Novel Plasma Biomarkers for Alzheimer’s Disease: Insights from Organotypic Brain Slice and Microcontact Printing Techniques

Abstract

Background:

Alzheimer’s disease (AD) is a severe neurodegenerative disorder characterized by beta-amyloid plaques and tau neurofibrillary tangles. The diagnosis of AD is complex, with the analysis of beta-amyloid and tau in cerebrospinal fluid being a well-established diagnostic approach. However, currently no blood biomarkers have been identified or validated for clinical use. In the present study, we will identify novel plasma biomarkers for AD using our well-established organotypic mouse brain slice model connected to microcontact prints. We hypothesize that AD plasma contains factors that affect endothelial cell migration and new vessel formation.

Methods:

In the present study, plasma from human patients is microcontact printed and connected to mouse brain slices. After 4 weeks in culture, laminin⁺ and lectin⁺ endothelial cells (ECs) and vessels are analyzed by immunostaining techniques. The most promising samples were processed by differential mass spectrometry.

Results:

Our data show that AD plasma significantly increased the migration length of laminin⁺ and lectin⁺ ECs along the microcontact prints. Using differential mass spectrometry, we could identify three potential biomarkers: C-reactive protein, basigin, and trem-like transcript 1 protein.

Conclusion:

Here we show that brain slices connected to human plasma prints allow the identification of novel human AD biomarkers with subsequent mass spectrometry. This technique represents a novel and innovative approach to translate research findings from mouse models to human applications.

Graphical abstract

Keywords

Alzheimer’s disease / biomarker / plasma / organotypic brain slice / vessel / microcontact printing / basigin / trem-like transcript 1 protein

1. Introduction

Alzheimer’s disease (AD) is a severe neurodegenerative disorder of advanced age that is characterized by progressive loss of cognitive abilities. It has been reported that more than 55 million people worldwide suffer from dementia, and 60–70% of them have AD [1, 2]. Definitive diagnosis can be made by histopathological demonstration of beta-amyloid (A\beta) plaques and tau neurofibrillary tangles in postmortem brain tissue. However, the early diagnosis is necessary to start early therapeutic treatment before the loss of nerve cells. It is widely accepted that biomarkers are found long before disease symptoms. Detection of these biomarkers will undoubtedly revolutionize the therapeutic options. Unfortunately, there are so far no accepted blood biomarkers, and only A\beta-42, A\beta-40, total tau and phosphorylated tau-181 proteins have been analyzed in cerebrospinal fluid (CSF) and have been introduced into routine practice [3].

Researchers have been working for many years to identify plasma biomarkers that can be used for diagnostic purposes in AD. The National Institute on Aging and Alzheimer’s Association (NIA-AA) published a guideline on this subject in 2011, updated it in 2018 under the name “research framework”, and finally revised this guideline on June 27, 2024 [4]. Many different candidate plasma molecules have been proposed, ranging from single biomarkers to multiple biomarker panels [5], but their reliability has been considered controversial [6]. In a remarkable study, amyloid precursor protein (APP) and A\beta plasma ratios were calculated by mass spectrometry and the results were reported to be promising [7]. To date, consistent and reliable results have not yet been obtained with plasma levels of A\beta. In the search for biomarkers for AD, tau proteins and phosphorylated forms (pTau181 and pT217) have the best potential [8, 9]. In addition, also molecules such as lipids, vitamins, metabolites, inflammatory molecules and cytokines, oxidative stress markers, non-coding RNAs, gut microbiome-derived molecules, and anti-A\beta antibodies may provide significant results that can serve as biomarkers [10, 11, 12, 13, 14, 15, 16].

There is evidence that AD is associated with dramatic vascular changes in the brain [17] and it is unclear whether vasculopathies are a cause or a consequence of the disease [18]. The most prominent changes observed in the cerebral vessels in Alzheimer’s disease are cerebral amyloid angiopathy (CAA) and morphological and degenerative changes in the affected areas. It has been shown that small vessels, including capillaries, have a deep association with A\beta fibrils and also that there are degenerative changes in the vascular feet of astroglial cells surrounding the blood vessels [19]. The increased permeability of the blood brain barrier (BBB) in AD leads to thickening, fragmentation, and degeneration in the basal lamina of blood vessels [20]. The “vascular hypothesis of AD” [21, 22] suggests that vascular changes begin before the clinical symptoms appear and cortical blood perfusion gradually deteriorates over the years [23, 24]. Thus, vascular changes may directly affect biomarkers profiles in AD and may provide new insights for the diagnosis of the disease. However, despite these published studies, the existence of results supporting the opposite view makes the issue controversial. Some studies suggest that, contrary to popular belief, the functions of blood vessels are not impaired in neurodegenerative diseases and the BBB remains intact [25, 26]. In AD, extracellularly accumulated A\beta is mostly cleared via the perivascular pathway, as A\beta has high aggregability and accumulates in the perivascular space, interferes with blood vessels, and ultimately leads to CAA [27, 28]. CAA associated with A\beta deposition results in impaired blood vessel function and ischemia of brain tissue [18]. A\beta toxicity also damages the smooth muscle and endothelial cells of the vessels [29, 30]. Besides A\beta and Tau accumulation, angiogenesis and inflammation are also frequently observed in AD [31, 32, 33, 34, 35, 36, 37, 38, 39].

Endothelial cells (EC) form the BBB and regulate the neuronal viability, as they synthesize and secrete angiogenic factors. EC also clear toxic substances such as A\beta-42 and prevent their accumulation. It is thought that the main factor responsible for A\beta accumulation in AD is an inadequate clearance through the BBB [40] and impaired BBB function is thought to be the main reason for decreased clearance [41] and EC play an important role in this process [42]. Additionally, A\beta accumulation in cerebral vessels also damages EC, triggering neuroinflammation and ultimately neurodegeneration and endothelial damage [43, 44, 45, 46]. EC are also key regulators of inflammation and angiogenesis [47]. Additionally, direct toxic effects of EC obtained from AD patients on healthy neurons have also been demonstrated [48]. One of the most important stages of angiogenesis is the aggregating of EC to form a tube [49]. In case of vascular dysfunction, activated EC start to produce factors and matrix components (e.g., laminin) and participate in the angiogenesis process [50].

Organotypic brain slice cultures are a revolutionary technique that has been used for over 40 years and provides the advantages of in vivo experiments in an in vitro setting [51] and are a bridge between in vivo and in vitro [51]. In order to apply substance directly onto brain slices, we developed the microcontact printing technique (µCP) [52, 53, 54, 55]. Organotypic culture models are very useful to investigate brain vessels to study their functions and pathologies [56, 57, 58, 59, 60, 61, 62]. EC are the basic epithelial cells of blood vessels and form the BBB and they have important functions in many physiological and pathological processes. Organotypic brain slice cultures are a very suitable model for the study of EC because the histological structure of blood vessels is largely preserved and has been used for this purpose in many studies [54, 60, 63]. Using this method, we used brain slices coupled to µCPs, and showed that blood vessels developed and extended specifically along the collagen-loaded lanes, suggesting that the combination of the two techniques could be a very suitable model for brain vessels [54].

In the current study, we aim to print human plasma samples onto semipermeable membranes using the µCP method and to evaluate the differences in angiogenesis and vascular cell function between organotypic brain slices cultured on these prints. According to our hypothesis, we suggest that plasma contains factors that can affect angiogenesis and migration of EC and subsequent formation of vessel. Our data show that plasma of AD patients affects mouse-derived EC and using mass spectrometry we identified 3 novel putative biomarkers in plasma.

2. Materials and Methods

In the present study we used organotypic brain slices, coupled them with microcontact prints and used immunostainings to detect EC and vessels. The most promising samples were used for differential mass spectometry to identify novel human biomarkers.

2.1 Human Plasma Samples

Plasma samples from three different groups, including healthy controls (n = 23), mild cognitive impairment (MCI) (n = 23) and AD patients (n = 22), were used in our study. Only Caucasian individuals aged 60 and over were included. Approval was received for the study from the Ethics Committee of Medical University of Innsbruck (AN2015.0159 351/4.7 405/5.5 (4484a), granted to Prim. Univ.-Prof. Dr. Josef Marksteiner) and all procedures were carried out according to the Helsinki Declaration. Written informed consent was obtained from all patients. Subjects were selected from the patients or their families/legal guardians under the care of Prim. Univ.-Prof. Dr. Josef Marksteiner in Landeskrankenhaus Hall/Tirol, Austria. Plasma samples were collected since 2014 and stored at –80 °C. The samples were never thawed during this period. To ensure sample standardization and eliminate the degradation effect, plasma samples collected in the same years were used in the study. The diagnostic processes of the patients were described in detail in a previous study [64]. Briefly, all subjects were administered the geriatric depression scale (GDS) and the minimental state examination (MMSE) for a comprehensive neuropsychological assessment. According to the results of these assessments, only patients diagnosed with AD and MCI were included in the study. Structural magnetic resonance imaging (MRI) of all patients was carried out with a 1.5 Tesla Siemens Symphony MRI device (Siemens Medical Solutions Inc. Malvern, PA, USA). Care was taken to exclude patients with other medical or neurological disorders that could lead to cognitive deficits. After the groups were determined, 10 mL of blood from each patient was taken into EDTA tubes. After these samples were centrifuged (2300 g, 5 min), the upper plasma phase was collected and stored at –80 °C (Fig. 1).

