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Authors: Antonio Real-Hohn
Summary: This protocol was developed to use common wet lab device (Real Time PCR machine) to detect viral genome release and native viral particle conversion. We used temperature ramping for inducing genome uncoating and fluorescent probes for RNA/DNA and/or proteins, to detect genome and/or capsid changes.

Proper citation: Antonio Real-Hohn 2018. Viral Genome Release and Native Viral Particle Conversion Measured Through Temperature Ramping in Real Time PCR Machine. protocols.io dx.doi.org/10.17504/protocols.io.p2xdqfn Copy   

•   Source: Protocols.io

Authors: Alan Cone
Group: Ju Lab
Summary: Quick crude protein extraction from budding or fission yeast.

Proper citation: Alan Cone 2016. Yeast Crude Protein Extraction. protocols.io dx.doi.org/10.17504/protocols.io.ebnbame Copy   

•   Source: Protocols.io

Authors: Hongfang Zhu

Proper citation: Hongfang Zhu 2017. Assay of anthocyanin biosynthetic enzyme activity. protocols.io dx.doi.org/10.17504/protocols.io.h2mb8c6 Copy   

•   Source: Protocols.io

Authors: HANNIGAN GD, GRICE EA, ET AL.
Group: VERVE Net, Club Grice
Summary: This protocol outlines the analysis used to plot contig coverage statistics, as well as sequence count and length stats. We begin with visualizing contig length vs coverage. We then visualize the distributions of sequence counts per sample as a probability density plot (similar idea as a histogram), and then do the same for median sequence length. Based on the methods from the following publication:Hannigan, Geoffrey D., et al. "The Human Skin Double-Stranded DNA Virome: Topographical and Temporal Diversity, Genetic Enrichment, and Dynamic Associations with the Host Microbiome." mBio 6.5 (2015): e01578-15.

Proper citation: HANNIGAN GD, GRICE EA, ET AL. 2016. Script R1: Virome Contig and Sequencing Statistics. protocols.io dx.doi.org/10.17504/protocols.io.eh5bb86 Copy   

•   Source: Protocols.io

Authors: Joachim Nwezeobi, Onyeyirichi Onyegbule, Chukwuemeka Nkere, Joseph Onyeka, Sharon van Brunschot, Susan Seal, John Colvin

Proper citation: Joachim Nwezeobi, Onyeyirichi Onyegbule, Chukwuemeka Nkere, Joseph Onyeka, Sharon van Brunschot, Susan Seal, John Colvin 2020. Whitefly DNA extraction, partial mtCO1 gene amplification and gel electrophoresis. protocols.io dx.doi.org/10.17504/protocols.io.bdvri656 Copy   

•   Source: Protocols.io

Authors: Celeste Karch, Rita Martinez, Jacob Marsh
Group: Neurodegeneration Method Development Community

Proper citation: Celeste Karch, Rita Martinez, Jacob Marsh 2019. Serial Dilution of Nucleofected iPSC Pools. protocols.io dx.doi.org/10.17504/protocols.io.x76frre Copy   

•   Source: Protocols.io

Authors: Chon-Ho Yen
Summary: Sialic acid is a generic term for the N- or O-substituted derivatives of neuraminic acid, a monosaccharide with a nine-carbon backbone. N-glycolylneuraminic acid (Neu5Gc or NGNA) is not synthesized in the human body, so most of the sialic acid in the body is N-acetylneuraminic acid (Neu5Ac or NANA), while other animals have two kinds of sialic acid (NANA and NGNA). There may be NGNA in biotech products manufactured from nonhuman animal source, and a detectable method is needed to determine the derivatives of sialic acid in the product.

