Visinin-like protein 1 (VILIP-1) belongs to the group of emerging biomarkers with the potential to support the early diagnosis of Alzheimer’s disease (AD). However, studies investigating the differential diagnostic potential in cerebrospinal fluid (CSF) are rare and are not available for blood.We set up a novel, sensitive single molecule array (Simoa) assay for the detection of VILIP-1 in CSF and serum. In total, paired CSF and serum samples from 234 patients were investigated: 73 AD, 18 behavioral variant frontotemporal dementia (bvFTD), 26 parkinsonian syndromes, 20 amyotrophic lateral sclerosis (ALS), 22 Creutzfeldt-Jakob disease (CJD), and 75 non-neurodegenerative control (Con) patients. The differential diagnostic potential of CSF and serum VILIP-1 was assessed using the receiver operating characteristic curve analysis and findings were compared to core AD biomarkers.r=0.32 (CI: 0.20–0.43), p<0.0001). VILIP-1 concentrations in CSF and serum were elevated in AD compared to Con (p<0.0001 and p<0.01) and CJD (p<0.0001 for CSF and serum), and an increase in CSF was observed already in early AD stages (p<0.0001). In the discrimination of AD versus Con, we could demonstrate a strong diagnostic potential for CSF VILIP-1 alone (area under the curve (AUC): 0.87), CSF VILIP-1/CSF Abeta 1-42 (AUC: 0.98), and serum VILIP-1/CSF Abeta 1-42 ratio (AUC: 0.89).CSF and serum VILIP-1 levels correlated weakly (We here report on the successful establishment of a novel Simoa assay for VILIP-1 and illustrate the potential of CSF and serum VILIP-1 in the differential diagnosis of AD with highest levels in CJD.The online version contains supplementary material available at 10.1186/s13195-022-01122-4. The analysis of cerebrospinal fluid (CSF) or blood-based biomarkers, reflecting pathophysiologic processes occurring in the central nervous system, might constitute one promising option for the (early) diagnosis of Alzheimer’s disease (AD). For AD, the analysis of amyloid-β peptide 1-42 (Abeta 1-42), total-tau (t-tau), and phosphorylated-tau (p-tau) in CSF is already implemented in the diagnostic guidelines , 6, and Due to its elevation in the CSF of AD and also in AD patients at a stadium of mild cognitive impairment (AD-MCI), VILIP-1 has been shown to be of diagnostic and prognostic value –22. HoweHere, we report on the development and validation of a highly sensitive immunoassay for the analysis of VILIP-1 in CSF and blood using single molecule array (Simoa) technology. We applied the novel assay to analyze VILIP-1 concentrations in six different diagnostic groups including AD, behavioral variant frontotemporal dementia (bvFTD), PD spectrum (including Parkinson’s disease (PD), Parkinson’s disease dementia (PDD), and dementia with Lewy bodies (DLB)), amyotrophic lateral sclerosis (ALS), Creutzfeldt-Jakob disease (CJD), and non-neurodegenerative controls (Con). Here, we evaluated the differential diagnostic potential of VILIP-1 CSF and blood levels for neurodegenerative diseases.All CSF and serum specimens examined in this study were from patients seen in the Departments of Neurology Ulm (between 2010 and 2020) and Göttingen (1997–2003). The study was approved by local Ethics Committees  and conducted following the Declaration of Helsinki. All participants gave their written informed consent to participate in the study.VILIP-1 was measured in the CSF and serum of 234 patients which were divided into six groups according to their diagnoses: AD, FTD, PD spectrum , ALS, CJD, and Con.n=18), the international criteria were used [n=22) were neuropathologically confirmed cases from the Department of Neurology in Göttingen  [The International Working Group 2 criteria were applied for the diagnosis of 73 AD patients . For theere used , 24. Tweere used . CJD patpathies) . The 20 pathies) . All 75 Using the clinical dementia rating scale (CDR) , the AD After lumbar puncture, CSF samples were centrifuged within 30min at 2000 g and supernatant aliquots were frozen in sterile polypropylene tubes at −80°C. Serum was extracted from blood samples by centrifugation  and stored likewise until analysis.Quantification of CSF t-tau, p-tau 181, and Abeta 1-42 was performed with commercially available ELISA kits . VILIP-1 serum and CSF levels were analyzed with a newly established Simoa assay. Intra- and interassay reproducibility was determined by analysis of CSF and serum sample triplicates in three different runs. The lower limit of quantitation (LLOQ) was defined as reported by Andreasson et al. . All meaAs capture and detection antibody, a polyclonal antibody specifically recognizing VILIP-1 was used . Biotinylation of the detector antibody was performed in a ratio 40:1 (biotin to antibody) according to the protocol provided by Thermo Fisher Scientific . Recombinant VILIP-1 was purchased from LSBio .y weighting was applied.All Simoa measurements were performed on a fully automated Simoa HD-1 Analyzer . Using a coating concentration of 0.2 mg/ml, the capture antibody was coupled to carboxylated paramagnetic beads  according to the protocol of the manufacturer. For the measurement of VILIP-1 in serum and CSF, the number of active beads with coupled antibodies was reduced and replaced by helper beads . Approximately 350,000 helper and 150,000 active beads were deployed for measuring one sample. The required amount of beads was washed twice on a magnetic separator with 1% BSA in PBS with 0.05% Tween-20 and was subsequently diluted in this buffer. The biotinylated VILIP-1 detection antibody was diluted in the same buffer to a concentration of 0.5 μg/ml. Streptavidin-β-galactosidase concentrate (SβG) was prepared in SβG-diluent  to a final concentration of 25pM. Resorufin-β-D galactopyranoside  substrate was used as provided by the manufacturer. Calibrators and 1:4 diluted CSF/serum samples (1% BSA in PBS with 0.05% Tween-20) were transferred into a 96-well plate  and placed into the HD-1. For assay configuration, a two-step assay protocol was chosen. In the first step, 25μl of bead and 20μl biotinylated antibody solution were incubated for 30 min (40 cadences) in a reaction cuvette  together with the sample, followed by several wash steps. In the second step, 100μl of SβG was added and incubated for 5 min and 15 s (7 cadences), followed by the addition of the substrate and automated imaging. Image analysis was performed by the HD-1 Analyzer software version 1.5 . As a regression model, a four-parameter logistic curve with 1/U test was applied. Kruskal-Wallis test with subsequent Dunn’s post hoc test was applied for comparisons of three or more