Quantitative Proteomics Evaluation of Human Multipotent Stromal Cell for β Cell Regeneration

Article

Quantitative Proteomics Evaluation of Human Multipotent Stromal Cell for b Cell Regeneration Graphical Abstract

Authors Miljan Kuljanin, Ruth M. Elgamal, Gillian I. Bell, Dimetri Xenocostas, Anargyros Xenocostas, David A. Hess, Gilles A. Lajoie

Donor hMSC

β-cell Regenerative hMSC Co nd

Correspondence

itio

Hyperglycemia β-cell regeneration

ne dM

[email protected] (D.A.H.), [email protected] (G.A.L.)

ed ia

Quantitative ELISA hMSC β-cell Regenerative Potency

Quantitative Secretome Proteomics PRM Validation

C13N15

SVM

Machine Learning

Highlights d

d

The secretome of hMSCs contains unique signatures useful for potency analyses Quantitative proteomics and machine learning predict b cell regenerative hMSCs

d

Secreted levels of IL-6 and CXCL8 are predictive of regenerative potency

d

Donor characteristics play an important role in the regenerative potency of hMSCs

Kuljanin et al., 2018, Cell Reports 25, 2524–2536 November 27, 2018 ª 2018 The Authors. https://doi.org/10.1016/j.celrep.2018.10.107

In Brief High-throughput quantitative assays that assess regenerative potency of human multipotent stromal cells (hMSCs) need to be established to evaluate their therapeutic potential. Kuljanin et al. develop a quantitative proteomics analyses of secreted proteins combined with in vivo mouse models to determine a protein signature that is predictive for b cell regeneration.

Cell Reports

Article Quantitative Proteomics Evaluation of Human Multipotent Stromal Cell for b Cell Regeneration Miljan Kuljanin,1,2 Ruth M. Elgamal,2,3 Gillian I. Bell,2 Dimetri Xenocostas,2 Anargyros Xenocostas,4 David A. Hess,2,3,* and Gilles A. Lajoie1,5,* 1Don

Rix Protein Identification Facility, Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 2Krembil Centre for Stem Cell Biology, Molecular Medicine Research Laboratories, Robarts Research Institute, London, ON, Canada 3Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 4Department of Medicine, Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 5Lead Contact *Correspondence: [email protected] (D.A.H.), [email protected] (G.A.L.) https://doi.org/10.1016/j.celrep.2018.10.107

SUMMARY

Human multipotent stromal cells (hMSCs) are one of the most versatile cell types used in regenerative medicine due to their ability to respond to injury. In the context of diabetes, it has been previously shown that the regenerative capacity of hMSCs is donor specific after transplantation into streptozotocin (STZ)-treated immunodeficient mice. However, in vivo transplantation models to determine regenerative potency of hMSCs are lengthy, costly, and low throughput. Therefore, a high-throughput quantitative proteomics assay was developed to screen b cell regenerative potency of donor-derived hMSC lines. Using proteomics, we identified 16 proteins within hMSC conditioned media that effectively identify b cell regenerative hMSCs. This protein signature was validated using human islet culture assay, ELISA, and the potency was confirmed by recovery of hyperglycemia in STZ-treated mice. Herein, we demonstrated that quantitative proteomics can determine sample-specific protein signatures that can be used to classify previously uncharacterized hMSC lines for b cell regenerative clinical applications. INTRODUCTION Human multipotent stromal cells (hMSCs) have been described as one of the most versatile cell types for use in regenerative medicine applications. Since their initial discovery in bone marrow (Pereira et al., 1995), hMSCs have been identified and isolated from several adult and fetal tissues (Via et al., 2012). The use of donor-matched or autologous hMSCs greatly increases their therapeutic potential in the clinical hematopoietic transplantation setting, bypassing the requirements for longterm immunosuppression to prevent graft-versus-host diseases that normally arise during allogeneic-cell-based therapies (Henschler et al., 2008). In addition, hMSCs have the capacity

to respond to injury and combat infection or diseases in all vascularized tissues within the body (Caplan and Correa, 2011). hMSCs harvested from multiple anatomical locations are equivalent in terms of surface marker expression and differentiation potential into adipose, cartilage, and bone tissues (Zazzeroni et al., 2017). However, hMSCs harvested from different sites and hMSCs propagated under different expansion conditions display very different gene expression profiles, which may have an impact on their function as well as their clinical relevance in cellular therapies (El-Badawy and El-Badri, 2016). For example, hMSCs harvested from amniotic fluid have been shown to have neonatal defense properties, while hMSCs harvested from bone marrow play functional roles in blood and bone formation (Tsai et al., 2007). One of the most useful properties that hMSCs possess is ample secretion of regenerative cytokines and immunomodulatory factors. hMSCs secrete a wide variety of growth factors and cytokines and chemokines that can induce cell proliferation and promote angiogenesis (Murphy et al., 2013). It has been well documented that hMSCs secrete pro-angiogenic cytokines, such as human growth factor (HGF), epidermal growth factors (EGFs), and vascular endothelial growth factors (VEGFs), that increase fibroblast, epithelial, and endothelial cell division as well as chemokines, such as stromal derived factor-1 (SDF-1 or CXCL12), that increase accessory cell recruitment to sites of injury (Chen et al., 2008). Alternatively, hMSCs also secrete extracellular vesicles that contain packaged peptides, proteins, membrane lipids, and nucleic acids, which can impact regenerative processes at distant sites via the bloodstream (Lai et al., 2016; Todorova et al., 2017). In addition to having proliferative and pro-angiogenic effects, hMSCs have also been implemented in anti-inflammatory, immunomodulatory, anti-apoptotic, anti-microbial, and most recently b cell regenerative applications (Gao et al., 2014; Hao et al., 2013; Murphy et al., 2013). Although hMSCs have been safely and successfully used in many clinical applications, direct characterization of secreted trophic factors is still lacking and could lead to further realization of their therapeutic potential (Ranganath et al., 2012). The therapeutic potential of each hMSC line is directly related to what they secrete and how much of each factor they secrete. For example, the vascular regenerative capacity of

2524 Cell Reports 25, 2524–2536, November 27, 2018 ª 2018 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

bone-marrow-derived hMSCs can be enriched by subset selection based on high aldehyde dehydrogenase activity (Sherman et al., 2017). In addition, the heterogeneous nature of hMSCs isolated from different donors, and the methods used to expand them in culture, both impact b cell regenerative potential in hyperglycemic mouse models (Bell et al., 2012a). Interestingly, the b cell therapeutic potential of hMSCs also diminishes with prolonged passage in culture (Bell et al., 2012b). These aforementioned studies relied on lengthy in vivo evaluation to characterize hMSCs with enhanced angiogenic and b cell regenerative properties. This process is inherently ineffective at screening a large number of donor-derived hMSCs lines for therapeutic efficacy relevant to cellular therapies. Therefore, thorough examination of proteins secretion by hMSCs using high-throughput technologies are needed to improve existing therapies and to tailoring each hMSC line isolated for a specific regenerative function. Extensive proteomic characterization of hMSCs that possess the ability to decrease blood glucose levels in hyperglycemia mouse models have revealed key protein signature that could be used for future potency screening (Kuljanin et al., 2017). We have previously shown that islet regenerative hMSC lines demonstrate increased secretion of pro-angiogenic, cell growth supportive factors alongside reduced secretion of common proinflammatory signals that were highly expressed in hMSCs that did not possess the ability to lower blood glucose levels. However, due to the relatively small sample size used, more in-depth analysis is required to determine whether a protein signature could be used to predict which hMSC lines possess the ability to initiate islet regeneration after transplantation into streptozotocin (STZ)-treated mice in vivo. Herein, we present a quantitative proteomic approach that can be used to predict the therapeutic potential of hMSC lines for the induction of b cell regenerative capacity. By using hMSC lines previously characterized for glucose lowering capacity in STZtreated mice, we developed a surrogate assay using human islets to assess b cell regenerative effects of hMSC conditioned media (CM) in vitro. Quantitative label-free proteomics was used to generate a training dataset, and by using machine learning algorithms, we were able to determine an unbiased protein signature of b cell regenerative hMSCs that was subsequently validated by using targeted quantitative proteomic approaches. In addition, secreted proteins from 16 previously uncharacterized cells lines were tested against this protein signature to predict the therapeutic potential of each hMSC line. Finally, these results were validated using the aforementioned in vitro human islet culture assay coupled with multiparametric flow cytometry, ELISA, and the potency was confirmed by quantitative in vivo recovery from hyperglycemia in STZ-treated mice. Finally, we have provided a protein signature that can be used to screen hMSC lines for use in b cell regenerative applications. RESULTS b Cell Regenerative Capacity of hMSC CM We have previously shown that hMSC transplants can be used to initiate regeneration of endogenous b cells after transplantation into STZ-treated NOD/SCID (non-obese diabetic/severe

