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Rich JM, Moses L, Einarsson PH, Jackson K, Luebbert L, Booeshaghi AS, Antonsson S, Sullivan DK, Bray N, Melsted P, Pachter L. The impact of package selection and versioning on single-cell RNA-seq analysis. bioRxiv 2024:2024.04.04.588111. [PMID: 38617255 PMCID: PMC11014608 DOI: 10.1101/2024.04.04.588111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.
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Affiliation(s)
- Joseph M Rich
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- USC-Caltech MD/PhD Program, Keck School of Medicine, Los Angeles, CA, 90033, USA
| | - Lambda Moses
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Pétur Helgi Einarsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Kayla Jackson
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- USC-Caltech MD/PhD Program, Keck School of Medicine, Los Angeles, CA, 90033, USA
| | - Laura Luebbert
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - A. Sina Booeshaghi
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Sindri Antonsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Delaney K. Sullivan
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | | | - Páll Melsted
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Lior Pachter
- Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
- Lead Contact
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2
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Luebbert L, Hoang C, Kumar M, Pachter L. Fast and scalable querying of eukaryotic linear motifs with gget elm. Bioinformatics 2024; 40:btae095. [PMID: 38377393 PMCID: PMC10927331 DOI: 10.1093/bioinformatics/btae095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/31/2024] [Accepted: 02/18/2024] [Indexed: 02/22/2024] Open
Abstract
MOTIVATION Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. RESULTS To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. AVAILABILITY AND IMPLEMENTATION The manual and source code are available at https://github.com/pachterlab/gget.
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Affiliation(s)
- Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States
| | - Chi Hoang
- California Institute of Technology, Pasadena, CA 91125, United States
| | - Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, United States
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Sullivan DK, Min KHJ, Hjörleifsson KE, Luebbert L, Holley G, Moses L, Gustafsson J, Bray NL, Pimentel H, Booeshaghi AS, Melsted P, Pachter L. kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq. bioRxiv 2024:2023.11.21.568164. [PMID: 38045414 PMCID: PMC10690192 DOI: 10.1101/2023.11.21.568164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.
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Affiliation(s)
- Delaney K Sullivan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | | | | | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | | | - Lambda Moses
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | | | - Nicolas L Bray
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Harold Pimentel
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - A Sina Booeshaghi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Páll Melsted
- deCODE Genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
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4
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Torok Z, Luebbert L, Feldman J, Duffy A, Nevue AA, Wongso S, Mello CV, Fairhall A, Pachter L, Gonzalez WG, Lois C. Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons. bioRxiv 2023:2023.05.17.541057. [PMID: 37292888 PMCID: PMC10245685 DOI: 10.1101/2023.05.17.541057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Maintaining motor skills is crucial for an animal's survival, enabling it to endure diverse perturbations throughout its lifespan, such as trauma, disease, and aging. What mechanisms orchestrate brain circuit reorganization and recovery to preserve the stability of behavior despite the continued presence of a disturbance? To investigate this question, we chronically silenced a fraction of inhibitory neurons in a brain circuit necessary for singing in zebra finches. Song in zebra finches is a complex, learned motor behavior and central to reproduction. This manipulation altered brain activity and severely perturbed song for around two months, after which time it was precisely restored. Electrophysiology recordings revealed abnormal offline dynamics, resulting from chronic inhibition loss, some aspects of which returned to normal as the song recovered. However, even after the song had fully recovered, the levels of neuronal firing in the premotor and motor areas did not return to a control-like state. Single-cell RNA sequencing revealed that chronic silencing of interneurons led to elevated levels of microglia and MHC I, which were also observed in normal juveniles during song learning. These experiments demonstrate that the adult brain can overcome extended periods of abnormal activity, and precisely restore a complex behavior, without recovering normal neuronal dynamics. These findings suggest that the successful functional recovery of a brain circuit after a perturbation can involve more than mere restoration to its initial configuration. Instead, the circuit seems to adapt and reorganize into a new state capable of producing the original behavior despite the persistence of some abnormal neuronal dynamics.
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Affiliation(s)
- Zsofia Torok
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | - Jordan Feldman
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | | | | | - Shelyn Wongso
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
| | | | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology; Pasadena, CA, USA
| | - Walter G. Gonzalez
- Department of Physiology, University of San Francisco; San Francisco, CA, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, CA, USA
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Luebbert L, Sullivan DK, Carilli M, Hjörleifsson KE, Winnett AV, Chari T, Pachter L. Efficient and accurate detection of viral sequences at single-cell resolution reveals novel viruses perturbing host gene expression. bioRxiv 2023:2023.12.11.571168. [PMID: 38168363 PMCID: PMC10760059 DOI: 10.1101/2023.12.11.571168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
More than 300,000 mammalian virus species are estimated to cause disease in humans. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.
