Single Molecular Observation of Self-Regulated Kinesin Motility

Kinesin-1 is an ATP-driven molecular motor that transports various cargoes in cells, a process that can be regulated by the kinesin tail domain. Here, kinesin ATPase activity and motility were inhibited <i>in vitro</i> by interacting the kinesin heavy chain C-terminal tail domain with the kinesin N-terminal motor domain. Though the tail domain can directly interact with microtubules, we found 70% of tail domains failed to bind in the presence of >100 mM (high) KCl, which also modulated the ATPase inhibition manner. These observations suggest that self-inhibition of kinesin depends on electrostatic interactions between the motor domain, the tail domain, and a microtubule. Furthermore, we observed self-regulated behavior of kinesin at the single molecule level. The tail domain did not affect motility velocity, but it did lower the binding affinity of the motor domain to the microtubule. The decrement in binding was coupled to ATPase inhibition. Meanwhile, the tail domain transfected into living cells not only failed to bind to microtubules but also inhibited the motor domain and microtubule interaction, in agreement with our <i>in vitro</i> results. Furthermore, at high potassium concentrations, the self-regulation of kinesin observed in cells was like that <i>in vitro</i>. The results favor a way tail inhibition mechanism where the tail domain masks the microtubule binding site of the motor domain in high potassium concentration

Germond et al. 2018a-Raman & gene expression datasets.xlsx

This file include 2 datasets used in Germond et al. 2018<div><br></div><div><b>Biological samples</b>: Eleven E. coli strains described in Suzuki et al. 2014 Nature Communication. One strain is the parental strain MDS42 and 10 others are evolved strains that acquired a resistance against antibiotic drugs during a 3 month period of evolution. Cell lines are labelled according to the antibiotic drug they were evolved in.</div><div><b><br></b><div><b>1st dataset: </b>Raman spectral dataset of 624 independent populations of Escherichia coli measured 5 times each by Raman spectroscopy. Average data of the five measurements are presented. Measurements were performed using a 532 nm excitation wavelength using 5 sec exposure per measure.</div></div><div><br></div><div><b>2nd dataset: </b>Transcriptome data of E coli strains as measured by microarray. The log10-transformed expression levels after quantile normalization are presented. Genes for which the expression value was below 2.0 were removed, as recommended by the manufacturer, leaving 2613 genes (out of 4600+) in the dataset. </div><div><br></div><div>Please refer to Germond et al. 2018a Communication Biology when using this dataset. See the publication for the detailed measurement procedures.</div

Distinct Modulated Pupil Function System for Real-Time Imaging of Living Cells

<div><p>Optical microscopy is one of the most contributive tools for cell biology in the past decades. Many microscopic techniques with various functions have been developed to date, i.e., phase contrast microscopy, differential interference contrast (DIC) microscopy, confocal microscopy, two photon microscopy, superresolution microscopy, etc. However, person who is in charge of an experiment has to select one of the several microscopic techniques to achieve an experimental goal, which makes the biological assay time-consuming and expensive. To solve this problem, we have developed a microscopic system with various functions in one instrument based on the optical Fourier transformation with a lens system for detection while focusing on applicability and user-friendliness for biology. The present instrument can arbitrarily modulate the pupil function with a micro mirror array on the Fourier plane of the optical pathway for detection. We named the present instrument DiMPS (<u>Di</u>stinct optical <u>M</u>odulated <u>P</u>upil function <u>S</u>ystem). The DiMPS is compatible with conventional fluorescent probes and illumination equipment, and gives us a Fourier-filtered image, a pseudo-relief image, and a deep focus depth. Furthermore, DiMPS achieved a resolution enhancement (pseudo-superresolution) of 110 nm through the subtraction of two images whose pupil functions are independently modulated. In maximum, the spatial and temporal resolution was improved to 120 nm and 2 ms, respectively. Since the DiMPS is based on relay optics, it can be easily combined with another microscopic instrument such as confocal microscope, and provides a method for multi-color pseudo-superresolution. Thus, the DiMPS shows great promise as a flexible optical microscopy technique in biological research fields.</p> </div

Biological applications of siDiMPS using green fluorescent protein.

