Characterization Technique for Exosomes: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Shijia Wu
,
Yalan Zhao
,
Zitong Zhang
,
Chao Zuo
,
Hongjun Wu
,
Yongtao Liu

Exosomes distributed by extracellular vesicles carry various information highly consistent with cells, becoming a new type of biomarker for tumor screening. However, although conventional characterization technologies can quantify size and morphology for exosomes, they are limited in related fields such as function tracing, protein quantification at unit point, and microstructural information.

  • exosome
  • tumor diagnosis
  • optical analysis technology
  • super-resolution microscope

1. Introduction

Exosomes, a novel tumor biomarker after circulating tumor cells and circulating tumor DNA [1][2][3], can be used in tumor diagnosis [4]. Biosignaling molecules of exosomes are exchanged by endocytosis, and thereby tumor cell activities such as growth, metastasis, drug resistance, and immune evasion are regulated [5][6]. Therefore, exosomes have important value in the early diagnosis of tumors, the monitoring of treatment progress, and prognosis as shown in Figure 1 [4][7][8][9].
Photonics 11 00101 g001
Figure 1. Exosome information transfer process and application [4].
Exosomes are membranous extracellular vesicles (EV) secreted by cells with a particle size of 30 to 150 nm [10]. The study found that the function of exosomes depends on the cell type from which they are derived, while it maintains the same genetic material as the donor cells [11]. Different analyses become one of the tumor-specific research targets during exosome activity. For example, exosomes derived from tumor cells carry many types of proteins, such as surface proteins, inclusions, enzymes, etc. Among them, the surface proteins such as CD9, CD63, and CD81 and the inclusion factors such as HSP70 and Alix are representative proteins for the isolation and identification of exosomes [12][13][14]. The differences in proteins can reflect information exchange between tumor cells and basal cells and between tumor cells and tumor cells, which regulates immune response, migration, differentiation, and other basic cellular functions [15][16][17][18]. For example, studies have found that exosomes derived from different cells are different in size, morphology, and composition [19]. The exosomes derived from tumor cells are large and contain more lipids and outer membrane proteins, which promote tumor cell growth, invasion, and metastasis [20]. The surface proteins of cancer exosomes are often different in different stages, which indicates that these proteins are closely related to the process of cancer. Similarly, the surface proteins of cancer exosomes from different sources are also different, which can be used for the early diagnosis of cancer [21][22]. Research has proven that ADAM10, metalloprotease, CD9, Annexin-1, and HSP70 are enriched in exosomes isolated from the pleural effusion or serum of breast cancer patients [23]. However, the exosomes derived from immune cells are smaller and include a variety of immune molecules, such as cytokines, antigens, antibodies, etc., which could regulate immune responses and antitumor effects [24]. More importantly, quantitative analysis is necessary to identify exosomes accurately. In addition, some studies believe that the morphology, quantity, and concentration of exosomes are different in the process of secretion [25], so real-time tracking and quantification can objectively analyze the delicate mechanism to achieve optimal anti-tumor effect. In conclusion, the morphological characterization and function of exosomes are the basis for exploring the fine physiological information and biochemical mechanisms in cellular biology [26][27].
Exosomes are emerging biomarkers of tumor liquid biopsy, and therefore it is particularly important to explore their biological information such as function tracing, protein quantification at unit point, and microstructural changes [28][29]. At present, much attention has been paid to their basic characteristics such as concentration, diameter, morphology, and particle tracking in high-throughput samples [30][31][32]. Conventional characterization methods can achieve the basic characterization of exosomes, yet they are subject to certain limitations. DLS can only obtain the size distribution of exosomes but cannot detect concentration [33]. Although flow cytometry (FCM) realizes multi-parameter detection, the analysis results are based on high-throughput samples and cannot obtain the morphological characteristics of a single EV [34]. EM can visually observe the morphological characteristics of a single EV, but it is not suitable for living cells, and the direction is limited in biological research [35]. Therefore, there is an urgent need for an exosome detection technique that can preserve the fluorescence specificity and achieve high-throughput sample single-molecule detection. In recent years, researchers have paid more attention to fluorescence microscopy. The fluorescence microscope has the advantage of live cell imaging and specific labeling; however, its resolution ranges from 200 nm to 500 nm, which cannot be applied to detecting exosomes. Therefore, how to improve the imaging resolution of optical microscopy and apply it to the imaging observation of subcellular structures such as exosomes has become a research hotspot in recent years. With the development of fluorescence microscopy, super-resolution imaging technology breaks through the diffraction limitation, which is conducive to the field of exosomes [26][36]. Compared with others, it has the advantages of high resolution, specific detection, and live-cell imaging to realize the qualitative and quantitative analysis of proteins of living or fixed cells and track the mechanism of exosomes [36].

2. Conventional Characterization Technologies

Due to the unique biological function of exosomes, an increasing amount of basic research is being concentrated on it [37][38][39][40][41]. Characterization technologies play important roles in the study of exosomes [30]. Generally speaking, various approaches for analysis are categorized into two primary types: biochemical analysis and physical analysis. Biochemical analysis mainly determines the source and composition of exosomes, including Western blot and enzyme-linked immunosorbent assay (ELISA), in which the specific binding of antibody antigens decides the effect qualitatively or quantitatively [42]. However, the disadvantage is that the morphological characteristics and concentration of exosomes cannot be obtained. 

2.1. Tunable Resistive Pulse Sensing

Tunable Resistive Pulse Sensing (TRPS) is based on Coulter’s principle. The suspension was mixed in the electrolyte, which could go through the nanopore chip with a specific aperture. The resistance between the two electrodes inside and outside changes instantaneously at the moment of passing through the nanopore, the result of which is a pulse signal as shown in Figure 2. The intensity and frequency of the signal are related to the size and number of exosomes. The expression of exosomes was counted by the pulse signal. In 2014, Maas proposed a method to characterize the concentration and size of EVs by the TRPS based on the qNano system [43]. In 2017, researchers pointed out that TRPS has promise in the quantitative and dimensional analysis of single-particle EV [44]. In 2018, Durak-Kozica analyzed EVs from endothelial cells for a short time and found that the diameter of EVs was 121.84 ± 0.08 and 115.82 ± 0.96 nm from microvascular and big vessels, respectively [45]. This technology enables the efficient quantification of size and number, which cannot be specifically analyzed for exosomes due to the principle of potential pulses.

Photonics 11 00101 g002
Figure 2. TRPS and EM (a) The principle of TRPS [43]. (b) TRPS reveals the size distribution of EVs derived from the TIME cell line (left) and the HUVEC line (right) [45]. (c) Particle concentration and size measurements for EV samples before and after INTERCEPT treatment (left), Simultaneous size and zeta potential measurements of untreated and INTERCEPT-treated EV samples and carboxylated polystyrene standard particles (right) [46]. (d) The principle of SEM (left) and TEM (middle) [47] and EVs scans of TEM [48], SEM [49], cryo-TEM [50] (right). (e) EM images of S. aureus without EVs and with EVs (left) [51]. Examples of EM images of individual cup-shaped EVs of different origins [19]. (f) Multivesicular bodies (MVBs) contained in cultured HMC-1 cells and extracellular vesicles in the surrounding growth medium [52].