groups. Cut-off calculations were done with receiver operating characteristic (ROC) analyses. By maximizing the Youden index (sensitivity+specificity−1) and by the selection of the best likelihood ratio, cut-off levels were determined. To assess significant associations between parameters, the Spearman rank correlation coefficient was calculated. For differences in sex distribution, the chi-square test was applied. For all evaluations, p<0.05 was considered statistically significant. Calculations were performed using the GraphPad Prism 7.0 software .For concentration differences between the two groups, the Mann-Whitney The Simoa assay covers a range from 0.7 to 1600 pg/ml. The intra-assay CV was 6% for CSF and 7% for serum. The interassay CV for CSF and serum was determined to be 11% and 8%, respectively. Samples were dilution stable between dilutions of 1:2 and 1:10 for CSF and 1:2 and 1:8 for serum. We calculated a LLOQ of 0.8 pg/ml. No cross-reaction with human albumin or immunoglobulin G was observed. More details on the performance of the assay can be found in Additional file In this study, we investigated the CSF and blood of 234 patients comprising 73 AD, 18bvFTD, 26 PD spectrum, 20 ALS, 22 CJD, and 75 non-neurodegenerative control patients. Demographic and clinical parameters of the patient groups are summarized in Table p<0.0001), PD spectrum (p<0.01), bvFTD (p<0.01), and ALS (p<0.001) . Serum VILIP-1 concentrations were elevated in AD compared to Con patients (p<0.01). CJD serum levels were significantly increased compared to every other group (p<0.001) , p=0.40) as well as serum (r=−0.05 (CI: −0.18–0.08), p=0.42) VILIP-1 concentrations in the entire cohort. In the Con group alone, age and serum VILIP-1 values also depicted no correlation (r=−0.08 (CI: −0.31–0.16), p=0.44) whereas age and CSF levels were correlating significantly (r=0.33 (CI: 0.11–0.53), p<0.01).We found no correlation between the age of the patients and CSF (r=0.32 (CI: 0.20–0.43), p<0.0001) , p<0.0001) and CSF p-tau 181  and negatively with CSF Abeta 1-42 (r=−0.27 (CI: −0.40 to −0.14), p<0.0001). Moreover, serum VILIP-1 also correlated with CSF t-tau (r=0.37 (CI: 0.25–0.048), p<0.0001) and CSF Abeta 1-42 (r=−0.19 (CI: −0.32 to −0.05), p<0.01) but not with p-tau 181 (r=0.12 (CI: −0.02–0.026), p=0.08)   and AD-dementia (p=0.02) groups were also increased compared to PD-MCI, whereas PD-MCI and PDD/DLB patient levels were not significantly increased compared to Con patients. In the serum analysis, only AD-dementia patients displayed significantly elevated VILIP-1 levels when compared to the Con group (p<0.0001).For the evaluation of the early diagnostic abilities of VILIP-1, we stratified the AD cases into one cohort of patients with AD-dementia and one with AD-MCI and compared the CSF and serum VILIP-1 level with the Con group. The AD-dementia and AD-MCI groups showed significantly increased CSF VILIP-1 concentrations compared to Con patients (01) Fig. A. The leUsing receiver operating characteristic (ROC) analyses, we assessed the discriminating potential of CSF and serum VILIP-1 in the comparison of AD versus the other neurodegenerative diseases with the exception of CJD Fig. A and AD The AUCs of CSF and serum VILIP-1 in the comparison of AD and the other neurodegenerative diseases except CJD were found to be lower with 0.77 (95% 0.70–0.85) and 0.59 (0.5–0.69), respectively, compared to the core markers, e.g., CSF p-tau 0.99 (0.97–1). For the comparison of AD vs. Con, the AUCs of CSF and serum VILIP-1 were calculated as 0.87 0.82–0.93) and 0.69 (0.60–0.77), respectively, with an increase to 0.98 (0.97–1) and 0.89 (0.84–0.95) when divided by CSF Abeta 1-42 which is comparable with, e.g., the AUC of 0.99 (0.99–1) for the ratio of CSF t-tau and CSF Abeta 1-42. The calculated AUCs of all markers and ratios can be found in Table –0.93 andn=11), PSP (n=7), LBD (n=1)) [In this study, we report on the development and validation of a novel, highly sensitive Simoa assay for the analysis of VILIP-1 in CSF and serum as well as the application of said immunoassay in a cohort of a variety of neurodegenerative diseases. In the performance testing, the assay yielded good results indicating a high sensitivity and reliability of the new quantitative VILIP-1 assay. Applying it to analyze VILIP-1 levels in different neurodegenerative diseases, we could show elevated CSF VILIP-1 concentrations in AD compared to Con patients confirming previous findings , 33, 34.D (n=1)) . Up to nD (n=1)) . One posOur findings show a high correlation of CSF VILIP-1 with t-tau and p-tau and a weaker, inverse correlation with Abeta 1-42 which supports earlier studies , 20. MorBlood VILIP-1 concentrations in the serum/plasma of AD patients have only been analyzed in one study . In 2011Serum VILIP-1 levels were significantly elevated in AD-dementia compared to Con but neither to any other neurodegenerative disease nor to AD-MCI patients. Furthermore, serum VILIP-1 levels of the AD-MCI cohort displayed no significant difference to the Con group as observed for CSF VILIP-1 levels. One possible explanation could be differences in the blood-cerebrospinal fluid barrier (BCB) dysfunction between the AD-dementia and AD-MCI groups. In fact, BCB impairment has been described in the pathogenesis of AD; however, studies so far could not reveal a significant correlation between cognitive function and BCB impairment , 38. AnoThe difference between CSF and blood VILIP-1 results is also reflected in the ROC analysis where with an AUC of 0.77 CSF VILIP-1 performs all right in the differential diagnosis of AD and well when compared to the Con group (AUC 0.87). Serum VILIP-1 on the other side shows only an AUC of 0.59 versus the other neurodegenerative diseases and an AUC of 0.69 versus the Con group. Applying the ratio CSF VILIP-1 to CSF Abeta 1-42 in the comparison with the other neurodegenerative diseases, the AUC increases substantially to 0.93 which is close to the AUC of CSF t-tau/ CSF Abeta 1-42 (0.98). These findings suggest that future analysis especially also the combination of blood VILIP-1 with blood Abeta 1-42, for which more and more measurement techniques emerge –41, coulAnother neuronal injury marker described in the literature is neurofilaments. Especially, neurofilament light (NfL) and heavy chain have been extensively studied as markers in neurological disorders . CSF andA biomarker yielding high specificity for AD, however, representing a different pathology in AD than VILIP-1 is p-tau . As our The strengths of our study are (i) the establishment of a novel, easy-to-use Simoa assay for the analysis of CSF and serum VILIP-1, (ii) the outline of a comprehensive study of CSF and serum VILIP-1 concentrations in a cohort of different neurodegenerative diseases, and (iii) the parallel analysis of CSF and serum samples in all cohorts. Limitations of the study are the lower number of AD-MCI compared to AD-dementia cases and the lack of a vascular dementia (VaD) control cohort.In the clinic, VILIP-1 analysis could be in addition relevant for patient stratification for clinical and as an additional readout in therapeutic trials for example for monitoring the effect of disease-modifying drugs on calcium ion homeostasis or synaptic/neuronal integrity. Further studies might focus on the analysis of VILIP-1 in VaD, on the combination of markers in blood, and especially on the investigation of blood VILIP-1 levels in follow-up samples.Additional file 1: Supplementary material. Description of assay parallelism and pre-analytical stability.