combined immunodeficiency) mice. However, hMSC lines represented a heterogeneous population, and the extent of regenerative capacity was cell line specific (Bell et al., 2012a). In addition, hMSCs with the capacity to induce islet regeneration after transplantation were rare, and only 15%–20% of hMSC lines were able to reduce chemically established hyperglycemia after transplantation. To determine whether each hMSC sample possessed b cell regenerative capacity, a lengthy 42-day in vivo experiment needed to be performed (Figure 1), making this strategy an inefficient method when used as a screening modality. Therefore, we sought to build a quantitative proteomic method, coupled with computational analyses, to determine a protein signature that could be used to predict b cell regenerative capacity using hMSC-generated CM (Figure 1). A training dataset was constructed using 3 previously in vivo validated regenerative and 3 nonregenerative hMSC lines (Bell et al., 2012a). These cell lines have been previously characterized for differentiation potential, hMSC cell surface expression, and proliferation and show no significant differences between regenerative and nonregenerative hMSCs. In addition, 4 previously uncharacterized hMSC lines were added to the training dataset, for a total of 10 hMSC lines, and were grouped based on their ability to maintain b cell survival after 7 days using human islet culture assays. Correlations between the in vivo regenerative potency of previously characterized cell lines and their ability to maintain b cell survival in vitro was assessed. The area under the curve (AUC) was determined for each cell line, and the correlation between the total live b cell number was determined to be 0.997, meaning that as the regenerative potency increases (i.e., decrease in AUC), the total number of live b cell increases (Table S1). These data suggest that the in vitro assay is a strong surrogate for assessing the regenerative potential of hMSCs in vivo and is a viable strategy for grouping hMSCs into regenerative and nonregenerative cohorts. b cell survival for 6 previously characterized and 4 uncharacterized hMSC lines was performed using multiparametric flow cytometry for FluoZin, 7AAD, and Annexin-V (Figures S1A and S1B). Representative flow cytometry plots are shown in Figures S1C–S1J. Concentrated CM from 3 islet regenerative hMSC lines showed significantly higher live b cell numbers compared to CM generated from 3 nonregenerative hMSC lines (Figure S1B), as previously shown (Kuljanin et al., 2017). The remaining hMSC lines that were used for test validation did not significantly improve b cell survival compared to unconditioned media controls (RPMI) and were thus classified as nonregenerative. When grouped, regenerative hMSC lines showed a significant increase in total viable b cells compared to nonregenerative hMSC lines after 7 days of culture, validating our in vitro functional assay used for initial class assignment. Building a Regenerative Protein Signature Using Proteomics Quantitative label-free proteomics was used to determine what secreted factors present in the CM best identify and segregate donor-derived hMSC lines into b cell regenerative or nonregenerative classes. Each hMSC line used for building the training dataset was grown to 80% confluency and washed 3 times with PBS, and media was conditioned to capture all secreted proteins after 24 hr using serum-free conditions, in duplicate.

Cell Reports 25, 2524–2536, November 27, 2018 2525

A

B

C

E D

Figure 1. Quantitative Proteomics Strategies to Evaluate b Cell Regenerative Potency of Donor-Derived hMSC Lines (A) Donor-derived hMSCs (n = 20) are isolated, expanded ex vivo, and transplanted into a hyperglycemic mouse model. Blood glucose is monitored for 42 days to determine endogenous b cell regenerative potential. Alternatively, donor-derived hMSCs are isolated and expanded ex vivo, and secreted proteins are captured by conditioning media (CM) for 24 hr using serum-free conditions. CM are digested and a protein signature of b cell regenerative hMSCs is determined using mass-spectrometry-based quantitative proteomics. (B) Principle component analysis displays good separation of regenerative and nonregenerative hMSCs. (C) Total proteins quantified in each hMSC cell line CM (n = 2). Data are shown as mean. (D) Quantitative data were filtered using GO cellular component to only include classically secreted proteins (590). Secretome generated from hMSC CM was compared to 3 previously published secretome datasets. (E) Representative volcano plot of differentially expressed proteins for regenerative versus nonregenerative hMSC CM. Classically secreted proteins are displayed in blue. A change greater than 2-fold is represented outside the boundaries. p < 0.05, false discovery rate (FDR) < 0.05. Also see Figure S1.

Secreted proteins were digested and subjected to single-shot proteomic analyses. Principle component analysis showed very good segregation of previously characterized regenerative hMSC lines versus nonregenerative hMSC lines (Figure 1B). On average, we quantified 2300 proteins per sample, and 88% of all quantified proteins were present in both replicates (Figure 2A). In total, 3038 unique proteins were quantified, with 2258 quantified in at least 70% of all samples (Figure 1C). Using gene ontology (GO) analysis, we filtered all quantified proteins to only include those with extracellular localization (590) (Figure 1D). Reproducibility of the quantitative proteomics data was assessed by looking at Pearson correlations between all secreted proteins within and between each replicate (Figure 2B). On average, samples replicates were highly correlated with a Pearson score of 0.98. Correlation between regenerative and nonregenerative samples was much lower, having an average

2526 Cell Reports 25, 2524–2536, November 27, 2018

score of 0.45, indicating that the expression levels of the quantified secreted proteins were very different. Interestingly, the correlation between regenerative samples was much lower than the correlation between nonregenerative samples, at 0.60 and 0.75, respectively, suggesting that there was more heterogeneity in secreted proteins quantified from regenerative samples. In addition, our comprehensive hMSC secreted dataset was compared to previously published data from our group and showed very reproducible results, with 406 overlapping proteins and an additional 184 secreted proteins identified in this study (Figure 1D). Comparatively, we also assessed the depth of the secretome using two previously published large secreted datasets. Compared with Meissner et al. (2013), which used activated macrophages to obtain a comprehensive time-resolved secretome, overlap was very low, with only 10% proteins identified in both studies. Low overlap in secreted proteins is most

B

Sample 1

Intracellular

Extracellular

1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6a 6b 7a 7b 8a 8b 9a 9b 10a 10b

0.75 0.50 0.25 41 proteins 0 0

E

0.4

0.6

0.8

0.75 0.50 0.25

1.0

Regenerative 27

16 protein

0

F

25 50 75 100 Protein Rank

1.00

0

Pearson Correlation

25 50 75 100 Protein Rank Nonregenerative

p<0.05

24 21

CXCL5

SFRP1

COL14A1

TINAGL1

NPTX1

CCL2

CXCL8

ORM1

SERPING1

PENK

HAPLN1

IL6

VCAM1

15

LAMC2

18

COL6A3

Relative Expression (Log2)

1.00

GDF15

D

C Posterior Probability

180

2245

hMSC Samples

155

Rep2

Posterior Probability

Rep1

1a 1b 2a 2b 3a 3b 4a 4b 5a 5b 6a 6b 7a 7b 8a 8b 9a 9b 10a 10b

A

Figure 2. Quantitative Label-free Proteomics Facilitates Efficient Classification of b Cell Regenerative and Nonregenerative Donor-Derived hMSC Lines (A) Each hMSC CM was analyzed in duplicate by LC-MS/MS. Greater than 80% of the quantified proteins were found in both replicates. (B) Label-free quantitative reproducibility within and between sample replicates. The intensity of each box represents the Pearson correlation between each sample for all secreted proteins. High correlation scores were obtained between sample replicates (>0.95). (C and D) Quantitative proteomics data were mined for a b cell regenerative signature using unbiased machine learning. A support vector machine identified 41 proteins that could be used accurately to segregate regenerative and nonregenerative hMSC lines (C). This included 18 classically secreted proteins and 23 intracellular proteins (D). (E) Classically secreted proteins were mined for a b cell regenerative signature using unbiased machine learning. A support vector machine identified 16 proteins could be used accurately to segregate regenerative and nonregenerative hMSC lines. (F) Label-free quantitative values for the top 16 proteins obtained from the support vector machine that were highly expressed in regenerative (green) (n = 6) and nonregenerative (gray) (n = 14) hMSC lines. Data are represented as mean ± SD. **p < 0.05, ***p < 0.01. See also Figure S2.