Proper citation: Chon-Ho Yen 2019. Analysis of the Derivatives of Sialic Acid in Pig Tissues. protocols.io dx.doi.org/10.17504/protocols.io.zd6f29e Copy   

•   Source: Protocols.io

Authors: Frank Aylward

Proper citation: Frank Aylward 2018. SYSB 3036 W04: Homology searches. protocols.io dx.doi.org/10.17504/protocols.io.vqse5we Copy   

•   Source: Protocols.io

Authors: Alba Balletbó
Group: iGEM Wageningen 2019
Summary: Gel electrophoresis is the standard lab procedure for separating DNA by size (e.g., length in base pairs) for visualization and purification. Electrophoresis uses an electrical field to move the negatively charged DNA through an agarose gel matrix toward a positive electrode. Shorter DNA fragments migrate through the gel more quickly than longer ones. Thus, you can determine the approximate length of a DNA fragment by running it on an agarose gel alongside a DNA ladder (a collection of DNA fragments of known lengths).

Proper citation: Alba Balletbó 2019. Gel Electrophoresis. protocols.io dx.doi.org/10.17504/protocols.io.7eehjbe Copy   

•   Source: Protocols.io

Authors: NEB
Group: New England Biolabs (NEB)

Proper citation: NEB 2016. Digestion Mixture for M0302 (v2). protocols.io dx.doi.org/10.17504/protocols.io.erhbd36 Copy   

•   Source: Protocols.io

Authors: Gert-Jan Hendriks, Lisa Anna Jung, Anton J.M. Larsson, Oscar Andersson Forsman, Michael Lidschreiber, Katja Lidschreiber, Patrick Cramer, Rickard Sandberg

Proper citation: Gert-Jan Hendriks, Lisa Anna Jung, Anton J.M. Larsson, Oscar Andersson Forsman, Michael Lidschreiber, Katja Lidschreiber, Patrick Cramer, Rickard Sandberg 2019. NASC-seq (new transcriptome alkylation-dependent single-cell RNA sequencing) protocol. protocols.io dx.doi.org/10.17504/protocols.io.whtfb6n Copy   

•   Source: Protocols.io

Authors: Jascha Grübel, Tyler Thrash, Christoph Hölscher, Victor Schinazi
Summary: Previous research in spatial cognition has often relied on simple spatial tasks in static environments in order to draw inferences regarding navigation performance. These tasks are typically divided into categories (e.g., egocentric or allocentric) that reflect different two-systems theories. Unfortunately, this two-systems approach has been insufficient for reliably predicting navigation performance in virtual reality (VR). In the present experiment, participants were asked to learn and navigate towards goal locations in a virtual city and then perform eight simple spatial tasks in a separate environment. These eight tasks were organised along four orthogonal dimensions (static/dynamic, perceived/remembered, egocentric/allocentric, and distance/direction). We employed confirmatory and exploratory analyses in order to assess the relationship between navigation performance and performances on these simple tasks. We provide evidence that a dynamic task (i.e., intercepting a moving object) is capable of predicting navigation performance in a familiar virtual environment better than several categories of static tasks. These results have important implications for studies on navigation in VR that tend to over-emphasise the role of spatial memory. Given that our dynamic tasks required efficient interaction with the human interface device (HID), they were more closely aligned with the perceptuomotor processes associated with locomotion than wayfinding. In the future, researchers should consider training participants on HIDs using a dynamic task prior to conducting a navigation experiment. Performances on dynamic tasks should also be assessed in order to avoid confounding skill with an HID and spatial knowledge acquisition.

Proper citation: Jascha Grübel, Tyler Thrash, Christoph Hölscher, Victor Schinazi 2017. Protocol for "Evaluation of a Conceptual Framework for Predicting Navigation Performance in Virtual Reality". protocols.io dx.doi.org/10.17504/protocols.io.jbdcii6 Copy   

•   Source: Protocols.io

Authors: Lukas Snoek, Tinka Beemsterboer
Group: Spinoza Centre, REC-L
Summary: The weekly quality control at the Spinoza Centre REC evaluates the signal quality/performance of the 3T Philips Achieva TX (R5, v3.0).