<p>(A,B) Fluorescence images of localized E-cadherin-GFP in a living cell obtained by conventional microscopy (A) and siDiMPS (B). Scale bars, 1 µm. The exposure time for the camera was 30.28 ms. The images shown are the averages of 100 acquired images (total frame rate, 0.3 Hz). Inserts, magnifications of the areas in orange rectangles, respectively. Scale bars, 200 nm. (C) One-dimensional fluorescence intensity profiles inside the yellow rectangle in A (blue, conventional) and B (red, siDiMPS). Arrowheads indicate the periodic peaks of fluorescence intensity. (D) Histogram of the distance between two peaks within the periodic localization of E-cadherin-GFP. Arrowheads and values represent the peaks in the histogram. (E, F) Fluorescence images of autophagosomes labeled with GFP-LC3 in a living cell acquired by conventional microscopy (E) and siDiMPS (F). Scale bars, 1 µm. Inserts, the intensity profiles of the one-dimensional fluorescence intensity profile (yellow line) in each panel. (G) siDiMPS time lapse images of a small organelle labeled with GFP-LC3. Scale bar, 400 nm. The exposure time for the camera was 30.28 ms. Images E, F and G are averages of 10 acquired images (total frame rate, 3 Hz). (H, I) Three dimensional images of autophagosome acquired by conventional microscopy (H) and siDiMPS (I). Left and middle panels are XY-image and XZ-image, respectively. Right panels are one-dimensional fluorescence intensity profile along longitudinal axis (Z) at the center of the images. Scale bars, 1 µm. The exposure time for the camera was 100 ms.</p

Individual quantum dots observation with 2 ms temporal resolutions with the siDiMPS.

<p>(A) Four typical fluorescence images of QDs captured by a high speed CCD camera with a conventional microscope (upper) and the DiMPS (lower). One pixel, 64.5 nm. (B) Ten PSFs of fluorescence QD images acquired with the DiMPS with 2 ms temporal resolution. The gray line is the fitted Gaussian curve, which indicates the spatial resolution (122 nm). (C, D) Fluorescent images of E-cadherin-QD on the cell membrane acquired by conventional microscopy (C) and DiMPS (D). Scale bars, 1 µm. Inserts are magnifications of the red rectangles. Scale bars, 200 nm. Dotted cyan circles indicate individual QDs. (E) Time courses of the dissociation of the E-cadherin complex (upper) and kymograph of the cyan broken line showing dissociation (lower left) and association (lower right). Numbers in the panels indicate elapsed time (ms). Position scale bars, 200 nm; time scale bar, 100 ms. (F) Time course of the cross-sections (cyan broken line) for the upper panel in E. Red and blue lines are fitting results using double Gaussian functions and two single Gaussian functions used to make the fitted double Gaussian, respectively.</p

Basic of the DiMPS.

<p>(A) Photograph of the DiMPS. (B) Schematic drawing of the DiMPS. S, spatial mask; BS, polarizing beam splitter; LCM, liquid crystal micro mirror array; P, prism mirror; L, lens; FP, Fourier plane; CP, conjugate plane. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044028#s4" target="_blank">methods</a> for the optics construction. (C) Typical modulations of the pupil function. The pupil function is the polarization pattern in the LCM. Black part shows where the light is blocked. The PSF is represented by the fluorescence distribution of a φ100 nm bead with an emission peak at 515 nm (Invitrogen). Red lines show the one-dimensional fluorescence intensity profile of the PSF along the transverse and longitudinal axes. Gray lines are the one-dimensional fluorescence intensity profile of a normal PSF. Scale bars, 1 µm.</p

Confocal and multi-color imaging with the DiMPS.

