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8.3: Sample preparation - Biology

8.3:  Sample preparation - Biology


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Prepare your samples for electrophoresis while the gel is curing by adding concentrated loading buffer. The loading buffer also contains glycerol, which makes the sample dense enough to sink to the bottom of the sample well.

Add 4 μL of 6X loading buffer directly to each of the 20 μL PCR reactions from the last lab. Briefly, centrifuge each tube to mix the dye and samples, if necessary. You will use half of each sample in your gels. Store the remaining sample in the refrigerator.


8.3: Sample preparation - Biology

Native PAGE Principle:

Native PAGE uses the same discontinuous chloride and glycine ion fronts as SDS-PAGE to form moving boundaries that stack and then separate polypeptides by charge to mass ratio. Proteins are prepared in a non-reducing non-denaturing sample buffer, which maintains the proteins' secondary structure and native charge density. Therefore you can easily see multiple bands from the camshot of your native PAGE gel if your target protein has polymerized forms in your sample. In native PAGE electrophoresis most proteins have an acidic or slightly basic pl (isoelectric point) (

3–8) and migrate towards the negative polar. If your protein's pl is larger than 8,9, for example, you should probably reverse the anode and run the native PAGE gel.

Learn more about Native-PAGE:

  • For the electrophoresis system, a bio-rad system is recommended.
  • For a 5ml native PAGE stacking gel

*: Added right before each use.

For a 10ml native PAGE separating gel:

Acylamide percentage 6% 8% 10% 12% 15%
Acrylamide/Bis-acrylamide (30%/0.8% w/v) 2ml 2.6ml 3.4ml 4ml 5ml
0.375M Tris-HCl(pH=8.8) 7.89ml 7.29ml 6.49ml 5.89ml 4.89ml
*10% (w/v) ammonium persulfate (AP) 100μl 100μl 100μl 100μl 100μl
*TEMED 10μl 10μl 10μl 10μl 10μl

*: Added right before each use.

Sample buffer (2x):

62.5 mM Tris-HCl, pH 6.8
25% glycerol Glycerol
1% Bromophenol Blue

25 mM Tris
192 mM glycine

Note: running buffer should be

pH 8.3. Do not adjust the pH.

Gel running protocol:

1. Prepare appropriate amount of separating gel in a small beaker, then add specific vol. of AP and TEMED and gently swirl the beacker to ensure a sufficient mixing. Pipet the gel solution into the gap between the glass plates of gel casting (Don't fully fill). Fill the rest space with water (isopropanol alternatively). Allow 20-30min for a complete gelation.

2. You can prepare the stacking gel solution while the separating gel is gelating. Prepare appropriate amount of stacking gel in a beacker and mix with 10% AP and 1% TEMED. Pour out the water in the first step and pipet the stacking gel solution into the gap and insert the comb. Allow 20-30min to let it gelate.

3. Mix your sample with sample buffer. Do not heat your sample!

4. Load the sample mixture and set an appropriate voltage to run the electrophoresis.

Note: It's better to put the system on ice and not set a relative high Volt in case the proteins degrade.

5. Stain as you would a standard Coomassie-blue protocol or proceed to a immuno-blotting procedure (western-blot).


8.3: Sample preparation - Biology

PAGE-SDS Laemmli Protocol


% Acrylamide 10% 10% 12% 12% 15%
Number of Minigels 5 8 5 8 5
1.5M TrisHCl pH 8.3 + 0.4% SDS 7.0 ml 10.5 ml 7.0 ml 10.5 ml 7.0 ml
30% Acrylamide 0.8% Methylene bis Acrylamide 9.3 ml 13.9 ml 11.3 ml 16.9 ml 13.9 ml
H 2 O 12.3 ml 18.4 ml 9.3 ml 13.9 ml 6.3 ml
10% APS 100 ul 150 ul 100 ul 150 ul 100 ul
TEMED 23 ul 35 ul 23 ul 35 ul 23 ul

% Acrylamide 10% 10% 12% 12% 15%
Number of Minigels 5 8 5 8 5
1.5M TrisHCl pH 8.3 + 0.4% SDS 7.0 ml 10.5 ml 7.0 ml 10.5 ml 7.0 ml
40% Acrylamide / Methylene bis Acrylamide (ratio: 37.5:1) 7.2 ml 10.8 ml 8.6 ml 12.9 ml 10.8 ml
H 2 O 14.4 ml 21.5 ml 12 ml 17.9 ml 9.4 ml
10% APS 100 ul 150 ul 100 ul 150 ul 100 ul
TEMED 23 ul 35 ul 23 ul 35 ul 23 ul

Add TEMED and APS at the end. Gently swirl the flask to mix, being careful not to generate bubbles. Pipette the solution to a level of 4cm of the top. Add 0.3ml of n-buthanol. A very sharp liquid interface will be visible within 10-20min. Let polymerize the gel for another hour at least. Rinse the surface of the gel with watter before pouring the stacking gel.

Stacking Gel

Number of Minigels 2 5 8
Number of Minigels 2 5 8
0.5M TrisHCl pH 6.8+ 0.4% SDS 2.5 ml 4.0 ml 5.2 ml 0.5M TrisHCl pH 6.8+ 0.4% SDS 2.5 ml 4.0 ml 5.2 ml
30 % Acrylamide 0.8% Methylene bis Acrylamide 1.0ml 1.5ml 2.0ml 40 % Acrylamide / Methylene bis Acrylamide (ratio: 37.5:1) 0.75 ml 1.1 ml 1.4 ml
H 2 O 6.4ml 9.6ml 12.8ml H 2 O 6.6 ml 10 ml 13.4ml
10% APS 100 ul 150 ul 200 ul 10% APS 100 ul 150 ul 200 ul
TEMED 10 ul 15 ul 20 ul TEMED 10 ul 15 ul 20 ul

Fill each sandwich with stacking gel solution and insert a comb into each place taking care not to trap any bubbles bellow the teeth. The gel should fully polymerized after 1hour. Cover gel with a nylon wrap. Keep gels no more than 2 weeks at 4°C.

Prior to adding the sample buffer, keep samples at 0°C. Add the SDS sample buffer (RT) to the sample (still on ice), and boil at 100°C immediately 3 to 5 min. DO NOT leave the sample in SDS sample buffer without heating endogenous proteases are very active in SDS sample buffer and can cause severe degradation. Once heated, sample could sit at RT for a short time until loading, or at -20°C for a long time.
For a gel thickness of 0.75mm and 15 wells applied 10-25ug protein of a complex mixture, when staining with Coomasie Blue and 0.5 to 5ug for samples for one or few proteins. If silver stain is used 10 to 100-fold less protein can be used.
Samples can be concentrated or interferences (salts, etc.) eliminated with TCA, acetone, TCA-DOC, ethanol, etc. (see attached Protocol). Potassium ions in particular must be removed since they precipitate the SDS.
Some proteins such as nuclear non-histone proteins and membrane proteins, require the presence of 8M urea in the SDS sample buffer to get complete solubilization.
Some membrane bound proteins undergo aggregation at temperatures above 40-50 °C . In this case incubate 30min at 40 °C with sample buffer.
A shift in the migration distances of proteins
with internal disulfide bridges could be observed by incubating samples in SDS in the presence or absence of reducing agents (mercaptoethanol, DTT, DTE, etc)