Phase-contrast magnetic resonance (PC-MR) is widely used in patients with idiopathic normal pressure hydrocephalus (iNPH), but its role in predicting prognosis remains controversial. To evaluate the effectiveness of preoperative PC-MR CSF flow measurement in predicting the clinical response to shunt surgery in patients with iNPH.Forty-six patients with definite iNPH were included between January 2018 and January 2022. PC-MR was used to evaluate CSF peak velocity (PV), average velocity, aqueductal stroke volume (ASV), net ASV, and net flow. The modified Rankin Scale (mRS), iNPH grading scale (iNPHGS), Mini-Mental State Examination (MMSE), and Timed 3-m Up and Go Test (TUG) were used for clinical assessment. The primary endpoint was the improvement in the mRS score 1 year after surgery, and the secondary endpoints were the iNPHGS, MMSE, and TUG scores at 1 year. Differences between shunt improvement and non-improvement groups, based on the clinical outcomes, were compared using the Mann-Whitney U-test, logistic regression models, and receiver operating characteristic curves. Correlations between CSF flow parameters and the baseline clinical outcomes were assessed using Spearman's correlation coefficient.No CSF parameters significantly differed between shunt improvement and non-improvement groups based on mRS and secondary outcomes. And all CSF parameters showed significant overlap in both shunt improvement and non-improvement groups based on mRS and secondary outcomes. Significant correlations between the mRS and iNPHGS scores, and PV, ASV, and net ASV were observed.While some preoperative PC-MR CSF flow parameters reflected the symptom severity of iNPH to a certain extent, they alone might not be ideal markers of shunt responsiveness. Idiopathic normal pressure hydrocephalus (iNPH) is an increasingly recognized treatable disease among older adults and is characterized by a triad of gait impairment, cognitive disturbances, and urinary incontinence, as well as the presence of normal cerebrospinal fluid (CSF) pressure on lumbar puncture , 2. SympCSF tap tests have been widely used to predict shunt responses in clinical practice. Meanwhile, the infusion test, as one of the useful diagnosis procedure for iNPH, have been also used to predict shunt responses in some clinical centers , 7. HoweTherefore, in this study, we used consistent MR scanning equipment and PC-MR scanning methods, collected multiple PC-MR CSF parameters, and focused on definite iNPH patients. The purpose of this study was to investigate whether PC-MR measurements of CSF parameters could be used as predictive markers of shunt outcomes in iNPH patients.The original cohort consisted of 61 consecutive iNPH patients who had undergone PC-MR within 1 week before diagnostic lumbar CSF puncture and received a shunt at the authors' hospital between January 2018 and January 2022. The inclusion criteria were as follows: 1) meeting the diagnostic criteria of the iNPH guidelines  meeting , age ≥60Based on both the iNPH guidelines and previous studies , 2, 14, Shunt improvement was defined as an improvement of 1 or more grades on the mRS, ≥1 point on the iNPHGS, and ≥3 points on the MMSE; and a decrease of >10% on the TUG. The primary endpoint was an improvement of ≥1 point (favorable outcome) on the mRS at 12 months after shunt placement. The secondary outcome measures were the iNPHGS, MMSE, and TUG findings.All patients underwent PC-MRI examinations using the same scanning sequence. To reduce heterogeneities in baseline aqueductal flow in PC-MR as much as possible, the scans of all patients with iNPH were performed at night (8–10 pm) . All imaQuantitative analysis of CSF flow parameters was performed using the flow quantification software provided with the MR scanner. The region of interest was manually defined along the outer border of the aqueduct by the unified opinions of three neuroradiologists with more than 15 years of experience in brain MRI interpretation to reduce the difference in measurement . CSF floThe net ASV was calculated by subtracting the PC-MRI-derived volumetric estimate of retrograde aqueductal flow from that of antegrade flow during one cardiac cycle , 19. Nett-test was used to identify significant differences among normally distributed data between the improvement and non-improvement groups, such as age. Similarly, the Mann-Whitney and chi-square tests (when appropriate) were used to test for differences in CSF flow parameters between shunt responders and non-responders. Associations between outcome and CSF flow parameters were assessed using logistic regression models, with results presented as odds ratios with 95% confidence intervals (CIs) in a forest plot. Receiver operating characteristic curves (ROCs) were used to evaluate the predictive effectiveness of the CSF flow parameters on the results. Correlations between the CSF flow parameters from PC-MR and clinical assessment parameters were determined using the Spearman correlation coefficient. Statistical significance was accepted at the level of 0.05 (two-tailed).All statistical analyses were performed using the SPSS version 24 . The Shapiro-Wilk test of normality was used to determine the distribution of variables. A The preoperative demographic characteristics, preoperative clinical characteristics, and PC-MR CSF flow results of the iNPH patients are summarized in One year after surgery, 28 patients showed improved mRS scores and iNPHGS scores. Twenty-five (74%) patients showed improvement on the MMSE by at least three levels (definitive improvement in cognition). Twenty-eight (61%) of the 46 patients tested