Natalia
Vtyurina
a,
Christoffer
Åberg
*b and
Anna
Salvati
*a
aDepartment of Nanomedicine & Drug Targeting, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713AV Groningen, the Netherlands. E-mail: a.salvati@rug.nl
bDepartment of Pharmaceutical Analysis, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713AV Groningen, the Netherlands. E-mail: christoffer.aberg@rug.nl
First published on 25th May 2021
Live cell imaging is a powerful tool to understand how nano-sized objects, such as the drug carriers used for nanomedicine applications, are taken up and trafficked by cells. Here we visualized human HeLa cells as they took up and trafficked nanoparticles of different sizes and quantified nanoparticle colocalization with different fluorescently-labelled intracellular compartments over time. This allowed us to obtain kinetic profiles of nanoparticle transport towards the lysosomes in individual cells. With a simple theoretical model, we determined the typical departure time of nanoparticles from the cell membrane and typical lysosome arrival time. We compared these kinetics parameters for nanoparticles of different sizes and determined how they vary in individual cells. We also performed a similar analysis for early endocytic compartments through which nanoparticles transit and discuss challenges in quantifying the colocalization in this case. The results show a high variability in intracellular trafficking kinetics between individual cells. Additionally, they help us to understand how nanoparticle properties affect their cellular uptake and intracellular distribution kinetics.
Unlike small molecules that access the cytosol via active transporters or passive diffusion across the cell membrane, nanoparticles enter cells by energy-dependent endocytic mechanisms.6–8 Following endocytosis, they are enclosed into endosomal compartments and are typically trafficked to the lysosomes.1,7,9–11 However, the details of the uptake mechanisms and intracellular processes are often not clarified, including how nanoparticle properties affect them.1,12 This knowledge can help the design of targeted nanomedicines.2,5,13
Within this context, we aimed at gaining a better understanding of nanoparticle behaviour at the subcellular level by visualizing living cells as they internalized and trafficked nanoparticles. Several methods can be used to study transport inside cells, each presenting some advantages and limitations. Many methods rely on perturbing transport in cells in different ways, for instance using chemical inhibitors or by silencing or knocking down the expression of key proteins involved in uptake and transport.1,7,14,15 However, the perturbation may induce a different response of the cells compared to what is observed under physiological conditions.1,15 In contrast, live cell imaging allows following uptake and intracellular transport of nanoparticles in cells expressing fluorescent markers, but otherwise unperturbed.9,16 Furthermore, imaging live cells takes advantage of observing the processes of interest using both spatial and temporal information.17 For instance, Vercaturen et al. used dynamic colocalization of nanoparticles with different intracellular compartments to characterize the mechanism of uptake of polyplexes and their intracellular trafficking,16 while in the work of Sandin et al. different Rab GTPase proteins were labeled in HeLa cells to monitor trafficking of carboxylated polystyrene nanoparticles along early and late endosomal compartments.9
In line with these studies, here we expressed key fluorescently labeled markers involved in uptake and intracellular trafficking in live cells to visualize nanoparticles as they enter cells and are trafficked to their final destination in the lysosomes. By acquiring rapid dual-colour 3D confocal fluorescence microscopy images of the same cells over time, we determined the kinetics of nanoparticle uptake and trafficking through early to late stages of endocytosis in individual cells.
Additionally, by applying a simple theoretical model we determined the typical nanoparticle departure time from the membrane and the typical time required to arrive in the lysosomes and compared the results for nanoparticles of different sizes. By quantifying colocalization in the same (live) cells over time, we have been able to determine relevant intracellular rate constants in individual cells and study their variability. Thus, we used live cell imaging not only to determine whether nanoparticles are internalized and trafficked to certain compartments, but also to obtain the kinetics of the processes involved and analyze how these vary in individual cells and as a function of nanoparticle properties, such as size.
