Synthetic gene circuits for cell state detection and protein tuning in human pluripotent stem cells
2022; Springer Nature; Volume: 18; Issue: 11 Linguagem: Inglês
10.15252/msb.202110886
ISSN1744-4292
AutoresLaura Prochazka, Yale S. Michaels, Charles Lau, Ross D. Jones, Mona Siu, Ting Yin, Diana Wu, Esther Jang, Mercedes Vázquez‐Cantú, Penney M. Gilbert, Himanshu Kaul, Yaakov Benenson, Peter W. Zandstra,
Tópico(s)3D Printing in Biomedical Research
ResumoArticle11 November 2022Open Access Source DataTransparent process Synthetic gene circuits for cell state detection and protein tuning in human pluripotent stem cells Laura Prochazka Laura Prochazka Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Yale S Michaels Yale S Michaels orcid.org/0000-0002-6857-5922 Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Charles Lau Charles Lau Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Ross D Jones Ross D Jones Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Formal analysis, Investigation, Writing - review & editing Search for more papers by this author Mona Siu Mona Siu Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Ting Yin Ting Yin Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Diana Wu Diana Wu Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Esther Jang Esther Jang Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Mercedes Vázquez-Cantú Mercedes Vázquez-Cantú Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Swiss Federal Institute of Technology (ETH) Zürich, Department of Biosystems Science and Engineering (D-BSSE), Basel, Switzerland Contribution: Formal analysis, Investigation, Writing - review & editing Search for more papers by this author Penney M Gilbert Penney M Gilbert orcid.org/0000-0001-5509-9616 Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada Contribution: Resources, Writing - review & editing Search for more papers by this author Himanshu Kaul Himanshu Kaul orcid.org/0000-0003-1586-7486 School of Engineering, University of Leicester, Leicester, UK Department of Respiratory Sciences, University of Leicester, Leicester, UK Contribution: Formal analysis, Funding acquisition, Investigation, Writing - review & editing Search for more papers by this author Yaakov Benenson Yaakov Benenson orcid.org/0000-0003-1880-6507 Swiss Federal Institute of Technology (ETH) Zürich, Department of Biosystems Science and Engineering (D-BSSE), Basel, Switzerland Contribution: Resources, Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Peter W Zandstra Corresponding Author Peter W Zandstra [email protected] orcid.org/0000-0002-9568-733X Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - review & editing Search for more papers by this author Laura Prochazka Laura Prochazka Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Yale S Michaels Yale S Michaels orcid.org/0000-0002-6857-5922 Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Charles Lau Charles Lau Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Ross D Jones Ross D Jones Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Formal analysis, Investigation, Writing - review & editing Search for more papers by this author Mona Siu Mona Siu Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Ting Yin Ting Yin Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Diana Wu Diana Wu Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Esther Jang Esther Jang Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Contribution: Formal analysis, Investigation Search for more papers by this author Mercedes Vázquez-Cantú Mercedes Vázquez-Cantú Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Swiss Federal Institute of Technology (ETH) Zürich, Department of Biosystems Science and Engineering (D-BSSE), Basel, Switzerland Contribution: Formal analysis, Investigation, Writing - review & editing Search for more papers by this author Penney M Gilbert Penney M Gilbert orcid.org/0000-0001-5509-9616 Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada Contribution: Resources, Writing - review & editing Search for more papers by this author Himanshu Kaul Himanshu Kaul orcid.org/0000-0003-1586-7486 School of Engineering, University of Leicester, Leicester, UK Department of Respiratory Sciences, University of Leicester, Leicester, UK Contribution: Formal analysis, Funding acquisition, Investigation, Writing - review & editing Search for more papers by this author Yaakov Benenson Yaakov Benenson orcid.org/0000-0003-1880-6507 Swiss Federal Institute of Technology (ETH) Zürich, Department of Biosystems Science and Engineering (D-BSSE), Basel, Switzerland Contribution: Resources, Supervision, Funding acquisition, Writing - review & editing Search for more papers by this author Peter W Zandstra Corresponding Author Peter W Zandstra [email protected] orcid.org/0000-0002-9568-733X Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada Contribution: Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - review & editing Search for more papers by this author Author Information Laura Prochazka1,2, Yale S Michaels3,4, Charles Lau1,2,3,4, Ross D Jones3,4, Mona Siu3,4, Ting Yin1,2, Diana Wu1,2, Esther Jang1,2, Mercedes Vázquez-Cantú1,2,5, Penney M Gilbert1,2,6, Himanshu Kaul7,8, Yaakov Benenson5 and Peter W Zandstra *,3,4 1Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada 2Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada 3Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada 4School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada 5Swiss Federal Institute of Technology (ETH) Zürich, Department of Biosystems Science and Engineering (D-BSSE), Basel, Switzerland 6Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada 7School of Engineering, University of Leicester, Leicester, UK 8Department of Respiratory Sciences, University of Leicester, Leicester, UK *Corresponding author. Tel: +1 604 822 2694; E-mail: [email protected] Molecular Systems Biology (2022)18:e10886https://doi.org/10.15252/msb.202110886 PDFDownload PDF of article text and main figures.PDF PLUSDownload PDF of article text, main figures, expanded view figures and appendix. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract During development, cell state transitions are coordinated through changes in the identity of molecular regulators in a cell type- and dose-specific manner. The ability to rationally engineer such transitions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein, we report the generation of synthetic gene circuits that can detect a desired cell state using AND-like logic integration of endogenous miRNAs (classifiers) and, upon detection, produce fine-tuned levels of output proteins using an miRNA-mediated output fine-tuning technology (miSFITs). Specifically, we created an "hPSC ON" circuit using a model-guided miRNA selection and circuit optimization approach. The circuit demonstrates robust PSC-specific detection and graded output protein production. Next, we used an empirical approach to create an "hPSC-Off" circuit. This circuit was applied to regulate the secretion of endogenous BMP4 in a state-specific and fine-tuned manner to control the composition of differentiating hPSCs. Our work provides a platform for customized cell state-specific control of desired physiological factors in hPSC, laying the foundation for programming cell compositions in hPSC-derived tissues and beyond. Synopsis Synthetic gene circuits with sense-and-respond capabilities were designed to detect cell states and perform cell-state-specific control of desired protein expression in human pluripotent stem cells (hPSCs). Circuits that detect the hPSC state (hPSC-On) were created using automated design and a model-guided circuit optimization process. The optimized circuits were merged with miSFITs fine-tuning technology and showed hPSC-specific actuation with precise control of the protein expression levels in hPSC. An empirical approach was used to create circuits that are repressed in hPSC (hPSC-Off) and applied as proof of concept for cell composition control of the three germ layers. Introduction Robust gene-regulatory programs enable stem cells to self-renew and differentiate by sensing and responding to stimuli in a defined manner. Crucially, these regulatory circuits are capable of integrating multiple internal and external input signals to achieve a high degree of specificity, resulting in lineage or cell-state-specific activation of effector molecules (Arnold & Robertson, 2009; Ruiz-Herguido et al, 2012). The production of effector molecules is often graded, where defined doses can lead to desirable proportions of downstream lineages (Zhang et al, 1998; Müller et al, 2012; Manfrin et al, 2019). The ability to engineer such gene-regulatory circuits into human pluripotent stem cells (hPSC) de novo would enable efficient production of desired cell types or tissues for research and regenerative medicine applications (Galloway et al, 2013; Lipsitz et al, 2016; Teague et al, 2016; Prochazka et al, 2017; Santorelli et al, 2019). With the goal to control human cell function, substantial effort has been directed toward synthetic gene circuit engineering in human cells (Tigges et al, 2009; Greber & Fussenegger, 2010; Wei et al, 2013; Duportet et al, 2014; Kiani et al, 2014; Morsut et al, 2016; Weinberg et al, 2017; Ausländer et al, 2018; Szenk et al, 2020), with recent exciting developments using hPSC (Lienert et al, 2013; Guye et al, 2016; Saxena et al, 2016; Gao et al, 2018; Velazquez et al, 2021). The majority of gene circuits implemented in human cells are logic gene circuits (Bronson et al, 2007; Rinaudo et al, 2007; Leisner et al, 2010; Auslände et al, 2012; Lohmueller et al, 2012; Cho et al, 2021). A handful of these circuits have been designed to detect cell-internal endogenous input signals, enabling restriction of circuit action to desired cell types or cell states (Xie et al, 2011; Baertsch et al, 2014; Prochazka et al, 2014; Miki et al, 2015; Angelici et al, 2016; Doshi et al, 2020). Here we define a cell state as discrete if it can be clearly discriminated from other cell states on the basis of a predefined set of molecular inputs that are detected and integrated by a circuit. The underlying circuit integrates the inputs in a function that can be approximated by Boolean logic and autonomously "decides" if a desired downstream molecule, the output, is produced at high (On) or low (Off) concentrations. One type of circuit that allows such discrete cell state discrimination is cell "classifiers" (Xie et al, 2011; Mohammadi et al, 2017). Cell classifiers have been designed to detect and logically integrate endogenous microRNAs (miRNAs) and have proven useful for a variety of applications such as the specific killing of cancer cells (Xie et al, 2011; Dastor et al, 2018) or for screening miRNA drug candidates (Haefliger et al, 2016). Additionally, single endogenous miRNAs have been employed to regulate synthetic genes to discriminate hPSCs from differentiated cells (Brown et al, 2007), for selection of PSC-derived mature cell types (Miki et al, 2015) or reprogrammed induced hPSC (di Stefano et al, 2011), and for specific killing of hPSC (Miki et al, 2015; Parr et al, 2016; Fujita et al, 2022). Interestingly, endogenous miRNAs have also been exploited to fine-tune expression levels of synthetic and natural genes in human cells (Michaels et al, 2019). Such graded production of proteins is crucial for many applications where precise intervention of physiological behavior is required (Michaels et al, 2019). Despite this progress, current implementations of cell classifiers result in arbitrary On and Off levels that are highly dependent on parameters such as the promoter strength and delivery system and thus are difficult to tune to the desired dose (Xie et al, 2011; Lapique & Benenson, 2014; Schreiber et al, 2016; Prochazka et al, 2017). Furthermore, miRNA-based systems implemented in stem cells typically operate with a single miRNA input and a single protein output (Brown et al, 2007; di Stefano et al, 2011; Parr et al, 2016; Fujita et al, 2022), limiting their applications. To date, no circuit has been reported that allows precise tuning of multiple desired proteins from desired discrete cell states, a function that stem cells perform continuously during development and would enable powerful control over stem cell differentiation. Here we design and implement synthetic gene circuits that are capable of performing cell-state-specific control of desired protein expression in hPSC. Specifically, we provide a platform for engineering gene circuits using transient transfections. The platform combines miRNA-based logic gate computations (Xie et al, 2011) for cell state detection, with miRNA silencing-mediated fine-tuners (miSFITs; Michaels et al, 2019) to enable precise tuning of the output dose by pre-selecting desired miSFITs targeted output constructs. We first outline the creation of a generic hPSC-specific circuit (hPSC-On circuit) using an automated miRNA identification and circuit design tool and a model-guided combinatorial screening approach. We next highlight an empirical approach for design and implementation of a minimal circuit that is silenced in hPSC (hPSC-Off circuit) and utilize this system for the autonomous induction of BMP4 dose-mediated hPSC microtissue patterning to achieve control over the proportions of differentiated cell types. Our platform lays the foundation for rapid, model-guided or empirical engineering of cell-state-specific circuits that have the ability to tune the output dose to desired levels in hPSC and their derivatives. Circuit design To establish our platform, we designed (i) a generic circuit that detects the pluripotent state, restricting circuit actuation to undifferentiated hPSCs (hPSC-On circuit; Fig 1A), and (ii) a minimal circuit that represses output production in hPSC (hPSC-Off circuit; Fig 1B). Our design uses a bow-tie architecture (Prochazka et al, 2014) that allows decomposition of the circuit into two modules: (i) a logic multi-input module that detects discrete cell states by recognizing a set of miRNAs (Xie et al, 2011) and (ii) a multi-output module that uses a library of miRNA mediated fine-tuners (miSFITs; Michaels et al, 2019) to independently tune the levels of multiple outputs to desired levels. Figure 1. Circuit design A, B. Schematic of generic circuit (A) and minimal circuit (B) with cellular performance (left) and circuit architecture (right). Logic input module (top) and output module (bottom). Act1 and Act2 are two different orthogonal synthetic transactivators, miR-FF4 is a synthetic miRNA-based repressor. Out is the output protein as indicated. (A) The generic circuit consists of one miRhigh sensor (inversion module) that recognizes up to two miRNAs (miR1, miR2 = OR gate) and two miRlow sensors each recognizing one miRNA (miR3, miR4). The circuit performance can be approximated with the Boolean logic function Output = miR1 OR miR2 AND NOT miR3 AND NOT miR4 and is designed to be active in hPSC (hPSC On). (B) The minimal circuit shows two miRlow sensors detecting miR1 and miR2 performing the logic function Output = NOT miR1 AND NOT miR2, designed to be inactive in hPSC (hPSC Off). In both circuits, the output module is controlled by Act2 and shows two protein outputs that are further controlled by miSFITs, an miR-17-based target library. C. Schematic of the miRNA-based targeting approach used to create the input module to detect endogenous miRNA inputs in a discrete manner. D. Schematic of the output fine-tuning using miSFITs. E. Flowchart and figure guidance for generic and minimal circuit development. Download figure Download PowerPoint The generic circuit contains an input module composed of two kinds of miRNA sensors, here named miRhigh and miRlow sensors (Fig 1A). miRlow sensors directly repress a synthetic transactivator, Act2, by placing four fully complementary repeats of a given miRNA in the 3′UTR of Act2, resulting in Act2 expression only when the given miRNA is absent or at low levels (Fig 1A and C). miRhigh sensors are double inversion modules, where the endogenous miRNA represses a transactivator Act1 through four fully complementary repeats in the 3′UTR of Act1 (Fig 1A and C). Act1 in turn induces a repressor, a synthetic miRNA termed miR-FF4, that does not exist in human cells (Xie et al, 2011; Schreiber et al, 2016). miR-FF4 in turn represses Act2 (Fig 1A). Following this cascade, Act2 expression is induced only if a given endogenous miRNA input is highly expressed. An miRhigh sensor can be targeted by two or more miRNAs, forming an OR gate, to improve the inversion performance and increase robustness to fluctuations in endogenous miRNAs (Xie et al, 2011; Schreiber et al, 2016; Fig 1A). Because all sensors converge on Act2, the integration of the miRNA signals can be approximated in an AND-like logic function (Prochazka et al, 2014) Output = miR1 OR miR2 AND NOT(miR3) AND NOT(miR4). This means that Act2 is produced at high levels only if highly expressed miRNAs are recognized by the miRhigh sensors AND if miRNAs that are not expressed or active in pluripotent state are recognized by miRlow sensors. If one of the miRNA inputs substantially differs to this profile, or multiple miRNAs differ slightly, Act2- and output expression is significantly repressed (Xie et al, 2011; Prochazka et al, 2014), thereby enabling discrete cell state detection. The minimal circuit, in contrast, operates only on miRlow sensors and has been built by targeting Act2 by four complementary repeats of two miRNAs that are highly expressed in hPSC, thereby repressing Act2 and with that the output expression (hPSC-Off circuit; Fig 1B and C). The minimal circuit performs the AND-like logic function Output = NOT(miR1) AND NOT(miR2). In both generic and minimal circuit, the output module is controlled by Act2 and thus, indirectly, by the endogenous miRNAs. Act2 can activate one or multiple protein outputs (Fig 1A and B). To fine-tune the expression levels of each output, we applied miSFITs, a previously reported approach that operates on a library of mutated target sites of miRNA-17 (Michaels et al, 2019). miR-17 is ubiquitously and strongly expressed among most human cell types, including hPSC (Data ref: Fogel et al, 2015a). To tune protein outputs, one repeat of an miRNA-17 target site variant was placed in the 3′UTR of the protein outputs (Fig 1D). Each variant contains different mutations in the target site leading to reduced binding strength of endogenous miR-17 and thus reduced repression. The decrease in repression strength depends on the position and identity of the mismatched nucleotides (Michaels et al, 2019). Thus, by selecting a desired mutant variant from the miSFITs library, expression of the output proteins can be tuned to desired levels (Michaels et al, 2019). The generic circuit has been rationally designed where the number and combination of miRhigh and miRlow sensors have been computationally predicted from miRNA expression data of hPSC and hPSC-derived cell states (Fig 1E, left). The design of the minimal circuit, in contrast, was empirical based on miRNAs and learnings from the generic circuit implementation (Fig 1E, right). Results Automated circuit design and miRNA validation In order to restrict circuit action to discrete cell states or cell types, a set of endogenous miRNAs that can clearly discriminate the cell state of interest (positive samples, here hPSC) from the other cell states (negative samples, here hPSC-derived differentiated cells) require to be identified. To address the challenge of manually selecting such a set of miRNAs, we have applied and further modified a previously developed computational platform that automates the miRNA candidate search and circuit design procedure (Mohammadi et al, 2017). This platform uses a mechanistic mathematical model that predicts circuit output production from miRNA expression data by seeking a set of miRNA inputs and underlying circuit with the largest classification margin (cMargin). In other words, the largest fold change in calculated circuit output levels between positive samples and negative samples (Mohammadi et al, 2017). In order to apply the platform to identify hPSC-specific miRNAs from different published miRNA sequencing sources, we have modified the miRNA pre-selection step of the algorithm to allow selection of miRNA sequences instead of miRNA names (see Materials and Methods). Using three different data sets, covering four hPSC lines and 15 hPSC-derived cell states (Fig 2A, Dataset EV1; Bar et al, 2008a; Data ref: Bar et al, 2008b; Lipchina et al, 2011a; Data ref: Lipchina et al, 2011b; Data ref: Fogel et al, 2015a; Fogel et al, 2015b), we identified a minimal set of miRNAs that allowed discrimination of hPSC from the other cell states with a cMargin of 1.16 corresponding to an average of 14.4-fold change between the hPSC group and the differentiated group (Fig 2A). The algorithm identified three miRNAs: miRNA-302a, which is highly expressed in hPSC and plays a critical role in maintenance of the pluripotent state (Lipchina et al, 2011a); and miR-489 and miR-375, which are not expressed in hPSC but are expressed at different levels in the negative samples (Fig 2A). miR-375 is a key regulator during differentiation of hPSC toward pancreatic progenitors and mature beta- and alpha-cells (Fogel et al, 2015b). The role of miR-489 has been described in cancer but not, to the best of our knowledge, in hPSCs or during development. The underlying logic function the circuit performs can be approximated with: Output = miR-302a AND NOT miR-489 AND NOT miR-375 (Fig 2A, right). By increasing the maximal circuit input number constraints and/or considering the unpruned circuit version, we found additional circuits with slightly improved performance (Fig EV1). We highlight that all identified circuits included miR-302a, miR-375, and miR-489 among other miRNAs (Fig EV1), supporting the importance of the three miRNAs for hPSC classification. We also note that an additional highly expressed miRNA input forming an OR gate with miR-302a might be beneficial for optimal circuit performance (Fig EV1). Figure 2. Automated circuit design and miRNA validation Summary of computationally identified circuit using a constraint of maximum three inputs. Shown are input miRNAs and predicted circuit output levels. Expression levels of identified miRNAs inputs are given as fold change over the pre-set input abundance threshold (t) of the total miRNA pool (where t = 1%) (left). Calculated circuit output levels are given as mol/cell (middle) and logic connectivity of the identified miRNA is depicted (right). EB, embryoid bodies; MPC, mesenchymal progenitor cells; NPC, neural progenitor cells; R-NSC, neural rosettes. miRNA expression data and nomenclature in Dataset EV1, raw output data and constraint files of the algorithm in Source data file. See also Fig EV1. Illustration of bidirectional miRNA sensor system. Bar chart showing relative DsRed expression of sensors containing the indicated miRNA target sites as four fully complementary repeats (discrete miRNA sensing). The control vector (n.t.) does not contain any target sites. Two-tailed unpaired t-tests were performed to compare the definitive endoderm derived from H1 (H1 DE) to H1 and the control vectors. For comparison to H1, data were normalized with the control vector (n.t.) before performing the t-test. *P-value = 0.011 and **P-value = 0.006 are both considered very significant. Representative scatterplots of HES-2 are shown on top. Bar chart showing relative DsRed expression of sensors containing different mutated target sequence of miR-17 as listed in Appendix Table S1. Additionally, a sensor containing no target (n.t.) and two wildtype (WT) T17 target sites with 1× and 4× repeats were tested. Two-tailed unpaired t-tests were performed to compare H1 and HES-2 for each sensor. *P-values < 0.01, were considered very significant (**), < 0.02 significant (*) and > 0.5 not significant. Representative scatterplots of HES-2 are shown on top. Data information: Each bar in (C) and (D) corresponds to mean ± s.d. from at least three biological replicates. See also Fig EV2. Source data are available online for this figure. Source Data for Figure 2 [msb202110886-sup-0004-SDataFig2.zip] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Summary of computationally identified circuits showing input miRNAs and predicted circuit output levels using different maximum input constraintsShown are identified circuits when ten or five maximum input numb
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