Protein Counting in Single Cancer Cells - Analytical Chemistry (ACS

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Protein Counting in Single Cancer Cells Stephanie M. Schubert, Stephanie R. Walter, Mael Manesse, and David R. Walt Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b00146 • Publication Date (Web): 26 Jan 2016 Downloaded from http://pubs.acs.org on January 28, 2016

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Analytical Chemistry

Protein Counting in Single Cancer Cells

Stephanie M. Schubert,† Stephanie R. Walter,† Mael Manesse,2 and David R. Walt*

Department of Chemistry, Tufts University, Medford, MA, 02144, USA †

These authors contributed equally to this work

Abstract The cell is the basic unit of biology and protein expression drives cellular function. Tracking protein expression in single cells enables the study of cellular pathways and behavior, but requires methodologies sensitive enough to detect low numbers of protein molecules with a wide dynamic range to distinguish unique cells and quantify population distributions. This study presents an ultrasensitive and automated approach for quantifying phenotypic responses with single cell resolution using single molecule array (SiMoA) technology. We demonstrate how prostate specific antigen (PSA) expression varies over several orders of magnitude between single prostate cancer cells, and how PSA expression shifts with genetic drift. Single cell SiMoA introduces a straightforward process that is capable of detecting both high and low protein expression levels. This technique could be useful for understanding fundamental biology and may eventually enable both earlier disease detection and targeted therapy.

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Introduction Cell development and activity are dictated by protein expression. Changes in protein concentration can affect the cell phenotype, resulting in dramatic consequences for processes such as cell growth, metabolism, and disease progression.1-3 There have been numerous single cell studies that measure mRNA using the transcriptome as a surrogate for the proteome.4-6 While these measurements are useful, others have shown that the amount of mRNA does not correlate with the amount of protein expressed.7-8 The genome provides a map for protein synthesis, but knowing the genome or the transcriptome does not directly correlate with knowing the proteome due to the stochastic nature of biological processes and other factors, such as the cellular microenvironment.7, 9 In order to truly understand the complexities of many biological processes, protein expression must be characterized at the single cell level. It is well known that the protein expression of a specific gene varies from cell to cell.10 Studying protein expression at the single cell level can yield insight regarding cellular functions and pathways, enabling the study of cell-to-cell variations and stochasticity.11-12 However, often studies of cellular biochemistry are based on bulk measurements of many cells and such ensemble experiments can only yield averages that may not be indicative of the actual population distributions present at the single cell level.13-14 Recent advances15 have successfully overcome experimental limitations in sensitivities to detect genetic,7,

16-17

proteomic,3,

11, 18-23

and

metabolomic2, 24 expressions at the single cell level. Flow cytometry in particular has contributed significantly to the field of single cell analysis.19,

25-28

Flow cytometry is capable of high

throughput (up to 1000 cells per second) and extensive multiplexed detection of membranebound and intracellular protein targets when coupled with mass spectroscopy.25, 29 In addition to

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cytometry approaches, several groups have developed methods for absolute quantification based on single molecule protein detection at the single cell level.11-12, 21, 23, 30 In this paper, we describe an alternative protein counting method using single molecule arrays (SiMoA) for quantitative single cell analysis, which compliments previous approaches and provides unique benefits in terms of sensitivity, dynamic range, sample considerations, and ease of implementation. Previous work using serum and other biofluids has shown that SiMoA can: dramatically improve detection limits compared to traditional ELISA,31 exhibit a wide dynamic range,32 enable multiplexed protein analysis,33 and increase throughput and reliability with automation,34-35 potentially making SiMoA an attractive platform for single cell protein studies. SiMoA can be performed on an unrestricted pool of native protein targets including intracellular, extracellular, and secreted targets without the need for additional amplification steps, external stimulation to enhance target production, or genetic engineering, all of which can introduce bias or alter the system.36-37 Finally, due to the commercial availability of SiMoA technology, assays are fully automated – thereby reducing human error – and can be easily transferred between laboratories and translated into a clinical setting where single cell analysis and serum-based tests could be performed simultaneously on the same instrument. Here, we demonstrate that SiMoA technology can be employed to fully quantify protein expression in single prostate cancer cells. In this proof-of-concept study, we demonstrate the quantification of intracellular PSA in two related prostate cancer cell lines: a low passage LNCaP cell line (LNCaPA) and an over sub-cultured LNCaP cell line that has undergone genetic drift (LNCaPB). These two LNCaP cell lines represent models for high and low protein expression, both of which are easily detected using SiMoA and require minimal sample preparation. Previous research has demonstrated that highly cultured LNCaP cells secrete significantly altered



