Journal: Computational and Structural Biotechnology Journal

Article Title: Application of microfluidic systems in modelling impacts of environmental structure on stress-sensing by individual microbial cells

doi: 10.1016/j.csbj.2021.11.039

Figure Lengend Snippet: Representative microscopic images of soil micromodels. Soil-micromodel microfluidics devices contained solid PDMS structures within a channel 1 mm in diameter and 10 mm in length, with the direction of fluid flow from top to bottom of the images; approximately one fifth of the channel length is presented in (A). Cells are distributed randomly through the open pore space, with examples of cell location highlighted at positions 1–4 in (A) and magnified in the corresponding close-ups presented in (B), where cells are indicated by arrows.

Article Snippet: Characterisation of decreased cellular response to copper inflow with increasing structure density in simplified structured environments. (A) Responses to copper of S. cerevisiae cells expressing pCUP1- GFP in microfluidic chambers (CellASIC ONIX II) with an average of either 0, 16 (±5), or 39 (±8) microspheres per trap, after 1 hr exposure to 200 μM copper sulfate.

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: Application of microfluidic systems in modelling impacts of environmental structure on stress-sensing by individual microbial cells

doi: 10.1016/j.csbj.2021.11.039

Figure Lengend Snippet: Characterisation of decreased cellular response to copper inflow with increasing structure density in simplified structured environments. (A) Responses to copper of S. cerevisiae cells expressing pCUP1- GFP in microfluidic chambers (CellASIC ONIX II) with an average of either 0, 16 (±5), or 39 (±8) microspheres per trap, after 1 hr exposure to 200 µM copper sulfate. Points represent individual cells, bars represent mean and standard deviation for 59, 58, or 62 cells (at the increasing microspheres per trap, respectively), with every cell being located in a separate structured environment (trap). CVs for single-cell % fluorescence increase were 63, 202, and 377% across cells in chambers with 0, 16, and 39 microspheres, respectively. (B) The Voronoi areas for individual cells determined across the same chambers as analysed in (A). Both (A) and (B) were analysed statistically by one-way ANOVA with Tukey’s multiple comparisons; ****p < 0.0001. (C) Correlation between single-cell Voronoi area and copper response (% fluorescence increase) of individual cells combined across the three structured and unstructured environments (Pearson’s r = 0.280, R 2 = 0.079, p < 0.0001, n = 179). (D) Copper responses of cells binned in 1000 µm 2 Voronoi-area intervals. Data shown are mean values ± SEM.

Article Snippet: Characterisation of decreased cellular response to copper inflow with increasing structure density in simplified structured environments. (A) Responses to copper of S. cerevisiae cells expressing pCUP1- GFP in microfluidic chambers (CellASIC ONIX II) with an average of either 0, 16 (±5), or 39 (±8) microspheres per trap, after 1 hr exposure to 200 μM copper sulfate.

Techniques: Expressing, Standard Deviation, Fluorescence

Journal: Computational and Structural Biotechnology Journal

Article Title: Application of microfluidic systems in modelling impacts of environmental structure on stress-sensing by individual microbial cells

doi: 10.1016/j.csbj.2021.11.039

Figure Lengend Snippet: Cell-cell variation in fluorescence-response differs between experimental systems and correlates with cell Voronoi area in the microsphere system (CellASIC). (A) Variation in single-cell copper response (CV) in shake-flask and microfluidic experiments in response to copper exposure. Data derived from those of , , . (B) Correlation between Voronoi area of cells (binned as shown) and variation (CV) in their individual copper responses. Pearson’s r = -0.8458, R 2 = 0.7154, p = 0.0081. Dotted lines represent 95% confidence intervals.

Article Snippet: Characterisation of decreased cellular response to copper inflow with increasing structure density in simplified structured environments. (A) Responses to copper of S. cerevisiae cells expressing pCUP1- GFP in microfluidic chambers (CellASIC ONIX II) with an average of either 0, 16 (±5), or 39 (±8) microspheres per trap, after 1 hr exposure to 200 μM copper sulfate.

Techniques: Fluorescence, Derivative Assay

Journal: Genome Research

Article Title: Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

doi: 10.1101/gr.149096.112

Figure Lengend Snippet: Promoter variants and experimental setup. ( A ) Illustration of hypothesized promoter dynamics induced by two distinct DNA-encoded strategies for increasing expression and a matched stochastic simulation of promoter switching, transcription, and translation ( right panel) for each strategy. For a simplified model in which promoters transition at some rate between transcriptionally inactive and active states ( top ), we hypothesize that addition of nucleosome disfavoring elements such as poly(dA:dT) tracts would increase the accessibility of the transcription factor binding site, thereby reducing the time that a factor molecule spends in search of its site ( middle ). In contrast, we hypothesize that increasing the affinity of a factor binding site would reduce the factor's dissociation rate. Note that both types of sequence changes result in a higher mean expression over the cell population, but with distinct hypothesized effects on promoter dynamics. Using the Gillespie algorithm, we simulate the kinetic scheme in three scenarios. In the simulation runs, we record, as a function of time, the promoter state (black–red line), mRNA levels (blue line), protein levels (black line), and the protein production rate (green line), which is the derivative of the protein levels with respect to time. In addition, we record protein production bursts (red line), when production is positive. The “normal” promoter ( top ) represents the reference point for the parameter changes. The fast promoter ( middle ) has an increased k on to simulate an increase in promoter accessibility [added poly(dA:dT)]. The “slow” promoter ( bottom ) has a decreased k off (with respect to the normal promoter) to simulate an increase in TF binding site affinity. The three example runs shown illustrate that although both parameters can increase the overall expression level, k on increases the frequency of production bursts, while k off increases the length of the bursts. ( B ) Illustration of promoter variants used in this study. All promoters are genomically integrated upstream of a yellow fluorescent protein (YFP) reporter and into a region that also contains an mCherry fluorescent protein driven by a constant TEF2 promoter. Promoter variants differ in the presence and length of two poly(dA:dT) tracts and in the affinity of the transcription factor binding site for either the Gcn4 or Gal4 transcriptional activators. ( C ) Representative YFP time-lapse microscopy images of four promoter variants, imaged in a microfluidic platform that supplies a continuous flow of medium. Each cell was followed over time, and its lineage, YFP, and mCherry signal intensity were extracted (Methods). ( D ) Single-cell flow cytometry data collected for the corresponding promoter variants from C , shown as a histogram of normalized YFP values ( top ) and as a scatterplot of YFP ( x -axis) against mCherry fluorescence ( bottom ).

Article Snippet: For fluorescence microscopy of single cells, cells were diluted to reach an optical density of 1 prior to the experiment in the desired medium and then diluted and loaded into the microfluidic imaging plate (The ONIX Microfluidic Perfusion System, CellASIC Corporation).

Techniques: Expressing, Binding Assay, Sequencing, Time-lapse Microscopy, Flow Cytometry, Fluorescence