Dataset Information

The Three-Dimensional Signal Collection Field for Fiber Photometry in Brain Tissue.

ABSTRACT: Fiber photometry is used to monitor signals from fluorescent indicators in genetically-defined neural populations in behaving animals. Recently, fiber photometry has rapidly expanded and it now provides researchers with increasingly powerful means to record neural dynamics and neuromodulatory action. However, it is not clear how to select the optimal fiber optic given the constraints and goals of a particular experiment. Here, using combined confocal/2-photon microscope, we quantitatively characterize the fluorescence collection properties of various optical fibers in brain tissue. We show that the fiber size plays a major role in defining the volume of the optically sampled brain region, whereas numerical aperture impacts the total amount of collected signal and, marginally, the shape and size of the collection volume. We show that ~80% of the effective signal arises from 10⁵ to 10⁶ ?m³ volume extending ~200 ?m from the fiber facet for 200 ?m core optical fibers. Together with analytical and ray tracing collection maps, our results reveal the light collection properties of different optical fibers in brain tissue, allowing for an accurate selection of the fibers for photometry and helping for a more precise interpretation of measurements in terms of sampled volume.

SUBMITTER: Pisanello M

PROVIDER: S-EPMC6399578 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The Three-Dimensional Signal Collection Field for Fiber Photometry in Brain Tissue.

Pisanello Marco M Pisano Filippo F Hyun Minsuk M Maglie Emanuela E Balena Antonio A De Vittorio Massimo M Sabatini Bernardo L BL Pisanello Ferruccio F

Frontiers in neuroscience 20190226

Fiber photometry is used to monitor signals from fluorescent indicators in genetically-defined neural populations in behaving animals. Recently, fiber photometry has rapidly expanded and it now provides researchers with increasingly powerful means to record neural dynamics and neuromodulatory action. However, it is not clear how to select the optimal fiber optic given the constraints and goals of a particular experiment. Here, using combined confocal/2-photon microscope, we quantitatively charac ...[more]

PMID: 30863275

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Project description:Tractions exerted by cells on their surroundings play an important role in many biological processes including stem cell differentiation, tumorigenesis, cell migration, cancer metastasis, and angiogenesis. The ability to quantify these tractions is important in understanding and manipulating these processes. Three-dimensional traction force microscopy (3DTFM) provides reliable means of evaluating cellular tractions by first measuring the displacement of fluorescent beads in response to these tractions in the surrounding matrix, and then using this measurement to compute the tractions. However, most applications of 3DTFM assume that the surrounding extra-cellular matrix (ECM) is non-fibrous, despite the fact that in many natural and synthetic environments the ECM contains a significant proportion of fibrous components. Motivated by this, we develop a computational approach for determining tractions, while accounting for the fibrous nature of the ECM. In particular, we make use of a fiber-based constitutive model in which the stress contains contributions from a distribution of nonlinear elastic fibers and a hyperelastic matrix. We solve an inverse problem with the nodal values of the traction vector as unknowns, and minimize the difference between a predicted displacement field, obtained by solving the equations of equilibrium in conjunction with the fiber-based constitutive model, and the measured displacement field at the bead locations. We employ a gradient-based minimization method to solve this problem and determine the gradient efficiently by solving for the appropriate adjoint field. We apply this algorithm to problems with experimentally observed cell geometries and synthetic, albeit realistic, traction fields to gauge its sensitivity to noise, and quantify the impact of using an incorrect constitutive model: the so-called model error. We conclude that the approach is robust to noise, yielding about 10% error in tractions for 5% displacement noise. We also conclude that the impact of model error is significant, where using a nonlinear exponential hyperelastic model instead of the fiber-based model, can lead to more than 100% error in the traction field. These results underline the importance of using appropriate constitutive models in 3DTFM, especially in fibrous ECM constructs.

Dataset Information

The Three-Dimensional Signal Collection Field for Fiber Photometry in Brain Tissue.

Publications

The Three-Dimensional Signal Collection Field for Fiber Photometry in Brain Tissue.

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