2.2. Electron Microscope

Electron Microscope (EM) is the most direct method to measure the size and morphology of a single EV [42]. It is divided into scanning electron microscope (SEM), transmission electron microscope (TEM), and cryo-transmission electron microscope (cryo-TEM). It is noted that EM can characterize the particle morphology and size of a single EV [53][54]. 

The outer membrane of EVs was imaged by a focusing electron beam, the results of which were secondary electrons with different emission amounts. The principle is shown on the left of Figure 2d. Min Kyo Jung et al. observed the detailed structure and specific protein of EVs stained by the negative staining technique [55]. Boz et al. observed the mechanism and morphology of hbEV by SEM [56]. Sokolova et al. obtained the morphological characteristics of exosomes derived from different cells including HEK110T, ECFC, and MSC [57]. The principle of TEM is that the electron beam collides with atoms of EVs, and the scattering angle is converted to acquire the changes in structure, as shown in the middle of Figure 2d. Compared with SEM, TEM has a higher resolution and can realize the imaging analysis of a single EV at the nanoscale [58]. Liu et al. successfully characterized exosomes extracted from breast tumor cells including their morphology and distribution [59]. Park et al. found the morphology of EVs derived from prostate cancer cells (PCa) was round and that the number of these EVs was significantly higher than that of normal cells [60]. It is worth noting that sample protocol is complicated and that the sample is in a vacuum during the process of imaging. In order to reduce the complexity of the protocol, the researchers further developed cryo-TEM to maintain the integrity and authenticity of the sample to the greatest extent [52]. This element changes the structure of the exosome from a round to a cup-like shape as shown in the right of Figure 2d. Conde-Vancells et al. realized the morphological characterization of exosomes derived from hepatocytes [50]. Kurtjak et al. identified the single, double, and multi-membrane morphology of CSF extracellular vesicles by combining cryo-TEM with atomic force microscopy (AFM) [61]. This proves that the cryo-TEM technique can not only restore the EV shape but also allow the analysis of the EV interior.

2.3. Dynamic Light Scattering

Dynamic light scattering (DLS) is an optical analysis method for measuring the size and distribution of sub-micron particles, and its basic principle is shown in Figure 3a. The Brownian motion of the particles causes the change in the light scattering signal, which is monitored by a digital autocorrelator to calculate the diffusion velocity and particle distribution of the particles [62]. The motion rate of small particles is higher, and its intensity fluctuation is larger. As a result, there is a swift and pronounced decline in the correlation curve. The sensitivity and FOV of DLS are better than the above papers, which can realize the basic characterization of large-size exosome nanoparticles. For smaller particles, the behavior of self-polymerization interferes with the light-intensity signal. Thus, it is impossible to realize the precision analysis and detection of high-concentration samples. In 2009, Lawrie et al. used DLS to characterize the size distribution of EV derived from red blood cells [63]. DLS analyzes all particles in a sample simultaneously, and therefore the information on the number or concentration of a certain category of particle cannot be provided [64]. For example, DLS provides a clear range of the diameter of EVs derived from ovarian cancer cells, but the concentration is difficult to analyze [65]. Therefore, DLS technology is often combined with other technologies to complete the characterization of EVs. For example, Tajik T combined DLS technology with electron microscopy, showed cannabis-derived EVs (CDEVs) can be considered exosome-like nanovesicles, and highlighted that CDEVs can be an ideal natural vehicle for bioactive phytocannabinoids, promoting the research of EVs in cancer diagnosis [66].

Figure 3. DLS and NTA (a) The principle of DLS [67]. (b) DLS to characterize the size distributions of microparticles within fresh-frozen plasma [68]. (c) The principle of NTA (left) [69] and EVs of fluorescent labeling on extracellular vesicles and fluorescence detection ability of NTA [70]. (d) Size distributions of three CD markers (CD9, CD63, and CD81) in multifluorescence by NTA (left) and fluorescent images of particles by NTA (right) [70]. 

2.4. Nanoparticle Tracking Analysis

Nanoparticle tracking analysis (NTA) is an optical method to assess nanoparticles [71]. When the laser irradiates a single nanoparticle, light intensity scattered from the sample is captured by a high-speed camera as shown in the left of Figure 3c. Compared with DLS, NTA can locate and track a single nanoparticle, so this technology has advantages in analyzing the particle size of complex samples. It is worth mentioning that NTA has the ability of fluorescence analysis. Different fluorescent particles can be measured by the relevant parameters, which can identify the sizes of various exosomes at the same time. In 2011, Dragovic et al. applied quantum dot-labeled fluorescent cell tracking peptide with NTA to confirm the origin of common vesicles in plasma [72].

2.5. Flow Cytometry

Size and morphology are the basic parameters for the characterization of exosomes. The characterization of functional parameters such as surface protein quantitative expression and signal transduction mode is of paramount importance. Flow Cytometry (FCM) realizes the rapid multi-parameter quantitative analysis of cells or submicron particles based on light scattering changes, and its basic principle is shown in Figure 4a. Scatters of light from particles suspended in a sheath stream reflect the size and density of the cells or particles, and they were acquired by a detector array. At the same time, the specific gene expression, protein expression, enzyme activity, ion concentration, and other biomolecular substances labeled by fluorescent dyes were specifically measured by different channels.
The sensitivity of traditional flow cytometry is limited to 300 to 500 nm [73], so it is obviously difficult to measure exosomes. Yan Xiaomei’s team developed nFCM by combining Rayleigh scattering with sheath flow single-molecule fluorescence detection technology, which enables the high-throughput analysis of exosomes with a size of 40 nm, as shown in Figure 4 [74][75]. Compared with traditional flow cytometry, the scattered light detection sensitivity of nFCM is improved by four to six orders.
Photonics 11 00101 g004
Figure 4. FCM (a) The principle of FCM [69]. (b) Histogram of particle size for an EV sample of HCT-15 cells [75]. (c) Heterogeneous fluorescent beads were analyzed by FCM [76]. (d) FCM detects CD9, CD63, and CD81 positive EVs [76]. (e) FCM analyzes the EV concentration and mean fluorescence intensity (MFI) of the anti-CD9-PE-stained EVs of the three individual EV preparations [76]. (f) Analysis of plasma EVs expressing CD45 and CD11b in GBM and normal donors [77]. 

