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Finding a easy and cheap method for dyeing dNTP

Finding a easy and cheap method for dyeing dNTP


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I want to measure OD to know the concentration of dNTP. Any idea for dyeing dNTP at cheapest price and easiest way ?


There may be such a dye, but I'm not aware of it. The standard way to measure dNTPs, and nucleic acids in general is by absorbance at 260 nm. This article has a table of molar extinction coefficients for dNTPs (Table 10.2). They are

wavelength molar extinction coefficient dATP 259 nm 15,200 dCTP 280 nm 13,100 dGTP 253 nm 13,700 dTTP 267 nm 9,600

In practice for routine work with DNA you can use 260 nm and assume that an absorbance of 1 corresponds to [DNA] = 50 µg ml-1. Using an average of the values in the Table, and disregarding the fact that these are actually at different wavelengths, and using an approximate average dNTP MW of 500, I calculate an absorbance for a 50 µg ml-1 solution of all 4 dNTPs as 1.3, so that's a reasonable agreement given all of the assumptions I've made.

There are fluorescent dyes that can be used to measure DNA, but these rely upon the presence of stacked bases, so are not useful for free dNTPs.

So, bad news if you don't have a UV spectrophotometer. There are plans available for construction of simple spectrophotometers using Lego and a few optoelectronic components. These tend to use LEDs as a light source, but I don't know if UV LEDs are available.


It will be difficult to achieve the level of wavelength resolution required to differentiate dNTPs, considering that you are doing a DIY experiment. Only very precise spectrophotometers can achieve it and they are expensive.

Another technique you can try is thin layer chromatography(TLC). It is cheap and you can easily do a DIY. But you need standards in order to differentiate NTP, NDP and NMP; the latter two might arise because of degradation during the experiment. People generally use a cocktail of anti-oxidants and protectants to minimize the degradation of nucleotides.

A strategy to go about this is to remove the phosphate using phosphatase and then do a TLC with Nucleosides (which are quite stable).

You can check this review on nucleic acid TLC.


Practical Introduction to Electrophoresis for Biology

The lab where I work is interested in the mechanics of basic biological processes, using yeast as a model organism.

A good friend, a physicist and technology marketing executive by training and profession, will hopefully be joining me in the lab. These are my informal notes to him to get him up to speed in a practical way for our lab, focusing on golden-oldies classical methods to start with. My hope is that he, and others, interested in making such a career transition into a bio lab will find this practical introduction useful as well.


Abstract

In this work we applied a flash photolysis technique to the study of the kinetic behavior of a synthetic flavylium salt in aqueous solution. The kinetic processes triggered by the light pulse of a common camera flash, are followed by means of a sligthly modified, computer controlled, spectrophotometer. Light induces reversible absorbance changes and two different transient species are evidenced prior the full recovery of the system. The kinetic behavior and the spectra of the transient species are reported. The proposed experiment is cheap and easy and allows students of all levels to become familiar with the concepts of transient decay kinetics and time resolved spectra.


Rethinking the Dyeing Process with Synthetic Biology

20% of the earth’s water pollution is caused by textile processing and 1,800 gallons of water are required to make a single pair of blue jeans. That is a staggering amount of fresh water required to make a single piece of clothing – but the industry is about to change.

UK-based Colorifix wants to reduce water waste throughout the dyeing process by leveraging the biological capabilities of microbes.

“As part of a University of Cambridge-led arsenic biosensor project, our team went to South Asia with our prototype biosensor, explained to people how it worked, and then asked people for a list of chemicals that they thought were worth monitoring,” says Dr. Orr Yarkoni, co-founder and CEO of Colorifix.

“When we came back [to the UK] and did our homework, we discovered that most of these concerning chemicals were from the textile industry, and dyeing was the major culprit for both water use and chemical use,” says Yarkoni, who then decided to use synthetic biology to produce the dyes and reduce contaminants, rather than simply monitor them.

After a few years of research with Dr. James Ajioka and Dr. David Nugent, Colorifix was spun out in 2016. Scientific progress was swift, and the team quickly looked at ways to reduce the environmental damage caused by the textile industry, beyond just the production of dyes.

Drs. Nugent (left) and Ajioka (right) in the Colorifix lab in Norwich, England. Image courtesy of Colorifix.