likely the results of difference cell lines used. Compared with Eichelbaum et al. (2012), which used pancreatic cancer and stromal cells lines, with azidohomoalainine (AHA) labeling used to identify newly synthesized proteins, overlap was also low, with 27% proteins identified in both studies. This low overlap is potentially a direct results of differences in cell lines used, as well as differences in quantitative techniques used to identify secreted proteins (Figure 1D). Nonetheless, to the best of our

knowledge, this is the most comprehensive secretome quantified from hMSCs. Lastly, a direct comparison of expression levels for each protein identified in regenerative and nonregenerative hMSCs was performed. In total, 405 proteins were found to be statistically and > 2-fold differentially expressed between regenerative and nonregenerative hMSCs, of which 131 were classically secreted proteins (Figure 1E). Upregulated proteins found in regenerative

Cell Reports 25, 2524–2536, November 27, 2018 2527

Table 1. Uncharacterized hMSC Lines Classified Based on Protein Expression Value Using the Support Vector Machine Cell Line

Probability Ra

Probability NRb

Class

1

16.9

83.1

NR

2

26.1

73.9

NR

3

20.1

80

NR

4

12.8

87.2

NR

5

95.7

4.3

R

6

17.6

82.4

NR

7

16.7

83.3

NR

8

12.6

87.4

NR

9

12.9

87.1

NR

10

20.6

79.4

NR

a

Regenerative b Non-regenerative

hMSCs included members of the Wnt-signaling family (WISP2, WNT5A, WNT5B, and SFRP1), while proteins upregulated in nonregenerative hMSCs mostly included pro-inflammatory factors (CXC, CC, and IL families). This is in agreement with previously published data highlighting nonregenerative hMSC CM secrete a more inflammatory signature, while regenerative hMSCs secrete proteins related to Wnt signaling (Kuljanin et al., 2017). Unbiased mining and protein marker selection of all detected proteins and secreted proteins from label-free quantitative proteomic data was achieved using the R package ‘‘geNetClassifier’’ with a support vector machine (SVM) (Devi Arockia Vanitha et al., 2014). Data mining was performed using the training dataset described above, using 3 regenerative and 7 nonregenerative hMSC lines. This method compared the patterns of differentially expressed proteins across multiple classes and provides a posterior probability for each protein (Devi Arockia Vanitha et al., 2014). The posterior probability is used to measure the strength each protein possesses to differentiate regenerative and nonregenerative hMSC lines. Using the full list of protein from the training dataset, and a cutoff of 0.95, the SVM selected 41 proteins that could reliably segregate regenerative and nonregenerative hMSC lines (Figure 2C). In total, this included 18 proteins annotated as classically secreted and 23 intracellular proteins (Figure 2D). Due to a small degree of cell death observed while culturing hMSCs in serum-free media (5%), we chose to filter the list for proteins that were annotated as classically secreted and performed the SVM again obtaining 16 proteins that were chosen for further validation (Figure 2E). A full list of all proteins and their corresponding posterior probabilities can be found in Tables S2 and S3. From the secreted list of proteins, 12 proteins were chosen with increased expression in nonregenerative hMSC CM and 4 proteins with increased expression in regenerative hMSC CM (Figure 2F). Included in the selected proteins were 6 inflammatory factors (IL-6, PENK, CXCL8, CCL2, GDF15, and CXCL5) that were all highly expressed in CM harvested from nonregenerative hMSC and 1 Wnt-related protein (SFRP) that was overexpressed in CM harvested from regenerative hMSCs. To confirm these findings,

2528 Cell Reports 25, 2524–2536, November 27, 2018

the relative expression of the top 16 proteins was assessed across each class. Indeed, 4 proteins with increased expression in regenerative hMSCs were 16- to 32-fold higher compared to nonregenerative hMSCs. In contrast, 12 proteins highly expressed in nonregenerative hMSCs were 4- to 32-fold higher compared to regenerative hMSCs. Lastly, the coefficient of variance (CV) was assessed for each protein chosen for classification. On average, label-free quantitative values showed very low variance with an average CV of 3.8% (Figure S2A). Testing the Regenerative Protein Signature CM from 10 previously uncharacterized cell lines was collected and proteomic analyses was performed to determine if accurate class assignment could be achieved using the quantitative protein signature generated by the SVM. Each hMSC line was grown and the CM was harvested as described above in duplicate. CM was first assessed using label-free quantitative proteomics, representing the test dataset. On average we quantified 2400 proteins per sample, with 90% of all quantified proteins present in both replicates. Reproducibility of the quantitative proteomics data was again assessed by looking at Pearson correlations between all secreted proteins within and between each replicate (Figure S2B). On average samples replicates were highly correlated with a Pearson score of 0.98. Interesting, one sample has relatively low correlation with the remaining 9 samples, with an average score of 0.45, suggesting its expression values for secreted proteins was much different. The remaining samples were highly correlative having an average score of 0.70. In addition, the CV was assessed for each protein chosen for classification obtained from the test dataset. Quantitative values showed very low variance with an average CV of 3.4% (Figure S2C). Relative expression levels from the training dataset were used to create cutoffs that uncharacterized samples must meet for classification as regenerative or nonregenerative. For example, the relative expression levels of proteins that were highly expressed, such as SFRP1 minus the SEM, were used to determine what the lowest value an uncharacterized hMSC line must meet to be considered regenerative (Figure S3). Samples that reached cutoffs that were representative of a regenerative samples (shaded area) received a score of +1, and those that did not received a score of 0. The sum of the score was used to assign the uncharacterized sample into either regenerative or nonregenerative cohorts. Uncharacterized hMSC lines that achieved a score of R8 (met > 50% of the cutoffs) were termed as regenerative, and samples that achieved a combined score of %7 were termed as nonregenerative (Table S4). In total, one uncharacterized hMSC line was determined to be regenerative (score 14), while the remaining unknown hMSC lines were determined to be nonregenerative (scores ranging from 0 to 4). To assess the efficiency of our scoring system, all label-free quantitative data obtained from the uncharacterized hMSC samples were tested against the SVM created using the training dataset to obtain a probability of class assignment (Devi Arockia Vanitha et al., 2014). The probability of assigning each uncharacterized cell line to either regenerative or nonregenerative class was calculated (Table 1). Using this approach, we obtained a call rate accuracy of 100%, meaning that all uncharacterized cell lines could be assigned with confidence to only one class. The sample that

was previously characterized as regenerative using our simple scoring system was classified as a regenerative hMSC line with a 95.7% probability. On the other hand, the remaining 9 uncharacterized hMSC lines were classified as nonregenerative, with probabilities ranging from 73.9% to 87.4%. Targeted Proteomics and Refinement of Protein Signature Targeted proteomics approaches, such as parallel reaction monitoring (PRM), has been widely used in drug screening and environmental toxicology and to identify metabolites as well as biomarkers for disease diagnostics (Picotti and Aebersold, 2012). Advantages PRM approaches possess over traditional data-driven mass spectrometry (MS)-based proteomic approaches are greater selectivity, sensitivity, and quantitative accuracy because few proteins are selected for quantification (Kiyonami et al., 2011). To further quantify the relative abundance of the candidate proteins obtained from the SVM, PRM was performed with in-house made, stable isotope labeled (SIL) peptide spike-in (glu-1-fibrinopeptide B: EGVNDNEEGFFSAR) that was purified using HPLC (>95%). hMSC CM was prepared for PRM analyses as described earlier in duplicate. The endogenous light peptide levels were first assessed using a targeted PRM approach and were determined to be below the limit of detection (LOD). Next, a 5-point standard curve, spanning 3 orders of magnitude (50 amol to 50 fmol), was constructed using the ratio of light to heavy peptides within the hMSC CM to account for matrix effects (Figure 3A) (Cappiello et al., 2008). To assess the relative abundance of each protein, the most abundant peptides were chosen per target (Table S5), and 4 transitions were chosen based on peak intensity for total fragment area integrations in Skyline with a dotp > 0.90 (MacLean et al., 2010). Peptides were chosen from a spectral library that was constructed using label-free quantitative data obtained from the training and testing datasets (Figure 3B). Each hMSC sample was spiked with 1 fmol/mL of SIL peptide and 1 mg of total protein, and 5 fmol of SIL peptide were injected onto the column and analyzed by PRM-LC-MS/MS (liquid chromatography-tandem mass spectrometry), in duplicate. The total integrated fragment area for each target was normalized to the spike-in SIL peptide, and the relative abundance of each target was determined using a standard curve (Figure 3C). Next, the fold change in the relative abundance of each peptide and corresponding protein was compared between regenerative and nonregenerative hMSCs. Highly overexpressed inflammatory markers that were identified using the label-free quantitative data, such as CXCL8 and IL-6, were again 27- and 17-fold higher in nonregenerative hMSCs compared to regenerative hMSCs, respectively (Figure 3D). Markers that were overexpressed in regenerative hMSCs such as NPTX and SFRP1 were found to only be 2.8- and 2.1-fold higher compared to nonregenerative hMSCs. This analysis also demonstrated good reproducibility with average CV values of 23% (Figure S3A). Interestingly, only 12/16 peptides showed differential abundances between regenerative and nonregenerative hMSCs greater than 2-fold, while 3 peptides showed CV values > 25%, suggesting that these peptides or proteins were the least favorable candidates moving forward.

