We want to thank all the students of the Synthetic Biology lab-SPST, TJU-for their general help, together with Zhi Li and Xiangyang Zhang for their assistance in FACS experiments.

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The authors declare no competing financial interest.

The protocol showed a possible complete workflow for synthetic gene digital circuits, following the &#34;Design-Build-Test-Learn&#34; (DBTL) biological engineering cycle and concerning both dry-lab and wet-lab experiments. Here, we focused on the CRISPR-Cas system, mainly dSpCas9, denAsCas12a, dLbCas12a, and the corresponding anti-CRISPR proteins, by designing and building in S. cerevisiae small transcriptional networks. Some of them mimicked Boolean gates, which are the basic components of digital circuits. All circuits described here permitted us to depict the properties and features of CRISPR-associated and anti-CRISPR proteins in S. cerevisiae. These results are essential to include these proteins in the scheme of gene digital circuits.
The DBTL concept provides a framework in synthetic biology, while many optimizations and improvements shall be made after testing a new artifact. For instance, in circuit 1, there was initially only one target site (one copy of lexOp) for dCas9/dCas12a-AD on the synthetic promoter upstream of yEGFP. After testing that circuit configuration, we found that it could achieve no more than a twofold activation15,16. Then, we assumed that by increasing the number of copies of lexOp, as in7, we could reach a higher transcriptional activation. Indeed, a higher fluorescence level was obtained by using three to six lexOp sites (Figure 1). Moreover, we further improved the performance of the circuits hosting dSpCas9 by engineering an scRNA, which is easier than fusing one or more ADs to a big protein like dSpCas9 (Figure 3). Additionally, by using a promoter of different strengths to produce the three AcrIIAs that we chose, we concluded that AcrIIA4 was the strongest inhibitor among them. Thus, we built a new NOT gate responsive to &#946;-estradiol by fusing the HBD(ER) to AcrIIA4 and exploiting the strong repression of AcrIIA4 on our best dSpCas9-based activator (Figure 4).
Similarly, we deeply characterized both the working of denAsCas12a and dLbCas12a in yeast and their interactions with three AcrVAs (Figure 5). For each dCas12a-AcrVA pair, we built a NOT (dCas12a was fused to an AD) and a YES (bare dCas12a) gate responsive to galactose. On the whole, dLbCas12a, together with AcrVA5, resulted in the best system to compute simple logic functions.
The method described here presented some critical steps. All protein DNA sequences were yeast codon-optimized to ensure higher expression in S. cerevisiae. To avoid unspecific targets of dSpCas9/denAsCas12a/dLbCas12a in the S. cerevisiae genome, we selected a bacterial operator such as lexOp. Moreover, strains containing the GAL1 promoter showed a significant growth delay that could limit the applicability of pGAL1 to synthetic gene circuits15.
Some modifications could also be brought to some steps in the overall method. In order to improve the efficiency of the digestion-ligation procedure, it is preferable to digest 10 &#181;g (overnight) of both the insert-containing plasmid and the acceptor vectors, rather than only 5 &#181;g in 1 h. In this way, a higher DNA concentration is reached after the elution step. The time for T4-ligation should be extended from 1 h (manufacturer&#39;s protocol) to 8 h. Finally, strains containing the dCas12a-VPR fusion protein should be diluted after a 24 h culture and grown for a further 12 h before running a FACS experiment. Under this condition, the variability among the fluorescence levels from different cells is no longer too high, and an acceptable standard deviation accompanies the mean value of the fluorescence intensity over a cell population.
In summary, this protocol explained how to simplify the design of gene digital circuits by making use of dCas proteins and, possibly, anti-CRISPR proteins. More importantly, we showed in detail how these families of proteins work in S. cerevisiae and which of them are the most promising for a future usage inside digital networks. An unsolved problem is the coupling of CRISPR-dCas/anti-CRISPR systems and chemicals, which represent the circuit inputs and cannot directly bind dCas proteins or anti-CRISPRs. Here, we bypassed the problem by using either the inducible GAL1 promoter or the HBD(ER) attached to AcrIIA4. However, a way to generalize the architecture of the circuit input layer is necessary to design synthetic gene digital circuits for different bioengineering areas such as metabolic engineering, biosynthesis, biosensing, biodiagnostics, and bioremediation.

