**6.1. The challenges of scaling-up BES**

Making the leap from the controlled laboratory scale use of these technologies into pilot scale systems that will inform us of their suitability to real applications is one of the most challenging aspects of research in this area. These challenges are mostly scientific, logistical, and financial in nature; here, we briefly discuss them. When conducting a pilot scale study, even with the best intentions and meticulous planning, it is not always easy to carry out the investigation strictly following the tenants of the scientific method. The use of replicas is a vital way of achieving a scientific method, the resulting reproducibility and reliability of the data collected is standard practice with all laboratory experiments. However, it is rarely considered at pilot scale, where the logistics and expense of operating one reactor are high enough without considering two. Ultimately, therefore most pilot scale studies are not highly scientific, and the information and conclusions we can draw from them are not as strong as those achieved in laboratory-based studies. This is unfortunate, as these studies will actually guide us into the real applications of the technologies developed initially in the laboratory. Achieving the rigor of laboratory testing within a field, a pilot scale study will be essential to take this technology forward.

The logistical problems of setting up a pilot scale project may individually seem like minor and highly surmountable problems, but can combine to have significant detriment to both the financial cost of the project and its outcomes. These problems, and the compromises that need to be made to overcome them, can have significant and long-lasting impacts into the study being undertaken. For example, a reactor planned for startup in the summer months can be delayed into the winter months which in the United Kingdom is a significant decrease in operating temperature, and would result in very slow microbial growth, and potentially a less active and effective biofilm forming. Without a replica reactor started in the summer months, we are unlikely to know the full impact of this on overall reactor performance.

For nitrogen in particular, this element can be concentrated (usually as ammonium) on the catholyte of the BES by migration and diffusion from the anode side. Due to the high pH of the catholyte, ammonium turns into ammonia gas which can be subsequently stripped from the off-gas [57]. The use of BES for nitrogen recovery has been explored using different waste streams, such as swine wastewater, landfill leachate or urine, and different reactor configurations offering encouraging results [58, 59]. Zamora et al. [60] demonstrated that

−1, which is

−1, which was significantly less than

−1

electrical energy required in a pilot scale BES for ammonia recovery is 1.4 kWh·kgN

0.96 kWh·kgN

134 Energy Systems and Environment

−1 [62].

**6.1. The challenges of scaling-up BES**

essential to take this technology forward.

with an associated energy consumption of 6.5 kWh·kgP

that needed by other struvite formation methods based on pH adjustment.

**6. Scaling-up BES for energy valorization of waste streams**

lower than other electrochemical nitrogen recovery technologies (for instance, 13 kWh·kgN

is needed to recover nitrogen for digestate using a conventional electrochemical cell [61]). Moreover, some studies have even reported a positive energy balance producing a surplus of

BES also represents an ideal technology to precipitate phosphorus, together with ammonium, in the form of struvite thanks to the relatively high pH in the catholyte as mentioned before. Furthermore, BES can be used to mobilize orthophosphate from the iron phosphate contained in digested sewage sludge [63]. Cusick et al. [64] reported a P precipitation efficiency of 85%

Therefore, the main advantage of using BES for nutrients recovery, compared to other technologies, is that they allow to limit the energy requirements by exploiting the energy content of the organic matter present in a waste [55]. Finally, although the first experiences with pilot plants have already been carried out and give hope to the development of this technology, the use of BES for nutrients recovery still needs optimization of operational parameters [65].

