Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters

Abstract

Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where...

Critical review of single-cell mechanotyping approaches for biomedical applications

“…In doing so we highlight developing approaches that combine low fabrication cost, microfluidic cell handling and the ability to modulate deforming forces, 52,75 in addition to recent developments supporting the potential for machine learning to enhance characterization. [76][77][78][79][80] We also identify directions for future work to improve the throughput, data quality and reliability of single-cell mechanotyping measurements.…”

“…Machine learning techniques for single-cell mechanotyping. The potential for machine learning techniques to advance single-cell mechanotyping technology has recently been proposed, namely as a complimentary data analysis tool utilizing any number of cell deformation techniques, [76][77][78][79]190,191 or as a standalone system capable of estimating cell deformability via associated morphological features, free from any form of mechanical probing. 80 Such machine learning approaches offer a methodology for characterising intrinsic cell stiffness properties, which is otherwise complicated owing to the complex biophysical nature and interplay between cellular components, as well as difficulties in calibration and standardization of mechanotyping load cases.…”

“…Machine learning tools are thus useful in characterising mechanical phenotypes where the relationship between cell stiffness and raw experimental data is nonlinear, noisy or otherwise complex. Machine learning algorithms applied in this manner have accordingly been trained to classify mechanotypes using complex cell trajectories under deformability-induced lift forces, 76 laser diffraction patterns under fluid shear forces, 79 optical images from RT-DC analysis 190 and acoustofluidic deformation forces. 77,191 The potential for machine learning techniques to identify mechanical characteristics from optical analysis alone has also been investigated.…”

Recent advances in deformation-assisted microfluidic cell sorting technologies

“…However, much of the research has focused on the methods for measuring single-cell deformability, with less emphasis on the application of cell deformability in cell sorting. [68][69][70][71][72] Given the potential of cell deformability in cell sorting, this review examines the different deformation-assisted microfluidic cell sorting methods to help researchers identify the most suitable technique for their application. To our knowledge, there are few specific reviews on deformability-assisted cell sorting.…”

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

“…Naturally, this trend permeates into the realm of microfluidics, where lab-on-a-chip technologies facilitate the rapid and efficient acquisition of substantial data from nanoscale and microscopic entities such as particles, vesicles, and biological cells. 1,2 In cell biology and medicine, a diverse array of single-cell technologies have been devised on microfluidic platforms, encompassing genome sequencing, 3 mechanical phenotyping, [4][5][6] and proteomics. 7,8 Among these, imaging flow cytometry has seen considerable integration with AI for automated handling and classification of large volumes of cell image data.…”