3. Super-Resolution Imaging Technology

The development of modern biology has promoted the rapid progress of microscopic imaging technology. Due to the limitation of optical diffraction, the minimum resolution of the traditional optical microscope is about half of the wavelength of incident light. Therefore, scientists have been constantly trying to break through it. The resolution of super-resolution imaging technology is below 200 nm, which obtains the same level of resolution as EM. Super-resolution microscopy can achieve real-time super-resolution imaging for organelle structure, interaction, protein function, etc. It provides a new analytical method for cell biology and breaks through the biomedical research status from the nano-scale.
At present, super-resolution imaging technology is divided into two categories. One is a single molecule localization imaging technique (SMLM) based on the random switching of the excited light of the fluorophores between on and off states, which includes stochastic optical reconstruction microscopy (STORM) and photoactivated localization microscopy (PALM). The other is the super-resolution imaging technology achieved based on light field regulation such as stimulated emission depletion (STED) and structured illumination microscopy (SIM).

3.1. Single Molecule Localization Imaging Technology

The basic principle of SMLM is based on the flicker of a single fluorescent molecule to locate a single molecule and then reconstruct super-resolution images. Compared with other technologies, SMLM has the advantages of low phototoxicity and low cell damage. It is more suitable for living cells, thus becoming a new super-resolution analysis method for exosomes in vivo observation. SMLM opens a new observation perspective for exosome-related studies.

3.1.1. Stochastic Optical Reconstruction Microscopy and Photoactivated Localization Microscopy Technology

PALM and STORM technologies are classical technologies in SMLM. In 2006, Eric Betzig et al. proposed the PALM technology [78], and Xiaowei Zhuang et al. proposed the STORM technology [79] at the same time. Both of them are based on single-molecule localization technology to achieve the super-resolution imaging of subcellular structure molecules. One of the key elements is the switched fluorophores. For example, PALM uses photoactivated green fluorescent protein (PA-GFP) to label the protein and irradiate the cell surface with different lasers so as to cause the fluorescence molecule cycle to complete the excitation localization process
One of the key points is the spatial and temporal resolution for SMLM. That requires more than 10,000 frames of images during the process of reconstruction, which needs much more time. The rapid development of EMCCD cameras has greatly improved imaging speed. In 2011, Zhuang Xiaowei’s group pictured extracellular vesicles with a high-speed EMCCD. The temporal resolution was improved to 0.5 s, which means that STORM has the potential to monitor live cell imaging in real-time [80].
In 2011, Zhuang’s team used the stage-specific neurite-associated protein (SNAP) label to label the Alexa Flour 467 optical switching probe to clathrin in living BS-C-1 cells. STORM technology was successfully used to obtain a 30 nm horizontal resolution and a 50 nm vertical resolution [80]. In 2012, Shim et al. determined the STORM membrane probe for live cell imaging through a large number of experiments and performed the super-resolution imaging of organelle membranes in live cells, reaching a spatial resolution of 20~60 nm [81]. In 2018, Zong Shenfei et al. discovered that silicon quantum dots (Si QD) have fluorescent scintillation behavior and applied them as SMLM imaging nanoprobes to stain CD63 of breast cancer cell (SKBR3)-derived EVs using CD63 aptamers fused with Si QD, achieving an imaging accuracy of about 30 nm. They demonstrated that Si QD can be used for the SMLM imaging of small objects such as exosomes. Moreover, Si QD has the characteristics of high biocompatibility and low cytotoxicity, which makes it a better choice of fluorophores for SMLM live cell imaging [82].

3.1.2. DNA-PAINT Technology

Similar to the STORM/PALM technology, DNA-PAINT also achieves super-resolution imaging by controlling the flicker of individual fluorophores. In 2014, Ralf Jungmann et al. proposed the DNA-PAINT technology, which uses reversible binding between complementary DNA sequences to produce an effect similar to the “flicker” of fluorescent molecules [83]. In double-strand DNA, one strand is connected to the fluorophores, called the imager strand, and the other is connected to the target molecule, called the docking strand. Due to the highly specific binding of the double-strand DNA, the imaging strand and the docking strand are bound spontaneously, producing single-molecule fluorescence in the focal plane [84] as shown in Figure 5. In addition, multi-channel fluorescence imaging can be realized by different proteins labeled with various docking strands.
Photonics 11 00101 g006
Figure 5. DNA-PAINT (a) The principle of DNA-PAINT [85]. (b) 3D DNA-PAINT images of COS-7 cells treated with DMSO or ES2 [86]. (c) Representative super-resolution images of 20 nm grid structures (left) and Diffraction-limited (DL) alongside the super-resolution (SR) of the microtubule network in a HeLa cell [87]. (d) Comparison of the widefield images and DNA-PAINT images [88]. 
The DNA-PAINT technology focused on reducing the resolution to the molecular level early. Then, it focused on the limitation of long acquisition times [89]. Long collection time is a basic limitation of SMLM technology, which is due to the need to collect enough photons to determine the central position of the fluorophores [90]. The common idea is to increase the binding frequency at a high concentration of the docking strand, but the concentration of the imaging strand influences the SNR of technology [91].
Proteins on exosomes can be quantitatively analyzed with high precision by DNA-PAINT. The different contents of proteins carried by exosomes such as CD9, CD63, CD81, HER2, EpCAM, and EGFR could achieve the classification of the course of tumorigenesis [92]. Chen et al. combined DNA-PAINT with machine learning to identify tumor cell types in two steps [85]. The first step is to distinguish between healthy cells and tumor cells and then to perform cross-typing on different types of tumor cells, among which the comparison results of breast cancer and pancreatic cancer show that the system can accurately and quickly distinguish the two types of cancer, which makes it possible for exosomes to achieve an early diagnosis of cancer through optical detection. 

3.2. Stimulated Emission Depletion Technology

In 1994, Hell et al. proposed stimulated emission depletion technology (STED) for the first time in theory. The principle of STED is that two laser beams are used for microscopic imaging, with one as excitation light for photon excitation and the other as loss light, which is a ring laser with zero central intensity [93]. The loss light should be coaxial with the excitation light, and the wavelength should match the emission wavelength of the fluorescence molecule as shown in the left of Figure 6. STED is a super-resolution imaging technology developed based on laser scanning confocal microscopy. Fast, direct imaging characteristics and a nanoscale observation scale are ideal for living cell research. However, the resolution of STED is affected by the ratio of lost optical power to probe saturation excitation power. The higher the relative power, the higher the imaging resolution, but it will also cause the rapid photobleaching of fluorophores, causing serious photodamage to cells. Therefore, the application of STED to live cell super-resolution requires the development of fluorescent probes.
Photonics 11 00101 g007
Figure 6. STED and SIM (a) The principle of STED (left) [94] and characterization of single EVs by UCNPs labeling using STED (right) [95]. (b) Visualization of extracellular sEVCs in HT29 colorectal carcinoma cell [96]. (c) Time-lapse STED imaging of the mitochondria [97]. (d) The principle of SIM (left) [94]. (e) SIM resolution was validated by a complete co-localization between genetic tag-based (CD63-GFP) and immunofluorescent imaging for the CD63 [98]. 