exhibited a >10% decrease in the TUG, and 10 (45%) showed improved continence.Patients with iNPH were divided into shunt improvement and non-improvement groups, based on the preoperative and postoperative mRS scores, and their PC-MR CSF parameters and preoperative clinical characteristics were compared . We did Subsequently, we divided iNPH patients into shunt improvement and non-improvement groups based on the secondary outcome measures  before and after shunt surgery, and compared their PC-MR CSF parameters . Similarp > 0.05). Under the primary result, the AUCs of the PC-MR CSF parameters were 0.58  for peak velocity, 0.54  for average velocity, 0.62  for ASV, 0.56  for net ASV and 0.52  for net flow, respectively  was comparable to that in previous studies , 22. AftIn addition to ASV, net ASV, which was used to diagnose iNPH, is another frequently noted PC-MR parameter. However, measurements of the net ASV are associated with technical difficulties similar to those of ASV , 17. TheIn addition to PC-MR parameter measurements, which easily vary, intraventricular CSF flow measurements in iNPH patients also show substantial variations. Jaeger et al. found that substantial variations in the mean wave amplitudes of iNPH patients over the course of several hours of intracranial pressure (ICP) monitoring are common, demonstrating that intracranial pulsatility is not stable, but rather a highly fluctuating phenomenon . These vMoreover, CSF flow itself undergoes dynamic changes, which increases the variability of PC-MR parameters over time, casting further doubt over the value of this method as a prognostic marker. Therefore, we believe that it is difficult to determine the prognosis of iNPH patients post-shunting using only aqueductal CSF parameters in PC-MR. Accordingly, our results question the clinical utility of PC-MR parameters, both with respect to its ability to diagnose iNPH and its value in predicting a clinically favorable shunt response.Furthermore, we found negative correlations between some PC-MR parameters and clinical symptoms, where the more severe the symptoms of iNPH, the lower the PC-MR CSF dynamics. Previous studies found that patients with iNPH have more severe symptoms with a longer duration , and thaOur study has some limitations. Although the number of iNPH patients in our study was relatively larger than that in previous PC-MR studies , 13, theIn conclusion, there were no significant differences in any PC-MR CSF parameters between iNPH patients with and without improvement after shunting. In addition, some PC-MR parameters were associated with symptom severity. Based on these findings, while PC-MR CSF flow parameters may reasonably reflect the severity of iNPH symptoms, used alone, they are not ideal markers for predicting shunt responsiveness in iNPH.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Shenzhen Second People's Hospital Bioethics Committee . The patients/participants provided their written informed consent to participate in this study.W-JH performed the experiments, analyzed the data, and wrote the original draft of the manuscript. X-jZ and Q-ZX performed the experiments, analyzed the data, and revised the manuscript. XZ contributed to the experiments. Q-ZX contributed to the data analysis. J-kC contributed to the experiment design and manuscript revision. JX designed the project, supervised the experiments, and drafted and revised the manuscript. All authors read and approved the final manuscript.This work was supported in part by the National Natural Science Foundation of China (Grant No. 82171913) and the Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A bone marrow aspirate revealed hypercellularity with primarily mature neutrophils  and a small aggregation of plasma cells . No BCR‐ABL1 transcript was detected. An initial diagnosis of chronic neutrophilic leukemia was made. Hydroxyurea was initiated, and leukocytosis gradually diminished. Subsequently, the patient complained of a right‐sided chest lump in June, 2020. Biopsy of the chest wall mass was performed, revealing a plasmacytoma with positive CD38  and granulocyte colony‐stimulating factor (G‐CSF) staining . Serum G‐CSF levels were also increased ; thus, a diagnosis of relapsed multiple myeloma with G‐CSF production was established. Pomalidomide and dexamethasone therapy was initiated, and serum G‐CSF levels decreased (788 pg/ml), and leukocyte counts were almost normalized 1 month after follow‐up.A 68‐year‐old man with a history of multiple myeloma  following autologous stem cell transplantation received a 9‐year maintenance therapy with lenalidomide (maintenance of complete remission). Upon follow‐up, severe leukocytosis was documented in June, 2020  generally can be caused by acute leukemia or myeloproliferative neoplasms. Other causes are reported as leukemoid reactions consisting severe infections, adverse drug reactions or G‐CSF producing tumors. G‐CSF elicits proliferation and activation of hematopoietic stem cell of the granulocyte lineage, leading to occurrence of severe leukocytosis. G‐CSF producing tumor is rare, and generally reported in patients with solid tumors. Although G‐CSF producing multiple myeloma is extremely rare, G‐CSF produced by multiple myeloma cells in the case can be the cause of severe leukocytosis, mimicking chronic neutrophilic leukemia. Physicians should pay attention for the rare disease when the severe leukocytosis appears.Severe leukocytosis (>50 × 10Hiroshi Ureshino and Masaharu Miyahara provided care for the patient or reviewed the case. Koichi Ohshima provided pathological diagnosis. Hiroshi Ureshino wrote the initial draft of this manuscript. All authors approved the final version. Written consent for publication was obtained from the patient.The authors declare no conflicts of interest.