To evaluate the power of each peptide to distinguish individual samples as regenerative or nonregenerative, we measured the area under the receiver operator characteristic (ROC) curve of each peptide (Figure 4A). For identification, one peptide (ENWVQR) corresponding to CXCL8, which was detected in higher abundances in nonregenerative hMSCs, showed the highest AUC (1.0) (Figure 4B). Conversely, (PPQCVDIPADLR) corresponding to SFRP, which was the most sensitive and selective protein detected in higher abundances in regenerative hMSCs, showed an AUC of 0.77 (Figure 4C). In total 10/16 peptides were able to accurately predict regenerative and nonregenerative hMSCs with high confidence (AUC > 0.7) (Figures S3B–S3O). In addition, correlation coefficients between the in vivo regenerative potency and the relative levels of each protein within hMSC CM were calculated (Table S6). The in vivo regenerative potency (AUC) was calculated as described above. Proteins that were highly expressed in nonregenerative hMSCs showed negative correlation with in vivo regenerative potency, while proteins that were highly expressed in regenerative hMSCs showed positive correlations with in vivo regenerative potency. For example, IL-6 and CXCL8, both of which were found to be upregulated in nonregenerative hMSC lines, possessed correlation coefficients of 0.819 and 0.889, respectively. This means as the regenerative potency in vivo increases (i.e., a decrease in the AUC), we observe a decrease in the levels of CXCL8 and IL-6 in the CM. On the other hand, SFRP1 that is upregulated in regenerative hMSCs has a correlation coefficient of 0.739, meaning as the regenerative potency in vivo increases (i.e., a decrease in the AUC), so do the levels of SFRP1 in the CM. To validate the performance of our PRM method, we sought to confirm these findings by measuring the absolute concentration of CXCL8 within the 20 hMSC generated CM samples by ELISA in duplicate. Using the ELISA, we obtained an AUC of 0.93 with 100% specificity above 11.05 pg/mL and a sensitivity of 75% (Figure 4D). In addition, the absolute concentration of IL-6 within the 20 hMSC generated CM samples was measured by ELISA in duplicate. Using the IL-6-ELISA, we obtained an AUC of 0.92 with 93.75% specificity above 2.18 pg/mL and a sensitivity of 75% (Figure 4E). Thus, these data suggest that regenerative hMSCs secrete less than 11.05 pg/mL of CXCL8 and less than 2.18 pg/mL of IL-6. Biological Validation of Uncharacterized Cell Lines Validation of each of the uncharacterized cell lines was performed using the human islet culture assay with CM described above. The total live b cell number was used to determine which hMSC lines had the ability to increase b cell survival as assessed by multiparametric flow analysis (Figure S4). We confirmed that only 1 out of 10 uncharacterized hMSC cell lines had significantly increased the total number of live b cells compared to unconditioned media control (RPMI) and that the remaining hMSC samples were not significantly different from control (Figure 5A). To further validate the predictive power of our proteomics assay, one regenerative and one nonregenerative hMSC line classified by the predictive assay were assessed by transplantation in vivo. STZ-treated (35 mg/kg/day, days 1–5), hyperglycemic (15–25 mmol/L) NOD/SCID mice were intravenously (i.v.) injected on day 10 with hMSCs (5.0 3 105 cells), and blood glucose Cell Reports 25, 2524–2536, November 27, 2018 2529

A

R-CXCL8

B 15

200

2

y4 y3

12 9 6 3

y4 y3

3000

y2

y2

b2

b2

150

Intensity (103 )

Intensity (103 )

Light:heavy Area Ratio

R =0.9995

NR-CXCL8

100

2000

1000

50

0 0

10

20

30

40

50

Analyte Concentration (fmol)

0

0 31.0

29.5 30.0 30.5 Retention Time (min)

C

VVIESLQDR_COL14A1

Peptides

WVQDSMDHLDK_CCL2

32.0

D

Samples EGVNDNEEGFFSAR

31.5

Retention Time (min)

IAVAQYSDDVK_COL6A3 EICLDPEAPFLK_CXCL5 ILTPEVR_GDF15 ENWVQR_CXCL8 FVGFPDK_HAPLN1 EALAENNLNLPK_IL6 AQGGDGVVPDTELEGR_LAMC2 CESQSTLDPGAGEAR_NPTX1 TEDTIFLR_ORM1 ELLETGDNR_PENK TLYSSSPR_SERPING1 PPQCVDIPADLR_SFRP1 ITGWGEETLPDGR_TINAGL1 QSTQTLYVNVAPR_VCAM1

-6

-4

-2

0

2

Log2(Fold Change) Regenerative/Non-regenerative Figure 3. Targeted Proteomic Refining of the Predictive Protein Signature for b Cell Regenerative hMSCs (A) 5-point standard curve spanning 3 orders of magnitude (50 amol to 50 fmol) using light and heavy isotope labeled peptides (glu-1-fibrinopeptide B: EGVNDNEEGFFSAR) as spike in standards. (B) Intensity of the most abundant fragments ions for CXCL8 quantified in regenerative (left) and nonregenerative (right) CM by PRM-LC-MS/MS. (C) Total integrated fragment area for each of the peptides selected for PRM evaluation across 20 hMSC CM samples. Total fragment area was normalized the standard peptide spike in (red), and the relative abundance of each peptide or protein was estimated using the standard curve. (D) Average peptide expression for all 16 peptides comparing regenerative (n = 6) and nonregenerative (n = 14) hMSC samples (shown as Log2 ratios). Data are represented as mean. See also Figures S2 and S3.

was monitored for 35 days (Figure 5B). Compared with PBS-injected control mice (n = 4) that remained severely hyperglycemic (>25 mmol/L) or mice transplanted with the nonregenerative hMSCs (n = 3), mice transplanted with regenerative hMSCs (n = 4) showed reduced hyperglycemia within 7 days post-transplant that was sustained out to day 35 (Figure 5B). Transplantation of regenerative hMSCs showed significantly reduced AUC for systemic blood glucose over the full 35 days (Figure 5C). Pancreas sections from mice injected with PBS, transplanted with nonregenerative and regenerative hMSC lines, and euthanized at day 35 were stained for murine insulin (Figures 5D–5F)

2530 Cell Reports 25, 2524–2536, November 27, 2018

to characterize the dynamics of islet regeneration after hMSC transplantation. Compared to mice injected with PBS, or mice transplanted with nonregenerative hMSCs, mice transplanted with regenerative hMSCs showed significantly increased islet number (Figure 5G), and total b cell mass (Figure 5H), but no difference in islet size (Figure 5I). These findings are in line with previous reports that regenerative hMSCs have the capacity to induce endogenous regeneration of murine islets (Bell et al., 2012a). Collectively, these data confirmed that the quantitative predictive assay could reliably identify b cell regenerative potency in hMSC lines using CM.

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ENWVQR_CXCL8 TLYSSSPR_SERPING1 ILTPEVR_GDF15 EALAENNLNLPK_IL6 FVGFPDK_HPLN1 PPQCVDIPADLR_SFRP1 EICLDPEAPFLK_CXCL5 ITGWGEETLPDGR_TINAGL1 ELLETGDNR_PENK AQGGDGVVPDTELEGR_LAMC2 CESQSTLDPGAGEAR_NPTX1 TEDTIFLR_ORM1 WVQDSMDHLDK_CCL2 QSTQTLYVNVAPR_VCAM1 IAVAQYSDDVK_COL6A1 VVIESLQDR_COL14A1

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Figure 4. Targeted Proteomics Validation Using ELISA Reveals CXCL8 and IL-6 as Accurate Segregators of b Cell Regenerative and Nonregenerative hMSCs (A) The area under the receiver operator characteristic (ROC) curve (AUC) was used to evaluate the ability of individual peptides to distinguish between b cell regenerative and nonregenerative hMSC lines. Peptides with high discriminative power are shown to the right of the blue lines (AUC R 0.70). (B and C) ROC analysis using targeted proteomics data for peptide corresponding to (B) CXCL8 and (C) SFRP1. CXCL8 displays the highest power of segregation between b cell regenerative and nonregenerative hMSCs. (D and E) Absolute protein concentration was quantified using ELISA for (D) CXCL8 and (E) IL-6. CXCL8 displays 100% specificity and 75% sensitivity below 11.0 pg/mL, while IL-6 displayed 93.75% specificity and 75% sensitivity below 2.2 pg/mL. See also Figure S4.

