Detection of Cancer Stem Cells from Patient Samples

Abstract

The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.

Full text

Academic Editor: Pablo
Martín-Vasallo
Received: 30 December 2024
Revised: 16 January 2025
Accepted: 17 January 2025
Published: 20 January 2025
Citation: Hakala, S.; Hämäläinen, A.;
Sandelin, S.; Giannareas, N.; Närvä, E.
Detection of Cancer Stem Cells from
Patient Samples. Cells 2025,14, 148.
https://doi.org/10.3390/
cells14020148
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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(https://creativecommons.org/
licenses/by/4.0/).
Review
Detection of Cancer Stem Cells from Patient Samples
Sofia Hakala , Anna Hämäläinen , Sanne Sandelin , Nikolaos Giannareas and Elisa Närvä *
Institute of Biomedicine and FICAN West Cancer Centre Laboratory, University of Turku and Turku University
Hospital, FI-20520 Turku, Finland; skhaka@utu.fi (S.H.); anna.e.hamalainen@utu.fi (A.H.);
sanne.sandelin@utu.fi (S.S.); nikolaos.giannareas@utu.fi (N.G.)
*Correspondence: elisa.narva@utu.fi
Abstract: The existence of cancer stem cells (CSCs) in various tumors has become in-
creasingly clear in addition to their prominent role in therapy resistance, metastasis, and
recurrence. For early diagnosis, disease progression monitoring, and targeting, there is
a high demand for clinical-grade methods for quantitative measurement of CSCs from
patient samples. Despite years of active research, standard measurement of CSCs has not
yet reached clinical settings, especially in the case of solid tumors. This is because detecting
this plastic heterogeneous population of cells is not straightforward. This review sum-
marizes various techniques, highlighting their benefits and limitations in detecting CSCs
from patient samples. In addition, methods designed to detect CSCs based on secreted
and niche-associated signaling factors are reviewed. Spatial and single-cell methods for
analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and
in vivo
imaging are discussed. Additionally, methods recently established in laboratories,
preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of
an ideal method as we look toward the future.
Keywords: cancer stem cell (CSC); diagnostics; immunohistochemistry (IHC); multiplex;
cytometry; single-cell RNA sequencing (scRNA-seq); liquid biopsy; immunoassay; medical
imaging; spatial transcriptomics
1. Introduction
Cancer stem cells (CSCs) have been studied for the past thirty years. From times of
theory and speculation, the existence and importance of CSCs have been recognized in all
major cancer types [
1
,
2
]. CSCs are a population of therapy-resistant, plastic, self-renewing
cells capable of initiating, metastasizing, and forming heterogeneous tumors [
3
]. Due to
these characteristics, CSCs encompass significant clinical value. Therefore, the detection
of CSCs from patient samples is becoming increasingly important. It is central not only to
have reliable methods to estimate the effectiveness of targeted therapies but also to enable
monitoring of disease progression and early diagnosis.
The main strategies that have been used to identify CSCs are based on their tumor ini-
tiation capacity (xenotransplantation), self-renewal ability (organoid formation), secretion
efficiency (drug efflux), and expression of CSC-specific factors (proteins, nucleotides, and
glycovariants). Of these, CSC-specific markers would be most optimal for clinical use. Over
the years, a full range of CSC markers across various cancer types have been identified, and
the list continues to expand. The most studied markers in clinical samples are CD44, CD133,
ALDH, CD24, CD34, and EpCAM, whereas the expression of pluripotent core factors such
as NANOG, SOX2, and OCT4 has become more evident in recent years [
4
–
6
]. In addition,
the CSC spatial microenvironment, or “niche”, which is crucial for CSC signaling and
Cells 2025,14, 148 https://doi.org/10.3390/cells14020148

Cells 2025,14, 148 2 of 27
function [
7
,
8
], provides an alternative way to identify CSCs through various signaling
factors and associated cell types.
One major challenge for quantitative CSC detection is the high inter- and intrapatient
heterogeneity and lack of exclusive CSC markers. However, single-cell-based multiparam-
eter methods can change these limitations into a successful detection method that could
quantitatively detect the full range stemness. This review aims to highlight CSC detection
methods (Figure 1) that would be most applicable for clinical use considering the special
characteristics of CSCs.
Cells 2025, 14, 148 2 of 29
[4–6]. In addition, the CSC spatial microenvironment, or “niche”, which is crucial for CSC
signaling and function [7,8], provides an alternative way to identify CSCs through various
signaling factors and associated cell types.
One major challenge for quantitative CSC detection is the high inter- and intrapatient
heterogeneity and lack of exclusive CSC markers. However, single-cell-based multipa-
rameter methods can change these limitations into a successful detection method that
could quantitatively detect the full range stemness. This review aims to highlight CSC
detection methods (Figure 1) that would be most applicable for clinical use considering
the special characteristics of CSCs.
Figure 1. Overview of the methods used to detect CSCs from patient samples. Methods are divided
based on sample type into analysis of tumor tissues, liquid biopsy, and in vivo medical imaging.
2. Spatial Analysis Methods for Tissues
Invasive operation is always performed before analysis of patient tissues. However,
surgical removal of tumors is a standard operation, where excessive material is often
available. In addition, fine-needle puncture can be used to obtain tissue that can be used
for diagnosis or monitoring of disease progression.
2.1. Conventional Immunohistochemistry
Chromogenic immunohistochemistry (IHC) is a straightforward, accessible, and af-
fordable standard method used in clinical practice for analyzing tissue samples. Im-
portantly, the stained slides are permanent and can be archived for long periods. The main
limitation of standard IHC is the restricted number of markers that can be used (one to
two per slide). This limitation can be alleviated to some extent by utilizing consecutive
serial sections. Further, the linear dynamic range of the signal intensity observed by
brightfield is narrow, resulting in semiquantitative data. In addition, IHC shows
Figure 1. Overview of the methods used to detect CSCs from patient samples. Methods are divided
based on sample type into analysis of tumor tissues, liquid biopsy, and in vivo medical imaging.
2. Spatial Analysis Methods for Tissues
Invasive operation is always performed before analysis of patient tissues. However,
surgical removal of tumors is a standard operation, where excessive material is often
available. In addition, fine-needle puncture can be used to obtain tissue that can be used
for diagnosis or monitoring of disease progression.
2.1. Conventional Immunohistochemistry
Chromogenic immunohistochemistry (IHC) is a straightforward, accessible, and afford-
able standard method used in clinical practice for analyzing tissue samples. Importantly,
the stained slides are permanent and can be archived for long periods. The main limitation
of standard IHC is the restricted number of markers that can be used (one to two per slide).
This limitation can be alleviated to some extent by utilizing consecutive serial sections.
Further, the linear dynamic range of the signal intensity observed by brightfield is narrow,
resulting in semiquantitative data. In addition, IHC shows interlaboratory variance and
requires a trained pathologist for analysis, which can be reduced to some extent by utilizing
modern digital analysis techniques [9,10].
Nevertheless, IHC has been the most commonly used technique for analyzing CSCs
from patient samples. Despite single-marker data, such as CD44, CD133, or ALDH1, a