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Affiliation(s)
- Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | - Delaney K Sullivan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles
| | - Maria Carilli
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | | | - Alexander Viloria Winnett
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
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6
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Moses L, Einarsson PH, Jackson K, Luebbert L, Booeshaghi AS, Antonsson S, Bray N, Melsted P, Pachter L. Voyager: exploratory single-cell genomics data analysis with geospatial statistics. bioRxiv 2023:2023.07.20.549945. [PMID: 37645732 PMCID: PMC10461913 DOI: 10.1101/2023.07.20.549945] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.
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Affiliation(s)
- Lambda Moses
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Pétur Helgi Einarsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Kayla Jackson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - A. Sina Booeshaghi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Sindri Antonsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | | | - Páll Melsted
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
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7
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Nichols AL, Blumenfeld Z, Luebbert L, Knox HJ, Muthusamy AK, Marvin JS, Kim CH, Grant SN, Walton DP, Cohen BN, Hammar R, Looger L, Artursson P, Dougherty DA, Lester HA. Selective Serotonin Reuptake Inhibitors within Cells: Temporal Resolution in Cytoplasm, Endoplasmic Reticulum, and Membrane. J Neurosci 2023; 43:2222-2241. [PMID: 36868853 PMCID: PMC10072302 DOI: 10.1523/jneurosci.1519-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/02/2022] [Accepted: 11/27/2022] [Indexed: 03/05/2023] Open
Abstract
Selective serotonin reuptake inhibitors (SSRIs) are the most prescribed treatment for individuals experiencing major depressive disorder. The therapeutic mechanisms that take place before, during, or after SSRIs bind the serotonin transporter (SERT) are poorly understood, partially because no studies exist on the cellular and subcellular pharmacokinetic properties of SSRIs in living cells. We studied escitalopram and fluoxetine using new intensity-based, drug-sensing fluorescent reporters targeted to the plasma membrane, cytoplasm, or endoplasmic reticulum (ER) of cultured neurons and mammalian cell lines. We also used chemical detection of drug within cells and phospholipid membranes. The drugs attain equilibrium in neuronal cytoplasm and ER at approximately the same concentration as the externally applied solution, with time constants of a few s (escitalopram) or 200-300 s (fluoxetine). Simultaneously, the drugs accumulate within lipid membranes by ≥18-fold (escitalopram) or 180-fold (fluoxetine), and possibly by much larger factors. Both drugs leave cytoplasm, lumen, and membranes just as quickly during washout. We synthesized membrane-impermeant quaternary amine derivatives of the two SSRIs. The quaternary derivatives are substantially excluded from membrane, cytoplasm, and ER for >2.4 h. They inhibit SERT transport-associated currents sixfold or 11-fold less potently than the SSRIs (escitalopram or fluoxetine derivative, respectively), providing useful probes for distinguishing compartmentalized SSRI effects. Although our measurements are orders of magnitude faster than the therapeutic lag of SSRIs, these data suggest that SSRI-SERT interactions within organelles or membranes may play roles during either the therapeutic effects or the antidepressant discontinuation syndrome.SIGNIFICANCE STATEMENT Selective serotonin reuptake inhibitors stabilize mood in several disorders. In general, these drugs bind to SERT, which clears serotonin from CNS and peripheral tissues. SERT ligands are effective and relatively safe; primary care practitioners often prescribe them. However, they have several side effects and require 2-6 weeks of continuous administration until they act effectively. How they work remains perplexing, contrasting with earlier assumptions that the therapeutic mechanism involves SERT inhibition followed by increased extracellular serotonin levels. This study establishes that two SERT ligands, fluoxetine and escitalopram, enter neurons within minutes, while simultaneously accumulating in many membranes. Such knowledge will motivate future research, hopefully revealing where and how SERT ligands engage their therapeutic target(s).
