Live single-cell imaging

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In systems biology, live single-cell imaging is a live-cell imaging technique that combines traditional live-cell imaging and time-lapse microscopy techniques with automated cell tracking and feature extraction, drawing many techniques from high-content screening. It is used to study signalling dynamics and behaviour in populations of individual living cells. [1] [2] Live single-cell studies can reveal key behaviours that would otherwise be masked in population averaging experiments such as western blots. [3]

Contents

In a live single-cell imaging experiment a fluorescent reporter is introduced into a cell line to measure the levels, localisation or activity of a signalling molecule. Subsequently, a population of cells is imaged over time with careful atmospheric control to maintain viability, and reduce stress upon the cells. Automated cell tracking is then performed upon these time series images, following which filtering and quality control may be performed. Analysis of features describing the fluorescent reporter over time, can then lead to modelling and generation of biological conclusions from which further experimentation can be guided.

History

The field of live single-cell imaging began with work demonstrating that green fluorescent protein (GFP), found in the jellyfish Aequorea victoria, could be expressed in living organisms. [4] This discovery allowed researches to study the localisation and levels of proteins in living single cells, for example the activity of kinases, [5] and calcium levels, through the use of FRET reporters, [6] as well as numerous other phenotypes. [7]

Generally, these early studies focused on the localisation and behaviour of these fluorescently labelled proteins at the subcellular level over short periods of time. However, this changed with pioneering studies looking at the tumour suppressor p53 [8] and the stress and inflammation related protein NF-κB, [9] revealing there levels and localisation respectively to oscillate over periods of several hours. Live single-cell approaches were also applied around this time to understand signalling in single-cell organisms including bacteria, where live studies allowed the dynamics of competence to be modelled, [10] and yeast revealing the mechanism underpinning coherent cell cycle entry. [11]

Experimental work flow

Fluorescent reporters

In any live single-cell study, the first step is to introduce a reporter for our protein/molecule of interest into a suitable cell line. Much of the growth in the field has come from improved gene editing tools such as CRISPR, this leading to development of a wide variety of fluorescent reporters. [12]

Fluorescent tagging uses a gene encoding a fluorescent protein that is inserted into the coding frame of the protein to be tagged. Texture and intensity features can be extracted from images of the tagged protein.

Molecules can also be tagged in vitro and introduced into the cell with electrophoresis. This enable the use of smaller and more photostable fluorophores but requires additional washing steps. [13]

By engineering expression of FRET reporter such that donor and emitter fluorophores are only in close proximity when an upstream signalling molecule is either active or inactive, the donor to emitter fluorescence intensity ratio can be used as a measure of signalling activity. For example, in key early work using FRET reporters for live single studies FRET reporters of Rho GTPase activity were engineered. [14]

Nuclear translocation reporters use engineered nuclear import and nuclear export signals, which can be inhibited by signalling molecules, to record signalling activity via the ration of nuclear reporter to cytoplasmic reporter. [15]

Live imaging

Live-cell imaging of fluorescently labelled cells must then be performed. This requires simultaneous incubation of cells in stress free conditions whilst imaging is being performed. There are several factors that must be taken into account when choosing imaging conditions such as phototoxicity, photobleaching, tracking ease, rate of change of signalling activity, and Signal to noise. These all relate to imaging frequency and illumination intensity.

Phototoxicity can result from being exposed to large amounts of light over long periods of time. Cells will become stressed, which can lead to apoptosis. High frequency and intensity imaging can cause the fluorophore signal to decrease through photobleaching. Higher frequency imaging generally makes automated cell tracking easier. Imaging frequencies should be able to capture necessary changes to signalling activity. Low intensity imaging or poor reporters may prevent low levels of signalling activity within the cell from being detected.