In 2011, researchers proposed a computational method and developed a corresponding piece of software for the automatic design of digital synthetic gene circuits1. A user had to specify the number of inputs (three or four) and fill in the circuit truth table; this provided all the necessary information to derive the circuit structure using techniques from electronics. The truth table was translated into two Boolean formulae via the Karnaugh map method2. Each Boolean formula is made of clauses that describe logic operations (sum or multiplication) among (part of) the circuit inputs and their negations (the literals). Clauses, in their turn, are either summed up (OR) or multiplied (AND) to compute the circuit output. Every circuit can be realized according to any of its two corresponding formulae: one written in POS (product of sums) form and the other in SOP (sum of products) representation. The former consists of a multiplication of clauses (i.e., Boolean gates) that contain a logic sum of the literals. The latter, in contrast, is a sum of clauses where the literals are multiplied.
Electric circuits can be realized, on a breadboard, by physically wiring different gates together. The electric current permits the exchange of signals among gates, which leads to the computation of the output.
In biology, the situation is more complex. A Boolean gate can be realized as a transcription unit (TU; i.e., the sequence &#34;promoter-coding region-terminator&#34; inside eukaryotic cells), where transcription or translation (or both) are regulated. Thus, at least two kinds of molecules establish a biological wiring: the transcription factor proteins and the non-coding, antisense RNAs1.
A gene digital circuit is organized into two or three layers of gates, namely: 1) the input layer, which is made of YES (buffer) and NOT gates and converts the input chemicals into wiring molecules; 2) the internal layer, which consists of as many TUs as there are clauses in the corresponding Boolean formula. If the circuit is designed according to the SOP formula, every clause in the internal layer will produce the circuit output (e.g., fluorescence) in a so-called distributed output architecture. If the product of sum (POS) formula is used, then a 3) final layer is required, which will contain a single multiplicative gate collecting the wiring molecules from the internal layer.
Overall, in synthetic biology, many different schemes can be designed for the same circuit. They differ in the number and the kind of both TUs and wiring molecules. In order to choose the easiest solution to be implemented in yeast cells, each circuit design is associated with a complexity score S, defined as
<img alt="Equation 1" src="/files/ftp_upload/64539/64539eq01.jpg" style="margin: auto;" />
where A represents the number of activators, R represents the number of repressors, and a is the amount of antisense RNA molecules. If either activators or repressors are absent from the circuit, their contribution to S is zero. Therefore, it is more difficult to realize a circuit scheme in the lab (high S) when it requires a high number of orthogonal transcription factors. This means that new activators and repressors shall be engineered de novo in order to realize the complete wiring inside the digital circuits. In principle, novel DNA-binding proteins can be assembled by using Zinc Finger proteins3 and TAL effectors4 as templates. However, this option appears too arduous and time-consuming; therefore, one should rely mostly on small RNAs and translation regulation to finalize complex gene circuits.
Originally, this method was developed to fabricate digital circuits in bacteria. Indeed, in eukaryotic cells, instead of antisense RNAs, it is more suitable to talk of microRNAs (miRNAs) or small interfering RNAs (siRNAs)5. However, the RNAi pathway is not present in the yeast S. cerevisiae. Hence, one should opt for fully transcriptional networks. Suppose that a circuit needs five activators and five repressors; its complexity score would be S = 32. Circuit complexity can be reduced by replacing the 10 transcription factors with a single dCas96 (nuclease deficient Cas9) fused to an activation domain (AD). As shown in7, dCas9-AD works as a repressor in yeast when binding a promoter between the TATA box and the TSS (transcription start site) and as an activator when binding well upstream of the TATA box. Thus, one can replace 10 transcription factors with a single dCas9-AD fusion protein and 10 sgRNAs (single guide RNAs) for a total complexity score of S = 11. It is quick and easy to synthesize ten sgRNAs, whereas, as previously commented, the assembly of 10 proteins would demand much longer and more complicated work.
Alternatively, one might use two orthogonal dCas proteins (e.g., dCas9 and dCas12a): one to fuse to an AD, and the other bare or in combination with a repression domain. The complexity score would increase by only one unit (S = 12). Hence, CRISPR-dCas systems are the key to the construction of very intricate gene digital circuits in S. cerevisiae.
This paper deeply characterizes the efficiency of both dCas9- and dCas12a-based repressors and activators in yeast. Results show that they do not demand a high amount of sgRNA to optimize their activity, so episomal plasmids are preferentially avoided. Moreover, dCas9-based activators are far more effective when using a scaffold RNA (scRNA) that recruits copies of the VP64 AD. In contrast, dCas12a works well when fused to the strong VPR AD directly. Furthermore, a synthetic activated promoter demands a variable number of target sites, depending on the configuration of the activator (e.g., three when using dCas12a-VPR, six for dCas9-VP64, and only one with dCas9 and a scRNA). As a repressor, dCas12a appears more incisive when binding the coding region rather than the promoter.
As a drawback, however, CRISPR-dCas9/dCas12a do not interact with chemicals directly. Therefore, they might be of no use in the input layer. For this reason, alternative Boolean gate designs containing anti-CRISPR proteins (Acrs) have been investigated. Acrs act on (d)Cas proteins and inhibit their working8. Hence, they are a means to modulate the activity of CRISPR-(d)Cas systems. This paper thoroughly analyzes the interactions between type II Acrs and (d)Cas9, as well as type V Acrs and (d)Cas12a in S. cerevisiae. Since Acrs are much smaller than Cas proteins, a NOT gate responsive to the estrogen &#946;-estradiol was built by fusing the hormone-binding domain of the human estrogen receptor9-HBD(hER)-to AcrIIA4. Besides, a handful of YES and NOT gates that expressed dCas12a(-AD) constitutively and AcrVAs upon induction with galactose were realized. At present, these gates serve only as a proof of concept. However, they also represent the first step toward a deep rethinking of the algorithm to carry out the computational automatic design of synthetic gene digital circuits in yeast cells.