Making the leap from the controlled laboratory scale use of these technologies into pilot scale systems that will inform us of their suitability to real applications is one of the most challenging aspects of research in this area. These challenges are mostly scientific, logistical, and financial in nature; here, we briefly discuss them. When conducting a pilot scale study, even with the best intentions and meticulous planning, it is not always easy to carry out the investigation strictly following the tenants of the scientific method. The use of replicas is a vital way of achieving a scientific method, the resulting reproducibility and reliability of the data collected is standard practice with all laboratory experiments. However, it is rarely considered at pilot scale, where the logistics and expense of operating one reactor are high enough without considering two. Ultimately, therefore most pilot scale studies are not highly scientific, and the information and conclusions we can draw from them are not as strong as those achieved in laboratory-based studies. This is unfortunate, as these studies will actually guide us into the real applications of the technologies developed initially in the laboratory. Achieving the rigor of laboratory testing within a field, a pilot scale study will be Financial problems can be broadly split into two main areas, finding funding to do this research and then using this funding to build practical systems. BES technologies and the complex microbiology they rely on mean that they do not fit easily into the standard technology readiness levels often used to identify different funding sources. Many of the fundamental elements of BES operate differently at different scales. There is therefore a need to do basic and fundamental science (TR level 1) such as sequencing and understanding microbial dynamics on reactors that are prototypes in an operational environment (TR level 7). Secondly, once funding is in place, different materials need to be sourced which are affordable to use at large scale but will still function in the BES. Thankfully low cost alternatives to most of the materials have now been found, with stainless steel replacing platinum cathodes [66] and cheap battery separators replacing ion exchange membranes [67]. In 2008, these two components were 85% of the costs [20]; however, in recent pilots, they account for less than 2% [67]. The carbon anode material at approximately 100 £·m−2 is now the greatest material cost. Furthermore, developing cheaper alternatives to ancillary equipments such as sensors and potentiostats, which are often expensive and not designed to be robust enough for field applications is another challenge.


**Author details**

Sophie Pilkinton, T.J. Hollingsworth, Brian Jerkins and Monica M. Jablonski\* Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN, United States

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**10. Yellow fever virus**

These human movements increase the risk of contamination of non-immune persons travel‐ ling in areas where contaminated vectors persist and, conversely, favour the introduction of

YF outbreaks are common in Africa despite the current knowledge of the disease transmis‐ sion and the availability of a vaccine. In Africa, YF cases are not uniformly distributed throughout the endemic area; rather, more cases are reported in West Africa compared to East and Central Africa. Genetic differences between genotypes of YF in Africa probably contribute to the observed distribution of YF outbreaks. Genetic and behavioral variation in mosquito vectors may also play a major role in the distribution of YF outbreaks. The other factors also contribute to the epidemiology of YF, including host genetic background, cli‐

Yellow fever virus is transmitted principally by insects (mosquitoes), but ticks (*Amblyomma variegatum*) may play a secondary and minor role in Africa. It was not until 1901 that yellow fever transmission to humans was associated with the blood-feeding by the *Ae. aegypti* mos‐ quito (Figure 2), which was a major breakthrough in understanding this dreadful disease. Dispatched to Cuba by the United States government to investigate the cause of YF, Walter Reed and colleagues confirmed that the primary mode of YF transmission to humans was the *Ae. aegypti* mosquito (Figure 2) and the in ground-breaking virologic studies demonstrat‐ ed that the disease was caused by an agent that could be filtered from the blood of infected individuals [49]. The reservoirof yellow fever virus is the susceptible vector mosquito spe‐ cies that remains infected throughout its life and can transmit the virus transovarially. Yel‐ low fever can persist as a zoonosis in the tropical areas of Africa and America, with nonhuman primates responsible for maintaining the infection. Man and monkey play the

mate, vaccination coverage, vertebrate hosts and movement of vertebrate hosts [48].

role of amplifiers of the amount of virus available for the infection of mosquitos [50].

**Figure 2.** *Aedes aegypti*, the primary disease vector for yellow fever (Photo by Muhammad Mahdi Kharim, published

the disease into previously YF free zones [47].