Contents

Library preparation Edit

The general steps to prepare a complementary DNA (cDNA) library for sequencing are described below, but often vary between platforms. [8] [3] [9]

  1. RNA Isolation:RNA is isolated from tissue and mixed with deoxyribonuclease (DNase). DNase reduces the amount of genomic DNA. The amount of RNA degradation is checked with gel and capillary electrophoresis and is used to assign an RNA integrity number to the sample. This RNA quality and the total amount of starting RNA are taken into consideration during the subsequent library preparation, sequencing, and analysis steps.
  1. RNA selection/depletion: To analyze signals of interest, the isolated RNA can either be kept as is, filtered for RNA with 3' polyadenylated (poly(A)) tails to include only mRNA, depleted of ribosomal RNA (rRNA), and/or filtered for RNA that binds specific sequences (RNA selection and depletion methods table, below). The RNA with 3' poly(A) tails are mainly composed of mature, processed, coding sequences. Poly(A) selection is performed by mixing RNA with poly(T) oligomers covalently attached to a substrate, typically magnetic beads. [10][11] Poly(A) selection has important limitations in RNA biotype detection. Many RNA biotypes are not polyadenylated, including many noncoding RNA and histone-core protein transcripts, or are regulated via their poly(A) tail length (e.g., cytokines) and thus might not be detected after poly(A) selection. [12] Furthermore, poly(A) selection may increased 3' bias, especially with lower quality RNA. [13][14] These limitations can be avoided with ribosomal depletion, removing rRNA that typically represents over 90% of the RNA in a cell. Both poly(A) enrichment and ribosomal depletion steps are labor intensive and could introduce biases, so more simple approaches have been developed to omit these steps. [15] Small RNA targets, such as miRNA, can be further isolated through size selection with exclusion gels, magnetic beads, or commercial kits.
  1. cDNA synthesis: RNA is reverse transcribed to cDNA because DNA is more stable and to allow for amplification (which uses DNA polymerases) and leverage more mature DNA sequencing technology. Amplification subsequent to reverse transcription results in loss of strandedness, which can be avoided with chemical labeling or single molecule sequencing. Fragmentation and size selection are performed to purify sequences that are the appropriate length for the sequencing machine. The RNA, cDNA, or both are fragmented with enzymes, sonication, or nebulizers. Fragmentation of the RNA reduces 5' bias of randomly primed-reverse transcription and the influence of primer binding sites, [11] with the downside that the 5' and 3' ends are converted to DNA less efficiently. Fragmentation is followed by size selection, where either small sequences are removed or a tight range of sequence lengths are selected. Because small RNAs like miRNAs are lost, these are analyzed independently. The cDNA for each experiment can be indexed with a hexamer or octamer barcode, so that these experiments can be pooled into a single lane for multiplexed sequencing.

Complementary DNA sequencing (cDNA-Seq) Edit

The cDNA library derived from RNA biotypes is then sequenced into a computer-readable format. There are many high-throughput sequencing technologies for cDNA sequencing including platforms developed by Illumina, Thermo Fisher, BGI/MGI, PacBio, and Oxford Nanopore Technologies. [16] For Illumina short-read sequencing, a common technology for cDNA sequencing, adapters are ligated to the cDNA, DNA is attached to a flow cell, clusters are generated through cycles of bridge amplification and denaturing, and sequence-by-synthesis is performed in cycles of complementary strand synthesis and laser excitation of bases with reversible terminators. Sequencing platform choice and parameters are guided by experimental design and cost. Common experimental design considerations include deciding on the sequencing length, sequencing depth, use of single versus paired-end sequencing, number of replicates, multiplexing, randomization, and spike-ins. [17]

Small RNA/non-coding RNA sequencing Edit

When sequencing RNA other than mRNA, the library preparation is modified. The cellular RNA is selected based on the desired size range. For small RNA targets, such as miRNA, the RNA is isolated through size selection. This can be performed with a size exclusion gel, through size selection magnetic beads, or with a commercially developed kit. Once isolated, linkers are added to the 3' and 5' end then purified. The final step is cDNA generation through reverse transcription.

Direct RNA sequencing Edit

Because converting RNA into cDNA, ligation, amplification, and other sample manipulations have been shown to introduce biases and artifacts that may interfere with both the proper characterization and quantification of transcripts, [18] single molecule direct RNA sequencing has been explored by companies including Helicos (bankrupt), Oxford Nanopore Technologies, [19] and others. This technology sequences RNA molecules directly in a massively-parallel manner.

Single-molecule real-time RNA sequencing Edit

Massively parallel single molecule direct RNA-Seq has been explored as an alternative to traditional RNA-Seq, in which RNA-to-cDNA conversion, ligation, amplifcation, and other sample manipulation steps may introduce biases and artifacts. [20] Technology platforms that perform single-molecule real-time RNA-Seq include Oxford Nanopore Technologies (ONT) Nanopore sequencing, [19] PacBio IsoSeq, and Helicos (bankrupt). Sequencing RNA in its native form preserves modifications like methylation, allowing them to be investigated directly and simultaneously. [19] Another benefit of single-molecule RNA-Seq is that transcripts can be covered in full length, allowing for higher confidence isoform detection and quantification compared to short-read sequencing. Traditionally, single-molecule RNA-Seq methods have higher error rates compared to short-read sequencing, but newer methods like ONT direct RNA-Seq limit errors by avoiding fragmentation and cDNA conversion. Recent uses of ONT direct RNA-Seq for differential expression in human cell populations have demonstrated that this technology can overcome many limitations of short and long cDNA sequencing. [21]

Single-cell RNA sequencing (scRNA-Seq) Edit

Standard methods such as microarrays and standard bulk RNA-Seq analysis analyze the expression of RNAs from large populations of cells. In mixed cell populations, these measurements may obscure critical differences between individual cells within these populations. [22] [23]

Single-cell RNA sequencing (scRNA-Seq) provides the expression profiles of individual cells. Although it is not possible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, patterns of gene expression can be identified through gene clustering analyses. This can uncover the existence of rare cell types within a cell population that may never have been seen before. For example, rare specialized cells in the lung called pulmonary ionocytes that express the Cystic Fibrosis Transmembrane Conductance Regulator were identified in 2018 by two groups performing scRNA-Seq on lung airway epithelia. [24] [25]

Experimental procedures Edit

Current scRNA-Seq protocols involve the following steps: isolation of single cell and RNA, reverse transcription (RT), amplification, library generation and sequencing. Single cells are either mechanically separated into microwells (e.g., BD Rhapsody, Takara ICELL8, Vycap Puncher Platform, or CellMicrosystems CellRaft) or encapsulated in droplets (e.g., 10x Genomics Chromium, Illumina Bio-Rad ddSEQ, 1CellBio InDrop, Dolomite Bio Nadia). [26] Single cells are labeled by adding beads with barcoded oligonucleotides both cells and beads are supplied in limited amounts such that co-occupancy with multiple cells and beads is a very rare event. Once reverse transcription is complete, the cDNAs from many cells can be mixed together for sequencing transcripts from a particular cell are identified by each cell's unique barcode. [27] [28] Unique molecular identifier (UMIs) can be attached to mRNA/cDNA target sequences to help identify artifacts during library preparation. [29]

Challenges for scRNA-Seq include preserving the initial relative abundance of mRNA in a cell and identifying rare transcripts. [30] The reverse transcription step is critical as the efficiency of the RT reaction determines how much of the cell's RNA population will be eventually analyzed by the sequencer. The processivity of reverse transcriptases and the priming strategies used may affect full-length cDNA production and the generation of libraries biased toward the 3’ or 5' end of genes.