3.3. Structured Illumination Microscopy Technology

Structured illumination microscopy technology (SIM) is a super-resolution imaging technology based on frequency domain modulation proposed by Gustafsson in 2000 [99]. A Moire fringe is used to transfer high-frequency information beyond the system cutoff frequency to the low-frequency part to realize signal detection and improve imaging resolution. Excited light can generate sinusoidal fringe patterns through the grate to irradiate the sample as shown in the left of Figure 6d. SIM usually only needs to capture nine frames of image, and the time resolution is much higher. In addition, excitation light intensity is also relatively low, which is suitable for living cells. However, as SIM is limited by the imaging principle, the lateral resolution can only reach about 150 nanometers, which means that although SIM has the advantage of temporal resolution compared with other super-resolution imaging technologies, it is limited by the spatial resolution and cannot meet the detection requirements of exosomes. In 2005, Gustafsson achieved a transverse resolution of 50 nm using Saturated Structured Illumination Microscopy (SSIM, Northrop Grumman PolyScientific, Charlotte, NC, US), but at the cost of severe light damage and reduced imaging speed [100]. Therefore, in recent years, researchers have tried to improve the spatial resolution of SIM while retaining the advantages of live-cell imaging.
At present, the two best-optimized SIM are patterned activation nonlinear SIM (PA NL-SIM) and Glazing Incidence SIM (GI-SIM). However, the resolution of both is better than 100 nm and cannot reach 50 nm. In 2015, Li et al. successfully observed images for F-actin in living COS-7 cells at the spatial resolution of 62 nm by PANL-SIM [101]. In recent years, researchers have tried to optimize algorithms to expand SIM resolution. In 2021, Boland et al. used a deep learning model to enlarge images and proposed a method to reconstruct a 3D SIM image stack to increase the axial resolution of SIM by two times [102]. In 2022, He et al. introduced deep image prior (DIP) during the process of post-processing to design an untrained convolutional network to recover high-frequency information lost in SIM, which successfully achieved a 1.4-fold increase in horizontal resolution [103]. In the same year, Butola et al. proposed a new algorithm that eliminated the requirement of illumination peak clarity and an understanding of illumination patterns, making the instrument simple and flexible to be used with various micro-objectives, achieving an increase in resolution of 2.6 to 3.4 times [104]. Due to the limitation of resolution, SIM is rarely used in exosome studies. In 2019, Choi et al. revealed the molecular morphology of a single EV, which is expected to be used in the exploration of cancer cell EV by combining with nanoflow cytometry [98]. It is believed that SIM technology will be widely used in exosome research after breaking through the limitation of spatial resolution.
The super-resolution imaging techniques have broken through the optical diffraction limit and been successfully applied to live-cell imaging. Based on the above description, SMLM has more advantages in exosome-related imaging research from the perspective of fluorescent probe requirements, imaging spatiotemporal resolution, and the advantages of live-cell imaging. Especially, the imaging resolution of SMLM could meet the requirement for observing the diameters of exosomes, so it is widely popular in exosome imaging research in live cells. STED technology has no special requirements for fluorescent probes, and the imaging resolution also meets the requirement for observing the diameters of exosomes, but the photodamage decreases the activity of live cells because of high power loss. As a result, it is not suitable for cells in vivo. Thus, it is urgent for STED to develop new fluorescent probes that are resistant to bleaching or low-loss optical thresholds. SIM technology with universal probes has superior imaging temporal resolution compared with the other two technologies. Due to the limitation of imaging principles, the imaging resolution cannot meet the requirement for observing exosomes at present. Some researchers found that nonlinear SIM may improve the resolution of traditional technology, which is good for exosomes. Above all, SMLM is currently the most effective super-resolution imaging technique for exosome research, and STED or SIM has also been optimized. It is promising for super-resolution imaging technology to explore more detailed biomedical issues such as subcellular structures and functional mechanisms with the advancement of various fields.

4. Summarize the Outlook

With developments in science and technology, optical technology has become the main means to explore exosome biogenesis and mechanisms of action, especially super-resolution imaging technology. It not only breaks through the diffraction limitation but also realizes three-color and multicolor imaging. It greatly combines the advantages of conventional characterization techniques and facilitates various biomedical studies based on exosomes, such as early diagnosis and typing of tumors. SIM and STED may limit the further study and application of exosomes due to photobleaching, phototoxicity, and a lack of resolution. Both algorithm optimization and the development of fluorescent probes have promoted the optimization of both, especially the development of nonlinear SIM technology, which promotes its application in exosomes. In contrast, SMLM research on exosomes has a wide range of application prospects. For example, the STORM technology can clearly and intuitively analyze the three-dimensional morphology of exosomes and functional information inside and outside cells with the advantages of super-resolution and rapid and real-time analysis. The results of basic research can lead to more medical applications. Chen Chen’s team has set a good example for us. At present, researchers are also actively developing various methods to improve super-resolution imaging technology to facilitate precision medicine about exosomes.
In the future, we can expect not only the development of various characterization techniques but also the combination of exosome characterization techniques with machine learning to greatly shorten the image reconstruction time and expand the possible directions of scientific technology and biomedical applications. In addition, nanoscale characterization imaging analysis provides a new observation for the structure and function analysis of exosomes and accelerates the process of basic research on exosomes. It objectively and systematically reveals the information exchange mechanism and interaction effect of exosome-based cell biology.
 