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concentrations of PSA.38 Although PSA is a secreted protein, we opted to quantify intracellular PSA content to directly access information related to protein synthesis, to reduce any conflation with the secretory pathway, and to highlight SiMoA as a method that can be employed for interrogating cytosomal components. Using SiMoA, we measure the impact of genetic drift on protein expression at the single cell level and show that LNCaPB cells have significantly depressed PSA expression compared to LNCaPA. These results indicate that genetic instability in cancer cells affect protein expression, and by extension, cancer progression, and also highlight the necessity of cell culture authentication. Importantly, we establish SiMoA as a unique and facile approach to count protein molecules with single cell resolution to reveal unbiased phenotypic information.

Experimental Procedures Materials. The SiMoA HD-1 Analyzer, SiMoA consumables, and PSA assay kits (ref 100683) were purchased from Quanterix Corporation. The PSA assay kit contains premade magnetic PSA capture beads, biotinylated PSA detection antibody, streptavidin-β-galactosidase (SBG), and resorufin β-D-galactopyranoside (RGP). Free PSA antigen (J63000, 96% free, MW = 30 kDa) was purchased from BiosPacific and diluted in 1% BSA in 1x PBS for calibration standards.

Cell Culture and Isolation. LNCaPA cells were obtained from ATCC (CRL-1740). LNCaPB cells were generously donated by the Kuperwasser lab (Tufts University School of Medicine). All cells were cultured in RPMI-1640 medium (A10491-01, Life Technologies) with 10% fetal bovine serum (26140-079, Life Technologies). Cultures were incubated at 37°C with 5% CO2.


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Medium replacement was carried out two to three times per week in a SterilGARD III Advance biosafety cabinet (SG403, The Baker Company). To isolate cells, culture plates were rinsed with 5 mL DPBS (14190-144, Life Technologies) and 3 mL of trypsin-EDTA (30-2101, ATCC) was added for 4-5 minutes then pipette mixed with 7 mL of complete growth medium to inhibit trypsin and suspend the cells. Cell suspension was centrifuged for 5 min at 130 g. Supernatant was aspirated, then the cell pellet was resuspended in complete growth medium. Cells were stained with Trypan Blue solution (15250-061, Life Technologies), washed 3x in DPBS, counted, and diluted to a concentration of 2 x 103 cells/mL in DPBS. Trypan Blue was used to identify dead cells and eliminate them from the isolation process. Cells were isolated by pipetting 1 µL of the washed cell solution into the cap of a flat, optically clear PCR tube and counting the number of cells contained in the droplet using an inverted microscope. Samples containing only one cell, as validated by eye, were selected for single cell analysis and stored individually in PCR tubes. Cells were isolated as quickly as possible following washing steps to reduce any secreted PSA in the bulk cell solution. Representative images of isolated cells are provided in the Supplementary Information (Supplementary Fig. 1). Isolated cells were lysed and analyzed the same day or stored in PCR tubes at -20°C until use. Since PSA is a secreted protein, the following controls were made to account for any PSA that may have been secreted into the cell solution after the washing steps. Controls were made by preparing a cell solution of 2 × 103 cells/mL which was left at room temperature for 30 minutes, centrifuging the solution for 5 minutes at 150 rcf to pellet the cells, and removing 1 uL of the supernatant to analyze via SiMoA under the same conditions employed for cell samples.



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For bulk experiments, low cell number samples were prepared via serial dilutions. Cell suspensions were washed, counted with a hemocytometer, and diluted to concentrations of 1 × 104, 5 × 103, 2.5 × 103, and 1 × 103 cells/mL.

