Process Analytical Utility of Raman Microspectroscopy in the Directed

Oct 6, 2015 - Continued advances toward cell-based therapies for human disease generate a growing need for unbiased and label-free monitoring of ...
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The process analytical utility of Raman microspectroscopy in the directed differentiation of human pancreatic insulin-positive cells Stanislav O. Konorov, H. Georg Schulze, Blair K. Gage, Timothy J. Kieffer, James M. Piret, Michael W. Blades, and Robin F. B. Turner Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b03295 • Publication Date (Web): 06 Oct 2015 Downloaded from http://pubs.acs.org on October 11, 2015

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The process analytical utility of Raman microspectroscopy in the directed differentiation of human pancreatic insulin-positive cells Stanislav O. Konorov,1,2§ H. Georg Schulze,1§ Blair K. Gage,3 Timothy J. Kieffer,3,4 James M. Piret,1,5 Michael W. Blades2* and Robin F. B. Turner1,2,6*

1. Michael Smith Laboratories, The University of British Columbia, 2185 East Mall, Vancouver, BC, Canada, V6T 1Z4 2. Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, BC, Canada, V6T 1Z1 3. Department of Cellular and Physiological Sciences, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC, Canada, V6T 1Z3 4. Department of Surgery, The University of British Columbia, 910 West 10th Avenue, Vancouver, BC, Canada, V5Z 4E3 5. Department of Chemical and Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BC, Canada, V6T 1Z3 6. Department of Electrical and Computer Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC, Canada, V6T 1Z4 § Equal contributions *

Corresponding Authors:

Turner, Email: [email protected], Fax: 604-822-2114 Blades, Email: [email protected], Fax: 604-822-2847

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2 Abstract Continued advances toward cell-based therapies for human disease generate a growing need for unbiased

and

label-free

monitoring

of

cellular

characteristics.

We

used

Raman

microspectroscopy to characterize four important stages in the 26-day directed differentiation of human embryonic stem cells (hESCs) to insulin-positive cells. The extent to which the cells retained spectroscopic features of pluripotent cells or developed spectroscopic features suggestive of pancreatic endocrine cells, as well as assessing the homogeneity of the cell populations at these developmental stages, were of particular interest. Such information could have implications for the utility of Raman microspectroscopy process analysis for the generation of insulin-positive cells from hESCs. Because hESC seeding density influences the subsequent pancreatic development, three different seeding density cultures were analyzed. Transcription factor and other marker analyses assessed the progress of the cells through the relevant developmental stages. Increases in the Raman protein-to-nucleic acid band ratios were observed at the final endocrine stage analyzed, but this increase was less than expected. Also, high glycogen band intensities, somewhat unexpected in pancreatic endocrine cells, suggested the presence of a substantial number of glycogen containing cells. We discuss the potential process analytical technology application of these findings and their importance for cell manufacturing.

Key words: beta-cells; insulin; definitive endoderm; pancreatic endocrine cells; human embryonic stem cells; directed differentiation; Raman microspectroscopy; process analytical technology; process analytical chemistry; glycogen

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3 Introduction Research to generate functional β-cells through the directed differentiation of human embryonic stem cells (hESCs) is driven by the need for large numbers of insulin secreting cells as part of developing a cellular transplantation therapy for diabetes.1 Protocols for directing the differentiation of hESCs into pancreatic endocrine cells have been established based on an understanding the biology of embryonic pancreas development.

2-6

Briefly, the first stage

specifies pluripotent cells to the endoderm germ layer via activation of transforming growth factor β (TGFβ) and WNT signalling. Next, the cells transition through the primitive gut tube stage to foregut endoderm in response to activation of fibroblast growth factor (FGF) and retinoic acid (RA) signalling as well as inhibition of the bone morphogenetic protein (BMP) and the sonic hedgehog (Shh) signalling pathways. Through the modulation of these and other pathways, further development follows staged induction of pancreatic progenitors and the later formation of hormone-positive cells. While the generation of definitive endoderm and later pancreatic progenitors is now a rather efficient process, further progress toward the maturation of these progenitor cells in vitro to functional glucose-responsive, insulin-secreting cells has only recently been made by two independent groups.2,3 Given that pancreatic progenitors do mature into insulin-producing β-cells in vivo,5,6 their developmental capacity has been established. This generation of fully competent β-cells in vivo has resulted in considerable efforts to scale-up the production of pancreatic progenitors for clinical trials.7,8 Whether producing pancreatic progenitors or mature β-cells in culture, eliminating the presence of pluripotent or multipotent cells that could give rise to teratomas or other neoplastic growths represents a major goal. The development of large-scale pancreatic progenitor and β-cell production processes should be accelerated by process analytical technology (PAT) if it can help ensure the uniformity, efficacy and safety of the cells generated. PAT monitoring and even feedback control of production should provide a valuable process development tool to improve biopharmaceutical manufacturing and ultimately product quality.9,10 To achieve on-line or close at-line monitoring of critical process parameters, new optical technologies have attributes that may prove to be very effective to enable non-contact analysis of live differentiating cell populations. Raman spectroscopy is a quantitative, information-rich optical technique increasingly used for the analysis of biological and pharmaceutical samples,9,11 bioprocess monitoring12,13 and quality control. It requires little or no sample processing, can be performed remotely via optical

