Emulsions are formed by shearing one liquid into a second immiscible one, often in the presence of surfactant, to create small droplets. The droplets can be remarkably stable, maintaining their shape and distribution for years 50 . Significant advances have been made in the past few years to produce emulsions that are monodisperse, with standard deviations in droplet size less than 5% 51,52,53,54 . Unlike the standard crossflow techniques for generating water-in-oil emulsions, in which the discontinuous phase is forced through narrow pores 52,54 or capillaries 52,55 into an open continuous phase, we accomplish droplet formation at the junction of two microfluidic channels containing water and an oil surfactant mixture. The water partially obstructs flow at the junction, but is not broken off at the channel interface as in traditional crossflow devices. Droplet formation is achieved by high shear forces generated at the leading edge of the water perpendicular to the oil flow, generating picoliter-scale droplets. Although the system remains at low Reynolds number, the flow is nonlinear because of interactions on the boundary between the two fluids. The two important effects are that the boundary is not static and that the motion of one fluid can entrain the other 56 . The resulting instability that drives droplet formation is a well known competition between surface tension and shear forces 57 .
1.2.3 Synthetic polymers
PEG (polyethylene glycol) is a biocompatible and biodegradable hydrophilic poly- meric network chains comprising a PEG diol with two hydroxyl end groups that can be changed into other groups to make hydrogels with different mechanical and chemical prop- erties. Even though PEG had a degradability, it is poor and negligible and it is also resis- tant to cell adhesion, which is not ideal for hydrogel droplet based applications for cell encapsulation. To increase the advantages, enzymatic peptide sequences can be used and injected into PEG hydrogels to let its degradation rate slow down and make them respon- sive to cells. The most common gelation method with PEG hydrogel is photo cross linking, which has a good in situ gelation at physiological temperature and pH. Furthermore, the light intensity can be minimized by choosing appropriate concentration and photo initiator chemistry, resulting in PEG gelation process more cell friendly.
Microfluidic spheroid formation platforms have been applied to HTS for long-term perfusion cell cultures and have maintained high-cell viability. In the past, numerous microfluidic platforms have been designed for formation of spheroids using microwells or U-shaped microstructures in the device. Microwell-based microfluidic platforms have been utilised more than other methods owing to their simplicity and easy operation [18-20]. Liu et al. designed a microfluidic device which had temporary U-shaped pneumatic microstructures for high-throughput spheroid formation, culture assessments, and drug efficacy tests . These platforms were often combined with a concentration gradient generator (CGG) as a mixing channel [22-24]. Such a channel can be controlled for precise flow control. Recently, Fan et al. reported a high-throughput drug screening brain cancer chip composed of a photo-polymerised hydrogel to form multiple cancer spheroids . They demonstrated that the culture array in association with a gradient generator was capable of forming spheroids, and for widespread parallel testing of drug responses. However, their microfluidic chip is difficult to be commercialised because of the short storage time of the hydrogel. In addition, because cells are injected through inlets, it is difficult for cells to go into the microwells through sub-channels, and their losses are thus high.
Table 5.1. The effects of various PDMS surface treatments on the background fluorescence signal by SYTO-9 adhesion to the microfluidic channel wall.
background signal varied widely between microfluidic measurements depending on the surface pre-treatment, device operation time, the concentration of SYTO-9, device material and laser intensity. In particular, the surface pre-treatment was found to be an important factor for reducing the adhesion of SYTO-9 to the channel wall. Table 5.1 shows the extent of SYTO-9 adhesion for PDMS surfaces treated using various surface treatment methods. In order to test this, microfluidic devices were fabricated, and treated with various surface treatment methods such as Aquapel, 0.2% Trichlorosilane in FC40, 100 µg/ml Bovine Serum Albumin (BSA), 1% pluronic P105 and 1% pluronic F108 in DI water for 5 minutes. Aquapel and 0.2% Trichlorosilane are typically used for hydrophobic glass surface coating, BSA is frequently used for microfluidic surface coating for reducing biomolecule interaction with the channel wall, and Pluronic P105 and F108 are polymers which inhibits the biomolecule adhesion to various types of surfaces. After flushing the device with DI water for 5 minutes, 10 µM SYTO-9 aqueous solution was flowed through the devices for 5 minutes at a flow rate of 10 µl/min and washed with DI water in order to remove any remaining unbound SYTO-9.