In order to set up the method, carboxylated polystyrene nanoparticles were selected as a model, because of their high fluorescence, resistance to photobleaching and stability in biological media.9,24–26 As a first step, in order to probe how nanoparticle properties affect intracellular distribution kinetics, nanoparticles of different sizes were used. Size is one of the key parameters that can be modulated in nanomedicine design, together with many other nanoparticle properties, such as material, charge, hydrophobicity, stiffness, as well as surface functionalization and addition of different types of surface ligands. In order to form a human biomolecular corona appropriate for testing on human cells, the nanoparticles were dispersed in medium containing 4 mg ml−1 human serum, roughly corresponding to the amount of proteins in standard cell culture medium supplemented with 10% bovine serum.27,28 This is an excess of proteins compared to the available nanoparticle surface area (ESI Table S1†). Dynamic light scattering confirmed that for all nanoparticles homogeneous dispersions were obtained in both PBS and the cell culture medium with serum (Fig. S1 and Table S2†).
In order to visualize different intracellular compartments, cells were labelled or transfected with constructs to express fluorescently labelled proteins of interest. Next, cells were exposed to nanoparticles of different sizes (40, 100 or 200 nm) in the form of a short “pulse” (of 5, 10, and 15 min, respectively) after which the cells were “chased” by acquiring 3D images at various times and these were analysed to determine colocalization (Fig. 1A illustrates this workflow). More in detail, cells were labelled with LysoTracker to stain the acidic compartments of the cells (mainly lysosomes, together with late endosomes and multivesicular bodies) and determine intracellular trafficking kinetics to the lysosomes. Indeed, after active uptake via different endocytic mechanisms, most nanoparticles are trafficked via the endosomes towards the lysosomes, as illustrated in Fig. 1B.1,7,9,11 Additionally, cells were transfected with a series of endocytic and membrane trafficking proteins carrying fluorescent tags. These included Rab5 and EEA1 to visualize the early endosomes; and clathrin light chain, caveolin-1 and pMyrPalm to stain different endocytic compartments (also depicted in Fig. 1B, together with examples of images of cells with the different labels). This selection was made to monitor eventual association of the nanoparticles with clathrin, caveolae or macropinosomes (pMyrPalm) and gain some information on the potential uptake mechanism.29,30
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Fig. 2 Workflow of image analysis to determine nanoparticle colocalization with lysosomes. (A) HeLa Kyoto cell with lysosomes stained by LysoTracker (red) and exposed for 10 min to 300 μg ml−1 of 100 nm carboxylated polystyrene nanoparticles (green). An example of an xy image and, on the right, the yz projection across the full cell volume of the raw 3D stack of images are shown. (B) Cell contours were manually defined based on the LysoTracker channel. (C) A 3D cell mask was generated. (D) The cell mask was applied to the nanoparticle and LysoTracker fluorescence channels to define the region of interest for the analysis. (E) Detected nanoparticle objects (green). (F) Detected lysosome objects (red). (G) Overlap of nanoparticle objects (green) and lysosome objects (red). (H) Detected nanoparticle objects that colocalize with lysosome objects (yellow). (I) Number of detected nanoparticle objects over time. (J) Number of detected lysosome objects. (K) Number of nanoparticle objects colocalized with lysosome objects. (L) Fraction of nanoparticle objects colocalized with lysosome objects. The results for individual cells are shown in grey and the mean and standard error over 8 cells are shown in purple. Scale bar 10 μm. Additional results for all individual cells are shown in Fig. S5.† |
Note also that what was identified as a nanoparticle “object” may contain several nanoparticles too close to be resolved optically. Movie S1† shows an example of a 3D reconstruction of a cell and the analysis performed and Fig. 2I–L show the data generated in 8 individual cells and their average.