In the amplification step, either PCR or in vitro transcription (IVT) is currently used to amplify cDNA. One of the advantages of PCR-based methods is the ability to generate full-length cDNA. However, different PCR efficiency on particular sequences (for instance, GC content and snapback structure) may also be exponentially amplified, producing libraries with uneven coverage. On the other hand, while libraries generated by IVT can avoid PCR-induced sequence bias, specific sequences may be transcribed inefficiently, thus causing sequence drop-out or generating incomplete sequences. [31] [22] Several scRNA-Seq protocols have been published: Tang et al., [32] STRT, [33] SMART-seq, [34] CEL-seq, [35] RAGE-seq, [36] Quartz-seq [37] and C1-CAGE. [38] These protocols differ in terms of strategies for reverse transcription, cDNA synthesis and amplification, and the possibility to accommodate sequence-specific barcodes (i.e. UMIs) or the ability to process pooled samples. [39]

In 2017, two approaches were introduced to simultaneously measure single-cell mRNA and protein expression through oligonucleotide-labeled antibodies known as REAP-seq, [40] and CITE-seq. [41]

Applications Edit

scRNA-Seq is becoming widely used across biological disciplines including Development, Neurology, [42] Oncology, [43] [44] [45] Autoimmune disease, [46] and Infectious disease. [47]

scRNA-Seq has provided considerable insight into the development of embryos and organisms, including the worm Caenorhabditis elegans, [48] and the regenerative planarian Schmidtea mediterranea. [49] [50] The first vertebrate animals to be mapped in this way were Zebrafish [51] [52] and Xenopus laevis. [53] In each case multiple stages of the embryo were studied, allowing the entire process of development to be mapped on a cell-by-cell basis. [8] Science recognized these advances as the 2018 Breakthrough of the Year. [54]

Experimental considerations Edit

A variety of parameters are considered when designing and conducting RNA-Seq experiments:

  • Tissue specificity: Gene expression varies within and between tissues, and RNA-Seq measures this mix of cell types. This may make it difficult to isolate the biological mechanism of interest. Single cell sequencing can be used to study each cell individually, mitigating this issue.
  • Time dependence: Gene expression changes over time, and RNA-Seq only takes a snapshot. Time course experiments can be performed to observe changes in the transcriptome.
  • Coverage (also known as depth): RNA harbors the same mutations observed in DNA, and detection requires deeper coverage. With high enough coverage, RNA-Seq can be used to estimate the expression of each allele. This may provide insight into phenomena such as imprinting or cis-regulatory effects. The depth of sequencing required for specific applications can be extrapolated from a pilot experiment. [55]
  • Data generation artifacts (also known as technical variance): The reagents (e.g., library preparation kit), personnel involved, and type of sequencer (e.g., Illumina, Pacific Biosciences) can result in technical artifacts that might be mis-interpreted as meaningful results. As with any scientific experiment, it is prudent to conduct RNA-Seq in a well controlled setting. If this is not possible or the study is a meta-analysis, another solution is to detect technical artifacts by inferring latent variables (typically principal component analysis or factor analysis) and subsequently correcting for these variables. [56]
  • Data management: A single RNA-Seq experiment in humans is usually 1-5 Gb (compressed), or more when including intermediate files. [57] This large volume of data can pose storage issues. One solution is compressing the data using multi-purpose computational schemas (e.g., gzip) or genomics-specific schemas. The latter can be based on reference sequences or de novo. Another solution is to perform microarray experiments, which may be sufficient for hypothesis-driven work or replication studies (as opposed to exploratory research).

Transcriptome assembly Edit

Two methods are used to assign raw sequence reads to genomic features (i.e., assemble the transcriptome):

  • De novo: This approach does not require a reference genome to reconstruct the transcriptome, and is typically used if the genome is unknown, incomplete, or substantially altered compared to the reference. [58] Challenges when using short reads for de novo assembly include 1) determining which reads should be joined together into contiguous sequences (contigs), 2) robustness to sequencing errors and other artifacts, and 3) computational efficiency. The primary algorithm used for de novo assembly transitioned from overlap graphs, which identify all pair-wise overlaps between reads, to de Bruijn graphs, which break reads into sequences of length k and collapse all k-mers into a hash table. [59] Overlap graphs were used with Sanger sequencing, but do not scale well to the millions of reads generated with RNA-Seq. Examples of assemblers that use de Bruijn graphs are Trinity, [58] Oases [60] (derived from the genome assembler Velvet[61] ), Bridger, [62] and rnaSPAdes. [63] Paired-end and long-read sequencing of the same sample can mitigate the deficits in short read sequencing by serving as a template or skeleton. Metrics to assess the quality of a de novo assembly include median contig length, number of contigs and N50. [64]
  • Genome guided: This approach relies on the same methods used for DNA alignment, with the additional complexity of aligning reads that cover non-continuous portions of the reference genome. [65] These non-continuous reads are the result of sequencing spliced transcripts (see figure). Typically, alignment algorithms have two steps: 1) align short portions of the read (i.e., seed the genome), and 2) use dynamic programming to find an optimal alignment, sometimes in combination with known annotations. Software tools that use genome-guided alignment include Bowtie, [66] TopHat (which builds on BowTie results to align splice junctions), [67][68] Subread, [69] STAR, [65] HISAT2, [70] and GMAP. [71] The output of genome guided alignment (mapping) tools can be further utilized by tools such as Cufflinks [68] or StringTie [72] to reconstruct contiguous transcript sequences (i.e., a FASTA file).The quality of a genome guided assembly can be measured with both 1) de novo assembly metrics (e.g., N50) and 2) comparisons to known transcript, splice junction, genome, and protein sequences using precision, recall, or their combination (e.g., F1 score). [64] In addition, in silico assessment could be performed using simulated reads. [73][74]

A note on assembly quality: The current consensus is that 1) assembly quality can vary depending on which metric is used, 2) assembly tools that scored well in one species do not necessarily perform well in the other species, and 3) combining different approaches might be the most reliable. [75] [76] [77]

Gene expression quantification Edit

Expression is quantified to study cellular changes in response to external stimuli, differences between healthy and diseased states, and other research questions. Transcript levels are often used as a proxy for protein abundance, but these are often not equivalent due to post transcriptional events such as RNA interference and nonsense-mediated decay. [78]

Expression is quantified by counting the number of reads that mapped to each locus in the transcriptome assembly step. Expression can be quantified for exons or genes using contigs or reference transcript annotations. [8] These observed RNA-Seq read counts have been robustly validated against older technologies, including expression microarrays and qPCR. [55] [79] Tools that quantify counts are HTSeq, [80] FeatureCounts, [81] Rcount, [82] maxcounts, [83] FIXSEQ, [84] and Cuffquant. These tools determine read counts from aligned RNA-Seq data, but alignment-free counts can also be obtained with Sailfish [85] and Kallisto. [86] The read counts are then converted into appropriate metrics for hypothesis testing, regressions, and other analyses. Parameters for this conversion are:

