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- Materials and Methods
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In eukaryotic cells, proteins can occupy multiple intracellular compartments and even move between compartments to fulfill critical biological functions or respond to cellular signals. Examples include transcription factors that reside in the cytoplasm but are mobilized to the nucleus as well as dual-purpose DNA repair proteins that are charged with simultaneously maintaining the integrity of both the nuclear and mitochondrial genomes. While numerous methods exist to study protein localization and dynamics, automated methods to quantify the relative amounts of proteins that occupy multiple subcellular compartments have not been extensively developed. To address this need, we present a rapid, automated method termed quantitative subcellular compartmentalization analysis (Q-SCAn). To develop this method, we exploited the facile molecular biology of the budding yeast, Saccharomyces cerevisiae. Individual subcellular compartments are defined by a fluorescent marker protein and the intensity of a target GFP-tagged protein is then quantified within each compartment. To validate Q-SCAn, we analyzed relocalization of the transcription factor Yap1 following oxidative stress and then extended the approach to multicompartment localization by examining two DNA repair proteins critical for the base excision repair pathway, Ntg1 and Ung1. Our findings demonstrate the utility of Q-SCAn for quantitative analysis of the subcellular distribution of multicompartment proteins.
In eukaryotic cells, subcellular compartments are functionally defined by the proteins present within them. While many proteins are localized to a single compartment, some proteins are localized to multiple compartments either constitutively or in response to cellular signals. For example, many transcription factors are localized to the cytoplasm until a signal triggers nuclear import [1, 2]. Such mobilization of proteins is an efficient strategy to alter local function in response to stimuli because transport of a preexisting pool of protein is more rapid than the de novo synthesis and localization of a comparable amount of protein. Other proteins simultaneously play roles in multiple compartments such as some DNA repair proteins that are localized to both the nucleus and mitochondria to maintain the integrity of the genomes in each of these cellular compartments .
Despite the biological significance of localizing proteins to multiple subcellular compartments, tools for quantifying the relative subcellular distribution of multicompartment proteins have not been extensively developed. Many protein localization studies employ manual scoring from microscopy data, relying on the heterogeneity of the cell population and human visual detection to provide a useful threshold [4-7]. However, these implicit thresholds are subjective and the process can be very labor-intensive. In addition, manual methods are only semi-quantitative as they are based on qualitative data. True quantification can be achieved by manually tracing the boundaries of the compartments of interest and then quantifying pixels within each compartment, but the laborious nature of this type of analysis means the number of cells that can be analyzed is effectively limited. Colocalization analysis , which has advanced greatly over the last decade and is widely available in image analysis software, is more suited to addressing questions about whether proteins and markers are spatially linked rather than about the distribution of a protein among distinct compartments. Photobleaching  and photoactivation techniques can be employed to examine dynamics ; however, these techniques require highly specialized experimental setups and are also limited to larger cells amenable to such techniques. Biochemical fractionation techniques can also provide quantifiable compartmentalization information on a population of cells [4, 11, 12], but microscopy-based techniques are superior to fractionation because micrographs preserve the spatial relationships and yield information on the single-cell level, not just the population average.
The limitations of the above techniques form a critical impediment to analyzing the steady-state distribution of proteins localized to multiple compartments. Development of advanced, automatable techniques that provide unbiased quantification of protein localization on a per-cell basis is becoming an active area of research. We have developed an approach to quantifying protein distribution among multiple compartments, which we term quantitative subcellular compartmentalization analysis (Q-SCAn). This microscopy-based method uses brightfield DIC images to identify cells, relies on a set of fluorescent markers to define subcellular compartments, and provides information about the amount of a protein of interest, marked by a third fluorescent reporter, within the identified compartments. By comparing the fluorescence intensities for each compartment, a localization index is calculated for each cell, yielding a quantitative measure of protein localization. Furthermore, the distribution of these localization indices can be compared between different cell types, conditions and time points to address the regulation of protein localization.
Here we describe the development of Q-SCAn in Saccharomyces cerevisiae and demonstrate its utility in measuring the single-cell localization of proteins by following the oxidative stress-induced relocalization of the transcription factor Yap1 . Next, we extend the approach to multicompartment localization by examining the nucleomitochondrial base excision repair (BER) protein Ntg1 . Finally, we apply the method to evaluate the localization of another nucleomitochondrial BER protein, Ung1 , which has not been previously analyzed in any quantitative manner. Our analysis of Ung1 provides new biological information about mechanisms of localization of Ung1 and thus insight into regulation of the BER pathway, demonstrating the utility of Q-SCAn for such studies. This work presents a novel method for quantifying the subcellular distribution of multicompartment proteins which can be immediately put to use and extended without specialized equipment or programming experience.
- Top of page
- Materials and Methods
- Supporting Information
The goal of the work presented here was to develop a rapid and quantitative method that could be used to assess the relative amount of a protein present in multiple compartments. Our results demonstrate the utility of Q-SCAn for such studies. Given the automated and rapid data analysis feasible with Q-SCAn, biological questions that examine changes in the localization of a dual-compartment protein can be readily addressed quantitatively. For example, a change in compartment-specific localization in response to a cellular signal over time could be analyzed as presented here for Yap1. In addition, quantitative information regarding the contribution of intracellular targeting signals to specific cellular compartments, such as described here for the DNA repair proteins Ntg1 and Ung1, can be readily collected and analyzed. Thus, Q-SCAn expands the tools available to address questions of protein distribution in a quantitative manner. Furthermore, Q-SCAn has been implemented in such a way as to ensure that the method can be readily employed by those that wish to analyze any protein of interest.
This study also demonstrates how Q-SCAn can be employed to gather new biological information about a protein of interest. While accumulation of Yap1 within the nucleus upon oxidative stress is a well-established biological response [7, 13, 22], our analysis reveals information about the extent of nuclear localization with time. Furthermore, we employed Q-SCAn to define the functional localization sequences of the DNA repair protein Ung1, which is localized both to the nucleus and mitochondria . Our analysis reveals that the NLS1 sequence constitutes the functional classical NLS in Ung1. However, altering both predicted classical NLS motifs did not eliminate all nuclear localization, indicating that Ung1 likely exploits a non-classical nuclear import pathway to ensure access to the nucleus. Such a mechanism using dual pathways to access the nucleus has also been observed for Ntg1 . Thus, in addition to facilitating experiments through rapid and automated data analysis, the quantitative results obtained using Q-SCAn provide novel information that is not revealed through conventional qualitative scoring methods.
A major strength of Q-SCAn is that the approach has been developed so that it can be easily applied by a user without any need for highly specialized equipment or software. Q-SCAn employs the open-source CellProfiler software package . This package is user-friendly and highly modular, allowing for any aspect of the Q-SCAn algorithm to be modified for different circumstances or to couple with other analyses. The CellProfiler package is also under active development and improvement at the Broad Institute, and technical assistance is readily available if required (www.cellprofiler.org).
There are a number of points that must be taken into consideration when implementing Q-SCAn. First, a marker protein for the compartment or compartments of interest will need to be designed. The fluorescence spectrum of fluorescent markers should not overlap with the fluorescence spectrum of the analyzed protein. Here we selected tdTomato and mCerulean coupled with GFP based on the spectral properties of these fluorescent proteins . Another consideration is that, as described here, the localization examined is not that of the endogenous protein, but rather that of a fluorescently tagged protein. Such tagged proteins are commonly used to obtain information about protein localization [28, 29] but both the presence of the tag and the level of the tagged-protein relative to endogenous protein need to be taken into account when interpreting results. Fluorescence noise should also be monitored. Although several noise-removal methods are implemented in Q-SCAn, some noise still remains. As a result, as the target protein level approaches the noise level, the localization index will approach equality. Finally, care must be taken to ensure that cells in the imaged fields are well-separated. While the cell-finding algorithm can readily separate cells that touch, there can be difficulty in separating cell clusters, which could inadvertently be treated as a single cell.
Development of methods to analyze protein distribution is an active area of research. A recent publication employed a dye to mark the S. cerevisiae plasma membrane to quantify recruitment of YFP-Ste5 to the plasma membrane upon pheromone signaling . Using this approach, the authors were able to calculate both the pheromone dose–response for Ste5 recruitment to the membrane and the dissociation constant for Ste5 from the membrane. While elements of this quantitative approach are similar to Q-SCAn, there are some significant differences. These authors employed a dye to mark the target compartment, but such dyes are not readily available to specifically mark all cellular compartments. In addition, the method was implemented using a special-purpose software, Cell-ID . While this program, like CellProfiler, is open-source, significant expertise would be required to use or modify the program. Another key distinction from Q-SCAn is that the authors employed defocused brightfield images to identify cells. While this approach facilitates cell-finding, obtaining these images on a non-automated microscope would be laborious and employing such images could result in a loss of information about internal cellular morphology and cell fitness. Given the different biological questions addressed by the two approaches, recruitment of a protein to the membrane  and movement of proteins between intracellular compartments (this report), these two methods complement one another to obtain quantitative biological information.
As a general method, there are numerous ways that Q-SCAn could be developed and extended, which is facilitated by implementation in CellProfiler. For example, the cell-finding algorithm could be replaced to identify mammalian cells, and the compartment-finding algorithms could be adapted to different types of markers including immunofluorescence. A third fluorescent protein from the deep red spectral range such as mPlum  could be introduced to mark a third compartment of interest. More than three fluorescent protein compartment markers could be employed if spectral imaging/linear unmixing is used to separate the signals. The localization distribution information could also be coupled to other information, such as cell size, compartment morphology or measures of the protein distribution within a compartment. With modifications, Q-SCAn could also be adapted to analyze localization within three-dimensional datasets . In addition to analyzing single images, Q-SCAn could further be employed to extract localization data from frames of a video, allowing quantitative analysis of the dynamics of protein relocalization within single cells. Q-SCAn could also have applications in diagnostics and therapeutics, as mislocalization of proteins is associated with disease processes including cancer, autoimmune disorders and degenerative disorders .
This study reports the development and utilization of Q-SCAn as a facile, quantitative analytical tool for providing broader and more detailed analysis of the localization of multicompartment proteins when compared to the current approaches available.