“Normally, [depositing a dye onto fabric] is done with chemicals or biologically-produced compounds, but without a biological agent. We are using the cells themselves to both produce and deposit the pigment into the fiber, so we are using biology for the entire process. Doing that is what allows us to save on water and energy and remove chemicals. We are using biology to transfer those pigments and dyes into the fiber,” explains Yarkoni.

Thus far, Colorifix has developed a suite of colors, each derived from natural pigments.

“Most of our pigments today are from insects, some microbiological ones, birds, and so forth. So yeah, we are looking everywhere for pigments right now. We’ve done a few pigments from underwater organisms as well,” says Yarkoni, who is quick to point out that the real benefit of Colorifix is their unique dyeing process, more so than the production of bio-based pigments.

In a previous interview with Vogue Australia, Yarkoni emphasized that their dyeing process uses 10 times less water and consumes 20 percent less energy, mainly because the company uses molasses, a sugar by-product, to feed the pigment-producing microbes, and replaces fixing chemicals with biology itself. The normal textile dyeing process often happens at very high temperatures – well over 100 degrees Celsius – which consumes a great deal of energy. Colorifix’s microbes can dye at 37 degrees and then be heat-inactivated at well under 100 degrees, which saves energy. But the company does not plan to stop there.

A sample of colored textiles from Colorifix’s bio-based dyeing process. Image courtesy of Colorifix.

“With regards to water, we’re excited to say that we’ve now successfully piloted both fermentation and dyeing using exclusively saltwater, so we now don’t need to touch fresh water in the process,” announced Yarkoni.

That’s a huge leap for the textile industry and could save thousands of gallons of freshwater throughout the dyeing process. The best part is that, despite these energy and water savings, the process for dyeing a textile remains largely unchanged.

“Our dye liquor essentially goes into their existing dye machine – they are all compatible – so all they need to do is change the dye liquor going into their machine,” says Yarkoni, referring to the aqueous solution of chemicals used to stain a garment. “The only difference is that [the dyeing facility] can get rid of all of their other chemicals, since the organism is fixing the dye and staining the fabric without those chemicals.”

From denim to dyes and everything in between, synthetic biology companies are answering the calls from a wasteful industry. Companies like Tinctorium, PILI and Colorifix are finding alternative ways to produce the same vibrant colors that appeal to consumers without the added toxicity, chemicals, and pollution.

We are entering a pivotal moment in the fashion industry, where bio-based, sustainable alternatives will be the new vogue.

Niko McCarty

Niko is a Bioengineering PhD student at the California Institute of Technology. He previously completed his Masters in Systems and Synthetic Biology at Imperial College London as a Fulbright Scholar.


RESULTS

Overview and optimization of DocMF system

The novel DocMF system measures protein-DNA interactions by examining the fluorescence signal change via on-chip sequential imaging before and after protein interaction with DNBs that contain DNA targets (Fig. 1 and movie S1). The DNBs are composed of sequencing adapters and inserts of random sequences to cover the full range of protein binding sites. Hundreds of millions of DNBs are first loaded onto the BGISEQ500 chips in a patterned array, and the insert regions are sequenced at a fixed length using the DNBSeq workflow (16). After obtaining the unique sequence information for each DNB, we reform single-stranded DNBs (ssDNBs) by stripping off the dye-labeled strand synthesized in sequencing. Subsequently, a native complementary strand is resynthesized to form dsDNA and end-labeled with fluorescent dyes. For DNA-cleaving proteins, a first image is acquired to record the location and the signal intensity of individual DNBs, i.e., reads. The protein of interest binds to its dsDNA targets and cleaves corresponding DNBs, leading to signal reduction or elimination of these DNBs during a second round of imaging (Fig. 1A). Specific motifs can be identified from the sequences of selected DNBs with signal elimination or reduction greater than a threshold (Fig. 1B) and verified in subsequent molecular assays. A slightly modified DocMF workflow is used to characterize protein-DNA binding preferences. In this protocol, DNBs are first imaged after incubating end-labeled dsDNA with DNA binding proteins for initial signal intensity. In the following step, an additional polymerase reaction called MDA is performed to replace the labeled strand. MDA leads to signal loss during the second imaging step as illustrated in fig. S1. However, if a protein of interest binds to its DNA targets and inhibits MDA, the signal from the DNBs containing protein binding sites would remain unchanged or be less affected than that of the control lane, which does not include protein incubation but retains the other steps.

