Abstract
Mesenchymal stem/stromal cells (MSCs) represent a heterogeneous cell population distributed throughout various tissues, demonstrating remarkable adaptability to microenvironmental cues and holding immense promise for disease treatment. However, the inherent diversity within MSCs often leads to variability in therapeutic outcomes, posing challenges for clinical applications. To address this heterogeneity, purification of MSC subpopulations through marker-based isolation has emerged as a promising approach to ensure consistent therapeutic efficacy. In this review, we discussed the reported markers of MSCs, encompassing those developed through candidate marker strategies and high-throughput approaches, with the aim of explore viable strategies for addressing the heterogeneity of MSCs and illuminate prospective research directions in this field.
Keywords: Mesenchymal stem/stromal cells, MSCs, Subpopulation, Cell markers, Identity
Introduction
MSCs (mesenchymal stem/stromal cells) are heterogenous cell populations, residing in various tissues (such as bone marrow, umbilical cord, teeth, adipose, and so on). Differing from other types of adult stem cells or terminal differentiated cells, the main function of MSCs is sensing and responding to micro-environmental disturbances. Due to their innate characteristics and functions, they have multiple ways to respond to micro-environmental changes, such as extracellular matrix modification, recruiting other cells (the immune cells, for example), secreting small factors with various functions (immune modulation and regeneration, for example) [1]. It is well-known that many diseases are resulting from micro-environment dysfunctions. Therefore, the MSCs have been intensively and extensively applied in treating different kinds of diseases. Both pre-clinical and clinical investigations have shown that the MSCs hold great promise in developing one new therapeutic approach for treating many kinds of diseases [1–5].
Soon after the first demonstration of MSCs, its therapeutic applications have been investigated for decades. Unfortunately, in contrast to the rapid growth of clinical trials, few of them eventually have been developed as applicable therapeutic products. In addition to other factors inducing the therapeutic inconsistency of MSCs, cell heterogeneity is one tough challenge in their way to achieve the expected clinical outcomes [1, 2, 4, 6, 7].
The heterogeneity of MSCs is reflected in different levels, such as the molecular levels (transcriptomics, proteomics, secretomics, and epigenomics), and the function levels (tri-lineage differentiation potentials, immunomodulatory capabilities, and regenerative activities) [2, 7, 8]. The heterogeneity of MSCs could be induced by various factors including the donor conditions (age, gender, health condition, genetic background, and so on), tissue origin, and the strategies to isolate and expand the MSCs (digestion enzyme, matrix protein, cell culture medium, passage number, and so on) [2, 6, 7, 9–12] (Fig. 1). The causes of MSC heterogeneity have been extensively described in the preceding reviews, and we will not delve into them further. Pluripotent stem cell derived MSC, which can avoid the heterogeneity induced by the aforementioned factors, is also an important category of MSCs [13, 14]. However, we will focus exclusively on MSCs derived from somatic cells under natural conditions in this review.
Fig. 1.
Illustration of factors inducing MSC heterogeneity and potential solutions. The MSC heterogeneity results from various factors, including donor conditions (age, gender, health conditions), tissue origin (bone, fat, placenta/umbilical cord, teeth), and the methods employed for isolating (plastic adherence, MACS, FACS) and expanding MSCs (2D, 3D bioreactor, 3D matrix). To address MSC heterogeneity and enhance their therapeutic stability, three primary strategies are currently employed. These strategies encompass standardizing the MSC production procedures and purifying MSC subpopulations by markers. MSC mesenchymal stem/stromal cell, MACS magnetic-activated cell sorting, FACS fluorescence-activated cell sorting, 2D 2 dimensional, 3D 3 dimensional
Among different strategies to reduce the heterogeneity and improve the therapeutic consistency of MSCs, purifying the homogenous MSC subpopulations is suggested to yield more consistent clinical outcomes [6]. MSC subpopulations refer to distinct groups or subsets within the broader MSC population that are identified based on specific characteristics or markers. These characteristics can include surface protein expressions, functional properties, gene expression profiles, or responsiveness to different environmental cues. According to the minimal criteria for defining MSCs, stated by the International Society for Cellular Therapy in 2006 [15], 55 MSC markers have been identified so far from different tissues and species (Fig. 2, Table 1).
Fig. 2.
Timeline of MSC marker identification
Table 1.
Basic information for MSC markers
| Name | Gene ID | Aliases | Full name | Year published | Positive (%) | Tissue | Species | References |
|---|---|---|---|---|---|---|---|---|
| STRO-1 | Antibody recogniziing unknown target | 1991 | 5–66.5 | Bone marrow | Human | 2070060 | ||
| 2004 | 6 | Bone marrow | Human | 14715641 | ||||
| 2009 | 6 | Bone marrow | Human | 19143868 | ||||
| 2010 | 13.3 | Bone marrow | Human | 20162565 | ||||
| 2011 | n.d | Adipose tissue | Rat | 21903091 | ||||
| 2011 | n.d | Bone marrow | Rat | 21208041 | ||||
| 2011 | n.d | Heart | Rat | 21208041 | ||||
| 2011 | n.d | Leg muscle | Rat | 21208041 | ||||
| 2011 | n.d | Aorta | Rat | 21208041 | ||||
| 2011 | n.d | Rectum | Rat | 21208041 | ||||
| 2011 | n.d | Adipose tissue | Human | 21208041 | ||||
| 2011 | n.d | Prostate tissue | Human | 21208041 | ||||
| 2017 | 11 | Tooth germ | Human | 28686984 | ||||
| 2019 | 1.0–9.6 | Gingiva | Human | 30260000 | ||||
| 2021 | 10 | Dental follicle | Human | 33278647 | ||||
| 2021 | 15 | Periodontal ligament | Human | 33278647 | ||||
| 2021 | 20 | Dental pulp | Human | 33278647 | ||||
| CD34 | 947 | CD34 molecule | 1991 | n.d | Bone marrow | Human | 1720038 | |
| 2007 | 1.8 | Bone marrow | Human | 17786605 | ||||
| 2016 | n.d | Placental amnion membrane | Human | 27405780 | ||||
| NT5E | 4907 |
NT; eN; NT5; NTE; eNT; CD73; E5NT; CALJA; |
5'-nucleotidase ecto | 1992 | n.d | Bone marrow | Human | 1316137 |
| 2006 | n.d | Chondrocyte | Murine | 16443378 | ||||
| 2006 | n.d | Osteoblast precursors | Human | 16418778 | ||||
| 2008 | n.d | Bone marrow | Human | 18086871 | ||||
| 2017 | 44.8–69.1 | Bone marrow | Murine | 28684854 | ||||
| 2018 | n.d | Bone marrow | Murine | 29451855 | ||||
| 2019 | n.d | Bone marrow | Murine | 31279774 | ||||
| 2021 | n.d | Adipose tissue | Human | 33407847 | ||||
| NGFR | 4804 |
CD271; p75NTR; TNFRSF16; p75(NTR); Gp80-LNGFR; |
Nerve growth factor receptor | 1993 | n.d | Bone marrow | Human | 7681701 |
| 2006 | n.d | Bone marrow | Human | 16977637 | ||||
| 2007 | n.d | Bone marrow | Human | 17395729 | ||||
| 2010 | 0.2–2.5 | Bone marrow | Human | 20179086 | ||||
| 2012 | n.d | Bone marrow | Human | 22268519 | ||||
| 2012 | n.d | Bone marrow | Human | 22048731 | ||||
| 2012 | 5.5 | Adherent dermal | Human | 22048731 | ||||
| 2012 | 31.4 | Embryonic face | Rat | 22982680 | ||||
| 2015 | 10.6 | Dental pulp | Human | 26674422 | ||||
| 2018 | 0.1–1.1 | Bone marrow | Human | 29482445 | ||||
| 2018 | 3.8–13 | Bone marrow | Human | 29915318 | ||||
| 2018 | 1.5–5.9 | Adipose tissue | Human | 29915318 | ||||
| 2018 | 0–0.5 | Amniotic fluid | Human | 29915318 | ||||
| 2018 | 0–0.5 | Cord blood | Human | 29915318 | ||||
| 2019 | n.d | Adipose tissue | Human | 30816233 | ||||
| 2021 | 19.1–22.1 | Adipose tissue | Human | 33653407 | ||||
| ENG | 2022 |
END; HHT1; ORW1; |
Endoglin | 1996 | n.d | Bone marrow | Human | 8652367 |
| 2000 | 98 | Bone marrow | Human | 10942523 | ||||
| 2005 | 33.7–46.5 | Bone marrow from young | Human | 16507351 | ||||
| 2005 | 36.7–43.9 | Bone marrow from old | Human | 16507351 | ||||
| 2006 | 1.9–2.8 | Bone marrow | Human | 16601078 | ||||
| 2010 | 1.2–82.0 | Adipose tissue | Human | 20153525 | ||||
| 2011 | n.d | Synovium | Rat | 21205995 | ||||
| ALCAM | 214 |
MEMD; CD166; |
Activated leukocyte cell adhesion | 1997 | n.d | Bone marrow | Human | 9276087 |
| 1998 | n.d | Bone marrow | Rat | 9556065 | ||||
| 1998 | n.d | Bone marrow | Rabbit | 9556065 | ||||
| 1998 | n.d | Bone marrow | Canine | 9556065 | ||||
| 1998 | n.d | Bone marrow | Human | 9556065 | ||||