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Affiliation(s)
- Aaron L Nichols
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91106
| | - Zack Blumenfeld
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91106
- Keck School of Medicine, University of Southern California, Los Angeles, California 90007
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91106
- Institute of Biology, Leiden University, 2333 BE Leiden, The Netherlands
| | - Hailey J Knox
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91106
| | - Anand K Muthusamy
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91106
| | - Jonathan S Marvin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Viginia 20147
| | - Charlene H Kim
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91106
| | - Stephen N Grant
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91106
| | - David P Walton
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91106
| | - Bruce N Cohen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91106
| | - Rebekkah Hammar
- Department of Pharmacy, Uppsala University, SE-751 23 Uppsala, Sweden
| | - Loren Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Viginia 20147
| | - Per Artursson
- Department of Pharmacy, Uppsala University, SE-751 23 Uppsala, Sweden
- Science for Life Laboratory Drug Discovery and Development Platform and Uppsala University Drug Optimization and Pharmaceutical Profiling Platform, Uppsala University, SE-751 23 Uppsala, Sweden
| | - Dennis A Dougherty
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91106
| | - Henry A Lester
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91106
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8
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Abstract
MOTIVATION A recurring challenge in interpreting genomic data is the assessment of results in the context of existing reference databases. With the increasing number of command line and Python users, there is a need for tools implementing automated, easy programmatic access to curated reference information stored in a diverse collection of large, public genomic databases. RESULTS gget is a free and open-source command line tool and Python package that enables efficient querying of genomic reference databases, such as Ensembl. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying required for genomic data analysis in a single line of code. AVAILABILITY AND IMPLEMENTATION The manual and source code are available at https://github.com/pachterlab/gget. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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9
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Nichols AL, Blumenfeld Z, Fan C, Luebbert L, Blom AEM, Cohen BN, Marvin JS, Borden PM, Kim CH, Muthusamy AK, Shivange AV, Knox HJ, Campello HR, Wang JH, Dougherty DA, Looger LL, Gallagher T, Rees DC, Lester HA. Correction: Fluorescence activation mechanism and imaging of drug permeation with new sensors for smoking-cessation ligands. eLife 2022; 11:85479. [DOI: 10.7554/elife.85479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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10
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Nichols AL, Blumenfeld Z, Fan C, Luebbert L, Blom AEM, Cohen BN, Marvin JS, Borden PM, Kim CH, Muthusamy AK, Shivange AV, Knox HJ, Campello HR, Wang JH, Dougherty DA, Looger LL, Gallagher T, Rees DC, Lester HA. Fluorescence activation mechanism and imaging of drug permeation with new sensors for smoking-cessation ligands. eLife 2022; 11:e74648. [PMID: 34982029 PMCID: PMC8820738 DOI: 10.7554/elife.74648] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/03/2022] [Indexed: 12/16/2022] Open
Abstract
Nicotinic partial agonists provide an accepted aid for smoking cessation and thus contribute to decreasing tobacco-related disease. Improved drugs constitute a continued area of study. However, there remains no reductionist method to examine the cellular and subcellular pharmacokinetic properties of these compounds in living cells. Here, we developed new intensity-based drug-sensing fluorescent reporters (iDrugSnFRs) for the nicotinic partial agonists dianicline, cytisine, and two cytisine derivatives - 10-fluorocytisine and 9-bromo-10-ethylcytisine. We report the first atomic-scale structures of liganded periplasmic binding protein-based biosensors, accelerating development of iDrugSnFRs and also explaining the activation mechanism. The nicotinic iDrugSnFRs detect their drug partners in solution, as well as at the plasma membrane (PM) and in the endoplasmic reticulum (ER) of cell lines and mouse hippocampal neurons. At the PM, the speed of solution changes limits the growth and decay rates of the fluorescence response in almost all cases. In contrast, we found that rates of membrane crossing differ among these nicotinic drugs by >30-fold. The new nicotinic iDrugSnFRs provide insight into the real-time pharmacokinetic properties of nicotinic agonists and provide a methodology whereby iDrugSnFRs can inform both pharmaceutical neuroscience and addiction neuroscience.
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Affiliation(s)
- Aaron L Nichols
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Zack Blumenfeld
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
- Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Chengcheng Fan
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
- Institute of Biology, Leiden UniversityLeidenNetherlands
| | - Annet EM Blom
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
| | - Bruce N Cohen
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Jonathan S Marvin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Philip M Borden
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Charlene H Kim
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Anand K Muthusamy
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
| | - Amol V Shivange
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Hailey J Knox
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
| | | | - Jonathan H Wang
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Dennis A Dougherty
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
| | - Loren L Looger
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Douglas C Rees
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadenaUnited States
- Howard Hughes Medical Institute, California Institute of TechnologyPasadenaUnited States
| | - Henry A Lester
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
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11
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Booeshaghi AS, Lubock NB, Cooper AR, Simpkins SW, Bloom JS, Gehring J, Luebbert L, Kosuri S, Pachter L. Reliable and accurate diagnostics from highly multiplexed sequencing assays. Sci Rep 2020; 10:21759. [PMID: 33303831 PMCID: PMC7730459 DOI: 10.1038/s41598-020-78942-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 11/24/2020] [Indexed: 11/09/2022] Open
Abstract
Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA.
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Affiliation(s)
- A Sina Booeshaghi
- Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | | | | | - Joshua S Bloom
- Octant Inc., Emeryville, CA, USA.,Department of Human Genetics, University of California, Los Angeles, Los Angeles, USA
| | - Jase Gehring
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA. .,Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
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