To ensure sequential imaging is feasible, it is crucial that the stripping step does not affect spatial information or damage the DNB structure. The BGISEQ500 chips used in this experiment are patterned arrays. Therefore, the sequential imaging does not affect the registration of DNB locations to the same extent that the CHAMP method using Miseq chips is affected (11). In addition, we tested a variety of stripping buffers and found that the formamide buffer had the least impact on DNB integrity, only breaking the hydrogen bonds between dsDNA without affecting DNB stability or detaching DNBs from the surface. Figure S2 shows that the sequencing quality scores, including Q30 (92.83 versus 90.32), Lag (0.15 versus 0.15), and RunOn (0.15 versus 0.12), remained unaffected after stripping with formamide buffer. In comparison, the stripping buffer with NaOH significantly decreased the Q30 from more than 90% to nearly 0.

After obtaining two images before and after protein-DNA interaction, we directly compared the raw signal intensity fold change of each DNB. If the protein cleaves DNA, the DNBs that have significant signal reduction can be retrieved (Fig. 1B), and the corresponding sequences are analyzed for motif identification (Materials and Methods). To measure protein-DNA binding interactions, we obtained the binding sequence information from these DNBs with minimal signal fold change compared with the control.

DocMF can characterize a broad range of endonuclease restriction sites (RSs)

After the system was established, we tested six restriction endonucleases (Eco RI, Bpu 10I, Age I, Nme AIII, Mlu I, and Bgl I) with different RS features. The type II restriction enzymes cleave DNA adjacent to or within their recognition sites (21), which have been extensively studied. The selected enzymes contain restriction sites (RSs) ranging from 6 to 11 bp and comprising normal palindromic sequences, nonpalindromic sequences, and degenerate bases. The DNB library contains a pool of synthetic random DNA fragments with a length of 50 nt. Forty of the 50 nucleotides of these random sequences were read using the BGISEQ-500RS High-throughput Sequencing Set (SE100). Images were taken before and after on-chip incubation of these enzymes for 2 hours or overnight. DNBs with FFI > 2 threshold (positive reads) were identified to screen for motifs (see Materials and Methods).

The exact length of restriction sites (RSs) (L) was obtained via a “seed assembly” method (Materials and Methods). We then calculated the site rates of all L-mers among positive reads (Materials and Methods). Using this method, for each of these six restriction endonucleases, we obtained the site rates of all L-mers (L is the predicted length of an RS) and drew a boxplot for the L-mers with the top 50 largest site rates (Fig. 2A). The motifs (colored orange in Fig. 2A) corresponding to the outliers in the boxplot, with the sum of site frequency of these outliers (colored green in Fig. 2A) >80%, were regarded as the DocMF-predicted restriction sites (RSs). Thus, for Eco RI, Bpu 10I, Age I, Nme AIII, and Mlu I, we obtained their restriction sites (RSs) from the outliers shown in Fig. 2A, because the site rate sums of these outliers were all larger than 80%. For Bgl I, however, the site rate sum of the outliers was only 7.65%, which is too small to be predicted as restriction sites (RSs) using DocMF. Thus, for Bgl I, we used the 11-mers (because 11 is the predicted length of the RS) with the top 372 largest site rates to predict the restriction sites (RSs) the site rate sum of these 372 motifs was larger than 80% (Fig. 2B). A sequence (19) representation (Fig. 2C) of these 372 sequences revealed that the RS for Bgl I was GCCNNNNNGGC, which agreed with the reported RS. These results demonstrated that our system coupled with an optimized bioinformatics method could reliably identify the DNA recognition site for different types of restriction enzymes.

(A) Box plots for the motifs with the top 50 log10(site rates). Outliers’ DNA sequences (orange) and the sum of outliers’ site rates (green) are shown. (B) Cumulative site rates for Bgl I. (C) A sequence logo representation of the 372 motifs for Bgl I.