The results showed that the number of nanoparticle objects and lysosomes remained relatively stable over the 6 h imaging period (Fig. 2I and J), confirming that with optimized imaging settings photobleaching was minimal. Instead, the number and fraction of nanoparticle objects colocalizing with lysosomes increased over time and plateaued within 6 h (Fig. 2K and L). This confirmed nanoparticle trafficking to the lysosomes, as previously observed for these and most other nanoparticles.1,7,9,10
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Fig. 3 Fitting a kinetic model to data for HeLa Kyoto cells exposed for 10 min to 300 μg ml−1 of 100 nm carboxylated polystyrene nanoparticles. (A) Fraction of nanoparticle objects colocalized with lysosome objects in individual cells (colors) and averaged over cells (black). (B) Fit of eqn (2) to the fraction of nanoparticle objects colocalized with lysosome objects in individual cells (colored) and averaged over all cells (black). Averages show the mean and standard error over 12 individual cells. (C) Three example fits of eqn (2) (solid line) to fraction of nanoparticle objects colocalized with lysosome objects (symbols) in individual cells. (D) Fraction of nanoparticle objects that ultimately end up colocalized with lysosome objects (fl) from the fits to individual cells shown in B. (E) Departure time of nanoparticle objects from the membrane (τ) from the fits shown in B. (F) Nanoparticle object arrival time to lysosomes (τl) calculated from the results shown in D and E. Error bars in panel D and E are uncertainties from the fit, while error bars in panel F are uncertainties propagated from those of fl and τ using Gauss’ formula. Additional results for all individual cells are shown in Fig. S5.† |
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Fig. 4 Fitting a kinetic model to data for HeLa Kyoto and HeLa cells exposed to 40, 100 and 200 nm carboxylated polystyrene nanoparticles. HeLa and HeLa Kyoto cells were exposed to 40, 100 and 200 nm carboxylated polystyrene nanoparticles and stained with LysoTracker, the cells were “chased” and nanoparticle colocalization with lysosomes was calculated (see Experimental section for details). Averages show the mean and standard error over various numbers of individual cells. (A) Average fraction of nanoparticle objects colocalized with lysosome objects: 40 (light purple), 100 (purple), 200 nm (dark purple) in HeLa Kyoto cells and corresponding model fits (dashed lines) (mean ± SE (n = 8–16)). (B) Average fraction of nanoparticle objects colocalized with lysosome objects: 40 (light blue), 100 (blue), 200 nm (dark blue) in HeLa cells (mean ± SE (n = 6–8)) and corresponding model fits (dashed lines). (C) Average fraction of nanoparticle objects colocalized with lysosome objects in HeLa Kyoto (purple circles) and HeLa (blue squares) cells exposed to 100 nm nanoparticles. (D) Fraction of nanoparticle objects that ultimately end up colocalized with lysosome objects fl extracted from the fits to averaged HeLa Kyoto (purple) and HeLa (blue) kinetic profiles shown in A and B. (E) Departure time of nanoparticle objects from the membrane (τ) extracted from the fits to averaged HeLa Kyoto (purple) and HeLa (blue) cell results shown in A and B. (F) Nanoparticle object arrival time to lysosomes (τl) calculated from the results shown in D and E. Error bars in panel D and E are uncertainties from the fit, while error bars in panel F are uncertainties propagated from those of fl and τ using Gauss’ formula. The results for all individual cells are shown in Fig. S4–S9.† |
Here k is the rate constant for the departure of nanoparticles from the membrane via all potential processes, while kl is the rate constant for a nanoparticle at the membrane arriving to a lysosome. We expected these two rate constants to be different, as not all nanoparticles at the membrane arrived to the lysosomes (Fig. 3A).