This entry is adapted from the peer-reviewed paper 10.3390/photonics11020101

References

  1. Wan, J.C.M.; Massie, C.; Garcia-Corbacho, J.; Mouliere, F.; Brenton, J.D.; Caldas, C.; Pacey, S.; Baird, R.; Rosenfeld, N. Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nat. Rev. Cancer 2017, 17, 223–238.
  2. Alix-Panabières, C.; Pantel, K. Circulating tumor cells: Liquid biopsy of cancer. Clin. Chem. 2013, 59, 110–118.
  3. Guo, L.C.; He, B. Extracellular vesicles and their diagnostic and prognostic potential in cancer. Transl. Cancer Res. 2017, 6, 599–612.
  4. Huang, T.; Deng, C.X. Current Progresses of Exosomes as Cancer Diagnostic and Prognostic Biomarkers. Int. J. Biol. Sci. 2019, 15, 1–11.
  5. Im, E.J.; Lee, C.H.; Moon, P.G.; Rangaswamy, G.G.; Lee, B.; Lee, J.M.; Lee, J.C.; Jee, J.G.; Bae, J.S.; Kwon, T.K.; et al. Sulfisoxazole inhibits the secretion of small extracellular vesicles by targeting the endothelin receptor A. Nat. Commun. 2019, 10, 1387.
  6. Junker, K.; Heinzelmann, J.; Beckham, C.; Ochiya, T.; Jenster, G. Extracellular Vesicles and Their Role in Urologic Malignancies. Eur. Urol. 2016, 70, 323–331.
  7. Akers, J.C.; Ramakrishnan, V.; Kim, R.; Phillips, S.; Kaimal, V.; Mao, Y.; Hua, W.; Yang, I.; Fu, C.C.; Nolan, J.; et al. miRNA contents of cerebrospinal fluid extracellular vesicles in glioblastoma patients. J. Neuro-Oncol. 2015, 123, 205–216.
  8. Minciacchi, V.R.; Freeman, M.R.; Di Vizio, D. Extracellular vesicles in cancer: Exosomes, microvesicles and the emerging role of large oncosomes. Semin. Cell Dev. Biol. 2015, 40, 41–51.
  9. Ciardiello, C.; Cavallini, L.; Spinelli, C.; Yang, J.; Reis-Sobreiro, M.; de Candia, P.; Minciacchi, V.R.; Di Vizio, D. Focus on Extracellular Vesicles: New Frontiers of Cell-to-Cell Communication in Cancer. Int. J. Mol. Sci. 2016, 17, 175.
  10. Liu, J.; Ren, L.; Li, S.; Li, W.; Zheng, X.; Yang, Y.; Fu, W.; Yi, J.; Wang, J.; Du, G. The biology, function, and applications of exosomes in cancer. Acta Pharm. Sin. B 2021, 11, 2783–2797.
  11. Duijvesz, D.; Luider, T.; Bangma, C.H.; Jenster, G. Exosomes as biomarker treasure chests for prostate cancer. Eur. Urol. 2011, 59, 823–831.
  12. Kimiz-Gebologlu, I.; Oncel, S.S. Exosomes: Large-scale production, isolation, drug loading efficiency, and biodistribution and uptake. J. Control. Release 2022, 347, 533–543.
  13. Andreu, Z.; Yáñez-Mó, M. Tetraspanins in extracellular vesicle formation and function. Front. Immunol. 2014, 5, 442.
  14. Berditchevski, F.; Odintsova, E. Tetraspanins as regulators of protein trafficking. Traffic 2007, 8, 89–96.
  15. Rezaie, J.; Akbari, A.; Rahbarghazi, R. Inhibition of extracellular vesicle biogenesis in tumor cells: A possible way to reduce tumorigenesis. Cell Biochem. Funct. 2022, 40, 248–262.
  16. Boucheix, C.; Rubinstein, E. Tetraspanins. Cell. Mol. Life Sci. 2001, 58, 1189–1205.
  17. He, C.; Zheng, S.; Luo, Y.; Wang, B. Exosome Theranostics: Biology and Translational Medicine. Theranostics 2018, 8, 237–255.
  18. He, M.; Crow, J.; Roth, M.; Zeng, Y.; Godwin, A.K. Integrated immunoisolation and protein analysis of circulating exosomes using microfluidic technology. Lab Chip 2014, 14, 3773–3780.
  19. Nikishin, I.; Dulimov, R.; Skryabin, G.; Galetsky, S.; Tchevkina, E.; Bagrov, D. ScanEV—A neural network-based tool for the automated detection of extracellular vesicles in TEM images. Micron 2021, 145, 103044.
  20. Kahlert, C.; Kalluri, R. Exosomes in tumor microenvironment influence cancer progression and metastasis. J. Mol. Med. 2013, 91, 431–437.
  21. Wang, X.; Huang, J.; Chen, W.; Li, G.; Li, Z.; Lei, J. The updated role of exosomal proteins in the diagnosis, prognosis, and treatment of cancer. Exp. Mol. Med. 2022, 54, 1390–1400.
  22. Hoshino, A.; Kim, H.S.; Bojmar, L.; Gyan, K.E.; Cioffi, M.; Hernandez, J.; Zambirinis, C.P.; Rodrigues, G.; Molina, H.; Heissel, S.; et al. Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers. Cell 2020, 182, 1044–1061.
  23. Risha, Y.; Minic, Z.; Ghobadloo, S.M.; Berezovski, M.V. The proteomic analysis of breast cell line exosomes reveals disease patterns and potential biomarkers. Sci. Rep. 2020, 10, 13572.
  24. Haraszti, R.A.; Didiot, M.C.; Sapp, E.; Leszyk, J.; Shaffer, S.A.; Rockwell, H.E.; Gao, F.; Narain, N.R.; DiFiglia, M.; Kiebish, M.A.; et al. High-resolution proteomic and lipidomic analysis of exosomes and microvesicles from different cell sources. J. Extracell. Vesicles 2016, 5, 32570.
  25. Zaborowski, M.P.; Balaj, L.; Breakefield, X.O.; Lai, C.P. Extracellular Vesicles: Composition, Biological Relevance, and Methods of Study. Bioscience 2015, 65, 783–797.
  26. Yang, B.; Chen, Y.; Shi, J. Exosome Biochemistry and Advanced Nanotechnology for Next-Generation Theranostic Platforms. Adv. Mater. 2019, 31, e1802896.
  27. Yu, D.; Li, Y.; Wang, M.; Gu, J.; Xu, W.; Cai, H.; Fang, X.; Zhang, X. Exosomes as a new frontier of cancer liquid biopsy. Mol. Cancer 2022, 21, 56.
  28. Hsu, M.T.; Wang, Y.K.; Tseng, Y.J. Exosomal Proteins and Lipids as Potential Biomarkers for Lung Cancer Diagnosis, Prognosis, and Treatment. Cancers 2022, 14, 732.
  29. Lee, Y.R.; Kim, G.; Tak, W.Y.; Jang, S.Y.; Kweon, Y.O.; Park, J.G.; Lee, H.W.; Han, Y.S.; Chun, J.M.; Park, S.Y.; et al. Circulating exosomal noncoding RNAs as prognostic biomarkers in human hepatocellular carcinoma. Int. J. Cancer 2019, 144, 1444–1452.