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		<tr>
			<td>0.1 mL PCR 8-strip tubes</td>
			<td>NEST</td>
			<td>403112</td>
			<td></td>
		</tr>
		<tr>
			<td>0.2 mL PCR tubes</td>
			<td>Axygen</td>
			<td>PCR-02-C</td>
			<td></td>
		</tr>
		<tr>
			<td>1.5 mL Microtubes</td>
			<td>Axygen</td>
			<td>MCT-150-C</td>
			<td></td>
		</tr>
		<tr>
			<td>15 mL Centrifuge tubes&#160;</td>
			<td>BIOFIL</td>
			<td>CFT011150</td>
			<td></td>
		</tr>
		<tr>
			<td>2 mL Microtubes</td>
			<td>Axygen</td>
			<td>MCT-200-C&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>50 mL Centrifuge tubes&#160;</td>
			<td>BIOFIL</td>
			<td>CFT011500</td>
			<td></td>
		</tr>
		<tr>
			<td>Agarose-molecular biology grade</td>
			<td>Invitrogen</td>
			<td>75510-019</td>
			<td></td>
		</tr>
		<tr>
			<td>Ampicillin sodium salt</td>
			<td>Solarbio</td>
			<td>69-52-3</td>
			<td></td>
		</tr>
		<tr>
			<td>Applied biosystems veriti 96-well thermal cycler</td>
			<td>Thermo Fisher Scientific</td>
			<td>4375786</td>
			<td></td>
		</tr>
		<tr>
			<td>AxyPrep DNA gel extraction kit</td>
			<td>Axygen</td>
			<td>AP-GX-250</td>
			<td></td>
		</tr>
		<tr>
			<td>BD FACSuite CS&#38;T research beads</td>
			<td>BD</td>
			<td>650621</td>
			<td>Fluorescent beads</td>
		</tr>
		<tr>
			<td>BD FACSVerse flow cytometer&#160;</td>
			<td>BD</td>
			<td>-</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Eppendorf</td>
			<td>5424</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge Sorvall ST 16R</td>
			<td>Thermo Fisher Scientific</td>
			<td>75004380</td>
			<td></td>
		</tr>
		<tr>
			<td>E. coli competent cells (Strain DH5&#945;)</td>
			<td>Life Technologies</td>
			<td>18263-012</td>
			<td></td>
		</tr>
		<tr>
			<td>ECL select Western Blotting detection reagent</td>
			<td>GE Healthcare</td>
			<td>RPN2235</td>
			<td></td>
		</tr>
		<tr>
			<td>Electrophoresis apparatus</td>
			<td>Beijing JUNYI Electrophoresis Co., Ltd</td>
			<td>JY300C</td>
			<td></td>
		</tr>
		<tr>
			<td>Flat 8-strip caps</td>
			<td>NEST</td>
			<td>406012</td>
			<td></td>
		</tr>
		<tr>
			<td>Gene synthesis company</td>
			<td>Azenta Life Sciences</td>
			<td></td>
			<td>https://web.azenta.com/zh-cn/azenta-life-sciences</td>
		</tr>
		<tr>
			<td>Goat anti-Mouse IgG (H+L) cross-adsorbed secondary antibody Alexa Fluor 568</td>
			<td>Invitrogen</td>
			<td>A-11004</td>
			<td></td>
		</tr>
		<tr>
			<td>HiFiScript cDNA synthesis kit</td>
			<td>CWBIO</td>
			<td>CW2569M</td>
			<td>Kit used in step 6.2.2.1</td>
		</tr>
		<tr>
			<td>Lysate solution (Zymolyase)</td>
			<td>zymoresearch</td>
			<td>E1004-A</td>
			<td></td>
		</tr>
		<tr>
			<td>Nikon Eclipse 80i fluorescence microscope</td>
			<td>Nikon</td>
			<td>-</td>
			<td>Fluorescence microscope</td>
		</tr>
		<tr>
			<td>Pipet tips&#8212;10 &#956;L</td>
			<td>Axygen</td>
			<td>T-300-R-S</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipet tips&#8212;1000 &#956;L</td>
			<td>Axygen</td>
			<td>T-1000-B-R-S</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipet tips&#8212;200 &#956;L</td>
			<td>Axygen</td>
			<td>T-200-Y-R-S</td>
			<td></td>
		</tr>
		<tr>
			<td>pRSII403</td>
			<td>Addgene</td>
			<td>35436</td>
			<td></td>
		</tr>
		<tr>
			<td>pRSII404</td>
			<td>Addgene</td>
			<td>35438</td>
			<td></td>
		</tr>
		<tr>
			<td>pRSII405</td>
			<td>Addgene</td>
			<td>35440</td>
			<td></td>
		</tr>
		<tr>
			<td>pRSII406</td>
			<td>Addgene</td>
			<td>35442</td>
			<td></td>
		</tr>
		<tr>
			<td>pRSII424</td>
			<td>Addgene</td>
			<td>35466</td>
			<td></td>
		</tr>
		<tr>
			<td>pTPGI_dSpCas9_VP64&#160;</td>
			<td>Addgene</td>
			<td>49013</td>
			<td></td>
		</tr>
		<tr>
			<td>Q5 High-fidelity DNApolymerase</td>
			<td>New England Biolabs</td>
			<td>M0491</td>
			<td></td>
		</tr>
		<tr>
			<td>Restriction enzyme-Acc65I</td>
			<td>New England Biolabs</td>
			<td>R0599</td>
			<td></td>
		</tr>
		<tr>
			<td>Restriction enzyme-BamHI</td>
			<td>New England Biolabs</td>
			<td>R0136</td>
			<td></td>
		</tr>
		<tr>
			<td>Restriction enzyme-SacI-HF</td>
			<td>New England Biolabs</td>
			<td>R3156</td>
			<td></td>
		</tr>
		<tr>
			<td>Restriction enzyme-XhoI</td>
			<td>New England Biolabs</td>
			<td>R0146</td>
			<td></td>
		</tr>
		<tr>
			<td>Roche LightCycler 96&#160;</td>
			<td>Roche</td>
			<td>-</td>
			<td>Real-Time PCR Instrument</td>
		</tr>
		<tr>
			<td>S. cerevisiae CEN.PK2-1C</td>
			<td>-</td>
			<td>-</td>
			<td>The parent strain. The genotype is: MATa; his3D1; leu2-3_112; ura3-52; trp1-289; 
			MAL2-8c; SUC2</td>
		</tr>
		<tr>
			<td>Stem-Loop Kit</td>
			<td>SparkJade</td>
			<td>AG0502</td>
			<td>Kit used in step 6.2.1.3</td>
		</tr>
		<tr>
			<td>T100 Thermal Cycler</td>
			<td>BIO-RAD</td>
			<td>186-1096</td>
			<td></td>
		</tr>
		<tr>
			<td>T4 DNA ligase</td>
			<td>New England Biolabs</td>
			<td>M0202</td>
			<td></td>
		</tr>
		<tr>
			<td>T5 Exonuclease</td>
			<td>New England Biolabs</td>
			<td>M0363</td>
			<td></td>
		</tr>
		<tr>
			<td>Taq DNA ligase</td>
			<td>New England Biolabs</td>
			<td>M0208</td>
			<td></td>
		</tr>
		<tr>
			<td>Taq DNA polymerase</td>
			<td>New England Biolabs</td>
			<td>M0495</td>
			<td></td>
		</tr>
		<tr>
			<td>TB Green Premix Ex Taq II (Tli RNaseH Plus)(2x) (SYBR Green I dye)</td>
			<td>Takara</td>
			<td>RR820Q</td>
			<td></td>
		</tr>
		<tr>
			<td>YeaStar RNA kit</td>
			<td>Zymo Research</td>
			<td>R1002</td>
			<td></td>
		</tr>
		<tr>
			<td>&#946;-estradiol</td>
			<td>Sigma-Aldrich</td>
			<td>E8875</td>
			<td></td>
		</tr>
	</tbody>
</table>

gene digital circuits based on crispr-cas systems and anti-crispr proteins

Synthetic gene Boolean gates and digital circuits have a broad range of applications, from medical diagnostics to environmental care. The discovery of the CRISPR-Cas systems and their natural inhibitors-the anti-CRISPR proteins (Acrs)-provides a new tool to design and implement in vivo gene digital circuits. Here, we describe a protocol that follows the idea of the "Design-Build-Test-Learn" biological engineering cycle and makes use of dCas9/dCas12a together with their corresponding Acrs to establish small transcriptional networks, some of which behave like Boolean gates, in Saccharomyces cerevisiae. These results point out the properties of dCas9/dCas12a as transcription factors. In particular, to achieve maximal activation of gene expression, dSpCas9 needs to interact with an engineered scaffold RNA that collects multiple copies of the VP64 activation domain (AD). In contrast, dCas12a shall be fused, at the C terminus, with the strong VP64-p65-Rta (VPR) AD. Furthermore, the activity of both Cas proteins is not enhanced by increasing the amount of sgRNA/crRNA in the cell. This article also explains how to build Boolean gates based on the CRISPR-dCas-Acr interaction. The AcrIIA4 fused hormone-binding domain of the human estrogen receptor is the core of a NOT gate responsive to &#946;-estradiol, whereas AcrVAs synthesized by the inducible GAL1 promoter permits to mimic both YES and NOT gates with galactose as an input. In the latter circuits, AcrVA5, together with dLbCas12a, showed the best logic behavior.