| 2002 | 5.7–91.4 | Bone marrow | Murine | 12070283 | ||||
| 2011 | 11.5–72.2 | Cartilage | Human | 21787134 | ||||
| 2011 | n.d | Bone marrow | Horse | 21782255 | ||||
| 2011 | n.d | Adipose tissue | Horse | 21782255 | ||||
| 2015 | n.d | Amniotic membrane | Porcine | 26540004 | ||||
| 2020 | n.d | Bone marrow | Human | 34493362 | ||||
| 2020 | n.d | Umbilical cord | Human | 34493362 | ||||
| MCAM | 4162 |
CD146; MUC18; HEMCAM; METCAM; MelCAM; |
Melanoma cell adhesion molecule | 1998 | n.d | Bone marrow | Human | 9529137 |
| 2003 | n.d | Bone marrow | Human | 12674330 | ||||
| 2003 | n.d | Dental pulp | Human | 12674330 | ||||
| 2007 | n.d | Bone marrow | Human | 17332507 | ||||
| 2007 | n.d | Dental pulp | Human | 17332507 | ||||
| 2007 | 1.3–1.7 | Endometrial tissue | Human | 17872908 | ||||
| 2011 | n.d | Bone marrow | Human | 21415267 | ||||
| 2016 | n.d | Bone marrow | Human | 26753846 | ||||
| 2016 | 12–25 | Umbilical cord | Human | 26841872 | ||||
| 2016 | 14.7–99.4 | Bone marrow | Human | 26941359 | ||||
| 2016 | 39.5 | Periapical cyst | Human | 27406247 | ||||
| 2019 | 60–90 | Bone marrow | Human | 31002939 | ||||
| 2020 | 34.6–65.6 | Bone marrow | Human | 32379908 | ||||
| 2021 | 11 | Gingival tissue | Human | 33777147 | ||||
| 2021 | n.d | Dental pulp | Human | 34461987 | ||||
| 2022 | 70–80 | Umbilical cord | Human | 35729643 | ||||
| THY1 | 7070 |
CD90; CDw90; |
Thy-1 cell surface antigen | 1999 | n.d | Bone marrow | Human | 10102814 |
| 2009 | n.d | Bone marrow | Murine | 19841085 | ||||
| 2009 | 95–100 | Bone marrow | Human | 18985728 | ||||
| 2009 | 32–75 | Bone marrow | Human | 18985728 | ||||
| 2009 | 38–96 | Amnion | Human | 18985728 | ||||
| 2009 | 63–91 | Chorion | Human | 18985728 | ||||
| 2014 | n.d | Incisor tooth | Murine | 25079316 | ||||
| 2016 | 98 | Dental pulp | Human | 27465541 | ||||
| 2016 | 98 | Adipose tissue | Human | 27465541 | ||||
| 2016 | 98 | Amniotic fluid | Human | 27465541 | ||||
| 2018 | 30 | Incisor pulp | Murine | 29371677 | ||||
| 2018 | 8.8–10.2 | Arterie | Human | 30008326 | ||||
| 2019 | n.d | Cardiac | Murine | 31353772 | ||||
| ITGA1 | 3672 |
VLA1; CD49a; |
Integrin subunit alpha 1 | 2000 | 22–89 | Bone marrow | Human | 10911362 |
| 2003 | 2.2–4.0 | Bone marrow | Human | 12877680 | ||||
| 2003 | 55 | Bone marrow | Human | 12883998 | ||||
| 2005 | 2.5–26.8 | Bone marrow | Human | 15676216 | ||||
| 2007 | 16.8 | Bone marrow | Human | 17694277 | ||||
| 2007 | 3.2–4.0 | Bone marrow | Human | 17109120 | ||||
| 2007 | 4–5 | Bone marrow | Murine | 17109120 | ||||
| 2007 | 1.0–1.2 | Bone marrow | Rat | 17109120 | ||||
| CD9 | 928 |
MIC3; MRP-1; BTCC-1; DRAP-27; TSPAN29; TSPAN-29; |
CD9 molecule | 2001 | 20–36 | Adipose tissue | Human | 11573204 |
| 2007 | 18.4–32.6 | Adipose tissue | Human | 17668233 | ||||
| Ly6a | 110,454 |
TAP; Sca1; Sca-1; Ly-6A.2; Ly-6A/E; Ly-6E.1; |
Lymphocyte antigen 6 family member A | 2003 | n.d | Bone marrow | Murine | 12732718 |
| 2003 | n.d | Bone marrow | Murine | 14616976 | ||||
| 2008 | 75.0–90.6 | Ear | Murine | 18599810 | ||||
| 2017 | n.d | Bone marrow | Murine | 27734598 | ||||
| 2021 | 20.3 | Lung | Murine | 34341173 | ||||
| 2022 | n.d | Lung | Murine | 35445270 | ||||
| CXCR4 | 7852 |
FB22; HM89; LAP3; LCR1; NPYR; WHIM; CD184; LAP-3; LESTR; NPY3R; NPYRL; WHIMS; HSY3RR; NPYY3R; WHIMS1; D2S201E; |
C-X-C motif chemokine receptor | 2004 |
Cell surface: 0–1; Intracellular: 83–98 |
Bone marrow | Human | 15251986 |
| 2006 | 30–56 | Bone marrow | Human | 16253981 | ||||
| 2006 | 11.5–21.6 | Cord blood | Human | 16410389 | ||||
| 2006 | 14.7–21.5 | Bone marrow | Human | 16410389 | ||||
| 2007 | 87.4–97.8 | Bone marrow | Human | 17606439 | ||||
| 2008 | n.d | Bone marrow | Human | 18334485 | ||||
| 2008 |
Cell surface: 0.5–4.1; Intracellular: 51–75 |
Bone marrow | Human | 18728032 | ||||
| 2012 |
Cell surface: 20.9–25.1; Intracellular: 71.8–83.2 |
Fetal blood | Human | 23197643 | ||||
| 2014 | 90–100 | Bone marrow | Rat | 24626964 | ||||
| 2014 | 8.4–11.0 | Umbilical cord | Rat | 25098450 | ||||
| 2017 | n.d | Bone marrow | Murine | 28352314 | ||||
| 2020 | 15.0–34.4 | Bone marrow | Murine | 32418119 | ||||
| CD274 | 29,126 |
B7-H; B7H1; PDL1; PD-L1; hPD-L1; PDCD1L1; PDCD1LG1; |
CD274 molecule | 2005 | n.d | Bone marrow | Murine | 15827960 |
| 2008 | n.d | Bone marrow | Murine | 18607390 | ||||
| 2020 | n.d | Bone marrow | Murine | 32509271 | ||||
| 2020 | n.d | Gingiva tissue | Human | 32707035 | ||||
| CD44 | 960 |
IN; LHR; MC56; MDU2; MDU3; MIC4; Pgp1; CDW44; CSPG8; H-CAM; HCELL; ECM-III; HUTCH-1; HUTCH-I; ECMR-III; Hermes-1; |
CD44 molecule | 2006 | n.d | Bone marrow | Murine | 16306150 |
| 2006 | 25.5–39.1 | Ap8c3 | Rat | 16306150 | ||||
| 2007 | n.d | Bone marrow | Murine | 17507906 | ||||
| 2012 | 38–52 | Bone marrow | Murine | 22654106 | ||||
| 2013 | n.d | Bone marrow | Human | 23847000 | ||||
| 2018 | n.d | Neural crest | Human | 29571051 | ||||
| GD2 | 2007 | 95 | Bone marrow | Human | 17264296 | |||
| STRO-3 | Antibody recogniziing TNSALP(tissue nonspecific alkaline phosphatase, a cell-surface glycoprotein) | 2007 | n.d | Bone marrow | Human | 8158854 | ||
| 2009 | n.d | Bone marrow | Sheep | 19231391 | ||||
| 2010 | n.d | Bone marrow | Sheep | 20850099 | ||||
| 2011 | n.d | Bone marrow | Human | 21155976 | ||||
| 2012 | n.d | Bone marrow | Sheep | 22404141 | ||||
| 2013 | n.d | Bone marrow | Sheep | 23658436 | ||||
| 2017 | n.d | Bone marrow | Sheep | 28173831 | ||||
| 2021 | n.d | Bone marrow | Human | 33045417 | ||||
| FZD9 | 8326 |
FZD3; CD349; |
Frizzled class receptor 9 | 2007 | n.d | Placenta | Human | 17288545 |
| 2007 | n.d | Bone marrow | Human | 17288545 | ||||
| 2008 | n.d | Placenta | Human | 17924962 | ||||
| 2011 | n.d | Placenta | Human | 20658518 | ||||
| PDGFRB | 5159 |
IMF1; KOGS; IBGC4; JTK12; PDGFR; PENTT; CD140B; PDGFR1; PDGFR-1; |
Platelet derived growth factor receptor beta | 2007 | 69.0–74.2 | Endometrial tissue | Human | 17872908 |
| SSEA-4 | Stage-specific embryonic antigen-4 | 2007 | 71 | Bone marrow | Murine | 17062733 | ||
| 2010 | 33.3 | Periodontal ligament | Human | 19945209 | ||||
| 2012 | 22.7 | Periodontal ligament | Human | 22895512 | ||||
| 2012 | 45.5 | Dental pulp | Human | 22266579 | ||||
| 2013 | 11–99.6 | Bone marrow | Human | 23330736 | ||||
| 2014 | n.d | Adipose tissue | Human | 25123923 | ||||
| 2017 | 70–86 | Bone marrow | Human | 29078802 | ||||
| KIT | 3815 |
PBT; SCFR; C-Kit; CD117; MASTC; |
KIT proto-oncogene, receptor tyrosine kinase | 2007 | n.d | Adipose tissue | Human | 17348807 |
| 2014 | 0.5 | Adipose tissue | Human | 24713343 | ||||
| 2014 | n.d | Adipose tissue | Murine | 24713343 | ||||
| ABCG2 | 9429 |
MRX; MXR; ABCP; BCRP; BMDP; MXR1; ABC15; BCRP1; CD338; GOUT1; MXR-1; CDw338; CDw388; UAQTL1; EST157481; |
ATP binding cassette subfamily G member 2 | 2011 | n.d | Lung | Murine | 21312316 |
| CD200 | 4345 |
MRC; MOX1; MOX2; OX-2; |
CD200 molecule | 2008 | n.d | Bone marrow | Human | 18086871 |
| 2012 | 7.5–69.6 | Bone marrow | Human | 22363701 | ||||
| 2012 | 0.4–0.5 | Umbilical cord blood | Human | 22363701 | ||||
| 2012 | 90 | Heart | Human | 22575528 | ||||
| 2012 | 25 | Bone marrow | Human | 22575528 | ||||
| 2012 | 0–10 | Adipose tissue | Human | 22575528 | ||||
| 2014 | 70.5 |
Full-term placenta (fetal origin) |
Human | 24721710 | ||||
| 2014 | 1.8 |
Full-term placenta (maternal origin) |
Human | 24721710 | ||||
| 2016 | 23–63.4 | Bone marrow | Human | 26773707 | ||||
| 2017 | 80 | Bone marrow | Murine | 28295880 | ||||
| PODXL | 5420 |
PC; PDX; PCLP; Gp200; gp135; PCLP-1; PODXL1; |
Podocalyxin like | 2009 | n.d | Bone marrow | Human | 18818395 |
| ITGA6 | 3655 |
JEB6; CD49f; VLA-6; ITGA6A; ITGA6B; |
Integrin subunit alpha 6 | 2009 | n.d | Bone marrow | Human | 18818395 |
| 2012 | n.d | Umbilical cord blood | Human | 22311737 | ||||
| 2013 | n.d | Bone marrow | Human | 23132820 | ||||
| 2013 | n.d | Umbilical cord blood | Human | 23132820 | ||||
| 2015 | 45.7–78.5 | Fetal bone marrow | Human | 26013602 | ||||
| 2015 | 11 | Adult bone marrow | Human | 26013602 | ||||
| 2020 | n.d | Aorsal skin | Murine | 31494092 | ||||
| 2021 | 5.3–17.7 | Adipose tissue | Rat | 33704842 | ||||
| STRO-4 | Antibody recogniziing Hsp90β | 2009 | 99.9 | Bone marrow | Human | 19327008 | ||
| 2009 | 92.3 | Adipose tissue | Human | 19327008 | ||||
| 2009 | 95 | Dental pulp | Human | 19327008 | ||||
| 2009 | 86.2 | Periodontal ligament | Human | 19327008 | ||||
| 2009 | 99.9 | Bone marrow | Sheep | 19327008 | ||||
| 2009 | 91.9 | Adipose tissue | Sheep | 19327008 | ||||
| 2009 | 99.9 | Dental pulp | Sheep | 19327008 | ||||
