Quantitative analysis of genetic expression responses to dynamic microenvironmental Professor Jeff Hasty, Chair Professor Stuart Brody Professor David Gough Professor Alexander Hoffmann
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After six challenging years filled with remarkable experiences and fascinating people, I reflect on my graduate journey, which was far from easy There were moments of doubt, questioning whether my past and future selves aligned with my aspirations Despite the difficulties, I owe my progress to the unwavering support of my colleagues, friends, and family, who have been instrumental in my success.
I express my deep gratitude to my professional colleagues, particularly Dr Hasty for his financial support and invaluable research guidance during my doctoral studies I am thankful for the collaborative and supportive environment at the Systems Biodynamics Lab, and I appreciate the editing assistance from Jesse, Chris, Mike, and Ben Special thanks to Jennifer and Natalie for their exceptional skills in yeast variant production, and to Matt and Dmitri for their artistry in computational modeling Additionally, I acknowledge Dr Groisman for providing a solid foundation in microfluidic design and fabrication, despite our challenging relationship.
I want to express my heartfelt gratitude to my friends who have been my pillars of support through both triumphs and challenges Jennifer, Chris, Lauren, and Jessica, your availability for coffee breaks at CUPs, walks at Mandeville, quick snacks at the “triple-S,” and happy hours at the Grove has been invaluable for my sanity and energy Jared, your unwavering friendship, from being my wedding groomsman to providing insights and spare parts when needed, means the world to me Pat, Peter, Karen, Jesse, Chris, and Joey, our Friday night poker games may have delayed my car payments, but the joy they bring is irreplaceable Diane, Jared, and Chris, I appreciate your efforts to keep me active on the tennis court and during early morning runs Ben, your assistance with data processing and timely answers has been a lifesaver, while Mike, you've been my go-to sounding board for frustrations and ideas—by the way, how many burritos have we shared?
I am profoundly grateful to my family for their unwavering love and support I want to extend my heartfelt thanks to my parents for encouraging my decision to attend graduate school, where I pursued both my master's and doctorate degrees Most importantly, I am thankful for my wife, Lisa, who has been by my side throughout this journey.
Since before graduate school, XIV has been a vital support in my journey, helping me understand that my efforts contribute not only to my future but also to our collective future Their unwavering encouragement has kept me focused and determined throughout every step of this process.
Chapter 2 includes excerpts from the publication by Volfson D and Hasty J, titled “Monitoring dynamics of single-cell gene expression over multiple cell cycles,” which was published in Molecular Systems Biology on November 22, 2005 The dissertation author is the first author of this study Additionally, parts of Chapter 2 are derived from the upcoming article by Pang WL, Bennett MR, Ostroff N, and Hasty J, focusing on “Metabolic gene regulation in a dynamically changing environment,” which is set to be submitted by February.
In 2007, the dissertation author was the primary contributor to a publication that includes a reprint of the work by Pang WL, Bennett MR, Ostroff N, and Hasty J, titled “Metabolic Gene Regulation in a Dynamically Changing Environment,” which is currently being prepared for submission to Nature by February.
Doctor of Philosophy in Bioengineering University of California, San Diego
Master of Science in Bioengineering University of California, San Diego
Bachelor of Science in Biochemical Engineering University of California, Davis
Teaching Assistant, Department of Bioengineering, University of Cal- ifornia, San Diego
Courses: Process Control (2003), Biotechnology Laboratory, (2001-
Teaching Assistant, Department of Chemical Engineering, University of California, San Diego Courses: Separation Processes
Assistant Development Engineer, Department of Biomedical Engi- neering, University of California, Davis
Web Developer, Paradux Concepts Laboratory Assistant, Genentech Inc
Research Intern, Process Control Lab, Department of Chemical En- gineering, University of California, Davis
1998-1999 Research Intern, Dairy Food Safety Lab, School of Veterinary Medi- cine, University of California, Davis
Pang WL*, Bennett MR*, Ostroff N*, and Hasty J (* equal contri- bution) Metabolic gene regulation in a dynamically changing envi- ronment In preparation
Grilly C*, Stricker J*, Pang WL, Bennett MR and Hasty J (* equal contribution) A synthetic gene network for tuning protein degra- dation in Saccharomyces cevevisiae Molecular Systems Biology In review
Cookson S*, Ostroff N*, Pang WL*, Volfson D and Hasty J (* equal contribution) Monitoring dynamics of single-cell gene expression over multiple cell cycles Molecular Systems Biology doi:10.1038/ msb4100032 Published online 22 November 2005
Zhang Q, Pang WL, Chen H, Cherrington J, Lipson K, Antonian
L, Shawver LK Application of LC/MS/MS in the quantitation of SU101 and SU0020 uptake by 3T3/PDGFr cells J Pharm Biomed Anal 28:701-9 (2002)
The 3rd Annual Conference on Systems and Pathways in Rhodes, Greece (2005) focused on the temporal manipulation of gene regulatory networks through microfluidics This innovative approach aims to connect computational simulations with real-world experiments, enabling high temporal resolution dynamics in biological research.
Genetic and phenotypic responses to fluctuating microenvironments Fifth Annual International Symposium: Systems Biology and Medi- cine Institute for Systems Biology, Seattle, Washington, USA (2006)
Systems dynamics on a chip 3rd International Conference on Path- ways, Networks, and Systems: Theory and Experiments October 2-7 Rhodes, Greece (2005) xv1
Quantitative analysis of genetic expression responses to dynamic microenvironmental perturbation by
Wvming Lee Pang Doctor of Philosophy in Bioengineering University of California, San Diego, 2007
Dynamic environments are prevalent in nature, influencing everything from metabolic rates to circadian rhythms and vascular structures One might expect that the regulatory systems governing cellular behavior would be finely tuned to respond to these variable conditions However, the analysis of cellular gene expression and regulation often overlooks highly dynamic perturbations, partly due to a lack of suitable technologies Recent advancements in microfluidic devices aimed at addressing biologically relevant questions offer promising solutions to this gap Additionally, new insights into the galactose metabolism in S cerevisiae suggest that similar pathways may be more fundamental than previously understood.
XViil memory systems, strengthens the need for the ability to examine gene regulation under complex and dynamic stimulation
This project developed advanced microfluidic technology for isolating and observing model host microbes, enabling long-term acquisition of high-resolution gene expression data at the single-cell level The innovative microfluidic system produced precise and continuous concentration waveforms, allowing for the exploration of the dynamic response of the galactose utilization pathway in S cerevisiae under varying nutrient conditions The findings revealed that pathway kinetics result in low-pass information filtration, while further investigations showed a coupling to glucose metabolism that ensures a robust global response despite challenges in galactose utilization These results highlight the significance of microfluidic platforms in quantitative biological research and the necessity of studying organisms in more natural environments to better understand the complex behaviors of gene systems.
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Quantitative biology 2.0 g q g kg Tà gà và vàng 1 I5 sẽ Ha (dt
As an undergraduate in biochemical engineering at UC Davis, I enrolled in upper-division biochemistry and cell biology courses to meet my technical elective requirements One notable course, Biology 104, focused on cellular pathways and their significance in cellular function Each lecture introduced a new cellular pathway, often prefaced by the instructor's remark that it was "sort of complicated." This perspective was understandable given that in late 1998, only a handful of microbial genomes, such as E coli K12-MGI655 and S cerevisiae S288C, had been sequenced, and techniques like green fluorescent protein (GFP) assays and gene expression microarrays were relatively new The course material, compiled from decades of meticulous research, highlighted the incomplete understanding of biological systems, as each new discovery added layers of complexity, making a fundamental grasp of biological processes increasingly elusive.
Recent advancements in biotechnology, including the proliferation of microarray technologies and the completion of genome sequences for major model organisms, have not resolved the challenges of predicting cellular behavior Despite extensive knowledge of gene networks, our understanding of cellular signaling and regulatory pathways remains complex The construction and analysis of synthetic systems have offered valuable insights into native cellular functions; however, even meticulously designed synthetic networks face inherent stochastic fluctuations from both external sources and internal dynamics, complicating accurate predictions.
Research on simple gene systems, particularly autoregulatory feedback loops, highlights the significant role of stochastic fluctuations in influencing system states These studies have prompted a focus on identifying and isolating sources of these fluctuations, as well as utilizing them to create stable genetic circuits By incrementally increasing the complexity of basic systems, researchers can effectively analyze, characterize, and optimize complex networks This methodology paves the way for the creation of "designer cells," which will have specific functionalities encoded in their genomes Achieving this objective necessitates a set of predictive laws akin to those established in chemistry, physics, and engineering, ultimately defining the field of quantitative biology.
Figure 1.1: Cartoon diagram of the galactose utilization pathway, abridged to the sensory, regulatory, and committal step towards galactose metabolism
Advancements in quantitative and synthetic biology are heavily influenced by the analysis of model genetic systems, similar to how artists draw inspiration from their experiences The detailed examination of gene regulatory networks can uncover new phenomena within established gene systems A prime example is the galactose utilization pathway, which is one of the most thoroughly understood inducible metabolic pathways in Saccharomyces cerevisiae.
In the same note, it is also an elegantly designed genetic switch[84] and for these reasons, it is an ideal target for many studies in gene regulatory dynamics/47, 109].
When yeast cells encounter galactose, it is transported into the cell via the Gal2p transporter, which operates actively at low galactose concentrations and passively at high concentrations Inside the cell, galactose activates Gal3p, which interacts with the transcriptional repressor Gal80p, preventing it from inhibiting the transcriptional activator Gal4p This allows Gal4p to recruit RNA polymerase, leading to the production of galactokinase (Gallp) and other proteins involved in galactose metabolism, such as Gal2p and Gal80p.
The galactose pathway's core switching mechanism relies on the localization of Gal80p, which, despite its weak binding affinity, is reinforced by a positive feedback loop involving galactose, Galdp, and Gal8p This state is further enhanced by an additional feedback loop created by Gal4p and Gal2p Conversely, the negative feedback loop from Gal80p destabilizes the main positive feedback from Gal3p, allowing the system to revert to a non-induced state in the absence of galactose This structure effectively forms a basic memory system within yeast cells Recent research by Acar and Van Oudenaarden highlights that the pathway's positive feedback redundancy makes it resilient to minor environmental changes, and intriguingly, the persistence of this memory can be adjusted by the intracellular levels of Gal80p, with lower concentrations leading to more persistent induced states, ultimately resulting in a monostable "always on" condition.
The galactose pathway is closely linked to glucose repression, a well-studied system characterized by a multi-feedback signaling mechanism Glucose signaling is regulated by several transcriptional repressors, including Rgt1p, Stdlp/Mthlp, Miglp, and Mig2p, with the process initiated by glucose-specific sensors Snf3p and Rgt2p These sensors, while resembling hexose transporters, do not transport glucose but instead transmit information about glucose binding to the membrane-bound kinase Yck1p, which phosphorylates co-regulators Mthlp and Stdlp Once phosphorylated, these co-regulators are marked for degradation by the ubiquitin-ligase SCF In the absence of glucose, Mthlp and Stdlp are recruited by Rgt1p, forming a regulatory complex that suppresses the expression of glucose-specific hexose transporters (Hxt1-4) and other proteins like Snf8p and Mig2p.
The glucose response uniquely adapts to varying concentrations of external inducers In high glucose environments (2-10% w/v), Mig2p represses the expression of high-affinity glucose transporters Hxt2p and Hxt4p, along with Snf8p and Mthlp, while exerting weak repression on other carbon utilization genes such as SUC, MAL, and GAL Conversely, at low to moderate glucose levels (>0.1%), Rgtlp's basal activity inhibits Mig2p, allowing for the expression of all four transporters Upon glucose entry into the cell, it triggers the dephosphorylation of cytosolic Miglp, which then translocates to the nucleus to inhibit other carbon utilization pathways through the Ssn6-Tup1 repressor complex.
The control loop involving glucose, Stdlp/Mthlp+Rgtlp, and Hxts primarily operates as a classic negative feedback system, while the interaction between glucose, Stdlp/Mthlp, and Stdlp/Mth1p degradation represents a unique positive feedback mechanism The key regulator of glucose response and subsequent catabolite repression is the swift alteration in the phosphorylation state of Miglp This process is closely aligned with the increased expression and degradation of Stdlp and Mth1p, leading to a degradation-based sequestration that facilitates the rapid silencing of the signaling network when glucose levels drop.
Dynamic environments and living cells 2 2 ee 6
The coupling of glucose and galactose pathways in S cerevisiae suggests that such complexity could not have developed in a stable environment In nature, biological systems encounter a variety of complex stimuli, including nutrient fluctuations, growth factors, radiative cycles, and mechanical forces Conversely, laboratory conditions often minimize or eliminate these external factors, raising concerns about whether laboratory behavior accurately reflects the true nature of biological systems.
Recent theoretical advancements in the experimental study of gene regulatory networks suggest that utilizing oscillatory input perturbations can yield more insightful frequency response analyses compared to traditional single-step inputs When a system is subjected to oscillatory inputs, it produces a corresponding oscillatory response that varies in amplitude and phase-shift, reflecting the dynamics of the underlying process By probing the system with multiple input frequencies, researchers can create a dynamic profile that captures the output amplitudes and phase-shifts relative to these frequencies This valuable data can be leveraged to optimize process gain and enhance temporal sensitivity.