50

in vivo regenerative potency (AUC) was calculated as described above. There 25 25 25 was no correlation found between regenAUC=0.78 AUC=0.93 AUC=0.92 erative potency and the age of the donor 0 0 0 (0.0491). We found very low correlation 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 Specificity (%) between the regenerative potency and Specificity (%) Specificity (%) the sex of the donor (0.162). A strong positive correlation was determined for the regenerative potency and the BMI of the donor (0.795). This Donor Characteristics To date, 38 donor-derived hMSC lines have been characterized means as the regenerative potency in vivo increases (i.e., a using the STZ-treated in vivo mouse model or using the quantita- decrease in the AUC), we observe a decrease in the BMI of the tive proteomics predictive model. Therefore, we sought to deter- donor. These data suggest that there is a correlation with the mine if there was any utility in pre-screening donors to increase BMI of the donor and the b cell regenerative potency of derived the likelihood of identifying more b cell regenerative hMSC lines, hMSCs and that pre-screening donors could add an additional using donor characteristics. All hMSC lines used were sampled at level of utility. random and were not pre-selected. First, the sex of the donor was considered (Figure 6A). In total, hMSCs isolated from 13 male and DISCUSSION 16 female donors were characterized as nonregenerative, while hMSCs derived from 5 male and 4 female donors were character- Our study demonstrates that CM derived from hMSCs can be ized as regenerative. Next, donor age was assessed (Figure 6B). used to predict the therapeutic potential for the paracrine inducIn our study, patients ranged from 21 to 66 years of age, and sur- tion of regeneration by corresponding hMSC lines. Importantly, prisingly we found no direct correlation between the ages of the using a quantitative proteomics approach, we showed that donors and whether the samples were regenerative. The average each hMSC line could be efficiently placed into a regenerative age of regenerative donors was 50.4, while the average age of or nonregenerative class based on hMSC secretory profiling. nonregenerative donors was 49.5. Lastly, when the weight or By employing the current proteomics strategy, and using the BMI of the donor was considered, we observed a clear trend machine learning algorithms, an unbiased protein signature of (Figure 6C). All hMSCs derived from donors that had a BMI in the b cell regenerative hMSCs could be efficiently determined. obese range (>30 kg/m2) were classified as nonregenerative Impressively, a panel of 16 proteins and their expression levels (15/15), and all of the hMSCs derived from donors that had a within hMSC CM could reliably predict the b cell regenerative poBMI in the overweight range (>24.9 to <29.9 kg/m2) were classi- tential of 16 uncharacterized donor-derived hMSC lines. Further fied as nonregenerative (13/13). In contrast, almost all hMSCs validation using targeted proteomics, and ELISA on selected derived from donors that possessed a BMI in the healthy range targets, confirmed our findings providing a high-throughput (<24.9) were classified as regenerative (8/10) (Figure 6D). Lastly, assay that could be used to efficiently screen up to 6 uncharaccorrelation coefficients between the in vivo regenerative potency terized hMSC lines within 24 hr. Notably, using patient informaand the donor characteristics were assessed (Table S7). The tion including donor sex, age, and BMI for 38 screened hMSC

Cell Reports 25, 2524–2536, November 27, 2018 2531

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Figure 5. Functional Validation of hMSC Lines Characterized by Quantitative Proteomics Using In Vitro and In Vivo Assays (A) Culturing human islet in CM generated from regenerative hMSC lines (sample 5) (n = 3) increased the proportion of live b cells after 7 days of culture compared to negative controls (black) (n = 3) and islets cultured in CM generated from nonregenerative hMSCs (samples 1–4 and 6–10) (n = 3). (B) hMSCs from one newly characterized regenerative and one nonregenerative hMSC line were translated into STZ-treated (35 mg/kg/ days 1–5) NOD/SCID mice on day 10, and blood glucose was monitored weekly until day 35. Mice transplanted regenerative hMSCs (green) (n = 4) showed reduced hyperglycemia from days 14 to 35 compared to mice injected with PBS (black) (n = 4) and mice transplanted with nonregenerative hMSCs (gray) (n = 3). (C) Mice transplanted with regenerative hMSCs showed significantly reduced systemic blood glucose levels over the full time course compared to mice injected with PBS or transplanted with nonregenerative hMSCs. (D–F) Representative photomicrographs of insulin expressing islets at day 35 in mice injected with PBS (D) or transplanted with nonregenerative hMSCs (E) and regenerative hMSCs (F). (G–I) Compared to mice injected with PBS or mice transplanted with nonregenerative hMSCs, mice transplanted with regenerative hMSCs showed significantly increased islet number (G) and b cell mass (H), with no difference in islet size (I). Arrowheads denote islets, and inlets show a 2.53 magnified view of islets outlined with a dotted box. Scale bars, 200 mm. Data are represented as mean ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001. See also Figure S5.

samples, we identified a strong correlation between donor BMI and b cell regenerative capacity, which could be used as a pre-screening modality, in conjunction with targeted proteomics approaches, to select the most potent regenerative hMSC lines for clinical applications. Combining previously characterized hMSC lines, a surrogate in vitro human islet survival assay, and our high-throughput proteomic screening techniques, we successfully demonstrate that hMSC CM contains a subset of 16 secretory proteins that predict

2532 Cell Reports 25, 2524–2536, November 27, 2018

b cell regenerative capacity according to our in vitro and in vivo validation assays. Within this list of proteins, we identified neuronal pentraxin 1 (NPTX1) to be highly upregulated in b cell regenerative hMSCs. NPTX1 has been shown to decrease apoptotic and oxidative stress pathways implicated in impaired insulin secretion and b cell failure using rat models (Schvartz et al., 2012). Thus, the protective role of pentraxin 1 was predicted with the islet survival culture assay, where hMSC lines classified as regenerative consistently showed increased b cell

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Figure 6. Donor Characteristics Reveal Body Mass Index as a Potential Prescreening Tool to Identify More b Cell Regenerative hMSC Lines

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(A) Equal number of male (n = 13) and female (n = 16) donors were characterized as regenerative or nonregenerative using in vivo mouse models or quantitative proteomic screens. (B) No direct correlation between donor age and b cell regenerative potency was observed. (C) A direct correlation between donor BMI and b cell regenerative potency was observed with samples characterized using in vivo mouse models (hexagon), quantitative proteomics (circle), or both (square). (D) All hMSC lines derived from patients that had BMIs in the obese category (>29.9) or in the overweight category (>24.9, <29.9) were classified as nonregenerative (gray). In contrast, 80% of samples derived from donors that has healthy BMIs (<24.9) were classified as regenerative (green). Data are represented as mean ± SD.

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survival compared to nonregenerative hMSC lines. In addition, expression of SFRP1, a Wntsignaling modulator, was consistently upregulated in b cell regenerative hMSCs. Wnt signaling has been previously characterized as an important pathway modulating b cell functions including cell proliferation and survival (Islam, 2010; Kuljanin et al., 2017; Rulifson et al., 2007). In addition, orosomucoid (ORM1) was upregulated in regenerative hMSCs, which has been shown to suppress pro-inflammatory responses (Lee et al., 2010) and is important in regulating metabolic homeostasis in type 2 diabetes (Sun et al., 2016). Lastly, the expression of extracellular matrix proteins such as collagen (COL14A1) was also found to be consistently upregulated in b cell regenerative hMSCs. Although collagen have not been thoroughly studied in the context of diabetes and b cells, evidence has suggested that other matrix proteins, such as matrix metalloproteases (MMPs), may be important in modulating inflammation and innate immunity (Rohani and Parks, 2015). Thus, b cell regenerative hMSC lines secreted several proteins consistent with the modulation of b cell survival, proliferation, and immunity. hMSC lines characterized as nonregenerative represented 80% of the samples screened and showed elevated levels of 6 pro-inflammatory markers. First, upregulated secretion of C-X-C motif chemokine 8 (CXCL8/IL8) has been linked to increased inflammatory responses within islets that can lead to downregulation of pancreas-specific transcription factors and upregulation of pancreatic progenitor cell specific factors, sug-