Given that cells were exposed to a “pulse” of nanoparticles, we made the idealized assumption that at t = 0 all nanoparticles were at the membrane and none in the lysosomes. Under this assumption, the solution to eqn (1) can be written as
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The left-hand side of this equation is the fraction of nanoparticles in the lysosomes as a function of time (Fig. 3A). It is characterized in terms of two parameters, namely fl, which is the fraction of nanoparticles that ultimately end up in the lysosomes (fl = kl/k) and τ, which is the typical time it takes nanoparticles to leave the membrane (τ = k−1). Thus, in this simple model the time-dependence is given at membrane level (in terms of τ or k) rather than along the endolysosomal pathway (in terms of kl). Fitting this simple model (eqn (2)) to data demonstrated that this model provides a good description of the data (Fig. 3A–C; and Fig. S5†) and it does so not only for the kinetics averaged over all cells, but also for individual cells.
From the fits, we also found the fraction of nanoparticles that ultimately end up in the lysosomes (fl; Fig. 3D) and the typical membrane departure time (τ; Fig. 3E). Compared to the latter, the more interesting parameter to describe the accumulation of the particles in lysosomes is the typical lysosome arrival time (τl = kl−1), which we can also calculate (τl = τ/fl). The typical lysosome arrival time τl (Fig. 3F) generally demonstrated the same trend as the membrane departure time (Fig. 3E). We also observed that the fraction of nanoparticles accumulating in the lysosomes, while certainly variable, was at least relatively uniform over the investigated cells (Fig. 3D). In contrast, the typical membrane departure (τ; Fig. 3E) and lysosomal arrival times (τl; Fig. 3F) were highly variable.
We note that we characterized the kinetics with a single transport step from membrane to lysosome. In a more extended model one could include further steps, for example, transport from membrane to early endosomes, from early endosomes to late endosomes and from late endosomes to lysosomes.31 In such an extended model, our current description would be an approximation. However, this simpler description was sufficient to describe the acquired data.
We then fitted the model to data averaged over all cells for each dataset. In agreement with the observed kinetic profiles (Fig. 4A–C), the fraction of nanoparticles that ultimately ended up in the lysosomes extracted from the applied model (fl) varied as a function of nanoparticle size in both cell lines (Fig. 4D). We stress that the fraction of nanoparticles that ultimately end up in the lysosomes is what one expects to have at the plateau, when the kinetic process is over. The majority of the kinetic curves in Fig. 4A–C have indeed plateaued, but not all. Specifically, we observe that the 100 nm particles in HeLa cells had not yet fully accumulated in the lysosomes when imaging was stopped (Fig. 4C). Nevertheless, the model allows us to extract the value we expect to observe at the plateau.
A variation was also observed for the membrane departure time (τ; Fig. 4E) and arrival time to the lysosomes (τl; Fig. 4F) for the different sizes. It would be interesting to study in more detail how the intracellular trafficking kinetics (membrane departure time and arrival time to the lysosomes) and the fraction of nanoparticles arriving into the lysosomes vary when cells are exposed to different amounts of nanoparticles. This could allow determining – for instance – whether uptake or intracellular trafficking compartments saturate and how the kinetics may be affected. The proposed methods and modelling can be used also to answer this kind of questions.
When comparing the results for the different nanoparticle sizes, the most prominent result appear to be that in HeLa cells, the 100 nm nanoparticles both leave the membrane and arrive in lysosomes far slower than the other sizes (Fig. 4E and F; blue). It is known that different uptake pathways sort the internalized cargoes into different early compartments, presumably with concomitant differences in intracellular processing kinetics, and then, in most cases, they converge to the lysosomes.11 Since it is likely that the nanoparticles of different sizes are internalized by cells via different mechanisms, this could be the underlying reason for the different kinetics. These results further illustrate how important it is for successful nanomedicine design to understand the details of nanoparticle uptake and intracellular trafficking. By changing nanomedicine design one expects not only uptake efficiency to be affected, but also the kinetics of intracellular trafficking and arrival to the lysosomes of the internalized material. However, the slower kinetics for the 100 nm nanoparticles could not be confirmed in HeLa Kyoto cells, where, if anything, there is an increase in both the departure and lysosomal arrival time with size (Fig. 4E and F; purple). It would be interesting to determine whether this comes from a different uptake mechanism in the two cell lines, as observed for instance for EGFR,23 or only a difference in the kinetics of these events for this particular nanoparticle size and type.
