Molecular crowding in single eukaryotic cells: Using cell environment biosensing and single-molecule optical microscopy to probe dependence on extracellular ionic strength, local glucose conditions, and sensor copy number

Abstract

The physical and chemical environment inside cells is of fundamental importance to all life but has traditionally been difficult to determine on a subcellular basis. Here we combine cutting-edge genomically integrated FRET biosensing to readout localized molecular crowding in single live yeast cells. Confocal microscopy allows us to build subcellular crowding heatmaps using ratiometric FRET, while whole-cell analysis demonstrates crowding is reduced when yeast is grown in elevated glucose concentrations. Simulations indicate that the cell membrane is largely inaccessible to these sensors and that cytosolic crowding is broadly uniform across each cell over a timescale of seconds. Millisecond single-molecule optical microscopy was used to track molecules and obtain brightness estimates that enabled calculation of crowding sensor copy numbers. The quantification of diffusing molecule trajectories paves the way for correlating subcellular processes and the physicochemical environment of cells under stress.

Comparison of endogenously expressed fluorescent protein fusions behaviour for protein quality control and cellular ageing research

“…The cells were then heat shocked at 42 °C for 30 min and sampled by collecting 50 ml culture before heat shock, right after and then 60 and 90 min after recovery at 30 °C. Proteins were extracted by boiling the cells in Laemmli buffer 42 , 43 . Protein concentration was determined using Pierce 660 nm assay (Thermo Scientific).…”

“…For timelapse microscopy, the strain expressing Hsp104-mSc-I was grown in YNB complete medium supplemented with 2% glucose, sub-cultured to OD 600 ~ 0.45 and subjected to 30 min heat-shock at 42 °C. The cells were then gently spun down and placed onto a 1% agarose pad perfused with YNB medium supplemented with 2% glucose (w/v) and sealed with a coverslip as described previously 43 . The sample was imaged in 11 Z-stacks every 5 min for 90 min at 30 °C using the TempModule S1 (Zeiss), Y-module S1 (Zeiss), Temperable insert S1 (Zeiss), Temperable objective ring S1 (Zeiss), and Incubator S1 230 V (Zeiss) to maintain the temperature.…”

Investigating molecular crowding during cell division in budding yeast with FRET

“…1.5 BK7 Menzel-Glazer glass coverslips, Germany) coated with 20 µL of 1 mg/mL Concanavalin A (ConA). Slide preparation was perform as previously described (Shepherd et al, 2020). After washing the ConA with 200ul of imaging media, 20 µL of cells were flowed in, the slide was incubated, inverted for 5 minutes in a humidified chamber for adhesion to the ConA coated coverslip and washed again with 200 µl of imaging media, sealed with nail varnish ready for imaging.…”

“…Alongside this, tracking single molecules with Slimfield microscopy has been used to resolve oligomerization states of molecular assemblies in vivo (Badrinarayanan et al, 2012;Laidlaw et al, 2021;Sun et al, 2019;Syeda et al, 2019;Wollman et al, 2017). The crGE sensor copy numbers (i.e., number of sensor molecules present per cell) were also measured in previous work (Shepherd et al, 2020), demonstrating that crowding readout in the cell was independent of local sensor concentration. Single crGE molecules were also tracked through the low peak emission intensity of mCerulean3 this had precluded single-molecule FRET measurement.…”

PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and simulatiOn, a flexible, extensible, beginner-friendly and optimized program for analysis of single-molecule microscopy data

“…the number of fluorescently labelled biomolecules present in any given tracked object) and copy number of molecular complexes in cells [8]- [14]. Multiple algorithms and software packages have been written and made available to researchers to analyze these super-resolution microscopy data either as standalone suites or as plugins for popular image analysis programs such as ImageJ [15]. However, limited software tools are available for stoichiometry determination and none are available, to our knowledge, exploiting the speed and extensibility of Python.…”