Journal: Cell Reports

Article Title: Quantitative Characterization of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control

doi: 10.1016/j.celrep.2019.03.081

Figure Lengend Snippet: Construction and Characterization of Yeast Strains Expressing Wild-Type or Mutant (A53T) Human α-Synuclein from the Galactose-Inducible Promoter (A) Schematic of the constructs to express either the human α-synuclein gene ( SNCA ) or the disease-associated A53T mutant in yeast. α-Synuclein is fused to a GFP reporter under the control of the galactose-inducible promoter P GAL1 . In each strain (WT or A53T), two copies of the construct were genomically integrated and at least one copy was expressed from a yeast centromeric plasmid (YCp) . A red fluorescent reporter (mCherry protein) under the control of the constitutive promoter P TEF2 was also integrated in both yeast strains. (B) Ten-fold serial dilutions of yeast strains expressing either WT and A53T in log-phase cultures were spotted on synthetic complete drop-out media supplemented with either glucose (uninduced) or galactose (induced) to assess cell viability upon α-synuclein induction . (C) Schematic illustration of the integrated experimental platform for automated microfluidics feedback control of α-synuclein in yeast cells.

Article Snippet: The control error e is defined as the difference between the desired α-synuclein level y r e f and the measured (or predicted, as in the case of the MPC algorithm) α-synuclein level y . Optimization was performed with the MATLAB function fmincon of the Optimization Toolbox .

Techniques: Expressing, Mutagenesis, Construct, Control, Plasmid Preparation

Journal: Cell Reports

Article Title: Quantitative Characterization of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control

doi: 10.1016/j.celrep.2019.03.081

Figure Lengend Snippet: Automated Microfluidics Feedback Control Enables Precise Regulation of α-Synuclein Expression over Time in Both WT α-Synuclein Strain and A53T Mutant Strain α-Synuclein-GFP fluorescence was quantified in each cell and normalized to the red fluorescence (mCherry protein) with a custom-made image processing algorithm . Light gray lines show representative examples of single-cell traces . Single-cell traces may start or end at different times, because of cells being born and cells being pushed out of the field of view. (A and B) Population-averaged α-synuclein-GFP fluorescence (blue with SD across cells in gray) for the WT α-synuclein strain (A) and the A53T mutant α-synuclein strain (B) grown in the microfluidics device in the presence of galactose. Examples of images below each time course show WT and A53T mutant α-synuclein-GFP at the indicated time points. (C and D) Population-averaged α-synuclein-GFP fluorescence (blue with SD in gray) was controlled to the reference target value (yellow) by automatically switching between glucose and galactose (brown) as computed in real time by the Model Predictive Control strategy . No α-synuclein inclusions were observed over the course of the experiment in both strains. Examples of images below each time course show (C) WT and (D) A53T mutant α-synuclein-GFP at the indicated time points. See also Figure S1 .

Article Snippet: The control error e is defined as the difference between the desired α-synuclein level y r e f and the measured (or predicted, as in the case of the MPC algorithm) α-synuclein level y . Optimization was performed with the MATLAB function fmincon of the Optimization Toolbox .

Techniques: Control, Expressing, Mutagenesis, Fluorescence

Journal: Cell Reports

Article Title: Quantitative Characterization of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control

doi: 10.1016/j.celrep.2019.03.081

Figure Lengend Snippet: Automated Microfluidics Feedback Control of α-Synuclein Expression at Nine Increasing Levels to Observe and Quantify α-Synuclein Aggregation Thresholds in Both WT α-Synuclein Strain and A53T Mutant Strain (A–F) Six control experiments were performed to increase α-synuclein protein expression stepwise in both the WT α-synuclein strain (A–C) and mutant A53T α-synuclein strain (D–F). Population-averaged α-synuclein expression (blue with SD in gray) was tightly regulated to the target expression levels (yellow) by automatically switching between galactose and glucose (brown) as directed by the controller . α-Synuclein-GFP fluorescence in single cells is normalized to mCherry fluorescence within each cell to enable comparison across cells, strains, and experiments. Examples of images below each time course show WT and A53T mutant α-synuclein-GFP at the indicated time points (A–F). Prior to the formation of inclusions, α-synuclein-GFP is mainly on the membrane, whereas inclusions appear as bright cytoplasmic spots. See also and .

Article Snippet: The control error e is defined as the difference between the desired α-synuclein level y r e f and the measured (or predicted, as in the case of the MPC algorithm) α-synuclein level y . Optimization was performed with the MATLAB function fmincon of the Optimization Toolbox .

Techniques: Control, Expressing, Mutagenesis, Fluorescence, Comparison, Membrane

Journal: Cell Reports

Article Title: Quantitative Characterization of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control

doi: 10.1016/j.celrep.2019.03.081

Figure Lengend Snippet: Single-Cell Time Course of α-Synuclein Expression Reveals Specific Aggregation Thresholds for Both the Wild-Type and Disease-Associated Mutant Forms α-Synuclein-GFP fluorescence was quantified in each cell and normalized to the red fluorescence (mCherry protein) with a custom-made image processing algorithm . ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Student’s t test. (A and B) Heatmap representation of single-cell α-synuclein-GFP fluorescence for the WT strain (A) and A53T strain (B) during the control experiments reported in Figure 3 , where α-synuclein-GFP increases in a stepwise fashion. Each row represents a single cell and each column a 5-min time interval. Single-cell time-lapse imaging was used to detect the aggregation time point (red dot in each row), defined as the moment at which α-synuclein inclusions become visible at the cell membrane. Only cells that formed inclusions are shown. Some cells did not show any inclusions during their lifespan because they did not reach the aggregation threshold. Of the cells that reach the aggregation threshold, the majority (83% for WT and 86% for A53T) exhibited inclusions. Single-cell traces may start or end at different times, because of cells being born and cells being pushed out of the field of view. (C) Distribution of the α-synuclein fluorescence at the aggregation time point across single cells in the WT (n = 53) and A53T (n = 64) α-synuclein strains. The aggregation threshold of the WT strain is significantly higher than the A53T strain (Student’s t test, p = 1.06 × 10 −23 ). (D) Distribution of the α-synuclein aggregation delay across single cells in the WT (n = 53) and A53T (n = 64) α-synuclein strains. Aggregation delay is defined as the time interval prior to the aggregation time point during which the α-synuclein-GFP fluorescence level was within 1 normalized unit from the aggregation threshold. The average aggregation delay of the A53T mutant strain is only a few minutes higher than the WT strain (Student’s t test, p = 0.0003). (E) Distributions of the cell-cycle duration across single cells before and after formation of α-synuclein inclusions in both the WT (n = 53) and A53T (n = 64) α-synuclein strains. The cell-cycle duration after α-synuclein inclusions appear increases significantly in both WT (Student’s t test, p = 7.29 × 10 −7 ) and A53T (Student’s t test, p = 1.41 × 10 −17 ) strains. Outliers were removed according to the boxplot rule.

Article Snippet: The control error e is defined as the difference between the desired α-synuclein level y r e f and the measured (or predicted, as in the case of the MPC algorithm) α-synuclein level y . Optimization was performed with the MATLAB function fmincon of the Optimization Toolbox .

Techniques: Expressing, Mutagenesis, Fluorescence, Control, Imaging, Membrane

Journal: Cell Reports

Article Title: Quantitative Characterization of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control

doi: 10.1016/j.celrep.2019.03.081

Figure Lengend Snippet: Single-Cell Automated Microfluidics Feedback Control Enables Real-Time Monitoring of Mutant A53T α-Synuclein Aggregation and Clearance Dynamics (A–D) Two single-cell control experiments are shown. α-Synuclein-GFP expression was quantified with an image processing algorithm and normalized to the red fluorescent reporter (mCherry protein; ). Cells were tracked in real-time using a custom algorithm . The budded phase of the cell cycle indicated by shaded gray areas was identified using a custom procedure . Bud formation (dashed blue line) and cell division (dotted blue line) are also shown. Red circles indicate α-synuclein-GFP fluorescence at cell divisions. (A) Automatic feedback control of A53T α-synuclein-GFP expression (blue) in a single cell at two different levels (6 and 10 in yellow) below and above the A53T aggregation threshold (7.4 in red). (B) Single-cell fluorescence images of the α-synuclein-GFP time course in (A). Images are taken at 5-min intervals. Red squares highlight images where α-synuclein-GFP fluorescence level is at or above the aggregation threshold. α-Synuclein-GFP inclusions appear only once the aggregation threshold is reached. (C) Automatic feedback control of A53T α-synuclein-GFP expression (blue) in a single cell at three different levels (6, 10, and 14 in yellow) below and above the A53T aggregation threshold (7.4 in red). To investigate clearance of α-synuclein inclusions, at the end of the control experiment (1,500 min), glucose was provided to the cells, thus inhibiting A53T α-synuclein-GFP expression. (D) Single-cell fluorescence images of the α-synuclein-GFP time course in (C). Red squares highlight images where α-synuclein-GFP fluorescence level is at or above the aggregation threshold. α-Synuclein-GFP inclusions appear once the aggregation threshold is reached. Upon glucose treatment (1,500 min), α-synuclein-GFP expression decreases (from 1,500 to 2,000 min), and inclusions are cleared. See also and .

Article Snippet: The control error e is defined as the difference between the desired α-synuclein level y r e f and the measured (or predicted, as in the case of the MPC algorithm) α-synuclein level y . Optimization was performed with the MATLAB function fmincon of the Optimization Toolbox .