gesting transformation of mature b cells toward a more immature endocrine cell phenotype, a phenomenon referred to as dedifferentiation (Negi et al., 2012). Second, upregulation of interleukin-6 (IL-6) within b cells has been shown to decrease GLUT2-expression, implicated in the loss of glucose sensing ability, as well as increased T cell responses and reduced regulatory T cell function (Van Belle et al., 2014). Third, C-C motif chemokine ligand 2 (CCL2) is involved in the recruitment of inflammatory monocytes toward islet or b cell populations (Burke et al., 2012). In addition, CCL2 plays a critical role in the clinical outcome of islet transplantation in patients with type 1 diabetes (T1D) by increasing macrophage recruitment, increasing destruction of b cells, and negatively impacting long-lasting insulin independence (Piemonti et al., 2002). Finally, the increased serum expression of CXCL5 is linked to obesity and the onset of type 2 diabetes (T2D) by impairing insulin secretion in response to glucose stimulation (Nunemaker et al., 2014). Another inflammatory protein that was expressed in nonregenerative hMSCs was growth differentiated factor 15 (GDF15). GDF15 has recently been described as a novel marker of inflammatory conditions associated with T2D. A commonly used glucose-lowering drug metformin is used to alleviate complications associated with T2D, and the concentration of GDF15 can be used as a biomarker that directly correlates with dosage amount and duration of metformin treatment (Gerstein et al., 2017). In addition, the concentration of GDF15 in the serum has been used as a valuable clinical marker for predicting transitions in albuminuria stages in patients with T2D (Hellemons et al., 2012), as well as cardiovascular risk in newly diagnosed T2D (Shin et al., 2016), and most recently for involvement in body weight management (Emmerson et al., 2017; Mullican et al., 2017). Lastly, GDF15 has been accurately used to predict future Obese

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Cell Reports 25, 2524–2536, November 27, 2018 2533

insulin resistance and impaired glucose control in obese nondiabetic individuals (Kempf et al., 2012). The final inflammatory protein that was found to be overexpressed in nonregenerative hMSCs was Proenkephalin A (PENK). Serum PENK levels have also been used as biomarkers to describe conditions associated with diabetes (van Hateren et al., 2015). Taken together, these data suggested that compared to regenerative hMSCs, nonregenerative hMSCs establish a more pro-inflammatory microenvironment not permissive of b cell regeneration. In agreement with the pro-inflammatory microenvironment established by nonregenerative hMSCs, GDF15 represents yet another protein responsible for mediating chronic islet inflammation (Breit et al., 2011). Furthermore, elevated levels of GDF15 in nonregenerative hMSCs could be directly correlated to the BMI of the donors that the cell lines were derived from. As mentioned previously, 90% of the nonregenerative hMSC lines were derived from donors that had BMIs > 25. Increased BMI is correlated with increased inflammation as well as increased risk of diabetes (Bays et al., 2007). Patients at risk or with long-term exposure to T2D-related inflammation may have altered hMSC function (Phadnis et al., 2009). Specifically, in mouse models of T2D, the therapeutic potential of endogenous bone-marrow-derived MSC was indeed impaired (Shin and Peterson, 2012). Also, hMSCs derived from patients with T2D showed gene expression profiles that were significantly altered in terms of cytokine secretion and immunomodulatory ability, suggesting states of ‘‘disease memory’’ within hMSC samples (de Lima et al., 2016). Taking all these considerations together, hMSCs derived from patients that are outside of the healthy BMI range or considered ‘‘pre-diabetic’’ can generate altered pro-inflammatory secretory profiles that warrant further investigation in eventual disease progression. In summary, our proteomic analyses demonstrated donorderived hMSC lines could be classified as b cell regenerative or nonregenerative using proteomic analyses of CM. In addition, our analyses demonstrated the rarity of finding a b cell regenerative hMSC lines without ex vivo manipulation. Also, we demonstrated that quantitative proteomics coupled with unbiased data mining can be used to determine sample-specific protein secretion signatures that can be used in a high-throughput fashion, which is significantly cheaper than lengthy in vivo mouse models, to classify previously uncharacterized donor-derived hMSC lines for b cell regenerative clinical applications. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d d

d

KEY RESOURCES TABLE CONTACT FOR REAGENT AND RESOURCE SHARING EXPERIMENTAL MODEL AND SUBJECT DETAILS B Animal maintenance and manipulations B Human subjects METHOD DETAILS B Generation of CM for culture and proteomic analysis B Human islet culture with hMSC CM B Chloroform methanol precipitation and protein digestion

2534 Cell Reports 25, 2524–2536, November 27, 2018

B

d

Liquid chromatography-tandem mass spectrometry (LC-MSMS) B Label-free proteomic data analyses B Support vector machine learning B Isotopically labeled peptide synthesis B Targeted proteomic and data analysis B ELISA B Immunohistochemistry analysis QUANTIFICATION AND STATISTICAL ANALYSIS B Cell counts B Statistical Analyses

SUPPLEMENTAL INFORMATION Supplemental Information includes five figures and eight tables and can be found with this article online at https://doi.org/10.1016/j.celrep.2018.10.107. ACKNOWLEDGMENTS We would like to thank Sarah Krause for her assistance in immunohistochemical staining and acquiring immunofluorescent images and Paula Pittock for mass spectrometry technical support. Studies were supported in part by a JDRF USA Strategic Research Agreement on Optimizing b-cell Regeneration (2-SRA-2015-60-Q-R) grant to D.A.H. and a Natural Sciences and Engineering Research Council of Canada (R-3095A03) grant to G.A.L. AUTHOR CONTRIBUTIONS Conceptualization, M.K., D.A.H., and G.A.L.; Methodology, M.K., R.M.E., G.I.B., D.X., A.X., D.A.H., and G.A.L.; Investigation, M.K., R.M.E., G.I.B., D.A.H., D.X., and G.A.L.; Writing – Original Draft, M.K., D.A.H., and G.A.L.; Writing – Review & Editing, M.K., R.M.E., G.I.B., D.A.H., and G.A.L; Funding Acquisition, D.A.H. and G.A.L.; Supervision, D.A.H. and G.A.L. DECLARATION OF INTERESTS The authors declare no competing interests. Received: June 14, 2018 Revised: October 1, 2018 Accepted: October 29, 2018 Published: November 27, 2018 REFERENCES Bays, H.E., Chapman, R.H., and Grandy, S.; SHIELD Investigators’ Group (2007). The relationship of body mass index to diabetes mellitus, hypertension and dyslipidaemia: comparison of data from two national surveys. Int. J. Clin. Pract. 61, 737–747. Bell, G.I., Broughton, H.C., Levac, K.D., Allan, D.A., Xenocostas, A., and Hess, D.A. (2012a). Transplanted human bone marrow progenitor subtypes stimulate endogenous islet regeneration and revascularization. Stem Cells Dev. 21, 97–109. Bell, G.I., Meschino, M.T., Hughes-Large, J.M., Broughton, H.C., Xenocostas, A., and Hess, D.A. (2012b). Combinatorial human progenitor cell transplantation optimizes islet regeneration through secretion of paracrine factors. Stem Cells Dev. 21, 1863–1876. Breit, S.N., Johnen, H., Cook, A.D., Tsai, V.W.W., Mohammad, M.G., Kuffner, T., Zhang, H.P., Marquis, C.P., Jiang, L., Lockwood, G., et al. (2011). The TGF-b superfamily cytokine, MIC-1/GDF15: a pleotrophic cytokine with roles in inflammation, cancer and metabolism. Growth Factors 29, 187–195. Burke, S.J., Goff, M.R., Updegraff, B.L., Lu, D., Brown, P.L., Minkin, S.C., Biggerstaff, J.P., Zhao, L., Karlstad, M.D., and Collier, J.J. (2012). Regulation of

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Shin, L., and Peterson, D.A. (2012). Impaired therapeutic capacity of autologous stem cells in a model of type 2 diabetes. Stem Cells Transl. Med. 1, 125–135. Shin, M.Y., Kim, J.M., Kang, Y.E., Kim, M.K., Joung, K.H., Lee, J.H., Kim, K.S., Kim, H.J., Ku, B.J., and Shong, M. (2016). Association between growth differentiation factor 15 (GDF15) and cardiovascular risk in patients with newly diagnosed type 2 diabetes mellitus. J. Korean Med. Sci. 31, 1413–1418. Sun, Y., Yang, Y., Qin, Z., Cai, J., Guo, X., Tang, Y., Wan, J., Su, D.F., and Liu, X. (2016). The acute-phase protein orosomucoid regulates food intake and energy homeostasis via leptin receptor signaling pathway. Diabetes 65, 1630–1641. Todorova, D., Simoncini, S., Lacroix, R., Sabatier, F., and Dignat-George, F. (2017). Extracellular vesicles in angiogenesis. Circ. Res. 120, 1658–1673. Tsai, M.-S., Hwang, S.-M., Chen, K.-D., Lee, Y.-S., Hsu, L.-W., Chang, Y.-J., Wang, C.-N., Peng, H.-H., Chang, Y.-L., Chao, A.-S., et al. (2007). Functional network analysis of the transcriptomes of mesenchymal stem cells derived from amniotic fluid, amniotic membrane, cord blood, and bone marrow. Stem Cells 25, 2511–2523.

