/journal.pcbi.1006378DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138422PMC
September 2018

Combinatorial Expression of and Defines Dopamine Neuron Populations with Distinct Projection Patterns and Disease Vulnerability.

eNeuro 2018 May-Jun;5(3). Epub 2018 Jun 13.

Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720.

Midbrain dopamine neurons project to numerous targets throughout the brain to modulate various behaviors and brain states. Within this small population of neurons exists significant heterogeneity based on physiology, circuitry, and disease susceptibility. Recent studies have shown that dopamine neurons can be subdivided based on gene expression; however, the extent to which genetic markers represent functionally relevant dopaminergic subpopulations has not been fully explored. Here we performed single-cell RNA-sequencing of mouse dopamine neurons and validated studies showing that and are selective markers for dopaminergic subpopulations. Using a combination of multiplex fluorescent hybridization, retrograde labeling, and electrophysiology in mice of both sexes, we defined the anatomy, projection targets, physiological properties, and disease vulnerability of dopamine neurons based on and/or expression. We found that the combinatorial expression of and defines dopaminergic subpopulations with unique features. dopamine neurons reside in the ventromedial VTA, send projections to the medial shell of the nucleus accumbens, and have noncanonical physiological properties. dopamine neurons are found in the VTA as well as in the ventromedial portion of the SNc, where they project selectively to the dorsomedial striatum. dopamine neurons represent a smaller VTA subpopulation, which is preferentially spared in a 6-OHDA model of Parkinson's disease. Together, our work provides detailed characterization of and expression in the midbrain and generates new insights into how these markers define functionally relevant dopaminergic subpopulations.
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http://dx.doi.org/10.1523/ENEURO.0152-18.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104179PMC
January 2019

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BMC Genomics 2018 Jun 19;19(1):477. Epub 2018 Jun 19.

Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.

Background: Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.

Results: We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.

Conclusions: Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.
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http://dx.doi.org/10.1186/s12864-018-4772-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6007078PMC
June 2018

Taste Perception of Antidesma bunius Fruit and Its Relationships to Bitter Taste Receptor Gene Haplotypes.

Chem Senses 2018 08;43(7):463-468

National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA.

It was shown more than 40 years ago that the ability to perceive the bitterness of the fruit of the Antidesma bunius tree is inversely correlated with the ability to perceive the well-studied bitter tastant phenylthiocarbamide (PTC). To determine if variants of the TAS2R38 gene, which encodes the PTC taste receptor, or variants in any of the other TAS2R bitter or TAS1R sweet receptor genes account for Antidesma taste perception, we recruited an independent subject sample and examined associations between these taste receptor gene haplotypes and Antidesma perception. Consistent with previous findings, almost none of our subjects who reported Antidesma juice as bitter was a PTC "responder" by previous definitions (i.e. a PTC taster). In our study, of the 132 individuals who perceived PTC as bitter, 15 perceived Antidesma as bitter, although these 15 subjects had very weak bitterness perception scores. Examination of TAS2R38 gene haplotypes showed that, of the subjects who perceive Antidesma as bitter, all carried at least one copy of the TAS2R38 AVI (PTC non-taster) haplotype. However, 86 subjects carried at least one AVI haplotype and failed to perceive Antidesma as bitter. No other TAS2R or TAS1R gene variants showed an association with Antidesma bitter, sweet, or sour perception. Our results show that TAS2R38 haplotypes are associated with differential perception of Antidesma berry juice bitterness, and that all those who perceive this bitterness carry at least one AVI haplotype. This indicates that the AVI haplotype is necessary for this perception, but that additional variable factors are involved.
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http://dx.doi.org/10.1093/chemse/bjy037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6108389PMC
August 2018

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.

Genome Biol 2018 02 26;19(1):24. Epub 2018 Feb 26.

Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, 9000, Belgium.

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.
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http://dx.doi.org/10.1186/s13059-018-1406-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251479PMC
February 2018

A general and flexible method for signal extraction from single-cell RNA-seq data.

Nat Commun 2018 01 18;9(1):284. Epub 2018 Jan 18.

CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, 75006, Paris, France.

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.
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http://dx.doi.org/10.1038/s41467-017-02554-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773593PMC
January 2018

Learning-dependent chromatin remodeling highlights noncoding regulatory regions linked to autism.

Sci Signal 2018 01 16;11(513). Epub 2018 Jan 16.

Department of Biomedical Sciences, Elson S. Floyd College of Medicine. Washington State University, Spokane, WA 99202, USA.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder that is associated with genetic risk factors. Most human disease-associated single-nucleotide polymorphisms (SNPs) are not located in genes but rather are in regulatory regions that control gene expression. The function of regulatory regions is determined through epigenetic mechanisms. Parallels between the cellular basis of development and the formation of long-term memory have long been recognized, particularly the role of epigenetic mechanisms in both processes. We analyzed how learning alters chromatin accessibility in the mouse hippocampus using a new high-throughput sequencing bioinformatics strategy we call DEScan (differential enrichment scan). DEScan, which enabled the analysis of data from epigenomic experiments containing multiple replicates, revealed changes in chromatin accessibility at 2365 regulatory regions-most of which were promoters. Learning-regulated promoters were active during forebrain development in mice and were enriched in epigenetic modifications indicative of bivalent promoters. These promoters were disproportionally intronic, showed a complex relationship with gene expression and alternative splicing during memory consolidation and retrieval, and were enriched in the data set relative to known ASD risk genes. Genotyping in a clinical cohort within one of these promoters ( promoter 6) revealed that the SNP rs6010065 was associated with ASD. Our data support the idea that learning recapitulates development at the epigenetic level and demonstrate that behaviorally induced epigenetic changes in mice can highlight regulatory regions relevant to brain disorders in patients.
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http://dx.doi.org/10.1126/scisignal.aan6500DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180319PMC
January 2018

A Matter of Taste: Lineage-Specific Loss of Function of Taste Receptor Genes in Vertebrates.

Front Mol Biosci 2017 28;4:81. Epub 2017 Nov 28.

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, United States.

Vertebrates can perceive at least five different taste qualities, each of which is thought to have a specific role in the evolution of different species. The avoidance of potentially poisonous foods, which are generally bitter or sour tasting, and the search for more nutritious ones, those with high-fat and high-sugar content, are two of the most well-known examples. The study of taste genes encoding receptors that recognize ligands triggering taste sensations has helped to reconstruct several evolutionary adaptations to dietary changes. In addition, an increasing number of studies have focused on pseudogenes, genomic DNA sequences that have traditionally been considered defunct relatives of functional genes mostly because of the presence of deleterious mutations interrupting their open reading frames. The study of taste receptor pseudogenes has helped to shed light on how the evolutionary history of taste in vertebrates has been the result of a succession of gene gain and loss processes. This dynamic role in evolution has been explained by the "less-is-more" hypothesis, suggesting gene loss as a mechanism of evolutionary change in response to a dietary shift. This mini-review aims at depicting the major lineage-specific loss of function of taste receptor genes in vertebrates, stressing their evolutionary importance and recapitulating signatures of natural selection and their correlations with food habits.
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http://dx.doi.org/10.3389/fmolb.2017.00081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712339PMC
November 2017

Injury Activates Transient Olfactory Stem Cell States with Diverse Lineage Capacities.

Cell Stem Cell 2017 Dec 22;21(6):775-790.e9. Epub 2017 Nov 22.

Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

Tissue homeostasis and regeneration are mediated by programs of adult stem cell renewal and differentiation. However, the mechanisms that regulate stem cell fates under such widely varying conditions are not fully understood. Using single-cell techniques, we assessed the transcriptional changes associated with stem cell self-renewal and differentiation and followed the maturation of stem cell-derived clones using sparse lineage tracing in the regenerating mouse olfactory epithelium. Following injury, quiescent olfactory stem cells rapidly shift to activated, transient states unique to regeneration and tailored to meet the demands of injury-induced repair, including barrier formation and proliferation. Multiple cell fates, including renewed stem cells and committed differentiating progenitors, are specified during this early window of activation. We further show that Sox2 is essential for cells to transition from the activated to neuronal progenitor states. Our study highlights strategies for stem cell-mediated regeneration that may be conserved in other adult stem cell niches.
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http://dx.doi.org/10.1016/j.stem.2017.10.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728414PMC
December 2017

Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference.

