Biology subjects

Nawaz, A. A.

Publications and source records attributed to Nawaz, A. A..

3 recordsLinked to original sources

bioRxiv2021

Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues

Biomedical research often relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on their physical properties. Sorting real-time deformability and fluorescence cytometry (soRT-FDC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with additional challenges including survival, changes in morphology, or presence of aggregates. Here, we introduce methods for robust analysis and sorting of single cells from mammalian nervous tissue and provide DNNs which are capable of distinguishing visually similar cells. Exemplarily, we employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye. Results provide evidence that the combination of machine learning and soRT-FDC allows label-free enrichment of target cells from dissociated tissues.

biophysics↗

bioRxiv2019

Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting

The identification and separation of specific cells from heterogeneous populations is an essential prerequisite for further analysis or use. Conventional passive and active separation approaches rely on fluorescent or magnetic tags introduced to the cells of interest through molecular markers. Such labeling is time- and cost-intensive, can alter cellular properties, and might be incompatible with subsequent use, for example, in transplantation. Alternative label-free approaches utilizing morphological or mechanical features are attractive, but lack molecular specificity. Here we combine image-based real-time fluorescence and deformability cytometry (RT-FDC) with downstream cell sorting using standing surface acoustic waves (SSAW). We demonstrate basic sorting capabilities of the device by separating cell mimics and blood cell types based on fluorescence as well as deformability and other image parameters. The identification of blood sub-populations is enhanced by flow alignment and deformation of cells in the microfluidic channel constriction. In addition, the classification of blood cells using established fluorescence-based markers provides hundreds of thousands of labeled cell images used to train a deep neural network. The trained algorithm, with latency optimized to below 1 ms, is then used to identify and sort unlabeled blood cells at rates of 100 cells/sec. This approach transfers molecular specificity into label-free sorting and opens up new possibilities for basic biological research and clinical therapeutic applications.

biophysics↗

bioRxiv2018

High-throughput microfluidic characterization of erythrocyte shape and mechanical variability

The motion of red blood cells (RBCs) in microchannels is important for microvascular blood flow and biomedical applications such as blood analysis in microfluidics. The current understanding of the complexity of RBC shapes and dynamics in microchannels is mainly based on several simulation studies, but there are a few systematic experimental investigations. Here, we present a combined study, which systematically characterizes RBC behavior for a wide range of flow rates and channel sizes. Even though simulations and experiments generally show good agreement, experimental observations demonstrate that there is no single well-defined RBC state for fixed flow conditions, but rather a broad distribution of states. This result can be attributed to the inherent variability in RBC mechanical properties, which is confirmed by a model that takes the variation in RBC shear elasticity into account. This represents a significant step toward a quantitative connection between RBC behavior in microfluidic devices and their mechanical properties, which is essential for a high-throughput characterization of diseased cells.\n\nSignificance StatementThe ability to change shape is crucial for the proper functioning of red blood cells under harsh conditions in the microvasculature, since their shapes strongly affect the flow behavior of whole blood. Our results from simulations and systematic experiments reveal the shapes and dynamics of red blood cells for different flow conditions and channel dimensions, generally in good agreement. However, in the experiments, cells do not exhibit a single well-defined shape for fixed flow conditions. We show that this distribution of shapes can be attributed to the variability in mechanical properties of red blood cells.

biophysics↗