From a biological standpoint, these measures are essential for characterizing gene expression responses and understanding their modulation by factors like gene mutations, deletions, and environmental variations Furthermore, frequency responses can reveal phenomena such as system resonances that are not detectable through basic step or pulse experiments, and can also elucidate previously uncharacterized network topologies.
Recent research has focused on the dynamics of the galactose pathway, emphasizing the need for dynamic perturbation of gene systems to enhance quantitative biological research Notable studies, including those by Acar and Van Oudenaarden, along with various computational analyses, investigate the behavior of cellular populations in stochastic environments.
Current techniques for monitoring gene expression 0
Recent advancements in cellular biology have unveiled intricate mechanisms involving DNA, RNA, and proteins, particularly their enzymatic functions A significant trend in this field is the utilization of fluorescent reporters, like green fluorescent protein and its variants, alongside high-throughput technologies such as flow cytometry and gene microarrays These innovative techniques enable researchers to analyze numerous cells and expression states with remarkable precision and efficiency, making them essential tools in systems biology research.
Despite their wide-ranging applications and precision, current techniques for analyzing cellular expression states have limitations, particularly when investigating gene expression dynamics For example, using a flow cytometer to analyze an inducible expression system is straightforward, as it generates fluorescence readouts from cells stimulated with varying inducers Similarly, gene or protein microarrays can provide a complete cellular profile However, in systems exhibiting bistability or oscillations, the fluorescence readouts may show bimodal distributions or high variance, indicating a more complex behavior than a single stable expression state Flow cytometry, while informative, lacks the fine-grained detail needed to understand the temporal dynamics of these states Additionally, experiments involving gene expression under dynamic perturbations with smooth waveforms challenge current technological limits, as the typical sampling rate of flow cytometers fails to capture rapid temporal characteristics, such as noise properties or feedback effects Mapping responses with adequate temporal resolution remains a significant technical hurdle.
Microfluidic devices 0 ee ee 9
Microfluidic devices have revolutionized quantitative analysis in research by enhancing capabilities and productivity through miniaturization, technological integration, parallel scalability, and cost-effective sample efficiency At their core, these devices consist of microscale channels equipped with inlets and outlets that connect to the macroscopic environment Enhanced functionality is achieved by incorporating additional microchannels and microchambers, which facilitate parallel on-chip processes, storage and processing of reaction species, and fluid flow control.
Recent advancements in microfluidic technology have led to the creation of innovative platforms for protein crystallization and structural analysis, a comprehensive device for DNA extraction and purification, and various systems designed to observe and quantify microbial growth and gene expression.
Microfluidic platforms are increasingly utilized to explore essential biological questions, including cellular migration, responses to shear stress and mechanical stimuli, and the intricacies of stem cell differentiation Their ability to isolate small reaction volumes and manipulate individual cells makes them invaluable tools in biological research.
Microfluidic devices serve as an ideal platform for studying various biological phenomena, including protein expression dynamics, long-duration gene expression, and population dynamics, due to their capability to isolate small reaction volumes and manipulate single cells These devices enable researchers to precisely and dynamically control the local environment while maintaining high temporal resolution in reporter readouts Consequently, advancements in microfluidic technology represent a significant intersection of fluid dynamics, physics, and microscale integration with life science research.
1.5.1 Modeling on-chip pressures and flows
Microfluidic devices can be effectively characterized through fluid flow simulations, facilitating both their design and performance evaluation This approach streamlines development cycles, ultimately conserving valuable materials and reducing fabrication time By utilizing the Navier-Stokes equations for incompressible fluid flow, engineers can optimize device functionality and efficiency.
;Íy+tv:Wv) =~Vp+ứg + u0VỶv 8 (1.2)
If one assumes the following dimensionless parameters,
An = Z8 uoL (L7) equations 1.1 and 1.2 become,
Under steady state conditions, e.g when there is no local acceleration of fluid particles, equation 1.9 is further simplified to,
Microfluidic flows typically occur at length scales of 10-100 micrometers, leading to the condition Npeô 1 In this regime, the viscous term Xz V'U significantly outweighs the internal term U- VU Therefore, it is reasonable to simplify Eqn 1.10 further under these conditions.
By solving Eqn 1.11 for flow through a cylindrical pipe of radius r and length L, one arrives at following solution,
AP=Q: q: (*) Suh (1.12) 1.12 which is similar to Ohm’s law for analog electrical circuits,
A similar analysis done on a channel with a rectangular cross-section[10] yields,
Figure 1.2: Node/Segment schematic of a fluidic “t”-junction and, d at (1.16)
Note for small values of a, ie w >> d, a tends toward unity and the fluidic resistance through such a rectangular channel is simply,
While equations 1.14 to 1.17 effectively describe flow in a single microfluidic channel, most microfluidic devices feature intricate networks of interconnected channels A basic example is the fluidic T-junction shown in figure 1.2, where fluid flow can be analyzed similarly to an electrical circuit using an analog of Ohm’s law This system includes three external nodes (open circles) and one internal node (black circle), with the external nodes representing fluidic access ports connected to external fluid and pressure supplies To fully characterize the flow within the device, it is essential to determine the flow direction and magnitude in each segment and the pressure at the internal node (node 3) This analysis parallels Kirchhoff’s current law, where the sum of electron flux through all circuit nodes equals zero; in fluidic systems, fluid flow (Q) takes the place of electron flux.
In this scenario, a positive value for Q; indicates fluid flowing into the node and a negative value indicates fluid exiting the node For the system depicted in Fig 1.2, Eqn 1.18 becomes, đa + Q,+Q.=0 (1.19)
Substituting Eqn 1.14 into Eqn 1.19 gives,
GŒ;=- ý — RB; (1.21) , and represents the fluidic conductance of segment ¿ Solving for the internal node, P3 gives,
GabeP3 = GaP) + GpPo + GeP4 (1.22) where,
The system illustrated in Fig 1.3 expands upon the configuration in Fig 1.2 by incorporating an additional external node and a connecting segment By applying the analysis outlined in Eqns 1.19-1.22, we can derive the corresponding results for this enhanced system.
GabeaP3 = GaP + GoP2 + GePy + GaPs (1.24)
Figure 1.3: Node/Segment schematic of a fluidic cross-junction
Comparing the solutions in Equations 1.24 and 1.22 reveals key characteristics of the analysis Firstly, the prefactor for the pressure at any internal node is the total sum of the conductances of all connected segments Secondly, the right-hand side of the equation defining an internal node consists of the sum of the pressures from neighboring nodes, each multiplied by their corresponding segment conductances.
In complex fluidic systems, such as those with multiple internal node points, it is essential to solve a system of equations to accurately characterize on-chip flow An illustrative example of this is the h-cross system depicted in Fig 1.4.
Performing a microfluidic open circuit analvsis described above gives the following system of equations,
—Ge Goede Ps 0 0 Ga G Py
Pe pre-multiplying both sides of Eqn 1.25 by the inverse of the LHS constant matrix gives the
Figure 1.4: Node/Segment schematic of a fluidic h-cross solution for the internal node pressures,
Py Gabe —Ge Ga Gp 0 0 Tạ
Ps —Ge Gede 0 0 Gq Ge P4
Now that all the node pressures are known, determining the segment flows simply requires applying Eqn 1.14 again First, however, it is necessary to generalize it for a system of equations,
The equation Qi = Gi Cpr Pe (1.27) represents a relationship where Gj is a matrix containing segment conductance values, while Cj is a constant matrix defining the connections between nodes P This framework is essential for understanding the interactions within the system.
16 the h-cross system described above, Eqn 1.27 becomes,
QO, 0 Gb oOo 0 0 0 1 -10 0 0 Py Ọ |=| 0 0 Ge 0 0 0 1 0 0-1 0 P3 (1.28)
The automation of on-chip pressure and flowrate determination streamlines the simulation of complex fluidic systems, allowing fluidic designers to specify only external pressures, segment geometry, and connectivity for efficient design without the need for repeated device fabrication and testing To support microfluidic development, I created a utility for simulating microfluidic device designs, detailed further in Appendix A.
For the devices discussed, macroscopic flow modeling effectively provided reliable estimates of operational characteristics The design of fluidic components was simplified, utilizing straight-walled channels with a rectangular cross-section, which eliminated the need for complex computational fluid dynamics methods.
Early microfluidic devices were primarily made by selectively etching glass and silicon substrates, a method that, although advanced for its time, posed safety risks and was time-consuming This technique restricted the variety and design of topographic features on the devices Additionally, the process of thermally annealing these glass devices to seal flow channels at temperatures exceeding 400°F complicated the pre-patterning of biological moieties on channel surfaces.
Recent advancements in rapid prototyping have seen the use of polydimethylsiloxane (PDMS), a clear silicone elastomer, for replica molding against photolithographically created master molds This technique leverages established UV photolithography processes on silicon substrates, enabling designers to explore a wide variety of channel geometries and topologies Additionally, PDMS can be easily bonded to glass or silicon substrates through straightforward chemical modifications at room temperature, enhancing its compatibility for biological surface modifications.
Cells, Constructs, and Culture Conditions 0
2.1.1 Parent and fluorescent variant strains
S cerevisiae was chosen as the model organism because of the abundance of knowledge regarding its galactose utilization pathway|120] and relatively straight forward methods for modifying its genome[51] More specifically, S cerevisiae K699, YPH499 and YPH500 (see Table 2.1 for genotypes) were used because they contained deletions of sev- eral metabolic pathways allowing for the use of auxotrophic selection markers in genetic transformations[95] These strains were commonly utilized in the yeast literature and are phenotypically different in several ways Because of their descendence from the S5288C strain, the two YPH strains, 499 and 500 do not utilize galactose anaerobically|77] More- over, and possibly related, YPH cells were observed to grow approximately 50 percent slower
Table 2.1: Yeast parent strains used for genetic transformations
YPH499 a uras-52 lys2-80lamber ade2-10lochre trp1-A63 his1-A200 leu2-Al
YPH500 a ura3-52 lys2-80lamber Ade2-10 1 ochre trp1-A63 his1-A200 leu2-A1
The K699 strain exhibits enhanced galactose utilization compared to YPH cells, requiring ten times more galactose to reach similar induction levels This difference is often linked to a GAL2 deficiency, which codes for the galactose-specific hexose transporter; however, our sequencing analysis found no mutations in GAL2 that would affect protein function Other research suggests a recessive mutation in MPI, a gene potentially allelic to GAL2, may contribute to this phenotypic variation Additionally, YPH cells have a deletion in the adenine biosynthesis pathway, leading to the accumulation of a flavin precursor and resulting in a reddish pigmentation Although this pigmentation does not adversely affect growth, it significantly increases the intracellular background during imaging with 558 nm light, approximately ten times higher than that of non-cellular images.
To assess the galactose pathway response, various yeast strains were engineered to express different combinations of fluorescent proteins, including yYEGFP, yEVenus (YFP), and yCerulean (CFP), utilizing modified pRS shuttle vectors and pKT integrating vectors.
Table 2.2: Excitation and emission spectral maxima of fluorescent proteins *SR101 is sulforhodamine
101, a low molecular weight, hydrophillic, red fluorescent dye that is used to track on-chip concentrations of inducing or repressing chemical species, or in microfluidic flow validation procedures
(nm) (nm) yEGFP 488 507 yEVenus 515 530 yCerulean 434 477
The vectors SR101* 586 605, pKT0090, and pKT0101a feature a flexible linker sequence connected to a fluorescent protein domain and a histidine (HIS5) auxotrophic marker gene A two-color variant, WLPY0O12, was developed by replacing the histidine marker in the pKT0090 vector with a URA& coding region, followed by a series of sequential transformation and selection steps.
Several pilot studies investigated cellular growth in batch culture and microfluidic devices using yYEGFP and yEVenus fusions to the Gall protein, but subsequent research opted for yCerulean fusions due to their enhanced spectral separation from sulforhodamine 101 (SR101, Sigma), a red fluorescent dye employed for tracking inducer concentrations Importantly, this substitution did not result in any negative effects.
The YPH Gal2p-yEVenus variants, WLPY008 and WPLY009, were developed to confirm Gal2p deficiency in the YPH lineage When observed under a microscope in 2% (w/v) galactose media, these cells exhibited a distinct "mottled" appearance In contrast, the WLPY010 and WLPY012 cells, derived from the K699 parent strain, displayed a uniformly fluorescent outer membrane under identical conditions.
The study examines GAL2 expression in S cerevisiae strains YPH499, YPH500, and K699, revealing deficiencies in GAL2 expression in YPH strains through the use of a yEVenus fusion YPH cells displayed a "mottled" appearance, contrasting with the uniform membrane distribution seen in K699 cells This observation suggests inadequate localization of Gal2p to the cytosolic membrane, leading to impaired utilization of galactose in the YPH lineage.
Table 2.3 outlines the yeast variants developed for this study, where a “-” denotes a fusion product and a “:” indicates non-fused products coexpressed from the same promoter Notably, strains WLPY001 and WLPY003 were utilized in a separate, unrelated project.