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Van Belle, T.L., Pagni, P.P., Liao, J., Sachithanantham, S., Dave, A., Bel Hani, A., Manenkova, Y., Amirian, N., Yang, C., Morin, B., et al. (2014). Beta-cell specific production of IL-6 in conjunction with a mainly intracellular but not mainly surface viral protein causes diabetes. J. Autoimmun. 55, 24–32. van Hateren, K.J.J., Landman, G.W.D., Arnold, J.F.H., Joosten, H., Groenier, K.H., Navis, G.J., Sparwasser, A., Bakker, S.J.L., Bilo, H.J.G., and Kleefstra, N. (2015). Serum Proenkephalin A Levels and Mortality After Long-Term Follow-Up in Patients with Type 2 Diabetes Mellitus (ZODIAC-32). PLoS ONE 10, e0133065. Via, A.G., Frizziero, A., and Oliva, F. (2012). Biological properties of mesenchymal stem cells from different sources. Muscles Ligaments Tendons J. 2, 154–162. €gge, U.I. (1984). A method for the quantitative recovery Wessel, D., and Flu of protein in dilute solution in the presence of detergents and lipids. Anal. Biochem. 138, 141–143. Zazzeroni, L., Lanzoni, G., Pasquinelli, G., and Ricordi, C. (2017). Considerations on the harvesting site and donor derivation for mesenchymal stem cells-based strategies for diabetes. CellR4 5, e2435.

STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Mouse Monoclonal anti-Insulin

Sigma

Cat#I2018; RRID:AB_260137

Anti-Mouse IgG (H+L) made in Horse, Peroxidase Labeled

MJS Biolynx

Cat#VECTPI2000; RRID:AB_2336177

ImmPACT DAB, Peroxidase

MJS Biolynx

Cat#VECTSK4105; RRID:AB_2336520

Antibodies

Chemicals, Peptides, and Recombinant Proteins AmnioMAX C-100 Basal Medium

ThermoFisher

Cat#17001074

7-AAD

BioLegend

Cat#420403

AmnioMAX C-100 Basal Medium

ThermoFisher

Cat#17001074

AmnioMAX C-100 Supplement

ThermoFisher

Cat#12556023

APC-Annexin-V

BioLegend

Cat#640920

FluoZin-3 AM cell permeant

ThermoFisher

Cat#F24195

D-(+)-glucose

Sigma

Cat#G7201

Amicon Ultra-15 Centrifugal Filter Units

Millipore Sigma

Cat#UFC900308

Streptozotocin

Sigma

Cat#S0130

FreeStyle Lite

Abbott

N/A

FreeStyle Lite Blood Glucose Strips

Abbott

N/A

Mouse-on-Mouse Immunodetection Kit, Basic

MJS Biolynx

Cat#VECTBMK2202; RRID:AB_2336833

Synthesis ready preloaded arginine TRT resin

Cambridge Isotopes

CAT#SRPR-ARG-CN-PK

Critical Commercial Assays CXCL8 (IL8) ELISA

R&D Systems

Cat#D800C

IL6 ELISA

ThermoFisher

Cat#EH2IL6

Human multipotent stromal cells

Bell et al., 2012a; 2012b

N/A

Human islets

IIDP

N/A

Jackson Lab.

N/A

AxioVision Microscope Software

Zeiss

Version 4.7

AxioVision Microscope Software

Zeiss

Version 4.8

Northern Eclipse

EMPIX Imaging

Version 7.0

MaxQuant

Max Planck Inst.

Version 1.5.8.3

Experimental Models: Cell Lines

Experimental Models: Organisms/Strains NOD.CB17-Prkdcscid/J Software and Algorithms

Perseus

Max Planck Inst.

Version 1.5.8.5

Graphpad

GraphPad

Version 6.01

Skyline

Skyline

Version 3.7.0.11317

RStudio

CRAN R Project

Version 3.5.0

Other Olympus BX50 Microscope

Olympus

N/A

Axio Imager 2 Microscope

Zeiss

N/A

EVOS FL Cell Imaging System

ThermoFisher

N/A

Q Exactive Plus

ThermoFisher

N/A

M-Class UPLC

Waters

N/A

MultiPep RS

Intavis

N/A

Cell Reports 25, 2524–2536.e1–e4, November 27, 2018 e1

CONTACT FOR REAGENT AND RESOURCE SHARING Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Gilles Lajoie ([email protected]). EXPERIMENTAL MODEL AND SUBJECT DETAILS Animal maintenance and manipulations Mouse protocols were reviewed and approved by the Animal Care Committee at the University of Western Ontario and all colonies were maintained following Canadian animal research guidelines. NOD/SCID mice (male and female) were initially obtained from Jackson Laboratories, were housed and used according to the guidelines of the Animal Use Protocol (2015-033). To induce hyperglycemia, STZ (Sigma) was dissolved in 0.1 M sodium citrate buffer (CAB) at pH 4.5, and 5 doses (days 1-5) were administered intraperitoneally (8 weeks of age) at 35 mg/kg/day within 15 min of dissolving. Hyperglycemia progression (> 15 mmol/L) was assessed by monitoring the blood glucose levels up to the day of transplantation on day 10. For transplantation, mice were anesthetized and 5e5 ex vivo expanded hMSC were injected into the tail vein. Negative control mice were injected with PBS via the tail vein. Mice were monitored weekly for non-fasted blood glucose by tail vein puncture using a FreeStyleTM glucometer (Abbott). Human subjects Human bone marrow from 13 male and 16 female healthy donors ages 21-66 was obtained after informed consent at the London Health Science Centre (London, Ontario, Canada). All studies were approved by the Human Research Ethics Board at the University of Western Ontario (REB 12934, 12252E). Human pancreatic islets were provided by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) funded Integrated Islet Distribution Program (IIDP) at the City of Hope (California, USA), NIH Grant #2EC4DK098085-02. METHOD DETAILS Generation of CM for culture and proteomic analysis After 4 days of culture, passage 3-4 (80% confluency), hMSC were washed twice with PBS to remove residual growth factors and replated in basal AmnioMaxTM media without supplement to collect proteins secreted by hMSC for 24 hr. Media conditioned by hMSC was filtered and centrifuged at 450 xg to remove any cellular debris. Cell viability was assessed using trypan blue staining and > 95% viability was used as a standard cutoff for all in vitro culture assays and secretome analyses. For in vitro culture assays, CM was generated fresh and concentrated the morning of each experiment. CM was assessed by NanoDropTM (A280) and protein amount was normalized to 0.40 mg/uL total protein and was used to culture human islets for 7 days. For proteomic analyses, CM was generated in duplicate and concentrated using 3kDa molecular weight cutoff filter units (Millipore) and stored at 80 C until processing. For proteomic analysis, concentrated CM was lyophilized overnight and re-suspended in 8.0 M urea, 50 mM ammonium bicarbonate (ABC), 10 mM dithiothreitol (DTT) and 2% SDS solution prior to protein quantification. Protein concentrations were subsequently determined using the Pierce 660 nm protein assay (ThermoFisher Scientific). Human islet culture with hMSC CM Human islets from 3 donors were obtained from the Integrated Islet Distribution Program (IIDP). Upon arrival, 200 islet equivalents were plated in RPMI media without serum (Invitrogen). CM was concentrated as described above and 25 mg total protein was added to islet cultures for 7 days. Negative control samples received no hMSC secreted proteins. Culture media was switched once during the 7 days of culture on day 4, by replacing 75% of the media with fresh RPMI and additional 25 mg total protein. After 7 days, islet were harvested and dissociated using 0.25% trypsin (ThermoFisher Scientific) and b cell content was estimated using FluoZin-3TM (Flz3) (ThermoFisher Scientific) and apoptosis was quantified using 7-AAD and Annexin-V (BioLegend). Flow cytometry was performed using a LSR II flow cytometer (BD Biosciences) and data was analyzed using FloJo software (V10) (Treestar). Chloroform methanol precipitation and protein digestion Protein extracts from hMSC in 8.0 M urea lysis buffer were reduced in 10 mM DTT for 30 min in the dark. Next, samples were alkylated with 100 mM iodoacetamide (IAA) for 45 min at room temperature in the dark. To facilitate the removal of incompatible detergents, €gge protocol reducing and alkylating reagents, proteins were precipitated using chloroform methanol according to the Wessel and Flu €gge, 1984). Briefly, 30 mg of protein extract from each sample was diluted to a final volume of 150 mL with 50 mM ABC, (Wessel and Flu and 600 mL of ice-cold methanol was added to each sample, followed by 150 mL of chloroform with thorough vortexing. Next, 450 mL of ice-cold deionized water was added before additional vortexing and centrifugation at 14000 xg for 5 min at room temperature. The upper aqueous phase was removed and an additional 450 mL of methanol was used to wash the protein pellet with vigorous vortexing and centrifugation at 14000 xg for 5 min at room temperature. The remaining chloroform methanol was discarded and the precipitated protein was dissolved in 100 mL of 50 mM ABC (pH 8.0) prior to protein digestion. For on-pellet protein digestion, a solution of LysC (Wako) (1:100 enzyme to protein ratio) was added to each precipitated samples and incubated in a ThermoMixer (ThermoFisher