sgRNA/crRNA expression by an RNA polymerase III-type promoter 
First, this work addressed the engineering of the transcriptional activation circuit (circuit 1) shown in Figure 1A. It contained three basic components: 1) the gene encoding for yEGFP (the reporter), which was preceded by a series of different synthetic promoters that provided target sites for dCas9/dCas12a-AD; 2) a yeast codon-optimized version of dCas9 or dCas12a fused to an activation domain (VP64 and VPR, respectively) and containing either one or two nuclear localization sequences (NLSs). Both dCas proteins were produced by a strong constitutive promoter-pGPD; and 3) an sgRNA/crRNA sequence that guided dCas9/dCas12a-AD to the target sites. The activation efficiency of dCas9/dCas12a-based activators was visualized and reflected by the fluorescence intensity of the reporter (measured via FACS experiments).
One dCas9 protein (dSpCas9) and two dCas12a proteins (denAsCas12a and dLbCas12a) were tested. A 3.36-fold activation was achieved with dSpCas9 extended, at its C terminus, with the VP64 AD and binding a synthetic promoter-upstream of yEGFP-containing six copies of the lexOp target site. The sgRNA was placed into an integrative shuttle vector and transcribed by the RNA polymerase III-dependent SNR52 promoter (SNR52i configuration, see Figure 1B). In the case of dCas12a, denAsCas12a-VPR returned the highest activation (4.45-fold) from a synthetic promoter with three operators when the crRNA was expressed via the SNR52i configuration (Figure 1C). Under the same conditions, dLbCas12a-VPR achieved its best fluorescence enhancement (3.21-fold) (Figure 1D). It should be noted that the comparison term, in every experiment, was a circuit whose sgRNA/crRNA could not bind the lex operators.
Multicopy plasmids are not necessary 
The SNR52i sgRNA expression cassette was replaced with an RGR structure expressed by a moderately strong promoter-pADH1. However, in both dCas9 and dCas12a cases, activation in the presence of an sgRNA/crRNA generated by the self-cleavage of RGR appeared comparable to or even lower than that achieved with an sgRNA/crRNA produced via SNR52i, despite the fact that pSNR52 was considered as a weak promoter (see Figure 1C,D for the first results obtained with dCas12a).
To further explore the connection between sgRNA/crRNA amount and activation efficiency, the two sgRNA/crRNA expression systems were inserted into an episomal plasmid, which can be taken up by the cell in 10-40 copies and generate a greater quantity of sgRNA/crRNA. As shown in Figure 2A, the activation by crRNA located on an integrative plasmid (either SNR52i or RGRi) was 1.4- to 2.4-fold higher than that achieved when the same crRNA was expressed by an episomal plasmid (either SNR52m or RGRm). The trend was confirmed by the sgRNA. In this case, the integrative plasmid guaranteed a 1.1- to 1.5-fold higher activation (Figure 2B). To exclude that the results were caused by a loss of the episomal plasmids, RT-qPCR was carried out to quantify the sgRNA/crRNA relative abundance in vivo. The results, in Figure 2C,D, verified that the episomal vector produced a much higher level of sgRNA/crRNA than the integrative vector, no matter the expression system (RGR or SNR52). These results showed that the SNR52 system could work even better than the RGR system, and a higher amount of sgRNA/crRNA in the cell did not guarantee a higher activation by the CRISPR-Cas system. Therefore, episomal plasmids should not be employed in the construction of gene digital circuits where dCas9/dCas12a-AD are used.
Scaffold RNA engineering 
An scRNA was engineered by extending the sequence of an sgRNA with MS2 hairpin structures that permitted to recruit VP64 AD when fused to the MS2 coat protein (MCP, see Figure 3A). In this way, no engineering of, nor modifications on, dCas9 were necessary. Two MS2 variants were tried: wt and f6. The scRNA containing the single MS2 hairpin-1&#215;MS2(wt) and 1&#215;MS2(f6)-gave a 5.27-fold and a 4.34-fold activation, respectively. However, the scRNA with a combination of the two hairpins-2&#215;MS2(wt+f6)-returned the overall highest activation in this study (7.54-fold, see Figure 3B). These results demonstrated that engineering an scRNA was far more effective than fusing any activation domains to dSpCas9 directly.
Boolean gate based on Acr proteins 
To further control and tune transcriptional activation by CRISPR-Cas systems, circuit 1 was modified with the insertion of a fourth TU for the expression of anti-CRISPR proteins (see Figure 4A for AcrIIAs and Figure 5A for AcrVAs). After showing that Acrs were effective, in S. cerevisiae, in contrasting the activation due to dCas9/dCas12a-AD, a new circuit was built (see Figure 5D) to test AcrVA action on dCas12a-based repressors (a previous work33 had already shown that AcrIIAs could inhibit dCas9-based gene down-regulation). The new, Acr-containing small networks worked as simple Boolean gates (YES and NOT), which might lead to a restyling of the input layer of more complex synthetic gene digital circuits.
AcrIIA4 is a strong inhibitor of dSpCas9 
Four different promoters with diverse strengths were used to drive the expression of three kinds of AcrIIs-AcrIIA2, AcrIIA4, and AcrIIA5. The results showed that the three AcrIIs worked in a dose-dependent manner in S. cerevisiae. When expressed by a strong promoter-pGPD-they reduced the fluorescence level reached by dSpCas9:scRNA_2&#215;MS2(wt+f6)-MCP-VP64 to 0.21, 0.11, and 0.13 of its value, respectively (Figure 4B). Since AcrIIA4 was the only one that caused high inhibition of fluorescence expression even when produced by a weak, synthetic promoter-genCYC1t_pCYC1noTATA-we could infer that AcrIIA4 was the strongest inhibitor among the three AcrIIs. Next, the HBD(ER) was fused to the C terminus of AcrIIA4 to build a &#946;-estradiol sensing device (see Figure 4C). In the presence of the estrogen &#946;-estradiol, AcrIIA4-HBD(ER) could translocate into the nucleus and then neutralize the function of the dSpCas9-based activator. The titration curve in Figure 4D shows that the circuit behaves like a NOT gate with an ON/OFF ratio approaching 2.3.
AcrVAs are repressors of dCas12a proteins 
A NOT gate was designed and built by inserting the pGAL1-driving AcrVA expression cassette into circuit 1. In this way, AcrVA synthesis, and the consequent repression of dCas12a-AD, could be induced by galactose (Figure 5A).&#160;As shown in Figure 5B,C, AcrVA1 hindered both denAsCas12a and dLbCas12a as activators by reducing fluorescence expression from 19% to 71%, depending on the circuit scheme. AcrVA4 and AcrVA5 cannot exert any action on denAsCas12a34. However, they performed a strong inhibition of dLbCas12a-based activators by reducing fluorescence expression up to 84% (AcrVA5) and 82% (AcrVA4). On the whole, AcrVA5 turned out to be the most reliable, among these three AcrVAs, in inhibiting dLbCas12a-based activators because it guaranteed, in different circuits, higher than 70% repression.
AcrVA1 action is concentration-dependent 
The relation between AcrVAs&#39; in vivo concentration and their inhibitory effects on both activators and repressors based on denAs/dLbCas12a was also studied. To this aim, each AcrVA was expressed under different promoters: the strong pGAL1, the medium-strength pTEF1, and the weak genCYC1t_pCYC1noTATA. As illustrated in Figure 5E, AcrVA1 displayed big fluctuations in its performance depending on the promoter that led its synthesis. AcrVA1 worked reasonably well only when produced by pGAL1. Under pTEF1 and genCYC1t_pCYC1noTATA, AcrVA1 showed some repression on the bare dLbCas12a only. AcrVA4 and AcrVA5, in contrast, appeared to be less sensitive to their concentration, especially when interacting with the bare dLbCas12a.