Median correlation of overlapping protein measurements across platforms in CSF (r~0.7) and plasma (r~0.6) was good, with more variability in plasma. The SomaScan technology provided the most measurements in plasma. Surprisingly, many proteins altered in AD CSF were found to be altered in the opposite direction in plasma, including important members of AD brain co-expression modules. An exception was SMOC1, a key member of the brain matrisome module associated with amyloid-β deposition in AD, which was found to be elevated in both CSF and plasma. Protein co-expression analysis on greater than 7000 protein measurements in CSF and 9500 protein measurements in plasma across all proteomic platforms revealed strong changes in modules related to autophagy, ubiquitination, and sugar metabolism in CSF, and endocytosis and the matrisome in plasma. Cross-platform and cross-biofluid proteomics represents a promising approach for AD biomarker development.Robust and accessible biomarkers that can capture the heterogeneity of Alzheimer’s disease and its diverse pathological processes are urgently needed. Here, we undertook an investigation of Alzheimer’s disease cerebrospinal fluid (CSF) and plasma from the same subjects (The online version contains supplementary material available at 10.1186/s13195-022-01113-5. Alzheimer’s disease (AD) is a growing public health problem with no available disease-modifying therapies. Multi-omic analyses of AD brain have illustrated the varied and complex pathophysiology beyond amyloid-β (Aβ) plaques and tau neurofibrillary tangles –5, but hProtein biomarkers represent a promising class of biomarkers for AD given the large diversity of potential markers, their direct role in subserving biological processes, and the fact that standard protein affinity-based measurement approaches are already deployed in most clinical laboratories around the world. Three main quantitative protein measurement technologies are currently available to conduct proteomic discovery experiments in biofluids at scale: mass spectrometry, multiplexed nucleic acid aptamers, and multiplexed antibodies. Mass spectrometry (MS) has been used most extensively to date in AD biofluid biomarker discovery research and provides a direct measurement of protein identity and abundance through the measurement of peptides , 6–8. ThIn order to further the development of robust biofluid biomarkers of AD that can reflect multiple pathological processes in brain, we conducted a proteomic analysis on CSF and plasma by applying each proteomic technology described above to the same discovery set of AD and control samples. By analyzing the same samples with each proteomic technology, we were able to conduct an in-depth cross-platform technical analysis to better understand the current strengths and limitations inherent in each platform for AD biofluid biomarker development in CSF and plasma, and increase confidence in proteins that show promise as potential AD biomarkers. Furthermore, because the CSF and plasma samples we analyzed were matched within subject, we were able to explore the relationship between CSF and plasma levels of promising AD biomarkers within subject. We leveraged the combined proteomic datasets to generate an AD protein co-expression network in CSF and plasma and explored how protein co-expression in these two fluids might be related to each other and to AD brain protein co-expression. We found strong co-expression signals for proteostasis, synaptic biology, sugar metabolism, complement, and TGF-β signaling in AD CSF, and endocytosis, matrisome, and complement in AD plasma. Multi-platform proteomic analysis of AD biofluids holds promise for identification of robust biomarkers for clinical translation.n=18) and AD (n=18) patients in the Emory Goizueta Alzheimer’s Disease Research Center (ADRC) , whose CSF and plasma were analyzed only by SomaScan. This subject was excluded from all direct cross-platform comparisons, and therefore, such analyses were restricted to N=35 subjects. For both CSF and plasma, mass spectrometry measurements were performed using isobaric tandem mass tags (TMT-MS) with pre-fractionation  was added to each sample after cooling the samples to room temperature, along with LysC (2.5 μg). After overnight digestion, 336 μl of 50 mM ammonium bicarbonate (ABC) was added to each sample to dilute the urea concentration to 1 M, along with trypsin (5 μg). After 12 h, the trypsin digestion was stopped by adding final concentration of 1% formic acid (FA) and 0.1% trifluoroacetic acid (TFA).Equal volumes (50 μl of each sample) of CSF were digested with lysyl endopeptidase  and trypsin (Thermo Fisher Scientific 90058). Briefly, each sample was reduced and alkylated with 1 μl of 0.5 M tris-2(-carboxyethyl)-phosphine (TCEP) and 5 μl of 0.4 M chloroacetamide (CAA) at 90°C for 10 min, followed by water bath sonication for 15 min. The same volume of 8 M urea buffer  to a final concentration of 6 M urea. LysC (4.5 μg) was used for overnight digestion at room temperature. Samples were then diluted to 1 M urea with 50 mM ABC. Trypsin (4.5 μg) was then added, and the samples were subsequently incubated for 12 h. The digestion was then stopped by adding final concentration of 1% FA and 0.1% TFA.To increase the depth of proteome coverage, immunodepletion of highly abundant proteins was performed as previously described . For CSF2PO4 (pH 8.5)] was added to each sample after cooling to room temperature, along with LysC (10 μg). After overnight digestion, 134.4 μl of 50 mM ABC was added to each sample to dilute the urea concentration to 1 M, along with trypsin (20 μg). After 12 h, the trypsin digestion was stopped by adding final concentration of 1% FA and 0.1% TFA.Equal volumes (2 μl of each sample) of plasma were digested with LysC and trypsin. Briefly, each sample was diluted 10-fold with 50 mM ABC, following by reduction and alkylation with 0.4 μl of 0.5 M of TCEP and 2 μl of 0.4 M CAA with heating at 90°C for 10 min. The samples were sonicated for 15 min with water bath sonication to help sample solubilization. Then, 8 M urea buffer [22.4 μl, 8 M urea, 10 mM Tris, 100 mM NaHg for 2 min. Sample flow-through was concentrated with a 3K Ultra Centrifugal Filter Device by centrifugation at 14,000×g for 30 min. Immunodepleted samples were diluted to equal volumes of 75 μl with phosphate-buffered saline. Immunodepleted plasma (60 μl) was then digested with LysC and trypsin using the same protocol used for CSF depleted samples.The High Select Top14 Abundant Protein Depletion Resin was also utilized for plasma samples prior to digestion. Following mixing, 500 μl of resin was aliquoted into each spin column. After the resin settled to the bottom of the spin column, 8 μL of each sample was added and depletion was performed by gentle rotation for 15 min at room temperature, followed by centrifugation at 1000×Before TMT labeling, the digested peptides were desalted using 50 mg of Sep-Pak C18 columns (Waters). Briefly, the columns were activated with 1 mL of methanol, then equilibrated with 2 × 1 mL 0.1% TFA. The acidified samples were loaded following by washing with 2 × 1 mL 0.1% TFA. Elution was performed with 1 mL 50% acetonitrile. To normalize protein quantification across batches, global internal standard (GIS) samples were generated for each sample set by combining 100 μl aliquots from each sample