| 2009 | 99.4 | Periodontal ligament | Sheep | 19327008 | ||||
| CSPG4 | 1464 |
NG2; MCSP; MCSPG; MSK16; CSPG4A; HMW-MAA; MEL-CSPG; |
Chondroitin sulfate proteoglycan 4 | 2009 | 13.3–89.8 | Bone marrow | Human | 19462316 |
| 2013 | n.d | Bone marrow | Murine | 24107994 | ||||
| 2013 | 95–100 | Bone marrow | Human | 23611563 | ||||
| NCAM1 | 4684 |
CD56; NCAM; MSK39; |
Neural cell adhesion molecule 1 | 2009 | n.d | Bone marrow | Human | 19066333 |
| 2016 | 22.8–95.9 | Bone marrow | Human | 27528376 | ||||
| 2019 | 1–35 | Bone marrow | Human | 30676001 | ||||
| PDGFRA | 5156 |
CD140A; PDGFR2; PDGFR-2; |
Platelet derived growth factor receptor alpha | 2009 | n.d | Bone marrow | Murine | 19841085 |
| 2012 | 5–10 | Bone marrow | Murine | 23154782 | ||||
| 2013 | 79–85 | Bone marrow | Human | 23776077 | ||||
| 2014 | 10–73 | Muscle | Human | 24743741 | ||||
| 2014 | n.d | Bone marrow | Human | 25454633 | ||||
| 2018 | n.d | White adipose tissue | Murine | 29378823 | ||||
| 2018 | n.d | Bone marrow | Murine | 29378823 | ||||
| 2018 | 6.8 | Bone marrow | Murine | 29529192 | ||||
| NES | 10,763 | Nbla00170 | Nestin | 2010 | n.d | Bone marrow | Murine | 20703299 |
| 2013 | n.d | Bone marrow | Human | 23776077 | ||||
| 2015 | n.d | Kidney | Murine | 25736496 | ||||
| 2019 | 1.8–2.3 | Bone marrow | Murine | 31029167 | ||||
| 2020 | 15.3–18.5 | Heart | Murine | 31991111 | ||||
| 2020 | 3.7–4.6 | Bone marrow | Murine | 31991111 | ||||
| VCAM1 | 7412 |
CD106; INCAM-100; |
Vascular cell adhesion molecule 1 | 2010 | n.d | Bone marrow | Murine | 20130212 |
| 2013 | 65 | Placental chorionic villi | Human | 23555021 | ||||
| 2013 | 32 | Bone marrow | Human | 23555021 | ||||
| 2013 | 7.4 | Umbilical cord | Human | 23555021 | ||||
| 2013 | 0.7 | Adipose tissue | Human | 23555021 | ||||
| 2013 | n.d | Bone marrow | Human | 24052950 | ||||
| 2016 | 57.5–68.3 | Placenta chorionic villi | Human | 27044487 | ||||
| 2020 | n.d | Umbilical cord | Human | 32597552 | ||||
| 2022 | n.d | Umbilical cord | Human | 35768999 | ||||
| ENTPD1 | 953 |
CD39; SPG64; ATPDase; NTPDase-1; |
Ectonucleoside triphosphate diphosphohydrolase 1 | 2011 | n.d | Bone marrow | Murine | 21176405 |
| 2013 | n.d | Synovial membrane | Human | 23804221 | ||||
| 2014 | 84.3 | Bone marrow | Human | 24043462 | ||||
| 2017 | n.d | Gingiva tissue | Human | 28210258 | ||||
| 2019 | n.d | Gingiva tissue | Human | 31076346 | ||||
| 2020 | n.d | Gingiva tissue | Human | 32565049 | ||||
| EPHB2 | 2048 |
DRT; EK5; ERK; CAPB; Hek5; PCBC; EPHT3; Tyro5; BDPLT22; |
EPH receptor B2 | 2011 | n.d | Bone marrow | Human | 21056708 |
| 2013 | n.d | Bone marrow | Human | 23413357 | ||||
| 2013 | n.d | Bone marrow | Human | 23711177 | ||||
| MX1 | 4599 |
MX; MxA; IFI78; IFI-78 K; lncMX1-215; |
MX dynamin like GTPase 1 | 2012 | n.d | Compact bone | Murine | 22385654 |
| SUSD2 | 56,241 |
W5C5; BK65A6.2; |
Sushi domain containing 2 | 2012 | 3.6–4.8 | Endometrial tissue | Human | 22469435 |
| 2013 | n.d | Bone marrow | Human | 23406305 | ||||
| 2021 | 5.1 | Placenta | Human | 33961124 | ||||
| ITGAV | 3685 |
CD51; MSK8; VNRA; VTNR; |
Integrin subunit alpha V | 2013 | 16 | Bone marrow | Human | 23776077 |
| 2013 | 76–82 | Bone marrow | Murine | 23776077 | ||||
| 2015 | 2.4–24 | Periodontal ligament | Human | 26674423 | ||||
| 2019 | 1.4 | Bone marrow | Murine | 31747966 | ||||
| 2021 | 13.4 | Heart | Murine | 33968928 | ||||
| TNFAIP6 | 7130 |
TSG6; TSG-6; |
TNF alpha induced protein 6 | 2014 | n.d | Bone marrow | Human | 25385603 |
| 2022 | 13.7–92.3 | Bone marrow | Murine | 36153571 | ||||
| 2022 | n.d | Placenta | Murine | 36153571 | ||||
| 2022 | n.d | Adipose tissue | Murine | 36153571 | ||||
| GLI1 | 2735 |
GLI; PPD1; PAPA8; |
GLI family zinc finger 1 | 2014 | 79.9–80.1 | Incisor pulp | Murine | 24506883 |
| 2015 | n.d | Craniofacial bone | Murine | 25799059 | ||||
| 2015 | 32 | Bone marrow | Murine | 25465115 | ||||
| 2017 | n.d | Bone marrow | Human | 28457748 | ||||
| 2017 | n.d | Bone marrow | Murine | 28457748 | ||||
| 2020 | n.d | Periodontal ligament | Murine | 32652075 | ||||
| 2020 | n.d | Pulp tissue | Murine | 32783935 | ||||
| 2020 | n.d | Lung | Human | 33046884 | ||||
| 2022 | n.d | Bone marrow | Murine | 36092701 | ||||
| LEPR | 3953 |
OBR; OB-R; CD295; LEP-R; LEPRD; |
Leptin receptor | 2014 | 0.3 | Bone marrow | Murine | 24953181 |
| 2016 | 0.2–0.4 | Bone marrow | Murine | 27053299 | ||||
| 2018 | n.d | Bone marrow | Murine | 33221380 | ||||
| EPHA2 | 1969 |
ECK; CTPA; ARCC2; CTPP1; CTRCT6; |
EPH receptor A2 | 2015 | n.d | Bone marrow | Human | 25684225 |
| 2015 | n.d | Adipose tissue | Human | 25684225 | ||||
| 2018 | n.d | Bone marrow | Human | 29941036 | ||||
| 2015 | 45.0–80.7 | Placenta | Human | 26700997 | ||||
| 2015 | n.d | Umbilical cord | Human | 26700997 | ||||
| 2018 | n.d | Umbilical cord | Human | 30342659 | ||||
| 2020 | n.d | Wharton's Jelly | Human | 32899389 | ||||
| BST2 | 684 |
CD317; HM1.24; TETHERIN; |
Bone marrow stromal cell antigen 2 | 2015 | 1–3 | Bone marrow | Human | 26070611 |
| 2022 | n.d | Bone marrow | Human | 35734183 | ||||
| ISLR | 3671 |
Meflin; HsT17563; |
Immunoglobulin superfamily containing leucine rich repeat | 2016 | n.d | Bone marrow | Murine | 26924503 |
| TLX1 | 3195 |
TCL3; HOX11; |
T cell leukemia homeobox 1 | 2016 | n.d | Bone marrow | Murine | 27939685 |
| 2019 | n.d | Bone marrow | Murine | 31320650 | ||||
| ALDH1A1 | 216 |
ALDC; ALDH1; HEL-9; HEL12; PUMB1; ALDH11; RALDH1; ALDH-E1; HEL-S-53e; |
Aldehyde dehydrogenase 1 family member A1 | 2017 | n.d | Adipose tissue | Human | 28233376 |
| 2023 | n.d | Adipose tissue | Human | 37261440 | ||||
| TNFRSF10D | 8793 |
DCR2; CD264; TRUNDD; TRAILR4; TRAIL-R4; |
TNF receptor superfamily member 10d | 2017 | 20–35 | Bone marrow | Human | 28962588 |
| LGR6 | 59,352 |
GPCR; |
Leucine rich repeat containing G protein-coupled receptor 6 | 2017 | n.d | Lung | Murine | 28886383 |
| LGR5 | 8549 |
FEX; HG38; GPR49; GPR67; GRP49; |
Leucine rich repeat containing G protein-coupled receptor 5 | 2017 | n.d | Lung | Murine | 28886383 |
| ROR2 | 4920 |
BDB; BDB1; NTRKR2; |
Receptor tyrosine kinase like orphan receptor 2 | 2017 | 6.7–40.9 | Bone marrow | Human | 28833807 |
| CMKLR1 | 1240 |
DEZ; ERV1; RVER1; ChemR23; CHEMERINR; |
Chemerin chemokine-like receptor 1 | 2017 | n.d | Bone marrow | Murine | 27733019 |
| 2022 | n.d | Bone marrow | Human | 35365767 | ||||
| 2022 | n.d | Bone marrow | Human | 35723360 | ||||
| SDC2 | 6383 |
HSPG; CD362; HSPG1; SYND2; |
Syndecan 2 | 2018 | 0.1–0.2 | Bone marrow | Human | 29979191 |
| 2020 | n.d | Umbilical cord | Human | 32169108 | ||||
| 2020 | n.d | Umbilical cord | Human | 33158246 | ||||
| HMMR | 3161 |
CD168; IHABP; RHAMM; |
Hyaluronan mediated motility receptor | 2019 | 1.3–1.8 | Umbilical cord | Human | 31068579 |
| F3 | 2152 |
TF; TFA; CD142; |
Coagulation factor III; tissue factor | 2020 | 20 | Umbilical cord | Human | 32252818 |
| 2023 | 9.5 | Wharton's Jelly | Human | 36504438 | ||||
| EPHA7 | 2045 |
EHK3; EK11; EHK-3; HEK11; |
EPH receptor A7 | 2020 | 5–20 | Peripheral tissues | Murine | 31471947 |
| SERPINF1 | 5176 |
OI6; OI12; PEDF; EPC-1; PIG35; |
Serpin family F member 1 | 2022 | n.d | Lung | Murine | 35445270 |
| S100A9 | 6280 |
MIF; NIF; P14; CAGB; CFAG; CGLB; L1AG; LIAG; MRP14; 60B8AG; MAC387; S100-A9; |
S100 calcium binding protein A9provided | 2023 | 9.5 | Wharton's Jelly | Human | 36504438 |
| LRRC75A | 388,341 |
FAM211A; C17orf76; |
Leucine rich repeat containing 75A | 2023 | n.d | Bone marrow | Human | 37263619 |
''n.d.' indicates 'not determined'
Function enrichment by GO (Gene Ontology) analysis indicates that these MSC markers mainly regulate the process of leukocyte migration, wound healing, cell chemotaxis, and so on (Fig. 3A). Although some markers are involved in multiple functions, some of them are also cross-interacted in a network way (Fig. 3B). KEGG (Kyoto Encyclopedia of Genes and Genomes) also indicates that these MSC markers are mainly involved in the signal pathways in PI3K-AKT, adhesion, and so on (Fig. 4A). Similar to the GO analysis, some markers regulated multiple pathways (Fig. 4B) and they are cross-interacted (Fig. 4C). Most of these MSC markers are localized on the cell membrane, which is suitable for cell purification with FACS (fluorescence-activated cell sorting) and MACS (magnetic-activated cell sorting), while some of them are also intracellularly or extracellularly localized (Table 2). Normally, MSCs enriched with specific makers have functional advantages (Table 2). However, in some cases, these enriched MSCs also have some disadvantages (Table 2).