Cells 2025,14, 148 3 of 27
correlation between survival and CSC expression can be demonstrated based on a meta-
study involving over 100 IHC patient studies [1].
2.2. Multiplex Techniques
Multiplex techniques increase the number of markers to be used simultaneously and
could provide a cost-effective method to detect heterogeneous subpopulations of CSCs
from patient tissues. Moreover, multiplexing includes spatial information on CSCs and
niche-associated cell types such as immune cells in their natural microenvironment. There
are various multiplexing techniques available that are compatible with standard clinical
instruments such as multiplex immunohistochemistry (mIHC), multiplex immunofluores-
cence (mIF) and multiplexed immunohistochemical consecutive staining on a single slide
(MICSSS). Of these, mIHC and mIF are based on tyramide signal amplification, enabling
the visualization of four to five markers simultaneously. MICSSS increases the number of
markers to ten, but it also extends the time required due to the multiple staining rounds,
which can also affect tissue integrity and antigenicity [10,11].
mIHC was recognized already in 2010 as a method to identify high-risk patients with
breast cancer by identifying putative CSCs [
12
]. Additionally, mIHC has been used to study
CSCs in gastric, cervical, ovarian, non-small lung, and head and neck cancer [
13
–
17
] and to
expose the stem cell niche [18].
In addition to the conventional multiplex techniques, several higher plex technologies
based on mass spectrometry, oligo-barcoded antibodies, and fluorescence are on their way
to clinic settings that require special instrumentation and expertise [
10
,
19
]. While these
technologies significantly increase the number of markers, they also raise costs, optimiza-
tion time, and analysis complexity. In addition, the tissue area and resolution obtained
with these technologies are still limited. However, advanced multiplex technologies may
soon provide valuable insights into CSC biology. In the next section, we highlight the
possibilities of spatial transcriptomics to capture CSCs.
2.3. Spatial Transcriptomics
Spatial transcriptomics covers all the methods that assign transcriptomics data to
the original location within a tissue [
20
,
21
]. This can be obtained through various ap-
proaches such as direct sequencing of microdissected areas and methods that involve in
situ hybridization [
22
,
23
], in situ sequencing [
24
], and in situ capturing [
25
] followed by
computational reconstruction of spatial data. Spatial transcriptomics is especially suitable
for studying CSC heterogeneity and revealing the spatial organization of niches and other
cell types. Importantly, through unbiased sequencing, new characteristics of CSCs can be
identified. Limitations of this technology include the inability to perfectly resolve single
cells, computational artifacts, the requirement for specific instrumentation, demanding
optimization, and cost [20,25].
The spatial transcriptomics platform Visium 10X genomics has been used to reveal
radial glial stem-like cells in the neuron-rich invasive niche of glioblastoma [
26
], the co-
location of CSCs and SPP1+ macrophage in the hypoxic region that determines the poor
prognosis of hepatocellular carcinoma [
27
], and the uniform location of CSCs in HPV-
negative oral squamous cell carcinoma [
28
] and to identify and characterize Wilms Tumor
CSCs in kidney cancer [
29
]. Additionally, microdissection and sequencing have revealed
the existence of distinct CSC-like populations in triple-negative breast cancer MDA-MB-231
xenografts, also confirmed in single-cell sequencing patient data [
30
]. Therefore, multiomic
approaches can be used to support the initial findings. For example, a spatial multiomic
approach was used to reveal a subpopulation of fibroblasts associated with cancer stemness
in human hepatocellular carcinoma [31].

Cells 2025,14, 148 4 of 27
3. Single-Cell Suspension Analysis Methods
Isolating suspension cells from body fluids, especially blood, is a relatively noninvasive
operation for a patient. Through careful tumor dissociation, single cells can also be obtained
from solid tumors via invasive procedures like tumor biopsies and surgeries. This section
discusses the following primary techniques for single-cell analysis: flow cytometry, mass
cytometry, and single-cell RNA sequencing (scRNA-seq). Other technologies applicable
after liquid biopsy are reviewed later.
3.1. Flow Cytometry
Flow cytometry is a powerful analytical technique widely used in CSC research and
clinical analysis, especially in the cases of peripheral blood, bone marrow aspirate, and
cerebrospinal fluid. Flow cytometry enables the simultaneous analysis of multiple cellular
features by measuring specific surface markers, intracellular proteins, and metabolic func-
tions [
32
–
36
]. Flow cytometry is highly sensitive [
37
,
38
], allowing researchers to identify
and isolate rare CSC populations from blood or tumor tissues. Moreover, the technique is
versatile, as it can adapt to various markers for different patient tumor types [
39
]. Despite
its strengths, flow cytometry has limitations. One limitation is the dependency on prior
knowledge of marker expression. This reliance can hinder the discovery of unexpected
CSC patterns, necessitating additional studies that are time-intensive and constrained by
limited sample availability. Further, the method can analyze only single viable cells, and
artifacts generated by tissue dissociation processing remain a challenge [
40
], which can
be reduced by using dissociation reagents that help preserve the integrity of cell surface
markers [
41
,
42
]. One drawback is its restriction to analyzing only one to twelve markers
per panel due to overlapping fluorochrome spectra, posing challenges for small tumor
biopsies [
43
]. However, spectral flow cytometry, an invasive technique used in immune
cell populations, overcomes this by capturing high-resolution spectral data from cells,
recording full emission spectra across all wavelengths instead of a single emission peak [
44
].
This enables the use of 30–50 markers per panel, conserving limited sample material and
allowing more comprehensive analyses than traditional cytometry [
45
–
49
]. However, nor-
malization is a key challenge in spectral flow cytometry [
50
,
51
]. Additionally, instrumental
issues, such as photon collection inefficiencies and inconsistent calibration over multiple
days, can lead to technical noise and errors like spreading artifacts obscuring biological
signals [52–54].
The classical identification of CSCs through flow cytometry relies on detecting a side
population, a small subset of cells that actively exclude DNA-binding dyes such as Hoechst
33342 due to heightened ABC transporter activity [
35
,
55
]. ABC transporters are integral
to the functionality of CSCs and chemoresistance, particularly in their capacity to expel
drugs and other compounds [
56
–
58
]. The side population approach has been employed
in various cancer types, including cancer cell lines of breast, glioma, colorectal, and B-cell
chronic lymphocytic leukemia patients [
59
–
62
]. Nonetheless, side population analysis
poses challenges. Not all CSCs are identified with this analysis, and results are influenced
by variability in staining protocols, dye concentrations, and gating techniques [
63
,
64
].
In addition, autofluorescence caused by riboflavin accumulation in membrane-bound
cytoplasmic structures bearing ATP-dependent ABCG2 transporters can be used to isolate
CSCs by flow cytometry [
65
]. Interestingly, these autofluorescent cells isolated from tumors
are distinct from the classical side population.
The classical study by Lapidot et al. (1994) used flow cytometry to show for the first
time the existence of CSCs by isolating CD34-positive and CD38-negative cells from patients
with human acute myeloid leukemia [
66
]. Flow cytometry analysis is currently an important
and indispensable tool in the diagnosis of mature B-cell lymphomas and leukemias [
67
].

