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Detection and quantification of bacterial endotoxins is important in a range of health-related contexts, including during pharmaceutical manufacturing of therapeutic proteins and vaccines. Here we combine experimental measurements based on nematic liquid crystalline droplets and machine learning methods to show that it is possible to classify bacterial sources ( Escherichia coli , Pseudomonas aeruginosa , Salmonella minnesota ) and quantify concentration of endotoxin derived from all three bacterial species present in aqueous solution. The approach uses flow cytometry to quantify, in a high-throughput manner, changes in the internal ordering of micrometer-sized droplets of nematic 4-cyano-4′-pentylbiphenyl triggered by the endotoxins. The changes in internal ordering alter the intensities of light side-scattered (SSC, large-angle) and forward-scattered (FSC, small-angle) by the liquid crystal droplets. A convolutional neural network (Endonet) is trained using the large data sets generated by flow cytometry and shown to predict endotoxin source and concentration directly from the FSC/SSC scatter plots. By using saliency maps, we reveal how EndoNet captures subtle differences in scatter fields to enable classification of bacterial source and quantification of endotoxin concentration over a range that spans eight orders of magnitude (0.01 pg mL −1 to 1 μg mL −1 ). We attribute changes in scatter fields with bacterial origin of endotoxin, as detected by EndoNet, to the distinct molecular structures of the lipid A domains of the endotoxins derived from the three bacteria. Overall, we conclude that the combination of liquid crystal droplets and EndoNet provides the basis of a promising analytical approach for endotoxins that does not require use of complex biologically-derived reagents ( e.g. , Limulus amoebocyte lysate). 

Liquid crystals (LCs) are fluids within which molecules exhibit long-range orientational order, leading to anisotropic properties such as optical birefringence and curvature elasticity. Because the ordering of molecules within LCs can be altered by weak external stimuli, LCs have been widely used to create soft matter systems that respond optically to electric fields (LC display), temperature (LC thermometer) or molecular adsorbates (LC chemical sensor). More recent studies, however, have moved beyond investigations of optical responses of LCs to explore the design of complex LC-based soft matter systems that offer the potential to realize more sophisticated functions ( e.g. , autonomous, self-regulating chemical responses to mechanical stimuli) by directing the interactions of small molecules, synthetic colloids and living cells dispersed within the bulk of LCs or at their interfaces. These studies are also increasingly focusing on LC systems driven beyond equilibrium states. This review presents one perspective on these advances, with an emphasis on the discovery of fundamental phenomena that may enable new technologies. Three areas of progress are highlighted; (i) directed assembly of amphiphilic molecules either within topological defects of LCs or at aqueous interfaces of LCs, (ii) templated polymerization in LCs via chemical vapor deposition, an approach that overcomes fundamental challenges related to control of LC phase behavior during polymerization, and (iii) studies of colloids in LCs, including chiral colloids, soft colloids that are strained by LCs, and active colloids that are driven into organized states by dissipation of energy ( e.g. bacteria). These examples, and key unresolved issues discussed at the end of this perspective, serve to convey the message that soft matter systems that integrate ideas from LC, surfactant, polymer and colloid sciences define fertile territory for fundamental studies and creation of future transformative technologies.