  • Sequencing depth/coverage: Although depth is pre-specified when conducting multiple RNA-Seq experiments, it will still vary widely between experiments. [87] Therefore, the total number of reads generated in a single experiment is typically normalized by converting counts to fragments, reads, or counts per million mapped reads (FPM, RPM, or CPM). The difference between RPM and FPM was historically derived during the evolution from single-end sequencing of fragments to paired-end sequencing. In single-end sequencing, there is only one read per fragment (i.e., RPM = FPM). In paired-end sequencing, there are two reads per fragment (i.e., RPM = 2 x FPM). Sequencing depth is sometimes referred to as library size, the number of intermediary cDNA molecules in the experiment.
  • Gene length: Longer genes will have more fragments/reads/counts than shorter genes if transcript expression is the same. This is adjusted by dividing the FPM by the length of a feature (which can be a gene, transcript, or exon), resulting in the metric fragments per kilobase of feature per million mapped reads (FPKM). [88] When looking at groups of features across samples, FPKM is converted to transcripts per million (TPM) by dividing each FPKM by the sum of FPKMs within a sample. [89][90][91]
  • Total sample RNA output: Because the same amount of RNA is extracted from each sample, samples with more total RNA will have less RNA per gene. These genes appear to have decreased expression, resulting in false positives in downstream analyses. [87] Normalization strategies including quantile, DESeq2, TMM and Median Ratio attempt to account for this difference by comparing a set of non-differentially expressed genes between samples and scaling accordingly. [92]
  • Variance for each gene's expression: is modeled to account for sampling error (important for genes with low read counts), increase power, and decrease false positives. Variance can be estimated as a normal, Poisson, or negative binomial distribution [93][94][95] and is frequently decomposed into technical and biological variance.

Spike-ins for absolute quantification and detection of genome-wide effects Edit

RNA spike-ins are samples of RNA at known concentrations that can be used as gold standards in experimental design and during downstream analyses for absolute quantification and detection of genome-wide effects.

  • Absolute quantification: Absolute quantification of gene expression is not possible with most RNA-Seq experiments, which quantify expression relative to all transcripts. It is possible by performing RNA-Seq with spike-ins, samples of RNA at known concentrations. After sequencing, read counts of spike-in sequences are used to determine the relationship between each gene's read counts and absolute quantities of biological fragments [11][96] In one example, this technique was used in Xenopus tropicalis embryos to determine transcription kinetics. [97]
  • Detection of genome-wide effects: Changes in global regulators including chromatin remodelers, transcription factors (e.g., MYC), acetyltransferase complexes, and nucleosome positioning are not congruent with normalization assumptions and spike-in controls can offer precise interpretation. [98][99]

Differential expression Edit

The simplest but often most powerful use of RNA-Seq is finding differences in gene expression between two or more conditions (e.g., treated vs not treated) this process is called differential expression. The outputs are frequently referred to as differentially expressed genes (DEGs) and these genes can either be up- or down-regulated (i.e., higher or lower in the condition of interest). There are many tools that perform differential expression. Most are run in R, Python, or the Unix command line. Commonly used tools include DESeq, [94] edgeR, [95] and voom+limma, [93] [100] all of which are available through R/Bioconductor. [101] [102] These are the common considerations when performing differential expression:

  • Inputs: Differential expression inputs include (1) an RNA-Seq expression matrix (M genes x N samples) and (2) a design matrix containing experimental conditions for N samples. The simplest design matrix contains one column, corresponding to labels for the condition being tested. Other covariates (also referred to as factors, features, labels, or parameters) can include batch effects, known artifacts, and any metadata that might confound or mediate gene expression. In addition to known covariates, unknown covariates can also be estimated through unsupervised machine learning approaches including principal component, surrogate variable, [103] and PEER [56] analyses. Hidden variable analyses are often employed for human tissue RNA-Seq data, which typically have additional artifacts not captured in the metadata (e.g., ischemic time, sourcing from multiple institutions, underlying clinical traits, collecting data across many years with many personnel).
  • Methods: Most tools use regression or non-parametric statistics to identify differentially expressed genes, and are either based on read counts mapped to a reference genome (DESeq2, limma, edgeR) or based on read counts derived from alignment-free quantification (sleuth, [104] Cuffdiff, [105] Ballgown [106] ). [107] Following regression, most tools employ either familywise error rate (FWER) or false discovery rate (FDR) p-value adjustments to account for multiple hypotheses (in human studies,

20,000 protein-coding genes or

Downstream analyses for a list of differentially expressed genes come in two flavors, validating observations and making biological inferences. Owing to the pitfalls of differential expression and RNA-Seq, important observations are replicated with (1) an orthogonal method in the same samples (like real-time PCR) or (2) another, sometimes pre-registered, experiment in a new cohort. The latter helps ensure generalizability and can typically be followed up with a meta-analysis of all the pooled cohorts. The most common method for obtaining higher-level biological understanding of the results is gene set enrichment analysis, although sometimes candidate gene approaches are employed. Gene set enrichment determines if the overlap between two gene sets is statistically significant, in this case the overlap between differentially expressed genes and gene sets from known pathways/databases (e.g., Gene Ontology, KEGG, Human Phenotype Ontology) or from complementary analyses in the same data (like co-expression networks). Common tools for gene set enrichment include web interfaces (e.g., ENRICHR, g:profiler, WEBGESTALT) [114] and software packages. When evaluating enrichment results, one heuristic is to first look for enrichment of known biology as a sanity check and then expand the scope to look for novel biology.

Alternative splicing Edit

RNA splicing is integral to eukaryotes and contributes significantly to protein regulation and diversity, occurring in >90% of human genes. [115] There are multiple alternative splicing modes: exon skipping (most common splicing mode in humans and higher eukaryotes), mutually exclusive exons, alternative donor or acceptor sites, intron retention (most common splicing mode in plants, fungi, and protozoa), alternative transcription start site (promoter), and alternative polyadenylation. [115] One goal of RNA-Seq is to identify alternative splicing events and test if they differ between conditions. Long-read sequencing captures the full transcript and thus minimizes many of issues in estimating isoform abundance, like ambiguous read mapping. For short-read RNA-Seq, there are multiple methods to detect alternative splicing that can be classified into three main groups: [116] [89] [117]

  • Count-based (also event-based, differential splicing): estimate exon retention. Examples are DEXSeq, [118] MATS, [119] and SeqGSEA. [120]
  • Isoform-based (also multi-read modules, differential isoform expression): estimate isoform abundance first, and then relative abundance between conditions. Examples are Cufflinks 2 [121] and DiffSplice. [122]
  • Intron excision based: calculate alternative splicing using split reads. Examples are MAJIQ [123] and Leafcutter. [117]

Differential gene expression tools can also be used for differential isoform expression if isoforms are quantified ahead of time with other tools like RSEM. [124]

Coexpression networks Edit

Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. [125] Their main purpose lies in hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. [125] RNA-Seq data has been used to infer genes involved in specific pathways based on Pearson correlation, both in plants [126] and mammals. [127] The main advantage of RNA-Seq data in this kind of analysis over the microarray platforms is the capability to cover the entire transcriptome, therefore allowing the possibility to unravel more complete representations of the gene regulatory networks. Differential regulation of the splice isoforms of the same gene can be detected and used to predict their biological functions. [128] [129] Weighted gene co-expression network analysis has been successfully used to identify co-expression modules and intramodular hub genes based on RNA seq data. Co-expression modules may correspond to cell types or pathways. Highly connected intramodular hubs can be interpreted as representatives of their respective module. An eigengene is a weighted sum of expression of all genes in a module. Eigengenes are useful biomarkers (features) for diagnosis and prognosis. [130] Variance-Stabilizing Transformation approaches for estimating correlation coefficients based on RNA seq data have been proposed. [126]

Variant discovery Edit

RNA-Seq captures DNA variation, including single nucleotide variants, small insertions/deletions. and structural variation. Variant calling in RNA-Seq is similar to DNA variant calling and often employs the same tools (including SAMtools mpileup [131] and GATK HaplotypeCaller [132] ) with adjustments to account for splicing. One unique dimension for RNA variants is allele-specific expression (ASE): the variants from only one haplotype might be preferentially expressed due to regulatory effects including imprinting and expression quantitative trait loci, and noncoding rare variants. [133] [134] Limitations of RNA variant identification include that it only reflects expressed regions (in humans, <5% of the genome), could be subject to biases introduced by data processing (e.g., de novo transcriptome assemblies underestimate heterozygosity [135] ), and has lower quality when compared to direct DNA sequencing.

RNA editing (post-transcriptional alterations) Edit

Having the matching genomic and transcriptomic sequences of an individual can help detect post-transcriptional edits (RNA editing). [3] A post-transcriptional modification event is identified if the gene's transcript has an allele/variant not observed in the genomic data.

Fusion gene detection Edit

Caused by different structural modifications in the genome, fusion genes have gained attention because of their relationship with cancer. [136] The ability of RNA-Seq to analyze a sample's whole transcriptome in an unbiased fashion makes it an attractive tool to find these kinds of common events in cancer. [4]

The idea follows from the process of aligning the short transcriptomic reads to a reference genome. Most of the short reads will fall within one complete exon, and a smaller but still large set would be expected to map to known exon-exon junctions. The remaining unmapped short reads would then be further analyzed to determine whether they match an exon-exon junction where the exons come from different genes. This would be evidence of a possible fusion event, however, because of the length of the reads, this could prove to be very noisy. An alternative approach is to use paired-end reads, when a potentially large number of paired reads would map each end to a different exon, giving better coverage of these events (see figure). Nonetheless, the end result consists of multiple and potentially novel combinations of genes providing an ideal starting point for further validation.

RNA-Seq was first developed in mid 2000s with the advent of next-generation sequencing technology. [139] The first manuscripts that used RNA-Seq even without using the term includes those of prostate cancer cell lines [140] (dated 2006), Medicago truncatula [141] (2006), maize [142] (2007), and Arabidopsis thaliana [143] (2007), while the term "RNA-Seq" itself was first mentioned in 2008 [144] The number of manuscripts referring to RNA-Seq in the title or abstract (Figure, blue line) is continuously increasing with 6754 manuscripts published in 2018. The intersection of RNA-Seq and medicine (Figure, gold line) has similar celerity. [145]

Applications to medicine Edit

RNA-Seq has the potential to identify new disease biology, profile biomarkers for clinical indications, infer druggable pathways, and make genetic diagnoses. These results could be further personalized for subgroups or even individual patients, potentially highlighting more effective prevention, diagnostics, and therapy. The feasibility of this approach is in part dictated by costs in money and time a related limitation is the required team of specialists (bioinformaticians, physicians/clinicians, basic researchers, technicians) to fully interpret the huge amount of data generated by this analysis. [146]

Large-scale sequencing efforts Edit

A lot of emphasis has been given to RNA-Seq data after the Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA) projects have used this approach to characterize dozens of cell lines [147] and thousands of primary tumor samples, [148] respectively. ENCODE aimed to identify genome-wide regulatory regions in different cohort of cell lines and transcriptomic data are paramount in order to understand the downstream effect of those epigenetic and genetic regulatory layers. TCGA, instead, aimed to collect and analyze thousands of patient's samples from 30 different tumor types in order to understand the underlying mechanisms of malignant transformation and progression. In this context RNA-Seq data provide a unique snapshot of the transcriptomic status of the disease and look at an unbiased population of transcripts that allows the identification of novel transcripts, fusion transcripts and non-coding RNAs that could be undetected with different technologies.

This article was submitted to WikiJournal of Science for external academic peer review in 2019 (reviewer reports). The updated content was reintegrated into the Wikipedia page under a CC-BY-SA-3.0 license ( 2021 ). The version of record as reviewed is: Felix Richter et al. (17 May 2021). "A broad introduction to RNA-Seq". WikiJournal of Science. 4 (2): 4. doi:10.15347/WJS/2021.004. ISSN 2470-6345. Wikidata Q100146647.


1 An introduction to statistics

1.3 Why biologists have to repeat everything

1.4 Why biologists have to bother with statistics

1.5 Why statistical logic is so strange

1.6 Why there are so many statistical tests

1.7 Using the decision chart

2 Dealing with variability

2.2 Examining the distribution of data

2.3 The normal distribution

2.4 Describing the normal distribution

2.5 The variability of samples

2.7 Presenting descriptive statistics and confidence limits

2.8 Introducing computer programs

2.9 Calculating descriptive statistics

2.10 Self-assessment problems

3 Testing for normality and transforming data

3.1 The importance of normality testing

3.3 What to do if your data has a significantly different distribution from the normal

3.4 Examining data in practice

3.6 The complete testing procedure

3.7 Self-assessment problems

4 Testing for differences from an expected value or between two groups

4.2 Why we need statistical tests for differences

4.3 How we test for differences

4.4 One- and two-tailed tests

4.5 The types of t test and their non-parametric equivalents

4.9 Introduction to non-parametric tests for differences

4.10 The one-sample sign test

4.11 The Wilcoxon matched pairs test

4.12 The Mann–Whitney U test

4.13 Self-assessment problems

5 Testing for differences between more than two groups: ANOVA and its non-parametric equivalents

5.3 Deciding which groups are different – post hoc tests

5.4 Presenting the results of one-way ANOVAs

5.5 Repeated measures ANOVA

5.6 The Kruskal–Wallis test

5.9 The Scheirer–Ray–Hare Test

5.11 Self-assessment problems

6 Investigating relationships

6.2 Examining data for relationships

6.5 Statistical tests for linear relationships

6.8 Studying common non-linear relationships

6.9 Dealing with non-normally distributed data: rank correlation

6.10 Self-assessment problems

7 Dealing with categorical data

7.2 The problem of variation

7.3 The x2 test for differences

7.4 The x2 test for association

7.7 Self-assessment problems

8.3 Excluding confounding variables

8.4 Replication and pseudoreplication

8.5 Randomisation and blocking

8.6 Choosing the statistical test

8.7 Choosing the number of replicates: power calculations

8.8 Dealing with your results

8.9 Self-assessment problems

9 More complex statistical analysis

9.1 Introduction to complex statistics

9.2 Experiments investigating several factors

9.3 Experiments in which you cannot control all the variables

9.4 Investigating the relationships between several variables

9.5 Exploring data to investigate groupings

10 Presenting and writing about statistics

10.1 Introduction – less is more!

10.2 The introduction section

10.5 The discussion section

10.6 The abstract or summary

Table S1: Critical values for the t statistic

Table S2: Critical values for the correlation coefficient r

Table S3: Critical values for the x2 statistic

Table S4: Critical values for the Wilcoxon T distribution

Table S5: Critical values for the Mann–Whitney U distribution

Table S6: Critical values for the Friedman x2 distribution

Table S7: Critical values for the Spearman rank correlation coefficient r


Sample Preparation with Nanomaterials. Next Generation Techniques and Applications. Edition No. 1