When comparing the results obtained in HeLa and HeLa Kyoto cells, no major differences were observed, with the noted exception of the 100 nm nanoparticles. However lysosomal colocalization seemed slightly higher in HeLa Kyoto, especially for the smaller 40 nm nanoparticles (Fig. 4D). We attempted to correlate the values of the kinetic parameters of individual cells with some basic characteristics of the cells. However, we could not find any obvious correlation between the fraction of colocalized nanoparticle objects, departure or arrival times and the amount of detected objects, cell volume and cell area (Fig. S4–9†).
In this context, we stress that we were able to fit the simple model to the kinetic data for individual cells (Fig. 3B and C, Fig. S4–S9†) rather than data averaged over cells. This allows extracting information on individual cell variability, another important aspect to consider. Such efforts would underpin a more detailed understanding of these processes that cannot be achieved at an average cell level. Indeed, average kinetic curves (Fig. 4A–C) implicitly hide a significant variability at the single-cell level, perhaps best appreciated by examining cell-to-cell variability in kinetic parameters (Fig. 3D–F and Fig. S4–S9† with the results on all individual cells). Given the significant effort in measuring the kinetics in individual cells in the way currently done (here or elsewhere),9,16,32–35 development of new methods with higher throughput is warranted. This could be achieved for instance by translating the approaches presented here to high content analysis, a high-throughput imaging method which is finding increasing applications in the nanomedicine field, including first examples of intracellular trafficking studies.36,37
We also note that even at the average cell level there are two conceivable averaging procedures: one may fit to the kinetic curves averaged over cells (Fig. 4A–C) or one may fit to kinetic curves of individual cells (Fig. 3B and C, Fig. S4–S9†) and subsequently average the resulting fitting parameters over cells. We performed both procedures, showing that overall the values and trends were comparable, but that the kinetic parameters extracted from averaged kinetic curves differ numerically from those extracted from individual cells (Fig. 4D–F and Fig. S10†). This is hardly surprising, but is nevertheless something to keep in mind when interpreting averaged kinetic curves.
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Fig. 5 Nanoparticle colocalization with early compartments (labelled by clathrin, caveolin-1, Rab5 and LysoTracker. (A) Averaged fraction of nanoparticle objects colocalized with clathrin (red) (mean ± SE (n = 20)), caveolin-1 (purple) (n = 8), Rab5 (green) (n = 5) and lysosome objects (blue) (n = 12) in HeLa Kyoto cells exposed for 10 min to 300 μg ml−1 100 nm carboxylated polystyrene nanoparticles and then “chased”. (B) Confocal image of a HeLa Kyoto cell expressing fluorescently labelled clathrin (mRFP-Clc, red) with LysoTracker stained lysosomes (green) 6 h after exposure for 5 min to 75 μg ml−1 of 40 nm carboxylated polystyrene nanoparticles (blue). The results for all individual cells are shown in Fig. S11.† |
Nanoparticles were observed to populate all these compartments within the first few hours, later reaching an apparent plateau at time scales comparable to those observed for arrival to the lysosomes (Fig. 5A and; Fig. S11† for the results in all individual cells. Movie S4† shows an example of a cell expressing labelled clathrin). This is a known limitation when using similar fluorescent constructs.38 With these constructs, the total concentration of the protein of interest can become much larger than its endogenous level and overexpressed markers are transported into the lysosomes, as indeed confirmed by co-staining (Fig. 5B and Movie S4†).