Cells 2025,14, 148 5 of 27
Recently, a high-dimensional spectral flow cytometry study with 20 antibodies was reported
to enable the identification of rare subpopulations that are significant to hematopoietic
stem cell hierarchies [47].
Furthermore, flow cytometry is an important tool for isolating rare cell populations
from tumor masses and identifying the CSCs using markers such as CD34, CD133, and
CD44, along with tumor-specific markers [
68
–
72
]. For the characterization of CSCs, marker
CD133 has been used for colorectal and brain tumors [
73
–
75
] whereas a combination of
CD44 and CD24 has been used as a standard tool in the case of breast cancers [
76
,
77
].
Similarly, for prostate cancer, the CSCs are identified using markers CD133, CD44, and
integrin α2β1 [78,79].
3.2. Mass Cytometry
Mass cytometry, also known as cytometry by time of flight (CyTOF), is a cutting-edge
technology designed for single-cell profiling. The technology has been used mainly for
immune cell profiling and is in use in over 200 clinical studies. By leveraging antibodies
conjugated to heavy metal isotopes, mass cytometry enables the detection of 40–60 proteins
and their post-translational modifications within individual cells. These labeled cells are
ionized in a mass spectrometer, where chemical bonds are broken, and metal ions are
separated based on their mass-to-charge ratio. This approach permits the simultaneous
analysis of numerous markers in a single sample, surpassing the capabilities of traditional
flow cytometry [
80
–
82
]. Therefore, the technique could be invaluable for exploring stem
cell heterogeneity.
Mass cytometry offers several advantages over traditional approaches. Unlike
fluorescent-based methods, it avoids spectral overlap, allowing the simultaneous quantifi-
cation of multiple proteins at the single-cell level [
83
]. Furthermore, its capacity to analyze
many parameters in a single sample reduces the number of cells required per experiment,
making it suitable for small or precious samples [
80
]. The ability to characterize spatial
marker distribution with imaging mass cytometry adds a valuable dimension to studying
tissue organization [
84
,
85
]. As with all techniques, mass cytometry has limitations. The
destruction of cells during analysis precludes recovery for downstream applications [
80
].
The flow rate is slower than that of flow cytometry, processing approximately 500 cells per
second versus several thousand by flow cytometers. Mass cytometry is also less sensitive
in detecting low-abundance molecules, and the reliance on heavy metal isotopes restricts
the ability to fully utilize the theoretical capacity of up to 135 channels [
80
,
81
]. Additionally,
the instruments and reagents are costly, making them less accessible to clinic settings than
other single-cell technologies.
The study on glioblastoma stem cells from patient samples underscores the critical
role of mass cytometry in understanding the complexity and heterogeneity of CSCs. Using
mass cytometry, researchers identified 15 distinct CSC subpopulations based on markers
such as CD15, CD44, CD133, and
α
6 integrin [
86
]. Notably, the subpopulation expressing
all four markers exhibited the highest self-renewal capacity and
in vivo
tumorigenicity,
highlighting its potential therapeutic relevance. Additionally, the study revealed differences
in stemness affecting signaling pathways, including MEK/ERK, Wnt, and Akt. Another
study of muscle-invasive bladder cancer identified a unique subset of CSCs (CD274+,
ALDH+) in tumor tissue using mass cytometry [
87
]. This contributed to the understanding
of the tumor microenvironment and the identification of key cell populations relevant
to prognosis and immunotherapy response in this cancer type. Overall, these findings
demonstrate that mass cytometry offers a more detailed and comprehensive view of
CSCs than traditional methods, allowing the identification of subpopulations that might
otherwise go unnoticed.