Fig. 3.
GO analysis of MSC markers. The bioinformatic analysis of GO enrichment of MSC markers was performed with Dotplotting (A), Cnetplotting (B). GO, gene ontology. MSC mesenchymal stem/stromal cell
Fig. 4.
KEGG analysis of MSC markers. The bioinformatic analysis of KEGG enrichment of MSC markers was performed with Dotplotting (A), Cnetplotting (B), and Emapplotting (C). Bioinformatic analysis was conducted with package ‘enrichplot’ in R. KEGG, kyoto encyclopedia of genes and genomes. MSC mesenchymal stem/stromal cell
Table 2.
Pros and cons of MSC markers
| Markers | Cellular localization | Generation | Pros | Cons | Refs |
|---|---|---|---|---|---|
| TNFAIP6 | Extracellular (Secreted) | 1st | Efficacy predictor of MSCs in treating inflammation | Low purfiying efficiency |
25385603 36153571 |
| Higher immune suppression activities improved therapeutic effects | |||||
| Improved therapeutic effects | |||||
| ALDH1A1 | Intracellular (Cytosol) | 1st | More primitive cell proliferation and tri-differentiation capabilites | 37261440 | |
| STRO-1 | Antibody recognizing unknown target | 1st | Multilineage differentiation capabilities | Heterogenous |
19143868 14715641 33278647 21903091 28686984 30260000 |
| Higher level of colony-forming activities | Expression level declines during passaging | ||||
| Higher level of proliferative rate | Not specific,also expressed in endothelium | ||||
| Higher level of immune suppression | |||||
| Better homing activities | |||||
| Expression level increases significantly during osteogenic differentiation | |||||
| ALCAM | Membrane | 1st | Purify human MSCs from fibroblasts | Not specific, expressed in other types of cells |
7535342 7760007 21787134 23280653 34493362 |
| More prone to differentiate into chondrocytes | |||||
| NT5E | Membrane | 1st | Identify the MSCs in different organs in vivo | Expression level decreases during the differentiation process |
16443378 16418778 18086871 29451855 31279774 28684854 33407847 34055772 |
| Higher level of colony-forming capabilities | |||||
| Higher immune suppression activities | |||||
| Much higher tri-differentiation abilities | |||||
| Higher level of regeneration cytokines | |||||
| Improved therapeutic effects | |||||
| Much more smaller with spindle and rod-like shapes | |||||
| ITGA1 | Membrane | 1st | Stronger colony-forming activity | Expression levels upregulated during osteogenic differentiation |
10911362 12877680 17694277 17109120 12883998 |
| Stronger differentiation capabilities | Not specific, also expressed in myofibroblasts | ||||
| Higher expression levels of THY-1 and ENG | |||||
| CXCR4 | Membrane | 1st | Contributes to MSCs homing | Expression level is low on the cell surface |
15251986 17606439 12357350 15837815 18728032 23197643 11283404 15153618 24626964 25098450 16410389 32418119 |
| Enhanced homing activities | Expression level decreases during passaging | ||||
| Expression level decreases during aging | |||||
| CD9 | Membrane | 1st | Higher NOS (nitric oxide synthase) expression | Expression level declines during in vitro expansion |
17668233 30356731 |
| Higher proliferation rate | Not specific,also expressed in lymphocytes | ||||
| Higher colony formation ratio | |||||
| Stronger cell adhesion capability | |||||
| Better engraftment | |||||
| Improved therapeutic effect | |||||
| CD44 | Membrane | 1st | Contributes to the MSC recruitment | Gradually up-regulated during cell expansion |
16306150 17507906 29571051 22654106 23847000 |
| Contributes to migration and adhesion of MSCs | Widely expressed in multiple types of cells | ||||
| Enhanced colony-forming capacity | Very low level in bone marrow MSCs | ||||
| Enhanced in vitro differentiation abilities | |||||
| GD2 | Membrane | 1st | Much higher proliferation | 17264296 | |
| Higher colony formation capabilities | |||||
| Better differentiation abilities | |||||
| MCAM | Membrane | 1st | Enhanced colony formation capabilities | Expression declines during in vitro expansion |
17956733 17872908 26753846 31002939 33777147 26841872 34461987 35729643 32379908 24188055 26941359 31070478 |
| Much higher colony-forming | Expressed in many cell type | ||||
| Much stronger chemotactic attraction | |||||
| Enhanced immune suppression activities | |||||
| Enhanced immune suppression and secretory capacities | |||||
| More prone to differentiate into vascular smooth muscle cell | |||||
| Less senescent phenotypes | |||||
| Faster proliferation rate | |||||
| Stronger stemness characteristics | |||||
| CD200 | Membrane | 1st | Much higher level of colony-forming activity | Down-regulated during differentiation |
18086871 24721710 28295880 26773707 |
| Enhanced immune suppression activities | Low level of CD200 expression in MSCs derived from adipose | ||||
| Contributes to immune suppression | Expression is not induced by IFN-γ in AD and UC derived MSCs | ||||
| Contributes to myeloid differentiation inhibition | |||||
| Prone to differentiate into the osteocytes | |||||
| Ly6a | Membrane | 1st | Contributes to maintain the stem cell state of MSCs | Heterogenous |
12732718 18599810 34341173 35445270 17379763 |
| Higher proliferation rate | Not specific,expressed in multiple types of stem cells | ||||
| Better immune suppression abilities | |||||
| THY1 | Membrane | 1st | Contributes to regulate the balance between osteoblasts and adipocytes differentiation | Not specific,expressed in the fibroblasts |
30089635 18985728 26718647 29371677 25739049 |
| Predict the immune suppressive function | |||||
| Proliferate faster and better differentiation capabilities | |||||
| ITGA6 | Membrane | 1st | Higher colony-forming activities | Not specific,expressed in multiple types of cells |
18818395 22311737 23132820 26013602 31494092 33704842 29720266 |
| Higher differentiation abilities | |||||
| Smaller size | |||||
| Better homing activities | |||||
| Better regeneration activity | |||||
| Better anti-apoptotic activities | |||||
| STRO-4 | Antibody recognizing the Hsp90β | 1st | Higher colony-forming activities | 19327008 | |
| Higher proliferation rate | |||||
| Higher multiple differentiation abilities | |||||
| ENTPD1 | Membrane | 1st | Contributes to suppress T cell proliferation |
21176405 24043462 28210258 32565049 23804221 31076346 |
|
| Contributes to suppress activation of B cells | |||||
| Better efficiency of chondrogenic and osteogenic differentiation | |||||
| Prevent osteoclastogenesis | |||||
| CSPG4 | Membrane | 1st | Enhanced proliferation abilities | Not specific, expressed in fibroblast and endothelial cells |
23611563 19462316 |
| Enhanced colony-forming abilities | |||||
| Enhanced differentiation abilities | |||||
| MX1 | Intracellular (cytosol, nuclear membrane) | 1st | Prone to differentiate into osteoblasts and regenerate the bone | 22385654 | |
| ITGAV | Membrane | 1st | Faster proliferating rate |
31747966 33968928 |
|
| Better abilities of colony-forming | |||||
| Better homing ability | |||||
| Better anti-inflammatory effects | |||||
| Therapeutic effects in the mouse model of myocardial infarction | |||||
| STRO-3 | Antibody recognizing the TNSALP | 1st | Higher proliferation and differentiation potencies |
18158854 19231391 20850099 21155976 22404141 23658436 26799116 28173831 33045417 |
|
| Have been applied in treating disease models | |||||
| GLI1 | Intracellular (nucleoplasm, cytosol) | 1st | Responsible for tissue regeneration after injury | Prone to differentiate into osteochondrogenic lineages |
24506883 27618218 29230039 33046884 36092701 25799059 25465115 28457748 32783935 |
| Contributes to tissue fibrosis | |||||
| Heterogenous | |||||
| ISLR | Extracellular (secreted) | 1st | Predict differentiation efficiency | Expression in fibroblast |
26924503 34676218 |
| Enhanced anti-fibrosis functions | |||||
| TNFRSF10D | Membrane | 1st | Reduced proliferation rate and differentiation efficiency | 28962588 | |
| Increased senescence phenotype | |||||
| EPHA7 | Membrane | 1st | Proliferate faster | 31471947 | |
| Higher level of colony-formation | |||||
| Higher level of differentiation capabilities | |||||
| SDC2 | Membrane | 1st | Enhanced colony forming |
29979191 32169108 33158246 34746723 |
|
| Enhanced immune suppression | |||||
| Enhanced regeneration activities | |||||
| Safe, feasible, and effective in clinics | |||||
| ENG | Membrane | 1st | Reduced adipogenic differentiation efficiency |
20153525 21205995 23069852 33800564 |
|
| Contravercy on the osteogenic and chondrogenic differentiation efficiency | |||||
| Not specific,expressed in activated ECs and immune cells | |||||
| NGFR | Membrane | 1st | Higher colony forming activity |
12135677 16977637 17395729 22268519 29482445 29915318 22048731 22982680 30816233 33653407 |
|
| Proliferate faster | |||||
| Enhanced immune suppression activity | |||||
| Higher tri-differentiation efficiency | |||||
| Improved therapeutic effects | |||||
| NES | Intracellular (intermediate filaments) | 1st | A MSC subpopulation constituting the niche for HSCs |
20703299 31029167 |
|
| Improved therapeutic effects | |||||
| FZD9 | Membrane | 1st | CD349− MSCs have better neovascularization abilities | 20658518 | |
| CD34 | Membrane | 1st | Higher colony forming activity | A marker of endothelial cells |
1720038 17786605 |
| PDGFRB | Membrane | 1st | Contributes to maintain MSC functions |
14766732 16210003 17872908 |
|
| Higher colony-forming activities | |||||
| SSEA-4 | Membrane | 1st | Enrich MSC populations | 17062733 | |
| NCAM1 | Membrane | 1st | Enhanced chondrogenic differentiation potentials | Not specific |
19066333 30676001 |
| Supporting long-term hematopoiesis | |||||
| VCAM1 | Membrane | 1st | Contributes to immune suppression function of MSCs | Colony-forming ability varies among different MSCs |
20130212 23555021 32597552 24052950 27044487 35768999 |
| Stronger ability to suppress immune responses | Expression level decreases during expansion | ||||
| Enhanced homing capacity | CD106 expression varies among MSCs derived from different tissues | ||||
| Better pro-angiogenic activity | |||||
| LEPR | Membrane | 1st | Much higher colony-forming activities |
24953181 27053299 |
|
| Promotes adipose differentiation of MSCs while inhibit osteogenesis | |||||
| PDGFRA | Membrane | 1st | Enhanced differentiation activities | Expressed in human skeletal muscle |
19841085 23776077 29529192 |
| Enhanced differentiation activities | Species differences | ||||
| SUSD2 | Membrane | 1st | Higher clonogenicity | 22469435 | |
| EPHB2 | Membrane | 1st | Improves intestinal homing abilities | 23413357 | |
| Promotes the intestinal stem cell regeneration | |||||
| KIT | Membrane | 1st | Better cell proliferative |
17348807 24713343 |
|
| Enhanced clonogenic activities | |||||
| EPHA2 | Membrane | 1st | Distinguish MSCs from fibroblasts | Not specific,expressed in epithelial cells |
26700997 30342659 32899389 28171762 32811512 |
| BST2 | Membrane | 1st | Up-regulated mRNA levels of immunosuppressive genes | The fresh CD317− BM-MSCs have better immune suppression activities |
26070611 35734183 unpublished data |
| Better regeneration capabilities | |||||
| Better immune suppression capabilities of UC-MSCs in CDM | |||||
| TLX1 | Intracellular | 1st | Better colony forming potentials |
27939685 31320650 |
|
| Better tri-lineage differentiation | |||||
| ROR2 | Membrane | 1st | Enhanced chondrogenic differentiation efficiency | 28833807 | |
| CD274 | Membrane | 1st | Enchanced immune suppression activities | Widely expressed on multiple types of cells, such as lymphocytes | 32707035 |
| Improved therapeutic effects | |||||
| ABCG2 | Membrane | 1st | Higher telomerase activity | 21312316 | |
| Better anti-inflammatory effects | |||||
| PODXL | Membrane | 1st | Higher colony-forming activities |
18818395 22311737 23132820 26013602 31494092 33704842 |
|
| Higher differentiation abilities | |||||
| Smaller size | |||||
| Better homing activities | |||||
| Better regeneration activity | |||||
| Better anti-apoptotic activities | |||||
| S100A9 | Extracellular (secreted) | 2st | Enhanced wound repair capabilities | 36504438 | |
| F3 | Membrane | 2st | Better regeneration abilities | Lower proliferation |
32252818 36504438 |
| LRRC75A | Intracellular (nucleoplasm) | 2st | Better VEGF production | 37263619 | |
| SERPINF1 | Extracellular (secreted) | 2st | Stable | 35445270 | |
| CMKLR1 | Membrane | 2st | Stronger immune suppression activities | Slower proliferation rate | 35365767 |
| Better osteogenic differentiation potential | Weaker adipogenic differentiation potentials | ||||
| HMMR | Membrane | 2st | Related to cell cycle status | 31068579 | |
| LGR5 | Membrane | 2st | Promote alveolar differentiation | A marker for epithelial stem cells |
26460010 28886383 |
| LGR6 | Membrane | 2st | Supports the airway differentiation | A marker for epithelial stem cells |
26460010 28886383 |
| Supports the Lgr5 + epithelial stem cells turnover |
There are various strategies available for biomarker discovery, and among them, two classic approaches stand out: the candidate biomarker strategy and the high-throughput screening strategy. The candidate biomarker strategy is based on existing biological knowledge, where one or more molecules or features possibly related to a specific disease or biological process are selected as candidate biomarkers. These candidates are then experimentally validated for their expression levels or variations under different conditions. This strategy relies heavily on a profound comprehensive understanding of medical domains and relevant biological processes [16]. In contrast, the high-throughput screening strategy employs techniques like genomics, transcriptomics, proteomics, lipidomics, and metabolomics to simultaneously analyze a large number of molecules and features. Through these techniques, it becomes feasible to detect thousands of molecules, facilitating the comparison of differences between heterogenous cell populations. Notably, this approach allows for the identification of biomarkers associated with specific functions without being reliant on prior knowledge [16–20]. It's worth noting that these strategies can be combined to enhance the comprehensive development of potential biomarkers. This integrated approach harnesses the biological knowledge of the candidate biomarker strategy while utilizing the technical capabilities of the high-throughput strategy to discover biomarkers associated with distinct functional subgroups in a more comprehensive and precise manner [16, 21–24]. In the realm of MSCs, with the use of prior knowledge and high-throughput technologies such as single-cell RNA sequencing (scRNA-seq), specific markers related to different functional subsets of mesenchymal stem cells can be more comprehensively and accurately mined [17–24].
Therefore, in the current review, we would discuss the MSC markers that have been identified so far. Furthermore, based on the identification approaches, these markers have been categorized into two groups: the 1st generation of MSC markers, which has been identified by the candidate biomarker strategy; and the 2nd generation of MSC makers, which has been identified by high-throughput screening approaches (Table 2).
Techniques of identifying mesenchymal stem cell subpopulations
In most studies reviewed in this paper, Flow cytometry and fluorescence-activated cell sorting (FACS) is predominantly utilized for sorting MSC subpopulations. FACS are the primary methods for identifying MSC subpopulations, celebrated for their precision and versatility in scientific research. These technologies use fluorescently labeled antibodies to target specific surface markers, allowing researchers to conduct multiparameter analyses [25, 26]. This facilitates simultaneous assessment of various markers and functional properties within MSC populations, aiding in the identification and isolation of distinct subpopulations based on differential expression of markers. Such detailed analysis provides crucial insights into MSC heterogeneity.
Another vital technique, immunomagnetic cell sorting, utilizes magnetic beads tagged with antibodies targeting specific surface markers for selective isolation of MSC subpopulations [27, 28]. This method ensures high specificity and efficiency, essential for distinguishing and harvesting functionally diverse MSC subsets.
Additionally, functional assays are integral for understanding the biological characteristics of MSC subpopulations. Immunomodulatory assays, for instance, involve co-culture setups with immune cells to evaluate MSCs' effects on immune cell proliferation, activation, and cytokine production [29–33]. These studies highlight the potential therapeutic uses of distinct MSC subsets in treating immune-related conditions. Differentiation assays, including those for osteogenic, adipogenic, and chondrogenic pathways, further elucidate the multilineage potential of MSC subpopulations, critical for identifying suitable cell sources for tissue engineering and regenerative medicine.
Gene expression profiling, through techniques such as RNA sequencing, provides deep insights into the transcriptomic landscapes that define specific functional states or lineage commitments within MSC populations [17–19, 23, 34]. These analyses help pinpoint molecular signatures characteristic of unique MSC subsets, enhancing our understanding of their heterogeneity.
Together, these techniques not only facilitate a comprehensive analysis of MSC heterogeneity but also specialize in pinpointing distinct MSC subpopulations. By employing these advanced methodologies, researchers can effectively characterize the diverse functional capacities and biological properties inherent to each subpopulation, significantly enhancing the precision of mesenchymal stem cell-based therapeutic strategies and the development of personalized regenerative medicine.
1st generation of MSC markers
Immune suppression related markers
Although the MSCs have been widely investigated in the animal models of different diseases, the only approved clinical product of MSCs is for GVHD (Graft Versus Host Disease) treatment in clinics [35, 36], because of their immune suppression capabilities. The immune modulation activity is one of those important contributors to the therapeutic effects of MSCs [1].
Extracellular secreted modulators
It has been demonstrated that TNFAIP6 (Tumor Necrosis Factor Alpha-Induced Protein 6) is a potential cell marker for mouse MSCs, irrespective of tissue origin and laboratory origin, with higher immune suppression activities and improved therapeutic effects [12]. However, the membrane expression level of TNFAIP6 is significantly lower than its cytoplasm level [12]. Indeed, TNFAIP6, also known as TSG6, is a small secreted protein with extracellular matrix remodeling and immunomodulation functions [37]. On the other hand, the importance of these secreted modulators, such as the TNFAIP6 having been characterized as one efficacy predictor of MSCs in treating inflammation in vivo [38], makes it necessary to develop novel strategies to purifying these MSC subpopulations for improving their therapeutic effects.
Extracellular ATP clearance
Dying or stressed cells could release ATP (Adenosine 5'-triphosphate) to the extracellular spaces and induce the pro-inflammatory cascade [39, 40]. The immune regulatory cells, such as Treg and MSCs, could express genes, such as ENTPD1 (Ecto-Nucleoside Triphosphate Diphosphohydrolase 1, also known as CD39) and NT5E (Ecto-5′-AMP-nucleotidase, also known as CD73), responsible for clearing these extracellular ATP [39, 41]. CD39 could hydrolyze the extracellular ATP, into ADP and then AMP; while CD73 converts AMP into adenosine [39, 40]. The extracellular adenosine has strong immune suppression activities via binding to the corresponding P1 receptors (including A1R, A2AR, A2BR, and A3R), and activating the downstream pathways (such as PKA, NF-κB, CREB, AKT, PI3K, ERK, JNK, and p38) [42]. Furthermore, the extracellular adenosine also regulates other cell functions, such as cell proliferation, adhesion, migration, invasion, tight junction formation, and vascular remodeling [39, 40, 42, 43].
The expression levels of CD39, CD73, and adenosine receptors could be induced by tissue damage, remodeling, and also the conditions of hypoxia and inflammation [41–43]. It has been demonstrated that MSCs express both CD73 and CD39 and could convert ATP into adenosine, resulting in suppressing T cell proliferation [44–47], and the activation of B cells [48]. The expression levels of CD73 modulate the proliferation and differentiation capabilities of MSCs [49, 50]. Its expression level decreases during the differentiation process [51].
The purified CD73+ MSCs have higher levels of colony-forming capabilities [52], even higher than the ENG+ and THY1+ MSCs [51]. In addition, CD73+ MSCs have much higher tri-differentiation abilities (adipocytes, osteoblasts, and chondrocytes) and higher immune suppression activities [52, 53]. Through EGFP reporter analysis in mice, CD73 could identify the MSCs in different organs in vivo [54, 55]. Furthermore, CD73+ MSCs are much more smaller with spindle and rod-like shapes, while CD73− MSCs are more polygonal larger cells [33]. CD73+ MSCs secrete higher levels of regeneration cytokines, such as VEGF, SDF-1α, and HGF than CD73− MSCs, and show improved therapeutic effects on the rat model of myocardial infarction [33]. Furthermore, CD73+CD39+ MSCs have great potential in bone regeneration, including better efficiency in chondrogenic and osteogenic differentiation [56], preventing osteoclastogenesis [57], and promoting bone formation via the Wnt/β-catenin pathway[58].
Other immune regulators
CD200 is an immune suppressor and promotes peripheral immune tolerance [59, 60]. Its immune suppression function works through binding to its receptor CD200R, which then activates multiple pathways, such as MAPK-ERK, p38 MAPK, and JNK, via Dok and p120-RasGAP [61], resulting in upregulating the downstream effectors including IDO (indoleamine-2,3-dioxygenase), TGF-β, and IL-10 [59]. A higher expression level of CD200 in MSCs correlates with enhanced immune suppression activities in vitro and in vivo [62]. CD200 expressed on MSCs recognizes and binds to its receptor CD200R, which is expressed on myeloid progenitors, resulting in myeloid differentiation inhibition and immune suppression [63]. CD200+ MSCs have much higher levels of colony-forming activity [51]. However, it has been demonstrated that the expression of CD200 is undetectable in MSCs derived from umbilical cord blood [64], or very low in MSCs derived from adipose [65]. In contrast, MSCs derived from the umbilical cord express higher levels of CD200 [65]. Interestingly, the pro-inflammatory cytokine IFN-γ upregulates the expression of CD200 in MSCs derived from bone marrow but not adipose or umbilical cord [65].
BST2 (bone marrow stromal cell antigen 2), also known as CD317, is a type of transmembrane glycoprotein involved in virus reproduction suppression and immune regulation [66]. Using the hTERT immortalized human bone marrow MSC colonies, it has been demonstrated that the MSCs from the CD317+ colony have increased cell areas and up-regulated mRNA levels of immunosuppressive genes than the CD317− MSCs in vitro [67]. Furthermore, CD317+ bone marrow-derived MSCs have better regeneration capabilities than the CD317− MSCs [68]. However, fresh CD317− MSCs isolated from human bone marrow have better immune suppression activities but not CD317+ MSCs [68]. However, our unpublished data show that CD317+ MSCs isolated from the human umbilical cord and expanded with chemically defined media have better immune suppression capabilities (unpublished data).