elution. All individual samples and GIS pooled standards were dried by speed vacuum (Labconco).Both depleted CSF and depleted plasma samples were divided into five TMT batches, labeled using an 11-plex TMT kit , and derivatized as previously described [For undepleted CSF and plasma samples, the TMT 16-plex kit  was used for labeling, which divided both CSF and plasma sample sets into 3 TMT batches with 12 samples plus 1 GIS in each batch. The sample and channel distribution were the same for CSF and plasma samples  and loaded onto a Water’s BEH column (2.1 mm × 150 mm with 1.7 μm particles). A Vanquish UPLC system (Thermo Fisher Scientific) was used to carry out the fractionation. Solvent A consisted of 0.0175% (vol/vol) NH4OH, 0.01125% (vol/vol) FA, and 2% (vol/vol) ACN; solvent B consisted of 0.0175% (vol/vol) NH4OH, 0.01125% (vol/vol) FA, and 90% (vol/vol) ACN. The sample elution was performed over a 25-min gradient with a flow rate of 0.6 mL/min with a gradient from 0 to 50% solvent B. A total of 192 individual equal volume fractions were collected across the gradient. Fractions were concatenated to either 48 or 96 fractions and dried to completeness using vacuum centrifugation.Dried samples were resuspended in high pH loading buffer  and loaded onto an Agilent ZORBAX 300 Extend-C18 column (2.1 mm × 150 mm with 3.5 μm beads). An Agilent 1100 HPLC system was used to carry out the fractionation. Solvent A consisted of 0.0175% (vol/vol) NH4OH, 0.01125% (vol/vol) FA, and 2% (vol/vol) ACN; solvent B consisted of 0.0175% (vol/vol) NH4OH, 0.01125% (vol/vol) FA, and 90% (vol/vol) ACN. The sample elution was performed over a 60 min gradient with a flow rate of 0.4 mL/min with a gradient from 0 to 60% solvent B. A total of 96 individual equal volume fractions were collected across the gradient and subsequently pooled by concatenation into 30 fractions and dried to completeness under vacuum centrifugation.Dried samples were resuspended in high pH loading buffer  were loaded and eluted using a Dionex Ultimate 3000 RSLCnano (Thermo Fisher Scientific) on an in-house packed 25 cm, 100 μm internal diameter (i.d.) capillary column with 1.9 μm Reprosil-Pur C18 beads  over a 60 min gradient. Mass spectrometry was performed with a high-field asymmetric waveform ion mobility spectrometry (FAIMS) Pro-equipped Orbitrap Eclipse (Thermo Fisher Scientific) in positive ion mode using data-dependent acquisition with 2-s top speed cycles. Each cycle consisted of one full MS scan followed by as many MS/MS events that could fit within the given 2-s cycle time limit. MS scans were collected at a resolution of 120,000 . All higher energy collision-induced dissociation (HCD) MS/MS spectra were acquired at a resolution of 30,000 . Dynamic exclusion was set to exclude previously sequenced peaks for 30 s within a 10-ppm isolation window. For batches 2 and 3, samples were eluted over a 21-min gradient. Mass spectrometry was performed the same as batch 1 except with a FAIMS compensation voltage of −45, and dynamic exclusion set to exclude previously sequenced peaks for 6 s within a 10-ppm isolation window.Mass spectrometry was performed the same as for CSF undepleted batches 2 and 3 except FAIMS compensation voltage was set at −40 and −60, and dynamic exclusion was set to exclude previously sequenced peaks for 20 s within a 10-ppm isolation window.All fractions (~1μg) were loaded and eluted using an Easy-nLC 1200 (Thermo Fisher Scientific) on an in-house packed 30 cm, 750 μm i.d. capillary column with 1.9 μm Reprosil-Pur C18 beads over a 120-min gradient. Mass spectrometry was performed with a Q-Exactive HFX (Thermo Fisher Scientific) in positive ion mode using data-dependent acquisition with a top 10 method. Each cycle consisted of one full MS scan followed by 10 MS/MS events. MS scans were collected at a resolution of 120,000 . All higher energy collision-induced dissociation (HCD) MS/MS spectra were acquired at a resolution of 45,000 . Dynamic exclusion was set to exclude previously sequenced peaks for 20 s within a 10-ppm isolation window.All raw files were searched using Proteome Discoverer  with Sequest HT. The spectra were searched against a human UniProt database downloaded April 2015 . We used this database for consistency with our prior brain proteomics study [n=27/36) missing values were excluded from consideration. The number of remaining protein isoforms after missing value control was 1128 in undepleted plasma, 2229 in undepleted CSF, 1385 in plasma depleted of highly abundant proteins, and 2944 in CSF depleted of highly abundant proteins.Only proteins that were identified and summarized as high confidence (<1% FDR) by Proteome Discoverer (PD) were used for analysis. The 3730 UniProt protein identifier accessions provided by PD were further annotated with Hugo Gene Nomenclature Committee (HGNC) official gene symbols. TMT-MS data were processed identically for both CSF and plasma, including fluid depleted of highly abundant proteins. TMT reporter intensities (abundances) that had not undergone normalization by Proteome Discoverer (PD) were used for analysis to preserve inherent protein abundance differences between control and AD subjects. Four separate datasets were used for analysis: CSF undepleted, CSF depleted, plasma undepleted, and plasma depleted of highly abundant proteins. For each dataset, batch correction was performed by dividing abundances for each protein within each batch by the global internal standard (GIS). GIS measurements were then removed, and proteins with more than 75% (NPX or RFU), then dividing by the median buffer signal. Protein assay-specific limit of detection (LOD) was defined as median log2 buffer signal plus 3 standard deviations (SD) of the assay’s buffer measurements. NPX background signal SD for PEA was defined from historically recorded background variance of the assays and included as a component of predetermined LOD, whereas SomaScan background SD was calculated from available buffer replicate data for the assays performed. Sample measurements in CSF or plasma that were below LOD were retained but considered as missing values. Olink repeated measurements of the same UniProt protein assayed in different panels (N=36 duplicated assays) were reduced to their representative single best replicate of the assay in one of the panels based on criteria including the highest pairwise correlation to other replicate assays, highest signal, and largest dynamic range.Olink NPX values were analyzed using the OlinkAnalyze R package v1.2.1. NPX values that were flagged with quality control (QC) warnings