F1000Res 2017 21;6:1158. Epub 2017 Jul 21.

Department of Statistics, University of California, Berkeley, Berkeley, CA, 94720, USA.

Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1) dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2) cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and pseudotimes; and (4) differential expression analysis along lineages.
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http://dx.doi.org/10.12688/f1000research.12122.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558107PMC
July 2017

A common -linked haplotype underlying non-syndromic hearing loss with enlargement of the vestibular aqueduct.

J Med Genet 2017 10 5;54(10):665-673. Epub 2017 Aug 5.

Otolaryngology Branch, National Institute on Deafness and Other Communication Disorders (NIDCD), Bethesda, Maryland, USA.

Background: Enlargement of the vestibular aqueduct (EVA) is the most common radiological abnormality in children with sensorineural hearing loss. Mutations in coding regions and splice sites of the gene are often detected in Caucasians with EVA. Approximately one-fourth of patients with EVA have two mutant alleles (M2), one-fourth have one mutant allele (M1) and one-half have no mutant alleles (M0). The M2 genotype is correlated with a more severe phenotype.

Methods: We performed genotype-haplotype analysis and massively parallel sequencing of the region in patients with M1 EVA and their families.

Results: We identified a shared novel haplotype, termed CEVA (Caucasian EVA), composed of 12 uncommon variants upstream of . The presence of the CEVA haplotype on seven of ten 'mutation-negative' chromosomes in a National Institutes of Health M1 EVA discovery cohort and six of six mutation-negative chromosomes in a Danish M1 EVA replication cohort is higher than the observed prevalence of 28 of 1006 Caucasian control chromosomes (p<0.0001 for each EVA cohort). The corresponding heterozygous carrier rate is 28/503 (5.6%). The prevalence of CEVA (11 of 126) is also increased among M0 EVA chromosomes (p=0.0042).

Conclusions: The CEVA haplotype causally contributes to most cases of Caucasian M1 EVA and, possibly, some cases of M0 EVA. The CEVA haplotype of defines the most common allele associated with hereditary hearing loss in Caucasians. The diagnostic yield and prognostic utility of sequence analysis of exons and splice sites will be markedly increased by addition of testing for the CEVA haplotype.
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http://dx.doi.org/10.1136/jmedgenet-2017-104721DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5880640PMC
October 2017

Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution.

Cell Stem Cell 2017 06 11;20(6):817-830.e8. Epub 2017 May 11.

Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA; QB3 Functional Genomics Laboratory, University of California, Berkeley, CA 94720, USA. Electronic address:

A detailed understanding of the paths that stem cells traverse to generate mature progeny is vital for elucidating the mechanisms governing cell fate decisions and tissue homeostasis. Adult stem cells maintain and regenerate multiple mature cell lineages in the olfactory epithelium. Here we integrate single-cell RNA sequencing and robust statistical analyses with in vivo lineage tracing to define a detailed map of the postnatal olfactory epithelium, revealing cell fate potentials and branchpoints in olfactory stem cell lineage trajectories. Olfactory stem cells produce support cells via direct fate conversion in the absence of cell division, and their multipotency at the population level reflects collective unipotent cell fate decisions by single stem cells. We further demonstrate that Wnt signaling regulates stem cell fate by promoting neuronal fate choices. This integrated approach reveals the mechanisms guiding olfactory lineage trajectories and provides a model for deconstructing similar hierarchies in other stem cell niches.
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http://dx.doi.org/10.1016/j.stem.2017.04.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484588PMC
June 2017

Normalizing single-cell RNA sequencing data: challenges and opportunities.

Nat Methods 2017 Jun 15;14(6):565-571. Epub 2017 May 15.

EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.
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http://dx.doi.org/10.1038/nmeth.4292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549838PMC
June 2017