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3.3. Single-Cell RNA Sequencing
ScRNA-seq is a transformative technology enabling detailed examination of gene
expression at the level of individual cells. Unlike bulk genomic methods that average gene
expression across cell populations, scRNA-seq captures cellular heterogeneity and provides
insights into processes like stem cell differentiation and drug responses in neighboring
cells [
88
,
89
]. First introduced in 2009, this technology has become more accessible and
widely used, allowing applications such as identifying rare cell populations and revealing
gene splicing patterns, co-regulated gene modules, and single-allele expression [
90
–
92
]. The
workflow typically includes cell isolation, messenger RNA (mRNA) capture, sequencing,
and bioinformatics analysis [
93
]. Different methods like Drop-seq, SCRB-seq, and Smart-
seq2 offer varying levels of sensitivity, sequencing depth, and cost, enabling customization
for specific research needs [
94
–
98
]. However, scRNA-seq has its limitations and challenges
considering clinical use. To maintain high RNA quality, tissue samples must be dissociated
and processed immediately after collection. This poses logistical challenges, particularly
in clinical environments where staff may not have specialized training or access to the
necessary equipment. The data analysis requires significant bioinformatics expertise and
computational resources, which may limit accessibility [
99
–
101
]. Furthermore, the limited
number of cells analyzed can reduce the statistical power needed to detect rare popula-
tions [
102
]. Transcription-level data does not always correlate with functional protein levels
in the samples [
103
], and it also lacks spatial information about cell organization within
tissues—an essential aspect for addressing certain biological questions.
Nevertheless, scRNA-seq has been instrumental in advancing our understanding of
CSCs [
104
]. Patel et al. (2014) uncovered the extensive cellular diversity and continuous
stem cell-related expression patterns in glioblastoma tumors using scRNA-seq [
105
]. In
bladder cancer, Yang et al. (2017) used scRNA-seq to successfully identify CSCs with
clonal homogeneity and mutations that enhance their self-renewal capabilities [
106
]. Sim-
ilarly, studies on triple-negative breast cancers have demonstrated significant inter- and
intratumoral heterogeneity [107,108].
Pan et al. (2020) applied scRNA-seq and pseudotemporal trajectory analysis to study
CSCs in collecting duct renal cell carcinoma, identifying CSC markers (e.g., BIRC5 and
CENPF) linked to poor prognosis [
109
]. Similarly, Fendler et al. (2020) used scRNA-seq in
clear cell renal cell carcinoma, revealing Wnt and Notch activation in CSCs and evaluating
inhibitors in sphere cultures, organoids, and patient-derived xenograft models [
110
]. Both
studies highlight CSC heterogeneity and therapeutic strategies to overcome drug resistance.
Moreover, a recent study utilized scRNA-seq on bone marrow samples from 41 patients with
primary refractory multiple myeloma to investigate resistance mechanisms [
111
]. The study
provided insights into early unresponsiveness or progression during first-line treatment,
guiding strategies to resensitize tumors to therapies. It also highlights the potential of
RNA sequencing to improve resistance prediction and therapeutic personalization in
clinical practice.
4. Analysis Methods for Biofluids
Liquid biopsy can be defined as the sampling method for any biofluid of the body.
In addition to blood, serum, plasma, urine, saliva, pleural effusion, and ascites fluid can
be classified as liquid biopsy samples. All the components that tumors secrete to blood
are together referred to as the tumor circulome [
112
]. It includes, for example, circulating
tumor cells (CTCs), circulating tumor DNA (ctDNA) and RNA (ctRNA), tumor-derived
extracellular vesicles (EVs), and protein markers.
Liquid biopsy has gained popularity over tissue biopsy because it offers semi- to
non-invasive, cost-effective, and easy sample collection from patients’ biofluids, with a

Cells 2025,14, 148 7 of 27
lower risk of complications [113]. Due to its repetitive nature, liquid biopsy results can be
even more reliable than traditional tissue biopsy results and provide a more comprehensive
representation of spatial and temporal tumor heterogeneity [
113
–
115
]. The drawbacks of
liquid biopsy are its lower sensitivity and specificity than those of tissue biopsy [
114
]. Con-
cerns have been raised about the detectability of rare cells and tumor circulome biomarkers
in comparison to other cell types and biomolecules present in the bloodstream [
115
,
116
].
The limitations of liquid biopsies in cancer diagnostics have been reviewed and listed by
De Rubis et al. (2019) [
113
]. In this chapter, we focus on the CSC-related circulome in the
blood as well as in other biofluids.
4.1. Detection of Circulating CSCs from a Blood Sample
CTCs are cells that have escaped into blood flow from the bulk tumor. Research has
shown that certain CTCs exhibit characteristics similar to those of CSCs. These features
are found to be more common in CTCs than in primary tumors [
117
–
122
]. As CTCs
represent only a small percentage of the cells circulating in a patient’s blood, and circulating
CSCs even smaller, CTC enrichment techniques are required for the isolation and analysis
of CSCs.
The enrichment methods can be divided into positive enrichment when choosing
CTCs or negative enrichment when deleting all the other cell types from a sample [
123
]. For
instance, the negative selection of the other circulome cells is one possible way to enrich the
circulating CSCs from patients’ blood [
124
,
125
]. A simple way to exclude red blood cells is
to use a red blood cell lysis buffer [
126
,
127
]. In addition, the enrichment techniques and
detection methods can be divided into label-dependent and label-free methods. Label-free
methods can utilize size, deformability, density, or electric charge for the enrichment of the
cells. Label-dependent methods mostly rely on the detection of CSC markers via antibodies
by immunocapture. Many studies are combining different label-free and label-dependent
enrichment and isolation methods to ensure the best result.
4.1.1. Label-Dependent Enrichment Methods for Circulating CSCs
CTCs have been traditionally detected by epithelial cell adhesion molecule EpCAM
expression. The first FDA-approved method for detecting CTCs called CellSearch utilizes
antibody-coated magnetic beads to detect the expression of EpCAM, CD45, and cytoker-
atins 8, 18, and 19 [
128
]. It is used in monitoring the state of patients with metastatic breast,
colorectal, or prostate cancer [
129
–
132
]. The CellSearch technique can be combined with
epithelial-to-mesenchymal transition (EMT) markers or CSC markers to detect circulating
CSCs [133,134].
As EpCAM is the most common CTC marker it is used in many circulating CSC
studies in combination with known CSC-associated markers. Koren et al. (2016) studied
the non-small-cell lung cancer CTCs using density gradient centrifugation followed by
EpCAM-labeled magnetic beads to enrich EpCAM-positive cells [
135
]. The gene expression
profiles analyzed by quantitative reverse transcription polymerase chain reaction (qRT-
PCR) showed overexpression of CSC marker ALDH1A1 in all the patients. Similarly, Tian
et al. (2018) used magnetic beads labeled with EpCAM antibody to capture the CTCs
from the blood of non-small cell lung cancer patients [
136
]. This study defined ALDH1 as
driving the stemness of the CTCs and its expression as a prognostic marker for non-small
cell lung cancer patients.
Hassan et al. (2022) studied the liver CSCs by analyzing EpCAM and CD133 expression
by flow cytometry and stemness-associated microRNA (miRNA) expression levels (miR-
1290 and miR-1825) by real-time PCR from the patient’s blood [
137
]. They concluded that
EpCAM and CD133, as well as examined miRNAs, could serve as diagnostic and prognostic