Techniques: Control, Mutagenesis, Expressing, Fluorescence

Journal: Cell Reports

Article Title: Quantitative Characterization of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control

doi: 10.1016/j.celrep.2019.03.081

Figure Lengend Snippet: Quantification of Autophagic and Proteasomal Contributions to the Clearance of Mutant A53T α-Synuclein We tested the effects of three compounds: (1) rapamycin (100 nM), an autophagy inducer; (2) PMSF (1 mM), an autophagy inhibitor; and (3) MG132 (50 μM), a proteasome inhibitor, in the mutant A53T α-synuclein strain deleted for the Pleiotropic Drug Resistance gene PDR5 encoding an efflux transporter to enable accumulation of the small molecules . Cells were grown overnight in the presence of galactose to induce formation of α-synuclein inclusions. At time 0 min, galactose was replaced by glucose, and α-synuclein-GFP fluorescence was quantified in individual cells in the microfluidics device using a custom-made image processing algorithm . Only cells that were present from the beginning to the end of the experiment were considered. (A) Population-averaged α-synuclein-GFP fluorescence computed from single-cell traces in each of the four conditions tested. t 1/2 is time necessary for α-synuclein-GFP fluorescence to become half of its initial value. (B–E) Representative single-cell time course (solid blue lines) in the Δpdr5 A53T α-synuclein yeast strain for each of the indicated conditions: untreated (B), rapamycin (C), MG132 (D), and PMSF (E). α-Synuclein-GFP fluorescence in each individual cell is normalized to the mean fluorescence during the calibration phase . The time at which α-synuclein inclusions disappear is indicated by a yellow line . The budded phase of the cell cycle is indicated by shaded gray areas. Bud formation (dashed blue line) and cell division (dotted blue line) are also shown. We defined four parameters as indicated in (B): time to first bud is defined as the time elapsed between the beginning of the experiment and the formation of the first bud of the cell. The mean fluorescence (solid black lines) is defined as the average fluorescence from the beginning of the experiment until the time to first bud. The drop of fluorescence at division is defined as the percentage decrease in fluorescence during the budded phase. The cell-cycle duration is defined as the time between two consecutive budding events. Experimental fluorescence at division (red circles) and model-predicted fluorescence, assuming a drop in fluorescence of 38% at division ( Jonas et al., 2018 ) caused by dilution (red squares), are also shown. (F–I) Distribution of time to first bud (F), mean fluorescence (G), duration of cell cycle (H), and drop at division (I) across single cells in the different conditions: untreated (n = 25), rapamycin (n = 14), MG132 (n = 24), and PMSF (n = 48). Solid black lines are the medians in each condition. Horizontal square brackets represent statistically significant pairwise comparisons with median values between conditions changing by at least 10%. Dashed horizontal square brackets represent pairwise comparison with median values between conditions changing less than 10%. ∗ p ≤ 0.1, ∗∗ p ≤ 0.05; Conover-Iman test of multiple comparisons using rank sums. The number of points in (H) and (I) is higher because each cell undergoes multiple divisions during the experiment; each point refers to a cell cycle in untreated (n = 91), rapamycin (n = 73), MG132 (n = 49), and PMSF (n = 137) condition. n.s., not significant. See also and and .

Article Snippet: The control error e is defined as the difference between the desired α-synuclein level y r e f and the measured (or predicted, as in the case of the MPC algorithm) α-synuclein level y . Optimization was performed with the MATLAB function fmincon of the Optimization Toolbox .

Techniques: Mutagenesis, Fluorescence, Comparison

Journal:

Article Title: Semi-Automatic Extraction Algorithm for Images of the Ciliary Muscle

doi: 10.1097/OPX.0b013e3182044b94

Figure Lengend Snippet: Example images of the ciliary body obtained with the Zeiss Visante Anterior Segment OCT. (A) An image where the scleral curvature appears relatively flat, and the caliper used to align a measurement 3 mm posterior to the scleral spur follows the contour of the sclera rather closely. (B) An image where the scleral curvature appears somewhat steeper, and the caliper used to align a measurement 3 mm posterior to the scleral spur cuts across the ciliary muscle rather than following the contour of the sclera.

Article Snippet: In summary, aims for this study were: list-behavior=enumerated prefix-word= mark-type=decimal max-label-size=0 To investigate and develop a correction for any image distortion in the binary files exported from the Visante Anterior Segment OCT by determining: list-behavior=enumerated prefix-word= mark-type=lower-alpha max-label-size=0 The appropriate pixel per mm conversion factor in Visante images for both air and the sclera/ciliary body; The general fidelity and level of geometric and refractive distortions present in the raw images obtained with the Visante; and To develop a semi-automatic algorithm for outlining and measuring the ciliary body in Visante images and assess the performance of the algorithm by determining: list-behavior=enumerated prefix-word= mark-type=lower-alpha max-label-size=0 The within-examiner and between-examiner variability for: list-behavior=enumerated prefix-word= mark-type=lower-roman max-label-size=0 Caliper measurements from the Visante analysis software; and The semi-automatic algorithm measurements; The number of images needed per subject to provide acceptably repeatable algorithm measurement in future studies; and The agreement between semi-automatic algorithm measurements and caliper measurements (Visante software).

Techniques: