skip to main content


Title: Distinguishing different modes of growth using single-cell data
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.  more » « less
Award ID(s):
1752024 1806818
NSF-PAR ID:
10342559
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
eLife
Volume:
10
ISSN:
2050-084X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Round spheres, straight rods, and twisting corkscrews, bacteria come in many different shapes. The shape of bacteria is dictated by their cell wall, the strong outer barrier of the cell. As bacteria grow and multiply, they must add to their cell wall while keeping the same basic shape. The cells walls are made from long chain-like molecules via processes that are guided by protein scaffolds within the cell. Many common antibiotics, including penicillin, stop bacterial infections by interrupting the growth of cell walls. Helicobacter pylori is a common bacterium that lives in the gut and, after many years, can cause stomach ulcers and stomach cancer. H. pylori are shaped in a twisting helix, much like a corkscrew. This shape helps H. pylori to take hold and colonize the stomach. It remains unclear how H. pylori creates and maintains its helical shape. The helix is much more curved than other bacteria, and H. pylori does not have the same helpful proteins that other curved bacteria do. If H. pylori grows asymmetrically, adding more material to the cell wall on its long outer side to create a twisting helix, what controls the process? To find out, Taylor et al. grew H. pylori cells and watched how the cell walls took shape. First, a fluorescent dye was attached to the building blocks of the cell wall or to underlying proteins that were thought to help direct its growth. The cells were then imaged in 3D, and images from hundreds of cells were reconstructed to analyze the growth patterns of the bacteria’s cell wall. A protein called CcmA was found most often on the long side of the twisting H. pylori. When the CcmA protein was isolated in a dish, it spontaneously formed sheets and helical bundles, confirming its role as a structural scaffold for the cell wall. When CcmA was absent from the cell of H. pylori, Taylor et al. observed that the pattern of cell growth changed substantially. This work identifies a key component directing the growth of the cell wall of H. pylori and therefore, a new target for antibiotics. Its helical shape is essential for H. pylori to infect the gut, so blocking the action of the CcmA protein may interrupt cell wall growth and prevent stomach infections. 
    more » « less
  2. Organisms switch their genes on and off to adapt to changing environments. This takes place thanks to complex networks of regulators that control which genes are actively ‘read’ by the cell to create the RNA molecules that are needed at the time. Piecing together these networks is key to fully understand the inner workings of living organisms, and how to potentially modify or artificially create them. Single-cell RNA sequencing is a powerful new tool that can measure which genes are turned on (or ‘expressed’) in an individual cell. Datasets with millions of gene expression profiles for individual cells now exist for organisms such as mice or humans. Yet, it is difficult to use these data to reconstruct networks of regulators; this is partly because scientists are not sure if the computational methods normally used to build these networks also work for single-cell RNA sequencing data. One way to check if this is the case is to use the methods on single-cell datasets from organisms where the networks of regulators are already known, and check whether the computational tools help to reach the same conclusion. Unfortunately, the regulatory networks in the organisms for which scientists have a lot of single-cell RNA sequencing data are still poorly known. There are living beings in which the networks are well characterised – such as yeast – but it has been difficult to do single-cell sequencing in them at the scale seen in other organisms. Jackson, Castro et al. first adapted a system for single-cell sequencing so that it would work in yeast. This generated a gene expression dataset of over 40,000 yeast cells. They then used a computational method (called the Inferelator) on these data to construct networks of regulators, and the results showed that the method performed well. This allowed Jackson, Castro et al. to start mapping how different networks connect, for example those that control the response to the environment and cell division. This is one of the benefits of single-cell RNA methods: cell division for example is not a process that can be examined at the level of a population, since the cells may all be at different life stages. In the future, the dataset will also be useful to scientists to benchmark a variety of single cell computational tools. 
    more » « less
  3. If you want to estimate whether height is related to weight in humans, what would you do? You could measure the height and weight of a large number of people, and then run a statistical test. Such ‘independence tests’ can be thought of as a screening procedure: if the two properties (height and weight) are not related, then there is no point in proceeding with further analyses. In the last 100 years different independence tests have been developed. However, classical approaches often fail to accurately discern relationships in the large, complex datasets typical of modern biomedical research. For example, connectomics datasets include tens or hundreds of thousands of connections between neurons that collectively underlie how the brain performs certain tasks. Discovering and deciphering relationships from these data is currently the largest barrier to progress in these fields. Another drawback to currently used methods of independence testing is that they act as a ‘black box’, giving an answer without making it clear how it was calculated. This can make it difficult for researchers to reproduce their findings – a key part of confirming a scientific discovery. Vogelstein et al. therefore sought to develop a method of performing independence tests on large datasets that can easily be both applied and interpreted by practicing scientists. The method developed by Vogelstein et al., called Multiscale Graph Correlation (MGC, pronounced ‘magic’), combines recent developments in hypothesis testing, machine learning, and data science. The result is that MGC typically requires between one half to one third as big a sample size as previously proposed methods for analyzing large, complex datasets. Moreover, MGC also indicates the nature of the relationship between different properties; for example, whether it is a linear relationship or not. Testing MGC on real biological data, including a cancer dataset and a human brain imaging dataset, revealed that it is more effective at finding possible relationships than other commonly used independence methods. MGC was also the only method that explained how it found those relationships. MGC will enable relationships to be found in data across many fields of inquiry – and not only in biology. Scientists, policy analysts, data journalists, and corporate data scientists could all use MGC to learn about the relationships present in their data. To that extent, Vogelstein et al. have made the code open source in MATLAB, R, and Python. 
    more » « less
  4. One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system. Biologists have previously succeeded in creating ‘minimal bacterial cells’. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive – in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system. Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose. Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity. 
    more » « less
  5. null (Ed.)
    People’s social interactions could influence their risk of developing various diseases, including cancer, according to population-level studies. In particular, studies have identified a so-called widowhood effect where a person’s risk of disease increases following the loss of a spouse. However, the cause of the widowhood effect remains debatable, as it can be difficult to separate the impact of lifestyle changes from biological changes in the individual following bereavement. It is not possible to use laboratory mice to identify a causal biological mechanism, because they do not form long-term relationships with a single partner (pair bonds). However, several species of deer mouse form pair bonds, and suffer from anxiety and stress if these bonds are broken. Naderi et al. used these mice to study the widowhood effect on the risk of developing cancer. First, Naderi et al. grew human lung cancer cells in blood serum taken from mice that were either in a pair bond or had been separated from their partner. The cancer cells grown in the blood of mice with disrupted pair bonds changed size and shape, indicating that these mice were more likely to develop cancer. This effect was not observed when the cells were grown in the blood of bonded deer mice or of another deer mouse species that does not form pair bonds. Naderi et al. also found that the activity of genes involved in the cancer cells’ ability to spread and to stick together was different in pair-bonded mice and in pair-separated mice. Next, Naderi et al. implanted lung cancer cells into the deer mice to study their effects on live animals. When cancer cells from the deer mice were transplanted into laboratory mice with a weakened immune system, the cells taken from pair-bonded deer mice were less likely to grow than the cells from deer mice with disrupted pair bonds. This suggests that the protective effects of pair bonding persist even after removal from the original mouse. These results provide evidence for a biological mechanism of the widowhood effect, where social experiences can alter gene activity relating to cancer growth. In the future, it will be important to determine whether the same applies to humans, and to find out if there are ways to mimic the effects of long-term bonds to improve cancer prognoses. 
    more » « less