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1. How Does a Plant Cell Build a New Cell Wall When Dividing?

   https://doi.org/10.3389/frym.2021.570769  

   Chang, Mingqin; Drakakaki, Georgia (June 2021, Frontiers for Young Minds)

   null (Ed.)

   If you live in an apartment or a house, you will notice that your home has different rooms separated by walls. A plant is just like your home, except there are many small rooms, called cells. Plant cells, like rooms, are also separated by cell walls. Cell walls are unique and are not found in animal cells. In a building, if you want to turn one large room into two small rooms, you build a new wall to divide it. This is similar to how a plant cell divides into two cells during cell division. To build a wall in a building, you need to employ construction workers, design the building plan, buy building materials, and finally assembly the wall. How does the plant cell take care of these different jobs? This article explains how the cell wall is built in a plant cell during cell division. 

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2. Recent Advances and New Perspectives in Capillary Electrophoresis-Mass Spectrometry for Single Cell “Omics”

   https://doi.org/10.3390/molecules24010042  

   DeLaney, Kellen; Sauer, Christopher; Vu, Nhu; Li, Lingjun (January 2019, Molecules)

   Accurate clinical therapeutics rely on understanding the metabolic responses of individual cells. However, the high level of heterogeneity between cells means that simply sampling from large populations of cells is not necessarily a reliable approximation of an individual cell’s response. As a result, there have been numerous developments in the field of single-cell analysis to address this lack of knowledge. Many of these developments have focused on the coupling of capillary electrophoresis (CE), a separation technique with low sample consumption and high resolving power, and mass spectrometry (MS), a sensitive detection method for interrogating all ions in a sample in a single analysis. In recent years, there have been many notable advancements at each step of the single-cell CE-MS analysis workflow, including sampling, manipulation, separation, and MS analysis. In each of these areas, the combined improvements in analytical instrumentation and achievements of numerous researchers have served to drive the field forward to new frontiers. Consequently, notable biological discoveries have been made possible by the implementation of these methods. Although there is still room in the field for numerous further advances, researchers have effectively minimized various limitations in detection of analytes, and it is expected that there will be many more developments in the near future. 

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3. Mapping Surface Charge Distribution of Single-Cell via Charged Nanoparticle

   https://doi.org/10.3390/cells10061519  

   Ouyang, Leixin; Shaik, Rubia; Xu, Ruiting; Zhang, Ge; Zhe, Jiang (June 2021, Cells)

   null (Ed.)

   Many bio-functions of cells can be regulated by their surface charge characteristics. Mapping surface charge density in a single cell’s surface is vital to advance the understanding of cell behaviors. This article demonstrates a method of cell surface charge mapping via electrostatic cell–nanoparticle (NP) interactions. Fluorescent nanoparticles (NPs) were used as the marker to investigate single cells’ surface charge distribution. The nanoparticles with opposite charges were electrostatically bonded to the cell surface; a stack of fluorescence distribution on a cell’s surface at a series of vertical distances was imaged and analyzed. By establishing a relationship between fluorescent light intensity and number of nanoparticles, cells’ surface charge distribution was quantified from the fluorescence distribution. Two types of cells, human umbilical vein endothelial cells (HUVECs) and HeLa cells, were tested. From the measured surface charge density of a group of single cells, the average zeta potentials of the two types of cells were obtained, which are in good agreement with the standard electrophoretic light scattering measurement. This method can be used for rapid surface charge mapping of single particles or cells, and can advance cell-surface-charge characterization applications in many biomedical fields. 

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4. Spatiotemporal force and motion in collective cell migration

   https://doi.org/10.1038/s41597-020-0540-5  

   Saraswathibhatla, Aashrith; Galles, Emmett E.; Notbohm, Jacob (June 2020, Scientific Data)

   Abstract Cells move in collective groups in biological processes such as wound healing, morphogenesis, and cancer metastasis. How active cell forces produce the motion in collective cell migration is still unclear. Many theoretical models have been introduced to elucidate the relationship between the cell’s active forces and different observations about the collective motion such as collective swirls, oscillations, and rearrangements. Though many models share the common feature of balancing forces in the cell layer, the specific relationships between force and motion vary among the different models, which can lead to different conclusions. Simultaneous experimental measurements of force and motion can aid in testing assumptions and predictions of the theoretical models. Here, we provide time-lapse images of cells in 1 mm circular islands, which are used to compute cell velocities, cell-substrate tractions, and monolayer stresses. Additional data are included from experiments that perturbed cell number density and actomyosin contractility. We expect this data set to be useful to researchers interested in force and motion in collective cell migration. 

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5. Distinguishing different modes of growth using single-cell data

   https://doi.org/10.7554/eLife.72565  

   Kar, Prathitha; Tiruvadi-Krishnan, Sriram; Männik, Jaana; Männik, Jaan; Amir, Ariel (December 2021, eLife)

   All cells – from bacteria to humans – tightly control their size as they grow and divide. Cells can also change the speed at which they grow, and the pattern of how fast a cell grows with time is called ‘mode of growth’. Mode of growth can be ‘linear’, when cells increase their size at a constant rate, or ‘exponential’, when cells increase their size at a rate proportional to their current size. A cell’s mode of growth influences its inner workings, so identifying how a cell grows can reveal information about how a cell will behave. Scientists can measure the size of cells as they age and identify their mode of growth using single cell imaging techniques. Unfortunately, the statistical methods available to analyze the large amounts of data generated in these experiments can lead to incorrect conclusions. Specifically, Kar et al. found that scientists had been using specific types of plots to analyze growth data that were prone to these errors, and may lead to misinterpreting exponential growth as linear and vice versa. This discrepancy can be resolved by ensuring that the plots used to determine the mode of growth are adequate for this analysis. But how can the adequacy of a plot be tested? One way to do this is to generate synthetic data from a known model, which can have a specific and known mode of growth, and using this data to test the different plots. Kar et al. developed such a ‘generative model’ to produce synthetic data similar to the experimental data, and used these data to determine which plots are best suited to determine growth mode. Once they had validated the best statistical methods for studying mode of growth, Kar et al. applied these methods to growth data from the bacterium Escherichia coli . This showed that these cells have a form of growth called ‘super-exponential growth’. These findings identify a strategy to validate statistical methods used to analyze cell growth data. Furthermore, this strategy – the use of generative models to produce synthetic data to test the accuracy of statistical methods – could be used in other areas of biology to validate statistical approaches. 

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