Background: Droplet‑based microfluidics is becoming an increasingly attractive alternative to microtiter plate tech‑ niques for enzymatic high‑throughputscreening (HTS), especially for exploring large diversities with lower time and cost footprint. In this case, the assayed enzyme has to be accessible to the substrate within the water‑in‑oil droplet by being ideally extracellular or displayed at the cell surface. However, most of the enzymes screened to date are expressed within the cytoplasm of Escherichia coli cells, which means that a lysis step must take place inside the drop‑ lets for enzyme activity to be assayed. Here, we take advantage of the excellent secretion abilities of the yeast Yarrowia lipolytica to describe a highly efficient expression system particularly suitable for the droplet‑based microfluidic HTS. Results: Five hydrolytic genes from Aspergillus niger genome were chosen and the corresponding five Yarrowia lipolytica producing strains were constructed. Each enzyme (endo‑β‑1,4‑xylanase B and C; 1,4‑β‑cellobiohydrolase A; endoglucanase A; aspartic protease) was successfully overexpressed and secreted in an active form in the crude supernatant. A droplet‑based microfluidic HTS system was developed to (a) encapsulate single yeast cells; (b) grow yeast in droplets; (c) inject the relevant enzymatic substrate; (d) incubate droplets on chip; (e) detect enzymatic activity; and (f ) sort droplets based on enzymatic activity. Combining this integrated microfluidic platform with gene expression in Y. lipolytica results in remarkably low variability in the enzymatic activity at the single cell level within a given monoclonal population (<5%). Xylanase, cellobiohydrolase and protease activities were successfully assayed using this system. We then used the system to screen for thermostable variants of endo‑β‑1,4‑xylanase C in error‑prone PCR libraries. Variants displaying higher thermostable xylanase activities compared to the wild‑type were isolated (up to 4.7‑fold improvement).
A number of efforts were expended to select the optimum scaffold and the activities of the predicted actives by SVM were estimated by using the MLRS model. Jorgenson group at University of Copenhagen developed many insilico models based on different classification methods such as binary QSAR, kNN, SVM, decision tree etc.
for developing inhibitors for P450 1A2, an important enzyme in drug metabolism. Here, SVM, kNN and random forest methods were found to be the best methods delivering models with high prediction accuracy with a Mathews correlation coefficient of 0.5 . Similar results were obtained by Khandelwal et al.  in their work on predicting pregnane X receptor activators using machine learning methods coupled with docking protocol. They observed that docking combined with regression yielded inferior results when compared with SVM and RF methods. Plewczynski et al.  have conducted extensive studies to assess a host of machine learning techniques such as SVM, random forest, ANN, k-nearest neighbour (kNN) classification with genetic- algorithm-optimized feature selection, trend vectors, naive Bayesian classification, and decision tree, for their capacity to recognize ligands from a large data collection of molecules. Interestingly, they obtained varying results from the stated methods; while some were good in retrieving actives, others yielded high enrichment scores. However, all the methods could correctly predict the recently reported ligands. It was concluded that no single method can be the most consistent one; rather a combination of methods is essential for better results in virtual screening.
The biochemical composition of different strains of microalgae has been extensively studied due to the various applications of algal biomass and metabolites (Guedes et al., 2011). This knowledge enhances strain selection for different applications such as biofuels, biopharmaceuticals and bioremediation. The choice of algal strain depends largely on the intended bioproducts, the bioprocess engineering environment it will be used and the cell genetic make up. In certain instances, strains are selected based on the knowledge of their genetic compositions and understanding of their metabolic pathways and growth rates. In other instances, selection is based on their ability to synthesize either extra or intra-cellular metabolites of high value. In general, most algae exhibit a similar biochemical composition as shown in Table 1-1.
Table 1.1: Approximate run times, yields, read lengths, costs, and sequencing error rates of different high-throughput sequencing technologies by mid 2011 [Glenn, 2011].
and dehybridized into two single strands, polymerase replicates the complementary of each strand by sequentially incorporating complementary nucleotides. For the irst time, Sanger et al. utilized terminating nucleotides that immediately stop the replication after incorporation. The DNA template is irst replicated multiple times (ampli ication) and then replicated in four solutions ( A , C , G , and T ) each of which contain all nucleotides and one terminating nucleotide in low concentration. After that, each solution contains en- tire copies and partial copies that end with the known terminating nucleotide, e.g. the solution with all four nucleotides and the terminating C contains pre ixes of the comple- mentary template strand that end with a C . A subsequent gel electrophoresis of the four sets is used to separate the pre ixes by their lengths and directly reveals the template se- quence. In the gel, a molecule migrates with a speed inversely proportional to its length.
render them hydrophobic (Devenish et al., 2013). The design file of the device used in this study (40 µm flow-focusing channel) is available from the author’s website: (Figure 1).
Figure 1. Design of the microfluidic device used for droplet generation. The device contains an inlet for the oil phase, an inlet for the aqueous phase (bacteria, agarose, medium) and an exit outlet. The droplets are formed at the flow-focusing geometry (picture inset). For this protocol, the channel width at the flow focusing part is 40 µm and the height of the channels is also 40 µm. Using this device, droplets with a diameter of 40-50 µm can be produced. Scale bar
Yu-Ting Liu, Xian-Bin Li,* Hui Zheng, Nian-Ke Chen,* Xue-Peng Wang, Xu-Lin Zhang, Hong-Bo Sun,* and Shengbai Zhang
Phase change memory (PCM) is an emerging non-volatile data storage technology concerned by the semiconductor industry. To improve the perfor- mances, previous efforts have mainly focused on partially replacing or doping elements in the flagship Ge-Sb-Te (GST) alloy based on experimental “trial- and-error” methods. Here, the current largest scale PCM materials searching is reported, starting with 124 515 candidate materials, using a rational high- throughputscreening strategy consisting of criteria related to PCM charac- teristics. In the results, there are 158 candidates screened for PCM materials, of which ≈68% are not employed. By further analyses, including cohesive energy, bond angle analyses, and Born effective charge, there are 52 materials with properties similar to the GST system, including Ge 2 Bi 2 Te 5 , GeAs 4 Te 7 , GeAs 2 Te 4 , so on and other candidates that have not been reported, such as TlBiTe 2 , TlSbTe 2 , CdPb 3 Se 4 , etc. Compared with GST, materials with close cohesive energy include AgBiTe 2 , TlSbTe 2 , As 2 Te 3 , TlBiTe 2 , etc., indicating possible low power consumption. Through further melt-quenching molecular dynamic calculation and structural/electronic analyses, Ge 2 Bi 2 Te 5 , CdPb 3 Se 4 , MnBi 2 Te 4 , and TlBiTe 2 are found suitable for optical/electrical PCM applica- tions, which further verifies the effectiveness of this strategy. The present study will accelerate the exploration and development of advanced PCM materials for current and future big-data applications.
A significant amount of work lies ahead in which we will test and evaluate primary patient biopsies in the Organo- Plate®, assess their longevity and retrospectively compare drug response to clinical outcome. Critical aspects to take into account will include stroma-tumor interaction. Fur- thermore, the model could be enhanced by including vas- cularity and aspects of the immune system. The OrganoPlate® platform is particularly suited for such com- plex co-cultures, as various cell types can be arranged in or- derly lanes, one next to the other, without the usage of artificial membranes. The challenge, however, may lie in the compatibility of the medium and matrix with all cell- types needed for the co-culture. Beyond optimization of cell culture conditions, the models need to be sufficiently robust and validated in order to improve their usability as an effective screening tool. While the current OrganoPlate®
CYP2D6 genotyping for multiple drug metabolism
The CYP450 enzyme system is one of the most important determinants of drug metabolism in humans. One of the central components of this pathway is the enzyme encoded by the CYP2D6 gene, which plays a role in metabolizing up to 25% of all available medications including the anticancer drug tamoxifen, antidepressants, antipsychotics, opioids, and beta-blockers, among others. 60 More than 70 alleles, which lead to either enhanced or decreased enzymatic expres- sion or efficiency, have been identified in this gene. 61 For example, studies have found that patients with a CYP2D6 poor metabolizer phenotype demonstrate a higher probability of adverse drug reactions to the antipsychotics haloperidol and risperidone. 62,63 While the clinical utility of such testing is still debated, 64,65 the desire to genotype many CYP2D6 markers in parallel has primarily been met with targeted array technologies. The Roche AmpliChip is FDA-approved and offered clinically; it uses a microarray with differential hybridization to probe 27 SNPs in CYP2D6, along with inser- tions and deletions, as well as two SNPs in CYP2C19. The Luminex xTAG bead-based array system is FDA-cleared to probe a similar number of CYP2D6 SNPs.
Lignocellulases are required for a wide range of applications, including the production of biofuels. While the production of biofuels from the renewable lignocellulosic biomass is gradually considered a promising way to replace fossil fuels, its bioconversion has been limited by the need for effective hydrolysis of lignin-carbohydrate complexes (LCC). This represents a major challenge in global efforts to utilize renewable resources in place of fossil fuels to meet the rising energy demands. Enzymatic hydrolysis is the most common process to degrade the cellulose and hemicellulose into fermentable sugars such as glucose and xylose, and multiple substrate enzymes are the most promising in this regards. This study has identified 38 beta-xylosidases encoding ORFs, ten of which have been confirmed through activity assay. None of the sequences identified in the shotgun sequence data were were identified in the genes that were confirmed through function-based assay. Some of these enzymes show potential industrial application through their ability of hydrolyse multiple substrates. Figure 58 summarises all enzymes which have been reported in this study.
HighThroughputScreening (HTS) has been used for over a quarter century to identify new drug candidates 1 . During the same period, the scale of the compound libraries and the complexity of methods employed has increased 2 . Early in the development of HTS some compounds were noted to respond frequently 3 leading to the concept of frequent hitters 3–6 and pan assay interference compounds (PAINs) 7–9 . Although specific structural motifs are known to be problematic, they also are found in pharmaceutically useful compounds 10–12 . Practitioners of HTS have learned to recognize a wide range of chemical and physical processes leading to apparent activity in HTS assays 13 . The science of active compound detection and corresponding statistical practice developed early 14,15 with many subsequent refinements 16–19 . The underpinning instrumental technologies of HTS have been influential for increasing the scale achievable in routine laboratory work and these technologies are widely deployed in the form of plate readers, lab robotics, and compound libraries accessible to researchers 20 . In addi- tion, due to funding policies considerable HTS data is publicly available 21 .
A new ultra-high-throughputscreening assay for the detection of cellulase activity was developed based on microfluidic sorting. Cellulase activity is detected using a series of coupled enzymes leading to the formation of a fluorescent product that can be detected on a chip. Using this method, we have achieved up to 300-fold enrichments of the active population of cells and greater than 90% purity after just one sorting round. In addition, we proved that we can sort the cellulase-expressing cells from mixtures containing less than 1% active cells. V C 2014 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License. [http://dx.doi.org/10.1063/1.4886771]
Hollfelder and co-workers extended the microfluidics system described above to a bacterial cytoplasmic expression system at a picoliter scale . In the first microfluidic device, an aqueous suspension of bacterial cells expressing the enzyme of interest is mixed together with a second aqueous stream at a flow-focusing junction. The second stream not only carries a substrate for PAS, but is also supplemented with cell lysis reagents. After mixing of both streams followed by droplet formation, E. coli cells are lysed and protein variants are released into the droplet; during off-chip incubation, active enzyme variants convert the substrate into a fluorescent product. Then, droplets are injected into the second microfluidic device, where they are sorted dielectrophoretically based on their fluorescence signal.
requirements for task decisions, as identified through the GDTA, were compared with specification of current interface action sequences and display content based on the AH models to identify potential usability issues with the existing software interfaces (for example, SAMI®). That is, we were able to determine whether particular screening method editor displays and action sequences led to the information operators needed for certain process decisions. In our previous work, we made direct comparison of the GDTA results and AH models to formulate interface design and automation functionality recommendations for enhancing the existing software applications used in the HTS process at CELISCA (Kaber et al., 2006).
Gergely Kosa 1* , Boris Zimmermann 1 , Achim Kohler 1 , Dag Ekeberg 2 , Nils Kristian Afseth 3 , Jerome Mounier 4 and Volha Shapaval 1
Background: Mucoromycota fungi are important producers of low- and high-value lipids. Mortierella alpina is used for arachidonic acid production at industrial scale. In addition, oleaginous Mucoromycota fungi are promising candi- dates for biodiesel production. A critical step in the development of such biotechnological applications is the selec- tion of suitable strains for lipid production. The aim of the present study was to use the Duetz-microtiter plate system combined with Fourier transform infrared (FTIR) spectroscopy for high-throughputscreening of the potential of 100 Mucoromycota strains to produce low- and high-value lipids.
Multi-atlas segmentation has proven useful for auto- mated region identification especially in clinical neuro- applications [24–27]. To overcome the limitation of many available software packages designed for clinical brain imaging, this approach has been implemented and employed for full and subregion segmentation of other organs (for whole body distribution and radiation dosim- etry) in several non-human species. Data variability—due to factors such as animal positioning, the non-rigidity of non-brain regions, general shape/distribution outliers and fluctuating contrast-to-noise—may occasionally cause segmentation failures, thus requiring strict QC and manual corrections. Nevertheless, we found in tim- ing experiments (not shown here) that multi-atlas seg- mentation followed by user editing of the automatically generated ROIs still significantly decreases processing time and observer variability compared to segmenting regions manually de novo.