DocMF can accurately identify the 5′-NGG-3′ PAM of SpCas9

CRISPR-Cas effectors are RNA-guided endonucleases that use a PAM as a DNA binding signal. The PAM is a short DNA sequence, normally less than 7 bp, that sits near the target DNA (termed protospacer) of the CRISPR-Cas system (22, 23). One widely used PAM identification approach transforms plasmids carrying randomized PAM sequences into E. coli in the presence or absence of the CRISPR-Cas locus. The frequency of a functional PAM sequence is significantly lower when the Cas protein is present (24).Thus, this PAM depletion assay (24) requires two sets of libraries for either 5′ or 3′ PAM identification and corresponding negative controls. The library size also needs to be large to cover most, if not all, PAM sequences. In addition, the plasmid depletion assay (24) is time-consuming and low throughput. In contrast, the DocMF system can simultaneously screen both 5′ and 3′ sequences for PAMs in a single experiment, generating coverage that is multiple orders of magnitude greater than the traditional method. One of the two PAM regions that is not recognized by the protein is used as an internal negative control.

In a proof-of-concept study, we evaluated the accuracy of DocMF by assessing the PAM requirements of SpCas9, the most widely used CRISPR-Cas system, from S. pyogenes. SpCas9 cleaves the dsDNA after binding to corresponding RNA, and this cleavage is reported from PAM depletion assays to be dependent on a 5′-NGG-3′ PAM sequence (25). The PAM DNB library used in DocMF is shown in Fig. 3A. The synthetic oligo region contained a known 23-nt SpCas9 protospacer sequence (colored orange in Fig. 3A) flanked by 5′ and 3′ PAM regions with 15 random nucleotides each (colored green in Fig. 3A). The sequence information of both PAM regions was obtained by a single-end sequencing of 50 nt.

(A) PAM DNB library preparation illustration. The synthetic oligo region contains a known 25-nt SpCas9 protospacer sequence (orange) flanked by 5′ and 3′ PAM regions with 15 random nucleotides each (green). Hundreds of copies of each random PAM-flanked protospacer are incorporated per DNB, and only copy is demonstrated. (B) The relative read frequency at both the 5′ end and the 3′ end for SpCas9. The X axis is all combinations of 7-nt sequences sorted by the difference between two ends in descending order. (C) PAM sequence for SpCas9.

The signal fold change was compared before and after SpCas9 on-chip incubation for 4 hours. Of 494,866,059 reads (DNBs), we obtained 366,913 DNBs that exhibited a fold change greater than 3 and could potentially be cleaved by SpCas9. The 7-nt sequences at both 5′ and 3′ PAM regions were retrieved from these DNBs for further analysis. The frequency of all 16,384 (4 7 ) PAM combinations for both 5′ and 3′ PAMs was calculated and plotted against the individual sequence in Fig. 3B. SpCas9 endonuclease was reported to only bind to the 3′ end of the target sequence. Therefore, 5′-randomized 7-nt sequences were used as the internal negative control. We applied the three sigma rule (26) to the 5′ sequences to define the cutoff for positive PAM signals. In other words, at the cutoff of 0.11, approximately 99.7% of data from the 5′ PAM region fell into background noise. This statistical cutoff resulted in 944 3′ PAM sequences that were preferably cut by SpCas9. A sequence logo (19) representation of the sequences revealed that SpCas9 preferred a 5′-NGG-3′ motif, although approximately 5.8% (55 of 944) of 5′-NAG-3′ and 1.6% (15 of 944) of 5′-NGA-3′ could also be recognized (Fig. 3C), which is in line with previous findings (2729).

DocMF enables sensitive in vitro detection of PAMs in different CRISPR-Cas systems

To further demonstrate the utility of DocMF in finding PAM sequences, we extended our study to two previously uncharacterized CRISPR-Cas systems, VeCas9 from Veillonella genus and BvCas12 from Butyricimonas virosa (24). VeCas9 has a Cas9 effector protein of 1064 amino acids, and BvCas12 protein is 1245 amino acids in length. Both proteins were expressed and purified as described in the Supplementary Materials and Methods. The crRNA and tracrRNA of VeCas9 were identified through small RNA sequencing, whereas the crRNA of BvCas12 was predicted in silico based on the previously reported Cas12a/Cpf1 orthologs (fig. S3). To interrogate the diversity of their PAM sequences, we conducted DocMF on VeCas9 and BvCas12 using the same DNB PAM library (Fig. 3A) used in the SpCas9 study. For VeCas9 experiments, we included three individual gRNA designs, crRNA:tracrRNA, sgRNA-1 with SpCas9 structure, and a truncated sgRNA-2 (fig. S3).