Proper citation: Lukas Snoek, Tinka Beemsterboer 2019. Weekly Quality Control. protocols.io dx.doi.org/10.17504/protocols.io.w8efhte Copy   

•   Source: Protocols.io

Authors: Jeffrey Lewis, Amanda Scholes
Group: Protist Research to Optimize Tools in Genetics (PROT-G), Lewis Lab
Summary: A generalizable protocol for measuring relative changes in gene expression via qPCR (cDNA synthesis, qPCR primer optimization, and qPCR analysis), with specific optimization for the budding yeast Saccharomyces cerevisiae.

Proper citation: Jeffrey Lewis, Amanda Scholes 2020. Quantitative (q)PCR and Differential Expression Analysis. protocols.io dx.doi.org/10.17504/protocols.io.bbgpijvn Copy   

•   Source: Protocols.io

Authors: Elena Hilario, Marcus Davy, Roy Storey, Richard Newcomb, Ross N Crowhurst, Roger P Hellens
Summary: A genome is assembled by connecting large scaffolds via a collection of thousands of mate-paired ends of various sizes (3-20 kbp). Terminal ends of long DNA fragments can be obtained by sequencing large insert clones by Sanger sequencing, a labour-intensive and expensive approach. Here we describe a high-throughput protocol to tag 11,520 BAC plasmids randomly with barcoded oligonucleotides at the end of a random hexamer by PCR.This method assisted the kiwifruit (Actinidia chinensis) genome assembly in a similar way as mate paired end datasets.  Although not fully sequenced, each randomly tagged BAC clone provides a collection of short sequence tags mapped to a genome across one or multiple scaffolds under three different scenarios: validate a single scaffold, join two scaffolds, or group several scaffolds by proximity only. Our method provides a simple way of cataloging BAC libraries to assist genome assembly projects.

Proper citation: Elena Hilario, Marcus Davy, Roy Storey, Richard Newcomb, Ross N Crowhurst, Roger P Hellens 2018. BACRB - Parallel Sequencing of Barcoded BAC Clones by random tagging PCR. protocols.io dx.doi.org/10.17504/protocols.io.p3adqie Copy   

•   Source: Protocols.io

Authors: Frank Aylward

Proper citation: Frank Aylward 2018. SYSB 3036 W11: Gene Expression and RNA-Seq. protocols.io dx.doi.org/10.17504/protocols.io.v4qe8vw Copy   