Sample Preparation with Nanomaterials: Next Generation Techniques for Sample Preparation delivers insightful and complete overview of recent progress in the use of nanomaterials in sample preparation. The book begins with an overview of special features of nanomaterials and their applications in analytical sciences. Important types of nanomaterials, like carbon nanotubes and magnetic particles, are reviewed and biological sample preparation and lab-on-a-chip systems are presented.

The distinguished author places special emphasis on approaches that tend to green and reduce the cost of sample treatment processes. He also discusses the legal, economical, and toxicity aspects of nanomaterial samples. This book includes extensive reference material, like a complete list of manufacturers, that makes it invaluable for professionals in analytical chemistry.

Sample Preparation with Nanomaterials offers considerations of the economic aspects of nanomaterials, as well as the assessment of their toxicity and risk. Readers will also benefit from the inclusion of:

  • A thorough introduction to nanomaterials in the analytical sciences and special properties of nanomaterials for sample preparation
  • An exploration of the mechanism of adsorption and desorption on nanomaterials, including carbon nanomaterials used as adsorbents
  • Discussions of membrane applications of nanomaterials, surface enhanced raman spectroscopy, and the use of nanomaterials for biological sample preparation
  • A treatment of magnetic nanomaterials, lab-on-a-chip nanomaterials, and toxicity and risk assessment of nanomaterials

Perfect for analytical chemists, materials scientists, and process engineers, Sample Preparation with Nanomaterials: Next Generation Techniques for Sample Preparation will also earn a place in the libraries of analytical laboratories, universities, and companies who conduct research into nanomaterials and seek a one-stop resource for sample preparation.

1 Nanomaterials (NMs) in Analytical Sciences
1.1 Introduction
1.2 Types of NMs
1.3 Applications of NMs
1.4 Conclusions
References

2 Special Properties of Nanomaterials (NMs) for SamplePreparation
2.1 Introduction
2.2 Mechanical Properties of NMs
2.3 Thermal Properties of NMs
2.4 Electrical Properties of NMs
2.5 Optical Properties of NMs
2.6 Magnetic Properties of NMs
2.7 Adsorption Properties of NMs
2.8 Conclusions
References

3 Adsorption Mechanism on Nanomaterials (NMs)
3. 1Introduction
3.2 Adsorption Process
3.3 Conclusions and Future Perspective
References

4 Carbon Nanomaterials (CNMs) as Adsorbents for SamplePreparation
4.1 Introduction
4.2 Carbon Nanomaterials (CNMs)
4.3 Adsorption on CNMs
4.4 Applications of CNMs
4.5 Conclusions
References

5 Membrane Applications of Nanomaterials (NMs)
5.1 Introduction935.2Traditional Membranes
5.2 Traditional Membranes
5.3 Carbon Nanomaterial-based Membranes
5.4 Nanoparticle-based Membranes
5.5 Molecularly Imprinted Polymer (MIP)-based Membranes
5.6 Conclusions
References

6 Surface-Enhanced Raman Spectroscopy (SERS) withNanomaterials (NMs)
6.1 Introduction
6.2 Theory of SERS
6.3 SERS Mechanisms
6.4 Determination of SERS Enhancement Factor
6.5 Selection Rules
6.6 Fabrications of SERS Substrates
6.7 Applications of SERS
6.8 Conclusions
References

7 Nanomaterials (NMs) for Biological Sample Preparations
7.1 Introduction
7.2 The Use of NMs in Diagnostic Platforms
7.3 NMs-based Lab-on-a-chip (LOC) Platforms
7.4 Biomedical Applications of NMs
7.5 Sensor Applications of NMs
7.6 Conclusions

8 Magnetic Nanomaterials for Sample Preparation
8.1 Introduction
8.2 Synthesis of Magnetic Nanoparticles
8.3 Solid-Phase Extraction (SPE)
8.4 Magnetic Solid-Phase Extraction (MSPE)
8.5 Conclusions and Future Trends
References

9 Lab on Chip with Nanomaterials (NMs)
9.1 Introduction
9.2 Lab-on-a-Chip (LOC) Concept
9.3 NM-Based LOC Platforms
9.4 Conclusions and Future Perspectives
References

10 Toxicity and Risk Assessment of Nanomaterials
10.1 Introduction
10.2 Hazard Assessment of Nanomaterials
10.3 Toxicity Mechanism of Nanomaterials
10.4 The Traditional Risk Assessment Paradigm
10.5 Strategies for Improving Specific Risk Assessment
10.6 Conclusions
References

11 Economic Aspects of Nanomaterials (NMs) for SamplePreparation
11.1 Introduction
11.2 Toxicity Concerns of NMs
11.3 Global Market for NM-Based Products
11.4 Conclusions
References

12 Legal Aspects of Nanomaterials (NMs) for SamplePreparation
12.1 Introduction
12.2 Safety Issues of NMs
12.3 Regulatory Aspects of NMs
12.4 Conclusions
References

13 Monitoring of Nanomaterials (NMs) in the Environment
13.1 Introduction
13.2 Toxicity and Safety Concerns of NMs
13.3 Main Sources and Transport Routes of Nanopollutants
13.4 Requirements of Analytical Approaches
13.5 Sampling of NMs in Environmental Samples
13.6 Separation of NMs in Environmental Samples
13.7 Detection Techniques for the Characterization of NMs
13.8 Conclusions
References

14 Future Prospect of Sampling
14.1 Introduction
14.2 Sampling
14.3 Sample Preparation
14.4 Green Chemistry
14.5 Miniaturization of Analytical Systems
14.6 Conclusions
References