  30. Zhu, L.; Sun, H.T.; Wang, S.; Huang, S.L.; Zheng, Y.; Wang, C.Q.; Hu, B.Y.; Qin, W.; Zou, T.T.; Fu, Y.; et al. Isolation and characterization of exosomes for cancer research. J. Hematol. Oncol. 2020, 13, 152.
  31. Liu, Y.; Zhou, Z.; Wang, F.; Kewes, G.; Wen, S.; Burger, S.; Ebrahimi Wakiani, M.; Xi, P.; Yang, J.; Yang, X.; et al. Axial localization and tracking of self-interference nanoparticles by lateral point spread functions. Nat. Commun. 2021, 12, 2019.
  32. Wu, H.J.; Huang, C.L.; Wang, L.W.; Li, Q.H.; Li, Y.J.; Zhang, L.H.; Zhu, D.W. Folate-targeted co-delivery polymersomes for efficient photo-chemo-antiangiogenic therapy against breast cancer and in vivo evaluation via OCTA_NIRF dual-modal imaging-3. Chin. Chem. Lett. 2022, 33, 5035–5041.
  33. Filipe, V.; Hawe, A.; Jiskoot, W. Critical evaluation of Nanoparticle Tracking Analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharm. Res. 2010, 27, 796–810.
  34. Libregts, S.; Arkesteijn, G.J.A.; Németh, A.; Nolte-'t Hoen, E.N.M.; Wauben, M.H.M. Flow cytometric analysis of extracellular vesicle subsets in plasma: Impact of swarm by particles of non-interest. J. Thromb. Haemost. 2018, 16, 1423–1436.
  35. Arraud, N.; Linares, R.; Tan, S.; Gounou, C.; Pasquet, J.M.; Mornet, S.; Brisson, A.R. Extracellular vesicles from blood plasma: Determination of their morphology, size, phenotype and concentration. J. Thromb. Haemost. 2014, 12, 614–627.
  36. Lai, J.J.; Chau, Z.L.; Chen, S.Y.; Hill, J.J.; Korpany, K.V.; Liang, N.W.; Lin, L.H.; Lin, Y.H.; Liu, J.K.; Liu, Y.C.; et al. Exosome Processing and Characterization Approaches for Research and Technology Development. Adv. Sci. 2022, 9, e2103222.
  37. Mashouri, L.; Yousefi, H.; Aref, A.R.; Ahadi, A.M.; Molaei, F.; Alahari, S.K. Exosomes: Composition, biogenesis, and mechanisms in cancer metastasis and drug resistance. Mol. Cancer 2019, 18, 75.
  38. Dai, J.; Su, Y.; Zhong, S.; Cong, L.; Liu, B.; Yang, J.; Tao, Y.; He, Z.; Chen, C.; Jiang, Y. Exosomes: Key players in cancer and potential therapeutic strategy. Signal Transduct. Target. Ther. 2020, 5, 145.
  39. Jiang, C.; Zhang, N.; Hu, X.; Wang, H. Tumor-associated exosomes promote lung cancer metastasis through multiple mechanisms. Mol. Cancer 2021, 20, 117.
  40. Zhao, S.; Mi, Y.; Guan, B.; Zheng, B.; Wei, P.; Gu, Y.; Zhang, Z.; Cai, S.; Xu, Y.; Li, X.; et al. Tumor-derived exosomal miR-934 induces macrophage M2 polarization to promote liver metastasis of colorectal cancer. J. Hematol. Oncol. 2020, 13, 156.
  41. Zhang, L.; Yu, D. Exosomes in cancer development, metastasis, and immunity. Biochim. Biophys. (BBA) Acta Rev. Cancer 2019, 1871, 455–468.
  42. Hartjes, T.A.; Mytnyk, S.; Jenster, G.W.; van Steijn, V.; van Royen, M.E. Extracellular Vesicle Quantification and Characterization: Common Methods and Emerging Approaches. Bioengineering 2019, 6, 7.
  43. Maas, S.L.; De Vrij, J.; Broekman, M.L. Quantification and size-profiling of extracellular vesicles using tunable resistive pulse sensing. J. Vis. Exp. 2014, 92, e51623.
  44. Maas, S.L.; Broekman, M.L.; de Vrij, J. Tunable Resistive Pulse Sensing for the Characterization of Extracellular Vesicles. Methods Mol. Biol. 2017, 1545, 21–33.
  45. Durak-Kozica, M.; Baster, Z.; Kubat, K.; Stępień, E. 3D visualization of extracellular vesicle uptake by endothelial cells. Cell. Mol. Biol. Lett. 2018, 23, 57.
  46. Vogel, R.; Pal, A.K.; Jambhrunkar, S.; Patel, P.; Thakur, S.S.; Reátegui, E.; Parekh, H.S.; Saá, P.; Stassinopoulos, A.; Broom, M.F. High-Resolution Single Particle Zeta Potential Characterisation of Biological Nanoparticles using Tunable Resistive Pulse Sensing. Sci. Rep. 2017, 7, 17479.
  47. Malenica, M.; Vukomanović, M.; Kurtjak, M.; Masciotti, V.; Dal Zilio, S.; Greco, S.; Lazzarino, M.; Krušić, V.; Perčić, M.; Jelovica Badovinac, I.; et al. Perspectives of Microscopy Methods for Morphology Characterisation of Extracellular Vesicles from Human Biofluids. Biomedicines 2021, 9, 603.
  48. McNamara, R.P.; Zhou, Y.; Eason, A.B.; Landis, J.T.; Chambers, M.G.; Willcox, S.; Peterson, T.A.; Schouest, B.; Maness, N.J.; MacLean, A.G.; et al. Imaging of surface microdomains on individual extracellular vesicles in 3-D. J. Extracell. Vesicles 2022, 11, e12191.
  49. Fakhredini, F.; Mansouri, E.; Mard, S.A.; Valizadeh Gorji, A.; Rashno, M.; Orazizadeh, M. Effects of Exosomes Derived from Kidney Tubular Cells on Diabetic Nephropathy in Rats. Cell J. 2022, 24, 28–35.
  50. Conde-Vancells, J.; Rodriguez-Suarez, E.; Embade, N.; Gil, D.; Matthiesen, R.; Valle, M.; Elortza, F.; Lu, S.C.; Mato, J.M.; Falcon-Perez, J.M. Characterization and comprehensive proteome profiling of exosomes secreted by hepatocytes. J. Proteome Res. 2008, 7, 5157–5166.
  51. Jeong, D.; Kim, M.J.; Park, Y.; Chung, J.; Kweon, H.S.; Kang, N.G.; Hwang, S.J.; Youn, S.H.; Hwang, B.K.; Kim, D. Visualizing extracellular vesicle biogenesis in gram-positive bacteria using super-resolution microscopy. BMC Biol. 2022, 20, 270.
  52. Zabeo, D.; Cvjetkovic, A.; Lässer, C.; Schorb, M.; Lötvall, J.; Höög, J.L. Exosomes purified from a single cell type have diverse morphology. J. Extracell. Vesicles 2017, 6, 1329476.
