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1. Cell morphology-based machine learning models for human cell state classification

   Li, Y. ; Nowak, C. ; Pham, U. ; Nguyen, K ; Bleris, L. ( May 2021 , npj systems biology and applications)

   null (Ed.)

   Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information. 

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2. Cell morphology-based machine learning models for human cell state classification

   https://doi.org/10.1038/s41540-021-00180-y  

   Li, Yi ; Nowak, Chance M. ; Pham, Uyen ; Nguyen, Khai ; Bleris, Leonidas ( May 2021 , npj Systems Biology and Applications)

   Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 livefvalue and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.

    

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3. Morphogenesis and cell ordering in confined bacterial biofilms

   https://doi.org/10.1073/pnas.2107107118  

   Zhang, Qiuting ; Li, Jian ; Nijjer, Japinder ; Lu, Haoran ; Kothari, Mrityunjay ; Alert, Ricard ; Cohen, Tal ; Yan, Jing ( July 2021 , Proceedings of the National Academy of Sciences)

   Biofilms are aggregates of bacterial cells surrounded by an extracellular matrix. Much progress has been made in studying biofilm growth on solid substrates; however, little is known about the biophysical mechanisms underlying biofilm development in three-dimensional confined environments in which the biofilm-dwelling cells must push against and even damage the surrounding environment to proliferate. Here, combining single-cell imaging, mutagenesis, and rheological measurement, we reveal the key morphogenesis steps ofVibrio choleraebiofilms embedded in hydrogels as they grow by four orders of magnitude from their initial size. We show that the morphodynamics and cell ordering in embedded biofilms are fundamentally different from those of biofilms on flat surfaces. Treating embedded biofilms as inclusions growing in an elastic medium, we quantitatively show that the stiffness contrast between the biofilm and its environment determines biofilm morphology and internal architecture, selecting between spherical biofilms with no cell ordering and oblate ellipsoidal biofilms with high cell ordering. When embedded in stiff gels, cells self-organize into a bipolar structure that resembles the molecular ordering in nematic liquid crystal droplets. In vitro biomechanical analysis shows that cell ordering arises from stress transmission across the biofilm–environment interface, mediated by specific matrix components. Our imaging technique and theoretical approach are generalizable to other biofilm-forming species and potentially to biofilms embedded in mucus or host tissues as during infection. Our results open an avenue to understand how confined cell communities grow by means of a compromise between their inherent developmental program and the mechanical constraints imposed by the environment.

    

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4. Aligned 2D carbon nanotube liquid crystals for wafer-scale electronics

   https://doi.org/10.1126/sciadv.abh0640  

   Jinkins, Katherine R. ; Foradori, Sean M. ; Saraswat, Vivek ; Jacobberger, Robert M. ; Dwyer, Jonathan H. ; Gopalan, Padma ; Berson, Arganthaël ; Arnold, Michael S. ( September 2021 , Science Advances)

   Semiconducting carbon nanotubes promise faster performance and lower power consumption than Si in field-effect transistors (FETs) if they can be aligned in dense arrays. Here, we demonstrate that nanotubes collected at a liquid/liquid interface self-organize to form two-dimensional (2D) nematic liquid crystals that globally align with flow. The 2D liquid crystals are transferred onto substrates in a continuous process generating dense arrays of nanotubes aligned within ±6°, ideal for electronics. Nanotube ordering improves with increasing concentration and decreasing temperature due to the underlying liquid crystal phenomena. The excellent alignment and uniformity of the transferred assemblies enable FETs with exceptional on-state current density averaging 520 μA μm −1 at only −0.6 V, and variation of only 19%. FETs with ion gel top gates demonstrate subthreshold swing as low as 60 mV decade −1 . Deposition across a 10-cm substrate is achieved, evidencing the promise of 2D nanotube liquid crystals for commercial semiconductor electronics. 

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5. Analyzing interfacial transport for water evaporating into dry nitrogen

   https://doi.org/10.1016/j.applthermaleng.2021.117910  

   Murray, B. ; Fox, M. J. ; Narayan, S. ( December 2021 , Applied thermal engineering)

   Markides, C. N. (Ed.)

   Designing air-water systems for industrial applications requires a fundamental understanding of mass accommodation at the liquid-vapor interface, which depends on many factors, including temperature, vapor concentration, and impurities that vary with time. Hence, understanding how mass accommodation changes over a droplet’s lifespan is critical for predicting the performance of applications leveraging evaporation. In this study, experimental data of water droplets on a gold-coated surface evaporating into dry nitrogen is coupled with a computational model to measure the accommodation coefficient at the liquid-vapor interface. We conduct this measurement by combining macroscopic observations with the microscopic kinetic theory of gasses. The experiments utilize a sensitive piezoelectric device to determine the droplet radius with high accuracy and imaging to measure the droplet contact angle. This setup also quantifies the trace amounts of non-volatile impurities in the droplet. For water droplets evaporating in a pure nitrogen stream, the accommodation coefficient directly relates to vapor flux over the droplet’s surface and is affected by the presence of impurities. We obtained a surface-averaged accommodation coefficient close to 0.001 across multiple water droplets evaporating close to room temperature. This quantification can aid in conducting a more accurate analysis of evaporation, which can assist in the improved design of evaporation-based applications. We believe the modeling approach presented in this work, which integrates the kinetic theory of gases to the macroscale flow behavior, can provide a basis for predicting evaporation kinetics in the presence of extremely dry non-condensable gas streams. 

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