| Literature DB >> 27515308 |
Emily T Camilleri1, Michael P Gustafson2, Amel Dudakovic1, Scott M Riester1, Catalina Galeano Garces1, Christopher R Paradise1, Hideki Takai3, Marcel Karperien4,5, Simon Cool6,7, Hee-Jeong Im Sampen8,9,10,11, A Noelle Larson1, Wenchun Qu12, Jay Smith13,14,15, Allan B Dietz2, Andre J van Wijnen16,17.
Abstract
BACKGROUND: Clinical translation of mesenchymal stromal cells (MSCs) necessitates basic characterization of the cell product since variability in biological source and processing of MSCs may impact therapeutic outcomes. Although expression of classical cell surface markers (e.g., CD90, CD73, CD105, and CD44) is used to define MSCs, identification of functionally relevant cell surface markers would provide more robust release criteria and options for quality control. In addition, cell surface expression may distinguish between MSCs from different sources, including bone marrow-derived MSCs and clinical-grade adipose-derived MSCs (AMSCs) grown in human platelet lysate (hPL).Entities:
Keywords: Adipose-derived mesenchymal stromal cells; CD markers; Flow cytometry; Human platelet lysate; Manufacturing; RNA-sequencing; Release criteria
Mesh:
Substances:
Year: 2016 PMID: 27515308 PMCID: PMC4982273 DOI: 10.1186/s13287-016-0370-8
Source DB: PubMed Journal: Stem Cell Res Ther ISSN: 1757-6512 Impact factor: 6.832
List of antibodies used for flow cytometry validation
| Antibody | Fluorophore | Company | Catalog number |
|---|---|---|---|
| CD163 | FITC | Beckman Coulter | B17492 |
| CD140B | PE | BD | 558821 |
| CD248 | Alexa Fluor 647a | Abgent | AP6756b |
| CD146 | PC5 | Beckman Coulter | A22364 |
| CD200 | PE-Cy7 | BD | 562125 |
| CD36 | APC | Beckman Coulter | A87786 |
| CD34 | APC-A750 | Beckman Coulter | A89309 |
| CD44 | Pac Blu | Beckman Coulter | B37789 |
| CD44 | PC7 | eBioscience | 25-0441 |
| CD276 | APC | eBioscience | 17-2769-41 |
| CD271 | FITC | Biolegend | 345104 |
| CD105 | PE | Beckman Coulter | A07414 |
| CD73 | PerCP eFluor710 | eBioscience | # 46-0739-42 |
| CD73 | PE | BD | #550257 |
| CD90 | FITC | Beckman Coulter | IM1839U |
| CD274 | PC7 | Beckman Coulter | A78884 |
| CD14 | ECD | Beckman Coulter | IM2707U |
| HLA-ABC | APC | eBioscience | 17-9983 |
| HLA-DR | Pac Blu | Beckman Coulter | A74781 |
| CD45 | Krome Orange | Beckman Coulter | A96416 |
APC allophycocyanin, FITC fluorescein isothiocyanate, PC7 R-phycoerythrin cyanin 7, PE R-phycoerythrin
a Custom Beckman Coulter-labeled antibody
Fig. 1Traditional phenotyping of clinical-grade adipose-derived mesenchymal stromal cells (AMSCs) expanded in human platelet lysate. a Clinical-grade AMSCs grown in human platelet lysate were expanded ex vivo and immunophenotyped using flow cytometry according to the release criteria presented in this table. b Representative flow cytometry scatter plots show AMSCs are a homogeneous population of cells and exhibit surface expression of standard cell surface markers, including CD105, CD44, CD73, and CD90, and are negative for HLA-DR. c Analysis of the flow cytometry release criteria across clinical-grade AMSCs from 15 donors demonstrated minimal variability in the population frequency (% Gated) of the surface markers. All AMSC donor cells were >85 % positive for CD90, CD105, CD73, CD44, and HLA-ABC, and were <85 % positive for HLA-DR, CD45, and CD14
Fig. 2Gene expression profiling and validation of cell surface markers across multiple mesenchymal cell types. a Gene expression of traditional markers by adipose-derived mesenchymal stromal cells (AMSCs) was analyzed using quantitative PCR (qPCR). AMSCs have relatively high expression levels of CD44, CD90, CD105, and CD73, and low or no expression of CD14 and CD45. To identify AMSC specific surface markers, high-throughput qPCR screening of 69 surface markers curated from the literature was performed on various mesenchymal cell types, including AMSCs (n = 4), bone marrow-derived stromal cells (BMSCs) (n = 2), primary bone cells () (n = 4), primary chondrocytes (C) (n = 4), and primary fibroblasts (F) (n = 4). b Hierarchical clustering analysis of qPCR data across multiple cell types shows that AMSCs have a unique phenotype at the gene expression level. c Comparison of different cell types revealed that classical surface markers, including CD44, CD73, and CD90, are expressed by not only AMSCs but also other cell types. Furthermore, nine non-classical markers were selected based on differential expression between the various mesenchymal cells. These markers, together with classical markers, were used to develop a novel antibody panel to characterize AMSCs
Fig. 3Validation of nine non-classical markers by flow cytometry among 15 clinical-grade adipose-derived mesenchymal stromal cells (AMSCs). Expression of five classical (a) and nine non-classical markers (b) was validated by flow cytometry across 15 additional freshly isolated and expanded AMSC donors. Surface marker expression was evaluated by the percentage of the gated cell population and mean fluorescence intensity (MFI). c Representative histograms of surface marker expression compared to unstained AMSCs (negative control). Of the non-classical markers, CD36 exhibited two cell populations which varied from patient-to-patient
