Single-point fluorescence correlation spectroscopy (FCS) allows measurements of fast diffusion and dynamic processes in the microsecond-to-millisecond time range. For measurements on living cells, image correlation spectroscopy (ICS) and temporal ICS extend the FCS approach to diffusion times as long as seconds to minutes and simultaneously provide spatially resolved dynamic information. However, ICS is limited to very slow dynamics due to the frame acquisition rate. Here we develop novel extensions to ICS that probe spatial correlations in previously inaccessible temporal windows. We show that using standard laser confocal imaging techniques (raster-scan mode) not only can we reach the temporal scales of single-point FCS, but also have the advantages of ICS in providing spatial information. This novel method, called raster image correlation spectroscopy (RICS), rapidly measures during the scan many focal points within the cell providing the same concentration and dynamic information of FCS as well as information on the spatial correlation between points along the scanning path. Longer time dynamics are recovered from the information in successive lines and frames. We exploit the hidden time structure of the scan method in which adjacent pixels are a few microseconds apart thereby accurately measuring dynamic processes such as molecular diffusion in the microseconds-to-seconds timescale. In conjunction with simulated data, we show that a wide range of diffusion coefficients and concentrations can be measured by RICS. We used RICS to determine for the first time spatially resolved diffusions of paxillin-EGFP stably expressed in CHOK1 cells. This new type of data analysis has a broad application in biology and it provides a powerful tool for measuring fast as well as slower dynamic processes in cellular systems using any standard laser confocal microscope.

Complex biological systems sense, process, and respond to their surroundings in real time. The ability of such systems to adapt their behavioral response to suit a range of dynamic environmental signals motivates the use of biological materials for other engineering applications. As a step toward forward engineering biological machines (bio-bots) capable of nonnatural functional behaviors, we created a modular light-controlled skeletal musclepowered bioactuator that can generate up to 300 μN (0.56 kPa) of active tension force in response to a noninvasive optical stimulus. When coupled to a 3D printed flexible bio-bot skeleton, these actuators drive directional locomotion (310 μm/s or 1.3 body lengths/min) and 2D rotational steering (2°/s) in a precisely targeted and controllable manner. The muscle actuators dynamically adapt to their surroundings by adjusting performance in response to "exercise" training stimuli. This demonstration sets the stage for developing multicellular bio-integrated machines and systems for a range of applications.bioactuator | stereolithography | tissue engineering | soft robotics U nderstanding complex biological systems requires uncovering the mechanisms through which integrated multicellular networks accomplish sensing, internal processing, and coordinated action in response to dynamic environmental signals. Attempting to reverse engineer these mechanisms for applications in regenerative medicine has been the focus of the burgeoning field of tissue engineering (1), and seminal advances in this field have targeted nearly every organ system in the body (2). These developments, in addition to improving the state of the art in therapeutics, have furthered our understanding of the design principles governing the organizational structure and function of natural biological systems. With this as a guide, we are ideally poised to start forward engineering biological machines, or bio-bots, capable of complex controllable nonnatural functional behaviors, thereby broadening the potential applications for building with biological materials.Before we can design bio-integrated machines for a range of applications, we must first engineer modular tissue building blocks that respond to external signals with complex functional behaviors. Observing and controlling the coordinated action of such building blocks in series and parallel will help us understand the emergent behavior of natural biological systems (3, 4). Nearly all machines require actuators, modules that convert energy into motion, to produce a measurable output in response to input stimuli. Efforts to manufacture bio-integrated actuators have targeted a range of cell types (5), including flagellated bacteria (6), cardiac muscle (7-9), and skeletal muscle (10-12). We previously demonstrated a millimeter-scale soft robotic device, or biobot, that uses the contractile force produced by electrically paced skeletal muscle to drive locomotion across a substrate (10). This bio-bot was the first demonstration of an untethered locomotive skeletal mu...

Fluorescence correlation spectroscopy (FCS) is a sensitive and widely used technique for measuring diffusion. FCS data are conventionally modeled with a finite number of diffusing components and fit with a least-square fitting algorithm. This approach is inadequate for analyzing data obtained from highly heterogeneous systems. We introduce a Maximum Entropy Method based fitting routine (MEMFCS) that analyzes FCS data in terms of a quasicontinuous distribution of diffusing components, and also guarantees a maximally wide distribution that is consistent with the data. We verify that for a homogeneous specimen (green fluorescent protein in dilute aqueous solution), both MEMFCS and conventional fitting yield similar results. Further, we incorporate an appropriate goodness of fit criterion in MEMFCS. We show that for errors estimated from a large number of repeated measurements, the reduced chi(2) value in MEMFCS analysis does approach unity. We find that the theoretical prediction for errors in FCS experiments overestimates the actual error, but can be empirically modified to serve as a guide for estimating the goodness of the fit where reliable error estimates are unavailable. Finally, we compare the performance of MEMFCS with that of a conventional fitting routine for analyzing simulated data describing a highly heterogeneous distribution containing 41 diffusing species. Both methods fit the data well. However, the conventional fit fails to reproduce the essential features of the input distribution, whereas MEMFCS yields a distribution close to the actual input.

Images obtained with a laser-scanning microscope contain a time structure that can be exploited to measure fast dynamics of molecules in solution and in cells. The spatial correlation approach provides a simple algorithm to extract this information. We describe the analysis used to process laser-scanning images of solutions and cells to obtain molecular diffusion constant in the microsecond to second timescale.

Precipitation of the 39-43-residue amyloid beta peptide (Abeta) is a crucial factor in Alzheimer's disease (AD). In normal as well as in AD-afflicted brain, the Abeta concentration is estimated to be a few nanomolar. Here we show that Abeta(1-40) precipitates in vitro only if the dissolved concentration is >14 microM. Using fluorescence correlation spectroscopy, we further show that the precipitation is complete in 1 day, after which the size distribution of Abeta monomer/oligomers in the solution phase becomes stationary in time and independent of the starting Abeta concentration. Mass spectra confirm that both the solution phase and the coexisting precipitate contain chemically identical Abeta molecules. Incubation at 68 degrees C for 1 h reduces the solubility by <12%. Together, these results show that the thermodynamic saturation concentration (C(sat)) of Abeta(1-40) in phosphate-buffered saline (PBS) at pH 7.4 has a well-defined lower limit of 15.5 +/- 1 microM. Divalent metal ions (believed to play a role in AD) at near-saturation concentrations in PBS reduce C(sat) only marginally (2 mM Mg(2+) by 6%, 2.5 microM Ca(2+) by 7%, and 4 microM Zn(2+) by 11%). Given that no precipitation is possible at concentrations below C(sat), we infer that coprecipitant(s), and not properties of Abeta(1-40) alone, are key factors in the in vivo aggregation of Abeta.