WLPYOOL | YPH500 GALI:yEGFP-PEST (1x integrated) TRP
WLPY002 | YPH500 GALI-yEGEP HIS
WLPY003 | YPH500 GALI-yEGEP, galS0A HIS, URA
WLPY004 | YPH500 GAL1-yCerulean (CFP) HIS
WLPY005 | YPH500 GALI-yEVenus (YFP) HIS
WLPY006 | YPH499 GAL1-yCerulean (CFP) HIS
WLP Y007 | YPH499 GALI-yEVenus (YFP) HIS
WLPY008 | YPH499 GAL2-yEVenus (YFP) HIS
WLPY009 | YPH500 GAL2-yEVenus (YFP) HIS
WLPY0O10 | K699 GAL1-yEVenus (YFP) HIS
WLPY011 | K699 GAL1-yCerulean (CFP) HIS
WLPY012 | K699 GAL1-yCerulean, GAL2-yEVenus (CFP/YFP) HIS, URA vector linker FP+stop aux marker
ES pKT0090 yEVenus HIS5 pKT0090a yEVenus URA3 pKT0101a yCerulean HISS
N 3 40bp reverse homologous primer sequence
PCR genome target gene Ị 3 sequence stop not included in homalageus homologous recombination recombination
Yeast transformation is achieved using pKT-derived fluorescent fusion protein vectors, where primers with 40bp of genomic homology flank the insert site to amplify the fusion reporter and selection marker from the plasmid vector The introduction of insert DNA into the yeast cells facilitates spontaneous homologous recombination at the targeted site, as defined by the primer sequences Successful transformants display fluorescence under specific induction and illumination conditions, along with auxotrophy for histidine (HIS5), uracil (URA3), or both in the case of double transformants.
Fluorescent yeast variants were developed through a documented transformation protocol that leverages the natural ability of S cerevisiae to integrate homologous DNA segments into its genome The parent strain cells were revived from frozen stock and cultured on solid yeast-peptone/dextrose (YPD) media supplemented with adenine at 30°C for two days.
A single colony was then selected and grown to saturation (ODgo90 4-6 approximately
To transform yeast cells, a culture was prepared by diluting 1 x 10% cells/mL in liquid YPD + ade media to an OD600 of 0.5, then grown to late log phase (OD600 1-2) The culture was washed in sterile 0.2m filtered water and concentrated in 1 mL of 0.1M Tris-EDTA buffered lithium acetate (LiAc buffer) at pH 8.0 DNA for transformation was generated through PCR amplification from plasmids, utilizing primers with 40bp homologous recombination sites for the yeast genomic GAL1 and flanking regions for the fluorescent protein, fusion linker, and auxotrophic selection marker The PCR products were purified with a Qiagen clean-up kit, yielding approximately 500-2000 ng/mL The transformation mixture consisted of 20-50 ng of DNA, 1 x 10^7 cells (100 µL concentrated cell suspension), and 20 µL of 10 µg/mL single-stranded carrier DNA, brought to 1 mL with LiAc buffer and 40% (w/v) PEG 3350 After incubating for 30 minutes at 30°C, cells underwent a heat shock at 42°C for 15 minutes Following heat shock, the culture was washed with sterile filtered water, resuspended in approximately 150 µL of fluid, and plated onto selective synthetic-dropout (SD) media with appropriate supplements for incubation.
2-4 days at 30°C Isolated colonies were picked, grown until saturation in selective media with 2% (w/v) glucose, and stored in 15% (w/v) glycerol at -80°C for later use
The transformation protocol, while generally reliable, proved inefficient for YPH cells, producing only 3-5 viable transformants per plate Discussions with colleague Mike Ferry revealed that this low yield was likely due to the rare occurrence of 40 bp homologous sequences targeting the correct genomic location Instead, the short homologous recombination anchors were more prone to targeting multiple undesired genomic sites This was evidenced by numerous attempts resulting in hundreds of “abortive” transformants per plate, characterized by their tiny colony size and inability to survive in selective liquid culture.
To enhance protocol efficiency, approximately 400 bp segments of genomic DNA flanking the target insertion site were amplified through a secondary round of PCR These “homology extenders” retained the 40 bp homologous recombination anchors from the original PCR primers When mixed with vector amplified DNA in equal ratios, the homology extenders facilitated the integration of the vector insert using the same cellular recombination mechanisms required for genomic incorporation This approach, featuring longer homology targeting regions, significantly increased the likelihood of successful recombination at the desired location Transformations conducted with this method demonstrated over 100-fold greater efficiency compared to those using only the 40 bp homologous sequences.
In this study, cells were cultivated under various media conditions, primarily on SD medium, which facilitated slower, more manageable growth and exhibited a lower fluorescence background compared to YP-based medium The cells were grown in three fundamental media states: neutral, inducing, and repressing The neutral media consisted of SD with appropriate auxotrophic selection supplements and 2% raffinose, a sugar that does not interfere with galactose or glucose metabolic pathways Induction media included varying concentrations of galactose alongside raffinose, which supported growth; cultures with raffinose grew nearly twice as fast at low galactose concentrations (= 1)/nPixels; if petSatCurr >= petSat, break; end; end;
The function `imadjust(I, [LoRng HiRng], [])` is used to adjust the intensity of an image The `imseg_getobjdata.m` function retrieves object data from segmented images, with the syntax `[nObjData, cols] = imseg_getobjdata(csIgqnt, seg, varargin)` The `opts` variable is set using the `getopts(varargin)` function The `csIgqnt` parameter is a cell array containing channel names and their corresponding image data for quantification, while the `seg` parameter is a structure that includes fields such as imgid, locid, channel, roi, imsize, objqntidx, and objallidx.
B serves as the mask for all objects within the image, encompassing those located at the borders L represents the label matrix for the objects designated for quantification Additionally, it is utilized to assess the background level of the image.
B = bndidx2mask(seg.imsize, seg.objallidx) ;
L = bwlabeln(imclearborder (B)); iImgID = seg.imgid; iLocID = seg.locid; esObjProps = {’area’, ‘centroid’, ‘pixelidxlist’, ‘boundingbox’,
The code snippet involves the use of 'conveximage', 'filledimage', and 'image' properties to analyze image regions with the `regionprops` function applied to the variable `L` The segmentation image dimensions are adjusted to align with the quantification image, ensuring accurate analysis across multiple channels.
Iqnt = csIgqnt{i,2}; m = mean(size(Iqnt)./seg.imsize); a a
Ignt = imresize(Iqnt, m); end; nBGLevel (i) = mean2(Ignt(~B)); end;
% loop over each object in the and log values into an array nObjData = []; for i0bjID=1:length(stObjData), stCurrObj = stObjData(iObjID) ;
% get subimage pixels for calculation nFL = []; for 1 = 1:nChannels,
% resize the quantification image to match the segmentation image Ignt = eslIqnt{i,2}; m = mean(size(Igqnt) /seg.imsize) ; if m ~= 1,
The calculations for subimage pixels involve extracting pixel values from the current object and converting them to double precision Statistical metrics such as mean, standard deviation, minimum, and maximum values are computed for each object, along with a background level To calculate the object perimeter, three types of images are utilized: native, filled, and convex, derived from the supplied binary region The perimeter is determined by summing the results from the binary perimeter function applied to each image type The data is then organized into a structured format for further analysis.
1ObjID, stCurrObj.Centroid, stCurrObj.BoundingBox, stCurrOb].Area, larPerim, nFL li nObjData = [nObjData; datarow] ; end; cols = {'LOCID’,’IMGID’,'OBJID’,’CENTX’,'CENTY’,’BBOXX’, 'BBOXY',
‘BBOXW’ ,’ BBOXH’ ,’ AREA’ ,’ PERIMNATIVE’ , ‘PERIMFILLED’ , ‘PERIMCONVEX’ };
40 for i = 1:nChannels, cols = {cols{:}, sprintf (’MEAN%d’, 1), sprintf (’STD%d’, i), sprintf (’MIN%d’, i), sprintf (’MAX%d’, i), sprintf (’BGLVL%d’', 1) end; bi
: - imsegauto.m function imsegauto(runinfo, seginfo, cvChannels, cvROIs) stand-alone segmentation code
AP ofP ol? the data directory specifed by the user named by img id uses full auto segmentation routine(s) and stores segmentation data to
% sExptPrefix, sExptID); sExpt Path = runinfo.path; sCorrPath = runinfo.corrpath;
The code initializes several variables related to image processing, including location ID, indices of images, and the total number of images It constructs the segmentation data path and checks if segmentation should be overwritten Additionally, it retrieves the segmentation algorithm, options, and display settings, while also preparing to read in correction images.
[N, F] = createcorrimgs(sCorrPath, [0:9], unique (cvChannels(:, 2)), unique(cvChannels(:,
% create data output directories if needed if “exist(sSegDataPath, ‘dir’), mkdir (sSegDataPath) ; end; if “exist (sprintf (’ mkdir(sprinté£ (' end; s/%03đ', sSegDataPath, iLocID), ‘dir’), s/%03đ', sSegDataPath, iLocID)); oP ol?
% preparation complete, start the segmentation process hProg = waitbar(0, ‘');
% set(hProg, ‘position’, [1, 25, iProgPos(3:4)]); icurriImg = 0; tLoopElaps = []; segprev = []; sFilePath = sprintf (’%s%s%03d', sSegDataPath, filesep, iLocID);
242 vildx, iCurriImg = iCurrImg + 1; nProgress = iCurrImg/nTotImgs; tLoopInit = clock; updateprogress(hProg, iCurrImg, nTotImgs, tLoopElaps) ;
2 5 check if there is already a segmentation file skip the segmentation process if there is ae ol?
2 % get the segmentation data directory listing sFileName = sprintf(’%04d.mat’, iCurrIdx); sFileFullPath = sprintf (’%s%s%s’, sFilePath, filesep, sFileName) ; stFiles esFiles dir(sFilePath);
{stFiles(~[stFiles.isdir] ).name}; if ismember(sFileName, csFiles) && ~bSegOverwrite, waltbar(nProgress, hProg, ‘Reading Existing Segmentation Data’); load(sFileFullPath); segprev = seg; if bDispSeg,
B = zeros(seg.imsize) ; B(seg.objallidx) = 1;
L = bwlabeln(B); end; else images only need to be read if new segmentation is to be performed this boosts performance waltbar(nProgress, hProg, ‘Reading Image Set’);
[cIall, cIseg, cIqnt] = readimgset(iLocID, iCurrIdx, sExptPath, evChannels, cvROIs, N, F); ° % ° 6 waltbar(nProgress, hProg, ‘’Segmenting Objects’);
To initialize the segmentation data structure, set the image ID to 1CurrTdx and the location ID to iLocID Assign the channel from cIseg{1} to seg.channel Calculate the region of interest (ROI) using the hash value of cvChannels and seg.channel, followed by another hash value from cvROIs Convert the ROI to a matrix format If the ROI is empty, assign it directly to seg.roi; otherwise, set seg.roi to a zero vector based on the size of cIseg{2} Finally, store the image size in seg.imsize, derived from the dimensions of clseg{2}.
[]; seg.objqntidx seg.objallidx if isempty(cIseg{2}), printf (’ERROR: Segmentation Image is Empty!’); end;
[seg, L] = feval(sSegAlgo, cIseg{2}, seg,
‘autoonly’, true, ‘mergeprev’, segprev}); segprev = seg; ° save (sFileFullPath, ‘seg’); if bDispSeg, end;
To display the segmented image, check if the figure exists using the command ‘exist (’hFigSeg’), hFigSeg = figure; end;’ Utilize the function imdispseg(cIseg{2}, L, ‘method’, sDispSegMeth) to visualize the segmentation Additionally, track the elapsed time for the loop execution with the command tLoopElaps = [tLoopElaps etime(clock, tLoopInit)].
1f bDispSeg, close (hFigSeg) ; clear hFigSeg end; close (hProg) ; clear hProg;
% output segmentation mask data to the specified data directory function imsegcollect (runinfo, seginfo, quantinfo, cvChannels,
% stand alone script to quantitate image intensity
% sExpt Prefix, sExptPath = runinfo.path; sCorrPath = runinfo.corrpath;
1LocID = runinfo.locid; imsegcollect.m cvROIs) sExptID) ;
Automated microscopy platform 0 00.00.0000 008 277
The imaging system employed was a Nikon Diaphot TMD advanced research-grade inverted epifluorescence microscope, featuring a five-position nosepiece with objectives ranging from 4x to 100x magnification It utilized a 100W HÀIX-4 halogen lamp for transmitted light, complemented by a neutral color balance filter, a green bandpass filter, and a 0.83NA ELWD condenser For fluorescence imaging, an EXFO X-Cite 120 mercury halide light source was integrated, equipped with an attenuation iris, an IR hot mirror, and a Chroma Sedat spectral excitation filter set in motorized filter wheels Both illumination paths were controlled by high-speed Uniblitz VS35 shutters, managed through a DMM-V4 four-channel shutter driver, while image acquisition was facilitated by a Hamamatsu Orca-ER cooled CCD camera.
The camera utilized in the setup features an EEE-1394 FireWire interface, connecting to the microscope through a 1.0x c-mount photocoupler from Diagnostic Instruments Stage translation and fine focus adjustments are motorized, employing a Prior Scientific Proscan-II XY motorized stage and a standard fine focus drive Additionally, the Proscan-II four-axis controller manages filter wheels, XY translation, and Z motion, ensuring precise autofocus capabilities.