2.2 Microcontact Printing of Human Plasma

The µCP method with collagen hydrogel was described in detail in a methodological review we published previously [65]. The master template was containing a total of 50 lanes, 8000 µm long, 50 µm wide with 50 µm spacing, and purchased from GeSiM www.gesim.de (accessed on 18.08.2024) (Fig. 1). Stamps were fabricated from polydimethylsiloxane (PDMS, Sylgard 184 Silicone Elastomer Kit, Dow, Seneffe, Belgium) using this template, as described in our previous methodological review [65]. µCP processes of plasma were carried out briefly as follows: 100 µL of lyophilized plasma sample (Fig. 1) was dissolved with 67 µL of type I bovine collagen solution (11179179001, Roche, Sigma-Aldrich, St. Louis, MO, USA). Subsequently, 10 µL of 100 mM (10\times) phosphate-buffered saline (PBS, pH 7.4, sterilized), 5 µL of 10 mM PBS, and for demonstrating the printing efficiency, 5 µL of red-fluorescent Alexa Flour 546 anti-rat antibody (1:40, A11081, Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) were added. For adjusting the pH to 7.2, 0.8 mL of 1N NaOH was added to the solution. Then, 12.5 µL of the crosslinker 4arm-poly (ethylene glycol) (PEG) succinimidyl succinate (JKA7006, Sigma-Aldrich, St. Louis, MO, USA) was added to the solution just before printing and the mixture was prepared.

The mixture (15 µL) was dropped onto the patterned sides of the stamps and a coverslip was placed on top to ensure even distribution. After waiting at 37 °C for 15 minutes, the coverslips were removed and the excess solution was scraped off the stamp surface with the edge of the coverslip. After the surface of the stamps was completely dry, they were placed on a semi-permeable membrane (HTTP02500, Isopore, Merck Millipore, Darmstadt, Germany) and 18 g of weight was added to the stamps to transfer the mixture to the membrane and kept at room temperature (RT) for 1 h. After the weights were removed, the stamps were left on the membranes O/N at +4 °C. The stamps were carefully removed from the membranes the next morning and the printing efficiency was checked by viewing the Alexa Fluor 546 anti-rat antibody under a fluorescent microscope (Olympus BX61, Tokyo, Japan). Membranes with insufficient printing efficiency were discarded and only membranes with appropriate printing were used in the study (Fig. 1). Then, in order to place the brain slices correctly aligned, a small dot was placed on the same side of all the prints with a marker pen. Membranes were sterilized under UV light for 20 min and kept in sterile medium in a 37 °C incubator at least for 15 min to equilibrate (Fig. 1).

2.3 Organotypic Brain Slices

Organotypic half brain slices were obtained according to the protocol we described in detail previously [65]. Briefly, 8–10-day old C57BL/6 mouse pups were quickly decapitated and their brains were removed under aseptic conditions. With care of sterility, brains were glued with a cyanoacrylate adhesive (Loctite 401, Henkel, Düsseldorf, Germany) onto the tissue holder with their frontal part facing up. The brains were trimmed in the coronal plane up to the hippocampus and 150 µm thick coronal slices were taken from the hippocampal level in sterile medium using a water-cooled vibratome (Leica, VT1000S, Nussloch, Germany). The slices were divided horizontally into two with a razor blade in the medium, and the upper part containing the hippocampus was placed on a microcontact printed semi-permeable extra membrane with 0.4 µm pore size (HTTP02500, Isopore, Merck Millipore, Darmstadt, Germany) in cell culture inserts (PICM03050, Millicell, Merck Millipore, Darmstadt, Germany) (Fig. 1). Slice cultures were carried out in 6-well plates (83.3920, Sarstedt, Nümbrecht, Germany) containing 1 mL of pH 7.2 minimum essential medium (MEM) (11012044, Gibco, Thermo Fisher Scientific, Waltham, MA, USA). To the medium, 5.12 mM NaHCO₃ (106329, Merck Millipore, Darmstadt, Germany), 31.5 mM glucose (49159, Merck Millipore, Darmstadt, Germany), 2 mM glutamine (100289, Merck Millipore, Darmstadt, Germany), 10% heat-inactivated horse serum (16050-122, HS, Gibco, Thermo Fisher Scientific, Waltham, MA, USA), 0.25\times HBSS (24020091, Gibco, Thermo Fisher Scientific, Waltham, MA, USA), 1\times antibiotic-antimitotic solution (A5955, Sigma-Aldrich, St. Louis, MO, USA) were added as a supplement. Slices were cultured for 4 weeks in an incubator at 37 °C with 5% CO₂. The medium was changed once a week (Fig. 1). After the culture period was completed, the slices were fixed with 4% PFA at +4 °C for 3 hours, then washed with PBS and stored in PBS containing 0.1% NaN₃ until the day of immunofluorescence. Animal studies were approved by the Austrian Ministry of Science and Research and were carried out in accordance with Austrian animal welfare and experimentation guidelines. Animal experiments are defined as “Organentnahme” in Austrian law.

2.4 Immunofluorescence for Endothelial Cells and Vessel (Laminin and Lectin)

For immunofluorescence labeling, we used the method we published previously [66]. For permeabilization, the slices were kept in 0.1% T-PBS (Triton X100-PBS) for 30 min at RT with gentle shaking and then washed with 10 mM PBS for 3 \times 3 min. Unwanted background staining in the slices was blocked by shaking with T-PBS 0.1% + BSA (11930.03, Serva, Heidelberg, Germany) 0.2% + horse serum (HS) (16050122, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) 20% for 30 minutes at RT. After withdrawing the blocking solution without washing, the slices were incubated with polyclonal rabbit anti-laminin primary antibody solution (1:500, L9393, SIGMA, St. Louis, MO, USA) for 48 h in +4 °C. After the incubation period, the slices were washed with PBS for 3 \times 3 min and incubated with Alexa Fluor 488 conjugated anti-rabbit secondary antibody (1:400, A21206, Invitrogen, Waltham, MA, USA), Texas red conjugated Lycopersicon Esculentum (Tomato) Lectin (1:100, 02451, Thermo Fisher Scientific, Waltham, MA USA) and 4^′,6-diamidino-2-phenylindole (1:10,000, D9542, DAPI, Sigma-Aldrich, St. Louis, MO, USA) diluted in T-PBS 0.1% + BSA 0.2% for 1 hour at room temperature with gentle shaking. Afterwards, the slices were washed again with PBS for 3 \times 3 min and then placed on glass slides and mounted with mowiol. Finally, imaging and microphotography were performed with a fluorescent microscope (Olympus BX61, Tokyo, Japan) using the green channel (ex 480/40 nm, em 527/30 nm) for Alexa 488 and the red channel (ex 535/50 nm, em 610/75 nm) for Texas red. A software connected to the camera (Openlab software, version 5.5.0, Improvision, Coventry, England) was used for camera control and imaging.

Confocal microscopic imaging and analysis were performed with a confocal microscope (Zeiss LSM 980, Carl Zeiss Microscopy Deutschland GmbH, 73447 Oberkochen, Germany) using the 20\times objective (0.8 NA, glycerol). The 488 nm laser was turned on to find the region of interest and the appropriate focus level. Imaging mode (4Y) and dyes (Alexa Fluor 488, Alexa Fluor 546, DAPI) were selected and airy scan calibration was performed. To acquire a Z-stack composite image, an interval between the first and last planes was set on the Z-axis in 4Y mode. The following parameters were used for deconvolution with Huygens Professional 23.10 software (Scientific Volume Imaging, Hilversum, Nederland): In the microscopic parameters window, lens immersion for glycerin (1.456), and mowiol as the mounting medium were set. In the operations window, the signal/noise ratio was set to 15 for red, 15 for green, and 10 for blue. Following deconvolution, acquired images were processed with the Imaris 10.0.1 software (Oxford Instruments, Abingdon, UK) for three-dimensional reconstruction.

2.5 Mass Spectrometry

To prepare the samples for analysis, plasma was first processed to deplete 14 of its most abundant proteins. Following the manufacturer’s protocol, this depletion was carried out using High Select™ Depletion Spin Columns (P/N: A36369, Thermo Scientific, Rockford, IL, USA). The proteins in the 400 µL sample flow-through were then reduced by adding 40 µL of dithiothreitol at a concentration of 100 mM in PBS buffer, incubated for 30 minutes. Next, to alkylate the free cysteines, 40 µL of 550 mM iodoacetamide prepared in PBS buffer was added, with samples kept in the dark at room temperature for 20 minutes. After being lyophilized to a volume of 200 µL, samples were diluted fivefold with acetonitrile, mixed by vortexing, and centrifuged at 16,000 \timesg for 5 minutes. Following two washes with ethanol, the pellet was dissolved in 100 µL of 100 mM triethylammonium bicarbonate buffer (TEAB) (pH 8.5). Proteins were digested with 1 µg of trypsin (Sequencing Grade Modified Trypsin, P/N: V5111, Promega, WI, USA) by agitating at 37 °C overnight. Peptides from this digestion were then labeled with TMT (TMT10plex™ Label Reagent Set, P/N A58332, Thermo Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. The combined samples were dried by lyophilization and reconstituted in 85 µL of 0.1% formic acid. The peptides were subsequently fractionated by high-pH reversed-phase chromatography on an XBridge Peptide BEH C18 column, 4.6 mm \times 250 mm, 300 Å, 5 µm (P/N 186003625, Waters, Milford, MA, USA), as described before [66].

For the liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) analysis, the digested peptides were injected into a nano-HPLC system (UltiMate 3000, Thermo Scientific, Waltham, MA, USA) interfaced with an Orbitrap Eclipse mass spectrometer (Thermo Scientific, Waltham, MA, USA), in line with a prior protocol [67]. Briefly, the peptides were separated using an acetonitrile gradient over a 142-minute total gradient on a homemade column (100 µm i.d. \times 17 cm) packed with 2.4 µm C18 material (Reprosil, Dr. A. Maisch HPLC GmbH, Ammerbuch-Entringen, Germany). The Orbitrap Eclipse operated in data-dependent acquisition mode with a three-second cycle. Survey full-scan MS spectra were recorded at a resolution of 120,000, while MS2 spectra were acquired at a resolution of 50,000. Fragmentation was performed using higher-energy collisional dissociation with a normalized collision energy of 38.

