SiCTeC: An inexpensive, easily assembled Peltier device for rapid temperature shifting during single-cell imaging

Metabolic rearrangement enables adaptation of microbial growth rate to temperature shifts

Temperature is a key determinant of microbial behaviour and survival in the environment and within hosts. At intermediate temperatures, growth rate varies according to the Arrhenius law of thermodynamics, which describes the effect of temperature on the rate of a chemical reaction. However, the mechanistic basis for this behaviour remains unclear. Here we use single-cell microscopy to show that Escherichia coli exhibits a gradual response to temperature upshifts with a timescale of ~1.5 doublings at the higher temperature. The response was largely independent of initial or final temperature and nutrient source. Proteomic and genomic approaches demonstrated that adaptation to temperature is independent of transcriptional, translational or membrane fluidity changes. Instead, an autocatalytic enzyme network model incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, resulting in a transient temperature memory. The model successfully predicts alterations in the temperature response across nutrient conditions, diverse E. coli strains from hosts with different body temperatures, soil-dwelling Bacillus subtilis and fission yeast. In sum, our model provides a mechanistic framework for Arrhenius-dependent growth.

Metabolomic rearrangement controls the intrinsic microbial response to temperature changes

Temperature is one of the key determinants of microbial behavior and survival, whose impact is typically studied under heat- or cold-shock conditions that elicit specific regulation to combat lethal stress. At intermediate temperatures, cellular growth rate varies according to the Arrhenius law of thermodynamics without stress responses, a behavior whose origins have not yet been elucidated. Using single-cell microscopy during temperature perturbations, we show that bacteria exhibit a highly conserved, gradual response to temperature upshifts with a time scale of ~1.5 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We find that this behavior is coupled to a temperature memory, which we rule out as being neither transcriptional, translational, nor membrane dependent. Instead, we demonstrate that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, which encodes a temperature memory and successfully predicts alterations in the upshift response observed under simple-sugar, low-nutrient conditions, and in fungi. This model also provides a mechanistic framework for both Arrhenius-dependent growth and the classical Monod Equation through temperature-dependent metabolite flux.

The Effects of Temperature on Cellular Physiology

Temperature impacts biological systems across all length and timescales. Cells and the enzymes that comprise them respond to temperature fluctuations on short timescales, and temperature can affect protein folding, the molecular composition of cells, and volume expansion. Entire ecosystems exhibit temperature-dependent behaviors, and global warming threatens to disrupt thermal homeostasis in microbes that are important for human and planetary health. Intriguingly, the growth rate of most species follows the Arrhenius law of equilibrium thermodynamics, with an activation energy similar to that of individual enzymes but with maximal growth rates and over temperature ranges that are species specific. In this review, we discuss how the temperature dependence of critical cellular processes, such as the central dogma and membrane fluidity, contributes to the temperature dependence of growth. We conclude with a discussion of adaptation to temperature shifts and the effects of temperature on evolution and on the properties of microbial ecosystems.

Impact and trends in embedding field programmable gate arrays and microcontrollers in scientific instrumentation

Microcontrollers and field-programmable gate arrays have been largely leveraged in scientific instrumentation since decades. Recent advancements in the performance of these programmable digital devices, with hundreds of I/O pins, up to millions of logic cells, >10 Gb/s connectivity, and hundreds of MHz multiple clocks, have been accelerating this trend, extending the range of functions. The diversification of devices from very low-cost 8-bit microcontrollers up to 32-bit ARM-based ones and a system of chip combining programmable logic with processors make them ubiquitous in modern electronic systems, addressing diverse challenges from ultra-low power operation, with sub-µA quiescent current in sleep mode for portable and Internet of Things applications, to high-performance computing, such as in machine vision. In this Review, the main motivations (compactness, re-configurability, parallelization, low latency for sub-ns timing, and real-time control), the possible approaches of the adoption of embedded devices, and the achievable performances are discussed. Relevant examples of applications in opto-electronics, physics experiments, impedance, vibration, and temperature sensing from the recent literature are also reviewed. From this bird-eye view, key paradigms emerge, such as the blurring of boundaries between digital platforms and the pervasiveness of machine learning algorithms, significantly fostered by the possibility to be run in embedded devices for distributing intelligence in the environment.