Quantitative phase imaging of adherent mammalian cells: a comparative study

Abstract

The quantitative phase imaging methods have several advantages when it comes to monitoring cultures of adherent mammalian cells. Because of low photo-toxicity and no need for staining, we can follow cells in a minimally invasive way over a long period of time. The ability to measure the optical path difference in a quantitative manner allows the measurement of the cell dry mass, an important metric for studying the growth kinetics of mammalian cells. Here we present and compare cell measurements obtained with three different techniques: digital holographic microscopy, lens-free microscopy and quadriwave lateral sheering interferometry. We report a linear relationship between optical volume density values measured with these different techniques and estimate the precisions of this measurement for the different individual instruments.

Computationally enhanced quantitative phase microscopy reveals autonomous oscillations in mammalian cell growth

“…Furthermore, for these methods strict proportionality with mass may not hold for all cell types, at different cell cycle stages, or across the full range of the cell mass distribution. For the reasons above, quantitative phase microscopy (QPM) has emerged as the method of choice for accurate dry mass measurements of attached cells down to a precision of less than 10 pg (note it is still more than 100 times less sensitive than the SMR) (18,(25)(26)(27). Furthermore, QPM is more readily available, as subtypes of the QPM technique like the quadriwave lateral shearing interferometry (QWLSI) (28), spatial light interference microscopy (29), ptychography (30), and digital holographic microscopy (31) have been commercialized.…”

A new ultradian rhythm in mammalian cell dry mass observed by holography

“…Cell dry mass calculation. The phase recovered from lens-free microscopy is proportional to the density and thickness of the specimen layer 34 . A relation is defined between the phase shift and the optical path difference (OPD), which quantifies the integral of the sample object refractive index difference with respect to the surrounding medium along the optical path:…”

Alternation of inverse problem approach and deep learning for lens-free microscopy image reconstruction

“…The first reconstruction is intended to be a fast www.nature.com/scientificreports/ step, with kernel K of Eq. (2) set to a Dirac distribution, limited to a low number of iterations (20) and with no attempt to phase unwrapping. For this first reconstruction the regularisation term is:…”

“…In comparison with the first reconstruction, the second reconstruction performs a larger amount of iterations (70) and takes into account the coherence of the illumination source, using a kernel K. In addition, the regularization term is more complex. This is the optimization scheme that has been previously published in 20 . The regularization term in the second reconstruction is set to:…”

“…Different deep learning approaches have been successful in improving lens-free holographic reconstruction results 16,17 but they do not address phase wrapping issues. A specific CNN could be trained to transform a lens-free reconstructed image into an image free of phase wrapping, as obtained with a quantitative phase imaging technique [18][19][20] . A similar approach have been used to transform images between different imaging techniques 21,22 .…”