<p>(A) Comparison of a confocal image (left), a high-pass filtered image obtained with the DiMPS combined with a confocal unit (middle), and siDiMPS combined with a confocal unit (right). Top, microtubules. Middle, actin bundles. Bottom, merged images of microtubules and actin bundles. Scale bars, 1 µm. Insets are enlarged images of the dotted yellow rectangles. (B, C) One dimensional fluorescence intensity profiles of the cyan lines in (A). (B) Microtubule. (C) Actin bundles. Blue, Red, and green lines were obtained from confocal, confocal + DiMPS, and confocal + siDiMPS images, respectively.</p

Raman images of cell-lines with differentiation capability.

<p>Raman images of Neuro2a (A) and 3T3L1 (D) cells before (left panel) and after (right panel) the induction of differentiation (inset; bright-field image). (B, E) Averaged Raman spectra of N2a (B) and 3T3L1 (E) cells before (blue) and after (red) induction of differentiation. Spectra are average of 15–27 cells. Spectra from the fibroblast cell-line NIH3T3 are also plotted (black). Peaks characteristic to cytochrome C are indicated with asterisks. (C, F) Score plots of Neuro2a (C) and 3T3L1 (F) cells before (blue) and after (red) the induction of differentiation calculated by PCA. For PCA analysis, raw spectra without averaging was used. Data from the fibroblast cell-line NIH3T3 are also plotted (black). Each marker shows averaged score values of the spectra obtained from single nuclei. Error bar shows SD of the score values from the same nuclei.</p

Resolution enhancement with the siDiMPS.

<p>(A, B, C) Pupil functions and fluorescence distributions of a φ100 nm bead with a non-masked (A), apodizing-masked (B), and superresolving-masked PSF (C). Upper panels, pupil functions; lower panels, PSF images in the longitudinal-transverse plane. (D) One-dimensional fluorescence intensity profiles of the non-masked (black), apodizing-masked (blue), and superresolving-masked PSF (red). Values indicate spatial resolution determined at FWHM. (E, F) Fluorescence distributions determined by subtracting an apodizing-masked PSF from a superresolving-masked one without (E), and with optimizing symmetry of PSF by adjusting lens L4 position along the light path (F). Left panels, PSF images in the longitudinal-transverse plane; middle and right panels, one-dimensional fluorescence intensity profile of each PSF along the transverse axis (middle) and longitudinal axis (right), respectively. Values indicate spatial resolution determined at FWHM. Red and gray lines are one dimensional fluorescence profiles of the non-masked (E) and extended PSF (F), respectively. All scale bars, 1 µm. (G) siDiMPS image of Alexa488-phalloidin stained actin bundles in a fixed cell. Top, non-masked. Middle, siDiMPS. Bottom, siDiMPS+deconvolution. Inserts, magnifications of the areas in yellow rectangles, respectively. Scale bars, 2 µm. The images were obtained with a 100 ms exposure time. (H) One-dimensional fluorescence intensity profiles of the yellow broken lines in G (black, conventional; green, siDiMPS; red, siDiMPS+deconvolution).</p

Visualizing Cell State Transition Using Raman Spectroscopy

<div><p>System level understanding of the cell requires detailed description of the cell state, which is often characterized by the expression levels of proteins. However, understanding the cell state requires comprehensive information of the cell, which is usually obtained from a large number of cells and their disruption. In this study, we used Raman spectroscopy, which can report changes in the cell state without introducing any label, as a non-invasive method with single cell capability. Significant differences in Raman spectra were observed at the levels of both the cytosol and nucleus in different cell-lines from mouse, indicating that Raman spectra reflect differences in the cell state. Difference in cell state was observed before and after the induction of differentiation in neuroblastoma and adipocytes, showing that Raman spectra can detect subtle changes in the cell state. Cell state transitions during embryonic stem cell (ESC) differentiation were visualized when Raman spectroscopy was coupled with principal component analysis (PCA), which showed gradual transition in the cell states during differentiation. Detailed analysis showed that the diversity between cells are large in undifferentiated ESC and in mesenchymal stem cells compared with terminally differentiated cells, implying that the cell state in stem cells stochastically fluctuates during the self-renewal process. The present study strongly indicates that Raman spectral morphology, in combination with PCA, can be used to establish cells' fingerprints, which can be useful for distinguishing and identifying different cellular states.</p></div