•   Source: Protocols.io

Authors: Azar Kordbacheh, Robert L. Wallace, Elizabeth J. Walsh
Summary: Sample collection and cultureAquatic plant samples were collected from habitats across the USA and a sediment sample from Australia (Tables S1.1, S1.2). Limnias melicerta and L. ceratophylli were identified and isolated from rehydrated sediments or by removing a piece of vegetation to which they were attached. Species identification was based on tube structure, shape of corona, antennae length, and the number of dorsal nodules [1]. Clonal lineages initiated from single females were cultured in modified MBL media [2] and fed a mixture of the algae Chlorella vulgaris Berijerinck, 1890 (The UTEX Culture Collection of Algae at the University of Texas at Austin [UTEX] strain 30) and Chlamydomonasreinhardtii Dangeard, 1888 (UTEX strain 90). Rotifers in this genus produce tubes of hardened secretions [3]; we added powdered carmine (Alfa Aesar, UK) to lab cultures to provide a supplementary matrix to aid tube construction and to increase their visibility in culture. Voucher specimens were deposited in the UTEP Biodiversity Collections at The University of Texas at El Paso (L. melicerta: UTEP:Zoo:43, 105-134; L. ceratophylli: UTEP:Zoo:32-42, UTEP:Zoo:52-61).Deposited specimens included approximately 10 individuals from each population preserved in 95% ethanol and 10 clonal individuals preserved in 4% buffered formalin for molecular analyses and identification, respectively. DNA Extraction and gene amplificationDNA was extracted from one individual of each clonal lineage by adding 13 µl Chelex®-100 (Bio-Rad Laboratories, CA, USA) and incubating at 100°C for 10 min. DNA templates were stored at -80°C until used for amplification. Number of clonal lineages examined from each population is given in Tables S1.1 and S1.2.An approximate 630 bp portion of the cytochrome c oxidase subunit I (COI) gene was amplified using the primers LCO1490: 5' -GGTCAACAAATCATAAAGATATTGG-3' and HCO2198: 5'-TAAACTTCAGGGTGACCAAAAAATCA-3' [4]. The entire nuclear internal transcribed spacer region (ITS) was amplified using the primers ITS4: 5'-TCCTCCGCTTATTGATATGC-3' and ITS5: 5'-GGAAGTAAAAGTCGTAACAAGG-3' [5], and 865 bp of the 18S rRNA gene was amplified using primers 3F: 5’-GTTCGATTCCGGAGAGGG-3’ as modified by Giribet et al. [6] and primer 18Sbi: 5’-CTAGAGTCTCGTTCGTTATCGG-3’ as modified by Whiting et al. [7].PCR reactions contained 10 µl of genomic DNA, 1 µl of each primer (500 ng/ µl), 22 µl HPLC grade sterile water, 1 µl GoTaq® G2 DNA Polymerase (Promega), 10 µl 5X PCR buffer B (10 mM MgCl2, pH 8.5) or 5X PCR buffer A (7.5 mM MgCl2, pH 8.5), followed by adding 5 µl dNTP mix (2.5 mM each of dATP, dCTP, dGTP, dTTP) at 80ºC. PCR cycles were run on a thermocycler (TECHNE TC-412) and consisted of an initial denaturation at 94ºC for 1 min, followed by denaturation at 94ºC for 1 min, annealing at 48ºC for 2 min and extension at 72ºC for 3 min for 35 cycles, and a final extension step at 72ºC for 7 min. To verify the size of amplification products we used electrophoresis, and we purified them using GENECLEAN® kits (MP Biomedicals, LLC) before sequencing. Sequencing was done at UTEP’s BBRC Genomic Analysis Core Facility on an Applied Biosystems 3130xl Genetic Analyzer using BigDye Terminator v3.1 Cycle Sequencing Kits (Applied Biosystems). GenBank accession numbers for all sequences obtained are given in Tables S1.1 (L. melicerta) and S1.2 (L. ceratophylli). The COI gene sequences of L. melicerta (accession number, KT870155.1) and L. ceratophylli(KT870157.1) from GenBank are not included in our analyses for two reasons. 1) The COI sequence of L. melicerta KT870155.1 is 330 bp, which was much shorter than COI sequences obtained in this study (623 bp). 