Brightfield/Phase Contrast Light Source
Ex Filter Wheel (for fluor bead AF only)
—— Shutter Neutral Density Filter (0.6 OD)
Fluor Light Source (for on-chip pattern AF) g SE g
Figure C.1: Imaging optical train and light paths
The imaging system utilized in this study was managed through a custom interface created in National Instruments LabVIEW, designed exclusively for image collection, similar in functionality to commercial platforms like ImagePro, MetaVue, and IPLab The primary motivation for employing LabVIEW was to reduce overall system costs while ensuring precise configurability However, the graphical nature and complexity of LabVIEW's programming language limit the presentation of detailed algorithms, with the complete source code accessible online at http://biodynamics.ucsd.edu.
Initialize locations (set pattern); repeat
Check channel indices for remaining images; if Acquisition is active and images remain then for Fach Location do Get XYZ coordinates;
Goto adjusted XYZ: for Each Channel do Query acquisition type:
Check elapsed times for each channel; if Acquiring single images
OR ( Acquiring an image sequence
AND the channel interval has elapsed ) then
Reset channel elapsed time to 0; endif endfor endfor endif until USER-STOP :
AND the current channel index is < max channel index
Waveform generation platform 2 ee ee 281
The waveform generation system utilized a pair of Bellofram voltage-to-pressure servo regulators, which operated on a 0-10V signal to control output pressure between 0-1 psi Voltage signals were produced through a LabVIEW program linked to the analog output channels of a PCI-6021 multifunction digital/analog input/output board The regulators were powered by a shared 12VDC source, with the input pressure supplied at a regulated 10 psi from house air The controlled pressure output was then channeled through a 23-gauge restriction segment to adjust the pressure to within a 0-12 inH2O range before being directed to fluid reservoirs Additionally, a 20-gauge bleed orifice was installed in parallel with the reservoir to address the lack of pressure relief from the upstream servo regulators.
Figure C.2: Waveform generation system configuration
Log Press Interval (sec) Interval between data points in pres- sure state data log
Log Press Data? Select this field to start/continue pressure data logging
The Log Press File contains the location of the pressure state log file, which records the frequency of the waveform in hertz (Hz), with a default setting of 10 Hz Additionally, it includes information on the duty cycle percentage, indicating the proportion of time a square wave remains high compared to low during one complete period.
The VI uses this parameter only if the signal type is a square wave The default is 50% std dev (s) standard deviation is the standard deviation of the generated noise The default is 1.0 mean interval (s) average length of time for intervals used by the random step function
The pulse function has a width of 282 seconds, while the delay is also measured in seconds The square wave duty cycle represents the percentage of time that the square wave stays high compared to when it is low over one complete period This parameter is utilized by the VI exclusively when the signal type is a square wave.
Global State (Frac) Global output state Controls any linked channels amplitude (%) square wave duty cycle is the percentage of time a square wave remains high versus low over one period
The VI uses this parameter only if the signal type is a square wave The default is 50%
AP 2 Configures output for analog pressure channel 2
Ch Channel selector for output pressure
Hi Hi voltage value for output pressure control
Lo Lo voltage value for output pressure control
Linked Output link to global state slider If selected the channel is controlled by the global state slider Correspondance to the global state depends on the linkage type
The LinkType Method establishes a connection between a channel and the global state, with two primary forms of linkage Direct linkage ensures that the channel state perfectly mirrors the global state, while inverted linkage reflects the channel state as the opposite of the global state.
Inverted signal type signal type is the type of waveform to generate
AP 1 Configures output for analog pressure channel 1
Ch Channel selector for output pressure
Hi Hi voltage value for output pressure control
Lo Lo voltage value for output pressure control
Linked Output link to global state slider If selected the channel is controlled by the global state slider Correspondance to the global state depends on the linkage type
The LinkType Method establishes a connection between a channel and the global state, offering two distinct approaches: direct linkage, where the channel state perfectly mirrors the global state, and inverted linkage, which reflects the opposite of the global state.
Const High interval (s) Value for constant *hi’ state interval length for random step function
Analog Pressure Monitor Graphical display of pressure state for all active channels.
RP —— Fal "stop": Mouse Up vb placeholder far stop button
Close snelog pressure requiators TEEEEEEEE!
Ow analog Pressure Monitor and Control Loop ignal type! captures control of the output slider pt tahien in raanual mnode
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Hierarchical states em fo) Timeout *
Tư ke B: Waveform Generator g luses @ LabVIEW built-in Function to create sine, square, sawtooth, and triangle waves prmaneaneaaren hosecedlibesiliboos:
286 fl True > ise Generator a LabVIEW built-in Gaus ce Ge andStepConstHighj : ¥ anstriia
TC: Tesla miero-Chemosflat uc cu cu Q va 290
Table D.1: Master mold feature height specifications ‘Photoresists are SU-8 unless otherwise specified +These layers were patterned additively (e.g no development step between this and the prior layer)
Layer Thickness Photoresist? Spin Speed
(um) (rpm) bead trap 2 2002 3000 chamber 4 2002 2000 flow channels 12 2010 2500 access port 38 2015 1000 thermal channel 260 2100 1000
D.1.2 Device Schematic and Port Assignments
2 um, bead trap im] 12 um, flow channel (| 300 um, temp control
4m, chamber IEl 38 um, access port
Figure D.1: Device schematic for TuC Inset displays a magnified view of the growth chamber
Table D.2: Port assignments for T2uC
1 C input, cell suspension Bin input, af/calibration bead loading Bout input, af/calibration bead outflow
W output, common waste oe & Ww
T2uC: Temporal Tesla micro-Chemostat 0.00.00 00 eee 292
D.2 T?uC: Temporal Tesla micro-Chemostat
Table D.3: Master mold feature height specifications ‘Photoresists are SU-8 unless otherwise specified + These layers were patterned additively (e.g no development step between this and the prior layer)
Layer Thickness Photoresist? Spin Speed
(um) (rpm) feeding channels 1 2001 3000 chamber 4 2002 2000 af pattern 4 2002 2000 flow network 10 2010 3000 chaotic mixer grooves? 2 2002 3000 thermal channel (1) 50 2050 3000 thermal channel (2) 260 2100 1000
D.2.2 Device Schematic and Port Assignments
1 um, filter barrier L] 4 um, AF pattern II 12 um, mixer groove 4um, chamber IEI 10 um, media channel |] 300 um, temp control
Figure D.2: Device schematic for T?1:C Insets display magnified views of the growth chamber and on-chip media switch
Table D.4: Port assignments for T2uC
1 We — output, cell suspension waste
Wm output, loading media waste
Ws output switching channel waste
5 output, aux waste for switch
B input, media 2 (blank) com ©› CC W bo
To assemble a bonded microfluidic device, you will need a total of eight microfluidic lines and eight fluid reservoirs, along with eight reservoir pressure tops For optimal performance, a multichannel pressure controller with at least four channels (eight channels recommended) is essential An inverted microscope equipped with an imaging camera, shuttered light sources for both transmitted and fluorescence imaging, and suitable acquisition software are also necessary Additionally, optional components include thermal control fluid lines and a heated/chilled water circulator for enhanced temperature management.