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4 fibers, combined with microscopy, used with aqueous samples, and, depending on the excitation frequency, is also non-perturbing and suitable for use with live cells.14 However, Raman scattering is not as efficient as some other optical methods such as fluorescence.14 Because Raman spectroscopy is more information-rich but less sensitive than fluorescence spectroscopy, it provides a complementary alternative to fluorescence-based methods with the great advantage that it does not require introduction of a foreign label. Raman microspectroscopy (RM) has been used successfully to investigate a wide range of hESC responses including niche formation,15 the effects of heat and cold stress,16 passaging,17 and pluripotency induction,18 and to quantify aspects of the cell cycle19 and cellular composition.20 Of special relevance are investigations on differentiation using both fixed21 and live hESCs. 13,22,23 We investigated the potential use of RM as a process analytical technique in the assessment of insulin-positive cells generated from differentiated hESCs. To this end we characterized cells at several stages of directed development to classify cell populations based on these stages and assess the cell homogeneity. In particular, it was determined whether or not the cells retained Raman spectroscopic features of pluripotent cells and/or developed characteristics of pancreatic progenitor cells. Because the hESC seeding density affects developmental trajectories in the generation of insulin-positive cells,24 three different groups of cells, seeded at different densities, were analyzed. Parallel analyses of macroscopic heterogeneity and cellular development at various culture stages were performed and compared to the RM results. Materials and methods Stem cell culture. Undifferentiated CA1S hESCs25-27 were cultured at 37°C, 5% CO2 on growth factor reduced-Matrigel-coated (BD Biosciences, Mississauga, ON, Canada) plates under feeder-free conditions in mTeSR1 medium (STEMCELL Technologies, Vancouver, BC, Canada) and enzymatically passaged every 2 to 3 days with a split ratio of 1:5 using Accutase (STEMCELL Technologies). The cells were maintained between 20% and 80% confluence. CA1S cells were also cultured as above, but allowed to grow without passaging for 24 h, 48 h (two samples), and 96 h (two samples) before spectroscopic examination. A detailed description of the pancreatic differentiation culture protocol, as well as the culture and preparation of human islet cells for Raman spectroscopy are described in the Supporting Information (SI).

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5 Raman spectroscopy. Spectra, covering the 300 cm-1 to 1800 cm-1 range, were obtained at room temperature from each mirror using a Raman microspectroscopy system (InVia, Renishaw, Gloucestershire, UK) in StreamLine™ mode with a 50x objective lens. The gold mirror increased the Raman signal almost 4 times compared to a standard glass-bottom Petri dish because both forward and backward-scattered signals from a two-pass beam path were collected while the encapsulating glass layer was too thin to generate a significant silica background signal.31 Raman scattering, generated with 100 mW of 785-nm excitation at the sample, was collected for 20 s per spectrum (i.e., from each pixel). Approximately 200 spectra were collected from each mirror by sampling 5 to 9 cells, using raster scanning with ~1.2 µm resolution, from areas where the deposited cells appeared present as a monolayer, rather than being stacked in multiple layers. Raman bands of relevance to this work, obtained from the literature, are listed in Table S-1 in the SI.32-34 Data Processing. Matlab 7.0 (The MathWorks, Natick, MA) was used for all spectral processing and data analyses. Spectra were processed with automated methods consisting of a small window moving average-based baseline-flattening,35 coincident two-dimensional second difference cosmic ray-induced spike removal,36 and an iterative Savitzky-Golay smoothing method.37 Thereafter, aberrant spectra were removed by visual inspection. We performed principal component analyses (PCA), a method that partitions the total variance of a hyperspectral data set into subsets of size-sorted mutually orthogonal variances, on the processed data. Because some of the cells were deposited on a different substrate with a Si calibration marker (521 cm-1), this region in all spectra was zeroed before PCA to remove any substrate influence. Safety considerations. Cells used in this work are considered Risk Group 2 biological material and were handled according to Containment Level 2 procedures. All chemicals were handled according to the relevant manufacturer’s protocols and material safety data sheet instructions. Raman spectroscopy employed a Class 3b laser and the appropriate radiation safety procedures were followed. Results and discussion Pancreatic endocrine differentiation of hESCs was performed in 7 stages over the course of 26 days (Figure 1A). During this period, cultures inoculated at low, middle, and high cell

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6 densities developed sequentially from the undifferentiated pluripotent state to more mature cell types, developing a considerable heterogeneity in cell morphology (Figure 1B). Throughout differentiation, LD seeded cells had the most diverse culture appearance, including cell clusters that were observed starting at day 11 of culture. Over this same timeline, Raman spectroscopy revealed culture macromolecular compositional changes.