This data showed that AcrVA4 and AcrVA5 generally performed better than AcrVA1 in inhibiting dLbCas12a-based transcription factors in S. cerevisiae. It should be noted that AcrVA5 has a peculiar working mechanism, since it acts as an enzyme that modifies LbCas12a permanently. However, as mentioned above, AcrVA5 (together with AcrVA4) cannot interact with denAsCas12a.
When AcrVAs were expressed under pGAL1, circuits become either YES gates (dCas12a was fused to an AD) or NOT gates (bare dCas12a). The former looked all highly performant, while the latter appeared to work better in the presence of AcrVA4 or AcrVA5.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64539/64539fig01.jpg" /> 
Figure 1: Transcriptional activation mediated by dCas9/dCas12a-AD. (A) Circuit 1 diagram. The synthetic promoters of yEGFP contained six (6x) and three (3x) copies of target sites for dCas9 or dCas12a, respectively. After that, dCas9/dCas12a-AD combines with sgRNA/crRNA; the synthetic promoter is targeted and activated due to the presence of the activation domains. (B) The best activation efficiency of dSpCas9-VP64 was achieved when there were six lexOp target sites on the synthetic promoter, and the sgRNA was transcribed by SNR52i. (C,D) The highest activation efficiency of dCas12a-VPR was obtained when three copies of lexOp were inserted into the synthetic promoter, and the crRNA was generated by SNR52i. SNR52i means that the sgRNA/crRNA was produced by pSNR52, and the expression cassette was placed inside an integrative shuttle vector. RGRi means an integrative plasmid hosting the RGR cassette to express sgRNA/crRNA was used. The negative control, &#34;-&#34;, represents a sgRNA/crRNA containing a scrambled spacer sequence that does not match with the lexOp site nor with any sequence along the yeast genome. &#34;bA&#34; indicates that the sgRNA/crRNA binds the antisense strand of target DNA, while &#34;bS&#34; stands for binding the sense strand. Each fluorescence level represents the mean value from at least three independent experiments (i.e., carried out on different days). Error bars are the standard deviation of the mean. <a href="https://www.jove.com/files/ftp_upload/64539/64539fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64539/64539fig02.jpg" /> 
Figure 2: The comparison of integrative and episomal plasmid-producing sgRNA/crRNA. (A) Activation efficiency of dLbCas12a-VPR:crRNA when targeting a single site on the promoter upstream of yEGFP. (B) dSpCas9-VP64:sgRNA activated n&#215; synthetic promoter (&#34;n&#34; stands for the number of lexOp target sites). (C,D) Normalized sgRNA/crRNA expression level15,16. &#34;i&#34; means that the sgRNA/crRNA expression cassette was placed into an integrative shuttle vector, while &#34;m&#34; stands for multicopy (i.e., episomal) plasmid. &#34;bA&#34;/&#34;bS&#34; indicates that the sgRNA/crRNA binds the antisense/sense strand of the DNA. &#34;ctrl&#34; is a negative control, where a scrambled sgRNA/crRNA was expressed. Each fluorescence level represents the mean value from at least three independent experiments (i.e., carried out on different days). Error bars are the standard deviation of the mean. <a href="https://www.jove.com/files/ftp_upload/64539/64539fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64539/64539fig03.jpg" /> 
Figure 3: The activation efficiency of the bare dSpCas9 in complex with an scRNA. (A) The schematic diagram of the interactions among scRNA, MCP-VP64, and the bare dSpCas916. The cap-like structure in purple represents MCP (MS2 coat protein). One MS2 hairpin (the purple structure in the scRNA) can recruit and bind two copies of MCP. Thus, an scRNA enables not only DNA-binding by dSpCas9 but also activation of gene expression through the recruitment of MCP-VP64. (B) The activation efficiency of dSpCas9:scRNA-MCP-VP64. Three kinds of scRNA were tested: one carrying the wild-type MS2 hairpin-1&#215;MS2(wt), another engineered with the f6 MCP aptamer-1&#215;MS2(f6), and the last one containing both hairpins-2&#215;MS2(wt+f6), which turned out to be the most performant. Each fluorescence level represents the mean value from at least three independent experiments (i.e., carried out on different days). Error bars are the standard deviation of the mean. <a href="https://www.jove.com/files/ftp_upload/64539/64539fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64539/64539fig04.jpg" /> 
Figure 4: AcrIIA-related circuits and results. (A) The AcrIIA expression cassette was inserted into circuit 1. This additional TU includes pGPD leading the expression of AcrIIAs to counteract dSpCas9:scRNA_2&#215;MS2(wt+f6)-MCP-VP64. (B) The inhibition efficiency of AcrIIAs on the best dSpCas9-based activator in Figure 3B. The black dashed line represents the fluorescence in the presence of the dSpCas9-based activator. The figures above each column show the inhibition efficiency calculated as the OFF/ON ratio (i.e., the fluorescence level in the presence of the AcrIIA divided by the fluorescence in the absence of any AcrIIA). In the legend, the strength of the four constitutive promoters gradually increases from up to down. (C) Diagram of the &#946;-estradiol sensing device (NOT gate) expressing AcrIIA4-HBD(hER). (D) Titration curve of the circuit in (C)16. The green curve refers to the change in fluorescence in the functional circuit. The black curve was derived from the strain without the expression of AcrIIA4-HBD(hER)-the negative control. The dashed line in gray marked the fluorescence plateau at the equilibrium. It was calculated as the mean of the fluorescence values at concentrations of &#946;-estradiol not lower than 125 nM. Each fluorescence level represents the mean value from at least three independent experiments (i.e., carried out on different days). Error bars are the standard deviation of the mean. <a href="https://www.jove.com/files/ftp_upload/64539/64539fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64539/64539fig05.jpg" /> 
Figure 5: AcrVA-related circuits and results. (A) The AcrVA expression cassette was inserted into circuit 1. The new TU contains the inducible promoter pGAL1 upstream of the AcrVA genes that neutralize the working of dCas12a-AD. The new circuit is a NOT gate regulated by galactose. (B,C) Results from the galactose-responsive NOT gate in (A). Here, pGAL1 drives the synthesis of AcrVAs, which then interact with dCas12a-AD15. The relative fluorescence corresponds to the OFF/ON ratio. (D) Galactose-responsive YES gate. It employs AcrVAs under the control of pGAL1 and the bare dCas12as that represses the synthesis of yEGFP. (E) Comparison of the inhibition efficiencies of AcrVAs expressed by promoters of different strength15. The groups &#34;AcrV effects on repression&#34; and &#34;bare dLb&#34; refer to the circuit in (D). The groups &#34;AcrV effects on activation&#34; and &#34;dLb-VPR&#34; are the results of the NOT gate in (A). Each fluorescence level represents the mean value from at least three independent experiments (i.e., carried out on different days). Error bars are the standard deviation of the mean. <a href="https://www.jove.com/files/ftp_upload/64539/64539fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Table 1: A list of all DNA sequences used in this study. <a href="https://www.jove.com/files/ftp_upload/64539/Suplementary Table 1.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 2: A list of primers used in this study. <a href="https://www.jove.com/files/ftp_upload/64539/Suplementary Table 2.xlsx" target="_blank">Please click here to download this File.</a>
Supplemental Coding Files: The R studio script to analyze the FCS files. <a href="https://www.jove.com/files/ftp_upload/64539/BDverse_analysis.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins at JoVE.com

