Real-time fluorescence and deformability cytometry — flow cytometry goes mechanics

Abstract

Cell mechanical characterization has recently approached the throughput of conventional flow cytometers. However, this very sensitive, label-free approach still lacks the specificity of molecular markers. Here we combine real-time 1D-imaging fluorescence and deformability cytometry (RT-FDC) to merge the two worlds in one instrument -opening many new research avenues. We demonstrate its utility using sub-cellular fluorescence localization to identify mitotic cells and test for their mechanical changes in an RNAi screen.Flow cytometry (FCM) is the gold standard for single cell characterization in biological research and numerous clinical applications 1,2 . As cells are flowed through the cytometer's cuvette, they are illuminated by a laser source, while detectors collect emitted fluorescence, as well as forward-and side-scattered light 2 . The analysis of scattered light provides information about cell size and shape in the absence of a molecular label.Recently, mechanics has emerged as an orthogonal label-free way to obtain information at the cellular scale. Importantly, a cell's mechanical state is tightly regulated in a cell-type and cell-state specific manner. Cell compliance therefore functions as a quantitative read-out of the state of the cytoskeleton 3-6 , which is influenced by progression through the cell cycle 7 , differentiation 8 , as well as patho-physiological processes, such as malignant transformation 9,10 and by immune challenge during infections 11 . Due to the recent advent of several microfluidic techniques with a massive increase in throughput (i.e., up to thousands of cells/sec), mechanical phenotyping can now be performed at measurement rates approaching those of conventional flow cytometers [12][13][14][15][16] . Amongst many other applications, this opens up the possibility of carrying out large-scale screens for the genes that regulate cell mechanics -something that was previously limited by the low throughput (i.e., 10 -100 cells/h) of techniques such as atomic force microscopy (AFM).Label-free approaches provide quantitative information about cells but lack molecular specificity. Such molecular-level information can, however, be acquired in parallel by combining these approaches with fluorescent probes 2 . In FCM the intracellular distribution of the fluorescent signal can be obtained by fluorescence pulse shape analysis 17,18 . Slit-scan flow cytometers detect fluorescence in a thin light sheet and achieve 1-D fluorescence imaging with improved resolution 19,20 . Line scan cameras 21 and advanced microscopy techniques based on non-linear optics 22,23 even implement 2-D imaging. Thus, mechanical phenotyping, which is very sensitive to changes in cell state, is an ideal complement to the use of fluorescent molecular probes. The combination of both approaches promises to open up entirely new possibilities for scientific exploration.Here, we present real-time fluorescence and deformability cytometry (RT-FDC), using a machine that combines the capabilities of 1D-resolved, fluorescence-based...

Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood

“…And the final increase in deformation from day 9 to 12 coincides with the addition of cytostatic drugs (vincristine, daunorubicin) to the methylprednisolone treatment. Dissecting this multifaceted response will be aided by adding simultaneous fluorescence identification of the cells in the future ( Rosendahl, 2017 ). Of note, of the conventional biomarkers and techniques that are used in the diagnosis of leukemia (see Supplementary file 2 ), only morphological analysis of air-dried Romanowsky-stained blood (or bone marrow) smears is traditionally applied to monitor treatment success in ALL.…”

“…Thus, label-free, disease-specific morpho-rheological blood signatures are a novel resource for generating hypotheses about the underlying molecular mechanisms. The availability of such parameters in real-time, easily combined with conventional fluorescence detection ( Rosendahl, 2017 ), are the necessary prerequisite for future sorting of morpho-rheologically distinct subpopulations, which then provides a novel opportunity for further molecular biological analysis. Of course, at present, MORE phenotyping provides a sensitive, but not a very specific marker.…”

“…For example, neutrophil softening could be a signature of different underlying pathological changes. In the future, fuller exploration of the large combinatorial space afforded by the multi-parametric response of the various blood cells, exploiting many additional morpho-rheological parameters in conjunction with machine learning, and inclusion of conventional fluorescence-based marker analysis ( Rosendahl, 2017 ) will further increase the specificity of this approach. Apart from now enabling realistic blood cell research ex vivo close to physiological conditions, delivering for example previously unavailable information about leukocyte activation kinetics, and after further in-depth studies of the phenomena reported here, MORE phenotyping could have a tangible impact on diagnosis, prognosis, and monitoring of treatment success of many hematological diseases as well as inflammatory, infectious, and metabolic disorders.…”

Single-cell mechanical phenotype is an intrinsic marker of reprogramming and differentiation along the mouse neural lineage

“…For combined assessment of surface marker expression and deformation, an RT-FDC setup with integrated fluorescence detection was used (Rosendahl et al, 2017). Prior to the measurements, cells were stained for 10 min with Anti-SSEA-1-APC (1:10, REA321, Miltenyi Biotec) and CD24-FITC (1:10, M1/69, Miltenyi Biotec) antibodies in a 0.3% BSA solution in PBS.…”

De novo identification of universal cell mechanics gene signatures

“…The copyright holder for this preprint (which this version posted April 27, 2021. ; https://doi.org/10.1101/2021.04. 26.441418 doi: bioRxiv preprint Urbanska, Ge, et al | BioRχiv 2021| 3 challenge of identifying novel targets determining the mechanical phenotype can be addressed on a large scale by performing screens using RNA interference [30][31][32] or smallmolecule compound libraries. Alternatively, the problem can be reverse-engineered, in that omics datasets for systems with known mechanical phenotype changes are used for prediction of genes involved in the regulation of mechanical phenotype in a mechanomics approach 4 .…”