were removed from further consideration. SomaScan RFU data and assay metadata for CSF and plasma were analyzed using the SomaDataIO R package v3.1.0. Olink NPX and SomaLogic SomaScan data included blank buffer replicate measurements (noise). Buffer measurements were used to calculate signal-to-noise (S:N) ratios for both unnormalized NPX or RFU abundance data. S:N ratios were calculated by subtracting the within-assay median buffer signal from the unlogged assay signal (2n=27/36) missing values. Because SomaScan CSF data had low signal, a second-pass filter step was applied to remove assays that did not meet an empirically derived S:N threshold. This threshold was determined by correlating SomaScan assays with Olink and TMT-MS undepleted assays at varying S:N cutoff values , and selecting the S:N value (≥0.45) that maximized median Pearson correlation with the other platforms. After application of first- and second-pass filters and removal of control aptamers, 3594 CSF and 7284 plasma human SomaScan assays were kept for subsequent analyses. For Olink assays, after applying the first-pass filter, 902 CSF and 1140 plasma proteins were kept for subsequent analyses.Both platform assays underwent a first-pass filter allowing up to 75%  and visualized using a custom in-house R script. The same procedure was used to determine ontology enrichment for network modules. Missing data  venneuler function. Enrichment of gene ontologies (GO) in different Venn categories was calculated as Fisher’s exact test .1-0 vennn=1932 CSF proteins, n=852 plasma proteins). Pairwise measurements were correlated using Pearson correlation on the difference in abundance between AD versus control subjects. Proteins that were discordant in their differential abundance, as well as proteins with negative Pearson rho across depleted and undepleted matched protein measurements across the 36 case samples, were considered significantly affected by depletion. In total, 32 proteins in CSF and 27 in plasma were censored from the TMT-MS depleted data due to effects of depletion on their abundance levels  . Absolute protein abundance differences in plasma were also assessed by calibrating relative platform signals to absolute plasma protein concentrations (n=4226) as provided in the Human Protein Atlas (HPA) on March 5, 2022 (https://www.proteinatlas.org/search). For the calibrated abundance calculations, unlogged abundance  was calibrated so that the geometric mean of control group measurements was set to the known absolute blood concentration with all individual measurements varying relative to this value. Missing values were considered as one-half the minimum assayed nonzero signal for geometric mean calculations. When multiple assays for a gene product were available within a platform, the assay with maximum mean signal was selected for calibration. HPA-calibrated values were plotted for all 4226 proteins regardless of presence in the platform as a ranked absolute abundance curve  measurements over all proteins after data processing as described above, which included signal cleanup, filtering on missingness, removal of proteins affected by top-14 highly abundant protein depletion, and, in the case of SomaScan CSF data, control of excessively low S:N assays. Volcano plots were made using a custom in-house script via the plotly (v4.9.2.1) R package function ggplotly. Individual volcano points were colored by membership in the 44 brain network modules described in Johnson et al. [Differences between AD and control were assessed on the logExternal Olink datasets used for correlation included plasma AD effect sizes from a Hong Kong-based cohort provided in Jiang et al.  Appendix2 fold change values that remained significant after FDR correction were correlated with log2 fold change values in the current study using Pearson correlation and Student’s p values as implemented in the WGCNA package verboseScatterplot function.Data provided in Jiang et al. and Whelan et al. was used directly for correlation without additional processing. AMP-PD Olink raw data from 212 study participants and all four 384-assay panels available  was loaded and processed in the same fashion as Emory Olink data. Four participants with a diagnosis of multiple systems atrophy were excluded. Final number of subjects analyzed was 118 PD and 90 control. As described above, values below LOD were censored as missing but otherwise retained for correlations, and proteins duplicated in the different panels were reduced to one representative assay. Final assay numbers included 1054 CSF and 1398 plasma assays. LogTMT-MS ion counts in fluid depleted of highly abundant proteins, SomaScan RFUs, and Olink unlogged NPX values, totaling 9589 assays in plasma and 7158 assays in CSF, were assembled for the 35 case samples commonly measured on all three platforms. Only truly missing values were considered as unavailable; values below LOD or those subject to S:N threshold-based filtering were retained. Data were transposed prior to removal of platform-specific effects as a batch effect using the TAMPOR algorithm. TAMPOR has been described previously for removal of nuisance batch effects in proteomic data and harmonization of different cohorts , 3. As aTo harmonize measurements across different proteomic platforms, we employed an adaptation of the TAMPOR algorithm (termed “transposed” TAMPOR) applied to relative abundance. SomaScan RFU, Olink 2^NPX, and TMT reporter abundance ratios were harmonized by considering common proteins across the different platforms as anchoring measurements. The central tendencies (intra-platform) of these common protein measurements are considered as robust as GIS measures within platform and are then normalized across platforms with the same median polish algorithm. The algorithm is therefore applied to samples as though they were proteins, and the proteins as though they were samples, in contrast to the standard TAMPOR algorithm described for batch correction in TMT data.n=101 in plasma and n=201 in CSF) to calculate the central tendency of data within and across platforms used for the denominators in the TAMPOR algorithm, as previously described [2 of the common proteins in all platforms. No missing values were present in the protein abundances used for network construction.Common proteins measured across all three platforms were used as the GIS  algorithm v1.69) was used for network generation. No outliers were detected using the WGCNA sample network connectivity outlier algorithm. The WGCNA blockwiseModules function was run on the CSF and plasma harmonized abundances with the following parameters: power=6.5 (CSF) or 11 (plasma), deepSplit=4, minModuleSize=10, mergeCutHeight=0.07, TOMdenom=“mean”, bicor correlation, signed network type, PAM staging and PAM respects dendro as TRUE, and a maxBlockSize larger than the total number of protein assays. Module memberships were then iteratively reassigned to enforce kME table consistency, as previously described . The res.69 was up values for multiple testing using the Benjamini-Hochberg method. The plasma network was compared to a SomaScan human serum network constructed from 4137 proteins as described in Emilsson et al. [Over-representation analysis (ORA) of module gene symbols between networks was determined using two-tailed Fisher’s exact test, followed by correction of n et al.  