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

CRISPR-Cas systems and anti-CRISPR proteins were integrated into the scheme of Boolean gates in Saccharomyces cerevisiae. The new small logic circuits showed good performance and deepened the understanding of both dCas9/dCas12a-based transcription factors and the properties of anti-CRISPR proteins.

Synthetic gene Boolean gates and digital circuits have a broad range of applications, from medical diagnostics to environmental care. The discovery of the ...

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1.6K Views.  Tianjin University. CRISPR-Cas systems and anti-CRISPR proteins were integrated into the scheme of Boolean gates in Saccharomyces cerevisiae. The new small logic circuits showed good performance and deepened the understanding of both dCas9/dCas12a-based transcription factors and the properties of anti-CRISPR proteins. 

Video: Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Three-dimensional Cell Culture Model for Measuring the Effects of Interstitial Fluid Flow on Tumor Cell Invasion

The growth and progression of most solid tumors depend on the initial transformation of the cancer cells and their response to stroma-associated signaling in the tumor microenvironment 1. Previously, research on the tumor microenvironment has focused primarily on tumor-stromal interactions 1-2. However, the tumor microenvironment also includes a variety of biophysical forces, whose effects remain poorly understood. These forces are biomechanical consequences of tumor growth that lead to changes in gene expression, cell division, differentiation and invasion3. Matrix density 4, stiffness 5-6, and structure 6-7, interstitial fluid pressure 8, and interstitial fluid flow 8 are all altered during cancer progression.
Interstitial fluid flow in particular is higher in tumors compared to normal tissues 8-10. The estimated interstitial fluid flow velocities were measured and found to be in the range of 0.1-3 &mu;m s-1, depending on tumor size and differentiation 9, 11. This is due to elevated interstitial fluid pressure caused by tumor-induced angiogenesis and increased vascular permeability 12. Interstitial fluid flow has been shown to increase invasion of cancer cells 13-14, vascular fibroblasts and smooth muscle cells 15. This invasion may be due to autologous chemotactic gradients created around cells in 3-D 16 or increased matrix metalloproteinase (MMP) expression 15, chemokine secretion and cell adhesion molecule expression 17. However, the mechanism by which cells sense fluid flow is not well understood. In addition to altering tumor cell behavior, interstitial fluid flow modulates the activity of other cells in the tumor microenvironment. It is associated with (a) driving differentiation of fibroblasts into tumor-promoting myofibroblasts 18, (b) transporting of antigens and other soluble factors to lymph nodes 19, and (c) modulating lymphatic endothelial cell morphogenesis 20.
The technique presented here imposes interstitial fluid flow on cells in vitro and quantifies its effects on invasion (Figure 1). This method has been published in multiple studies to measure the effects of fluid flow on stromal and cancer cell invasion 13-15, 17. By changing the matrix composition, cell type, and cell concentration, this method can be applied to other diseases and physiological systems to study the effects of interstitial flow on cellular processes such as invasion, differentiation, proliferation, and gene expression.

Interstitial fluid flow is elevated in solid tumors and can modulate tumor cell invasion. Here we describe a technique to apply interstitial fluid flow to cells embedded in a matrix and then measure its effects on cell invasion. This technique can be easily adapted to study other systems.

The growth and progression of most solid tumors depend on the initial transformation of the cancer cells and their response to stroma-associated signaling ...

Cell Patterning on Photolithographically Defined 
Parylene-C: SiO2 Substrates

Cell patterning platforms support broad research goals, such as construction of predefined in vitro neuronal networks and the exploration of certain central aspects of cellular physiology. To easily combine cell patterning with Multi-Electrode Arrays (MEAs) and silicon-based &#8216;lab on a chip&#8217; technologies, a microfabrication-compatible protocol is required. We describe a method that utilizes deposition of the polymer parylene-C on SiO2&#160;wafers. Photolithography enables accurate and reliable patterning of parylene-C at micron-level resolution. Subsequent activation by immersion in fetal bovine serum (or another specific activation solution) results in a substrate in which cultured cells adhere to, or are repulsed by, parylene or SiO2 regions respectively. This technique has allowed patterning of a broad range of cell types (including primary murine hippocampal cells, HEK 293 cell line, human neuron-like teratocarcinoma cell line, primary murine cerebellar granule cells, and primary human glioma-derived stem-like cells). Interestingly, however, the platform is not universal; reflecting the importance of cell-specific adhesion molecules. This cell patterning process is cost effective, reliable, and importantly can be incorporated into standard microfabrication (chip manufacturing) protocols, paving the way for integration of microelectronic technology.