Figure 1 (A) Schematic of differentiation stages between undifferentiated hESCs and more developed polyhormonal pancreatic endocrine cells across 26-days of culture. (B) Culture morphologies of low, middle, and high density seeded cultures (LD: 1.3, MD: 3.6 and HD: 5.2 x 104 cells/cm2 respectively) for Days 1, 5, 11 and 26 of differentiation. Scale bar is 200 µm. (C) Mean hESC (Day 0) Raman spectrum (black traces) superimposed on mean Day 1, 5, 11 and 26 spectra from LD (blue), MD (green), and HD (red) cultures. These spectra were normalized to the composite nucleic acid band at 784 cm-1, showing progressive increases in protein- and glycogen-related bands relative to nucleic acids when exposed to a directed differentiation protocol.

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7 Figure 1C shows the mean normalized Raman spectrum for each of these cultured populations of cells. Although normalization of these Raman spectra is complicated by many factors,18,38 the spectra were normalized to the 784 cm-1 composite nucleic acid Raman bands to show relative changes. Because the Day 0 and Day 1 spectra were highly similar, the colored Day 1 traces are mostly obscured by the black Day 0 traces. From Day 1 to 26, progressive amplitude increases (relative to the nucleic acid bands), were observed for the protein-related bands (e.g., 1003 cm-1). The glycogen bands (e.g., 485 cm-1) also increased, especially after Day 11 of the differentiation. Multivariate analyses, such as PCA, are useful for extracting and correlating complex information from multiple, even subtle, changes between spectra. PCA is commonly used to discriminate between spectra from different cell types.14,18,39 PCA decomposes the total variance of the spectral data set into orthogonal sub-variances ranked in declining order of their contribution (e.g. as percentage) to the overall variance. PCA was initially carried out on the spectral data set from all of the cultures to assess to what extent Raman microspectroscopy would allow us to discriminate between the LD, MD and HD groups, and then to obtain useful information from the principal components (PCs) on the possible bases for such discrimination. It can be seen from Figure 2A that the Day 26 spectra could be separated, though not completely, from spectra at earlier stages of differentiation based on spectral scores for principal components two and three (PC2, PC3). These PCs also permitted us distinguishing to some extent between the culture groups at earlier stages of differentiation, e.g., based on PC2 and PC4 as shown in Figure 2(B - E). In Figure 2F, PC2, PC3, and PC4 for the complete set are shown with their percentage variance contributions. Because it was difficult to examine equal amounts of material per group, the overall Raman intensities between groups varied and this variation was reflected in PC1. Therefore, the PC1 variance was excluded from the overall variance contributions. Normalization was not used, for example as in Figure 1C to nucleic acids, because this would remove nucleic acid-related variances from the spectra and subsequent PCs based on these spectra. Though, in such a case, PCA can still be used for discrimination between groups, the relative contributions of macromolecular components to the PCs are distorted, compromising inferences about such contributions from these PCs.

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8 Prominent features of PC2 were that glycogen bands were generally positive while protein and DNA bands were negative. In PC3, glycogen and RNA bands were positive, while protein and DNA bands were negative. Thus, in Figure 2A spectra with high glycogen levels tended to lie to the right while spectra with high glycogen and high RNA (~811 cm-1 band) levels tended to lie towards the top. PC4, with high glycogen and protein but low nucleic acid levels, did allow discrimination between many of the density groups at every stage of differentiation (Figure 2, panels B - E). We could not discriminate between all groups at all time points using the spectral scores for these principal components as might be expected for cultures where individual cells differentiate at varied rates and also for cultures that differed based only due to 2- or 4-fold differences in initial seeding density. Nonetheless, these PCs implied that proteins, glycogen, and RNA levels were key factors permitting discrimination.

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9 Figure 2 PCA on a combined set of spectra from all cultures provided some ability to discriminate between cultures. Scores based on principal components 2 to 4 were used to reveal the extent of separation between (A) all cultures (e.g. PC2 vs. PC3) and (B - E) between density groups at different stages of development (e.g. PC2 vs. PC4). (F) Discrimination between groups appeared to be due to differently prominent glycogen-related, protein-related, and NA-related peaks in PC2, PC3, and PC4.

