scholarly journals Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing

2020 ◽  
Vol 11 (4) ◽  
pp. 2213 ◽  
Author(s):  
Hidenao Yamada ◽  
Amane Hirotsu ◽  
Daisuke Yamashita ◽  
Osamu Yasuhiko ◽  
Toyohiko Yamauchi ◽  
...  
Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 590
Author(s):  
Jennifer Cauzzo ◽  
Nikhil Jayakumar ◽  
Balpreet Singh Ahluwalia ◽  
Azeem Ahmad ◽  
Nataša Škalko-Basnet

The rapid development of nanomedicine and drug delivery systems calls for new and effective characterization techniques that can accurately characterize both the properties and the behavior of nanosystems. Standard methods such as dynamic light scattering (DLS) and fluorescent-based assays present challenges in terms of system’s instability, machine sensitivity, and loss of tracking ability, among others. In this study, we explore some of the downsides of batch-mode analyses and fluorescent labeling, while introducing quantitative phase microscopy (QPM) as a label-free complimentary characterization technique. Liposomes were used as a model nanocarrier for their therapeutic relevance and structural versatility. A successful immobilization of liposomes in a non-dried setup allowed for static imaging conditions in an off-axis phase microscope. Image reconstruction was then performed with a phase-shifting algorithm providing high spatial resolution. Our results show the potential of QPM to localize subdiffraction-limited liposomes, estimate their size, and track their integrity over time. Moreover, QPM full-field-of-view images enable the estimation of a single-particle-based size distribution, providing an alternative to the batch mode approach. QPM thus overcomes some of the drawbacks of the conventional methods, serving as a relevant complimentary technique in the characterization of nanosystems.


2020 ◽  
Author(s):  
Patrick M. McCall ◽  
Kyoohyun Kim ◽  
Anatol W. Fritsch ◽  
J.M. Iglesias-Artola ◽  
L.M. Jawerth ◽  
...  

ABSTRACTMany compartments in eukaryotic cells are protein-rich biomolecular condensates demixed from the cyto- or nucleoplasm. Although much has been learned in recent years about the integral roles condensates play in many cellular processes as well as the biophysical properties of reconstituted condensates, an understanding of their most basic feature, their composition, remains elusive. Here we combined quantitative phase microscopy (QPM) and the physics of sessile droplets to develop a precise method to measure the shape and composition of individual model condensates. This technique does not rely on fluorescent dyes or tags, which we show can significantly alter protein phase behavior, and requires 1000-fold less material than traditional label-free technologies. We further show that this QPM method measures the protein concentration in condensates to a 3-fold higher precision than the next best label-free approach, and that commonly employed strategies based on fluorescence intensity dramatically underestimate these concentrations by as much as 50-fold. Interestingly, we find that condensed-phase protein concentrations can span a broad range, with PGL3, TAF15(RBD) and FUS condensates falling between 80 and 500 mg/ml under typical in vitro conditions. This points to a natural diversity in condensate composition specified by protein sequence. We were also able to measure temperature-dependent phase equilibria with QPM, an essential step towards relating phase behavior to the underlying physics and chemistry. Finally, time-resolved QPM reveals that PGL3 condensates undergo a contraction-like process during aging which leads to doubling of the internal protein concentration coupled to condensate shrinkage. We anticipate that this new approach will enable understanding the physical properties of biomolecular condensates and their function.


Author(s):  
Björn Kemper ◽  
Arne Bokemeyer ◽  
Steffi Ketelhut ◽  
Lenz Philipp ◽  
Bettenworth Dominik

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yair Rivenson ◽  
Tairan Liu ◽  
Zhensong Wei ◽  
Yibo Zhang ◽  
Kevin de Haan ◽  
...  

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