Before using DocMF, a PAM depletion assay (24) was first performed on VeCas9 for methodology comparison (Supplementary Materials and Methods). As shown in fig. S4 (A and B), with 4.62 Gb of sequencing data, we observed 508 sequences with a threshold of 3 for Alicyclobacillus acidoterrestris C2c1 (AacC2c1), a positive control in the depletion assay, and correctly identified the reported PAM, 5′-TTN-3′ (24). However, with even more sequencing data (7.33 Gb) for VeCas9, 0 and 74 distinct sequences were found with thresholds of 3 and 0.8, respectively (fig. S4, C and D). The results for VeCas9 were quite similar to our negative control sets (data not shown), and thus, we failed to detect correct PAM sequences for VeCas9 using the traditional depletion method. The failure could be attributed to either weak VeCas9 protein expression or function in E. coli cells. In addition, the low sensitivity (

20× coverage for each 7-nt PAM sequence) of the E. coli depletion assay could only aggravate the problems.

Using DocMF, DNBs with signal fold change above threshold were selected for further analysis. In the read frequency plot (Fig. 4, A and B), the 5′ PAM region of VeCas9 (with sgRNA-1) and the 3′ PAM region of BvCas12 showed no protein binding pattern, and their corresponding 3 SDs (0.09 for VeCas9 and 0.075 for BvCas12) were used to set cutoff lines. As a result, 4947 and 5580 unique PAM sequences were determined to be cleaved by VeCas9 and BvCas12, respectively. Both CRISPR-Cas systems conveyed large PAM families as illustrated in consensus sequences and sequence logo, two common PAM reporting schemes (Fig. 4, C to E and G) (22, 23). The consensus sequences of VeCas9 were revealed as 5′-NNARRNN-3′, or NYARRMY for an even more dominant set of PAM sequences by frequency plot (Fig. 4C), while sequence logo reported 5′-NNNRR-3′ PAM sequences (Fig. 4E). VeCas9 with the other gRNAs showed a similar pattern (fig. S5). Over 99% of PAMs with the short sgRNA-2 were found with at least one of the other two RNAs, indicating the high reproducibility of this DocMF method. Slight difference in PAM of BvCas12 was also observed between PAM reporting methods. Consensus sequence and sequence logo reported 5′-TYTN-3′ (Fig. 4D) or YYN (Fig. 4G), respectively. However, these two reporting systems ignored the correlation among all seven positions and might introduce some incorrect active PAMs if randomly combining each position.

(A) The relative read frequency at both the 5′ end and the 3′ end for VeCas9. (B) The relative read frequency at both the 5′ end and the 3′ end for BvCas12. Consensus PAM sequence by frequency plot with all detected 7-nt sequences for VeCas9 (C) and BvCas12 (D). PAM sequence by sequence logo for VeCas9 generated by all detected 7-nt sequences (E) and by the top 1000 7-nt sequences from FET analysis (F). PAM sequence by sequence logo for BvCas12 generated by all detected 7-nt sequences (G) and by the top 1000 7-nt sequences from FET analysis (H). (I) In vitro validation of VeCas9 PAM sequences. Nine 7-nt sequences each above/below the cutoff were selected. The FET ranking numbers are shown in red. NC, negative control. (J) In vitro validation of BvCas12 PAM sequences. Five 7-nt sequences above the cutoff and two 7-nt sequences below the cutoff were selected.

To interrogate the relative activity of each PAM, two methods were applied, FET and the PAM wheel. FET was introduced to sort the PAM sequences. FET is a widely used test to determine whether the difference between two groups is significant. Therefore, one particular PAM with a smaller P value according to FET indicated that its relative read frequency, or cutting efficiency, was more significant compared with one with a larger P value. After ranking the PAMs in order, we examined the consensus PAM sequences for the top 1000 sequences (Fig. 4, F and H). A slightly distinct PAM consensus sequence, 5′-NNARR-3′ for VeCas9 and 5′-TTTN-3 for BvCas12, was observed under these stringent selection criteria, which correlated better with the frequency plotting results (Fig. 4, C and D). To further validate the FET prediction, an in vitro nuclease assay was performed with randomly selected PAM sequences. PCR products containing individual PAMs and a common protospacer were incubated with either Cas9 or Cas12/Cpf1 proteins at 37°C for 1 hour. Reactions with 50 ng of input were run on TAE gels, and the remaining input quantity was used to calculate cleavage efficiency. As demonstrated in Fig. 4 (G and H), the PAMs with higher FET ranking numbers (in red) had less input remaining, indicating better cutting efficiency. The least ranked PAM gave minimal cutting, the product of which was almost not visible on agarose gels whose sensitivity is several orders of magnitudes lower than NGS. The consistency suggested that we could use our FET prediction to select the most active PAMs for in vivo gene editing.

A PAM wheel was also used to comprehensively understand the PAM sequences and their base dependence. The PAM wheel, derived from interactive Krona plots, captures individual PAM sequence and their relative activity, including the ones with low enrichment (20). It can be also expanded at any sector of the wheel to better view a subset of sequences and study the function of those PAMs. Figure 5 (A and B) depicts the respective PAM wheels for VeCas9 and BvCas12. For VeCas9, there is a strong base dependence between position 3 and 4. If position 3 had a base R (A or G), position 4 tended to have R (>80% fig. S6) and a small but notable level of C (>0%). If position 3 is Y (T or C), position 4 favored R only (

99%). T is the least favored base at position 4 or 5, which agrees with the in vitro cutting results from Fig. 5C (lanes 9 and 10). The gel also demonstrated that NYARRMY (the consensus PAM based on the most dominant consensus sequence lane 1 in Fig. 4C), NNARRNN (lanes 2 to 4), and ACAAGCC (58th ranked sequence as positive control lane 11) were cut more efficiently than NNCRRNN (lane 5), NNTRRNN (lane 6), or NNGRRNN (lane 7), which explains why A comprised 47% at position 3, while the other three bases were each between 16 and 19% (Fig. 5A and fig. S6). For the BvCas12 PAM wheel shown in Fig. 5B, we found that position −4 was random when both positions −2 and −3 were Y (T/C). So PAM YYN generated more cutting products than YRN in Fig. 5D (lanes 1 and 2). However, position −4 tended to be T if one of the −2 or −3 positions was not Y. As a result, we observed slightly more cutting with T than V at position −4, when position −2 and −3 are either RY or YR (Fig. 5D lanes 7 to 10). R at position −2 also dictated that position −3 will be Y (100% fig. S6). As shown in Fig. 5D, the BvCas12 system demonstrated clear cutting on 5′-TTTN-3′ or TYTN (Fig. 5D). Our data suggested that both VeCas9 and BvCas12 had a set of relaxed PAM sequences that were comprehensively captured by DocMF.

(A) PAM wheel for VeCas9. The upper yellow box gives an indication about each position of the PAM sequence, and the arrow illustrates the orientation of each base. The area of a sector of the ring for one base at one particular position represents its frequency at this position. (B) PAM wheel for BvCas12. (C) In vitro validation of VeCas9 PAM wheel results. NYARRMY is the consensus sequence based on positional frequency, while ACAAGCC is 58th FET ranked sequence included as a positive control. (D) In vitro validation of BvCas12 PAM wheel results. NNNTTTN or NNNTYTN is the consensus sequence based on positional frequency, while AATTTTG is 70th FET ranked sequence included as positive control. NC (negative control): positive PAMs incubated with corresponding protein but without any sgRNA.

DocMF can accurately identify protein binding sites

Protein-DNA interactions have been characterized in many high-throughput platforms including microarrays, HT-SELEX, and CHAMP (4, 11). We modified the DocMF workflow mentioned above to detect protein-DNA binding motifs. The steps remained unchanged until the natural complementary strand was resynthesized to form 50-bp dsDNA and end labeled with fluorescent dyes. After binding the protein of interest to its dsDNA targets and washing off any excess, we acquired the first images to record signal intensity. After this first imaging, an on-chip incubation with MDA reaction buffer, dNTPs, and a polymerase with strong strand displacement was performed at 30°C for 30 min to synthesize a second complementary strand using the ssDNB as template (fig. S1). Consequently, the original fluorescent strand would be replaced and displaced from DNBs, leading to signal drop when there was no protein binding to prevent MDA. To test this idea, we used a well-studied protein, dCas9, and removed its endonuclease activity through point mutations in its endonuclease domains HNH and RucV (17). The point mutations D10A and H840A changed two important residues for endonuclease activity, which ultimately results in its deactivation. Although dCas9 lacks endonuclease activity, it remains capable of binding to its gRNA and the DNA strand that is being targeted because the binding is mediated through its REC1, BH, and PI domains (30). Moreover, dCas9 has previously been shown not to bind to its target sequence when there is no PAM (NGG) present (31). Unlike the studies above, the reads with fluorescence fold change below the threshold were considered positive, indicating the DNBs that dCas9 could recognize and bind. In addition, we included a negative control lane without dCas9 incubation in the same process, since BGISEQ-500 has two lanes on a single chip. Approximately 95% of a total of 253M reads from the negative control lane lost half of the signal intensity (data not shown). We chose a signal change at 0.5 as threshold (image 2/image 1) and retrieved 14,371,289 of 335,497,075 and 15,647,574 of 337,529,837 reads from experimental and control lanes, respectively. We introduced a reliable relative binding strength concept to evaluate the binding strength for each 7-nt sequence. Similarly, the data at the 5′ end fitting a normal distribution were regarded as background noise, and the three sigma rule was adopted to define the cutoff at 0.135. After deducting the noises, we observed the NGG sequence was essential for dCas9’s binding (Fig. 6), consistent with previous findings. This suggests that with the modified DocMF, workflows can be harnessed as a general tool for identifying DNA binding motifs.

(A) The relative binding strength at both the 5′ end and the 3′ end for dCas9. The X axis is all combinations of 7-nt sequences and is automatically sorted by letter using Excel. (B) Sequence logo for dCas9 was generated by all detected 7-nt sequences based on those with the highest relative binding strength.


Methyl Blue Staining on Yeast

The methylene blue staining procedure is used to measure yeast viability based on the assumption that the methylene blue will enter the cells and be broken down by living yeast cells that produce the enzymes which breaks down methylene blue, leaving the cells colourless. The non- viable cells do not produce this enzyme (or enzymes) and as such the methylene blue that enters the cells are undegraded causing the cells to remain coloured (the oxidized form concentrates intracellularly).

The coloured and colourless cells are then counted using a haemocytometer and the number of viable and nonviable cells determined in a given area, the result would be then used to estimate the number of cells in the original sample.

This is an easy, quick and cheap method to determine the amount of viable yeast present in a sample though this is not the best method for a number of reasons. A major reason is that methylene blue rapidly becomes toxic to the yeast and as such preparations should be examined within 10 minutes of preparation.

The older the cells become, the less likely that they are to take up the methylene blue dye from solution since as the yeast age, they deposit lipid and/or sugar in their cell membrane (in the form of free sterols[predominantly ergosterol and zymosterol with minor portions of lanosterol and fecosterol] and phospholipids[phosphotidylcholine and phosphotidylethanolamine with minor portions of phosphotidylinositol, phosphotidylserine and phosphotidyl-glycerol]) as a survival mechanism to protect their internal mechanisms from the buildup of waste in the external environment.

This means that cells which are not viable would not take up the dye and due to their age and not their ability to break down the methylene blue (as a result of their viability) would remain colourless and be determined to be viable (a false positive). The test itself is also not very accurate since yeast might not be evenly distributed in the original sample and depending on the sample taken for determination , may yield higher or lower viability counts than are really present in the original sample.

This viability count and cell count is absolutely essential since the yeasts are the producers of the ethanol through the breakdown of sugars (catabolism of sugars by glycolysis to pyruvate, which is then converted to CO2 and ethanol) present in the wort/must (the pitching rate is a measure of the number of viable yeast present in a particular stock being used for fermentation). These processes can only be performed by viable yeast cells and consequently the greater the umbers of viable yeasts in the sample, the higher the rate of ethanol production. The expected yield of ethanol (alcohol) can also be calculated based on the number of viable yeast present in a particular stock being used for fermentation (along with the sugar present in the wort). This is very important in industrial alcohol production to determine the required alcohol content of the final product and the viability of the yeast pitched must be greater then 85% or it is not used.