During non-specific,21,23 heat stress-induced,16 and niche formation-related15 hESC differentiation, increases occur in the ratio of the 757 cm-1 tryptophan to 784 cm-1 composite nucleic acids Raman bands. This “R4” protein-to-nucleic acids ratio increases with the cytoplasmic-to-nuclear content ratio in both live and fixed cells as they differentiate.23 To determine if the pancreatic directed differentiation protocol produced a trend similar to that produced by non-specific differentiation by medium containing 10% serum, we evaluated changes in R4 for each of the cell groups. Figure 3A shows the mean Raman spectrum for each of the directed differentiation groups and indicates the bands used to calculate R4. After 1, 5, 11, and 26 days of serum differentiation, the R4 was previously reported to be approximately 0.5, 0.7, and 0.9, and 1.0, respectively.21 In contrast, lesser increases were observed in this ratio for the directed differentiation, as shown in Figure 3B. This increase was significant for all Day 26 relative to Day 0 groups (p < 0.01; two tailed t-tests, Bonferroni corrected for six tests). Interestingly, R4 was uniformly lower for all groups (p < 0.01; two tailed t-tests, Bonferroni corrected for six tests) on day 1. Compared to the serum-cultured cells, the overall more modest R4 values suggested that directed pancreatic differentiation endoderm progenitor spectra were more similar to undifferentiated hESC spectra. These results are consistent with what may have been the lesser maturity of the progenitor cells in the directed pancreatic cell protocol. Specifically, Figure 3(B) shows that by day 11, the bulk of spectra in all groups had R4 values between ~0.4 and ~0.6, much lower than the ~0.9 expected for mature cells at the same time point and closer to the ~0.4 of hESCs. By day 26, R4 values had increased to between ~0.6 and ~0.8, but were still much lower than our previously proposed R4 = 1 threshold for differentiated cells.23 In fact, only the LD group had any spectra that exceeded the 0.9 R4 observed after 11 days in medium containing serum (see Figure S-2). These spectra suggest that for a few cells, non-specific differentiation was occurring in the LD group, consistent with bright-field microscopic observations of distinct multicellular aggregates interspersed within these cultures (Figure 1B). Because no other Day 11 group had spectra that met this criterion, the pancreatic differentiation protocol was apparently less restrictive in low

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10 density-seeded cultures between germ layer induction and day 11. These results are also consistent with our findings in previous studies that at the end of definitive endoderm induction more Oct4 is present in cells seeded at low density compared to cultures seeded at higher densities.24 By day 26, the number of spectra with high R4 values in all groups had increased, with the greatest number again in the LD group as visible in Figure 3(C) and Figure S-2. In this protocol the MD group was found to be the most efficient seeding density for generation of Cpeptide, as opposed to glucagon (cf. Figure 4C below). The lower MD, compared to LD, group variability (thus greater uniformity) suggested the potential utility of R4 for monitoring efficient targeted differentiation in PAT applications. We have reported that glycogen can be present in substantial amounts in live17 and fixed20 undifferentiated CA1S cells and varies considerably as a result of passaging.17 Glycogen levels are low after passaging and reach high levels later in maintenance cultures.16 Figure 4A shows that by Day 26, glycogen ratios (relative to nucleic acids) overall increased substantially relative to Day 0 (p < 0.001; two tailed t-tests, Bonferroni corrected for three tests). Glycogen ratios for CA1S cells at different stages of the growth curve were obtained by culturing cells for 24, 48, and 96 h without passaging. They are presented in Figure 4A, also as black error bars, and showed that glycogen ratios expected for hESCs had an upper limit near 0.5. However, the glycogen ratios for endocrine progenitor cells produced by this protocol were substantially above 0.5 and unlikely to have been observed in a hESC culture. Figure 4B shows the variation in spectral glycogen ratios as a function of differentiation time. The substantial increase in average glycogen ratio as well as the substantial increase in distribution of these ratios when reaching the endocrine progenitor stage (Figure 4B) coincided with the emergence of glucagon secretion in these cells (Figure 4C). Glycogen is stored as granules in cells leading to an uneven distribution in cultures with some high local glycogen concentrations.20 Even when a culture on average contains very few glycogen storing and/or glycogen utilizing cells, the presence of those relatively high local glycogen concentrations will result in some spectra with spectral intensities contributed by glycogen. As other spectra will be devoid of such contributions, glycogen-specific interspectral variations in the hyperspectral data set will exist that could, in principle, be observed in the principal components of the hyperspectral data. We found the glycogen marker band ~ 480 cm-1 (Figure 4D; red arrow) to be present in one of the principal components (PCs) of every culture.

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11 Because the ultimate objective of the differentiation protocol was to generate hormone positive endocrine cells, it was of particular interest whether insulin could be discerned in our spectra. Insulin was immunofluorescently detected in the cells by day 26, as shown in Figure 5A, and increased secretion of C-peptide in the medium (a proinsulin derivative) between days 13 and 26 (Figure 4C).