CD274, also known as PD-L1 (programmed death ligand 1), is a type I transmembrane protein and is widely expressed on multiple types of cells, such as lymphocytes [69]. Its expression can be induced by pro-inflammatory cytokines, such as interferon-γ (IFN-γ), TNF-α, and IL-17 [69, 70]. And it has strong immune suppression activities through binding to its receptor PD-1 [71]. It has been demonstrated that PD-L1 is expressed in MSCs [70, 72, 73]. PD-L1+ MSCs have enhanced immune suppression activities and improved therapeutic effects on the collagen-induced mouse model of arthritis [74].
MX1, for ‘myxovirus resistance’, is the gene responsible for virus immunity and an important component of interferon pathway [75]. It has been demonstrated that Mx1+ MSCs are clonogenic at the single-cell level and have tri-differentiation abilities [32]. Although its antivirus mechanism remains unsolved, the Mx1+ MSCs might also have immune regulatory functions.
Cell adhesion related markers
In addition to the important role of MSCs in modulating immune responses [1], another critical function is regulating cell adhesion, including both the cell adhesion and migration of MSCs, as well as the recruitment and adhesion of other types of cells, such as lymphocytes.
Mediating cell migration
CD44 is an important adhesion molecule involved in recruiting immune cells or stem cells into the inflammatory or injured tissues, via interacting with hyaluronic acid (HA), which is expressed in the injured/inflammatory sites [76, 77].Their interactions induce conformational changes of CD44, recruit adaptor proteins, and lead to cytoskeletal rearrangement, resulting in the activation of various signaling pathways that involve cell growth, adhesion, and migration [76, 77]. In addition, CD44 also functions as a co-receptor to regulate the activities of other receptors, such as VEGFR, EGFR, FGFR and PDGFR [78]. CD44 is widely expressed in multiple types of cells, including MSCs, and it also contributes to MSC recruitment [79, 80]. Its expression level is further induced by PDGF [79]. The migration and adhesion of MSCs depend on CD44-HA (hyaluronic acid) interaction [79, 80]. Therefore, CD44 is a potentially important cell surface marker for MSC purification [81]. However, later investigations indicate that freshly isolated mouse/human MSCs derived from bone marrow express very low levels of CD44 [82, 83]. MSCs show enrichment in the CD44− fractions, as evidenced by their marker expression, colony-forming capacity, and in vitro differentiation abilities [82, 83]. Interestingly, CD44 is gradually up-regulated during cell expansion, even for the CD44− fractions of MSCs [82, 83]. Thus, the CD44 expression levels after in vitro expansion, may not reflect their original cell identity [82]. The CD44+ MSCs have enhanced colony-forming capacity and differentiation abilities [84].
MCAM (melanoma cell adhesion molecule), also known as CD146, is involved in cell-ECM (extracellular matrix) interactions [85, 86]. Upregulation of CD146 could switch cell–cell adhesion to cell-ECM adhesion by interacting with its ligands in the ECM, preparing cells for migration and invasion by secreting related cytokines and proteins [85, 86]. CD146 is expressed in many cell types, especially in those cells constituting blood vessels, such as endothelial cells [86] and MSCs [87–91]. And it has been proposed that CD146 is an MSC marker of multipotency [90–93]. CD146+ MSCs have a much stronger chemotactic attraction [94–97], and enhanced immune suppression activities in vitro and in vivo [27, 97–99]. Higher levels of CD146 expression correlate with a faster proliferation rate, enhanced multilineage differentiation potentials, stronger stemness characteristics, and less senescent phenotypes [98–100]. However, Tormin et al. have demonstrated that the colony-forming cells are exclusively enriched in the CD271+ population of MSCs in human bone marrow, regardless of the expression level of CD146 [101]. Within the CD271+ MSCs, both CD146+ and CD146− share similar genotypes and phenotypes [101]. Furthermore, other studies have also demonstrated that CD146+ and CD146− share similar levels of MSC marker expression, colony-forming, proliferation and differentiation capabilities [94, 96, 102, 103]. And the CD146− MSCs even proliferate significantly faster than the CD146+ population [103]. Higher expression of CD146 also indicates more prone to differentiate into vascular smooth muscle cells [103]. In MSCs derived from human dental cysts, CD146Low MSCs have higher levels of cell proliferation, colony-formation, and osteogenesis [102].
SDC2 (Syndecan-2), also known as CD362, is a type of transmembrane heparan sulfate proteoglycan, involved in modulating cell adhesion, proliferation, migration, and apoptosis through its interactions with the extracellular matrix and various proteins, such as proteases and cytokines. These interactions induce downstream pathway activations through intracellular protein partners [104]. CD362 is mainly expressed in MSCs [104]. CD362+ MSCs have enhanced colony forming, immune suppression and regeneration activities [105–107]. Furthermore, both Phase 1 and Phase 2 clinical studies show that CD362+ MSCs are safe, feasible, and effective in treating COVID-19 infections [108].
Mediating lymphocyte adhesion
VCAM1 (vascular cell adhesion molecule 1), also known as CD106, mediates cell–cell adhesion and plays an important role in mediating the rolling, adhesion, and migration of circulating lymphocytes on the endothelium under inflammatory conditions [109–111]. The CD106 is induced by pro-inflammatory cytokines in MSCs [112], and is involved in the immune suppression function of MSCs [113]. CD106+ MSCs derived from placenta and umbilical cord have stronger abilities to suppress immune responses [112, 114] and better pro-angiogenic activity, with enhanced promoting endothelial cell proliferation and migration [28, 115]. Furthermore, CD106+ MSCs have enhanced homing capacity [28, 112].
ITGA1 (integrin subunit alpha 1) is identified in the very late stage of activated T cells. ITGA1 is the major component of the ECM by binding to collagens (mainly collagen I and IV) and laminin, supporting the migration and activation of leukocytes, such as T cells, NK cells, NKT cells, and monocytes, especially the long-term activated or resident T cells [116, 117]. The ITGA1 has been proposed as an MSC marker for human bone marrow [118–120]. The ITGA1+ MSCs have stronger colony-forming activity [118, 119].
CD9, also known as MRP1 (motility related protein-1), is widely expressed in many cell types, including MSCs and lymphocytes, and is involved in regulating cell migration and invasion through integrin receptors [121, 122]. It has been demonstrated that CD9 is involved in the recognition and binding between MSCs and lymphocytes [123]. CD9+ human MSCs have higher NOS (nitric oxide synthase) expression, proliferation rate, colony formation ratio, and stronger cell adhesion capability, resulting in better engraftment and improved therapeutic effects in the mouse model of hindlimb ischemia [124, 125].
Other adhesion molecules
THY1, also known as CD90, is a small membrane protein located in the lipid raft [126]. Although CD90 does not have an intracellular domain, it is involved in cell adhesion, migration, proliferation, and apoptosis through modulating the cell–cell and cell–matrix interactions via binding to its ligands, such as integrins, syndecan, CD90 and CD97 [126, 127]. CD90 has been identified as an important marker for MSCs from different species and tissues [128–132], and could be a potential marker for predicting the immune suppressive function of MSCs [133, 134]. Later studies also indicate that CD90+ MSCs have a faster proliferation rate and better differentiation capabilities [135–137]. However, CD90 is also expressed in the fibroblasts, which might induce fibrosis [138].
Other adhesion genes also have been demonstrated as potential MSC makers, such as the SUSD2 [139–141], ALCAM [142, 143], NCAM1 [144–149], CD51 (also known as ITGAV) [150, 151], and ITGA6 (also known as CD49f) [152] (Tables 1, 2).
Regeneration related markers
Ephrin receptors
The Ephrin receptors (EphA and EphB), which can be recognized by ephrin ligands, play an important role in modulating multiple cellular functions, such as the self-renewal and differentiation of stem cells [153–158]. Proteomics studies indicate that EphA2 is expressed in the MSCs from human bone marrow and umbilical cord, and regulates the functions of MSCs [159, 160]. Follow-up studies showed that EphA2 could be a cell surface marker to distinguish MSCs from fibroblasts [161]. Furthermore, EphA7+ MSCs proliferate faster and have higher levels of colony formation and differentiation capabilities [162]. And EphB2+ MSCs have improved intestinal homing abilities and promoted the intestinal stem cell regeneration [31]. It has been demonstrated that Eph/ephrin pathway is also involved in the cell migration of MSCs [163–166], and is essential for suppressing the proliferation of activated T cells by MSCs [167].
PDGFR
PDGFR (platelet-derived growth factor receptor), including PDGFRA and PDGFRB (also known as CD140α and CD140β, respectively), plays an important role in embryonic development and organogenesis, particularly in regulating the proliferation, migration, and differentiation of MSCs in various organs [168–171]. Although both CD140α and CD140β have been identified as MSC markers [91, 168, 169], their investigation also indicates that CD140α is the negative selection marker for human MSCs derived from bone marrow, which differs from mouse MSCs [172].
Wnt pathway
FZD9, also known as CD349, is a receptor for Wnt ligands and activates β-catenin signaling pathway, which is involved in embryonic development and stem cell renewal [173, 174]. It has been demonstrated that CD349 is expressed in MSCs from both bone marrow and placenta, and proposed as a feasible marker for MSC isolation [175, 176]. Although both CD349+ and CD349− MSCs show similar levels of MSC marker expression and differentiation abilities, the CD349− MSCs have better neovascularization abilities than the CD349+ MSCs [177].
ROR2 is a tyrosine kinase-like orphan receptor, which can be activated by Wnt5a and regulate the tissue polarity and cell movement through downstream WNT/PCP (planar cell polarity) signaling pathway [178, 179]. It has been demonstrated that ROR2+ MSCs derived from human bone marrow have enhanced chondrogenic differentiation efficiency [30].
Others
ALDH (aldehyde dehydrogenase) belongs to the metabolic enzyme family, which is involved in regulating glycolysis/gluconeogenesis and the detoxification of aldehydes via oxidation [180, 181]. It plays an important role in cell survival, proliferation, differentiation, and has been characterized as a classical stem cell marker [180, 181]. In human adipose tissues, the ALDHHigh MSCs represent a more primitive subpopulation than the ALDHLow MSCs, from the perspectives of cell proliferation and tri-differentiation capabilities [182, 183].
STRO-1 can bind to an uncharacterized cell surface antigen, and identify around 10% of mononuclear cells in the human bone marrow [184]. Purified STRO-1+ cells from human bone marrow have higher levels of colony-forming activity, proliferative rate, multilineage differentiation capabilities, and immune suppression activities by expressing higher levels of immune inhibitory factors (IL-8, LIF, IDO, HLA-G, VCAM1, TGF-β, and IL-10) [185], suggesting that STRO-1 is a potential MSC marker [184, 186]. Later study showed that STRO-1+ MSCs have better homing activities than STRO-1− MSCs in the bone marrow, spleen, muscle, liver and kidney, while STRO-1− MSCs are more prone to be trapped in the lung [187].