Fig. 4Expression of novel markers by quantitative PCR (qPCR) and flow cytometry. Gene expression data were compared to flow cytometry data for two donors [adipose-derived mesenchymal cell (AMSC) donors 1 and 4]. Highly abundant markers showed good concordance (top panel) between the techniques, whereas lower abundance markers showed variability (bottom panel). In particular, CD200 and CD274 were not correlated. MFI mean fluorescence intensity
Fig. 5Effect of cryopreservation on surface marker expression. Flow cytometry for all 14 markers was performed on samples from 5 adipose-derived mesenchymal stromal cell (AMSC) donors before cryopreservation (pre-freeze), immediately after rescue from cryopreservation (post-thaw), and 4 days after revival from cryopreservation (4d Culture). a AMSCs were >90 % positive for classical surface markers across all manufacturing conditions, except CD34 which was a negative marker. b Non-classical surface markers exhibited variability both in population positivity (%Gated) and mean fluorescence intensity (MFI) as cells were processed through the various manufacturing conditions. One-way ANOVA and post-hoc testing were performed to identify variables that were statistically significant at p < 0.05
Fig. 6High-resolution RNA-sequencing (RNA-seq) analysis of surface marker gene expression by proliferating and confluent adipose-derived mesenchymal stromal cells (AMSCs). a Gene expression profiling using quantitative PCR for 69 cell surface protein-encoding genes reveals some surface markers are differentially expressed between proliferating (~70–80 % confluent) and confluent (100 % confluent) AMSCs. Values indicate fold-change (Log10 transformed) of confluent over proliferating, and are averages of samples from four different AMSC donors. Fold-change analysis shows markers that are differentially expressed between proliferating and confluent cultures. To further evaluate differential surface marker expression, RNA-seq was performed on proliferating and confluent AMSCs from four different donors. b Expression values for 551 cell surface genes expressed at a magnitude >0 reads per kilobase per million (RPKM) by all AMSC donors were extracted from the RNA-seq data set and subjected to hierarchical clustering analysis, which revealed distinct expression patterns for proliferating and confluent cells. c Representative graphs of genes derived from RNA-seq analysis shows CD292/BMPR1A was constitutively expressed, CD168/HMMR was only expressed by proliferating cells, and CD106/VCAM1 was only expressed in confluent cells
Top 25 categories from a DAVID 6.7 analysis of cell surface proteins expressed on adipose-derived mesenchymal stromal cells
| Constitutive | Proliferating | Confluent | |
|---|---|---|---|
| ( | ( | ( | |
| Rank | Category (enrichment score) | Category (enrichment score) | Category (enrichment score) |
| 1 | Membrane (48.26) | Signal (28.07) | Membrane (24.12) |
| 2 | Signal (47.47) | Membrane (27.82) | Signal (18.65) |
| 3 | Plasma membrane (28.33) | Cell adhesion (14.15) | Plasma membrane (11.82) |
| 4 | Cell adhesion (9.99) | Plasma membrane (12.38) | Tetraspanin (3.97) |
| 5 | Signal transduction (8.17) | Immunoglobulin-like (6.25) | Protease/peptidase (3.95) |
| 6 | Tetraspanin (7.20) | Tetraspanin (5.88) | ABC transporter (3.93) |
| 7 | Lipoprotein (5.52) | Integrin (5.75) | Growth factor binding (3.82) |
| 8 | Angiogenesis (5.11) | Response to wounding (4.85) | Cell adhesion (3.39) |
| 9 | B cell activation/Fas pathway (5.08) | Immunoglobulin-like V set (4.57) | Secreted (3.30) |
| 10 | Cell migration (4.86) | Metalloprotease (4.47) | Semaphorin (2.95) |
| 11 | Apoptosis (4.68) | Cell–cell adhesion (4.37) | Immunoglobulin-like (2.95) |
| 12 | Semaphorin (4.63) | Transferase (3.71) | Immune cell activation (2.55) |
| 13 | Immune response (4.38) | Semaphorin/integrin (3.56) | Vesicle-mediated transport (2.40) |
| 14 | Calcium-mediated signaling (4.20) | Transport (3.40) | Cytokine–cytokine receptor interaction (2.33) |
| 15 | Cytokine binding (4.12) | Extracellular matrix-receptor interaction (3.37) | Cell migration (2.29) |
| 16 | Activation immune response (4.00) | Cell motion (3.36) | Integrin (2.24) |
| 17 | Cell motion (3.68) | Differentiation (3.04) | Glycoprotein metabolic process (2.07) |
| 18 | Protein kinase cascade (3.64) | Epidermal growth factor (2.88) | Cell proliferation (1.96) |
| 19 | Integrin (3.31) | Low-density lipoprotein (2.32) | Membrane fraction (1.93) |
| 20 | Tyrosine protein kinase (3.04) | Biosynthetic process (2.20) | Immunoglobulin V-set (1.84) |
| 21 | Immune cell activation (3.02) | Secreted (2.13) | Regulation of transcription (1.56) |
| 22 | Signal-anchor (3.01) | Immune response (2.11) | Response to wounding (1.48) |
| 23 | Low density lipoprotein receptor (2.91) | Membrane fraction (2.04) | Regulation of immune activation (1.38) |
| 24 | Stress-activated protein kinase (2.90) | Magnesium ion binding (1.92) | Protein kinase cascade (1.18) |
| 25 | Wnt receptor pathway (2.88) | Cytokine binding (1.75) | Carbohydrate binding (1.07) |