For MS data analysis, Proteome Discoverer software (version 3.1, Thermo Scientific, Waltham, MA, USA) was used. MS/MS spectra were searched against the Uniprot human reference proteome database (EMBL-EBI, Cambridgeshire, UK) (last updated on 27/03/2024) using the Sequest HT search engine (The University of Washington’s Proteomics Resource, Seattle, WA, USA). Parameters were set as follows: trypsin specificity allowing up to two missed cleavages, fixed carbamidomethyl modification on cysteine, and variable oxidation on methionine. The precursor mass tolerance was 10 ppm, with a fragment mass tolerance of 20 mmu. A maximum false discovery rate (FDR) of 1% was applied to both protein and peptide identifications. Protein quantification was performed by calculating fold changes based on TMTpro reporter ion intensities in MS2 spectra. Additionally, sequence similarity between human and mouse was examined using the NCBI BLAST+ software (Version 2.16.0, National Library of Medicine, Bethesda, MD, USA) and database (EMBL’s European Bioinformatics Institute Hinxton, Cambridgeshire) [68].

2.6 Data Analysis and Statistics

Using the method described by Cohen [69], it was calculated that in order for the effect size suggested as 0.40 between the 3 groups to be statistically significant, a minimum of 66 cases should be studied, with a Type I error of 5% and a Test Power of 80%.

Quantitative analyzes were performed in a blinded manner. Counts included only cells that had migrated, as well as new vessel-like formation that had extended significantly beyond the brain slice and were positioned on the lanes. Long, thin structures formed by tightly connected cells that labeled strongly positive for laminin and had small and flattened nuclei morphologically similar to those in EC were accepted as vessel-like structures (Fig. 2). Counts and measurements were made in an area of 300 µm length and 6 lanes width in all samples using the ImageJ software (version 1.54; National Institute of Health, Bethesda, MD, USA). Pixel-to-µm ratio was calculated using a micrometric ruler and the software was calibrated according to this result. After calibration, the average migration distance of the cells was measured with the segmented line tool of the software. Using the cell counter tool of the software, the averages of laminin and lectin positive cells in all groups were calculated. All data were presented as mean \pm standard error of the mean (SEM). Statistical analysis was carried out with one-way ANOVA followed by a Fisher LSD post hoc test. p 0.05 was considered significant.

3. Results

3.1 Epidemiology of the Patients

Plasma from 23 healthy controls, 23 MCI and 22 AD patients, including more females, was used to ensure gender balance (Table 1). The mean age of the subjects was approximately 73 years and was significantly higher in the AD group (Table 1). The mean MMSE scores were 29.7 in the control group, 27.8 in the MCI group, and 19.7, which was significantly lower in the AD group. While no significant difference was found between the groups for the GDS score averages, they were 2.2 for the control group, 4.3 for the MCI group, and 3 for the AD group (Table 1).

3.2 Immunostainings with Laminin and Lectin

Immunolabeling with laminin and lectin was performed to EC and vessels in the brain slices and double co-labeling was applied with these two markers. It was observed that the blood vessels in the brain slice and the EC migrating out of the slice were labeled with both laminin and lectin (Fig. 2). In addition to these cells, it was observed that there was another cell population that migrated out of the slices along the lanes and was observed as only lectin⁺ and these cells surrounded the vessel-like structures (Fig. 2). Almost all of the cells migrating out of the brain slices were localized on the µCP lanes (Figs. 2,3). These cells were observed either as single cells, in groups that came together and began to compact, or as tightly connected cells.

Our data show that single EC migrated along the µCP lanes (Fig. 3A), but with the time of prolonged culturing these EC became denser and more compacted, their size decreased and cells flattened (Fig. 3B). This is consistent with the morphology of the endothelial cell, finally forming vessel-like structures (Fig. 3C). Confocal microscopic imaging showed that the vessel-like structures consisted mainly of laminin⁺ cells and that these cells were tightly interlocked and formed solid cellular cords (Fig. 3D,E). A lectin⁺ cell population was also observed around these vessel-like structures. These cells appeared to surround the vessel-like structures from the outside and have a close connection with them (Fig. 3E, arrows).

3.3 Quantitative Effects of µCP-Plasma on Laminin⁺ and Lectin⁺ Endothelial Cells

Quantification shows that plasma alone significantly reduced the number of laminin⁺ (Fig. 4A) and lectin⁺ (Fig. 4B) cells, when compared to PBS-loaded collagen. No effect on EC was seen when plasma was loaded from healthy controls, MCI patients or AD patients (Fig. 4A,B). However, when we compared the migration lengths of the cells, then plasma from AD patients significantly increased the migration of laminin⁺ (Fig. 4C) and lectin⁺ (Fig. 4D) cells along the µCPs. Plasma alone did not affect the migration capacity. compared to a PBS-loaded control.

3.4 Quantitative Effects of µCP-Plasma on Laminin⁺ Vessels

In the next step, the newly formed vessels were quantified. It was unexpected that only some of the slices reacted and formed new vessels (17.4–31.8% of the slices; Fig. 5). When quantified all slices approx. 0.3–0.5 new formed vessels were seen (Fig. 5, red bar), when only those slices were counted where new vessels were found, then this number increased to 1.0–1.4 vessels per slice (Fig. 5, blue bar). No difference was seen in the new formed vessels between the groups (Fig. 5).

Based on the results in Table 2, we selected the top two controls and top two AD samples (Fig. 6) and performed mass spectrometry. After depletion of the 14 most prominent proteins in plasma (e.g., albumin, immunoglobulins, fibrinogen, transferrin, …), we quantified approximately 1400 proteins in plasma with an overlap of 1396 proteins (Fig. 6).

Based on these results we further restricted the number of proteins by the keywords “vessel” or “angiogenesis” or “vascular”, 3 putative biomarkers were identified: C-reactive protein, basigin and trem-like transcript 1 protein (Table 2).

Taken together, our data show that plasma from Alzheimer patients significantly increased the migration length of laminin⁺ and lectin⁺ EC along the plasma microcontact prints, but not the number of EC. Based on these findings, we selected the best representing plasma samples and performed mass spectrometry using a differential approach and we identified 3 potential biomarkers: C-reactive protein, basigin and trem-like transcript 1 protein.

4. Discussion

In the present study, we coupled organotypic mouse brain slices to microcontact prints loaded with human plasma. We found that plasma from AD patients enhanced the migration of laminin⁺ and lectin⁺ EC. Using the most promising samples, we identified three potential biomarkers using differential mass spectrometry: C-reactive protein, basigin and trem-like transcript 1 protein.

4.1 Microcontact Printing of Human Plasma and Effects on Mouse-Derived Endothelial Cells

There is only one study investigating the effects of plasma directly on EC [70] and have mostly been conducted by evaluating the effects of isolated plasma factors [71, 72]. In an early study, when plasma and serum were compared to cultured endothelial cells, it was reported that plasma application reduced proliferation compared to serum [73]. In our study in brain slices, we concluded that plasma reduced proliferation regardless of whether it was taken from healthy or diseased individuals and we hypothesized that plasma-derived factors inhibit EC proliferation or have toxic properties. Such a toxic response was seen when thrombotic thrombocytopenic patient plasma was applied to cultured EC [74]. Although we cannot exclude toxic effects, this is unlikely, and we believe that human-derived factors inhibit the migration of mouse EC. Indeed, there are many factors in plasma that support (vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), platelet derived growth factor (PDGF), and epidermal growth factor (EGF)) or inhibit (transforming growth factor beta (TGF-\beta), \alpha and \gamma interferon, prostaglandin E2 (PGE2), and endostatin) EC migration [75, 76, 77, 78, 79]. In addition, plasma also contains high amounts of extracellular matrix proteins (albumin, immunoglobulins, fibrinogen, transferrin but also collagen) which directly affect the migration capacity of EC. Clearly, it is known that cytokines or growth factors modulate the proliferation, differentiation and migration of EC [80]. In future studies, it will be interesting to measure proliferation rates in the mouse slice and compare the expression levels of these factors in printed plasma samples over time. Our study is the first to evaluate vascular cells and new vessel development dynamics in organotypic mouse brain slice cultures activated by human plasma. Compared to single cell cultures, brain slices with preserved original histological structure, is a novel and innovative method and the coupling to human plasma may allow to identify novel biomarkers, especially in AD. However, in a very complex disease like AD, it is difficult to explain and translate the results, as the disease is a very heterogenous disorder, including plaque deposition, neurofibrillary tangles, cell death, glial activation and inflammation in the brain. In addition, peripheral effects may also reflect changes seen in the blood and may not directly be linked to the brain.

4.2 Laminin⁺ Endothelial Cells

Laminin is an extracellular multi-adhesive glycoprotein that polymerizes in the basement membrane (BM) of blood vessels and, together with type IV collagen, forms its major skeletal structure [81]. Laminin also has important functions in cell proliferation and differentiation, which is largely mediated through integrin receptors on the cell surface [82, 83]. Laminin has important functions in the formation and maintenance of the BBB and angiogenesis and in our study, we used the laminin antibody to directly label EC and newly formed vessel-like structures. We observed that the vessel-like structures along the lanes were specifically and intensely labeled with the laminin antibody [84]. Collagen-containing matrix structures have been used in in vitro angiogenesis studies for many years and EC show vessel formation within days or weeks [85, 86]. However, it has been shown that this effect is markedly reduced when laminin is used as the extracellular matrix component [81]. Thus, laminin is one of the most essential proteins required for new vessel formation under physiological and pathological conditions and is highly suitable to analyze EC and subsequent vessel formation.