Live-cell tracking

Following live-cell imaging, automated tracking software is then employed to extract time series data from videos of cells. Live-cell tracking is generally split into two steps, image segmentation of cells or their nuclei and cell/nuclei tracking based on these segments. Many challenges still exist in this stage of a live single-cell imaging study. [16] However recent progress has been highlighted in the field first objective comparison of single-cell tracking techniques. [17]

Quantitative phase imaging (QPI) is particularly useful for live-cell tracking. As QPI is label-free, it does not induce phototoxicity, nor does it suffer from the photobleaching associated with fluorescence imaging. [18] QPI offers a significantly higher contrast than conventional phase imaging techniques, such as phase-contrast microscopy. The higher contrast facilitates more robust cell segmentation and tracking than achievable with conventional phase imaging. [19]

New techniques that use a combination of traditional image segmentation techniques and deep learning to segment cells are also becoming more widely used as well. [20]

Data analysis

In the final stage of a live single-cell imaging study, modelling and analysis of time series data extracted from tracked cells is performed. Pedigree tree profiles can be constructed to reveal heterogeneity in individual cell response and downstream signalling. [21] [22] Refining and compressing data from video-based single-cell tracking can provide relevant inputs for big data analysis, contributing to the identification of biomarkers for enhanced diagnosis and prognosis. [23] A large overlap between analysis of single-cell live data, and modelling of biological systems using ordinary differential equations exists. Results from this key data analysis step will drive further experimentation, for example by perturbing aspects of the system being studied and then comparing signalling dynamics with those of the control population.

Applications

By analysing the signalling dynamics of single cells across entire populations, live single-cell studies are now letting us understand how these dynamics affect key cellular decision making processes. For example, live single-cell studies of the growth factor ERK revealed it to possess digital all-or-nothing activation. [24] Moreover, this all-or-nothing activation was pulsatile, and the frequency of pulses in turn determined whether mammalian cells would commit to cell cycle entry or not. In another key example, live single-cell studies of CDK2 activity in mammalian cells demonstrated that bifurcation in CDK2 activity following mitosis, determined whether cells would continue to proliferate or enter a state of quiescence; [25] now shown, using live single-cell methods, to be caused by stochastic DNA damage inducing upregulation of p21, which inhibits CDK2 activity. [26] Moving forward, live single-cell studies will now likely incooperate multiple reporters into single-cell lines to allow complex decision making processes to be understood, however challenges still remain in scaling up live single-cell studies.

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<span class="mw-page-title-main">Cell cycle</span> Series of events and stages that result in cell division

The cell cycle, or cell-division cycle, is the series of events that take place in a cell that causes it to divide into two daughter cells. These events include the duplication of its DNA and some of its organelles, and subsequently the partitioning of its cytoplasm, chromosomes and other components into two daughter cells in a process called cell division.

<span class="mw-page-title-main">Green fluorescent protein</span> Protein that exhibits bright green fluorescence when exposed to ultraviolet light

The green fluorescent protein (GFP) is a protein that exhibits green fluorescence when exposed to light in the blue to ultraviolet range. The label GFP traditionally refers to the protein first isolated from the jellyfish Aequorea victoria and is sometimes called avGFP. However, GFPs have been found in other organisms including corals, sea anemones, zoanithids, copepods and lancelets.

<span class="mw-page-title-main">Fluorescent tag</span>

In molecular biology and biotechnology, a fluorescent tag, also known as a fluorescent label or fluorescent probe, is a molecule that is attached chemically to aid in the detection of a biomolecule such as a protein, antibody, or amino acid. Generally, fluorescent tagging, or labeling, uses a reactive derivative of a fluorescent molecule known as a fluorophore. The fluorophore selectively binds to a specific region or functional group on the target molecule and can be attached chemically or biologically. Various labeling techniques such as enzymatic labeling, protein labeling, and genetic labeling are widely utilized. Ethidium bromide, fluorescein and green fluorescent protein are common tags. The most commonly labelled molecules are antibodies, proteins, amino acids and peptides which are then used as specific probes for detection of a particular target.