e2 Cell Reports 25, 2524–2536.e1–e4, November 27, 2018

Scientific) at 37 C for 4 hr at 1000 RPM, followed by trypsin/LysC (1:50) (Promega) solution was added to each sample and incubated at 37 C overnight at 1000 RPM. The next day, an additional aliquot of trypsin (1:100 ratio) was added for 4 hr, prior to acidifying with 10% formic acid (FA) (pH 3-4). The peptide concentrations were estimated using a Pierce BCA assay (ThermoFisher Scientific). Liquid chromatography-tandem mass spectrometry (LC-MSMS) Approximately 1 mg of each sample was injected onto a Waters M-Class nanoAcquity HPLC system (Waters) coupled to an ESI Orbitrap mass spectrometer (Q Exactive Plus) (ThermoFisher Scientific) operating in positive mode. Buffer A consisted of mass spectrometry grade water with 0.1% FA and buffer B consisted of acetonitrile with 0.1% FA (ThermoFisher Scientific). All samples were trapped for 5 min at a flow rate of 5 mL/min using 99% buffer A and 1% buffer B on a Symmetry BEH C18 Trapping Column (5 mm, 180 mm x 20 mm, Waters). Peptides were separated using a Peptide BEH C18 Column (130 A˚, 1.7 mm, 75 mm x 250 mm) operating at a flow rate of 300 nL/min at 35 C (Waters). Samples were separated using a non-linear gradient consisting of 1%–7% buffer B over 1 min, 7%–23% buffer B over 95 min and 23%–35% buffer b over 45 min, before increasing to 98% buffer B and washing. Settings for data acquisition on the Q Exactive Plus for both LFQ and PRM methods are outlines in Table S8. Label-free proteomic data analyses All MS raw files were searched in MaxQuant version 1.5.8.30 using the Human Uniprot database (updated May 2015 with 20, 264 entries) (Cox and Mann, 2008; UniProt Consortium, 2014). For all database searches, missed cleavages were set to 3, cysteine carbamidomethylation was set as a fixed modification and Oxidation (M), N-terminal Acetylation (protein) and Deamidation (NQ) were set as a variable modifications (max. number of modifications per peptide = 5), peptide length R 6. Precursor mass deviation was left at 20 ppm and 4.5 ppm for first and main search, respectively. Fragment mass deviation was left at 20 ppm. Protein and peptide FDR was left to 0.01 (1%) and decoy database was set to revert. Match between runs was enabled and all other parameters left at default. Bioinformatics analysis was performed using Perseus version 1.5.8.5. Briefly, protein lists were loaded into Perseus and proteins were identified by site, reverse and contaminants were removed. When using the match between runs feature, datasets were filtered for proteins containing a minimum of 1 unique peptide and quantified in 6 out of 10 different hMSC lines (training dataset). Support vector machine learning Data mining and protein marker selection of label-free quantitative proteomic data was achieved using the R package ‘‘geNetClassifier support vector machine’’ (SVM). Briefly, a text document was constructed that included all proteins or just contained classically secreted proteins identified within the initial training dataset. Two groups were created (regenerative and nonregenerative hMSC) based on classification obtained from previous in vivo and data generated from in vitro human islet culture assays. The posterior probability, or the predictive power, of each protein within the dataset was determined and exported. Proteins that achieved a posterior probability of > 0.95 were further evaluated. Uncharacterized hMSC lines were assessed against the classifier in the same manor. Label-free quantitative data for each uncharacterized hMSC line was imported into the SVM constructed from the training dataset, and the probability of assigning each cell line was determined. Isotopically labeled peptide synthesis Solid phase peptide synthesis was achieved using the 96-well format on the MultiPep RS system (Intavis). Peptides were synthesized using fmoc-chemistry on heavy (13C15N) labeled arginine preloaded chlorotrityl chloride resin (Cambridge Isotopes). Crude peptides were purified using an Agilent 1100 pump systems on a C18 column (Agilent). Peptide sequence and purity were determined using a quadrupole mass spectrometer (MicroMass Quattro micro). Peptide purify was assessed using LC-MSMS and further evaluated using mass spectrometry. Targeted proteomic and data analysis Targeted proteomic analyses was performed using a Q Exactive Plus operating in PRM mode (ThermoFisher Scientific). Briefly, a targeted inclusion list was added, allowing acquisition of specific peptides selected based on a spectral library constructed from combining data-dependent acquisition runs from the previously described datasets created using Skyline V3.7.0.11317. Protein lists were filtered to only include proteins that had posterior probabilities of > 0.95. In addition, missed cleavages were set to zero, and peptide length was limited to 16 amino acids. Peptides were chosen for targeting by order of pick intensity, meaning the most abundant peptides for each target were measured for a total of 16 targets corresponding to 16 peptides. A scheduled list was exported using a retention time window of ± 7.0 min. For each PRM sample, 20 mg of peptide was lyophilized and resuspended in 1 fmol/mL solution of ‘‘heavy gfp.’’ PRM raw files were processed using skyline and total fragment area, which corresponded to the 4 most intense fragment ions (excluding y1), were chosen for automatic integration. The total integrated fragment area of each peptide was determined and the ratio of target: heavy gfp was used to determine the relative abundance of each protein using a standard curve. ELISA Condition media was generated from 20 different hMSC lines as described above without concentration. CXCL8 (R&D Systems) and IL6 (ThermoFisher Scientific) protein levels within the CM were measured in duplicate using ELISA according to the manufacturer’s instructions.

Cell Reports 25, 2524–2536.e1–e4, November 27, 2018 e3

Immunohistochemistry analysis Pancreata were frozen in optimal cutting temperature media and sectioned (10 mm) such that each slide contained 3 sections > 150 mm apart. Sections were fixed in formalin, blocked with mouse serum, incubated with mouse insulin antibody and detected with peroxidase-labeled anti-mouse antibody (Vector). Criteria for islet enumeration required a minimum of 10 insulin+ cells. Islet size and number were quantified using light microscopy counting all islets within three sections per mouse. Islet circumference was calculated using AxioVision software. b cell mass was calculated by: b cell area/total area x pancreas weight. All histological quantification was performed by manual counting by at least two different people in a blinded fashion. QUANTIFICATION AND STATISTICAL ANALYSIS Cell counts Quantitative analyses were performed by manually counting cells or nuclei on immunostained section of the mouse pancreas in a blinded fashion. Specifically, every tenth section was counted and photographs were taken of the entire pancreas section by two different people. For islet size and b cell mass quantification, colorimetric insulin was used to define regions on interest. Statistical Analyses All statistical details of experiments can be found in the results section and in the figure legends and the number of bone marrow derived cell lines, proteomic replicates, mice used, or human pancreatic donors are represented as n. Data are represented as mean ± S.E.M., unless otherwise stated in the figure legends, and were considered significant if p < 0.05 using ANOVA with Tukey’s post hoc test. For proteomic data analyses was conducted using build in multiple sample t tests using permutation based FDR (p < 0.05, FDR < 0.05) in Perseus. Fold changes were considered significant if they were > 2-fold differentially expressed between samples. For all other analyses, data were analyzed for significance using Graph Pad prism V6.01 (GraphPad Software).

e4 Cell Reports 25, 2524–2536.e1–e4, November 27, 2018