Single Molecule Array Analysis. Isolated single cells were lysed by adding 64 µL Lysis Buffer 17 (895943, R&D Systems) to the PCR tube containing the cell sample. For dilution-based bulk samples containing low numbers of cells, 10 µL from each prepared concentration (corresponding to approximately 100, 50, 25, or 10 cells) was lysed with 55 µL Lysis Buffer 17. Cell lysates were then transferred to a 96-well PCR plate (10011-228, VWR) and diluted with 75 µL diluent (1% bovine serum albumin in 1x PBS) to obtain a total sample volume of 140 µL per single cell or cell dilution sample. All cell samples and assay reagents (capture beads, detection antibody, SBG, and RGP) were loaded into the appropriate reagent bays in the HD-1 analyzer. The SiMoA HD-1 Analyzer was employed for all PSA assays. The HD-1 analyzer is a fully automated system for digital ELISA that carries out all incubation, washing, and imaging steps.34-35 Complete descriptions of the process and benefits of digital ELISA and SiMoA technology are available in the literature.31, 34, 39 Briefly, the three-step PSA assay involves a 15 minute incubation of the PSA capture beads with the cell lysate sample, a 5 minute detection antibody incubation, and a 5 minute SBG incubation. Wash cycles are carried out after each incubation step. Finally, beads are exposed to RGP, loaded into individual wells within the microarrays contained on SiMoA Discs (100227), sealed with oil, and imaged. Wells that contain a bead and increase in fluorescence intensity between the first and last image are considered active wells containing the full enzyme-labeled immunocomplex. The number of average enzymes per bead (AEB) is calculated, based on the number of active wells and the total number


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of beads, using Poisson statistics and the digital or analog methods previously described.32 PSA calibration curves were fit with a 4-parameter logistic weighted regression to determine protein concentrations in the samples from measured AEB values. A representative calibration curve is provided in the Supplementary Information (Supplementary Fig. 2).

Results Cell line verification. LNCaPA was purchased directly from ATCC, while LNCaPB was obtained through a collaborator. The authenticity of LNCaPA was certified by ATCC upon purchase. Short tandem repeat (STR) profiling of LNCaPB was carried out using the Promega Cell Line Authentication Sample Collection Kit. The STR profiles, compared in Supplementary Table 1, show that LNCaPB exhibits an 88% match to LNCaPA. The apparent 12% genetic drift in LNCaPB is attributed to the extensive sub-culturing of this cell line and the genetic instability of cancer cells.38 Herein, we quantify how a 12% genetic drift in highly sub-cultured cells alters the PSA expression from single cells.

SiMoA analysis and comparison of PSA in single LNCaPA and LNCaPB cells. SiMoA was employed to determine PSA expression in single LNCaP cells. A representative PSA calibration curve is shown in Supplementary Figure 2. The limit of detection (LOD) for PSA in this assay was 0.0043 ± 0.0022 pg/mL, equating to ~12,000 PSA molecules in 140 µL (i.e. the volume used to prepare cell lysate samples). Cells were thoroughly washed to remove any secreted PSA in the bulk solution, manually isolated, lysed, and measured using the SiMoA HD-1 analyzer as described in the Experimental section. Figure 1 illustrates a schematic of the single cell SiMoA process.



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The PSA content of single LNCaPA (n=124) and single LNCaPB (n=68) cells were measured using the SiMoA platform. A dot plot of the number of PSA molecules measured in each single cell sample is shown in Figure 2. Figure 2 illustrates the range in the number of PSA molecules detected in individual cells from both the LNCaPA and LNCaPB cell lines. In both cell lines, the number of PSA molecules spans over two orders of magnitude, reflecting the large degree of cell-to-cell variability within the same homogeneous population. The number of PSA molecules observed in single LNCaPA cells ranged from 4.34 × 104 – 1.52 × 107, with a mean of 2.15 × 106 molecules per cell. In contrast, the LNCaPB cells ranged from 7.71 × 103-1.19 × 106 molecules, with a mean of 7.04 × 104 molecules per cell. These numbers equate to an average PSA concentration per cell of 1.79 µM (53.7 µg/mL) and 0.0585 µM (1.76 µg/mL) for LNCaPA and LNCaPB, respectfully, assuming a cell volume of 2 pL. A previous study by Pinzani and coworkers using immuno-qPCR determined the median number of PSA molecules per LNCaP cell to be approximately 3.3 × 106.40 Our results from the LNCaPA cell line (median = 1.3 × 106 PSA molecules) are similar to those observed by immunoqPCR, as one might expect since both cell lines were obtained directly from ATCC. The slightly lower median value obtained in our measurements could be explained by our focus on intracellular PSA as opposed to total (intracellular and secreted) PSA. In comparison, median PSA concentration for our single cell measurements of the LNCaPB cell line (median = 3.5 × 104 PSA molecules) is significantly lower than what Pinzani et al. determined, which can be attributed to the extensive subculturing and genetic drift of this cell line. Figure 2 also shows the histogram analysis comparing the distribution of PSA expression across both cell lines. Interestingly, we observe two distinct distributions of PSA expression with minimal overlap between the LNCaPA and LNCaPB cell lines. The vast difference in PSA


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content between LNCaPA and LNCaPB cell lines illustrates how crucial cell line verification is and how genetic drift can greatly alter cellular biology and experimental results. Although the two cell lines are closely related, we measure over a 30-fold difference in PSA protein expression. The sensitivity of SiMoA allows for single molecule protein counting from single cells over a wide range of protein concentrations with minimum sample handling. As described in the Experimental Section, controls were carried out to estimate PSA secretion from a cell solution as a conservative gauge of any background PSA that could accrue in the supernatant while cell samples were manually isolated. Since single cells were isolated in 1 µL droplets from a solution containing 2 × 103 cells/mL, we wanted to mimic this process to measure how much PSA would be secreted if cells were not isolated quickly. We prepared a 1 mL solution of 2 × 103 cells/mL for both cell lines and allowed PSA secretion to occur over 30 minutes. SiMoA analysis was performed on 1 µL aliquots of the supernatant, thus representing the number of PSA molecules/µL secreted by 2 × 103 cells in 30 minutes. On average, 1 uL of the supernatant contained 1.2 × 105 ± 7 × 104 PSA molecules for the LNCaPA cell line (n=16), and 1 × 104 ± 1 × 104 molecules for the LNCaPB cell line (n=11). These levels overlap with the lower range of PSA measured from single cells for LNCaPA and LNCaPB, indicating that detectable secretion of PSA into the supernatant may occur if cells are not isolated quickly after washing steps are carried out. In our experiments, all efforts were made to minimize background PSA in the supernatant by isolating cells immediately following washing steps.

SiMoA Analysis of PSA in Bulk Cells. For comparison with our single cell analysis, we analyzed low numbers of LNCaP cells to obtain ensemble averages of PSA. In these bulk experiments, cell suspensions were washed, counted, and diluted via serial dilutions to



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concentrations of 1x104, 5x103, 2.5x103, and 1x103 cells/mL; 10 µL from each prepared concentration (corresponding to 100, 50, 25, and 10 cells, respectively) was lysed and analyzed using SiMoA. Figure 3 shows the average PSA concentration observed for eight replicates containing low cell numbers of LNCaPA and LNCaPB cells. As expected, PSA concentration increases linearly with increasing cell numbers for both LNCaPA and LNCaPB (Supplementary Fig. 3. R2 = 0.972 and 0.992, respectively). Similar to our findings from single cells, we observe a 30-fold difference in the magnitude of PSA expression between LNCaPA and LNCaPB cells. Despite this agreement, we find that dilution-based measurements actually underestimate PSA expression compared to single cell studies. For dilution-based measurements, we observed an average value of 3.75 × 107 PSA molecules for 100 LNCaPA cells compared to 1.27 × 106 PSA molecules in 100 LNCaPB cells. Based on these values, one would extrapolate that single LNCaPA cells contain 3.75 × 105 PSA molecules on average while single LNCaPB cells contain an average of 1.27 × 104 molecules. However, single cell analysis (vide supra) returned average PSA values over five times higher for both cell lines. The likely cause of this discrepancy when measuring protein concentrations using dilution methodologies is that initial cell counts using hemocytometers may be inaccurate. In addition, cells can stick to tubes and pipettes used in the dilution process, altering the cell count and introducing significant error when cell numbers are low. These experiments further illustrate the value of true single cell protein counts compared to concentrations extrapolated from bulk measurements.

Discussion The work reported here focuses on advancing the important field of single molecule/single cell proteomics. Using an automated SiMoA platform, we demonstrate protein


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quantification from single cells using a single molecule counting technology that can be easily implemented without genetic engineering, fluorescent labeling, complicated microfluidic platforms, or rigorous and time consuming data analysis methodologies. To showcase the power of single cell SiMoA technology, we used both high and low passage prostate cancer cell lines of the same origin. The LNCaPA and LNCaPB cell lines exhibited a 12% genetic difference according to STR profiling and represent high and low PSA-expressing traits, respectively. The variations in PSA content between the LNCaPA and LNCaPB cells were measured at the single cell level and the averages between these cell lines were found to differ by 30-fold. The substantial decline in PSA production in the LNCaPB cell line due to this genetic variation has significant implications for the need to standardize cell lines across scientific studies. Our work demonstrates the range and sensitivity of SiMoA with its capability to count low numbers of protein molecules from individual cells. We have demonstrated that SiMoA can be applied to study molecules that are not highly abundant or that are down regulated within single cells. Our technique employs a simple isolation scheme that requires only a standard microscope and a commercially available instrument, making it straightforward to translate the approach to other cell lines and proteins. In addition, since SiMoA is an ELISA based technique, there is no risk of bias arising from an amplification step, as is the case with techniques such as immuno-PCR. Compared to cytometry-based approaches, single cell SiMoA could be further improved in terms of throughput. For the PSA assay used in this experiment, we could analyze 100 single cell samples in under 3 hours with a sensitivity of 0.004 pg/mL (or 12,000 PSA molecules per 140 µL cell lysate). It should be noted that the experimental time for the assay does not increase linearly with the number of samples because all samples are run simultaneously. Recent reports