<span class="mw-page-title-main">Immunofluorescence</span> Technique used for light microscopy

Immunofluorescence(IF) is a light microscopy-based technique that allows detection and localization of a wide variety of target biomolecules within a cell or tissue at a quantitative level. The technique utilizes the binding specificity of antibodies and antigens. The specific region an antibody recognizes on an antigen is called an epitope. Several antibodies can recognize the same epitope but differ in their binding affinity. The antibody with the higher affinity for a specific epitope will surpass antibodies with a lower affinity for the same epitope.

<span class="mw-page-title-main">Fluorescence recovery after photobleaching</span>

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<span class="mw-page-title-main">Förster resonance energy transfer</span> Photochemical energy transfer mechanism

Förster resonance energy transfer (FRET), fluorescence resonance energy transfer, resonance energy transfer (RET) or electronic energy transfer (EET) is a mechanism describing energy transfer between two light-sensitive molecules (chromophores). A donor chromophore, initially in its electronic excited state, may transfer energy to an acceptor chromophore through nonradiative dipole–dipole coupling. The efficiency of this energy transfer is inversely proportional to the sixth power of the distance between donor and acceptor, making FRET extremely sensitive to small changes in distance.

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<span class="mw-page-title-main">GCaMP</span> Genetically encoded calcium indicator

GCaMP is a genetically encoded calcium indicator (GECI) initially developed in 2001 by Junichi Nakai. It is a synthetic fusion of green fluorescent protein (GFP), calmodulin (CaM), and M13, a peptide sequence from myosin light-chain kinase. When bound to Ca2+, GCaMP fluoresces green with a peak excitation wavelength of 480 nm and a peak emission wavelength of 510 nm. It is used in biological research to measure intracellular Ca2+ levels both in vitro and in vivo using virally transfected or transgenic cell and animal lines. The genetic sequence encoding GCaMP can be inserted under the control of promoters exclusive to certain cell types, allowing for cell-type specific expression of GCaMP. Since Ca2+ is a second messenger that contributes to many cellular mechanisms and signaling pathways, GCaMP allows researchers to quantify the activity of Ca2+-based mechanisms and study the role of Ca2+ ions in biological processes of interest.

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<span class="mw-page-title-main">SmURFP</span>

Small ultra red fluorescent protein (smURFP) is a class of far-red fluorescent protein evolved from a cyanobacterial phycobiliprotein, α-allophycocyanin. Native α-allophycocyanin requires an exogenous protein, known as a lyase, to attach the chromophore, phycocyanobilin. Phycocyanobilin is not present in mammalian cells. smURFP was evolved to covalently attach phycocyanobilin without a lyase and fluoresce, covalently attach biliverdin and fluoresce, blue-shift fluorescence to match the organic fluorophore, Cy5, and not inhibit E. coli growth. smURFP was found after 12 rounds of random mutagenesis and manually screening 10,000,000 bacterial colonies.

Genetically encoded voltage indicator is a protein that can sense membrane potential in a cell and relate the change in voltage to a form of output, often fluorescent level. It is a promising optogenetic recording tool that enables exporting electrophysiological signals from cultured cells, live animals, and ultimately human brain. Examples of notable GEVIs include ArcLight, ASAP1, ASAP3, Archons, SomArchon, and Ace2N-mNeon.

Optogenetics began with methods to alter neuronal activity with light, using e.g. channelrhodopsins. In a broader sense, optogenetic approaches also include the use of genetically encoded biosensors to monitor the activity of neurons or other cell types by measuring fluorescence or bioluminescence. Genetically encoded calcium indicators (GECIs) are used frequently to monitor neuronal activity, but other cellular parameters such as membrane voltage or second messenger activity can also be recorded optically. The use of optogenetic sensors is not restricted to neuroscience, but plays increasingly important roles in immunology, cardiology and cancer research.

References

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