STRO-3, which recognizes TNSALP (tissue nonspecific alkaline phosphatase, a cell-surface glycoprotein), also identifies a MSC subpopulation with higher proliferation and differentiation potencies [188, 189]. The STRO-3+ MSCs have been identified in various species and tissues and applied in treating various disease models [189–196].
STRO-4 is a monoclonal antibody recognizing the cell surface expressed chaperone protein, Hsp90β. STRO-4+ MSCs have higher colony-forming activities, proliferation rates, and multiple differentiation abilities [29].
TLX1, also known as Hoxa11, belongs to Hox gene family which is essential for patterning during embryonic development. It has been demonstrated that the Hoxa11-lineage marked (Hoxa11-eGFP) could identify the multi-potent MSCs in the mouse bone marrow [197]. Hoxa11+ MSCs have better colony forming potentials and tri-lineage differentiation abilities [198, 199].
Transcription factor GLI1, the effector of the Hh signaling pathway, which regulates tissue development and homeostasis, has been used to mark MSCs in vivo [24, 200–202]. These Gli1+ MSCs are responsible for tissue regeneration after injury [200, 203–206]. However, the Gli1+ MSCs have the tendency to differentiate into osteochondrogenic lineages [201, 204]. Furthermore, the Gli1+ MSCs also contribute to tissue fibrosis [205, 207, 208].
ISLR, also known as Meflin, is a glycoprotein (cell membrane located or secreted) with anti-fibrosis functions through interacting with BMP7 (bone morphogenetic protein 7) and inhibiting TGF-β pathway and myofibroblast differentiation [209]. It has been demonstrated that Meflin is one MSC marker, and its expression positively correlates with its differentiation efficiency [210, 211].
Sca-1 (stem cell antigen-1) has been characterized as a common marker in multiple types of stem cells, such as hematopoietic stem cells and MSCs [212, 213]. It has been demonstrated that mouse MSCs derived from bone marrow and ear express high levels of Sca-1 [214–216]. The expression of Sca-1 is fundamental for maintaining the stem cell state of MSCs [22, 213, 215, 217]. Furthermore, they have higher proliferation rates and better immune suppression abilities [22, 217]. Other common stem cell markers, such as SSEA-4 (stage-specific embryonic antigen-4), KIT, and ABCG2, have also been identified as MSC markers [218–227].
Neuron related markers
Interestingly, the MSCs express some neural development related genes and some of them have been identified as MSC markers, such as CSPG4 (chondroitin sulfate proteoglycan 4) [228–230], GD2 (Disialoganglioside) [231], CD271 [232–238], and Nestin [239–243]. Whether the expression of neuron related genes indicates the dedifferentiated state of MSCs or potential interactions between MSCs and neurons remains unclear and needs further investigation.
Other markers
Since the first demonstration of MSCs, the ENG (Endoglin), also known as CD105, has been identified as a classical MSC marker [15, 128]. Using CD105 to purify MSCs is feasible and efficient in human bone marrow and adipose [244–248]. Furthermore, CD105+ MSCs have increased osteogenic and chondrogenic differentiation efficiency, and reduced adipogenic differentiation efficiency [248, 249]. However, controversial results also show that a low expression level of CD105 is correlated with increased osteogenic and chondrogenic differentiation [250]. Indeed, as a coreceptor of the TGF-β superfamily, CD105 is involved in regulating osteogenic differentiation [251–253].
The SDF1-CXCR4 is the major pathway responsible for cell recruitment and retention [254, 255]. CXCR4 is expressed in human MSCs and contributes to the MSCs homing process [256–260]. For example, in the mouse model of osteogenesis imperfecta, the human MSCs migrate into the bone marrow through the SDF1-CXCR4 pathway and reduce the fracture rate [261]. Furthermore, in the rat model of ischemic brain injuries, rat MSCs migrate into the injured sites of the brain and show therapeutic effects via the SDF1-CXCR4 pathway [262, 263]. Although the expression level of CXCR4 is high in MSCs, few the on the cell surface [256, 261]. However, Honczarenko et al. have demonstrated that the surface expression of CXCR4 is up to around 43% [264], indicating that some factors might induce the cell surface expression of CXCR4, such as culture conditions, stimuli, and passage numbers [265]. Indeed, the expression level of CXCR4 decreases during passaging [264, 266] and aging [267]. The cell membrane localization of CXCR4 is induced by cytokine stimulation (such as SDF-1) [256, 261, 268] or 3D culture conditions [269].
Some other MSC markers have also been demonstrated, such as LepR (Leptin receptor) [270–273], CD34 [274–276], and TNFRSF10D [277]. However, their functions in MSCs remain unclear. Purifying MSC subpopulation with one single maker has many disadvantages (Table 2). Therefore, the combination of multiple markers is a promising strategy to improve the efficiency and efficacy of MSC subpopulation purification. It has been demonstrated that the PODXLhi/ITGA6hi MSCs have better activities of colony formation, differentiation, proliferation, homing activities, regeneration activity, and anti-apoptotic activities [152, 278–282]. The PDGFR+Sca-1+ MSCs could differentiate into both mesenchymal and endothelial at single-cell level with enhanced self-renewal and multipotency abilities [129], and the CD146+PDGFRβ+ MSCs have higher levels of colony-forming activities [91]. Combining PDGFRα and other markers, such as Ly6a, Sca-1, and CD51, would further enrich the MSC subpopulation with enhanced colony-forming and differentiation activities [129, 243, 283, 284]. On the other hand, identifying novel MSC markers with novel high-throughput technologies is also critical for both MSC subpopulation purification and understanding the heterogeneity of MSCs.
2nd generation of MSC maker identification-high-throughput approach
The emergence and development of high-throughput technologies (genomics, transcriptomics, proteomics, lipidomics, metabolomics, and so on) have revolutionized various fields of life sciences [285–287]. These high-throughput technologies have not only expedited the pace of research but also transformed our understanding of life itself by providing a comprehensive and intricate view of biological systems. Since the introduction of Illumina's Solexa sequencing technology in 2005, a new era has been heralded by paving the way for high-throughput technologies [288]. This pioneering approach, built upon parallel sequencing principles, enables the simultaneous analysis of millions of DNA fragments, dramatically boosting sequencing efficiency. Notably, this breakthrough laid the foundation for subsequent advancements, with other platforms such as 454 Life Sciences, Ion Torrent, and PacBio also contributing to the progress of high-throughput technologies [285].
The impact of high-throughput technology extends far beyond genomics, reverberating profoundly across various domains of omics research. In the realm of transcriptomics, we can now unravel intricate gene regulatory networks by simultaneously analyzing the expression of thousands of genes. Technologies like RNA-Seq have empowered scientists to assess gene expression patterns across different conditions, tissues, or developmental stages, shedding light on cellular processes and signaling pathways [286]. High-throughput mass spectrometry techniques in proteomics offer a swift and comprehensive understanding of protein–protein interactions, modifications, and functions within cells. These methods allow researchers to identify and quantify proteins in complex samples, revealing insights into cellular processes, biomarker discovery, and disease mechanisms [287]. In lipidomics, mass spectrometry-based methods have enabled the comprehensive analysis of lipid molecules in biological samples, uncovering lipid profiles associated with health and disease [289]. Similarly, metabolomics, utilizing high-throughput mass spectrometry and nuclear magnetic resonance (NMR) techniques, offers insights into the global metabolite composition of cells or organisms, contributing to our understanding of metabolic pathways and disease biomarkers [290]. Epigenomics, focusing on epigenetic modifications like DNA methylation and histone modifications, benefits from high-throughput techniques such as DNA methylation arrays and next-generation sequencing. These tools provide a genome-wide view of epigenetic modifications, aiding in deciphering their roles in gene regulation, development, and disease [291].
Among these high-throughput technologies, single-cell RNA sequencing technology (scRNA-seq) is a significant innovation in the field of MSCs that has sparked widespread interest in recent years [17–24]. By deciphering the gene expression of each individual cell within a cell population, this technique reveals the astonishing complexity of cellular diversity and heterogeneity, bringing about a revolutionary breakthrough in cellular biology research [292, 293]. Distinct from traditional bulk RNA sequencing methods, scRNA-seq can precisely analyze cell function and types, regardless of sample heterogeneity [293–295].
Several novel MSC markers have been discovered since the application of scRNA-seq technology in the MSC field, such as the LRRC75A+ MSCs with enhanced VEGF production [23]; the CMKLR1+ MSCs with improved immune suppression capabilities [19]; the F3+ and S100A9+ MSCs with better regenerative activities [17, 18, 34].
Among these novel MSC markers identified by scRNA-seq, the CMKLR1+ subpopulation with enhanced immune suppression capabilities [19] has been investigated in detail. The CMKLR1 (Chemokine-like receptor 1), also known as CCRL2 (chemokine C–C motif receptor-like 2), is the transmembrane receptor for chemoattractant chemerin, involved in recruiting and migrating of lymphocytes and immune suppression via its ligand resolvin E1, an important anti-inflammatory mediator [296]. Furthermore, it has been demonstrated that the CMKLR1+ MSCs have better osteogenic differentiation potential and weaker adipogenic differentiation potentials than the CMKLR1− MSCs [19]. Indeed, the CMKLR1 pathway regulates the differentiation balance between the osteoblastogenic and adipogenic MSCs [297]. However, their data also indicate that inhibiting the CMKLR1 pathway promotes the osteoblastogenic differentiation of MSCs and suppresses the adipogenic differentiation of the mouse MSCs [297]. Whether the controversial data resulting from species differences needs further investigation [298–300].
The applications of scRNA-seq not only promote the identification of novel MSC markers, but also uncover new potential functions of MSCs. The MSC marker F3 [17, 18], also known as CD142 or thromboplastin, is a transmembrane glycoprotein and a receptor for coagulation factors, which is involved in platelet activation and coagulation development after tissue injury [301, 302]. The discovery of F3 in MSCs might indicate that MSCs play an important role in blood clot formation at the site of injury.
The extracellular matrix modification function of MSCs is well-known [303, 304]. However, the extracellular matrix microenvironment is a highly complex and dynamic biological component and is critical for the functions of MSCs, including the immune modulation function and stem cell characteristics [3, 305, 306]. Identifying new extracellular matrix-related MSC markers, such as Serpinf1 [22] and HMMR [20], would enhance our understanding of MSC biology in greater depth and breadth..
In addition, scRNA-seq is a powerful strategy for investigating the heterogeneity of MSCs. Purifying a homogenous MSC subpopulation is proposed to have improved therapeutic advantages [17, 19, 23, 307]. However, it has been demonstrated that the Gli1+ MSCs are still heterogenous, as revealed by scRNA-seq [24]. Furthermore, scRNA-seq can also uncover the diversity of functions and interactions among different MSC subpopulations. Two major MSC subpopulations (Lgr5+ and Lgr6+) residing in the mouse lung have completely different functions, uncovered by scRNA-seq analysis [21]. Lgr6+ MSCs support the airway differentiation, while the Lgr5+ MSCs promote alveolar differentiation [21]. In the human umbilical cord, four different MSC subpopulations (proliferative, niche-supporting, metabolism-related, and biofunctional MSCs) have been revealed by scRNA-sequencing [17].