The device features eight fluidic ports paired with eight fluid reservoirs, including four designated as "waste" reservoirs (labeled W, Ws, Wm, and We) for fluid exit, and four "media/input" reservoirs for fluid entry An auxiliary waste port, labeled "S" for "shunt," is included for the integrated media switch To ensure optimal functionality, all output reservoirs must contain only sterile filtered water, and all input media should also be sterile filtered when possible, minimizing particulate debris that could obstruct microfluidic channels.
The input reservoirs are designated as M, I, and B, representing loading media, "induction" media, and blank media, respectively The M reservoir holds unaltered growth medium, typically matching the media used for preparing the cell suspension for overnight culture The I reservoir contains growth media infused with chemical agents that trigger cellular responses, such as inducers, activators, or repressors Conversely, the B reservoir consists of media identical to that in the I reservoir but without the chemical agents, ensuring consistency in composition.
The "MM" reservoir, along with reservoirs "I" and "B," must incorporate a flow tracing dye, such as sulforhodamine 101 at a concentration of 0.01 mg/mL (Sigma $7563), essential for preparing the integrated media switch for operation Typically, the "I" reservoir is the one that contains the additional tracer dye.
The unlabeled input reservoir is designated for cell suspension and is the only reservoir not reused in future experiments To minimize cellular fouling of the device during operation, this reservoir is transformed into an output (waste) reservoir Further details will be provided below.
Line Attachment and Device Wetting
1 Secure the device to the microscope stage insert place the device on the microscope (4x or 10x magnification) If available, do not secure rotational movement of the stage insert at this point in time Inspect the device for any defects that could impair its performance — i.e channel leakage due to poor bonding, or channel blockage due to
296 dust particles Discard defective devices
To ensure optimal performance, position all reservoirs on the gravity towers and verify that all fluid connection lines are properly primed and free of bubbles Connect the pressure control lines to the tops of the syringes and adjust the source pressure on the pressure controller to 5 psi.
To optimize MOCA simulations, adjust the reservoir heights to their designated operational positions: 8 inH2O for Wm, 8 inH2O for Wc, 10 inH2O for Ws, 19.5 inH2O for 5, 14.5 inH2O for M, 20 inH2O for I, 20 inH2O for B, and 3 inH2O for Cells These specific heights are crucial for each experimental setup.
4 Connect the Ws, Wm, S reservoirs to ports 5, 2, and 7 on the device, respectively, and activate pressure to the reservoirs
Depending on slight variations in chip manufacturing and chip age, the following may happen in any order This is normal and does not impair the function of the device
(a) When fluid appears at port 4, attach the M reservoir to it
(b) When fluid appears at port 6, attach the I reservoir to it
(c) When fluid appears at port 8, attach the B reservoir to it
After a few seconds, fluid should emerge from either port 1 or port 3, which can serve as the entry point for cell suspension (C) or the exit for cell waste (We) If no fluid is observed at these ports within 5 minutes, it is necessary to pressurize both ports.
To facilitate fluid flow through the device, connect the M and Wm reservoirs Once a fluid bead is visible, attach the We reservoir to its designated port and deactivate any extra pressure It is important to refrain from connecting the cell suspension reservoir at this stage.
7 Place the device on the microscope Inspect all channels for pockets of air Check to see if the chamber and microchannels that connect it to the integrated media switch are appropriately wetted If there are air pockets anywhere in the chip, remove them by supplying 5 psi of pressure to ALL attached reservoirs Once all air pockets have been removed from the device, turn off the external supply pressure
Media Switch Setup and Operation
1 Move the field of view (4x or 10x magnification) to the media switching region (see device schematic), and illuminate using appropriate fluorescence settings for the tracer dye used If possible, rotationally square and lock down the device at this point in time
2 Set the source pressure on the controller to 5 psi
3 Purge the fluid that may have back flowed into ports 6 and 8 by pressurizing reservoirs
I and B, simultaneously You should see an interface form between the dyed and undyed media midway across the output channel
To ensure proper purging of the ports, monitor the field histogram and identify the maximum field intensity value Once the maximum intensity stabilizes and shows minimal fluctuation, it indicates that the ports are adequately purged, allowing for the safe removal of pressure.
5 Manually adjust the height of the I and B reservoirs so that the interface between the input streams occurs exactly in the middle of the output stream
6 If acomputer controlled concentration waveform generator is installed skip to step 11.
To begin, close all pressure valves and decrease the source pressure to 0 psi Next, open the pressure valve to the B reservoir and slowly increase the source pressure until the fluid interface shifts from the output channel to one of the adjacent overflow bypass channels Record this pressure as the operational pressure for the switch.
Repeat the previous step, but this time pressurizing the I reservoir, to ensure that the switch operation pressure is consistent for both states
Proceed to the cell loading procedures
Replace the pressure connections to the I and B reservoirs with the output lines from the waveform generation system
In the LabVIEW control panel, set the “LO” and “HI” voltages for both analog output channels to 0 volts and 5 volts, respectively
Link both channels to the global control and select one channel to be linked “inverted”
For optimal results, it is advised to connect the dye-free reservoir (B) in such a way that a 100% output state aligns with the highest dye concentration, and the opposite holds true The subsequent steps will be based on this configuration.
Glial Network Stimulator Quy vẻ vn 303
Table D.5: Master mold feature height specifications ‘Photoresists are SU-8 unless otherwise specified
Layer Thickness Photoresist’ Spin Speed
(um) (rpm) filter channels 1 2001 3000 perfusion channels 10 2010 3000 stimulant channels chamber squeeze-thru barrier chamber /loading channels 50 2050 3000
D.3.2 Device Schematic and Port Assignments chamber
L] 50 um, chamber / loading [] 10 um, media handling 1 ym, filter barrier
Figure D.3: Device schematic for glial network stimulation device Insets display magnified views of the growth chamber and on-chip media switch.
Table D.6: Port assignments for the glial network stimulation device
3 5 output, aux waste for switch
Oo M input, primary perfusion media
6 Ws output chamber + stimulant outflow waste
7 We — output, cell suspension + overflow perfusion waste
DynaGrad: Dynamic Chemical Gradient Device 2
Table D.7: Master mold feature height specifications ‘Photoresists are SU-8 unless otherwise specified
* Bacterial and yeast devices require patterning the gradient outflow channel with adhesion molecules such as polylysine (bacterial) or Conavalin-A (yeast)
Layer Thickness Photoresist’ Spin Speed
(um) (rpm) bacterial device† 6 2005 2500 yeast device? 10 2010 3000 mammalian device ĐỒ 2050 3000
D.4.2 Device Schematic and Port Assignments lo conc