Figure 3 (A) Shown are mean Raman spectra from undifferentiated hESCs and these cells, seeded at low, medium, and high densities into a pancreatic differentiation protocol. Spectra were normalized to the composite nucleic acid band at 784 cm-1. The R4 ratio of a protein band (757 cm-1 of tryptophan) to the composite nucleic acid band (784 cm-1) has been used before as an indicator of (non-specific) differentiation.23 (B) Over 26 days of differentiation, the modest increase in the R4 differentiation marker (means and standard errors) suggested that cells remained relatively immature. (C) Histograms of the data shown in (B) summarize the general increase in R4 values and its variability upon differentiation, especially after Day 11.

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12

Figure 4 (A) Glycogen content increased relative to nucleic acids with differentiation time. After 26 days of differentiation, cells had significantly more glycogen than undifferentiated cells. (B) Glycogen ratios and their distributions increased during development, especially after reaching the foregut endoderm stage when (C) glucagon and C-peptide secretion also increased based on 24-hour static hormone secretion levels of culture media between days 13 and 26, determined with radioimmunoassays of samples from different seeding densities. (D) The glycogen marker band (~480 cm-1; solid red arrow), along with other prominent glycogen bands (dashed arrows), was also present in the principal components of Raman spectra from each of the cultures suggesting the presence of residual pluripotent cells, and/or, differentiated cells with increased glycogen levels.

Both insulin and somatostatin contain disulfide bridges (three in insulin, one in somatostatin) that produce Raman bands at ~520 cm-1 (S-S stretch) and 662 cm-1 (C-S stretch),40 but the latter overlaps with a nucleic acid band near 666 cm-1, thus the presence of insulin and somatostatin may only cause the position of the nucleic acid peak to appear shifted to lower wavenumbers.

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13 However, the 520 cm-1 band is relatively free from overlap by other bands. PCA was performed on the Day 26 HD group spectra to assess evidence of the presence of insulin and somatostatin. Hints of insulin presence41 came from the presence of a band near 520 cm-1 in the PC4 of the Day 26 HD group spectra as shown in Figure 5B. As not all cells within the Day 26 cultures were hormone positive (Figure 5A), the spectral detection of insulin was quite challenging given the relatively low abundance (6-10%)28 of insulin-positive cells and the results were inconclusive.

Figure 5 (A) Immunofluorescent detection of polyhormonal endocrine cells expressing insulin (blue), glucagon (green) and somatostatin (red) in 26-day differentiated cultures and adult human pancreatic tissue. Nuclei are counterstained with DAPI (white) and the scale bar for all four panels is 100 µm. (B) PC4 of the Day 26 HD group spectra had a disulfide peak (arrow) suggesting the presence of insulin in cells differentiated towards pancreatic endocrine cells.

Though the differentiation protocol does not produce fully mature β-cells, we also compared Day 11 and Day 26 spectra with spectra from human islet cells and performed PCA on these spectra. The results (Figure S-3) suggested that differentiating cells were acquiring some features of β-cells (e.g. increased scattering from disulfide bonds, weaker scattering from tryptophan) but were still immature, as expected. These results suggest that the various cultured cell groups can be discriminated based on their changing macromolecular compositions with proteins, nucleic acids and glycogen being of particular import. Thus, R4 differentiation status and glycogen-related information could be used to determine the differentiation stage and/or track the differentiation trajectory. If, for example, the LD group Day 26 high glycogen levels were due to the presence of hepatocytes42 and

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14 hepatocytes were the desired endpoint, then the developmental stage could be determined based on this information. This is shown in Figure 6 using scaled data from Figures 3B and 4A such that their maxima and minima were between 1 and 0, respectively. Specifically, high glycogen and comparatively high protein (R4) levels could suggest hepatocytes, while high glycogen and low protein levels could indicate the presence of hESCs. In contrast, low glycogen levels along with high R4 could suggest that a deviation from the desired hepatocyte trajectory had occurred.

Figure 6 The potential PAT utility of Raman spectroscopic monitoring is shown by examining a combination of R4 and glycogen ratios. These ratios could be used to determine the developmental stage of cells as they progress along the LD directed differentiation trajectory. Oval shows the known range of variation for glycogen ratios at day 0. Such ranges of variation, appropriately established all along the differentiation trajectory, could be used to determine if differentiation is proceeding within acceptable boundaries.

Though deploying the LD group as example here, the use of different seeding densities also helped in visualizing a broader trajectory envelope. The developmental trajectory can also be further refined by utilizing a higher temporal resolution as well as other Raman bands. For example, by using all the stages shown in Figure 1A and all 16 macromolecular ratios that change significantly upon differentiation.23 Finally, from the extended culturing of hESCs without passaging, the ranges of variation of R4 and glycogen ratios could be determined for ‘normal’ hESCs (Figure 6 ovals). Cultured hESCs with ratios falling outside these limits could therefore be considered abnormal and unacceptable for product generation. Establishing such limits, for all relevant variables, all along the desired differentiation trajectory and keeping the developmental trajectory within these limits could therefore be used to control product quality. Thus, the accuracy and precision of a developmental protocol could be evaluated. A high accuracy would mean that the desired endpoint was achieved, while a high precision would imply that a high yield and less

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15 contamination with unwanted cells were obtained. It is our view that such an approach has clear and direct relevance to PAT development suitable for therapeutic cell generation. Conclusions Raman spectroscopy was used to characterize spectra from cell cultures seeded at three different densities and subjected to a pancreatic endocrine differentiation protocol. Using PCA, cultures could be classified based on their PC scores, suggesting that successfully differentiating cultures can be distinguished from others. Based on the Raman peaks prominent in PCs, these cell populations appeared to differ primarily in protein, RNA, and glycogen content. Glycogen, which occurs in CA1S hESCs and is also commonly found in hepatocytes and myocytes, was present in all cultures, and was especially high in end-stage cultures. Thus, cultured cell populations were heterogeneous and may have contained pluripotent and/or undesirably differentiated cells. Because we could follow the developmental trajectory of differentiating cells toward pancreatic endocrine cells, and detect the emergence of unwanted characteristics, we concluded that Raman spectroscopy has process analytical potential for therapeutic cell-generating bioprocesses. Acknowledgements We gratefully acknowledge C. Sherwood (Michael Smith Laboratories, UBC, Vancouver, BC) for assistance with cell culture related work. BKG’s PhD funding was provided by the Natural Sciences and Engineering Research Council (NSERC), the Michael Smith Foundation for Health Research (MSFHR), and The University of British Columbia (UBC). Research funding was provided by NSERC, the Canadian Institutes of Health Research (CIHR), the CIHR Regenerative Medicine and Nanomedicine Initiative, the Canada Foundation for Innovation (CFI), and the British Columbia Knowledge Development Fund. Supporting Information Available This information is available free of charge via the Internet at http://pubs.acs.org/.

References (1) Gage, B. K.; Wideman, R. D.; Kieffer, T. J. In Islets of langerhans; Islam, M. S. Ed.;