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have indicated that the SiMoA HD-1 Analyzer throughput can be further improved to process up to 66 samples/hour.35 One limiting factor for high throughput single cell analysis is the manual isolation method that was employed here, which requires an additional ~1 hour of sample preparation prior to experiment. We anticipate that future applications of single cell SiMoA could be combined with high throughput cell isolation techniques, such as ensemble-decision aliquot ranking (eDAR),41-42 to concentrate and analyze rare cells. Since many pathways involve cascades of molecular events, monitoring multiple proteins simultaneously is necessary to gain a complete picture. Thus, multiplexed analysis over a wide dynamic range is particularly beneficial for single cell protein studies. Although beyond the scope of this work, SiMoA analysis can easily be multiplexed to analyze multiple proteins for cellular dynamics and correlation studies within individual cells. Single cell SiMoA, although not applicable to live cells, enables single molecule based protein quantification and presents many exciting applications. Molecular mechanisms, pathways, and cell heterogeneity at the single cell level can be studied to potentially enable both earlier disease detection and targeted therapy by identifying rare cells in a population. For example, one might use SiMoA to analyze differences between circulating tumor cells (CTCs) to determine if a subpopulation of particularly aggressive cells existed in a population of indolent cells. Proteomic analysis of CTCs from clinical samples may be useful for early cancer detection, to identify the CTC phenotype and tissue of origin, and to guide therapy. A combination of serum-based and cell-based detection afforded by SiMoA could be highly advantageous in diagnostics applications. In conclusion, the work described here represents a sensitive and robust system for the quantification of protein molecules from single cells using single molecule counting. This technique provides an important new tool for the field of single cell analysis.


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Author Contributions SMS, MM, and DRW conceived the idea. SMS, SRW, and MM designed and carried out the experiments. SRW and SMS analyzed the data and wrote the manuscript. SMS, SRW, and DRW reviewed, revised, and discussed the data and manuscript.

Supporting Information Contains additional information, including a STR Profile Comparison of LNCaPA and LNCaPB cells, representative bright field images of a single LNCaPB cell inside a 1 µL droplet, SiMoA calibration curve for PSA, and a linear fit of PSA content in low cell counts of LNCaPA and LNCaPB cells.



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Acknowledgments We kindly thank Eleanor S. Johnson and Daniel T. Chiu at the University of Washington for their guidance and assistance with single cell isolation techniques. We also thank the Kuperwasser lab from Tufts University School of Medicine for their generous gift of the LNCaP cells (LNCaPB). This work was supported by the US Department of Defense BC100510 (W81XWH-11-1-0814).

Conflict of Interest Disclosure David R. Walt is the scientific founder and a board member of Quanterix, Corp. All remaining contributing authors declare no competing financial interests.


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Figure Legends Figure 1. Experimental scheme for single cell SiMoA analysis. Cells were isolated manually by visual inspection under a microscope. The cells were then lysed and loaded into the SiMoA HD1 analyzer, which performed the subsequent incubations with capture beads, detection antibody, and enzyme conjugate. After forming the immunocomplex on the beads, enzyme substrate was added, the beads were loaded into an array of wells, and the wells were sealed for imaging.

Figure 2. (Left) Dot plot showing the number of molecules detected in single LNCaPA and LNCaPB cells. Each dot represents a single cell. The average of each population is represented with a bar. (Right) Histograms illustrating the log-normal distribution of PSA molecules in individual LNCaPA (grey) and LNCaPB (blue) cells.

Figure 3. Plot of PSA concentration at low cell numbers of both LNCaPA (black) and LNCaPB (blue) cells. Cell numbers (10, 25, 50, and 100 cells) were estimated via serial dilution.



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Figures

Figure 1.

Figure 2.


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Figure 3.



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Protein Counting in Single Cancer Cells - Analytical Chemistry (ACS

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