Single-cell isolation

2023
Agustin Rolandelli, Hanna J Laukaitis-Yousey, Haikel N Bogale, Nisha Singh, Sourabh Samaddar, Anya J O’Neal, Camila R Ferraz, Matthew Butnaru, Enzo Mameli, Baolong Xia, Tays M. Mendes, Rainer L. Butler, Liron Marnin, Francy ECabrera Paz, Luisa M Valencia, Vipin S Rana, Ciaran Skerry, Utpal Pal, Stephanie E Mohr, Norbert Perrimon, David Serre, and Joao HF Pedra. 2023. “Tick hemocytes have pleiotropic roles in microbial infection and arthropod fitness.” bioRxiv, Pp. 2023.08.31.555785. Publisher's VersionAbstract
Uncovering the complexity of systems in non-model organisms is critical for understanding arthropod immunology. Prior efforts have mostly focused on Dipteran insects, which only account for a subset of existing arthropod species in nature. Here, we describe immune cells or hemocytes from the clinically relevant tick Ixodes scapularis using bulk and single cell RNA sequencing combined with depletion via clodronate liposomes, RNA interference, Clustered Regularly Interspaced Short Palindromic Repeats activation (CRISPRa) and RNA-fluorescence in situ hybridization (FISH). We observe molecular alterations in hemocytes upon tick infestation of mammals and infection with either the Lyme disease spirochete Borrelia burgdorferi or the rickettsial agent Anaplasma phagocytophilum. We predict distinct hemocyte lineages and reveal clusters exhibiting defined signatures for immunity, metabolism, and proliferation during hematophagy. Furthermore, we perform a mechanistic characterization of two I. scapularis hemocyte markers: hemocytin and astakine. Depletion of phagocytic hemocytes affects hemocytin and astakine levels, which impacts blood feeding and molting behavior of ticks. Hemocytin specifically affects the c-Jun N-terminal kinase (JNK) signaling pathway, whereas astakine alters hemocyte proliferation in I. scapularis. Altogether, we uncover the heterogeneity and pleiotropic roles of hemocytes in ticks and provide a valuable resource for comparative biology in arthropods.Competing Interest StatementThe authors have declared no competing interest.
2023.08.31.555785v1.full_.pdf
2021
Ashley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, and Erica Larschan. 2021. “TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data.” Nucleic Acids Res, 49, W1, Pp. W641-W653.Abstract
Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein-DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein-DNA binding data, and protein-protein interaction networks. TIMEOR's user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.
2020
A.M. Conard, N. Goodman, Hu, Y, N. Perrimon, R. Singh, C. Lawrence, and E. Larschan. 9/15/2020. “TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data [NOTE: A modified final version was published in NAR and is now available].” BioRxiv. Publisher's VersionAbstract
Uncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.
2020.09.14.296418v1.full_.pdf
Justin A Bosch, Shannon Knight, Oguz Kanca, Jonathan Zirin, Donghui Yang-Zhou, Yanhui Hu, Jonathan Rodiger, Gabriel Amador, Hugo J Bellen, Norbert Perrimon, and Stephanie E Mohr. 2020. “Use of the CRISPR-Cas9 System in Drosophila Cultured Cells to Introduce Fluorescent Tags into Endogenous Genes.” Curr Protoc Mol Biol, 130, 1, Pp. e112.Abstract
The CRISPR-Cas9 system makes it possible to cause double-strand breaks in specific regions, inducing repair. In the presence of a donor construct, repair can involve insertion or 'knock-in' of an exogenous cassette. One common application of knock-in technology is to generate cell lines expressing fluorescently tagged endogenous proteins. The standard approach relies on production of a donor plasmid with ∼500 to 1000 bp of homology on either side of an insertion cassette that contains the fluorescent protein open reading frame (ORF). We present two alternative methods for knock-in of fluorescent protein ORFs into Cas9-expressing Drosophila S2R+ cultured cells, the single-stranded DNA (ssDNA) Drop-In method and the CRISPaint universal donor method. Both methods eliminate the need to clone a large plasmid donor for each target. We discuss the advantages and limitations of the standard, ssDNA Drop-In, and CRISPaint methods for fluorescent protein tagging in Drosophila cultured cells. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Knock-in into Cas9-positive S2R+ cells using the ssDNA Drop-In approach Basic Protocol 2: Knock-in into Cas9-positive S2R+ cells by homology-independent insertion of universal donor plasmids that provide mNeonGreen (CRISPaint method) Support Protocol 1: sgRNA design and cloning Support Protocol 2: ssDNA donor synthesis Support Protocol 3: Transfection using Effectene Support Protocol 4: Electroporation of S2R+-MT::Cas9 Drosophila cells Support Protocol 5: Single-cell isolation of fluorescent cells using FACS.
2014
Benjamin E Housden, Shuailiang Lin, and Norbert Perrimon. 2014. “Cas9-based genome editing in Drosophila.” Methods Enzymol, 546, Pp. 415-39.Abstract

Our ability to modify the Drosophila genome has recently been revolutionized by the development of the CRISPR system. The simplicity and high efficiency of this system allows its widespread use for many different applications, greatly increasing the range of genome modification experiments that can be performed. Here, we first discuss some general design principles for genome engineering experiments in Drosophila and then present detailed protocols for the production of CRISPR reagents and screening strategies to detect successful genome modification events in both tissue culture cells and animals.

2014_Methods Enzymol_Housden.pdf