**8.6. Genetic and behavioral variation**

216 Encephalitis

**9. Yellow fever vectors**

under the GNU free documentation licences)

Ever since the causative agent of YF disease YFV, was first isolated in 1927 from a Ghanaian patient named Asibi [50], the Asibi YFV strain is still widely used by the scientists of today. YFV is the prototype member of the family Flaviviridae(from the Latin flavus, meaning yel‐ low), and genus Flavivirus, which get their name from the Latin word for yellow (flavus). The genome is a single-stranded, positive-sense RNA, 10,500 - 11,000 nucleotides in length. The genus Flavivirus contains approximately 70 viruses, and the major flavivirus diseases are yellow fever (YF), dengue, West Nile, Japanese encephalitis, and tick-borne encephalitis [51]. Unlike other mosquito-borne flaviviruses, YFV has a tropism for the liver and causes a viscerotropic disease whereas many other mosquito-borne flaviviruses have a tropism for the brain, or in the case of the DEN viruses they target cells of reticuloendothelial origin [52].

It was one of the earliest viruses to be identified and linked to human disease. Although substantial variation exists among strains, they can be grouped into monophyletic geo‐ graphical variants, called topotypes. African isolates are usually grouped into two topo‐ types, associated with East and West Africa [53,54], although some studies have argued for up to five [55]. Two more have been identified from South America, although one has not been recovered since 1974, suggesting that it may be extinct in the wild. There is no evidence for a difference in virulence between the topotypes [56]. YF activity often occurs in areas af‐ ter increases in temperature and rainfall that will favor increased biodiversity, including in‐ creased numbers of animals and arthropods while reduced rainfall limits mosquito vector density [49]. It has been known for over 50 years that increased temperatures are associated with enhanced transmission of YF virus [56] due to shortened extrinsic incubation period and increased biting by mosquitoes of vertebrate hosts [49].


**5. Conclusions**

The combination of inorganic and organic N substrates reduced the total and mineral N runoff losses compared with inorganic N alone in coarse-textured sand. The higher N uptake by the plant cover provides strong evidence that the combined use of inorganic and organic sources has the potential for increasing NUE by reducing fertiliser losses in surface runoff and synchronising better the N availability with plant requirements. Inorganic N fertiliser can be applied at low rates to provide sufficient N at the early growth stage, with organic substrates, such as composted manures, supplying N at later stages, particularly when the plant growth rate is high.

Furthermore, the potentially slower release of N from organic materials such as CropUpTM may reduce the impacts of other loss pathways (e.g., leaching and sub-surface lateral flow) relative to highly soluble inorganic N fertilisers, particularly when the release rate better matches plant N uptake requirements. These organic materials have widespread applications to sandy soils not only in agriculture, but also in other activities that involve a high N application to sand, such as golf courses and sporting ovals.

**Author Contributions:** Conceptualisation, I.P., C.P.-L. and M.R.; methodology, I.P., M.R. and D.M.; formal analysis, I.P. and D.M.; investigation, I.P.; resources, C.P.-L., I.T., B.S. and B.H.; data curation, I.P.; writing—original draft preparation, I.P.; writing—review and editing, I.P., C.P.-L., M.R., I.T., B.S., D.M. and B.H.; supervision, M.R.; funding acquisition, C.P.-L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Australian Government's Cooperative Research Centres Projects, grant number CRCPFIVE000015.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank the Cooperative Research Centres Projects (grant number CRCPFIVE000015) for funding this project.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


*DOI: http://dx.doi.org/10.5772/intechopen.103688 Advances in Graphene Platforms for Drug Delivery in Cancer and Its Biocompatibility*

physiological systems in order to decide whether a nanobiotechnological product should be tested in humans [3, 4].

The incorporation of nanomaterials into biological systems requires strategies for manipulating the ligands bound to the surface to make them more polar and biocompatible [5]. Nanomaterials must be soluble to have the biological application, and this is achieved by adding functional groups (functionalization). An ideal ligand must meet the following requirements: (1) provide stability and solubility to the nanomaterial in biological buffers; (2) maintain high resistance to photobleaching and other photophysical properties in aqueous media; (3) have functional groups that can conjugate biomolecules (conjugation), and (4) minimize the overall hydrodynamic

**Figure 1.**

*Application of GQDs platforms for cancer treatment. Cellular targets and effects of GQDs platforms in cell lines and experimental animals.*

size [6, 7]. Quantum dots (QDs) are among the most popular nanomaterials: they are semiconductor nanoparticles with photoluminescent properties and a wide variety of applications.

Functionalized QDs are very useful in biomedicine because they can be modified with a great variety of molecules and small biological polymers, which help improve their bioactivity and reduce their toxic effects [8–10]. Thanks to these characteristics, QDs can bind effectively to cell membranes, meaning they can be employed as excellent probes for cell detection, diagnosis, imaging, and delivery of therapeutic agents. Due to the great coupling achieved between QDs and biomolecules, today these are used as a tool for biological goals, to improve the efficacy of drug release control and significantly reduce toxicity [11–13]. At present, a wide range of studies on GQD platforms are mainly focused on cancer treatment (**Table 1** and **Figure 1**). This chapter will review the advances in all these areas, as well as aspects related to the toxicity and biocompatibility of GQDs.


**5.1.3 Urban growth and major roads**

Figure 2 shows that urban growth is more influenced by the roads along the railway. If before independence the railway played a role in the location of industrial areas, the urban railway had not developed, now it has not facilitated access to downtown as a centre of employment and therefore has not polarized urban growth. The roads in Matadi and Bandundu, as well as routes to the drop in Lukaya and to Lake Ma-Vallée, correspond to areas of urban growth today.

Despite the role of highways in urban growth, there are areas that develop latest far from downtown and away from these axes. Indeed, an urban extension area has been observed since the 1990s, south and east of the camp which houses the faculty of the University of Kinshasa. This is the area south Cogelos and neighbourhoods Tchad, Mandela and Department Plateau to the east. These areas develop in the absence of urban amenities. Indeed, they are connected to almost none of the service water supply of REGIDESO. The population is supplied fitted to the sources (Mayi ya Libanga, Mayi ya Niwa, Mayi ya Zamba) or the fountain. This is the case in the district of Mbiti. Where the water is high, people dig a well. The lack of urban amenities does not limit urban growth in Kinshasa. The bottom line for people is to have a home.

The quantitative analysis of urban growth compared to major communication axes, made in the GRASS software, shows that 47.5% of the growth took place at more than 1 km of main roads. Beyond this 1 kilometre threshold, the advantage of proximity to the main roads can be neglected and the neighbourhood effect becomes more important in the sense that people settled near existing neighbourhoods, but at a greater distance from the road.

This helps to highlight the fact that the major communication axes are not always, or are no longer, a major factor in urban growth.


**3. Results**

### *3.1. Detection of Immune Cell Accumulation around Biomaterial*

It was shown that biomaterial triggered the recruitment of macrophages at the site of the bone wound and enhanced wound healing [1,7,32,54]. Rat femur was operated on with biomaterial (with the Masquelet induced membrane technique), and three weeks later the whole bone together with femur and biomaterial was harvested. The non-operated femur from the second leg of this rat was used as a control (called non-operated femur). The Masquelet induced membrane technique consists of two different operative phases. In the first operation, a fixator was placed around the bone, and the missing bone fragments were filled with biomaterial. Three weeks after surgery, a Masquelet induced membrane was formed around the biomaterial. Three weeks later, during the second operation, the biomaterial was removed, but the Masquelet induced membrane remained, and the biomaterial was replaced with a bone graft. To examine the architecture and the regeneration region of the bone, cryosections of femurs were analyzed by Hematoxylin and phloxin (HP) histological coloration (Figure 2). The control femur was used as a reference to observe bone and muscle in their native structure (Figure 2A). In the operated femur, the Masquelet induced membrane, the regenerated region close to the Masquelet induced membrane, and the biomaterial were observed (Figure 2B). Some immune cells were identified by their structure at higher microscope magnification in the regeneration region (Figure 2C), hypothesized as macrophages.

**Figure 2.** Histologic and microscopic analysis of rat femur: ( **A**–**C**) histologic analysis of the femurs; histopathological image of ( **A**) non-operated; and (**B**) operated femurs of rats stained with hematoxylin and phloxin; ( **C**) expanded view: high magnification image of the area within the red rectangle in image (**B**); ( **D**,**E**) identification of macrophages M1 and M2 in the operated femur; anti-CD68 (Alexa488, turquoise fluorescence), labeling the M1 and M2 macrophages; anti-CD206 (Alexa568, red fluorescence) labeling the M2 macrophages and satellite cells; nuclear staining with DAPI (blue fluorescence).

In situ visualization of macrophages is quite problematic. The cluster of differentiation (CD) CD68 protein is one of the most common monocytes/macrophages marker proteins [55], but in other mononuclear phagocyte cells and non-hematopoietic cells (mesenchymal stem cells, fibroblast, endothelial, and tumor cells) weak expression can be

detected [56]. Other markers such as CD206 and CD163 mostly recognize M2 macrophages, but CD206 protein is also expressed in satellite and CD163 protein in dendritic cells [56,57]. In situ identification of human and mouse M2 macrophages can be performed by double immunolabeling with CD206 or CD163 together with CD68 antibodies (Table 1) [47,58].

**Table 1.** Cell phenotypes.

The same region was examined by the traditional immunostaining method (with anti-CD68 and anti-CD206 antibodies) to detect if M1-like or M2-like macrophages were localized in the regeneration region (Table 1). We observed M1-like macrophages expressing CD68 protein only and M2-like macrophages expressing both CD68 and CD206 proteins (Figure 2D). In the absence of a primary antibody (negative control), no expression of these markers was detected (Figure 2E).

To go further in our investigation, immunostaining was performed on both the nonoperated femur and the operated femur (Figure 3) in large sections (2 mm × 1.2 mm). Using bright field microscopy, the non-operated femur showed a representative architecture of the bone (Figure 3A) and an absence of fluorescence signal (Figure 3B). In the operated femur, the three expected regions (the regeneration region, the Masquelet induced membrane, and surrounding muscle) were observed (Figure 3C). Using CD68 and CD206 immunostaining, both M1-like and M2-like macrophages were detected (Figure 3D). However, the M1-like and M2-like macrophage repartition differed among the three zones (Figure 3E–G). A similar amount of M1-like and M2-like macrophages were observed in the regenerating region (Figure 3E) compared to the interface (between the regeneration region and Masquelet induced membrane) region, where mostly M1-like macrophages (Figure 3F) and a predominantly M2-like macrophage population were detected (Figure 3G).

Another widely accepted macrophage marker is the ionized calcium-binding adaptor molecule 1 (Iba1) (Table 1) [59–61]. Labeling with the anti-Iba1 antibody together with the anti-CD206 antibody showed very similar results (Figure S1). The only difference that has been detected is that fewer M1-like macrophages have been detected by Iba1 labeling in the interface region than with CD68 labeling. This result suggests that single CD68 positive cells include other cells than macrophages that have not yet been identified.

In summary, both M1-like and M2-like macrophages were identified in the injured region of the operated femur. After quantification of 1000 cells, 16.55% (CD68/CD206) and 17.5% (Iba1/CD206) were identified as M2-like macrophages compared to 6.17% (CD68/CD206) and 4.1% (Iba1/CD206) of M1-like macrophages (Table 2). To confirm this result, immunostaining with an anti-CD163 antibody was performed because this protein is also expressed by M2-like macrophages [16]. Next, the CD163 and CD206 co-labeling was performed to identify the M2-like macrophages and satellite cells in the same pictures (Figure S2). In this condition, 21.6% of M2-like macrophages were identified (Figure 4A and Table 1), while no expression of the protein was detected in the negative control (Figure 4B).

**Table 2.** Quantification of M1-like and M2-like macrophages in the regeneration region of the operated femur (quantitative analysis, based on random examination of 1000 cells in each of the conditions).

**Figure 3.** Identification of M1-like and M2-like macrophages in the rat femurs: (**A,B**) immunolabeling with anti-CD68 and CD206 antibodies of the non-operated; and (**C**–**G**) operated femurs; bright-field image together with immunolabeling of the non-operated (**A**) and operated (**C**) femurs; (**E**–**G**) expanded view: high magnification image of the area within the red rectangle in image C; anti-CD68 (Alexa488, turquoise fluorescence), labeling the M1 and M2 macrophages; anti-CD206 (Alexa568, red fluorescence) labeling the M2 macrophages and satellite cells; nuclear staining with DAPI (blue fluorescence); thin arrow: M1-like macrophages; thick arrow: M2-like macrophages.

**Figure 4.** Identification of M2-like macrophages in the operated femur: (**A**) immunolabeling of the operated femur with anti-CD163; (**B**) negative control of the immunolabeling; anti-CD163 (Alexa488, turquoise fluorescence), labeling the M2 macrophages; nuclear staining with DAPI (blue fluorescence).

### *3.2. Detection of Macrophages in Other Regions*

Previously we showed the capacity to detect macrophages using immunostaining in a non-decalcified cryo-fixed bone in the regeneration region (region 1). We further examined whether the different regions of the femur far from the injury region contained macrophages with phenotypic differences. In this context, three other regions were investigated: the muscle far from the bone or the wound (region 2), the periosteum (region 3), and the bone (region 4) (Figures 5 and S3). No difference between operated and non-operated femur tissues were observed (Figure 5). In region 2, no macrophages were present; only satellite cells could be detected (Figure 5B,C). In region 3, both M1-like and M2-like macrophages were detected close to osteoclasts, as it is multinucleated cells expressing CD68 (Figure 5D,E). In region 4, several cells expressing CD68 protein were detected. This result seemed to be coherent as macrophages were also localized in the bone marrow. Surprisingly, cells expressing only the CD206 protein were also detected (Figure 5F,G). This result requires further examination. No expression of both CD68 and CD206 markers was detected in the negative control (Figure S3).

### *3.3. Detection of Macrophages Based on Their Autofluorescence Feature*

It is well known that bone tissue has strong autofluorescence [62,63]. This makes it rather difficult to analyze the operated tissue with immunolabeling (using fluorophorelabeled antibody) because there was an abundant autofluorescence in the regenerating region (Figure 6). Autofluorescence may appear from structural proteins, such as collagen and elastin, but other endogenous fluorophores, such as Flavin-type molecules, are also often localized in cells [64,65]. In particular, immune cells, such as macrophages and granulocytes contain a large amount of phagosome/phagolysosome in their cytosol [66]. Sections of the operated femur tissues had relatively intense (excitation with 488 nm (Figure 6A–D,F) and excitation with 568 nm (Figure 6B–D,F)) autofluorescences exclusively in the cells with endosome-like structure (Figure 6E,G). The sections of the operated femur were subjected to different treatments, TrueVIEW Autofluorescence Quenching Kit, Blue Evans, or Black Soudan, to get rid of endogenous fluorescence (data not shown). Unfortunately, the endogen fluorescence was resistant to all treatments used so far.

**Figure 5.** Identification of M1-like and M2-like macrophages in the rat femurs: (**A**) presentation of the different regions of the femurs; (**B**,**D**,**F**) operated; and (**C**,**E**,**G**) non-operated femurs labeled with anti-CD68 and anti-CD206 antibodies; anti-CD68 (Alexa488, turquoise fluorescence), labeling the M1 and M2 macrophages; anti-CD206 (Alexa568, red fluorescence) labeling the M2 macrophages and satellite cells; nuclear staining with DAPI (blue fluorescence); thin arrow: satellite cells; arrowhead: osteoclast.

**Figure 6.** Observation of the autofluorescence in the operated rat femur: (**A**) detected autofluorescence after excitation with light (488 wavelengths, turquoise fluorescence); (**B**) detected autofluorescence after excitation with light (568 wavelengths, red fluorescence); (**C**) nuclear staining with DAPI (blue fluorescence); (**D**) merged image; (**E**) bright-field image; (**F**) expanded view: high magnification image of the area within the red rectangle in image (**D**); (**G**) expanded view: high magnification image of the area within the red rectangle in image (**E**).