High-throughput techniques for purifying MSC subpopulations
Throughout the developmental trajectory of scRNA-seq, various innovative platforms have emerged, each catering to different research needs based on their unique principles and features. The 10 × Genomics Chromium system is one widely used platform. It employs droplet technology to combine individual cells with specific molecular barcode particles, enabling high-throughput cell capture and transcriptome sequencing [308]. Similarly, Drop-seq utilizes droplet technology to encapsulate cells and molecular barcode beads in droplets, providing a cost-effective option for large-scale cell sequencing [308]. For studies requiring more accurate and comprehensive gene expression information, SMART-seq2 is an ideal choice. Its principle involves introducing specific sample labels after reverse transcription of RNA, allowing individual processing and sequencing of each cell's RNA for deeper insights [309]. For large-scale sample processing, CEL-seq2 proves to be a powerful selection, utilizing cell-specific molecular barcode primers to provide unique identification for each cell [310]. Additionally, the C1 platform, also known as Fluidigm C1, is an advanced single-cell analysis technology platform. It combines microfluidics technology and real-time fluorescence PCR technology, enabling high-throughput capture, processing, and analysis of individual cells. The workflow of the C1 platform includes key steps such as cell capture, lysis, reverse transcription, and amplification, resulting in high-quality single-cell transcriptome data. The C1 platform can be applied to various types of cell analysis, offering crucial support for cellular biology research [311].
Apart from the aforementioned platforms, other unique single-cell sequencing platforms continue to advance the field of cell analysis. For instance, inDrop, a platform similar to Drop-seq, utilizes microfluidic chips for cell capture, boasting high-throughput performance. Its distinctive design involves encapsulating cells and molecular barcode beads together in droplets, enhancing efficiency and accuracy in cell capture and analysis [308]. Moreover, sci-ATAC-seq is another notable platform that not only focuses on single-cell gene expression but also integrates transcriptome and chromatin accessibility information, providing researchers with more comprehensive data [312]. SPLiT-seq, a high-throughput single-cell sequencing technology, simultaneously captures the transcriptomes of thousands of cells. Through specialized fragmentation tags, cellular lysates are split into multiple fragments, each containing a cell-specific molecular barcode. This tag design enables the concurrent amplification of RNA fragments from multiple cells in a single reaction, achieving high-throughput cell capture and sequencing [313].
The exploration of the majority of these markers (S100A9, F3, LRRC75A, SERPINF1, CMKLR1, GL1) in the context of scRNA-seq applications has primarily relied on the 10 × Genomics Chromium system [17–19, 22–24], while the CD168 identification was conducted with the C1 platform [20] and the interaction between Lgr5+ and Lgr6+ MSCs were carried out by using the SMART-seq2 technology [21]. The reliability of these three platforms for developing novel MSC markers has been successfully validated. However, other single-cell sequencing platforms have yet to be applied in the MSC marker field so far. Their respective unique advantages, however, suggest they still hold immense potential for the development of new and effective MSC markers.
Beyond accelerating the pace of analysis, these technologies facilitate the simultaneous analysis of expansive datasets, laying bare the intricate network of molecular mechanisms and relationships that drive biological systems [285]. With the wide application of high-throughput technologies in biomedicine, we also had a deeper understanding of the complexity of biological systems and sought to go beyond the limitations of single omics. The rise of high-throughput technologies not only accelerated data generation but also paved the way for the emergence of multi-omics. This approach, fueled by the copious data generated, marries different omics layers (genomics, transcriptomics, proteomics, and metabolomics) into a comprehensive narrative of biological intricacies. By merging diverse omics datasets, multi-omics integration offers a more comprehensive biological context, enhancing accuracy and facilitating meaningful interpretation of findings [314, 315]. Notably, multi-omics technologies have already been applicated in MSCs [316–319]. Gao et al. demonstrated the utility of multi-omics analysis in understanding the immunosuppressive efficacy of MSCs, shedding light on cellular senescence and PD-L1 expression through single-cell transcriptome and proteomic data analysis [319]. Their findings underscore the potential of multi-omics approaches in discovering new effective MSC markers. This indicates that multi-omics is a feasible strategy to find new effective MSC subpopulations.
Enhanced therapeutic efficacy of marker-sorted MSC subpopulations
Above-mentioned MSC subpopulations exhibit enhanced therapeutic efficacy in various disease models, offering tailored treatment approaches for regenerative medicine and immunotherapy. CD73+ MSCs, characterized by heightened regeneration cytokine secretion and colony-forming capabilities, have shown promising results, particularly in myocardial infarction models [33, 52]. Similarly, CD200+, CD317+, and PD-L1+ MSC subpopulations demonstrate superior colony-forming activity and immune modulation, with CD317+ MSCs exhibiting notable immune suppression capabilities [51, 68, 74]. Moreover, functional diversity is evident among MSC subpopulations. For instance, CD146+ MSCs exhibit strong chemotactic attraction and immune suppression, while CD362+ MSCs display enhanced colony formation and immune suppression, validated in COVID-19 clinical trials [27, 94–99, 105–108]. Additionally, CD106+ MSCs from placenta and umbilical cord tissues demonstrate potent immunomodulation and pro-angiogenic activities [28, 112, 114, 115]. Furthermore, ITGA1+ MSCs and STRO-4+ MSCs exhibit robust colony-forming and proliferation rates [29, 118, 119].
In the clinical translation of marker-based sorting, personalized therapeutic interventions are becoming increasingly feasible. However, further clinical studies are needed to validate the efficacy and safety of these approaches. Overall, leveraging the unique properties of marker-sorted MSC subpopulations holds great promise for advancing regenerative medicine and immunotherapy, offering tailored treatments for diverse medical conditions.
Conclusions and perspectives
Although the therapeutic applications of the MSCs have great promises, challenges still need to be overcome [320, 321]. And the heterogeneity of MSCs constitutes one of those important barriers before their clinical application [6, 7]. Through bioinformatic analysis of the RNA-seq data from different labs and tissues, it is shown that the isolation and expansion procedures induce more heterogeneity than the tissue origin [12]. It should be noted that purifying and expanding the MSCs in vitro is a kind of stress similar to tissue damage in vivo, which might affect the molecular pathways and functions of MSCs [322]. Indeed, the expanded MSCs in vitro are very different from their counterpart in vivo [323]. The MSC expansion strategy would select the cell population which could adapt to these stimuli and stresses [322], indicating the necessity of standardizing the MSC processing procedures and developing a full chemical defined medium [6, 10–12]. Therefore, selecting the suitable MSC subpopulations with specific markers based on their functions and applications is necessary and mandatory [6, 7].
So far, the quest for identifying markers of MSCs has been incessant. The emergence of advanced high-throughput multi-omics techniques offers a promising avenue for discovering novel markers. In this review, numerous MSC subpopulations identified through marker-based sorting have demonstrated significant therapeutic efficacy in animal models. These subpopulations mainly exhibit enhanced therapeutic effects through their potent immunosuppressive capabilities, which have been validated across various animal models of inflammation [38, 68, 74, 112, 114]. Additionally, some subpopulations possess superior homing and regenerative properties, contributing to tissue repair in the rat model of myocardial infarction and the mouse model of hindlimb ischemia [31, 33, 124, 125]. These findings offer promising directions for future therapeutic applications of MSCs. Furthermore, ongoing advances in understanding and manipulating the properties of MSC subpopulations hold great promise for the development of more targeted and effective therapies in regenerative medicine and immune modulation.
However, upon evaluating the majority of currently developed markers, a trend becomes apparent: many subpopulations that are sorted tend to revert to an unsorted state after multiple generations of in vitro proliferation. For instance, during the isolation of MSCs using markers like MCAM, CD9, CXCR4, and STRO-1, their expression diminishes upon subsequent in vitro expansion and cultivation [100, 124, 264, 266, 324]. This situation might indicate that the sole reliance on biomarkers cannot purify consistent and stable subpopulations of MSCs, and eventually achieve successful applications in clinical medicine.
Under diverse physiological or pathological conditions, MSCs exhibit various forms of plasticity, including alterations in morphology, surface markers, secretion, differentiation, proliferation, migration, and apoptotic potential [325]. This plasticity is intimately linked to the microenvironment surrounding MSCs, where physical, chemical, and biological factors impact MSCs’ functions through distinct mechanisms [325]. These mechanisms might involve critical processes like signaling pathway modulation and cellular reprogramming, ultimately influencing MSCs’ capabilities [325]. Illustrating the immunomodulatory role of MSCs exemplifies this phenomenon. During the acute phase or relapse of inflammation, effector T cells secrete pro-inflammatory cytokines, including IFN-γ, TNF, IL-1, and IL-7. These pro-inflammatory cytokines stimulate MSCs to produce substantial amounts of IDO (indoleamine 2,3-dioxygenase) and chemokines. Chemokines serve to attract activated T cells toward MSCs. The elevated concentration of IDO metabolites stemming from this process directly inhibits T cells, resulting in an overall attenuation of the immune response and promotion of tissue repair [1, 326]. On the contrary, in chronic inflammation or during remission, the concentration of anti-inflammatory cytokines, such as TGF-β, increases while pro-inflammatory cytokines decline. Consequently, the production of IDO by MSCs drops below the immunosuppressive threshold. Despite the continued expression of chemokines albeit at lower levels, recruited T cells are not restricted, thus exacerbating the inflammatory immune response [1, 326].
As previously highlighted, MSCs are characterized by their exceptional plasticity. The exclusive focus on purifying MSC subpopulations could potentially impose certain limitations. A more intricate strategy revolves around carefully shaping the extracellular environment of MSCs through deliberate in vitro cultivation, a process terming ‘MSC education’. The objective of this educational initiative is to tap into the inherent variability present within the cell population, steering it towards a consistent manifestation of the intended functions. Across a spectrum of models, diverse categories of educated MSCs have unveiled a range of distinctive functionalities [327–329]. For instance, when BM-MSCs are exposed to WNT5a secreted by gastric cancer cells, a noticeable upregulation of α-SMA expression and an amplified capacity for driving tumorigenesis have been observed [327]. Furthermore, the exosomes released by MSCs primed with neonatal serum have proven capable of expediting the healing of cutaneous wounds by actively stimulating angiogenesis [328]. Notably, MSCs that have undergone a process of education through exposure to chemotherapy have emerged as critical mediators in facilitating communication between MSCs and tumor-initiating cells within specific tumor contexts. This communication is achieved through the selective secretion of cytokines and/or chemokines [329]. In the realm of immune regulation, differently educated MSCs can even exhibit contrasting functionalities. Waterman et al. found that MSCs educated by T-cell signaling (referred to as MSC-I) are primarily geared toward producing pro-inflammatory factors, while MSCs educated by TLR3 signaling (referred to as MSC-II) predominantly express immune-suppressive factors [330]. Similarly, MSCs educated by immune factors such as IFN-γ and TNF-α also demonstrate enhanced immune-suppressive capabilities [1, 331].
To sum up, the development of MSC markers, bolstered by high-throughput techniques, holds substantial potential. Looking at the broader field of MSC research, in addressing the challenge of inconsistent therapeutic efficacy due to MSC heterogeneity, MSC education also presents a viable avenue alongside MSC markers.
Author contributions
Si Chen and Bowei Liang wrote the draft; Jianyong Xu revised the manuscript.
Funding
Guangdong Basic and Applied Basic Research Foundation (2021A1515012484, 2023A1515010181); The fundamental research project of the Shenzhen Science and Technology Innovation Commission (20231120113324002).
Data availability
Not applicable.
Declarations
Competing interests
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Si Chen and Bowei Liang have contributed equally to this work and share first authorship.
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