4.3 Lectin⁺ Endothelial Cells

Lectins are a class of proteins responsible for cell-cell interaction, signaling pathways, and several innate immune responses [87]. It is well known that the vasculature can be immunohistochemically stained with lectin specific antibodies, but also with lectins coupled to fluorescent dyes, such as, e.g., lectins of plant origin with fluorescent properties, such as red fluorescent tomato lectin [88]. Fluorescently conjugated lectin is a protein that shows high affinity to carbohydrates, and binds to macromolecules on the surface of EC [89]. While, laminin labels mostly the basement membrane of EC, lectin can be considered as an additional specific marker for EC. Thus, in our study, we used fluorescently conjugated lectin to strengthen our data with 2 independent markers for EC. It was very interesting to see, that two different cell populations were found. While some cells were both laminin⁺ and lectin⁺, the second group of cells was observed as only lectin⁺. This is clearly shown by a higher number of lectin⁺ EC, compared to laminin⁺ cells. Our data show that vessel-like structures were composed of laminin⁺/lectin⁺ double positive cells. On the other hand, only lectin⁺ cells were attached to these structures and surrounded them, which we could verify using confocal microscopy. It seems possibly that the only lectin⁺ cells on the surface of EC may represent pericytes [90, 91, 92]. Pericytes play a role in angiogenesis, BBB formation and maintenance [93, 94] and many studies show that pericytes play a role in AD vasculopathy [95, 96, 97, 98]. A previous study has shown that the number of pericytes in the cerebral vessels increases in progression of AD and that pericyte damage is seen as one of the early signs in dementia [99]. Thus, the use of both markers, laminin and lectin, provide better and more accurate stainings of EC and vessel.

4.4 Migration of Endothelial Cells and Vessel Formation

EC migration is a process during vasculogenesis and angiogenesis, as well as in the healing of damaged vessels [100]. Vascular alterations in AD cause hypoxia and the up-regulation of proangiogenic mediators [101], such as altered enhanced or dysfunctional vascularization [102] and formation of new blood vessels [33]. In our study, we observed an increase in migration of vascular cells in the AD group, but not an increase in the number of new vessel-like formations. It can be suggested that the pro-angiogenic factors present in AD plasma stimulate the migration and angiogenesis in vitro, but this is not sufficient for the completion of new vessel formation. In the present study we investigated vessel by immunostainings, but we cannot fully prove that these vessels are tubes and fully active, thus we term them “vessel-like structures”. Our data may provide evidence that the 4-week period is too short for the formation of new “vessel-like structures” and it may be necessary to culture slices for a longer time period. It will be interesting to study also NOTCH and VEGF receptors, as they may play a role in the formation of new vessels [103, 104, 105].

4.5 Mass Spectrometry to Identify New Biomarkers

In order to find new biomarkers, we selected the best samples, based on the migration capacity of laminin⁺ cells and compared controls with AD using differential mass spectrometry and proteomics and we identified 3 potential biomarkers for AD selective for the keywords “vessel” or “angiogenesis” or “vascular”: C-reactive protein, basigin and trem-like transcript-1 protein.

4.5.1 C-Reactive Protein (CRP) and AD

C-reactive protein (CRP) is an acute phase reactant protein primarily synthesized in hepatocytes and is elevated in the blood after neuroinflammation [106]. There are numerous studies suggesting that this protein is associated with APOE \epsilon4 [107, 108]. In homozygous individuals carrying two APOE \epsilon4 alleles, CRP blood levels increase, cognitive scores decrease, and CSF Tau levels increase [109]. Definitely, CRP is found in A\beta plaques and neurofibrillary tangles [110, 111]. CRP has neurotoxic effects in vitro at concentrations similar to serum AD levels [112]. There are conflicting results in the literature regarding the relationship between CRP blood levels and AD. Although one study reported that increased blood CRP levels are associated with the risk of AD [113], there are also studies reporting no association [114], or even suggesting an inverse association between increased CRP levels and the risk of AD [115]. CRP has also angiogenic properties [116, 117, 118] as CRP is related to angiogenesis after brain tissue damage [116]. According to our data, decreased plasma CRP levels may have a promoting effect on cell migration of EC. Testing this hypothesis with brain slice cultures on CRP-printed membranes would be useful to show the effects of CRP on different stages of angiogenesis. It will be interesting to show the relationship between monomeric and pentameric CRP [117] in future studies.

4.5.2 Basigin and AD

Basigin (CD147) is a transmembrane glycoprotein that belongs to the IgG superfamily and has been shown to stimulate the synthesis of many different types of matrix metalloproteinases (MMP). Basigin has also an important role in tumor invasion and metastasis [119, 120]. In AD, basigin is an integral regulatory subunit of the \gamma-secretase and regulates A\beta production [121]. Alternatively, basigin activates MMPs and is a key protein in degradation of A\beta [122]. Basigin is also expressed in EC and plays an important role in the maintenance of the BBB [123] and promotes endothelial cell migration by stimulating the synthesis of VEGF, MMPs and hyaluronan during angiogenesis [124]. Thus, our data may suggest that basigin could play a role in the pathological angiogenesis seen in AD possibly interacting with VEGF or MMPs. Regarding basigin and plasma levels in AD, no publications were found in a PubMed search, thus our data may show for the first time, that basigin could be a novel putative plasma biomarker for AD. Definitely, it will be interesting to perform commercial ELISAs for basigin to measure plasma levels in AD versus controls.

4.5.3 Trem-Like Transcript-1 Protein and AD

Trem-like transcript-1 protein (TLT-1, TREML1) is a transmembrane receptor protein that is a member of the Triggering receptor expressed on myeloid cells family. It is expressed in various immune cells, megakaryocytes and platelet alpha granules and plays an essential role in platelet aggregation and hemostasis [125, 126]. Soluble TLT-1 found in plasma plays a protective role in AD and has anti-inflammatory properties [127]. In our PubMed search, we could not find any studies directly addressing the relationship between TLT-1 and AD pathogenesis or plasma biomarkers. However, it is extremely interesting to note, that activated platelets express TLT-1 and release it into the plasma. First, TLT-1 colocalizes with VEGF in platelets and it stimulates angiogenesis by affecting EC and platelet activation and possibly angiogenic processes [128]. Second, TLT-1 is also involved in cell adhesion and migration processes [129]. And finally, and most importantly, there are several reports that activated platelets migrate into the brain and contribute to the progression of AD. In our lab, we have shown that platelets migrate into the human AD brain [130] and damage cortical vessel causing inflammation [62]. It seems possible that activated platelets cross the damaged BBB in AD and release platelet-derived beta-amyloid(40), which may contribute to the plaque development (see review [131]). Platelet-derived TLT-1 may play a potent role in this process.

4.6 Limitations of the Study

There are some definite limitations (weaknesses) in our study: (a) Printing plasma onto polycarbonate membranes is a process that requires knowledge and experience and has a high probability of error as print results can sometimes be inconsistent. To prevent this, we prepared multiple prints from each plasma sample. To assess the quality of the prints, we added a fluorescently conjugated antibody (Alexa 546) to the loading mixture and only selected the best samples that met our quality standards for study. (b) AD is a disorder seen in elderly individuals. Therefore, the 8–10-day old animals we used in our study cannot fully represent the AD group. Conducting the study with adult animal brain slices will largely eliminate this disadvantage. However, the success rate of long-term culture methods with brain slices from adult animal subjects is still quite low. (c) Since we used mouse brain tissue in our experiments, the differences between species reduce the reliability of the results to some extent. However, we assume that the factors in human plasma are effective due to the homological similarity between mouse and human. (d) Obtaining organotypic brain slices suitable for culture is a process that requires experience and manipulation skills. Sometimes, even in the results of subjects in the same group, deviations can be seen, although we pay utmost attention to obtain slices with similar quality standards. (e) In our study, we used immunolabeling methods to show vascular cells with laminin and lectin. Although protein expressions provide very useful information in showing functional and pathological changes in experimental groups, the results can be evaluated semi-quantitatively. In our future studies, quantitative evaluation of gene expression as well as protein production will provide more accurate confirmation of our findings and hypotheses. (f) Although histological structure and cell-to-cell interactions are largely preserved in brain slices, blood circulation and axons of some neurons are lost due to the cutting process. These disadvantages, together with the inadequacies of the in vitro environment, limit the ability of the experiment to represent in vivo conditions. (g) AD is a disease that develops over a long period of time and progresses over many years. Therefore, a 4-week culture period may not be sufficient to represent the effects of the disease. Additionally, this period may not be long enough for the development of new blood vessels and significant differences in their numbers to be detected. (h) The plasma we used in the study were collected many years ago and are well-preserved samples. However, we do not have the genetic data of these subjects that may be relevant to our study (such as APOE ϵ4, PSEN1, PSEN2, APP, TREM) and cannot be obtained retrospectively. In future studies, including expression and mutation analyses of these genes will allow us to better understand the pathogenesis of AD and related angiogenesis. (i) Our study was conducted with a cross-sectional method in nature. It can only give us information about a certain stage of AD. However, we are confident that in the future, we will obtain much more reliable and useful information if we can combine this complex neurobiological technique with longitudinal methods. (j) We were able to conclude that the new structures formed along the lanes were blood vessels based solely on laminin and lectin labeling. Additional labeling such as type IV collagen and endothelial-specific intercellular junction complex proteins would be useful to support this view. Furthermore, electron microscopic examination would also support our arguments by visualizing lumen formation and tight junction-like complexes between EC. (k) The study of three biomarkers identified by mass spectrometry method in only 2 AD and 2 healthy control plasma samples is numerically insufficient for significance tests. In our current study, only 4 samples could be analyzed with this method, which is quite cost intensive. In our future studies, it is planned to allocate a budget for experiments where statistical analyses can be performed with this method and to study a sufficient number of samples. (l) Finally, since we worked with brain tissue from mice, our results have limited potential to represent AD in humans. However, protein amount changes shown by advanced methods such as mass spectrometry can be a guide for the early diagnosis of the disease.

5. Conclusion

In the present study, we coupled organotypic mouse brain slices to microcontact prints loaded with human plasma. We found that plasma from AD patients enhanced the migration of laminin⁺ and lectin⁺ EC. Using the most promising samples, we identified three potential biomarkers using differential mass spectrometry: C-reactive protein, basigin and trem-like transcript-1 protein. Based on our results we hypothesize that inhibition or the activation of one of these 3 molecules may influence endothelial cell migration and possibly formation of new vessel. The next steps to identify the potential role of these 3 plasma biomarkers in AD will be: (1) we will develop a sensitive human ELISA to analyze these 3 factors in plasma to demonstrate a significant difference between healthy controls and AD and (2) we will also generate recombinant human proteins and load these proteins into the µCP lanes to proof that indeed migration of EC and vessel formation is modified. Our present study is an innovative novel approach to find new human biomarkers in a mouse cell culture model.

In conclusion for a broader readership: AD is a severe neurodegenerative disorder and it would be helpful for the patients to get an early and accurate diagnosis. So far only CSF biomarkers are available, but we need non-invasive blood-plasma biomarkers. There is an extensive search worldwide to identify new biomarkers in blood, but none has yet entered clinical routine. In this study we connected our extensive experience on brain slices (more than 20 years) with our novel innovative method of microcontact printing and aimed to find new biomarkers in plasma. We identified 3 novel human AD biomarkers, which could have a potential to diagnose AD. It will be essential to use very sensitive and fast assays (e.g., ELISAs, chemiluminescence, single molecule array services (SIMOA), PCR) to detect these 3 biomarkers and compare controls versus AD with a high number of patients (>50). This will be the basis to introduce a novel biomarker panel into the clinical routine and give the patient a fast diagnosis in the progression of dementia.

Availability of Data and Materials

All data reported in this paper will be shared by the corresponding author upon request.

References

[1] Global status report on the public health response to dementia. 2021. Available at: https://www.who.int/publications/i/item/9789240033245 (Accessed 28 November 2024).

[2] World Health Organization. Global action plan on the public health response to dementia 2017–2025. World Health Organization: Geneva. 2017.

[3] Mulder C, Verwey NA, van der Flier WM, Bouwman FH, Kok A, van Elk EJ, et al. Amyloid-beta(1-42), total tau, and phosphorylated tau as cerebrospinal fluid biomarkers for the diagnosis of Alzheimer disease. Clinical Chemistry. 2010; 56: 248–253. https://doi.org/10.1373/clinchem.2009.130518.

[4] Jack CR, Jr, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimer’s & Dementia. 2024; 20: 5143–5169. https://doi.org/10.1002/alz.13859.

[5] Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K, et al. Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nature Medicine. 2007; 13: 1359–1362. https://doi.org/10.1038/nm1653.

[6] Björkqvist M, Ohlsson M, Minthon L, Hansson O. Evaluation of a previously suggested plasma biomarker panel to identify Alzheimer’s disease. PLoS ONE. 2012; 7: e29868. https://doi.org/10.1371/journal.pone.0029868.

[7] Kaneko N, Yamamoto R, Sato TA, Tanaka K. Identification and quantification of amyloid beta-related peptides in human plasma using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Proceedings of the Japan Academy. Series B, Physical and Biological Sciences. 2014; 90: 104–117. https://doi.org/10.2183/pjab.90.104.

[8] Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE, et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nature Medicine. 2020; 26: 379–386. https://doi.org/10.1038/s41591-020-0755-1.

[9] Palmqvist S, Janelidze S, Quiroz YT, Zetterberg H, Lopera F, Stomrud E, et al. Discriminative Accuracy of Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders. JAMA. 2020; 324: 772–781. https://doi.org/10.1001/jama.2020.12134.

[10] Varesi A, Carrara A, Pires VG, Floris V, Pierella E, Savioli G, et al. Blood-Based Biomarkers for Alzheimer’s Disease Diagnosis and Progression: An Overview. Cells. 2022; 11: 1367. https://doi.org/10.3390/cells11081367.

[11] Gustaw-Rothenberg KA, Siedlak SL, Bonda DJ, Lerner A, Tabaton M, Perry G, et al. Dissociated amyloid-beta antibody levels as a serum biomarker for the progression of Alzheimer’s disease: a population-based study. Experimental Gerontology. 2010; 45: 47–52. https://doi.org/10.1016/j.exger.2009.10.003.

[12] Weldon Furr J, Morales-Scheihing D, Manwani B, Lee J, McCullough LD. Cerebral Amyloid Angiopathy, Alzheimer’s Disease and MicroRNA: miRNA as Diagnostic Biomarkers and Potential Therapeutic Targets. Neuromolecular Medicine. 2019; 21: 369–390. https://doi.org/10.1007/s12017-019-08568-0.

[13] Kumar P, Dezso Z, MacKenzie C, Oestreicher J, Agoulnik S, Byrne M, et al. Circulating miRNA biomarkers for Alzheimer’s disease. PLoS ONE. 2013; 8: e69807. https://doi.org/10.1371/journal.pone.0069807.

[14] Kiko T, Nakagawa K, Tsuduki T, Furukawa K, Arai H, Miyazawa T. MicroRNAs in plasma and cerebrospinal fluid as potential markers for Alzheimer’s disease. Journal of Alzheimer’s Disease. 2014; 39: 253–259. https://doi.org/10.3233/JAD-130932.

[15] Bronzuoli MR, Iacomino A, Steardo L, Scuderi C. Targeting neuroinflammation in Alzheimer’s disease. Journal of Inflammation Research. 2016; 9: 199–208. https://doi.org/10.2147/JIR.S86958.

[16] Humpel C. Identifying and validating biomarkers for Alzheimer’s disease. Trends in Biotechnology. 2011; 29: 26–32. https://doi.org/10.1016/j.tibtech.2010.09.007.

[17] Grammas P. Neurovascular dysfunction, inflammation and endothelial activation: implications for the pathogenesis of Alzheimer’s disease. Journal of Neuroinflammation. 2011; 8: 26. https://doi.org/10.1186/1742-2094-8-26.

[18] Govindpani K, McNamara LG, Smith NR, Vinnakota C, Waldvogel HJ, Faull RL, et al. Vascular Dysfunction in Alzheimer’s Disease: A Prelude to the Pathological Process or a Consequence of It? Journal of Clinical Medicine. 2019; 8: 651. https://doi.org/10.3390/jcm8050651.

[19] Miyakawa T. Vascular pathology in Alzheimer’s disease. Psychogeriatrics. 2010; 10: 39–44. https://doi.org/10.1111/j.1479-8301.2009.00294.x.

[20] Cortes-Canteli M, Iadecola C. Alzheimer’s Disease and Vascular Aging: JACC Focus Seminar. Journal of the American College of Cardiology. 2020; 75: 942–951. https://doi.org/10.1016/j.jacc.2019.10.062.

[21] de la Torre JC, Mussivand T. Can disturbed brain microcirculation cause Alzheimer’s disease? Neurological Research. 1993; 15: 146–153. https://doi.org/10.1080/01616412.1993.11740127.

[22] de la Torre J. The Vascular Hypothesis of Alzheimer’s Disease: A Key to Preclinical Prediction of Dementia Using Neuroimaging. Journal of Alzheimer’s Disease. 2018; 63: 35–52. https://doi.org/10.3233/JAD-180004.

[23] Hays CC, Zlatar ZZ, Wierenga CE. The Utility of Cerebral Blood Flow as a Biomarker of Preclinical Alzheimer’s Disease. Cellular and Molecular Neurobiology. 2016; 36: 167–179. https://doi.org/10.1007/s10571-015-0261-z.

[24] Binnewijzend MAA, Benedictus MR, Kuijer JPA, van der Flier WM, Teunissen CE, Prins ND, et al. Cerebral perfusion in the predementia stages of Alzheimer’s disease. European Radiology. 2016; 26: 506–514. https://doi.org/10.1007/s00330-015-3834-9.

[25] Thomsen MS, Kostrikov S, Routhe LG, Johnsen KB, Helgudóttir SS, Gudbergsson JM, et al. Remodeling of the brain angioarchitecture in experimental chronic neurodegeneration. Neurobiology of Disease. 2025; 204: 106761. https://doi.org/10.1016/j.nbd.2024.106761.

[26] Bien-Ly N, Boswell CA, Jeet S, Beach TG, Hoyte K, Luk W, et al. Lack of Widespread BBB Disruption in Alzheimer’s Disease Models: Focus on Therapeutic Antibodies. Neuron. 2015; 88: 289–297. https://doi.org/10.1016/j.neuron.2015.09.036.

[27] Sheikh AM, Yano S, Tabassum S, Mitaki S, Michikawa M, Nagai A. Alzheimer’s Amyloid β Peptide Induces Angiogenesis in an Alzheimer’s Disease Model Mouse through Placental Growth Factor and Angiopoietin 2 Expressions. International Journal of Molecular Sciences. 2023; 24: 4510. https://doi.org/10.3390/ijms24054510.

[28] Hampel H, Hardy J, Blennow K, Chen C, Perry G, Kim SH, et al. The Amyloid-β Pathway in Alzheimer’s Disease. Molecular Psychiatry. 2021; 26: 5481–5503. https://doi.org/10.1038/s41380-021-01249-0.

[29] Xu J, Chen S, Ku G, Ahmed SH, Xu J, Chen H, et al. Amyloid beta peptide-induced cerebral endothelial cell death involves mitochondrial dysfunction and caspase activation. Journal of Cerebral Blood Flow and Metabolism. 2001; 21: 702–710. https://doi.org/10.1097/00004647-200106000-00008.

[30] Davis J, Cribbs DH, Cotman CW, Van Nostrand WE. Pathogenic amyloid beta-protein induces apoptosis in cultured human cerebrovascular smooth muscle cells. Amyloid. 1999; 6: 157–164. https://doi.org/10.3109/13506129909007321.

[31] Jefferies WA, Price KA, Biron KE, Fenninger F, Pfeifer CG, Dickstein DL. Adjusting the compass: new insights into the role of angiogenesis in Alzheimer’s disease. Alzheimer’s Research & Therapy. 2013; 5: 64. https://doi.org/10.1186/alzrt230.

[32] Tarkowski E, Issa R, Sjögren M, Wallin A, Blennow K, Tarkowski A, et al. Increased intrathecal levels of the angiogenic factors VEGF and TGF-beta in Alzheimer’s disease and vascular dementia. Neurobiology of Aging. 2002; 23: 237–243. https://doi.org/10.1016/s0197-4580(01)00285-8.

[33] Buée L, Hof PR, Delacourte A. Brain microvascular changes in Alzheimer’s disease and other dementias. Annals of the New York Academy of Sciences. 1997; 826: 7–24. https://doi.org/10.1111/j.1749-6632.1997.tb48457.x.

[34] Perlmutter LS, Chui HC, Saperia D, Athanikar J. Microangiopathy and the colocalization of heparan sulfate proteoglycan with amyloid in senile plaques of Alzheimer’s disease. Brain Research. 1990; 508: 13–19. https://doi.org/10.1016/0006-8993(90)91111-s.

[35] Sundberg C, Nagy JA, Brown LF, Feng D, Eckelhoefer IA, Manseau EJ, et al. Glomeruloid microvascular proliferation follows adenoviral vascular permeability factor/vegf-164 gene delivery. The American Journal of Pathology. 2001; 158: 1145–1160. https://doi.org/10.1016/S0002-9440(10)64062-X.

[36] Salminen A. Hypoperfusion is a potential inducer of immunosuppressive network in Alzheimer’s disease. Neurochemistry International. 2021; 142: 104919. https://doi.org/10.1016/j.neuint.2020.104919.

[37] Erickson MA, Banks WA. Blood-brain barrier dysfunction as a cause and consequence of Alzheimer’s disease. Journal of Cerebral Blood Flow and Metabolism. 2013; 33: 1500–1513. https://doi.org/10.1038/jcbfm.2013.135.

[38] Vagnucci AH, Jr, Li WW. Alzheimer’s disease and angiogenesis. Lancet. 2003; 361: 605–608. https://doi.org/10.1016/S0140-6736(03)12521-4.

[39] Humpel C. Chronic mild cerebrovascular dysfunction as a cause for Alzheimer’s disease? Experimental Gerontology. 2011; 46: 225–232. https://doi.org/10.1016/j.exger.2010.11.032.

[40] Iturria-Medina Y, Sotero RC, Toussaint PJ, Mateos-Pérez JM, Evans AC, Alzheimer’s Disease Neuroimaging Initiative. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nature Communications. 2016; 7: 11934. https://doi.org/10.1038/ncomms11934.

[41] Zhang YL, Wang J, Zhang ZN, Su Q, Guo JH. The relationship between amyloid-beta and brain capillary endothelial cells in Alzheimer’s disease. Neural Regeneration Research. 2022; 17: 2355–2363. https://doi.org/10.4103/1673-5374.335829.

[42] Yamazaki Y, Kanekiyo T. Blood-Brain Barrier Dysfunction and the Pathogenesis of Alzheimer’s Disease. International Journal of Molecular Sciences. 2017; 18: 1965. https://doi.org/10.3390/ijms18091965.

[43] Nelson AR, Sweeney MD, Sagare AP, Zlokovic BV. Neurovascular dysfunction and neurodegeneration in dementia and Alzheimer’s disease. Biochimica et Biophysica Acta. 2016; 1862: 887–900. https://doi.org/10.1016/j.bbadis.2015.12.016.

[44] Solis E, Jr., Hascup KN, Hascup ER. Alzheimer’s Disease: The Link Between Amyloid-β and Neurovascular Dysfunction. Journal of Alzheimer’s Disease. 2020; 76: 1179–1198. https://doi.org/10.3233/JAD-200473.

[45] Zuliani G, Cavalieri M, Galvani M, Passaro A, Munari MR, Bosi C, et al. Markers of endothelial dysfunction in older subjects with late onset Alzheimer’s disease or vascular dementia. Journal of the Neurological Sciences. 2008; 272: 164–170. https://doi.org/10.1016/j.jns.2008.05.020.

[46] Kalaria RN, Hedera P. Differential degeneration of the cerebral microvasculature in Alzheimer’s disease. Neuroreport. 1995; 6: 477–480. https://doi.org/10.1097/00001756-199502000-00018.

[47] De Caterina R, Massaro M, Scoditti E, Annunziata Carluccio M. Pharmacological modulation of vascular inflammation in atherothrombosis. Annals of the New York Academy of Sciences. 2010; 1207: 23–31. https://doi.org/10.1111/j.1749-6632.2010.05784.x.

[48] Grammas P, Moore P, Weigel PH. Microvessels from Alzheimer’s disease brains kill neurons in vitro. The American Journal of Pathology. 1999; 154: 337–342. https://doi.org/10.1016/S0002-9440(10)65280-7.

[49] Dixelius J, Jakobsson L, Genersch E, Bohman S, Ekblom P, Claesson-Welsh L. Laminin-1 promotes angiogenesis in synergy with fgf by distinct regulation of the gene and protein expression profile in endothelial cells. The Journal of Biological Chemistry. 2004; 279: 23766–23772. https://doi.org/10.1074/jbc.M311675200.

[50] Yousif LF, Di Russo J, Sorokin L. Laminin isoforms in endothelial and perivascular basement membranes. Cell Adhesion & Migration. 2013; 7: 101–110. https://doi.org/10.4161/cam.22680.

[51] Humpel C. Organotypic brain slice cultures: A review. Neuroscience. 2015; 305: 86–98. https://doi.org/10.1016/j.neuroscience.2015.07.086.

[52] Steiner K, Humpel C. Effects of Ischemia on the Migratory Capacity of Microglia Along Collagen Microcontact Prints on Organotypic Mouse Cortex Brain Slices. Frontiers in Cellular Neuroscience. 2022; 16: 858802. https://doi.org/10.3389/fncel.2022.858802.

[53] Steiner K, Yilmaz SN, Gern A, Marksteiner J, Faserl K, Villunger M, et al. From Organotypic Mouse Brain Slices to Human Alzheimer Plasma Biomarkers: A Focus on Microglia. Biomolecules. 2024; 14: 1109. https://doi.org/10.3390/biom14091109.

[54] Steiner K, Humpel C. Beta-Amyloid Enhances Vessel Formation in Organotypic Brain Slices Connected to Microcontact Prints. Biomolecules. 2023; 14: 3. https://doi.org/10.3390/biom14010003.

[55] Yilmaz SN, Steiner K, Marksteiner J, Faserl K, Villunger M, Sarg B, et al. From Organotypic Mouse Brain Slices to Human Alzheimer’s Plasma Biomarkers: A Focus on Nerve Fiber Outgrowth. Biomolecules. 2024; 14: 1326. https://doi.org/10.3390/biom14101326.

[56] Kovács R, Papageorgiou I, Heinemann U. Slice cultures as a model to study neurovascular coupling and blood brain barrier in vitro. Cardiovascular Psychiatry and Neurology. 2011; 2011: 646958. https://doi.org/10.1155/2011/646958.

[57] Bendfeldt K, Radojevic V, Kapfhammer J, Nitsch C. Basic fibroblast growth factor modulates density of blood vessels and preserves tight junctions in organotypic cortical cultures of mice: a new in vitro model of the blood-brain barrier. The Journal of Neuroscience. 2007; 27: 3260–3267. https://doi.org/10.1523/JNEUROSCI.4033-06.2007.

[58] Chip S, Zhu X, Kapfhammer JP. The analysis of neurovascular remodeling in entorhino-hippocampal organotypic slice cultures. Journal of Visualized Experiments. 2014; e52023. https://doi.org/10.3791/52023.

[59] Ravin R, Suarez-Meade P, Busse B, Blank PS, Vivas-Buitrago T, Norton ES, et al. Perivascular invasion of primary human glioblastoma cells in organotypic human brain slices: human cells migrating in human brain. Journal of Neuro-Oncology. 2023; 164: 43–54. https://doi.org/10.1007/s11060-023-04349-9.

[60] Morin-Brureau M, De Bock F, Lerner-Natoli M. Organotypic brain slices: a model to study the neurovascular unit micro-environment in epilepsies. Fluids and Barriers of the CNS. 2013; 10: 11. https://doi.org/10.1186/2045-8118-10-11.

[61] Li Q, Han X, Wang J. Organotypic Hippocampal Slices as Models for Stroke and Traumatic Brain Injury. Molecular Neurobiology. 2016; 53: 4226–4237. https://doi.org/10.1007/s12035-015-9362-4.

[62] Kniewallner KM, Foidl BM, Humpel C. Platelets isolated from an Alzheimer mouse damage healthy cortical vessels and cause inflammation in an organotypic ex vivo brain slice model. Scientific Reports. 2018; 8: 15483. https://doi.org/10.1038/s41598-018-33768-2.

[63] Duport S, Robert F, Muller D, Grau G, Parisi L, Stoppini L. An in vitro blood-brain barrier model: cocultures between endothelial cells and organotypic brain slice cultures. Proceedings of the National Academy of Sciences of the United States of America. 1998; 95: 1840–1845. https://doi.org/10.1073/pnas.95.4.1840.

[64] Marksteiner J, Imarhiagbe D, Defrancesco M, Deisenhammer EA, Kemmler G, Humpel C. Analysis of 27 vascular-related proteins reveals that NT-proBNP is a potential biomarker for Alzheimer’s disease and mild cognitive impairment: a pilot-study. Experimental Gerontology. 2014; 50: 114–121. https://doi.org/10.1016/j.exger.2013.12.001.

[65] Steiner K, Humpel C. Long-term organotypic brain slices cultured on collagen-based microcontact prints: A perspective for a brain-on-a-chip. Journal of Neuroscience Methods. 2023; 399: 109979. https://doi.org/10.1016/j.jneumeth.2023.109979.

[66] Schmidt O, Weyer Y, Baumann V, Widerin MA, Eising S, Angelova M, et al. Endosome and Golgi-associated degradation (EGAD) of membrane proteins regulates sphingolipid metabolism. The EMBO Journal. 2019; 38: e101433. https://doi.org/10.15252/embj.2018101433.

[67] Indelicato E, Faserl K, Amprosi M, Nachbauer W, Schneider R, Wanschitz J, et al. Skeletal muscle proteome analysis underpins multifaceted mitochondrial dysfunction in Friedreich’s ataxia. Frontiers in Neuroscience. 2023; 17: 1289027. https://doi.org/10.3389/fnins.2023.1289027.

[68] Madeira F, Madhusoodanan N, Lee J, Eusebi A, Niewielska A, Tivey ARN, et al. The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024. Nucleic Acids Research. 2024; 52: W521–W525. https://doi.org/10.1093/nar/gkae241.

[69] Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd edn. Routledge: New York, NY. 1988.

[70] Mitra D, Jaffe EA, Weksler B, Hajjar KA, Soderland C, Laurence J. Thrombotic thrombocytopenic purpura and sporadic hemolytic-uremic syndrome plasmas induce apoptosis in restricted lineages of human microvascular endothelial cells. Blood. 1997; 89: 1224–1234.

[71] Wall RT, Harker LA, Quadracci LJ, Striker GE. Factors influencing endothelial cell proliferation in vitro. Journal of Cellular Physiology. 1978; 96: 203–213. https://doi.org/10.1002/jcp.1040960209.

[72] Kalghatgi S, Friedman G, Fridman A, Clyne AM. Endothelial cell proliferation is enhanced by low dose non-thermal plasma through fibroblast growth factor-2 release. Annals of Biomedical Engineering. 2010; 38: 748–757. https://doi.org/10.1007/s10439-009-9868-x.

[73] Gospodarowicz D, Ill C. Extracellular matrix and control of proliferation of vascular endothelial cells. The Journal of Clinical Investigation. 1980; 65: 1351–1364. https://doi.org/10.1172/JCI109799.

[74] Burns ER, Zucker-Franklin D. Pathologic effects of plasma from patients with thrombotic thrombocytopenic purpura on platelets and cultured vascular endothelial cells. Blood. 1982; 60: 1030–1037.

[75] Bagavandoss P, Wilks JW. Specific inhibition of endothelial cell proliferation by thrombospondin. Biochemical and Biophysical Research Communications. 1990; 170: 867–872. https://doi.org/10.1016/0006-291x(90)92171-u.

[76] Khorana AA, Sahni A, Altland OD, Francis CW. Heparin inhibition of endothelial cell proliferation and organization is dependent on molecular weight. Arteriosclerosis, Thrombosis, and Vascular Biology. 2003; 23: 2110–2115. https://doi.org/10.1161/01.ATV.0000090671.56682.D7.

[77] Takehara K, LeRoy EC, Grotendorst GR. TGF-beta inhibition of endothelial cell proliferation: alteration of EGF binding and EGF-induced growth-regulatory (competence) gene expression. Cell. 1987; 49: 415–422. https://doi.org/10.1016/0092-8674(87)90294-7.

[78] Joyce NC. Proliferative capacity of corneal endothelial cells. Experimental Eye Research. 2012; 95: 16–23. https://doi.org/10.1016/j.exer.2011.08.014.

[79] James D, Nam HS, Seandel M, Nolan D, Janovitz T, Tomishima M, et al. Expansion and maintenance of human embryonic stem cell-derived endothelial cells by TGFbeta inhibition is Id1 dependent. Nature Biotechnology. 2010; 28: 161–166. https://doi.org/10.1038/nbt.1605.

[80] Marcelo KL, Goldie LC, Hirschi KK. Regulation of endothelial cell differentiation and specification. Circulation Research. 2013; 112: 1272–1287. https://doi.org/10.1161/CIRCRESAHA.113.300506.

[81] Kubota Y, Kleinman HK, Martin GR, Lawley TJ. Role of laminin and basement membrane in the morphological differentiation of human endothelial cells into capillary-like structures. The Journal of Cell Biology. 1988; 107: 1589–1598. https://doi.org/10.1083/jcb.107.4.1589.

[82] Stamati K, Priestley JV, Mudera V, Cheema U. Laminin promotes vascular network formation in 3D in vitro collagen scaffolds by regulating VEGF uptake. Experimental Cell Research. 2014; 327: 68–77. https://doi.org/10.1016/j.yexcr.2014.05.012.

[83] Simon-Assmann P, Orend G, Mammadova-Bach E, Spenlé C, Lefebvre O. Role of laminins in physiological and pathological angiogenesis. The International Journal of Developmental Biology. 2011; 55: 455–465. https://doi.org/10.1387/ijdb.103223ps.

[84] Schéele S, Sasaki T, Arnal-Estapé A, Durbeej M, Ekblom P. Monoclonal anti-mouse laminin antibodies: AL-1 reacts with laminin alpha1 chain, AL-2 with laminin beta1 chain, and AL-4 with the coiled-coil domain of laminin beta1 chain. Matrix Biology. 2006; 25: 301–305. https://doi.org/10.1016/j.matbio.2006.03.004.

[85] Maciag T, Kadish J, Wilkins L, Stemerman MB, Weinstein R. Organizational behavior of human umbilical vein endothelial cells. The Journal of Cell Biology. 1982; 94: 511–520. https://doi.org/10.1083/jcb.94.3.511.

[86] Madri JA, Williams SK. Capillary endothelial cell cultures: phenotypic modulation by matrix components. The Journal of Cell Biology. 1983; 97: 153–165. https://doi.org/10.1083/jcb.97.1.153.

[87] Raposo CD, Canelas AB, Barros MT. Human Lectins, Their Carbohydrate Affinities and Where to Find Them. Biomolecules. 2021; 11: 188. https://doi.org/10.3390/biom11020188.

[88] Battistella R, Kritsilis M, Matuskova H, Haswell D, Cheng AX, Meissner A, et al. Not All Lectins Are Equally Suitable for Labeling Rodent Vasculature. International Journal of Molecular Sciences. 2021; 22: 11554. https://doi.org/10.3390/ijms222111554.

[89] Robertson RT, Levine ST, Haynes SM, Gutierrez P, Baratta JL, Tan Z, et al. Use of labeled tomato lectin for imaging vasculature structures. Histochemistry and Cell Biology. 2015; 143: 225–234. https://doi.org/10.1007/s00418-014-1301-3.

[90] Brown LS, Foster CG, Courtney JM, King NE, Howells DW, Sutherland BA. Pericytes and Neurovascular Function in the Healthy and Diseased Brain. Frontiers in Cellular Neuroscience. 2019; 13: 282. https://doi.org/10.3389/fncel.2019.00282.

[91] Nakagomi T, Kubo S, Nakano-Doi A, Sakuma R, Lu S, Narita A, et al. Brain vascular pericytes following ischemia have multipotential stem cell activity to differentiate into neural and vascular lineage cells. Stem Cells. 2015; 33: 1962–1974. https://doi.org/10.1002/stem.1977.

[92] Özen I, Deierborg T, Miharada K, Padel T, Englund E, Genové G, et al. Brain pericytes acquire a microglial phenotype after stroke. Acta Neuropathologica. 2014; 128: 381–396. https://doi.org/10.1007/s00401-014-1295-x.

[93] Armulik A, Genové G, Mäe M, Nisancioglu MH, Wallgard E, Niaudet C, et al. Pericytes regulate the blood-brain barrier. Nature. 2010; 468: 557–561. https://doi.org/10.1038/nature09522.

[94] Sengillo JD, Winkler EA, Walker CT, Sullivan JS, Johnson M, Zlokovic BV. Deficiency in mural vascular cells coincides with blood-brain barrier disruption in Alzheimer’s disease. Brain Pathology. 2013; 23: 303–310. https://doi.org/10.1111/bpa.12004.

[95] Lendahl U, Nilsson P, Betsholtz C. Emerging links between cerebrovascular and neurodegenerative diseases-a special role for pericytes. EMBO Reports. 2019; 20: e48070. https://doi.org/10.15252/embr.201948070.

[96] Goncalves A, Antonetti DA. Transgenic animal models to explore and modulate the blood brain and blood retinal barriers of the CNS. Fluids and Barriers of the CNS. 2022; 19: 86. https://doi.org/10.1186/s12987-022-00386-0.

[97] Sweeney MD, Sagare AP, Zlokovic BV. Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nature Reviews. Neurology. 2018; 14: 133–150. https://doi.org/10.1038/nrneurol.2017.188.

[98] Angulo-Urarte A, Graupera M. When, where and which PIK3CA mutations are pathogenic in congenital disorders. Nature Cardiovascular Research. 2022; 1: 700–714. https://doi.org/10.1038/s44161-022-00107-8.

[99] Nation DA, Sweeney MD, Montagne A, Sagare AP, D’Orazio LM, Pachicano M, et al. Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nature Medicine. 2019; 25: 270–276. https://doi.org/10.1038/s41591-018-0297-y.

[100] Lamalice L, Le Boeuf F, Huot J. Endothelial cell migration during angiogenesis. Circulation Research. 2007; 100: 782–794. https://doi.org/10.1161/01.RES.0000259593.07661.1e.

[101] Grammas P, Samany PG, Thirumangalakudi L. Thrombin and inflammatory proteins are elevated in Alzheimer’s disease microvessels: implications for disease pathogenesis. Journal of Alzheimer’s Disease. 2006; 9: 51–58. https://doi.org/10.3233/jad-2006-9105.

[102] Baloyannis SJ, Baloyannis IS. The vascular factor in Alzheimer’s disease: a study in Golgi technique and electron microscopy. Journal of the Neurological Sciences. 2012; 322: 117–121. https://doi.org/10.1016/j.jns.2012.07.010.

[103] De Smet F, Segura I, De Bock K, Hohensinner PJ, Carmeliet P. Mechanisms of vessel branching: filopodia on endothelial tip cells lead the way. Arteriosclerosis, Thrombosis, and Vascular Biology. 2009; 29: 639–649. https://doi.org/10.1161/ATVBAHA.109.185165.

[104] Lobov IB, Renard RA, Papadopoulos N, Gale NW, Thurston G, Yancopoulos GD, et al. Delta-like ligand 4 (Dll4) is induced by VEGF as a negative regulator of angiogenic sprouting. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104: 3219–3224. https://doi.org/10.1073/pnas.0611206104.

[105] Noguera-Troise I, Daly C, Papadopoulos NJ, Coetzee S, Boland P, Gale NW, et al. Blockade of Dll4 inhibits tumour growth by promoting non-productive angiogenesis. Nature. 2006; 444: 1032–1037. https://doi.org/10.1038/nature05355.

[106] Sproston NR, Ashworth JJ. Role of C-Reactive Protein at Sites of Inflammation and Infection. Frontiers in Immunology. 2018; 9: 754. https://doi.org/10.3389/fimmu.2018.00754.

[107] Kravitz BA, Corrada MM, Kawas CH. Elevated C-reactive protein levels are associated with prevalent dementia in the oldest-old. Alzheimer’s & Dementia. 2009; 5: 318–323. https://doi.org/10.1016/j.jalz.2009.04.1230.

[108] Tachibana A, Iga JI, Ozaki T, Yoshida T, Yoshino Y, Shimizu H, et al. Serum high-sensitivity C-reactive protein and dementia in a community-dwelling Japanese older population (JPSC-AD). Scientific Reports. 2024; 14: 7374. https://doi.org/10.1038/s41598-024-57922-1.

[109] Tao Q, Alvin Ang TF, Akhter-Khan SC, Itchapurapu IS, Killiany R, Zhang X, et al. Impact of C-Reactive Protein on Cognition and Alzheimer Disease Biomarkers in Homozygous APOE ɛ4 Carriers. Neurology. 2021; 97: e1243–e1252. https://doi.org/10.1212/WNL.0000000000012512.

[110] Duong T, Nikolaeva M, Acton PJ. C-reactive protein-like immunoreactivity in the neurofibrillary tangles of Alzheimer’s disease. Brain Research. 1997; 749: 152–156. https://doi.org/10.1016/s0006-8993(96)01359-5.

[111] Iwamoto N, Nishiyama E, Ohwada J, Arai H. Demonstration of CRP immunoreactivity in brains of Alzheimer’s disease: immunohistochemical study using formic acid pretreatment of tissue sections. Neuroscience Letters. 1994; 177: 23–26. https://doi.org/10.1016/0304-3940(94)90035-3.

[112] Duong T, Acton PJ, Johnson RA. The in vitro neuronal toxicity of pentraxins associated with Alzheimer’s disease brain lesions. Brain Research. 1998; 813: 303–312. https://doi.org/10.1016/s0006-8993(98)00966-4.

[113] Engelhart MJ, Geerlings MI, Meijer J, Kiliaan A, Ruitenberg A, van Swieten JC, et al. Inflammatory proteins in plasma and the risk of dementia: the rotterdam study. Archives of Neurology. 2004; 61: 668–672. https://doi.org/10.1001/archneur.61.5.668.

[114] Dik MG, Jonker C, Hack CE, Smit JH, Comijs HC, Eikelenboom P. Serum inflammatory proteins and cognitive decline in older persons. Neurology. 2005; 64: 1371–1377. https://doi.org/10.1212/01.WNL.0000158281.08946.68.

[115] O’Bryant SE, Waring SC, Hobson V, Hall JR, Moore CB, Bottiglieri T, et al. Decreased C-reactive protein levels in Alzheimer disease. Journal of Geriatric Psychiatry and Neurology. 2010; 23: 49–53. https://doi.org/10.1177/0891988709351832.

[116] Slevin M, Matou S, Zeinolabediny Y, Corpas R, Weston R, Liu D, et al. Monomeric C-reactive protein–a key molecule driving development of Alzheimer’s disease associated with brain ischaemia? Scientific Reports. 2015; 5: 13281. https://doi.org/10.1038/srep13281.

[117] Turu MM, Slevin M, Matou S, West D, Rodríguez C, Luque A, et al. C-reactive protein exerts angiogenic effects on vascular endothelial cells and modulates associated signalling pathways and gene expression. BMC Cell Biology. 2008; 9: 47. https://doi.org/10.1186/1471-2121-9-47.

[118] Badimon L, Peña E, Arderiu G, Padró T, Slevin M, Vilahur G, et al. C-Reactive Protein in Atherothrombosis and Angiogenesis. Frontiers in Immunology. 2018; 9: 430. https://doi.org/10.3389/fimmu.2018.00430.

[119] Kanyenda LJ, Verdile G, Boulos S, Krishnaswamy S, Taddei K, Meloni BP, et al. The dynamics of CD147 in Alzheimer’s disease development and pathology. Journal of Alzheimer’s Disease. 2011; 26: 593–605. https://doi.org/10.3233/JAD-2011-110584.

[120] Kataoka H, DeCastro R, Zucker S, Biswas C. Tumor cell-derived collagenase-stimulatory factor increases expression of interstitial collagenase, stromelysin, and 72-kDa gelatinase. Cancer Research. 1993; 53: 3154–3158.

[121] Nahalkova J, Volkmann I, Aoki M, Winblad B, Bogdanovic N, Tjernberg LO, et al. CD147, a gamma-secretase associated protein is upregulated in Alzheimer’s disease brain and its cellular trafficking is affected by presenilin-2. Neurochemistry International. 2010; 56: 67–76. https://doi.org/10.1016/j.neuint.2009.09.003.

[122] Vetrivel KS, Zhang X, Meckler X, Cheng H, Lee S, Gong P, et al. Evidence that CD147 modulation of beta-amyloid (Abeta) levels is mediated by extracellular degradation of secreted Abeta. The Journal of Biological Chemistry. 2008; 283: 19489–19498. https://doi.org/10.1074/jbc.M801037200.

[123] Cui D, Yamamoto K, Ikeda E. High-Mannose-Type Glycan of Basigin in Endothelial Cells Is Essential for the Opening of the Blood-Brain Barrier Induced by Hypoxia, Cyclophilin A, or Tumor Necrosis Factor-α. The American Journal of Pathology. 2024; 194: 612–625. https://doi.org/10.1016/j.ajpath.2023.11.007.

[124] Muramatsu T. Basigin (CD147), a multifunctional transmembrane glycoprotein with various binding partners. Journal of Biochemistry. 2016; 159: 481–490. https://doi.org/10.1093/jb/mvv127.

[125] Washington AV, Schubert RL, Quigley L, Disipio T, Feltz R, Cho EH, et al. A TREM family member, TLT-1, is found exclusively in the alpha-granules of megakaryocytes and platelets. Blood. 2004; 104: 1042–1047. https://doi.org/10.1182/blood-2004-01-0315.

[126] Giomarelli B, Washington VA, Chisholm MM, Quigley L, McMahon JB, Mori T, et al. Inhibition of thrombin-induced platelet aggregation using human single-chain Fv antibodies specific for TREM-like transcript-1. Thrombosis and Haemostasis. 2007; 97: 955–963.

[127] Carrasquillo MM, Allen M, Burgess JD, Wang X, Strickland SL, Aryal S, et al. A candidate regulatory variant at the TREM gene cluster associates with decreased Alzheimer’s disease risk and increased TREML1 and TREM2 brain gene expression. Alzheimer’s & Dementia. 2017; 13: 663–673. https://doi.org/10.1016/j.jalz.2016.10.005.

[128] Washington AV. Abstract 535: Trem Like Transcript-1 (TLT-1) Regulates Both Leukocytes and Endothelial Cells to Mediate Immunohemostasis. Arteriosclerosis, Thrombosis, and Vascular Biology. 2016; 36: A535–A535. https://doi.org/10.1161/atvb.36.suppl_1.535.

[129] Schmoker AM, Perez Pearson LM, Cruz C, Colon Flores LG, Branfeild S, Pagán Torres FD, et al. Defining the TLT-1 interactome from resting and activated human platelets. Journal of Proteomics. 2020; 215: 103638. https://doi.org/10.1016/j.jprot.2020.103638.

[130] Kniewallner KM, Wenzel D, Humpel C. Thiazine Red(+) platelet inclusions in Cerebral Blood Vessels are first signs in an Alzheimer’s Disease mouse model. Scientific Reports. 2016; 6: 28447. https://doi.org/10.1038/srep28447.

[131] Humpel C. Platelets: Their Potential Contribution to the Generation of Beta-amyloid Plaques in Alzheimer’s Disease. Current Neurovascular Research. 2017; 14: 290–298. https://doi.org/10.2174/1567202614666170705150535.

Funding

Scientific and Technological Research Council of Türkiye(1059B192300559)

TUBITAK-2219-International Postdoctoral Research Fellowship Program for Turkish Citizens