### *3.4. Investigation of Macrophage Fluorescence by Microspectroscopic Analysis*

As the endogen fluorescence was very strong in the bone tissue, further investigation was needed for macrophage characterization. Turquoise fluorescence was detected in macrophages, but the autofluorescence prevented any conclusion as both specific fluorescence (coming from the immune signal) and autofluorescence were detected in a mixed signal (Figure 7A). Microspectroscopical analysis in situ facilitates the differentiation between specific fluorescence and autofluorescence. The ratio between the intensity of specific fluorescence and autofluorescence should be higher than three to conclude that the autofluorescence is not disturbing. This technique was applied to excited signals at 488 nm to separate them from their characteristic emission spectra. The emission spectrum of specific fluorescence reached a maximum at 520 nm, whereas the emission spectrum of autofluorescence peaked at approximately 550 nm (Figure 7B). The intensity of the specific CD68 fluorescence was approximately the same as the intensity of the autofluorescence (Figure 7B). Using this technique, two subtracted images were extracted from Figure 7C: the autofluorescence signal (Figure 7D) and the specific CD68 fluorescence of the macrophages (Figure 7E). In the resulting image, we can distinguish autofluorescence from the CD68 specific signal in cells (Figure 7E).

**Figure 7.** Identification of the in situ autofluorescence by microspectroscopy analysis. In the operated femur, macrophages were labeled with anti-CD68 antibody. Tissues were excited at 488 nm. (**A**) Emission of the autofluorescence and the antigen-specific fluorescence (Alexa Fluor 488) with turquoise fluorescence. (**B**) Microspectroscopycal analysis of Alexa Fluor 488 emission (turquoise line) and autofluorescence (red line). (**C**) Separation of autofluorescence emission (red fluorescence) and antigen-specific fluorescence (Alexa Fluor 488) (turquoise fluorescence). (**D**) Emission of the autofluorescence (red fluorescence). (**E**) Emission of the antigen-specific fluorescence (Alexa Fluor 488) (turquoise fluorescence). Nuclear staining with DAPI (blue fluorescence). Fine arrow: macrophages.

### *3.5. Detection of IL-1β and IL-6 Cytokines in the Operated Femur*

The presence of M1-like and mostly M2-like macrophages was shown earlier in the wounded bone tissue with immunofluorescence techniques. The next step was to determine the M2-like macrophages based on their cytokine expressions. Each subtype of M2-like macrophages (M2a, M2b, M2c, or M2d) secretes a different panel of cytokines. The next step was to identify the types of cytokines produced in the operated femur using RT-qPCR. All three markers showed higher expression in the operated femur (2.2 times more CD68, 1.61 times more CD163, and 2.29 times more CD206 mRNA) (Figure 8). Among the tested genes, two cytokines showed much higher expression, IL-1β (6.89 times more) and IL-6 (4.44 times more), in the operated femur compared to the non-operated femur (Figure 8). The only M2 macrophage that produces these two cytokines is the M2b macrophage (Figure 1). The in situ DIG technique is a commonly used, non-radioactive IHS method because it is very sensitive, but it has the limitation that it only allows analysis of a single gene in a sample.

**Figure 8.** Expression of the different marker genes and cytokines in the operated femur; qRT-PCR analysis of the different gene expressions. This result is the average of three measurements.

### *3.6. Identification of Macrophage Phenotype Using Both I DIG and In Situ HCR Techniques*

In situ HCR was chosen to further characterize the macrophages. The advantage of this technique is that it is more sensitive than FISH, and unlike in situ DIG, it allows the identification of several genes at once. Our next question was whether we could detect M2blike macrophages in situ using this technique in the operated rat bone. Since cytokines are usually secreted [48,67], immunostaining is not suitable to distinguish between different subtypes. Indeed, it is quite challenging to identify which cells produce the secreted protein. For this reason, an in situ hybridization technique was used to reveal the subtypes of M2-like macrophages as it allows the detection of the mRNA coding for those cytokines.

For the first time, in situ hybridization with a digoxigenin-labeled cRNA probe (in situ DIG) was used because the labeling remains stable, and it is an advantage to examine the labeling architecture. This technique is the most widely used non-radioactive ISH method because it is very sensitive, unfortunately, only one mRNA per section can be detected [68]. The expression of β-actin, IL-1β, IL-6, CD68, and CD163 genes was identified (Figure 9). The β-actin was used as a positive control (Figure 9A). In the absence of a specific probe, no expression was observed (Figure 9B). The mRNA expression of CD68, CD163, IL-1β, and IL-6 genes (Figure 9C–F) was detected around the biomaterial. Both CD68 (Figure 9C) and IL-1β mRNA expression (Figure 9D) was very intense. The CD163 (Figure 9E) and IL-6 (Figure 9F) mRNA expressions were much weaker, but a higher magnification showed a clear labeling (Figure 9G,H). In addition, β-actin, CD68, CD163, IL-1β, and IL-6 signals were quantified in the operated femur. Upregulation of these genes was detected in the Masquelet induced membrane region.

The same labeling was performed on the non-operated femur sections. In the nonoperated section, abundant expression of the β-actin mRNA was detected (Figure S4A), and in the negative control (Figure S4B), expression was not observed. The CD68 (Figure S4C) and IL-1β mRNA (Figure S4D) expressions were intense in the bone marrow, but no expression in the other part of the femur was detected. No expression was found with a small magnification of the CD163 (Figure S4E) and IL-6 (Figure S4F) mRNA. The CD163 expression was detectable only with higher magnification in the periosteum (Figure S4G). IL-6 mRNA was present in the bone marrow (Figure S4H).

**Figure 9.** In situ hybridization in the operated rat femur: (**A**) expression of β-actin mRNA (positive control); (**B**) negative control; (**C**) CD68 mRNA; (**D**) IL-1β mRNA; (**E**) CD163 mRNA; (**F**) IL-6 mRNA; (**G**) expanded view: high magnification image of the area within the red rectangle in image (**E**); (**H**) expanded view: high magnification image of the area within the red rectangle in image (**F**); (**I**) the stained area of the picture was quantitatively analyzed using ImageJ. The Masquelet induced membrane region was compared with the control region (**C**) (n = 3). \*\* *p* < 0.01, \* *p* < 0.5, ns = not significant, compared to control region.

Because co-labeling was not possible with the in situ DIG technique, in situ hybridization coupled with hybridization-chain-reaction detection (in situ HCR) was performed on cryosections of the bone. Indeed, three different mRNA expressions were co-detected, taking into account the rapid loss of signal of fluorophores within two weeks after labeling. Using the in situ DIG method, a high expression of IL-1β and IL-6 was detected in the regenerating bone. To examine whether M2-like macrophages were expressing these two cytokines, in situ HCR was performed with CD163, IL-1β, and IL-6 probes altogether (Figure 10A). Macrophages co-expressing the three signals were identified in the regenerated region of the operated femur, while no expression was detected in the negative control (Figure 10B). By random examination of the fluorescence of 100 cells labeled with a CD163 probe, the percentage of the M2b-like macrophages was approximately 68% of the M2-like macrophages.

**Figure 10.** Identification of the M2b-like macrophages in the operated femur by in situ hybridization combined with hybridization-chain-reaction detection (in situ HCR): (**A**) in situ hybridization combined with hybridization-chain-reaction detection (in situ HCR); (**B**) negative control: Probe-IL-6 (Alexa488, turquoise fluorescence), Probe-IL-1β (Alexa546, red fluorescence), Probe-CD163 (Alexa647, yellow fluorescence), nuclear staining with DAPI (blue fluorescence).