Cell Patterning on Photolithographically Defined 
Parylene-C: SiO2 Substrates

This protocol describes a microfabrication-compatible method for cell patterning on SiO2. A predefined parylene-C design is photolithographically printed on SiO2 wafers. Following incubation with serum (or other activation solution) cells adhere specifically to (and grow according to the conformity of) underlying parylene-C, whilst being repulsed by SiO2 regions.

Cell patterning platforms support broad research goals, such as construction of predefined in vitro neuronal networks and the exploration of certain ...

Investigating Receptor-ligand Systems of the Cellulosome with AFM-based Single-molecule Force Spectroscopy

Cellulosomes are discrete multienzyme complexes used by a subset of anaerobic bacteria and fungi to digest lignocellulosic substrates. Assembly of the enzymes onto the noncatalytic scaffold protein is directed by interactions among a family of related receptor-ligand pairs comprising interacting cohesin and dockerin modules. The extremely strong binding between cohesin and dockerin modules results in dissociation constants in the low picomolar to nanomolar range, which may hamper accurate off-rate measurements with conventional bulk methods. Single-molecule force spectroscopy (SMFS) with the atomic force microscope measures the response of individual biomolecules to force, and in contrast to other single-molecule manipulation methods (i.e.&#160;optical tweezers), is optimal for studying high-affinity receptor-ligand interactions because of its ability to probe the high-force regime (&#62;120 pN). Here we present our complete protocol for studying cellulosomal protein assemblies at the single-molecule level. Using a protein topology derived from the native cellulosome, we worked with enzyme-dockerin and carbohydrate binding module-cohesin (CBM-cohesin) fusion proteins, each with an accessible free thiol group at an engineered cysteine residue. We present our site-specific surface immobilization protocol, along with our measurement and data analysis procedure for obtaining detailed binding parameters for the high-affinity complex. We demonstrate how to quantify single subdomain unfolding forces, complex rupture forces, kinetic off-rates, and potential widths of the binding well. The successful application of these methods in characterizing the cohesin-dockerin interaction responsible for assembly of multidomain cellulolytic complexes is further described.

Cellulosomes are multienzyme complexes designed for digesting cellulose. AFM-based SMFS was used to study the mechanical properties and folding configuration of cellulosome-associated protein assemblies. We present a complete workflow for protein immobilization, data acquisition, and data analysis to study the interactions of individual receptor-ligand complexes involved in cellulosome assembly.

Cellulosomes are discrete multienzyme complexes used by a subset of anaerobic bacteria and fungi to digest lignocellulosic substrates. Assembly of the ...

A Microfluidic-based Electrochemical Biochip for Label-free DNA Hybridization Analysis

Miniaturization of analytical benchtop procedures into the micro-scale provides significant advantages in regards to reaction time, cost, and integration of pre-processing steps. Utilizing these devices towards the analysis of DNA hybridization events is important because it offers a technology for real time assessment of biomarkers at the point-of-care for various diseases. However, when the device footprint decreases the dominance of various physical phenomena increases. These phenomena influence the fabrication precision and operation reliability of the device. Therefore, there is a great need to accurately fabricate and operate these devices in a reproducible manner in order to improve the overall performance. Here, we describe the protocols and the methods used for the fabrication and the operation of a microfluidic-based electrochemical biochip for accurate analysis of DNA hybridization events. The biochip is composed of two parts: a microfluidic chip with three parallel micro-channels made of polydimethylsiloxane (PDMS), and a 3 x 3 arrayed electrochemical micro-chip. The DNA hybridization events are detected using electrochemical impedance spectroscopy (EIS) analysis. The EIS analysis enables monitoring variations of the properties of the electrochemical system that are dominant at these length scales. With the ability to monitor changes of both charge transfer and diffusional resistance with the biosensor, we demonstrate the selectivity to complementary ssDNA targets, a calculated detection limit of 3.8 nM, and a 13% cross-reactivity with other non-complementary ssDNA following 20 min&#160;of incubation. This methodology can improve the performance of miniaturized devices by elucidating on the behavior of diffusion at the micro-scale regime and by enabling the study of DNA hybridization events.

We present a microfluidic-based electrochemical biochip for DNA hybridization detection. Following ssDNA probe functionalization, the specificity, sensitivity, and detection limit are studied with complementary and non-complementary ssDNA targets. Results illustrate the influence of the DNA hybridization events on the electrochemical system, with a detection limit of 3.8 nM.

Miniaturization of analytical benchtop procedures into the micro-scale provides significant advantages in regards to reaction time, cost, and integration ...

Patterning Bioactive Proteins or Peptides on Hydrogel Using Photochemistry for Biological Applications

There are many biological stimuli that can influence cell behavior and stem cell differentiation. General cell culture approaches rely on soluble factors within the medium to control cell behavior. However, soluble additions cannot mimic certain signaling motifs, such as matrix-bound growth factors, cell-cell signaling, and spatial biochemical cues, which are common influences on cells. Furthermore, biophysical properties of the matrix, such as substrate stiffness, play important roles in cell fate, which is not easily manipulated using conventional cell culturing practices. In this method, we describe a straightforward protocol to provide patterned bioactive proteins on synthetic polyethylene glycol (PEG) hydrogels using photochemistry. This platform allows for the independent control of substrate stiffness and spatial biochemical cues. These hydrogels can achieve a large range of physiologically relevant stiffness values. Additionally, the surfaces of these hydrogels can be photopatterned with bioactive peptides or proteins via thiol-ene click chemistry reactions. These methods have been optimized to retain protein function after surface immobilization. This is a versatile protocol that can be applied to any protein or peptide of interest to create a variety of patterns. Finally, cells seeded onto the surfaces of these bioactive hydrogels can be monitored over time as they respond to spatially specific signals.

In this method, we use photopolymerization and click chemistry techniques to create protein or peptide patterns on the surface of polyethylene glycol (PEG) hydrogels, providing immobilized bioactive signals for the study of cellular responses in vitro.

There are many biological stimuli that can influence cell behavior and stem cell differentiation. General cell culture approaches rely on soluble factors ...

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

This protocol describes how to make agarose-based tissue-mimicking phantoms and demonstrates how to determine their optical properties using a conventional optical system with an integrating sphere. Measuring systems for the acquisition of the diffuse reflectance and total transmittance spectra are constructed with a broadband white light source, a light guide, an achromatic lens, an integrating sphere, a sample holder, an optical fiber probe, and a multi-channel spectrometer. An acrylic mold consisting of two rectangular acrylic pieces and a U-shaped acrylic piece is constructed to create an epidermal phantom and a dermal phantom with whole blood. The application of a sodium dithionite (Na2S2O4) solution to the dermal phantom enables the researcher to deoxygenate hemoglobin in red blood cells distributed in the dermal phantom. The inverse Monte Carlo simulation with the diffuse reflectance and total transmittance spectra measured by a spectrometer with an integrating sphere is performed to determine the absorption coefficient spectrum &#181;a(&#955;) and the reduced scattering coefficient spectrum &#181;s&#39;(&#955;) of each layer phantom. A two-layered phantom mimicking the diffuse reflectance of human skin tissue is also demonstrated by piling up the epidermal phantom on the dermal phantom.