To avoid such complications, it is preferable to ensure that the protein of interest is expressed from its genomic locus under its native promoter, for instance using CRISPR/Cas9 genome editing.39,40 To overcome this limitation, we restricted the colocalization analysis to the objects located at the cell edges by manually removing in 3D the interior of the cells where most lysosomes are found (Fig. 6A and B for HeLa Kyoto cells expressing labelled clathrin exposed to 100 nm nanoparticles; see extended Experimental section in ESI for details†). The fraction of nanoparticles colocalized with clathrin now remained stable over 6 h at around 20% (Fig. 6C and Fig. S12† for the results in all individual cells).
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Fig. 6 Overcoming overexpression limits when quantifying colocalization between nanoparticles and labelled endocytic proteins. (A) Example of an xy view of the 3D z-stack image of a HeLa Kyoto cell expressing fluorescently labelled clathrin (mRFP-Clc, red) and exposed for 10 min to 300 μg ml−1 of 100 nm nanoparticles (green). (B) The same cell as in A after removing the inner volume to restrict analysis to the cell borders. (C) Averaged fraction of nanoparticle objects colocalized with clathrin in “whole” HeLa Kyoto cells (solid line) exposed to 100 nm nanoparticles and “cell edges” region (dashed line) (mean ± SE (n = 20)). (D) Averaged fraction of colocalized nanoparticle objects at cell edges in HeLa Kyoto cells expressing fluorescently labelled Rab5 (green) or clathrin (pink) after exposure for 5 min to 75 μg ml−1 of 40 nm nanoparticles; and cells expressing fluorescently labelled Rab5 (blue) and clathrin (purple), after exposure for 10 min to 300 μg ml−1 of 100 nm nanoparticles (mean ± SE (n = 6–9)). Data in panel D are based on images acquired with a DeltaVision microscope (see Experimental section for details). The results for all individual cells are shown in Fig. S12.† |
Even when starting imaging earlier after addition of the nanoparticles and sampling every 10 min instead of 20 min, we did not observe a sharp transition of nanoparticles through these early compartments, but rather a stable colocalization at around 20% (Fig. 6D and Fig. S12† for the results in all individual cells). Similar results were also obtained for the 40 nm nanoparticles and cells expressing fluorescently labelled clathrin and Rab5 (Fig. 6D), suggesting that some nanoparticles are still present in these compartments even two hours after exposure. Additionally, movies acquired at faster rate in the first minutes of chase clearly showed some colocalization events with all of the markers tested, including clathrin, caveolin-1, pMyrPalm to follow macropinocytosis, and Rab5 and EEA1 for early endosomes (Movies S5–9† show an overview of typical events in labelled cells exposed to nanoparticles), suggesting that some nanoparticles do transit all of these compartments. A closer inspection of the images recorded also showed that events of colocalization of nanoparticles with these early endocytic markers could be observed both at early (∼15 min) “chase” time, but also at very late “chase” times (∼2.5 h). Some of these events may be related to technical issues, such as nanoparticles still present in the extracellular medium or stuck on the glass desorbing and becoming available for uptake at much later “chase” times. Additionally, some lysosomes are present also at cell edges and thus part of these colocalized objects may still be in the lysosomes. Nevertheless, similar to what was observed for nanoparticle departure time from the membrane or arrival time to the lysosomes, these late colocalization events may also suggest a very heterogeneous behaviour in the uptake events within a cell, with some nanoparticles stuck on the membrane for long time before their actual internalization, as indeed observed in the recorded movies.
The low colocalization observed with multiple endocytic compartments (Fig. 6C and D for clathrin and Movies S5–9† for all markers investigated) may also suggest that within the same cell nanoparticles may be internalized via different pathways. Other studies have reported similar observations.1,9,16,32,41,42 If multiple pathways are present, the source of this variability should be determined, as well as how cells decide which pathway to activate for each individual nanoparticle.
Footnote |
† Electronic supplementary information (ESI) available: Extended experimental methods, additional results of nanoparticle characterization and colocalization analysis in individual cells for all cells, nanoparticles and marker tested, movies of live cells expressing the different markers and exposed to nanoparticles. See DOI: 10.1039/d1nr00901j |
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