Cells 2025,14, 148 8 of 27
markers for liver cirrhosis or hepatocellular carcinoma. Varillas et al. (2019) studied CTCs
and CSCs in pancreatic ductal adenocarcinoma with a geometrically enhanced mixing
microfluidic chip containing antibodies against EpCAM and CD133 [
138
]. Additionally,
they used label-free density gradient centrifugation to enrich the cells. A total of 78%
of their samples were positive for the CSC marker CD133. They showed that although
circulating CSCs are rare compared to other cells in a patient’s blood, they can still be
isolated and identified.
Immuno-based methods can also be used for the negative enrichment of CSCs [
139
,
140
].
Mihalcioiu et al. (2023) utilized blood apheresis to collect CTCs from selected breast cancer
patients to find CTCs with metastatic abilities [
141
]. Cells remaining after CD45-negative
selection expressed markers such as CK8, EpCAM, ALDH1, CD44, and CXCR4.
4.1.2. Label-Free Enrichment Methods for Circulating CSCs
The most used method for label-free enrichment of CTCs and circulating CSCs from
blood samples is density gradient centrifugation as it is a simple and cost-effective standard
method for blood samples [
127
,
142
–
144
]. Some of the label-free enrichment methods are
partly developed to overcome the limitations of EpCAM-dependent selection as some of
the circulating CSCs could fully or partially undergo the EMT and lose the expression of
EpCAM in the process [140,141,145,146].
Microfluidic chips can be designed as label-free. Hyun et al. (2016) studied CTCs in
metastatic breast cancer patients’ blood using a parallel multi-orifice flow fractionation
(p-MOFF) chip that characterized the cells by size, shape, and density [
145
]. By further
analysis, they identified patients’ CTCs positive for CSC marker ALDH1A1. Notably, only
4 of the 24 patients had solely EpCAM-positive CTCs, which underlines the weakness of
EpCAM in CSC detection. Lin et al. (2017) developed a microfluidic labyrinth that uses
size-based separation [
147
]. They were able to separate subpopulations of CTCs and found
CSC-like cells by gene expression analysis. The labyrinth technology was later used by
Wan et al. (2019) to analyze CTCs from hepatocellular carcinoma [
148
]. As they performed
immunostaining, they were able to detect CD44-positive populations in all stages of the
cancer. Also, the FDA-cleared Parsortix PC1 system is a label-free microfluidic-based
technique for cell categorization by size and deformability [
149
,
150
]. This technique was
later used to isolate the CTCs of non-small cell lung cancer and then analyze the gene
expression profiles with qPCR. The results showed that the CSC markers NANOG and
PROM1 were associated with a poor prognosis [118].
Another label-free cell enrichment tool utilizes dielectrophoresis and field-flow to
detect differences in polarization and categorize cells from blood samples [
151
]. This
tool has been used to detect CTCs that have passed through the EMT or gained the CSC
phenotype in early-stage breast cancer [
152
]. Of 47 patients, 55–74% had epithelial CTCs
(CK+, EpCAM+, or E-cadherin+), 57–72% had EMT-CTCs (vimentin+ or β-catenin+), and
9–22% had CSC-CTCs (CD44+ and CD24low) based on immunofluorescence staining.
4.2. Detection of CSCs from Other Biofluids
Depending on the type of cancer, CSCs can also be detected and isolated from other
biofluids. In particular, ovarian cancer research has been using the ascites fluid to isolate
the CSCs [
153
–
157
]. In these studies, ascites CSCs have been isolated utilizing, e.g., centrifu-
gation and negative enrichment techniques followed by enzyme-linked immunosorbent
assay (ELISA), qRT-PCR, or flow cytometry analysis. In addition, isolated CSCs have also
been studied by spheroid and adherent cell cultures
in vitro
[
154
,
155
,
157
]. Kuroda et al.
(2013) isolated CSC-like cells from ascites using anti-CD326 (EpCAM) microbeads and then
measured the ALDH1 activity of the cells [
158
]. High ALDH1 expression correlated with

Cells 2025,14, 148 9 of 27
a poorer prognosis in serous and clear cell adenocarcinoma samples. Studies like these,
one after another, have shown the correlation between CSCs and the poor prognosis of
ovarian cancers.
In the case of, e.g., breast and lung cancers, malignant pleural effusion can emerge.
This fluid has also been shown to contain CSCs, which have been studied directly with
IHC, immunofluorescence, RT-PCR, flow cytometry, and genomic DNA analysis [
159
–
162
].
In addition, floating tumor cells with CSC features have been found in cerebrospinal
fluid collected with lumbar puncture from breast cancer patients with leptomeningeal
metastasis [
163
]. In this study, CSCs were enriched through centrifugation and subsequently
analyzed using flow cytometry. The analysis revealed overexpression of the markers
Syndecan-1 (CD138), MUC-1 (CD227), CD45, CD34, CD24, CD44, and CD133.
Biomarkers can also be found in saliva. In some cancer types, especially in oral cancers,
tumor-specific DNA, RNA, or proteins are often found in saliva samples [
164
]. Kamarajan
et al. (2017) analyzed saliva together with tissue and plasma samples to study head and
neck squamous cell carcinoma CSC metabolomics by UPLC-MS/MS (ultra-performance
liquid chromatography–mass spectrometry) [
165
]. They showed that glutaminolysis plays
a role in regulating ALDH levels in these cells and induces stemness.
4.3. Detection Methods for CSC-Derived Extracellular Vesicles
EVs released by cells into the extracellular space can contain various surface markers
and biomolecules of the cell of origin, making them valuable for cancer diagnosis. CSCs
are proposed to have elevated secretion of EVs compared to cancer cells [
166
,
167
]. Both
cancer cell- and CSC-derived EVs have been shown to promote cancer progression and
metastasis [
168
–
171
]. Additionally, CSC-derived EVs have been shown to carry CSC-
related RNAs that can cause stemness in surrounding cells [
169
,
172
–
175
]. The role of
CSC-derived EVs in cancer has been extensively reviewed by Naghibi et al. (2023) and Su
et al. (2021) [176,177].
By detecting CSC-derived EVs from biofluids, one can predict CSCs’ existence. The
most obvious way to detect CSC-derived EVs is to use known EV and CSC markers together.
In a recent study, ovarian cancer patient-derived EVs from urine and ascites fluid beforeand
after chemotherapy were isolated and characterized using ultracentrifugation, CD9-coated
magnetic beads, and flow cytometry using markers CD81, CD117, and EpCAM [
178
]. Also,
CSC-derived exosomes have been isolated from melanoma patients’ serum and ascites
fluid from pancreatic cancer patients with ultracentrifugation and an exosome isolation
kit [179,180].
CSC-derived EVs have also been detected using label-free methods. Surface-enhanced
Raman scattering (SERS) is a technique that significantly enhances Raman scattering sig-
nals [
181
]. SERS provides a highly sensitive, label-free method that requires minimal
sample preparation and allows sample recovery [
182
]. Haldavnekar et al. (2022) used SERS
to detect and trap CSC-derived EVs from cancer patients’ blood by self-functionalized 3D
networks of nanosensors [
183
]. Interestingly, the EVs derived from CSCs differed from
those derived from the bulk tumor cancer cells. The same research group later used this
technique to detect CSC-derived EVs from glioblastoma and was able to find differences in
molecular signatures between non-cancerous EVs and CSC-derived EVs [
184
]. Remarkably,
an extremely small sample volume, 5
µ
L, was used for the analysis. Although this tech-
nique needs more investigation, it could be used on a patient’s plasma or blood to support
other diagnostic tools.

Cells 2025,14, 148 10 of 27
4.4. Detection Methods for Secreted CSC-Specific Proteins, RNA, and DNA
Detection of circulating secreted proteins has been the most common way to diagnose
and monitor cancer. Many studies have used CSC-associated proteins as markers for CSC
existence or cancer progression [
185
–
189
]. Protein biomarkers can be detected, for instance,
by ELISA, Western blot, or mass spectrometry.
Certain freely floating secreted RNAs in biofluids have also been associated with CSC
appearance [
172
,
190
,
191
]. For example, CSC-related miRNAs have been detected in the
serum of colorectal cancer patients [
192
], and CSC-related mRNA expression in the urine
of bladder cancer patients has been measured by qRT-PCR [193].
ctDNA analysis, by sequencing and amplification-based techniques, has been used
for cancer detection and diagnosis for years. Currently, these new nanobiotechnologies,
such as SERS, offer an easier and more cost-effective option. These technologies and their
potential use for detecting CSC were reviewed earlier by Sun et al. 2024 [116].
5. Analysis Methods for Niche-Associated Factors
The CSC niche is part of the tumor microenvironment, which typically consists of
various cell types, including cancer cells, stromal and endothelial cells, cancer-associated
fibroblasts, extracellular matrix, signaling molecules, intrinsic factors, blood vessels, im-
mune cells, networks of cytokines and growth factors, and other cellular and acellular
components, such as exosomes [
194
–
197
]. Currently, the composition of CSCs, immune
cells, and niche characteristics in the tumor microenvironment can be studied using various
technologies mentioned in this review, including IHC, mIHC [
198
], scRNA-seq [
199
,
200
],
flow cytometry [
201
], whole-body medical imaging (PET and MRI) [
202
], imaging mass
cytometry [
84
,
203
], and multiplex ion beam imaging (MIBI) [
204
]. In addition to these,
CSCs can be detected by chemokines and cytokines, which can be detected by ELISA,
Luminex, and chromatography assays, which are discussed next.
5.1. Immunoassays—ELISA and Luminex
ELISA is an antibody-antigen-based analytical method for qualitative and quantitative
analyses. The assays are performed in a plate format, where the blood, plasma, or serum
sample antigens are attached to a solid phase. The antigens are allowed to react with
specific antibodies, which are then detected by a secondary antibody labeled with an
enzyme [
205
,
206
]. ELISA is a simple and cost-effective assay with varying specificity,
sensitivity, and efficiency. The preparation of specific antibodies can be labor-intensive
and costly. In addition, the assay suffers from a high likelihood of false-positive and false-
negative results, compromising reliability [
205
]. Based on ELISA assays, CSCs have been
found to express high levels of IL-8, IL-1
β
, IL-6, TNF
α
, VEGF, PTGS2, CXCR1, IL-15, and
IL-13/IL-13RA2 in the blood and serum samples of cancer patients [207–212].
The Luminex assay is a multiplex biometric ELISA-based immunoassay that con-
tains shaded microspheres conjugated to a monoclonal antibody specific to the target pro-
tein [206,213]. The Luminex assay can be performed on patient blood, plasma, and serum
samples and allows the simultaneous measurement of up to 100 analytes with high sensi-
tivity, a dynamic range, high throughput, and minimal sample requirements [
206
,
214
,
215
].
However, Luminex assays can be complex, and variability may arise from lot numbers,
kit components, antibodies, analyte standards, assay performance, instrumentation, data
analysis, and calibration [
213
,
214
,
216
]. CSCs in patient blood and plasma samples have
been shown to correlate with significantly high levels of the cytokines including IL-1
β
,
IL-2, IL-7, IL-8, IL-15, G-CSF, IFN-
γ
, TNF
α
, VEGF, and FGF as measured by Luminex
assay [215,217–219].

Cells 2025,14, 148 11 of 27
5.2. Chromatography
Chromatography is based on the principle that molecules in a mixture applied to a
stable phase are separated by the movement of the mobile phase depending on the molecu-
lar properties of molecules related to absorption, partitioning, affinity, and differences in
molecular weights [
220
]. The chromatographic method is efficient and rapid but requires
detailed knowledge of the substances to be separated [220–222].
Chromatographic methods are commonly combined with immunoassays, flow cytom-
etry, and mass cytometry. For instance, molecules are separated by chromatography, such
as HPLC (high-performance liquid chromatography), FPLC (fast protein liquid chromatog-
raphy), and CE (capillary electrophoresis) and detected by mass cytometry [
219
,
221
,
222
].
Chromatographic methods have been used with Luminex to detect elevated levels of the
following cytokines in plasma samples from cancer patients: IL-6, IL-17, IFNα, IFNγ, and
TNFRII [219].
6. Medical Imaging Methods
Non-invasive imaging with positron emission tomography (PET), single-photon emis-
sion tomography (SPECT), or magnetic resonance imaging (MRI) has great potential for
developing diagnostic methods for CSCs, enabling whole-body imaging revealing CSCs in
their natural environment. Non-invasive imaging-based detection would be key, especially
for detecting small or widespread metastatic lesions, and provides an opportunity for
frequent monitoring of a therapy’s effectiveness. The limited number of CSC markers to
be used simultaneously is a clear disadvantage of these techniques, although multi-tracer
tumor imaging is possible [
202
]. Further, the use of tracers requires careful timing, as after
intravenous administration, the tracers will slowly move to a specific location. In addition,
unspecific signals are generated through the accumulation of tracers in certain organs, such
as the liver, bladder, kidneys, and spleen. However, these non-invasive imaging methods
could be used to expose patients’ susceptibility to CSC treatments directed against certain
receptors. Importantly, antibody/peptide-assisted imaging methods offer the chance for
targeted delivery of therapy agents or therapeutic radiation to the tumor.
6.1. Magnetic Resonance Imaging
In MRI, CSC-specific antibodies or peptides can be conjugated with superparamagnetic
nanoparticles, which can be imaged after intravenous injection, resulting in a resolution
of 0.3–2 mm within a tissue. Several options have been tested to image CSCs
in vivo
in
tumor xenograft models. For example, CD133- and EpCAM-positive cells in mice have
been successfully imaged
in vivo
with MRI with antibody-coated nanoparticles
[223–225]
.
In addition, hyaluronan-modified magnetic nanoparticles have been developed to image
and target CD44-positive cells in mice based on the ability of CD44 receptors to bind
hyaluronan [
226
–
228
]. Additionally, CSCs positive for the fibronectin variant were imaged
with variant-specific fibronectin peptide ligand particles [
229
] and later used for the targeted
delivery of chemotherapeutic agents [
230
]. In addition, increased uptake of ferritin or
glutamine by CSCs has been tested to reveal the location of CSCs and as a theranostic
strategy with MRI [231,232].
6.2. Positron Emission Tomography
For PET or SPECT imaging, CSC-specific antibodies can be conjugated with positron-
emitting or gamma-emitting radioactive tracers, reaching a resolution of 0.6–5 mm
in vivo
depending on the instrument. CD133, CD44, EpCAM, or CXCR4 antibody/peptide cou-
pled with 64Cu, 89Zr, 18F, 68Ga, or 125I tracers have been successfully used to image
CSCs
in vivo
in tumor xenograft models [
233
–
246
]. Initial phase I clinical studies using

Cells 2025,14, 148 12 of 27
89Zr-labelled CD44 antibody showed specific tumor uptake with an acceptable safety
profile
[247,248]
. As anticipated, both non-specific and specific uptake was observed in
normal tissues. Synthetic peptide 68Ga-PentixaFor, which specifically binds CXCR4, a
marker that has been suggested to be a CSC marker, has been used successfully in over
1000 patients in multiple studies to image disease progression in hematologic and solid
tumors, as reviewed in Lindenberg et al. 2024 [249].
7. Discussion
Based on this extensive review, the detection of CSCs from clinical patient samples
is achievable using the various techniques summarized in Figures 2and 3. Samples
for analysis can be obtained directly from tumors, but non-invasive measurements from
biofluids or through direct medical imaging can also be used. Importantly, multiple studies
have demonstrated that detecting CSCs would be highly relevant for patients. However,
some of the traditional analysis methods should be developed further or replaced to achieve
clinically relevant results.
Cells 2025, 14, 148 12 of 30
depending on the instrument. CD133, CD44, EpCAM, or CXCR4 antibody/peptide
coupled with 64Cu, 89Zr, 18F, 68Ga, or 125I tracers have been successfully used to image
CSCs in vivo in tumor xenograft models
[233,234,235,236,237,238,239,240,241,242,243,244,245,246]. Initial phase I clinical studies
using 89Zr-labelled CD44 antibody showed specic tumor uptake with an acceptable
safety prole [247,248]. As anticipated, both non-specic and specic uptake was
observed in normal tissues. Synthetic peptide 68Ga-PentixaFor, which specically binds
CXCR4, a marker that has been suggested to be a CSC marker, has been used successfully
in over 1000 patients in multiple studies to image disease progression in hematologic and
solid tumors, as reviewed in Lindenberg et al. 2024 [249].
7. Discussion
Based on this extensive review, the detection of CSCs from clinical patient samples is
achievable using the various techniques summarized in Figure 2 and Figure 3. Samples
for analysis can be obtained directly from tumors, but non-invasive measurements from
biouids or through direct medical imaging can also be used. Importantly, multiple
studies have demonstrated that detecting CSCs would be highly relevant for patients.
However, some of the traditional analysis methods should be developed further or
replaced to achieve clinically relevant results.
Figure 2. Summary of current techniques for detecting CSCs from patient samples. Immunohisto-
chemistry (IHC) [
10
], multiplex immunohistochemistry (mIHC) [
10
], multiplex immunofluorescence

Cells 2025,14, 148 13 of 27
(mIF) [
10
], multiplexed immunohistochemical consecutive staining on a single slide (MICSSS) [
10
],
higher plex technologies [
10
,
25
], flow cytometry [
33
,
37
,
43
,
45
,
61
], spectral flow cytometry [
44
],
mass cytometry [
80
,
84
,
86
], single-cell RNA sequencing (scRNAseq) [
89
,
91
,
100
,
102
,
111
], qRT-
PCR
[123,192,193]
, antibody-coated magnetic beads [
128
,
135
,
136
,
178
], surface-enhanced raman scat-
tering (SERS) [
182
–
184
], chromatography [
220
], ELISA assay [
205
,
206
], Luminex [
206
,
213
,
216
], mi-
crofluidic chips [
138
,
145
,
147
–
150
], density gradient centrifu-gation [
127
,
142
–
144
] as well as imaging
techniques positron emission tomography (PET) [
250
], and magnetic resonance imaging (MRI) [
250
]
are concluded in this table. NA = Not applicable.
Cells 2025, 14, 148 15 of 31
Figure 3. The illustrative detection capacity of currently available techniques to detect CSCs. The
techniques are divided into single-cell analysis, biofluid analysis, spatial analysis, and in vivo im-
aging methods based on the sample type.
CSCs are a plastic population of cells with high heterogeneity. In addition, CSC mark-
ers are subject to variability, influenced by genetic or epigenetic changes, environmental
conditions, or drug treatments. Therefore, a single miracle marker cannot be found or
used, which could lead to drastic underestimation of the quantity of CSCs. The future of
CSC isolation and detection is in multiparameter assays. Importantly, the field should be
Figure 3. The illustrative detection capacity of currently available techniques to detect CSCs. The
techniques are divided into single-cell analysis, biofluid analysis, spatial analysis, and
in vivo
imaging
methods based on the sample type.

Cells 2025,14, 148 14 of 27
CSCs are a plastic population of cells with high heterogeneity. In addition, CSC mark-
ers are subject to variability, influenced by genetic or epigenetic changes, environmental
conditions, or drug treatments. Therefore, a single miracle marker cannot be found or
used, which could lead to drastic underestimation of the quantity of CSCs. The future of
CSC isolation and detection is in multiparameter assays. Importantly, the field should be
open to new factors to be identified. Instead of classical surface markers, modern tech-
nologies might result in the identification of new pan-CSC indicators. Such breakthroughs
would significantly advance CSC research and improve the development of assays and
optimizations of new targeted cancer therapies.
In addition to the methods discussed in this review, recent technological developments
offer intriguing spatial methods that could be especially suitable for CSCs. One of the new
potential label-free universal detection techniques for CSCs is their morphological features,
which could be analyzed using deep learning tools on patient samples [
251
]. Before
performing this, it is essential to identify CSCs and correlate the results with morphology
across a substantial number of patient samples to train the algorithm. To achieve this,
several new technologies in addition to the spatial transcriptomic methods discussed earlier
have emerged that allow spatial mapping of 50–100 markers in tissue sections and the
identification of new factors. Deep visual proteomics (DVP) allows single-cell resolution
proteomic profiling from tissue slides [
252
], and spatial multimodal omics approaches
allow simultaneous analysis of transcriptomes with epigenomes or proteomes [
253
]. An
additional benefit of these techniques is the possibility of visualizing niche-associated cells,
such as immune cells, that could further support diagnosis.
Recent advancements in single-cell techniques, such as flow cytometry, mass cytome-
try, scRNA-seq, and higher plex technologies, have significantly enhanced our ability to
study CSCs. These multiparameter methods are likely to reveal, in the near future, the
true complexity of the inter- and intrapatient heterogeneity of CSCs suggested by initial
studies [
26
–
31
,
47
,
87
,
88
,
106
–
112
]. Importantly, these technologies can result in the identifi-
cation of new diagnostic and therapeutic targets as multiple markers in new combinations
in addition to unbiased sequencing are utilized.
The detection of CSCs, CSC-derived EVs, and CSC biomarkers from biofluids would
be highly valuable for cancer diagnosis. Multiple studies have shown the correla-
tion of their existence in patients’ biofluids with metastasis, relapse, and poor progno-
sis
[118,125,136,137,142,144,168–171,254]
. Already existing CTC and EV enrichment de-
tection techniques offer great possibilities for CSC detection from biofluids. However, it
is important to note that selecting only EpCAM-positive cells could lead to a loss of a
significant proportion of circulating CSCs [140,141,145].
The future of
in vivo
clinical imaging and targeting of CSCs might be in multimodal
PET/MRI devices and the use of super probes [
250
]. Combining these techniques would
allow high spatial resolution and superior visualization of soft tissues via MRI, where
outlines of tissues and organs are visible, in addition to extremely high sensitivity and
limitless penetration depth via PET, which can also measure biochemical changes. Bimodal
PET/MRI super probes could be used to deliver drugs in a targeted fashion, such as through
the low pH of tumors or image-guided release. However, many challenges remain to be
overcome, such as biocompatibility, pharmacokinetics, targeting efficiency, and toxicity.
The final step in the field is to adopt laboratory assays for standard clinical use. An
ideal assay should be non-invasive and produce quantitative data revealing the complete
essence of CSC presentation within a patient. In the future, the “stemness” of a patient
sample could be as simple and routine as hemoglobin measurement. This goal might be
overly optimistic as it would require the identification of a universal panel of stemness in-
dicators resulting in one simple value bypassing the cancer type and inter- and intrapatient

Cells 2025,14, 148 15 of 27
heterogeneity and variability. However, the field should strive for an easy clinical readout
rather than overly complicated data that can arise from new research tools. Importantly,
the results obtained should have prognostic value. Significantly, the data should direct
treatment decision-making and point to certain targeted therapies. All this will require
collaborative development of CSC-directed therapies along with clinical diagnostic assays.
To establish all this, a comprehensive set of assays will be necessary to correlate the full
heterogeneity of the CSC burden within the primary tumor, at metastatic sites, and in the
blood, along with the secreted factors found in biofluids and observed through medical
imaging. To conclude, various assays and large-scale clinical studies will be needed to
establish clinically relevant assays.
Author Contributions: Conceptualization, E.N., S.H., A.H., S.S. and N.G.; writing—original draft
preparation, S.H., A.H., S.S., N.G. and E.N.; writing—review and editing, S.H., A.H., S.S., N.G. and
E.N.; visualization, S.H., A.H., S.S., N.G. and E.N.; supervision, E.N.; project administration, E.N.;
funding acquisition, S.H., A.H., S.S., N.G. and E.N. All authors have read and agreed to the published
version of the manuscript.
Funding: This research was funded by Jane and Aatos Erkko Foundation, Finnish Cultural Founda-
tion, ImmuDocs Doctoral Pilot, iCANDOC Doctoral education pilot in precision cancer medicine,
Svenska Kulturfonden, Turku Doctoral Programme of Molecular Medicine (TuDMM), and Drug
Research Doctoral Programme (DRDP).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
CE capillary electrophoresis
CSC cancer stem cell
CTC circulating tumor cell
ctDNA circulating tumor DNA
ctRNA circulating tumor RNA
CyTOF cytometry by time Of flight
DVP deep visual proteomics
ELISA enzyme-linked immunosorbent assay
EMT epithelial-to-mesenchymal transition
EV extracellular vesicle
FDA Food and Drug Administration
FPLC fast protein liquid chromatography
HPLC high-performance liquid chromatography
IHC immunohistochemistry
MIBI multiplex ion beam imaging
MICSSS multiplexed immunohistochemical consecutive staining on a single slide
mIF multiplex immunofluorescence
mIHC multiplex immunohistochemistry
miRNA microRNA
MRI magnetic resonance imaging
mRNA messenger RNA
PCR polymerase chain reaction

Cells 2025,14, 148 16 of 27
PET positron emission tomography
p-MOFF parallel multi-orifice flow fractionation
qPCR quantitative polymerase chain reaction
qRT-PCR quantitative reverse transcription polymerase chain reaction
scRNA-seq single-cell RNA sequencing
SERS surface-enhanced Raman scattering
SPECT single photon emission tomography
UPLC-MS/MS ultra-performance liquid chromatography–mass spectrometry
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