Here, we demonstrate how agarose-based tissue-mimicking optical phantoms are made and how their optical properties are determined using a conventional optical system with an integrating sphere.

This protocol describes how to make agarose-based tissue-mimicking phantoms and demonstrates how to determine their optical properties using a ...

A Droplet-Based Microfluidic Approach and Microsphere-PCR Amplification for Single-Stranded DNA Amplicons

Droplet-based microfluidics enable the reliable production of homogeneous microspheres in the microfluidic channel, providing controlled size and morphology of the obtained microsphere. A microsphere copolymerized with an acrydite-DNA probe was successfully fabricated. Different methods such as asymmetric PCR, exonuclease digestion, and isolation on streptavidin-coated magnetic beads can be used to synthesize single-stranded DNA (ssDNA). However, these methods cannot efficiently use large amounts of highly purified ssDNA. Here, we describe a microsphere-PCR protocol detailing how ssDNA can be efficiently amplified and separated from dsDNA simply by pipetting from a PCR reaction tube. The amplification of ssDNA can be applied as potential reagents for the DNA microarray and DNA-SELEX (Systematic evolution of ligands by exponential enrichment) processes.

This work provides a method for the fabrication of droplet-based microfluidic platforms and the application of polyacrylamide microspheres for microsphere-PCR amplification. The microsphere-PCR method makes it possible to obtain single-stranded DNA amplicons without separating double-stranded DNA.

Droplet-based microfluidics enable the reliable production of homogeneous microspheres in the microfluidic channel, providing controlled size and ...

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

The limitations of cell-based synthetic biology are becoming increasingly apparent as researchers aim to develop larger and more complex synthetic genetic regulatory circuits. The analysis of synthetic genetic regulatory networks in vivo is time consuming and suffers from a lack of environmental control, with exogenous synthetic components interacting with host processes resulting in undesired behavior. To overcome these issues, cell-free characterization of novel circuitry is becoming more prevalent. In vitro transcription and translation (IVTT) mixtures allow the regulation of the experimental environment and can be optimized for each unique system. The protocols presented here detail the fabrication of a multilayer microfluidic device that can be utilized to sustain IVTT reactions for prolonged durations. In contrast to batch reactions, where resources are depleted over time and (by-) products accumulate, the use of microfluidic devices allows the replenishment of resources as well as the removal of reaction products. In this manner, the cellular environment is emulated by maintaining an out-of-equilibrium environment in which the dynamic behavior of gene circuits can be investigated over extended periods of time. To fully exploit the multilayer microfluidic device, hardware and software have been integrated to automate the IVTT reactions. By combining IVTT reactions with the microfluidic platform presented here, it becomes possible to comprehensively analyze complex network behaviors, furthering our understanding of the mechanisms that regulate cellular processes.

The fabrication process of a PDMS-based, multilayer, microfluidic device that allows in vitro transcription and translation (IVTT) reactions to be performed over prolonged periods is described. Furthermore, a comprehensive overview of the hardware and software required to automate and maintain these reactions for prolonged durations is provided.

The limitations of cell-based synthetic biology are becoming increasingly apparent as researchers aim to develop larger and more complex synthetic genetic ...

Methods for In Vivo Biomechanical Testing on Brachial Plexus in Neonatal Piglets

Neonatal brachial plexus palsy (NBPP) is a stretch injury that occurs during the birthing process in nerve complexes located in the neck and shoulder regions, collectively referred to as the brachial plexus (BP). Despite recent advances in obstetrical care, the problem of NBPP continues to be a global health burden with an incidence of 1.5 cases per 1,000 live births. More severe types of this injury can cause permanent paralysis of the arm from the shoulder down. Prevention and treatment of NBPP warrants an understanding of the biomechanical and physiological responses of newborn BP nerves when subjected to stretch. Current knowledge of the newborn BP is extrapolated from adult animal or cadaveric BP tissue instead of in vivo neonatal BP tissue. This study describes an in vivo mechanical testing device and procedure to conduct in vivo biomechanical testing in neonatal piglets. The device consists of a clamp, actuator, load cell, and camera system that apply and monitor in vivo strains and loads until failure. The camera system also allows monitoring of the failure location during rupture. Overall, the presented method allows for a detailed biomechanical characterization of neonatal BP when subjected to stretch.

Methods for In Vivo Biomechanical Testing on Brachial Plexus in Neonatal Piglets

Presented here are methods to perform in vivo biomechanical testing on brachial plexus in a neonatal piglet model.

Neonatal brachial plexus palsy (NBPP) is a stretch injury that occurs during the birthing process in nerve complexes located in the neck and shoulder ...

Electrowetting-based Digital Microfluidics Platform for Automated Enzyme-linked Immunosorbent Assay

Electrowetting is the effect by which the contact angle of a droplet exposed to a surface charge is modified. Electrowetting-on-dielectric (EWOD) exploits the dielectric properties of thin insulator films to enhance the charge density and hence boost the electrowetting effect. The presence of charges results in an electrically induced spreading of the droplet which permits purposeful manipulation across a hydrophobic surface. Here, we demonstrate EWOD-based protocol for sample processing and detection of four categories of antigens, using an automated surface actuation platform, via two variations of an Enzyme-Linked Immunosorbent Assay (ELISA) methods. The ELISA is performed on magnetic beads with immobilized primary antibodies which can be selected to target a specific antigen. An antibody conjugated to HRP binds to the antigen and is mixed with H2O2/Luminol for quantification of the captured pathogens. Assay completion times of between 6 and 10 min were achieved, whilst minuscule volumes of reagents were utilized.

Electrowetting-based digital microfluidic is a technique that utilizes a voltage-driven change in the apparent contact angle of a microliter-volume droplet to facilitate its manipulation. Combining this with functionalized magnetic beads enables the integration of multiple laboratory unit operations for sample preparation and identification of pathogens using Enzyme-linked Immunosorbent Assay (ELISA).

Electrowetting is the effect by which the contact angle of a droplet exposed to a surface charge is modified. Electrowetting-on-dielectric (EWOD) exploits ...