  53. Gardiner, C.; Di Vizio, D.; Sahoo, S.; Théry, C.; Witwer, K.W.; Wauben, M.; Hill, A.F. Techniques used for the isolation and characterization of extracellular vesicles: Results of a worldwide survey. J. Extracell. Vesicles 2016, 5, 32945.
  54. Issman, L.; Brenner, B.; Talmon, Y.; Aharon, A. Cryogenic transmission electron microscopy nanostructural study of shed microparticles. PLoS ONE 2013, 8, e83680.
  55. Jung, M.K.; Mun, J.Y. Sample Preparation and Imaging of Exosomes by Transmission Electron Microscopy. J. Vis. Exp. 2018, 131, e56482.
  56. Božič, D.; Hočevar, M.; Kisovec, M.; Pajnič, M.; Pađen, L.; Jeran, M.; Bedina Zavec, A.; Podobnik, M.; Kogej, K.; Iglič, A.; et al. Stability of Erythrocyte-Derived Nanovesicles Assessed by Light Scattering and Electron Microscopy. Int. J. Mol. Sci. 2021, 22, 12772.
  57. Sokolova, V.; Ludwig, A.K.; Hornung, S.; Rotan, O.; Horn, P.A.; Epple, M.; Giebel, B. Characterisation of exosomes derived from human cells by nanoparticle tracking analysis and scanning electron microscopy. Colloids Surf. B Biointerfaces 2011, 87, 146–150.
  58. Cizmar, P.; Yuana, Y. Detection and Characterization of Extracellular Vesicles by Transmission and Cryo-Transmission Electron Microscopy. Methods Mol. Biol. 2017, 1660, 221–232.
  59. Liu, Z.; Xue, H.; Chen, Q.; Yang, G. A method for extraction of exosomes from breast tumour cells and characterisation by transmission electron microscopy. J. Microsc. 2023, 292, 117–122.
  60. Park, Y.H.; Shin, H.W.; Jung, A.R.; Kwon, O.S.; Choi, Y.J.; Park, J.; Lee, J.Y. Prostate-specific extracellular vesicles as a novel biomarker in human prostate cancer. Sci. Rep. 2016, 6, 30386.
  61. Kurtjak, M.; Kereïche, S.; Klepac, D.; Križan, H.; Perčić, M.; Krušić Alić, V.; Lavrin, T.; Lenassi, M.; Wechtersbach, K.; Kojc, N.; et al. Unveiling the Native Morphology of Extracellular Vesicles from Human Cerebrospinal Fluid by Atomic Force and Cryogenic Electron Microscopy. Biomedicines 2022, 10, 1251.
  62. Stetefeld, J.; McKenna, S.A.; Patel, T.R. Dynamic light scattering: A practical guide and applications in biomedical sciences. Biophys. Rev. 2016, 8, 409–427.
  63. Palmieri, V.; Lucchetti, D.; Gatto, I.; Maiorana, A.; Marcantoni, M.; Maulucci, G.; Papi, M.; Pola, R.; De Spirito, M.; Sgambato, A. Dynamic light scattering for the characterization and counting of extracellular vesicles: A powerful noninvasive tool. J. Nanopart. Res. 2014, 16, 2583.
  64. Perpetuo, L.; Ferreira, R.; Thongboonkerd, V.; Guedes, S.; Amado, F.; Vitorino, R. Urinary exosomes: Diagnostic impact with a bioinformatic approach. Adv. Clin. Chem. 2022, 111, 69–99.
  65. Gercel-Taylor, C.; Atay, S.; Tullis, R.H.; Kesimer, M.; Taylor, D.D. Nanoparticle analysis of circulating cell-derived vesicles in ovarian cancer patients. Anal. Biochem. 2012, 428, 44–53.
  66. Tajik, T.; Baghaei, K.; Moghadam, V.E.; Farrokhi, N.; Salami, S.A. Extracellular vesicles of cannabis with high CBD content induce anticancer signaling in human hepatocellular carcinoma. Biomed. Pharmacother. 2022, 152, 113209.
  67. Hassan, P.A.; Rana, S.; Verma, G. Making sense of Brownian motion: Colloid characterization by dynamic light scattering. Langmuir 2015, 31, 3–12.
  68. Lawrie, A.S.; Albanyan, A.; Cardigan, R.A.; Mackie, I.J.; Harrison, P. Microparticle sizing by dynamic light scattering in fresh-frozen plasma. Vox Sang. 2009, 96, 206–212.
  69. Szatanek, R.; Baj-Krzyworzeka, M.; Zimoch, J.; Lekka, M.; Siedlar, M.; Baran, J. The Methods of Choice for Extracellular Vesicles (EVs) Characterization. Int. J. Mol. Sci. 2017, 18, 1153.
  70. Cho, S.; Yi, J.; Kwon, Y.; Kang, H.; Han, C.; Park, J. Multifluorescence Single Extracellular Vesicle Analysis by Time-Sequential Illumination and Tracking. ACS Nano 2021, 15, 11753–11761.
  71. Vestad, B.; Llorente, A.; Neurauter, A.; Phuyal, S.; Kierulf, B.; Kierulf, P.; Skotland, T.; Sandvig, K.; Haug, K.B.F.; Øvstebø, R. Size and concentration analyses of extracellular vesicles by nanoparticle tracking analysis: A variation study. J. Extracell. Vesicles 2017, 6, 1344087.
  72. Dragovic, R.A.; Gardiner, C.; Brooks, A.S.; Tannetta, D.S.; Ferguson, D.J.; Hole, P.; Carr, B.; Redman, C.W.; Harris, A.L.; Dobson, P.J.; et al. Sizing and phenotyping of cellular vesicles using Nanoparticle Tracking Analysis. Nanomed. Nanotechnol. Biol. Med. 2011, 7, 780–788.
  73. van der Pol, E.; Coumans, F.A.; Grootemaat, A.E.; Gardiner, C.; Sargent, I.L.; Harrison, P.; Sturk, A.; van Leeuwen, T.G.; Nieuwland, R. Particle size distribution of exosomes and microvesicles determined by transmission electron microscopy, flow cytometry, nanoparticle tracking analysis, and resistive pulse sensing. J. Thromb. Haemost. 2014, 12, 1182–1192.
  74. Hu, Y.; Tian, Y.; Di, H.; Xue, C.; Zheng, Y.; Hu, B.; Lin, Q.; Yan, X. Noninvasive Diagnosis of Nasopharyngeal Carcinoma Based on Phenotypic Profiling of Viral and Tumor Markers on Plasma Extracellular Vesicles. Anal. Chem. 2022, 94, 9740–9749.
  75. Liu, H.S.; Tian, Y.; Xue, C.F.; Niu, Q.; Chen, C.; Yan, X.M. Analysis of extracellular vesicle DNA at the single-vesicle level by nano-flow cytometry. J. Extracell. Vesicles 2022, 11, e12206.
  76. Ricklefs, F.L.; Maire, C.L.; Reimer, R.; Dührsen, L.; Kolbe, K.; Holz, M.; Schneider, E.; Rissiek, A.; Babayan, A.; Hille, C.; et al. Imaging flow cytometry facilitates multiparametric characterization of extracellular vesicles in malignant brain tumours. J. Extracell. Vesicles 2019, 8, 1588555.
