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In-line holographic microscopy with model-based analysis

Nature Reviews Methods Primers volume 2, Article number: 83 (2022) Cite this article

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Abstract

An in-line holographic microscope is an optical microscope outfitted with a coherent light source, such as a laser. Light scattered by the specimen interferes with the transmitted beam, and the intensity of that interference pattern constitutes a hologram. Unlike a conventional photograph, a hologram contains information about the phase of the scattered light that is useful for measuring the composition and 3D arrangement of microscopic objects in the specimen. This Primer presents an overview of experimental methods and discusses three recent analysis techniques: fitting scattering models to the hologram; using machine learning to localize and classify the specimen; and a hybrid approach that uses machine learning to initialize fits. The combination of holographic microscopy and model-based analysis is well suited to applications where precise, quantitative results are needed with high acquisition speed. Such applications include studying properties of heterogeneous colloidal dispersions, measuring colloidal interactions, monitoring stresses in soft materials, detecting molecular binding and aggregation, and following the motion of microorganisms in three dimensions. We discuss the reproducibility and current limitations of each method. Finally, we anticipate directions for future development and provide an outlook on the integration between experiment and computational analysis, an emerging paradigm for microscopy.

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Fig. 1: Hologram formation and analysis.
Fig. 2: Detailed set-up of an in-line holographic microscope.
Fig. 3: Data normalization for hologram analysis.
Fig. 4: Approaches to quantitative analysis of holograms.
Fig. 5: Motion and tracking.
Fig. 6: Property estimation and characterization.

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Acknowledgements

Work at Harvard is supported by the National Science Foundation under grant DMR-2011754 and by the Department of Defense through the National Defense Science & Engineering Graduate Fellowship Program. Work at UNSW Sydney was supported by the Human Frontier of Science Program Grant (RPG0029/2020 to A.W.), and A.W. was supported by the Australian Research Council Discovery Early Career Award (DE210100291). Work at NYU was supported by the National Science Foundation under grants DMR-2104837, DMR-1420073 and DMR-0922680, and by the National Institutes of Health under grant R44TR001590.

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Authors and Affiliations

  1. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Caroline Martin & Vinothan N. Manoharan

  2. Department of Physics and Center for Soft Matter Research, New York University, New York, NY, USA

    Lauren E. Altman & David G. Grier

  3. School of Chemistry, UNSW Sydney, Sydney, New South Wales, Australia

    Siddharth Rawat & Anna Wang

  4. Department of Physics, Harvard University, Cambridge, MA, USA

    Vinothan N. Manoharan

Authors
  1. Caroline Martin
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  2. Lauren E. Altman
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  3. Siddharth Rawat
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  4. Anna Wang
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  5. David G. Grier
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Contributions

Introduction (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Experimentation (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Results (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Applications (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Reproducibility and data deposition (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Limitations and optimizations (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Outlook (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.); Overview of the Primer (C.M., L.E.A., S.R., A.W., D.G.G. and V.N.M.).

Corresponding author

Correspondence to Vinothan N. Manoharan.

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Competing interests

D.G.G. is a founder of Spheryx, Inc., which manufactures instruments for holographic particle characterization. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Methods Primers thanks Radim Chmelik; Laurence Wilson, who co-reviewed with Sam Matthews; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

CATCH: https://github.com/laltman2/CATCH

HDF5: https://www.hdfgroup.org/solutions/hdf5/

HoloPy: http://holopy.readthedocs.io/

pylorenzmie: https://github.com/davidgrier/pylorenzmie

Glossary

Holograms

2D intensity patterns resulting from the interference between light scattered from an object and a reference beam.

Fringes

The bright or dark bands in an image that are produced by the interference of light.

Holographic microscopy

The use of a microscope with a coherent or semi-coherent light source to record holograms of microscopic objects.

Reconstruction

The process of illuminating a hologram with a beam such that the hologram acts as a diffraction grating.

Tomograms

Images recorded by a penetrating wave that represents a cross section of a 3D object.

Colloidal particles

Nanoparticles or microparticles suspended in a fluid or other medium.

Scattering

The interaction of electromagnetic radiation with an object resulting in a change in the direction of the light.

Coherent

Light that has a narrow distribution of frequencies and a well-defined phase, such that interference can be observed.

Collimated

A beam of light with parallel rays.

Objective lens

A lens or collection of lenses that focus and magnify light to form an image.

Tube lens

A lens or series of lenses that focus parallel rays to form an image on a sensor or eyepiece.

Speckle

Fluctuating bright and dark regions in an image that arise from extraneous scattering and interference.

Spherical aberration

A type of optical aberration in which rays nearer the edge of a lens are deflected more than those near its axis.

Capillary action

Flow driven by interfacial tension.

Syringe pumps

Volumetrically controlled pumps that deliver fluid by moving a syringe piston, typically resulting in a constant flow rate.

Pressure pumps

Pumps that are pressure-driven and have controllers to maintain constant pressure.

Dynamic range

The range of intensities that a sensor can record.

Quantum efficiency

A measure of the sensitivity of a detector, determined by how many incident photons are converted into electrons.

Dark count

The intensity recorded by a sensor or camera in the absence of a signal.

Bayesian parameter estimation

A statistical inference technique yielding the probability distribution of the parameters of a model given the data.

Marginalized uncertainties

The uncertainties in a model parameter determined by accounting for correlations with other parameters.

Markov-chain Monte Carlo

(MCMC). A numerical method that uses a biased random walk through the parameter space to both estimate a probability distribution and integrate it.

Convolutional neural networks

(CNNs). Machine-learning algorithms that use convolution layers to process higher-dimensional image data.

Support vector machines

Machine-learning algorithms that distinguish data points using hyperplanes in a high-dimensional parameter space.

ReLU

The rectified linear activation function, a piecewise function that returns zero for negative inputs and returns the input for positive inputs.

Brownian motion

The random motion of particles suspended in a medium due to collisions with the surrounding molecules.

Polydispersity

The distribution of sizes within a sample.

Graticule

A set of parallel lines with known spacing used for measuring scale.

Lossy encoding

Methods of compressing or transferring data that approximate or down-sample the data.

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Martin, C., Altman, L.E., Rawat, S. et al. In-line holographic microscopy with model-based analysis. Nat Rev Methods Primers 2, 83 (2022). https://doi.org/10.1038/s43586-022-00165-z

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