2) The COI sequence of L. ceratophylli KT870157.1 grouped with cryptic species M of L. melicerta in phylogenetic analyses. Additional 18S rRNA sequences (L. melicerta: KM873599.1, L. ceratophylli: KM873598.1) and a COI sequence from L. melicerta (KT870154.1) were included from GenBank. Sinantherina socialis(Linneaus, 1758) and Ptygura pilula (Cubitt, 1872) were included as outgroups in phylogenetic analyses for the COI gene. Floscularia conifera (Hudson, 1886) andPtygura brachiata (Hudson, 1886) were used as outgroup taxa in phylogenetic analyses based on ITS region. For analysis of 18S rRNA sequences, we used Collotheca campanulata as the outgroup (Table S1.1).Genetic DiversityFinchTV v 1.4.0 [8] was used to check sequences manually, especially for potential double peaks in the ITS region sequences. The ITS region alignment was uploaded to the SeqPhase online tool (http://seqphase.mpg.de/seqphase/) to phase the sequences as described by Flot [9]. Contigs for all sequences were made using CAP 3 [10] and were aligned using MAFFT v 7 [11]. Mesquite v 3.2 [12] was used to manually check the alignments and to translate COI gene sequences to proteins. To measure substitution saturation, we used DAMBE v 6 [13]. Number of polymorphic sites, number of parsimony informative sites, number of haplotypes, haplotype diversity (h), and nucleotide diversity (π) were calculated using DnaSp v 5.10.01 [14], and uncorrected pairwise sequences distances ("p") were calculated in Mega v 7.0 [15]. A haplotype network was constructed using the median joining method in Network v 5.0.3 [16]. Species delimitation Models for sequence evolution were TPM2uF+I+G for the COI gene, TPM1uF+I for the ITS region, and JC for the 18S rRNA gene as determined using Jmodeltest2 [17,18] available at the Cipres Science Gateway 3.3 [19]. To construct the phylogenetic trees, Bayesian analysis was run for 107 generations with two parallel runs and a 25% burn in period using MrBayes v 3.2.6 on XSEDE high-throughput computing resources available at Cipres Science Gateway [19]. Phylogenetic analyses were implemented in BEAST and *BEAST [20] using GTR+I+G model of sequence evolution for the COI gene and GTR+I model for the ITS region. TPM2uF and TPM1uF models are not available in BEAST. However, both of these models are classified under the GTR model. Thus GTR was used in both instances.To determine the number of evolutionary entities (putative cryptic species), we used Generalized Mixed Yule Coalescent (GMYC, [21]), Poisson Tree Process (PTP, [22]), Automatic Barcoding Gap Discovery (ABGD, [23]), *BEAST v 1.8.3 [20], and Bayesian Species Delimitation (BSD) implemented in Bayesian Phylogenetics and Phylogeography software (BPP v 3.1, [24–26]). We used BEAST v 1.8.3 [20] to construct ultrametric trees and *BEAST v 1.8.3 [20] for species delimitation. Both analyses were run for the COI gene and ITS region sequences separately for 107 generations, with sampling every 1,000 generations. Tracer v 1.6.0 [27] was used to check the effective sample size (ESS>200) and to verify convergence. Consensus trees were obtained using TreeAnnotator v 1.8.3 with a 25% burn in. Ultrametric trees were used for species delimitation in single threshold and multiple threshold GMYC [28] (http://species.h-its.org/gmyc/, accessed June 12, 2018), Bayesian GMYC (bGMYC) [29] and PTP methods. bGMYC was run using the R package bGMYC v 1.0.2 for 100,000 iterations with sampling every 1,000 iterations. We ran PTP by uploading the ultrametric trees to the online tool available at http://species.h-its.org/ptp/(accessed June 12, 2018) andused default settings. ABGD delimitation was done by uploading the sequence alignment to the online tool available at wwwabi.snv.jussieu.fr/public/abgd/ under the default settings (accessed June 12, 2018).*BEAST v 1.8.3 [20] was run under assumptions regarding the number of species for both the COI gene and ITS region sequences. Lineages having posterior probabilities > 0.90 were retained in *BEAST analyses. To run BSD, the phylogenetic tree based on Bayesian inference was used as the guide tree, and we used the joint species delimitation and tree estimation method (unguided species delimitation) that does not rely on the topology of the guide tree.Isolation by Distance Geographic distance matrices were constructed using Geographic Distance Matrix Generator v 1.2.3 [30]. To test the correlation between genetic variation and geographic distances (log transformed; km) among populations, Mantel tests with 10,000 permutations were run using the R package ecodist v 1.2.9 [31]. Trophi morphologyTrophi were prepared for scanning electron microscopy (SEM) by dissolving rotifer tissue in ~5% sodium hypochlorite, rinsing with deionized water 10-15 times, and air-drying on circular cover slips at room temperature [32]. Trophi were coated with gold/palladium using a Gatan 682 PECS sputter coater. SEM images were obtained at 20 kV using a Hitachi S-4800 system. Trophi were prepared for individuals from one clonal lineage from each examined population. We used a geometric morphometric approach to study variation in the shape and size of 92 trophi for L. melicerta and 60 trophi for L. ceratophylli among putative cryptic species. This method uses Cartesian coordinates for a set of anatomical landmarks [33]. SEM images were obtained from caudal and frontal views of the trophi. Using TPS series software [34], nine landmarks on the caudal view and 10 on the frontal view of the trophi were digitized (Fig 1). Configuration of landmarks were analyzed using Generalized Procrustes Analysis [33]. Trophi size was calculated as Centroid Size (CS): i.e., the square root of the sum of squared distances between landmarks and their centroid [35]. Variation in the shape of trophi based on landmarks was examined using Discriminant Analysis in SPSS v 24 [36]. Because trophi centroid size was not normally distributed, variation in size among putative cryptic species was tested using a non-parametric Kruskal-Wallis test, and between the two morphospecies using a non-parametric Man-Whitney U test implemented in SPSS v 24 [36]. References1. Wallace RL, Kordbacheh A, Walsh EJ. Key to the currently recognized species of Limnias Schrank, 1803 (Rotifera, Monogononta, Gnesiotrocha, Flosculariidae). Zootaxa. 2018;4442: 307–318. doi.org/10.11646/zootaxa.4442.2.7.2. Stemberger RS. A general approach to the culture of planktonic rotifers. Can J Fish Aquat Sci. 1981;38: 721–724. doi:10.1139/f81-095.3. Wallace RL, Nogrady T, Snell TW, Ricci C. Rotifera 1: Biology, ecology and systematics. 2nd ed. Nogrady T, editor. Kenobi Productions, 2006, 293 pp. 4. Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome coxidase subunit I from diverse metazoan invertebrates. Mol Mar Biol Biotechnol. 1994;3: 294–299. doi:10.1371/journal.pone.0013102.5. White T, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis M, Gelfand D, Shinsky J, White T, editors. PCR Protocols: A guide to methods and applications. Academic Press, 1990, pp. 315–322. citeulike-article-id:671166.6. Giribet G, Carranza S, Baguna J, Riutort M, Ribera C. First molecular evidence for the existence of a Tardigrada + Arthropoda clade. Mol Biol Evol. 1996;13: 76–84. 7. Whiting MF, Carpenter JC, Wheeler QD, Wheeler WC. The Strepsiptera problem: phylogeny of the holometabolous insect orders inferred from 18S and 28S ribosomal DNA sequences and morphology. Syst Biol. 1997;46: 1–68. 8. Geospiza.com. FinchTV. Seattle, WA; 2014: http://www.geospiza.com/Products/finchtv.shtml.9. Flot JF. seqphase: a web tool for interconverting phase input/output files and fasta sequence alignments. Mol Ecol Resour. 2010;10: 162–166. doi:10.1111/j.1755-0998.2009.02732.x.10. Huang X, Madan A. CAP3: A DNA sequence assembly program. Genome Res. 1999;9: 868–877. 11. Katoh K, Standley DM. MAFFT. Multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30: 772–780. doi:10.1093/molbev/mst010.12. Maddison WP, Maddidon DR. Mesquite: a modular system for evolutionary analysis. 2017: Version 3.2. http://mesquiteproject.org. 13. Xia X. DAMBE6: New tools for microbial genomics, phylogenetics and molecular evolution. J Hered. 2017;108: 431–437. 14. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25: 1451–1452. doi:10.1093/bioinformatics/btp187.15. Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol. 2015;33: 1870-4. doi:10.1093/molbev/mst197.16. Bandelt HJ, Forster P, Rohl A. Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol. 1999;16: 37–48. 17. Darriba D, Taboada GL, Doallo R, Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat Methods. 2012;9: 772. http://dx.doi.org/10.1038/nmeth.2109.18. Guindon S, Gascuel O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003;52: 696–704. doi:10.1080/10635150390235520.19. Miller MA, Pfeiffer W, Schwartz T. Creating the CIPRES science gateway for inference of large phylogenetic trees. 2010. Gateway Computing Environments Workshop, 2010, pp. 1–8. doi:10.1109/GCE.2010.5676129.20. Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol Biol Evol. 2012;29: 1969–1973. doi:10.1093/molbev/mss075.21. Pons J, Barraclough TG, Gomez-Zurita J, Cardoso A, Duran DP, Hazell S, et al. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst Biol. 2006;55: 595–609. doi:10.1080/10635150600852011.22. Zhang J, Kapli P, Pavlidis P, Stamatakis A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics. 2013;29: 2869–76. doi:10.1093/bioinformatics/btt499.23. Puillandre N, Lambert A, Brouillet S, Achaz G. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol Ecol. 2012;21: 1864–1877. doi:10.1111/j.1365-294X.2011.05239.x.24. Rannala B, Yang Z. Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. Genetics. 2003;164: 1645–56. doi:10.1073/pnas.0913022107.25. Yang Z, Rannala B. Bayesian species delimitation using multilocus sequence data. Proc Natl Acad Sci. 2010;107: 9264–9269. doi:10.1073/pnas.0913022107.26. Yang Z, Rannala B. Unguided species delimitation using DNA sequence data from multiple loci. Mol Biol Evol. 2014;31: 3125–3135. doi:10.1093/molbev/msu279.27. Rambaut A, Suchard MA, Xie D, Drummond AJ. Tracer v1.6. http://beast.bio.ed.ac.uk/Tracer. citeulike-article-id:13604975.28. Fujisawa T, Barraclough TG. Delimiting species using single-locus data and the generalized mixed yule coalescent approach: a revised method and evaluation on simulated data sets. Syst Biol. 2013; 62: 707–724. doi:10.1093/sysbio/syt033.29. Reid NM, Carstens BC. Phylogenetic estimation error can decrease the accuracy of species delimitation: a Bayesian implementation of the general mixed yule coalescent model. BMC Evol Biol. 2012;12: 196. doi:10.1186/1471-2148-12-196.30. Ersts PJ. Geographic distance matrix generator version 1.2.3. Nat Hist. 2006; http://biodiversityinformatics.amnh.org/open_source/gdmg/documentation.php.31. Goslee SC, Urban DL. The ecodist package for dissimilarity-based analysis of ecological data. J Stat Software. 2007;22: 1–19. https://www.jstatsoft.org/v022/i07.32. Segers H. Rotifera of some lakes in the floodplain of the River Niger (Imo State, Nigeria). New species and other taxonomic considerations. Hydrobiologia. 1993;250: 39–61. doi:10.1007/BF00007494.33. Adams DC, Rohlf FJ, Slice DE. Geometric morphometrics: Ten years of progress following the revolution. Ital J Zool. 2004;71: 5–16. doi:10.1080/11250000409356545.34. Rohlf FJ. The tps series of software. Hystrix. 2015;26: 1–4. doi:10.4404/hystrix-26.1-11264.35. Cavalcanti MJ, Monteiro LR, Lopes PRD. Landmark-based morphometric analysis in selected species of serranid fishes (Perciformes: Teleostei). Zool Stud. 1999;38: 287–294. 36. IBM Corp. 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Proper citation: Azar Kordbacheh, Robert L. Wallace, Elizabeth J. Walsh 2018. Protocol used in Evidence supporting cryptic species complexes within two sessile microinvertebrates, Limnias melicerta and L. ceratophylli (Rotifera, Gnesiotrocha). protocols.io dx.doi.org/10.17504/protocols.io.tppemmn Copy   

•   Source: Protocols.io

Authors: Miriam Schalamun, Benjamin Schwessinger
Group: High molecular weight DNA extraction from all kingdoms, Plantae
Summary: Extraction of high quality DNA for long read sequencing e.g. the Oxford Nanopore Optimized for DNA extraction from eucalyptus grandis and eucalyptus pauciflora. This protocol contains an optional Chloroform clean up step which is necessary for eucalyptus but might not be for other tissue. For long DNA fragments don't vortex the DNA sample. 

Proper citation: Miriam Schalamun, Benjamin Schwessinger 2017. High molecular weight gDNA extraction after Mayjonade et al. optimised for eucalyptus for nanopore sequencing. protocols.io dx.doi.org/10.17504/protocols.io.khkct4w Copy   

•   Source: Protocols.io

Authors: Aizea Morales-Kastresana, Joshua Welsh, Jennifer Jones
Group: Translational Nanobiology Section
Summary: Recent advances in high resolution flow cytometry (HRFC), which show improvements in both light scatter and fluorescence sensitivity have resulted in the development of techniques that better isolate, stain and analyze single EVs(Boing et al., 2014; Groot Kormelink et al., 2016; Morales-Kastresana, Musich, Welsh, Telford, Demberg, Wood, Bigos, Ross, Kachynski, Dean, Feton, et al., 2019; Morales-Kastresana et al., 2017; Stoner et al., 2016; van der Vlist, Nolte-'t Hoen, Stoorvogel, Arkesteijn, & Wauben, 2012). Below, we describe protocols to fluorescently label EVs using CFDA-SE (hereinafter called CFSE), as well as antibodies targeted at specific EV surface proteins. We also provide guidelines for residual dye and antibody removal, appropriate data acquisition by HRFC and EV counting by HRFC. Figure 1summarizes these protocols.The EVs used in this protocol are derived from the DC2.4 cell line, and bone marrow derived dendritic cells (BMDCs). The DC2.4 cell line are immature dendritic cells (DCs) with very low expression of typical DC markers on their surface (unpublished observation and(Hargadon, Forrest, & Reddy, 2012)) and that release a morphologically homogeneous population of EVs (~130 nm in diameter). DC2.4 EVs will be used to demonstrate a CFSE staining in Basic Protocol 1, as well as being used as a negative control for antibody-based staining methods (Basic Protocol 2). Bone marrow dendritic cell (BMDC)-derived EVs are more heterogeneous in diameter (100-200 nm) and composition(Morales-Kastresana, Musich, Welsh, Telford, Demberg, Wood, Bigos, Ross, Kachynski, Dean, Felton, et al., 2019), and express DC markers such as MHC-II. BMDC EVs will be used to demonstrate antigen-specific staining with fluorochrome-conjugated antibodies in Basic Protocol 2. DC2.4 and BMDC-derived EVs were isolated by serial ultracentrifugation, with concentration and diameter distribution characterized by NTA, as described before(Morales-Kastresana, Musich, Welsh, Telford, Demberg, Wood, Bigos, Ross, Kachynski, Dean, Feton, et al., 2019; Morales-Kastresana et al., 2017).

Proper citation: Aizea Morales-Kastresana, Joshua Welsh, Jennifer Jones 2020. Detection and Sorting of Extracellular Vesicles and Viruses using nanoFACS. protocols.io dx.doi.org/10.17504/protocols.io.bj6xkrfn Copy   

•   Source: Protocols.io

Authors: G-Biosciences
Group: G-Biosciences
Summary: The UPPA-PROTEIN Concentrate™ kit is used to quantitatively concentrate dilute protein samples as low as 1ng/ml into a small volume. Protein precipitation and concentration are not affected by the presence of detergents, chaotropics, or other common laboratory agents.

Proper citation: G-Biosciences 2017. UPPA-PROTEIN Concentrate™. protocols.io dx.doi.org/10.17504/protocols.io.e5gbg3w Copy   

•   Source: Protocols.io