Pioneering technique paves way for fast and cheap fabrication of rapid medical diagnostic tools

Example 100-micron wide 3D-printed microchannel scaffolds, shown next to a 20p coin - the cost to print 1000 of these channels. Credit: University of Bristol

New technology developed by the University of Bristol has the potential to accelerate uptake and development of on-chip diagnostic techniques in parts of the world where rapid diagnoses are desperately needed to improve public health, mortality and morbidity.

Microfluidic devices underpin lab-on-a-chip (LOC) technologies which are developed to provide the rapid diagnoses at that are needed at point of care (POC) for the swift and effective treatment of many diseases.

Researchers at Bristol have developed a fast, reliable and cost-effective alternative for producing the soft-lithographic moulds used for fabricating microfluidic devices, published in the journal PLOS ONE. This discovery means fabrication of microfluidic devices (with channel dimensions

width of a human hair) is now both accessible and affordable using simple, low-cost 3-D-printing techniques and the open-source resources developed by the team.

"Previously, techniques for producing the soft-lithographic scaffolds/moulds (microfluidic channel patterns) were time-consuming and extremely expensive, while other low-cost alternatives were prone to unfavourable properties. This development could put LOC prototyping into the hands of researchers and clinicians who know the challenges best, in particular those in resource-limited settings, where rapid diagnostics may often have the greatest impact," said lead author of the study, Dr. Robert Hughes.

Dye-mixing inside a microfluidic chip made using 3D-printed interconnecting channel scaffolds. Credit: University of Bristol

"This technique is so simple, quick & cheap that devices can be fabricated using only everyday domestic or educational appliances and at a negligible cost (

0.05% of cost of materials for a single microfluidic device). This means researchers and clinicians could use our technique and resources to help fabricate rapid medical diagnostic tools, quickly and cheaply, with minimal additional expertise or resources required," said co-author, Mr Harry Felton.

"The simplicity and minimal cost of this technique, as well as the playful click-and-connect approach developed, also makes it suitable for hobbyists and educational use, to teach about microfluidics and the applications of lab-on-a-chip technology," said co-author Ms Andrea Diaz Gaxiola.

"It is our hope that this will democratise microfluidics and lab-on-a-chip technology, help to advance the development of point-of-care diagnostics, and inspire the next generation of researchers and clinicians in the field," said Dr. Hughes.

Simplified flow-diagram of the low-cost technique for fabricating microfluidic devices. Resulting channels can be applied directly to a glass surface with no additional treatment. Credit: University of Bristol

The next step for the team is to identify potential collaborators in both research and education to help demonstrate the impact this technology could have in both settings by developing and supporting outreach activities and applications for on-chip diagnostic testing.


Science works in strange ways. The experiment, which requires a few basic metal materials and a small digital clock, also calls for an uncooked spud and America's least expensive coin -- the penny. After setting up an electrical circuit with the objects, the potato's natural acidity interacts with the penny's copper to complete one half of this natural "battery's" circuit.

Science fair students, like real scientists, should work to gain consent from people before including them in an experiment.


Affiliations

Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

Scott Sherrill-Mix, Young Hwang, Aoife M. Roche, Abigail Glascock, Susan R. Weiss, Yize Li, Louis J. Taylor & Frederic D. Bushman

Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

Scott Sherrill-Mix, Jevon Graham-Wooten & Ronald G. Collman

Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

Leila Haddad, Peter Deraska, Caitlin Monahan, Andrew Kromer & Arupa Ganguly

Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA

Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA


Be safe and follow some simple rules

  • Never use the same pots and utensils for dyeing that you use for cooking.
  • Wear rubber gloves and use a face mask when measuring mordants and dyes.
  • Work in a well ventilated area.
  • Dispose of used mordants and dye baths safely.

Well there you have it. A simple and practical guide to the use of mordants and fixitives in your natural dyeing products!

Whilst all this talk may seem a little off-putting at first, hopefully you now see how simple and fun the whole affair really is!

But… if you’re still feeling a bit shell shocked, do consider my Natural Dyeing Bootcamp. I cover 4 stunning natural dyeing projects in detail that don’t require mordants or fixatives at all!

Not sure where to get started? Check out my 30 day Natural Dyeing Boot Camp! Try It Now


Watch the video: Deoxynucleotide dNTP vs Dideoxynucleotid ddNTP (October 2022).