4. Discussion

Given the urgent need to control the COVID-19 pandemic, vaccine development is being accelerated into the clinical trials phase [4] , [7] , [8] , even though understanding of the immunologic features of the antigens of SARS-CoV-2 remains poor. In this phase I trial, a study was performed to investigate the safety and immunogenicity of this inactivated vaccine in 191 subjects. The data collected show several notable features. First, the clinical safety observations among the 191 subjects suggest that there were no severe adverse reactions related to vaccination, and the most frequently reported events were mild, including redness, itching and swelling at the inoculation site and a few cases of slight fatigue there were no significant differences between the vaccine and control groups. These data support the clinical safety of this vaccine. However, based on the current concern about the possibility of immunopathology due to SARS-Cov-2 infection [16] , we extended our safety observations to the investigation of variations in immune cell populations and cytokine levels in the peripheral blood. The test results suggested that there were no abnormalities in most of the 48 detected cytokines and the proportions of immune cells. Second, not only did serological detection show the presence of neutralizing antibodies, antibodies against the S protein, the N protein and the complete virion antigens were also found in ELISA assays to have been elicited in the vaccinated population, and there were dynamic alterations in the levels of these antibodies based on the dose and the vaccination schedule. However, the medium and high doses in both the 0, 14 and 0, 28 schedule groups led to 100% seroconversion of ELISA anti-S antibody after two inoculations, and interestingly, the medium dose group assigned to the 0, 14 schedule reached 100% seroconversion of the neutralizing antibody with the highest GMT value. However, the high-dose group exhibited lower seroconversion and GMT values. According to our understanding, this result might be due to the medium dose providing suitable signal stimulation to the immune system or the limited sample size. Therefore, further investigations in phase II and III trials are necessary. Additionally, the neutralizing antibody can neutralize different pandemic strains with diverse mutations. However, the GMT of neutralizing antibodies measured in this trial is obviously lower than the GMTs observed in the trials of mRNA vaccines developed by Moderna and Pfizer [17] , [18] this difference suggests that characteristic immune responses are elicited by mRNA vaccines and vaccines against the inactivated virus and that these vaccines should be evaluated based upon their clinical protective efficacy [19] . At the time of the antibody response, a CTL response with IFN-gamma specificity against the S, N and virion antigens was detected in immunized individuals in comparison with individuals receiving the placebo this suggests that any one of these three antigens enables the specific activation of T cells even if the immune response does not show dose-dependent effects. These immunological indexes indicate that a systemic immune response was elicited by our vaccine in the human population. To examine the genetic diversity of the specific immunity elicited by the vaccine, we examined the mRNA gene profile of the PBMCs from vaccinated individuals and found that most of the expressed mRNA genes were related to various signaling pathways of the innate and adaptive immune systems, and the immune functions were upregulated in comparison with the placebo group. Here, activation of the multiple signaling pathways involved in the immune response resulted in variations in hundreds of genes related to activation of innate immunity at day 7 after booster immunization regardless of the immunization schedule however, the cytokines that were found to have elevated levels in COVID-19 patients had only mild variations and were at levels similar to those in the placebo control group, which corresponded with those detected in the serum. The activation of genes related to T cells, B cells, DCs and mononuclear cells/macrophages with varying dynamics is evidence of the immune resonse elicited by the vaccine. All the data obtained in this trial support the safety and immunogenicity of this inactivated vaccine and are encouraging with regard to further studies of its efficacy in the future.

A limitation of this study is the lack of analysis of the protective efficacy of the vaccine and the lack of a comparative transcriptional analysis of PBMCs from vaccinated individuals and COVID-19 patients the latter comparison was not possible because few blood samples have been obtained from COVID-19 patients in mainland China at this time.


The Exams

The external assessment

The IB uses a criterion based approach to assessment. This means that students work is judged against identified levels of attainment and not against the work of other students. At the grade award meeting which takes place once all the papers have been marked levels of attainment, in the form of grade boundaries are agreed. There is consideration of the difficulty of the exams and the performance of students and a committee of people are involved.

Several methods of assessment are used: Assessment criteria when the task in open ended, Markbands with a range of level descriptors used to judge students answers to some extended response questions, Analytic markschemes where a particular kind of response is required, and Marking notes are used to clarify assessment criteria.

Examiners are monitored throughout the marking process and ever effort is made to ensure consistency in marking.

Standard Level

Multiple choice questions are quite challenging and students need practice with the style.

Each question has a "distractor" or answer which is almost correct. Usually one or two answers are quite obviously wrong.

The data analysis questions at the beginning of this paper require good skills in the interpretation of graphs but also an ability to cope with unfamiliar material.

The short answer questions are the least complicated part of the external assessment. If a student knows the biology these questions resemble the work a student often does using text books and worksheets in lessons. The challenge is to be prepared to answer questions on any part of the IB Biology guide.

There is a choice of two extended response questions, students must answer all three sections of one question. Care is required in reading the questions, in particular the command term so that students give the right sort of answers. Often there is one part which requires a diagram.

Historically, many students find this exam easier than paper 2 because there is less syllabus coverage and students can prepare more thoroughly for the option material. There are two or three questions in section A which will test knowledge of practical experiment skills and analysis of results from any part of the core topics and the prescribed experiments. Section B resembles the short answer section of paper 2 except that it covers only material from the chosen option. Students should be trained in choosing the correct option and only answering questions from one option.

Internal Assessment

This investigation is assessed by the teacher in school and a sample of work is moderated by the IB and adjustments made to marks so that all centres are awarding marks in a similar standard. For further details see The Investigation pages.

Higher Level

Although the length of all the exams for HL is longer than the SL exams, the structure of all examinations and the investigation is the same as for SL.

The only exception is in Paper 2 where students will have a choice of two out of three extended response questions. The weighting of paper two is slightly less that the SL paper 2 to allow for assessment of the extra HL material in the option paper.


Riffle Sample Splitters with chutes

Riffle sample Splitters, also called Sample Dividers with Chutes, allow dividing samples into two representative subsamples with a good accuracy.

Riffle Sample Splitter is the most universally used sampling device for preparing representative splits of dry, free-flowing granular product.

The technique is rapid and the equipment is economical.

It is precisely designed to reduce the bulk of material to a convenient representative size for laboratory analysis. When used properly, it provides an accuracy that is recognized through out the industry

With Riffle Sample Splitters, a homogenous, dry, free-flowing sample is poured evenly into the hopper / funnel. The material flows through the alternately arranged passages in the opposite direction (chutes / riffle bank) into the two collecting pans under the dividing head outlets. With every operation the feed sample is divided in two representative subsamples. The operation can be repeated as many times as necessary, until the required dividing quantity has been obtained.


8.3: Sample preparation - Biology

There are two methods to transform E. coli cells with plasmid DNA - chemical transformation and electroporation. For chemical transformation, cells are grown to mid-log phase, harvested and treated with divalent cations such as CaCl2. Cells treated in such a way are said to be competent. To chemically transform cells, competent cells are mixed with the DNA , on ice, followed by a brief heat shock. Then, cells are incubated with rich medium and allowed to express the antibiotic resistant gene for 30-60 minutes prior to plating. For electroporation, cells are also grown to mid-log phase but are then washed extensively with water to eliminate all salts. Usually, glycerol is added to the water to a final concentration of 10% so that the cells can be stored frozen and saved for future experiments. To electroporate DNA into cells, washed E. coli are mixed with the DNA to be transformed and then pipetted into a plastic cuvette containing electrodes. A short electric pulse, about 2400 volts/cm, is applied to the cells causing smalls holes in the membrane through which the DNA enters. The cells are then incubated with broth as above before plating.

For chemical transformation, there is no need to pre-treat the DNA. For electroporation, the DNA must be free of all salts so the ligations are first precipitated with alcohol before they are used.

Experimental Design :

To determine the efficiency of transformation, a positive control transformation should be done using 1 ng of uncut plasmid DNA, e.g. pUC19. The efficiency of transformation is calculated as the number of transformants/&mug of input DNA. A negative control should also be included that contains cells with no added DNA.

A negative control with cells only (no added DNA) should also be included.