  77. Aibaidula, A.Z.; Fain, C.E.; Garcia, L.C.; Wier, A.; Bouchal, S.M.; Bauman, M.M.; Jung, M.Y.; Sarkaria, J.N.; Johnson, A.J.; Parney, I.F. Spectral flow cytometry identifies distinct nonneoplastic plasma extracellular vesicle phenotype in glioblastoma patients. Neuro-Oncol. Adv. 2023, 5, vdad082.
  78. Betzig, E.; Patterson, G.H.; Sougrat, R.; Lindwasser, O.W.; Olenych, S.; Bonifacino, J.S.; Davidson, M.W.; Lippincott-Schwartz, J.; Hess, H.F. Imaging intracellular fluorescent proteins at nanometer resolution. Science 2006, 313, 1642–1645.
  79. Rust, M.J.; Bates, M.; Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 2006, 3, 793–795.
  80. Jones, S.A.; Shim, S.H.; He, J.; Zhuang, X. Fast, three-dimensional super-resolution imaging of live cells. Nat. Methods 2011, 8, 499–508.
  81. Shim, S.H.; Xia, C.; Zhong, G.; Babcock, H.P.; Vaughan, J.C.; Huang, B.; Wang, X.; Xu, C.; Bi, G.Q.; Zhuang, X. Super-resolution fluorescence imaging of organelles in live cells with photoswitchable membrane probes. Proc. Natl. Acad. Sci. USA 2012, 109, 13978–13983.
  82. Zong, S.; Zong, J.; Chen, C.; Jiang, X.; Zhang, Y.; Wang, Z.; Cui, Y. Single molecule localization imaging of exosomes using blinking silicon quantum dots. Nanotechnology 2018, 29, 065705.
  83. Jungmann, R.; Avendaño, M.S.; Woehrstein, J.B.; Dai, M.; Shih, W.M.; Yin, P. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 2014, 11, 313–318.
  84. Tholen, M.M.E.; Tas, R.P.; Wang, Y.; Albertazzi, L. Beyond DNA: New probes for PAINT super-resolution microscopy. Chem. Commun. 2023, 59, 8332–8342.
  85. Chen, C.; Zong, S.; Liu, Y.; Wang, Z.; Zhang, Y.; Chen, B.; Cui, Y. Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification. Small 2019, 15, e1901014.
  86. Liu, D.A.; Tao, K.; Wu, B.; Yu, Z.; Szczepaniak, M.; Rames, M.; Yang, C.; Svitkina, T.; Zhu, Y.; Xu, F.; et al. A phosphoinositide switch mediates exocyst recruitment to multivesicular endosomes for exosome secretion. Nat. Commun. 2023, 14, 6883.
  87. Auer, A.; Strauss, M.T.; Schlichthaerle, T.; Jungmann, R. Fast, Background-Free DNA-PAINT Imaging Using FRET-Based Probes. Nano Lett. 2017, 17, 6428–6434.
  88. Zhu, M.; Zhang, L.; Jin, L.; Chen, J.; Zhang, Y.; Xu, Y. DNA-PAINT Imaging Accelerated by Machine Learning. Front. Chem. 2022, 10, 864701.
  89. van Wee, R.; Filius, M.; Joo, C. Completing the canvas: Advances and challenges for DNA-PAINT super-resolution imaging. Trends Biochem. Sci. 2021, 46, 918–930.
  90. McEvoy, A.L.; Greenfield, D.; Bates, M.; Liphardt, J. Q&A: Single-molecule localization microscopy for biological imaging. BMC Biol. 2010, 8, 106.
  91. Nieves, D.J.; Gaus, K.; Baker, M.A.B. DNA-Based Super-Resolution Microscopy: DNA-PAINT. Genes 2018, 9, 621.
  92. Li, W.; Li, C.; Zhou, T.; Liu, X.; Liu, X.; Li, X.; Chen, D. Role of exosomal proteins in cancer diagnosis. Mol. Cancer 2017, 16, 145.
  93. Hell, S.W.; Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: Stimulated-emission-depletion fluorescence microscopy. Opt. Lett. 1994, 19, 780–782.
  94. Sezgin, E. Super-resolution optical microscopy for studying membrane structure and dynamics. J. Phys. Condens. Matter 2017, 29, 273001.
  95. Huang, G.; Liu, Y.; Wang, D.; Zhu, Y.; Wen, S.; Ruan, J.; Jin, D. Upconversion nanoparticles for super-resolution quantification of single small extracellular vesicles. eLight 2022, 2, 20.
  96. Valcz, G.; Buzás, E.I.; Kittel, Á.; Krenács, T.; Visnovitz, T.; Spisák, S.; Török, G.; Homolya, L.; Zsigrai, S.; Kiszler, G.; et al. En bloc release of MVB-like small extracellular vesicle clusters by colorectal carcinoma cells. J. Extracell. Vesicles 2019, 8, 1596668.
  97. Yang, X.; Yang, Z.; Wu, Z.; He, Y.; Shan, C.; Chai, P.; Ma, C.; Tian, M.; Teng, J.; Jin, D.; et al. Mitochondrial dynamics quantitatively revealed by STED nanoscopy with an enhanced squaraine variant probe. Nat. Commun. 2020, 11, 3699.
  98. Choi, D.; Montermini, L.; Jeong, H.; Sharma, S.; Meehan, B.; Rak, J. Mapping Subpopulations of Cancer Cell-Derived Extracellular Vesicles and Particles by Nano-Flow Cytometry. ACS Nano 2019, 13, 10499–10511.
  99. Gustafsson, M.G. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc. 2000, 198, 82–87.
  100. Gustafsson, M.G. Nonlinear structured-illumination microscopy: Wide-field fluorescence imaging with theoretically unlimited resolution. Proc. Natl. Acad. Sci. USA 2005, 102, 13081–13086.
  101. Li, D.; Shao, L.; Chen, B.C.; Zhang, X.; Zhang, M.; Moses, B.; Milkie, D.E.; Beach, J.R.; Hammer, J.A., 3rd; Pasham, M.; et al. ADVANCED IMAGING. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics. Science 2015, 349, aab3500.
  102. Boland, M.A.; Cohen, E.A.K.; Flaxman, S.R.; Neil, M.A.A. Improving axial resolution in Structured Illumination Microscopy using deep learning. Philos. Trans. Ser. A Math. Phys. Eng. Sci. 2021, 379, 20200298.
  103. He, Y.; Yao, Y.; He, Y.; Huang, Z.; Luo, F.; Zhang, C.; Qi, D.; Jia, T.; Wang, Z.; Sun, Z.; et al. Surpassing the resolution limitation of structured illumination microscopy by an untrained neural network. Biomed. Opt. Express 2023, 14, 106–117.
  104. Butola, A.; Acuna, S.; Hansen, D.H.; Agarwal, K. Scalable-resolution structured illumination microscopy. Opt. Express 2022, 30, 43752–43767.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback