Single‐cell profiling screen identifies microtubule‐dependent reduction of variability in signaling
2018; Springer Nature; Volume: 14; Issue: 4 Linguagem: Inglês
10.15252/msb.20167390
ISSN1744-4292
AutoresC. Gustavo Pesce, Stefan Zdraljevic, W. J. Peria, Alan Bush, María Victoria Repetto, Daniel Rockwell, Richard Yu, Alejandro Colman‐Lerner, Roger Brent,
Tópico(s)CRISPR and Genetic Engineering
ResumoArticle4 April 2018Open Access Source DataTransparent process Single-cell profiling screen identifies microtubule-dependent reduction of variability in signaling C Gustavo Pesce C Gustavo Pesce Abalone Bio, Inc., Richmond, CA, USA Search for more papers by this author Stefan Zdraljevic Stefan Zdraljevic orcid.org/0000-0003-2883-4616 Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA Search for more papers by this author William J Peria William J Peria orcid.org/0000-0003-4246-2572 Fred Hutchinson Cancer Research Center, Seattle, WA, USA Search for more papers by this author Alan Bush Alan Bush IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina Search for more papers by this author María Victoria Repetto María Victoria Repetto IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina Search for more papers by this author Daniel Rockwell Daniel Rockwell Abalone Bio, Inc., Richmond, CA, USA Search for more papers by this author Richard C Yu Richard C Yu Abalone Bio, Inc., Richmond, CA, USA Search for more papers by this author Alejandro Colman-Lerner Alejandro Colman-Lerner orcid.org/0000-0002-2557-8883 IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina Search for more papers by this author Roger Brent Corresponding Author Roger Brent [email protected] orcid.org/0000-0001-8398-3273 Fred Hutchinson Cancer Research Center, Seattle, WA, USA Search for more papers by this author C Gustavo Pesce C Gustavo Pesce Abalone Bio, Inc., Richmond, CA, USA Search for more papers by this author Stefan Zdraljevic Stefan Zdraljevic orcid.org/0000-0003-2883-4616 Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA Search for more papers by this author William J Peria William J Peria orcid.org/0000-0003-4246-2572 Fred Hutchinson Cancer Research Center, Seattle, WA, USA Search for more papers by this author Alan Bush Alan Bush IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina Search for more papers by this author María Victoria Repetto María Victoria Repetto IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina Search for more papers by this author Daniel Rockwell Daniel Rockwell Abalone Bio, Inc., Richmond, CA, USA Search for more papers by this author Richard C Yu Richard C Yu Abalone Bio, Inc., Richmond, CA, USA Search for more papers by this author Alejandro Colman-Lerner Alejandro Colman-Lerner orcid.org/0000-0002-2557-8883 IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina Search for more papers by this author Roger Brent Corresponding Author Roger Brent [email protected] orcid.org/0000-0001-8398-3273 Fred Hutchinson Cancer Research Center, Seattle, WA, USA Search for more papers by this author Author Information C Gustavo Pesce1, Stefan Zdraljevic2, William J Peria3, Alan Bush4, María Victoria Repetto4, Daniel Rockwell1, Richard C Yu1, Alejandro Colman-Lerner4,‡ and Roger Brent *,3,‡ 1Abalone Bio, Inc., Richmond, CA, USA 2Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA 3Fred Hutchinson Cancer Research Center, Seattle, WA, USA 4IFIBYNE-UBA-CONICET and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina ‡These authors contributed equally to this work *Corresponding author. Tel: +1 206 667 1482; E-mail: [email protected] Molecular Systems Biology (2018)14:e7390https://doi.org/10.15252/msb.20167390 PDFDownload PDF of article text and main figures. 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 Populations of isogenic cells often respond coherently to signals, despite differences in protein abundance and cell state. Previously, we uncovered processes in the Saccharomyces cerevisiae pheromone response system (PRS) that reduced cell-to-cell variability in signal strength and cellular response. Here, we screened 1,141 non-essential genes to identify 50 "variability genes". Most had distinct, separable effects on strength and variability of the PRS, defining these quantities as genetically distinct "axes" of system behavior. Three genes affected cytoplasmic microtubule function: BIM1, GIM2, and GIM4. We used genetic and chemical perturbations to show that, without microtubules, PRS output is reduced but variability is unaffected, while, when microtubules are present but their function is perturbed, output is sometimes lowered, but its variability is always high. The increased variability caused by microtubule perturbations required the PRS MAP kinase Fus3 and a process at or upstream of Ste5, the membrane-localized scaffold to which Fus3 must bind to be activated. Visualization of Ste5 localization dynamics demonstrated that perturbing microtubules destabilized Ste5 at the membrane signaling site. The fact that such microtubule perturbations cause aberrant fate and polarity decisions in mammals suggests that microtubule-dependent signal stabilization might also operate throughout metazoans. Synopsis A genetic screen for pheromone signal variability mutants, and subsequent experiments, showed that cytoplasmic microtubule plus end function stabilizes signaling. Perturbing plus end function impacted signal transmission by Fus3 MAPK, as well as cell polarity determinations. Screen identifies yeast microtubule mutants with increased pheromone signal variability. Such mutations, and other perturbations affecting cytoplasmic plus end function, cause erratic signaling. Signal variability affects gene expression and polarity decisions. There is substantial signal variability due to the effect of Fus3 MAP Kinase at the cell membrane signaling site. Introduction Cell signaling systems transmit information about the external environment, enabling cells to respond to extracellular signals. Accurate signal transmission and response of individual cells, and coherence in cell population response, are critical for the choreographed sequence of signal and response during embryonic development, and for regulated cell division and differentiation during tissue maintenance in the adult. Variability in cell responses is well recognized and widespread, from Escherichia coli infected with phages (Delbrück, 1945), to mammalian cells subjected to pro-apoptotic signals (Spencer et al, 2009 and Appendix). However, the means by which cells transmit and respond to signals accurately, and so manifest coherent population responses, remain largely unknown. We and others have studied cell-to-cell variability, using the cell fate decision system that controls mating in Saccharomyces cerevisiae, the pheromone response system (PRS) (Colman-Lerner et al, 2005; Yu et al, 2006; Paliwal et al, 2007; Ricicova et al, 2013). The PRS has elements prototypic for many other signaling systems: It uses a GPCR, which, when bound by pheromone, couples via a G-protein to a scaffold-dependent MAPK cascade (Dohlman & Thorner, 2001; Fig 1). In this cascade, there are two partially redundant MAP kinases, Fus3 and Kss1, each able to activate downstream steps. After activation, receptors, G-proteins, and the scaffold concentrate into a membrane patch (Suchkov et al, 2010; Ventura et al, 2014; Ismael et al, 2016) here called the signaling site. The cell converts extracellular ligand concentration into an occupancy measurement (Brent, 2009) by determining the ratio of ligand-occupied to unoccupied receptors (Bush et al, 2016) and transmitting that information accurately, via negative feedback (Yu et al, 2008) and "push–pull" mechanisms (Andrews et al, 2016). Signaling causes outputs including induction of genes at appropriate levels (here called "system output") that depend on a set of proteins that constitute the signaling arm of the PRS. Determination of the direction of a gradient of pheromone concentration, and subsequent growth toward a mating partner, depends on a partly overlapping set of proteins, the polarity determination arm of the system. Our previous work quantified system output by expression from PRS-responsive and control reporter genes. It separated the cell-to-cell variability in output into two contributions. The first of these was from cell-to-cell variability in the pathway subsystem, P (includes all events upstream of the promoter of the reporter gene), quantified as η2(P) (η2 = variance/mean2), and here called "pathway variability". The second contribution was from variability in events related to reporter gene expression, either due to (i) preexisting differences in the general capacity of cells to express genes into proteins, G, quantified as η2(G), and here called "variability in gene expression" or (ii) rapid-acting changes in gene expression due to "intrinsic noise", which we quantified as η2(γ). In this previous work, we made the assumption that cell-to-cell differences in (P) were composed of η2(L), (differences in L, the capacity component of the signal transmission subsystem at the start of the experiment) and η2(λ), (rapid-acting changes in signal during the measurement) but we could not separate η2(L) and η2(λ) experimentally. Figure 1. The signaling arm of the yeast pheromone response system (PRS)Binding of the ligand, α-factor, to a seven-helix transmembrane receptor, Ste2, in the MATa cell depicted, causes the dissociation of the α subunit of a trimeric G-protein, Gpa1, from the βγ dimer, Ste4/Ste18. This event causes the recruitment to the plasma membrane of the scaffold protein Ste5, leading to the assembly and activation of the MAP kinase cascade (MAPKKK Ste11, MAPKK Ste7) and the detachment from the scaffold of the Erk1/2-like MAPKs Fus3 and Kss1 (Dohlman & Thorner, 2001). In the cytoplasm, activated Fus3 and Kss1 phosphorylate targets including Ste5 (Bhattacharyya et al, 2006; Malleshaiah et al, 2010), and in the nucleus, they phosphorylate Dig1, Dig2, and Ste12 (Tedford et al, 1997). These events comprise the pathway subsystem, P; that is, the subsystem that transmits the signal to the promoters of inducible genes. Activation of Ste12 leads to the induction of approximately 100 pheromone-responsive genes (PRGs) (Roberts et al, 2000) and their expression via the expression subsystem G (defined in the text). Download figure Download PowerPoint Four lines of evidence show that cell-to-cell variation and pathway variability, η2(P), is under active control. First, pathway subsystem output P correlates negatively with gene expression capacity G, indicating a compensatory mechanism that reduces variability in system output (Colman-Lerner et al, 2005). Second, mutations in either of the PRS MAPKs Kss1 and Fus3 affect η2(P), and do so differently (Colman-Lerner et al, 2005). Third, maintenance of the matching dose-response relationship between system output and system activity, which reduces the amplification of stochastic noise η2(λ) during signal transmission at an intermediate point requires the action of negative feedback from Fus3 (Yu et al, 2008). Fourth, we showed recently (Bush et al, 2016) that a push–pull mechanism suppresses cell-to-cell differences in signal-dependent gene expression caused by changes in the abundance of the receptor. Here, we hypothesized that there might be additional mechanisms that regulate (or suppress) variability in transmitted signal. Results Large-scale screen identifies genes whose products affect pathway variability To identify genes that affected cell-to-cell variability, we first constructed a whole genome collection of yeast carrying the necessary reporters and mutations in each non-essential gene. To do this, we extended established methods facilitating genetic crosses of arrayed collections (Tong et al 2004, see Appendix). During our initial characterization of cell-to-cell variability phenotypes in our collection we found that, for many gene deletions, the patches of post-sporulation segregants contained varying numbers of colonies of genetically variant haploids, likely arising from chromosomal mis-segregation during meiosis (Hughes et al 2000), same sex diploid formation (Giaever & Nislow, 2014) and possibly also from mutations present in some cells in the starting collection. While such heterogeneity, if present, might have not had a large impact on phenotypes studied before in similar collections (Jonikas et al, 2009; Neklesa & Davis, 2009; Wolinski et al, 2009; Ayer et al, 2012), our measures of cell-to-cell variability were very sensitive to it. We thus generated our collection from clonal cultures derived from single colonies. To screen the mutant collection, we optimized a flow cytometry adaptation of our microscopic methods to measure single-cell responses. In this screen, we arrested cell cycle progression by inhibition of the Cdc28-as2 mutant protein with the inhibitor 1NM-PP1-NM, incubated cells with pheromone in well plates, stopped the response by the addition of cycloheximide, and allowed time for the fluorophores to fully mature. We first tested this method on the reference strain. We extended our previous characterization of the dose response of pathway variability η2(P) to a broader range of different pheromone concentrations (0.1–30 nM). Consistent with previous microscopic measurements at just two doses (Colman-Lerner et al, 2005), the fine-grained dose response showed that η2(P) decreased monotonically with increasing pheromone (Fig EV1). We thus were reassured about using this approach to screen the arrayed mutant collection. Click here to expand this figure. Figure EV1. Time-dependent output and dose response of the reporter genes used in the screenWe stimulated SGA85 cells with the indicated concentrations of pheromone and measured the accumulated fluorescent protein by flow cytometry as detailed in Materials and Methods. A, B. Average fluorescence output of the pheromone-inducible PPRM1-mRFP reporter, O (A) and the constitutive PACT1-YFP reporter, G (B), in A.U., measured at four different doses over time. C. Estimating pathway variability (η2(P)). Panel shows a scatter plot, with one point per cell, of PPRM1-mRFP vs. PACT1-YFP, in 500 cells stimulated with 20 nM pheromone for 3 h. The amount of pathway variability (η2(P) + η2(γ)) (a quantity very close to η2(P), see Appendix) is a measure of the average width of this distribution of plotted points about the identity line, drawn in pink. D. Dose dependence of pathway variability and output. Plot shows pathway variability η2(P) + η2(γ) (blue), and output (gray) in SGA85 cells, as a function of pheromone dose after 180 min. Download figure Download PowerPoint For the primary screen, we assayed 1,141 strains from the collection (996 randomly selected and 145 bearing a deletion in a non-essential kinase or phosphatase (Appendix Table S2). We screened these for expression related variables (Table 1) that allowed us to compute pathway output (P) and/or variability in it (η2(P)), system output (O) and variability in it, and a proxy for gene expression capacity, G. Screened strains corresponded to more than 1/4 of the non-essential yeast genes. Appendix Table S2 shows the numerical results. Table 1. Variables measured in isogenic cell populations Variable name Short expression Calculated as Median pheromone response system (PRS) output O, or Median (inducible RFP) Median constitutive or control output G, or Median (constitutive XFP) Cell-to-cell variability in PRS output η2(O), η2(PPRM1-mRFP) or η2(mCherry) Cell-to-cell variability in constitutive or control output (representing general gene expression capacity) η2(G), η2(PACT1-YFP) or η2(PBMH2-YFP) Cell-to-cell variability in signal transmission η2(P), or η2(L + λ) σ2(mRFPi/ −YFPi/ ) Signal strength P O/G σ2 is variance, μ is average, means the average of B. Quantities in bold type are those used for selection and/or clustering analysis below. From these screened strains, we selected gene deletions for follow-up "secondary screen" studies, based on their η2(P) and average output (O) phenotypes (Fig 2A–C). We chose selection thresholds that lay in the tails of the distributions of values measured for the 52 separate cultures of the reference strain included in the screen. From the low dose (0.6 nM pheromone) data, we selected mutants with high or low median pheromone system output (O) (Fig 2A) or high or low η2(P) (Fig 2B). From the high dose (20 nM pheromone) data, we only selected mutants that showed high η2(P) (Fig 2C). Figure 2D and E shows η2(P) vs. P, at 0.6 nM (D) and 20 nM (E) pheromone doses, for all measured strains. Figure 2. Selection of mutants for follow-up studiesPlots show distributions of values for 991 randomly selected non-essential deletion strains, and 102 additional strains with deletions of a non-essential kinase or phosphatase, and two wild-type strains. Values were derived from flow cytometry data obtained after 3 h of stimulation with pheromone. Blue vertical bars indicate the thresholds used to select mutants for secondary screens (see Appendix). A. PRS output, O (median mRFP signal), in 0.6 nM pheromone. B. Estimated pathway variability η2(P) in 0.6 nM pheromone C. Estimated η2(P) in 20 nM pheromone (see Appendix Table S1). D, E. Signaling variability vs. transmitted signal P (median mRFP/median YFP) for all 1,093 strains screened. Plots show an estimate of η2(P) vs. P for the same dataset displayed in Fig 4A–C. The contour lines show the expected dependence of variability on output for outputs proportional to a Poisson random variable (lower noise at higher outputs), with proportionality constants logarithmically spaced from 10−5 to 1. Purple Xs are independent replicates of the reference SGA 85 strain. Their spread gives an indication of the limits of this primary screen. The SGA 85 swarm lies below the 0.158 contour at 20 nM but above it at 0.6 nM, indicating that variability at the low dose is higher than expected from the same Poisson processes taking place at 20 nM. At 0.6 nM, Δbim1 and Δgim4 showed somewhat greater, and at 20 nM substantially greater, pathway variability than reference cells. See Appendix Table S2 for a list of all strains and their corresponding raw output and variability values. Source data are available online for this figure. Source Data for Figure 2 [msb167390-sup-0008-SDataFig2.xlsx] Download figure Download PowerPoint For the secondary screen, we isolated three fresh independent haploid segregants and assayed them by flow cytometry as above (see Appendix for a complete description of primary and secondary screens). These screens identified 50 deletion strains (Table 2) that reproducibly showed changes in O or η2(P). Table 2. Genes found in the screen Gene name Screen, criteria Important for Description ARG82 U,2 AA metabolism Inositol polyphosphate multikinase ERV46 U,1,4,5 Cargo transport ER vesicle protein, component of COPII complex; required for membrane fusion HIS1 U,1,4,5 AA metabolism ATP phosphoribosyltransferase UGA1 U,5 AA metabolism Gamma-aminobutyrate (GABA) transaminase SLA1 U,2,3 Actin binding Cytoskeletal protein binding protein; required for assembly of the cortical actin cytoskeleton SAP155 K,2,3,5 Cell cycle Protein required for function of the Sit4 protein phosphatase YER068C-A U,5 Dubious open reading frame Dubious open reading frame/overlaps with ARG5, ARG6 acetylglutamate kinase and N-acetyl-gamma-glutamyl-phosphate reductase YIL032C U,5 Dubious open reading frame Dubious open reading frame/next to BCY1 ERG3 U,1,5 Ergosterol biosynthesis C-5 sterol desaturase GAL83 K,1 Glucose repression One of three possible beta-subunits of the Snf1 kinase complex GUP1 U,2,3 Glycerol metabolism, protein folding Plasma membrane protein involved in remodeling GPI anchors PPZ1 K,5 Ion homeostasis Serine/threonine protein phosphatase Z, isoform of Ppz2p; involved in regulation of potassium transport, which affects osmotic stability, cell cycle progression, and halotolerance FUS1 U,5 Mating Membrane protein localized to the shmoo tip AAT2 U,5 Metabolism Cytosolic aspartate aminotransferase involved in nitrogen metabolism PTC6 K,2,3 Metabolism Mitochondrial type 2C protein phosphatase (PP2C) GIM4 U,5 Microtubule chaperone/Protein folding Subunit of the heterohexameric cochaperone prefoldin complex PAC10 GIM2 U,5 Microtubule chaperone/Protein folding Subunit of the heterohexameric cochaperone prefoldin complex BIM1 U,4,5 Microtubule end binding Microtubule plus-end-binding protein MSH1 U,5 Mitochondrial homeostasis Escherichia coli MutS homolog, binds DNA mismatches, required for mitochondrial function BUB1 K,4,5 Mitosis Protein kinase required for cell cycle checkpoint, delays entry into anaphase until kinetochores bound by opposing microtubules ELM1 K,2 Morphogenesis Serine/threonine protein kinase that regulates cellular morphogenesis HSL1 K,2 Morphogenesis Nim1-related protein kinase; regulates the morphogenesis and septin checkpoints NUP60 U,5 Nuclear transport FG-nucleoporin component of central core of the nuclear pore complex SXM1 U,4,5 Nuclear transport Nuclear transport factor (karyopherin) CBR1 U,5 Respiration Microsomal cytochrome β reductase RTC3 U,4,5 RNA metabolism Protein of unknown function involved in RNA metabolism CKA1 U,1,4,5 Signaling Alpha catalytic subunit of casein kinase 2 (CK2) CKB1 U,5 Signaling Beta regulatory subunit of casein kinase 2 (CK2) CKB2 K,1 Signaling Beta' regulatory subunit of casein kinase 2 (CK2) FUS3 K,3,5 Signaling Mitogen-activated serine/threonine protein kinase (MAPK), part of PRS HOG1 K,3 Signaling Mitogen-activated protein kinase involved in High Osmolarity (HOG) pathway KSS1 K,3 Signaling Mitogen-activated protein kinase (MAPK); functions in PRS and signal transduction pathways that control filamentous growth and pheromone response PBS2 K,5 Signaling MAP kinase kinase of the HOG signaling pathway SSK2 K,3 Signaling MAP kinase kinase kinase of HOG1 signaling pathway FAR1 U,1 Signaling/cell cycle/polarization CDK inhibitor, nuclear anchor, recruited by Ste18-Ste4 at polarity patch SIP1 U,4,5 Signaling/glucose repression Alternate beta-subunit of the Snf1 protein kinase complex KAR4 U,5 Signaling/mating Transcription factor required for activation of some pheromone-responsive genes STE50 U,1,4 Signaling/mating Adaptor protein, in PRS helps connect Ste20 MAPKKKK to Ste11 MAPKKK SKY1 K,3 Splicing SR protein kinase (SRPK); varied functions, regulates proteins involved in mRNA metabolism and cation homeostasis, helps some LexA fusion proteins bind operator KIN3 K,5 Stress Non-essential serine/threonine protein kinase; possible role in DNA damage response OCA1 K,1 Stress Protein tyrosine phosphatase; required for cell cycle arrest in response to oxidative damage of DNA CTK1 K,4 Transcription regulation Catalytic (alpha) subunit of C-terminal domain kinase I (CTDK-I); phosphorylates RNA pol II DEP1 U,5 Transcription regulation Component of the Rpd3L histone deacetylase complex, variously needed for activation and repression, regulates DNA replication origin timing SUM1 U,2,4,5 Transcription regulation Transcriptional repressor that regulates middle-sporulation genes; required for mitotic repression of middle-sporulation-specific genes; also acts as general replication initiation factor; involved in telomere maintenance, chromatin silencing SWI5 U,5 Transcription regulation Part of Mediator and Swi/Snf nucleosome remodeling complexes UME6 U,4,5 Transcription regulation Meiotic transcription regulator, DNA binding, recruits variously Sin3/Rpd3 repressor (HDAC) and Ime1 activator. RPL12A U,1,4 Translation Ribosomal 60S subunit protein L12A RPL19B U,1,4,5 Translation Ribosomal 60S subunit protein L19B RPL34A U,4,5 Translation Ribosomal 60S subunit protein L34A ECM15 U,1,4,5 Unknown Possibly tetrameric, non-essential protein, unknown function VPS64 U,5 Vacuole metabolism Required for cytoplasmic proteins to enter vacuole Selection criteria codes (see Fig 2A–C): (1) O(0.6 nM) < 3.39; (2) O(0.6 nM) > 7.72; (3) η2(P(0.6 nM)) < 0.027; (4) η2(P(0.6 nM)) > 0.054; (5) η2(P(20 nM)) > 0.019. Genes from the strains in the unbiased (U) screen and the strains in the non-essential kinase and phosphatase screen (K) screen that showed altered PRS system output (O), low or high pathway variability (η2(P)) at low pheromone (0.6 nM), or high variability (η2(P)) at high dose (20 nM). Table shows gene name, screen from which it was selected and selection criteria, overall functional class, and a brief description of its molecular role or activity. We did an additional follow-up "tertiary screen" on duplicate independent isolates of 44 of the haploid deletion strains (Appendix Table S6). For this screen, we used microscope-based quantification of the fluorescent protein reporters. Although we did not seek to gain biological insight from observation of effects of these gene deletions on cell morphology, this microscope-based quantification of fluorescence signal had two advantages. First, it allowed us to rule out the possibility that putative single-cell values were actually derived from clumps of several cells. None of the mutant cultures we imaged was affected by these problems. Second, it allowed us to measure another variable, gene expression noise, η2(γ), by simultaneous quantitation of the two fluorescent protein reporters (CFP and mRFP) driven by PPRM1 (accurate CFP measurements were not possible in the flow cytometer). The tested mutants showed values of η2(γ) that were typical of the reference strain. The only significant differences were in O, η2(O), and η2(P). Mutant genes define different axes of quantitative system behavior To gain insight into the different phenotypes caused by these gene deletions, we grouped the mutant strains in the secondary screen using a hierarchical clustering approach based on the five variables we measured by flow cytometry, at low and high pheromone dose (Fig 3 and Appendix Table S2). Fourteen of the 19 cultures of the reference strain grouped together in one cluster (cluster I), one in cluster IIa, two in cluster IIIa, one in cluster IIIb, and one in cluster Vc. With a few exceptions (for example ∆ckb1, ∆his1, and ∆sky1) either all or all but one of the independent segregants bearing each gene deletion grouped in the same subcluster. Taken together with the results of the tertiary screen, these results show that differences in variability in strains with different gene deletions were due to the mutations. Since all 19 cultures of the SGA85 reference cells were isogenic, that five of these cultures grouped into different clusters highlight the fact that these high-throughput flow cytometric assays sometimes perform inconsistently. Similarly, since our independent haploid segregants came from crosses with an otherwise isogenic MATα strain, we believe that the observed infrequent grouping of any single deletion's isolates into multiple clusters most likely reflects measurement anomalies rather than uncharacterized genetic differences between the MATa and MATα parents of the strains. Figure 3. Cluster analysis of 50 genes identified as affecting variability and or pheromone response outputHierarchical clustering of values derived from flow cytometry measurements from 198 cell populations (19 replicates for reference strain SGA85, four independent segregants each for 17 deletions from the kinases or phosphatase set and three independent segregants each for 37 deletions from the unbiased set). We used the Pearson correlation metric to assess distance between strains and the average linkage method to form clusters. Before clustering, we first log-transformed the data and then median centered each row (each strain). Each strain had the following 10 measurements (five after induction with 20 nM pheromone and five after induction with 0.6 nM pheromone): O (pheromone system output), G (gene expression output), and η2(O), η2(G) and η2(P), the three cell-to-cell variability measurements. The panel shows these values as a "heat map", from red (higher than the median) to black (equal to the median) to green (lower than the median). The signature pattern for each cluster or subcluster is represented with a color bar with 10 blocks, one for each measurement (gray indicates that that the measurement may take any value). Rightmost column shows representative deletion strains for each subcluster. The asterisk next to the last row of the re
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