For most cloning applications, we use DH5&alpha host cells. These cells are compatible with lacZ blue/white selection procedures, are easily transformed, and good quality plasmid DNA can be recovered from transformants. One notable exception is when transforming with plasmid constructs containing recombinant genes under control of the T7 polymerase. These constructs are typically transformed into DH5&alpha for the cloning phase, but need to be transformed into a different bacterial strain, BL21(DE3) for expression of the recombinant protein (BL21 strains carry the gene for expression of the T7 polymerase).

Electroporation of E. coli:

  • Sterile centrifuge bottles &ndash 250 ml for GSA rotor
  • SOB medium
  • E. coli host strain such as DH5&alpha
  • WB (10% redistilled glycerol, 90% distilled water, v/v) chilled to 4°C&ndash need 500 ml of WB for each 250 ml of culture
  • tRNA (5-10 µg/ml &ndash used as a mass carrier to increase the efficiency of precipitation)
  • 5 M ammonium acetate
  • 100% ethanol
  • 70% ethanol
  • 0.5X TE or EB (10 mM Tris, pH 8.3)
  • SOC medium
  • transformation plates

I. Preparation of E. coli cells for electroporation.

1. Use a fresh colony of DH5&alpha (or other appropriate host strain) to inoculate 5 ml of SOB (without magnesium) medium in a 50 ml sterile conical tube. Grow cells with vigorous aeration overnight at 37°C.

2. Dilute 2.5 ml of cells into 250 ml of SOB (without magnesium) in a 1 liter flask. Grow for 2 to 3 hours with vigorous aeration at 37°C until the cells reach an OD550 = 0.8.

3. Harvest cells by centrifugation at 5000 RPM in a GSA rotor for 10 min in sterile centrifuge bottles. (Make sure you use autoclaved bottles!).

4. Wash the cell pellet in 250 ml of ice-cold WB as follows. First, add a small amount of WB to cell pellet pipet up and down or gently vortex until cells are resuspended. Then fill centrifuge bottle with ice cold WB and gently mix. NOTE- the absolute volume of WB added at this point is not important.

5. Centrifuge the cell suspension at 5,000 RPM for 15 min and carefully pour off the supernatant as soon as the rotor stops. Cells washed in WB do not pellet well. If the supernatant is turbid, increase the centrifugation time.

6. Wash the cell pellet a second time by resuspending in 250 ml of sterile ice-cold WB using the same technique described above. Centrifuge the cell suspension at 5000 RPM for 15 min.

7. Gently pour off the supernatant leaving a small amount of WB in the bottom of the bottle. Resuspend the cell pellet in the WB - no additional WB needs to be added &ndash and the final volume should be about 1 ml. Cells can be used immediately or can be frozen in 0.2 ml aliquots in freezer vials using a dry ice-ethanol bath. Store frozen cells at -70°C.

II. Preparing DNA for Electroporation

DNA for electroporation must have a very low ionic strength and a high resistance. The DNA may be purified by either dilution, precipitation or dialysis.

  • For transformation of purified plasmid DNA, dilute DNA in 10 mM Tris pH 8-8.3 to about 1-50 ng/µl (do not use TE). Use 1 µl for transformation.
  • For ligation reactions, use the following procedure.

Purifying DNA by Precipitation:

1. Add 5 to 10 &mug of tRNA to a 20 &mul ligation reaction in a 1.5 ml tube. Add 22 &mul 5M ammonium acetate (or an equal volume of ligation reaction with added tRNA). Mix well.

2. Add 100 &mul absolute ethanol (or 2.5 volumes of ligation reaction, tRNA and salt). Ice 15 min.

3. Centrifuge at >12,000 x g for 15 min at 4°C. Carefully decant the supernatant.

4. Wash the pellet with 1 ml of 70% ethanol. Centrifuge at >12,000 x g for 15 min at room temperature. Remove the supernate.

5. Air dry the pellet (speed vac okay but don't overdry).

6. Resuspend the DNA in EB buffer (10 mM Tris-HCl, pH 8.3) or 0.5X TE buffer [5 mM Tris-HCl, 0.5 mM EDTA (pH 7.5)] to a concentration of 10 ng/ul of DNA. For ligation reactions, it is convenient to resuspend in 10 µl. Use 1 &mul per transformation of 20 &mul of cell suspension.

III. Electroporation.

1. Mark the required number of micro centrifuge tubes. Place the required number of Micro-electroporation Chambers on ice. Fill the temperature control compartment of the Chamber Safe with

250 ml of ice-water slurry and place the Chamber Rack in the Chamber Safe.

2. Thaw an aliquot of cells that have prepared as in Section I and aliquot 20 µ l of cells to the required number of microfuge tubes on ice. Add 1 µ l of the DNA (or ligation reaction) prepared as in Section II.

3. Using a micro pipette, pipette 20 µ l of the cell-DNA mixture between the bosses in a Micro-Electroporation Chamber. Do not leave an air bubble in the droplet of cells the pressure of a bubble may cause arcing and loss of the sample. Place the chamber in a slot in the Chamber Rack and note its position. Repeat the process if more than one sample is to be pulsed. Up to 4 samples can be placed in the Chamber Rack at one time. Handle the chambers gently to avoid accidentally displacing the sample from between the bosses.

4. Close the lid of the Chamber safe and secure it with the draw latch.

5. Plug the pulse cable into the right side of the Chamber safe.

6. Turn the chamber selection knob on top of the Chamber Safe to direct the electrical pulse to the desired Micro-Electroporation Chamber.

7. Set the resistance on the Voltage Booster to 4 k&Omega set the Pulse Control unit to LOW and 330 µ F double check connections.

8. Charge the Pulse Control unit by setting the CHARGE ARM switch on the Pulse Control unit to CHARGE and then pressing the UP voltage control button until the voltage reading is 5 to 10 volts higher than the desired discharge voltage. For E. coli, the standard conditions are 2.4 kv, which means setting the Pulse Control unit to 405 volts (400 volts is the desired discharge voltage + 5). The voltage booster amplifies the volts by

6-fold such that the total discharge voltage is 2400 volts, or 2.4 kv. The actual peak voltage delivered to the sample will be shown on the Voltage Booster meter after the pulse is delivered.

9. Set the CHARGE/ARM switch to the ARM position. The green light indicates that the unit is ready to deliver a DC pulse. Depress the pulse discharge TRIGGER button and hold for 1 second.

NOTE: The DC voltage display on the Pulse Control unit should read <10 volts after a pulse has been delivered. If not, discharge the capacitor using the DOWN button.

10. For additional samples, turn the chamber selection knob to the next desired position and repeat steps 8 and 9 until all samples are pulsed.

11. For ampicillin selection, inoculate the samples into 2 ml of SOC medium and shake for 30 minutes (for amp), 60 minutes (for Kan) to allow expression of the antibiotic gene. Plate cells on LB medium with appropriate antibiotic or screening reagent (e.g. 100 µg/ml ampicillin, and/or 40 &mul of 20 mg/ml X-Gal, XP, and 40 &mul of 100 mM IPTG) .

This Web page is maintained by Julie B. Wolf, UMBC
Last updated on 3/2/2010

is designed for students interested in careers in industrial and biomedical sciences.


Watch the video: Μικροσκοπική παρατήρηση Φυτικών-Ζωικών κυττάρων (October 2022).