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16 Springer: Dordrecht, Netherlands, 2014; pp 1-37. (2) Rezania, A.; Bruin, J. E.; Arora, P.; Rubin, A.; Batushansky, I.; Asadi, A.; O'Dwyer, S.; Quiskamp, N.; Mojibian, M.; Albrecht, T.; Hsuan, Y.; Yang, C.; Johnson, J. D.; Kieffer, T. J. Nat. Biotechnol. 2014, 32, 1121–1133. (3) Pagliuca, F. W.; Millman, J. R.; Gürtler, M.; Segel, M.; Van Dervort, A.; Ryu, J. H.; Peterson, Q. P.; Greiner, D.; Melton, D. A. Cell 2014, 159, 428-439. (4) D'Amour, K. A.; Agulnick, A. D.; Eliazer, S.; Kelly, O. G.; Kroon, E.; Baetge, E. E. Nat. Biotechnol. 2005, 23, 1534–1541. (5) Kroon, E.; Martinson, L. A.; Kadoya, K.; Bang, A. G.; Kelly, O. G.; Eliazer, S.; Young, H.; Richardson, M.; Smart, N. G.; Cunningham, J.; Agulnick, A. D.; D'Amour, K. A.; Carpenter, M. K.; Baetge, E. E. Nat. Biotechnol. 2008, 26, 443–452. (6) Rezania, A.; Bruin, J. E.; Riedel, M. J.; Mojibian, M.; Asadi, A.; Xu, J.; Gauvin, R.; Narayan, K.; Karanu, F.; O'Neil, J. J.; Ao, Z.; Warnock, G. L.; Kieffer, T. J. Diabetes 2012, 61, 2016–2029. (7) Gutteridge, A.; Rukstalis, J. M.; Ziemek, D.; Tie, M.; Ji, L.; Ramos-Zayas, R.; Nardone, N. A.; Norquay, L. D.; Brenner, M. B.; Tang, K.; McNeish, J. D.; Rowntree, R. K. PLoS One, 2013, 8, e56024. (8) Schulz, T. C.; Young, H. Y.; Agulnick, A. D.; Babin, M. J.; Baetge, E. E.; Bang, A. G.; Bhoumik, A.; Cepa, I.; Cesario, R. M.; Haakmeester, C.; Kadoya, K.; Kelly, J. R.; Kerr, J.; Martinson, L. A.; McLean, A. B.; Moorman, M. A.; Payne, J. K.; Richardson, M.; Ross, K. G.; Sherrer, E. S.; Song, X.; Wilson, A. Z.; Brandon, E. P.; Green, C. E.; Kroon, E. J.; Kelly, O. G.; D’Amour, K. A.; Robins, A. J. PLoS One, 2012, 7, e37004. (9) Streefland, M.; Martens, D. E.; Beuvery, E. C.; Wijffels, R. H. Eng. Life Sci. 2013, 13, 212–223. (10) Junker, B. H.; Wang, H. Y. Biotechnol. Bioeng. 2006, 95, 226–261. (11) Lin, H.; Marjanović, O.; Lennox, B.; Šašić, S.; Clegg, I. M. Appl. Spectrosc. 2012, 66, 272-281. (12) Abu-Absi, N. R.; Kenty, B. M.; Cuellar, M. E.; Borys, M. C.; Sakhamuri, S.; Strachan, D. J.; Hausladen, M. C.; Li, Z. J. Biotechnol. Bioeng. 2011, 108, 1215-1221. (13) Pascut, F. C.; Kalra, S.; George, V.; Welch, N.; Denning, C.; Notingher, I. Biochim. Biophys. Acta 2013, 1830, 3517-3524. (14) Chan, J. W.; Lieu, D. K. J. Biophotonics 2009, 2, 656-668. (15) Konorov, S. O.; Schulze, H. G.; Piret, J. M.; Aparicio, S. A.; Turner, R. F. B.; Blades, M. W. Appl. Spectrosc. 2011, 65, 1009-1016. (16) Schulze, H. G.; Konorov, S. O.; Aparicio, S. A.; Piret, J. M.; Blades, M. W.; Turner, R. F. B. Appl. Spectrosc. 2011, 65, 1380-1386. (17) Konorov, S. O.; Schulze, H. G.; Piret, J. M.; Turner, R. F. B.; Blades, M. W. J. Raman Spectrosc. 2010, 42, 1135-1141. (18) Tan, Y.; Konorov, S. O.; Schulze, H. G.; Piret, J. M.; Blades, M. W.; Turner, R. F. B. Analyst 2012, 137, 4509-4515. (19) Konorov, S. O.; Schulze, H. G.; Piret, J. M.; Blades, M. W.; Turner, R. F. B. Anal. Chem. 2013, 85, 8996-9002. (20) Konorov, S. O.; Schulze, H. G.; Atkins, C. G.; Piret, J. M.; Aparicio, S. A.; Turner, R. F. B.; Blades, M. W. Anal. Chem. 2011, 83, 6254–6258. (21) Konorov, S. O.; Schulze, H. G.; Caron, N. J.; Piret, J. M.; Blades, M. W.; Turner, R. F. B. J. Raman Spectrosc. 2010, 42, 576-579.

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Analytical Chemistry

17 (22) Chan, J. W.; Lieu, D. K.; Huser, T.; Li, R. A. Anal. Chem. 2009, 81, 1324-1331. (23) Schulze, H. G.; Konorov, S. O.; Caron, N. J.; Piret, J. M.; Blades, M. W.; Turner, R. F. B. Anal. Chem. 2010, 82, 5020–5027. (24) Gage, B. K.; Webber, T. D.; Kieffer, T. J. PloS One 2013, 8, e82076. (25) Caron, N. J.; Gage, B. K.; O'Connor, M. D.; Eaves, C. J.; Kieffer, T. J.; Piret, J. M. Biotechnol. Bioeng. 2013, 110, 2706-2716. (26) Amps, K.; Andrews, P. W.; Anyfantis, G.; Armstrong, L.; Avery, S.; Baharvand, H.; Baker, J.; Baker, D.; Munoz, M. B.; Beil, S.; Benvenisty, N.; Ben-Yosef, D.; Biancotti, J. C.; Bosman, A.; Brena, R. M.; Brison, D.; Caisander, G.; Camarasa, M. V.; Chen, J.; Chiao, E.; Choi, Y. M.; Choo, A. B.; Collins, D.; Colman, A.; Crook, J. M.; Daley, G. Q.; Dalton, A.; De Sousa, P. A.; Denning, C.; Downie, J.; Dvorak, P.; Montgomery, K. D.; Feki, A.; Ford, A.; Fox, V.; Fraga, A. M.; Frumkin, T.; Ge, L.; Gokhale, P. J.; Golan-Lev, T.; Gourabi, H.; Gropp, M.; Lu, G.; Hampl, A.; Harron, K.; Healy, L.; Herath, W.; Holm, F.; Hovatta, O.; Hyllner, J.; Inamdar, M. S.; Irwanto, A. K.; Ishii, T.; Jaconi, M.; Jin, Y.; Kimber, S.; Kiselev, S.; Knowles, B. B.; Kopper, O.; Kukharenko, V.; Kuliev, A.; Lagarkova, M. A.; Laird, P. W.; Lako, M.; Laslett, A. L.; Lavon, N.; Lee, D. R.; Lee, J. E.; Li, C.; Lim, L. S.; Ludwig, T. E.; Ma, Y.; Maltby, E.; Mateizel, I.; Mayshar, Y.; Mileikovsky, M.; Minger, S. L.; Miyazaki, T.; Moon, S. Y.; Moore, H. D.; Mummery, C.; Nagy, A.; Nakatsuji, N.; Narwani, K.; Oh, S. K.; Olson, C.; Otonkoski, T.; Pan, F.; Park, I. H.; Pells, S.; Pera, M. F.; Pereira, L. V.; Qi, O.; Raj, G. S.; Reubinoff, B.; Robins, A.; Robson, P.; Rossant, J.; Salekdeh, G. H.; Schulz, T. C.; Sermon, K.; Sheik, M. J.; Shen, H.; Sherrer, E.; Sidhu, K.; Sivarajah, S.; Skottman, H.; Spits, C.; Stacey, G. N.; Strehl, R.; Strelchenko, N.; Suemori, H.; Sun, B.; Suuronen, R.; Takahashi, K.; Tuuri, T.; Venu, P.; Verlinsky, Y.; Ward-van Oostwaard, D.; Weisenberger, D. J.; Wu, Y.; Yamanaka, S.; Young, L.; Zhou, Q. Nat. Biotechnol. 2011, 29, 1132-1144. (27) Caron, N. J.; Harper, W.; Chaudhry, M. A.; Hoesli, C. A.; Sherwood, C. S.; O’Connor, M.; Eaves, C. J.; Piret, J. M. In Cell Culture Engineering XI: Sunshine Coast, Queensland, Australia, 2008. (28) Bruin, J. E.; Erener, S.; Vela, J.; Hu, X.; Johnson, J. D.; Kurata, H. T.; Lynn, F. C.; Piret, J. M.; Asadi, A.; Rezania, A.; Kieffer, T. J. Stem Cell Res. 2014, 12, 194-208. (29) Gage, B. K.; Baker, R. K.; Kieffer, T. J. Islets 2014, 6, e29236. (30) Konorov, S. O.; Schulze, H. G.; Blades, M. W.; Turner, R. F. B. Anal. Chem. 2014, 86, 9399-9404. (31) Konorov, S. O.; Glover, C. H.; Piret, J. M.; Bryan, J.; Schulze, H. G.; Blades, M. W.; Turner, R. F. B. Anal. Chem. 2007, 79, 7221-7225. (32) Movasaghi, Z.; Rehman, S.; Rehman, I. Appl. Spectrosc. Rev. 2007, 42, 493-541. (33) De Gelder, J.; De Gussem, K.; Vandenabeele, P.; Moens, L. J. Raman Spectrosc. 2007, 38, 1133-1147. (34) Lyng, F.; O'Faolain, E.; Conroy, J.; Meade, A.; Knief, P.; Duffy, B.; Hunter, M.; Kelehan, P.; Byrne, J. H. Exp. Mol. Pathol. 2007, 82, 121-129. (35) Schulze, H. G.; Foist, R. B.; Okuda, K.; Ivanov, A.; Turner, R. F. B. Appl. Spectrosc. 2012, 66, 757-764. (36) Schulze, H. G.; Turner, R. F. B. Appl. Spectrosc. 2014, 68, 185-191. (37) Schulze, H. G.; Foist, R. B.; Ivanov, A.; Turner, R. F. B. Appl. Spectrosc. 2008, 62, 1160-1166. (38) Schulze, H. G.; Konorov, S. O.; Okuda, K.; Piret, J. M.; Blades, M. W.; Turner, R.

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18 F. B. J. Raman Spectrosc. 2012, 43, 360–369. (39) Ichimura, T.; Chiu, L.-d.; Fujita, K.; Kawata, S.; Watanabe, T. M.; Yanagida, T.; Fujita, H. PLoS One 2014, 9, e84478. (40) Hilderink, J.; Otto, C.; Slump, C.; Lenferink, A.; Engelse, M.; van Blitterswijk, C.; deKoning, E.; Karperien, M.; van Apeldoorn, A. PLoS One 2013, 8, e78148. (41) Ortiz, C.; Zhang, D.; Xie, Y.; Davisson, V. J.; Ben-Amotz, D. Anal. Biochem. 2004, 332, 245-252. (42) Agarwal, S.; Holton, K. L.; Lanza, R. Stem Cells 2008, 26, 1117-1127.

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