Serum nen et al. . OverlapZsummary composite z score for 8 underlying network parameters was calculated and visualized by circular heatmap as significance , corresponding to the Zsummary scores obtained.Pairwise, directional preservation between CSF and plasma, plasma and CSF, and brain to each of the biofluid networks and vice versa was performed using the WGCNA (v1.69) modulePreservation function with 500 permutations after harmonizing protein assay labels as described above. p values.Cell type-specific enriched marker gene symbol lists were used as previously published to perform Fisher’s exact one-tailed test for enrichment . Benjamit test. Comparisons among three or more groups were performed with Kruskal-Wallis nonparametric ANOVA or standard ANOVA with Tukey or post hoc pairwise comparison of significance. P values were adjusted for multiple comparisons by false discovery rate (FDR) correction according to the Benjamini-Hochberg method where indicated. Z score conversion of normalized protein data and normalized protein eigenproteins or synthetic eigenproteins were calculated as fold of standard deviation from the mean. At n=36, we were powered to detect a correlation rho of 0.45 at 80% power and p=0.05. For pairwise comparisons between AD and control groups considering all proteins measured, we were powered to detect a fold change of ~1.2 to ~1.3 depending on platform and fluid based on the power calculation method described by Bi and Liu [All statistical analyses were performed in R (v4.0.2). Boxplots represent the median, 25th, and 75th percentile extremes; thus, hinges of a box represent the interquartile range of the two middle quartiles of data within a group. The farthest data points up to 1.5 times the interquartile range away from box hinges define the extent of whiskers (error bars). Correlations were performed using the biweight midcorrelation function as implemented in the WGCNA R package or Pearson correlation. Comparisons between two groups were performed by a two-sided  and Liu .Additional file 1: Supplementary Figure 1. Signal-to-Noise Analysis in SomaScan. (A-D) Signal-to-noise was analyzed in the SomaScan 7000 data in CSF and plasma. (A) The frequency of median relative fluorescence units (RFU) for each protein assay in CSF across n=36 subjects and the frequency of median RFU for the buffer control of each aptamer reagent (left), with frequency of calculated signal:noise ratio for each aptamer reagent (right). (B) The same analysis as shown in (A), except in plasma. (C) The median correlation across all proteins commonly measured in the SomaScan 7000 platform with those in the Olink 1196 and TMT-MS platforms at a given minimum SomaScan signal:noise threshold, in CSF (left) and plasma (right). A signal:noise (S:N) ratio of 0.45 is indicated on the curves . (D) Frequency of aptamer median S:N in CSF and plasma prior to (left) and after (right) applying a S:N filter of 0.45. Total aptamer numbers in each fluid before and after applying the filter are provided. Numbers include both human and non-human assays but no control probes, with a <75% missingness threshold by limit of detection applied prior to S:N filter. (Insets) S:N from 0-10. Supplementary Figure 2. Missing Values.  The number of quantified protein assays by percent missingness across n=36 samples in CSF and plasma for the targeted SomaScan and Olink platforms, and the number of quantified proteins across TMT batches in the TMT-MS depleted and undepleted analyses (A). The SomaScan analysis includes application of the signal-to-noise (S:N) filter in CSF. “All” in each panel indicates the total number of human measurements for each platform and analysis, including assays that fall below S:N threshold in SomaScan, or do not pass QC criteria in Olink, or proteins that are identified but not quantified by reporter ions in MS . (B) The number of proteins removed after applying a <75% missingness filter. The SomaScan analysis also includes application of the S:N filter in CSF. Supplementary Figure 3. Effects of Highly Abundant Protein Depletion. (A-C) Frequency distribution of the correlation of TMT-MS measurements before and after depletion of the top 14 most highly abundant plasma proteins, in CSF (left) and plasma (right) (A). The vertical red line indicates the median correlation across all measurements after depletion. (B) Correlation of proteins between depleted and undepleted CSF, considering all proteins (left), proteins that are significantly different between AD and control in the depleted analysis and that have a corresponding measurement in the undepleted analysis , and proteins that are significantly different between AD and control in the undepleted analysis and that have a corresponding measurement in the depleted analysis . The individual proteins are colored according to whether they are correlated across paired measurements of the same subjects in depleted and undepleted measurements. (C) Same as in (B), except in plasma. Correlations were performed by Pearson test. Supplementary Figure 4. Proteomic Coverage with Undepleted Fluid and Unique Ontology Coverage. (A-C) Number and overlap of proteins measured by TMT-MS in undepleted fluid, Olink 1196, and SomaScan 7000 platforms in CSF (left) and plasma (right) from n=36 subjects. The threshold for inclusion was measurement in at least 9 subjects . CSF measurements on the SomaScan 7000 platform underwent signal-to-noise filtering  prior to subsequent analyses. (B) Number and overlap of proteins measured in CSF and plasma by TMT-MS in undepleted fluid. (C) Gene ontology of proteins uniquely measured in each platform in CSF and plasma as shown in Fig. z score of 1.96. Supplementary Figure 5. Correlation of AD Minus Control Differences Between Platforms Across Subjects. (A) Correlation between Olink and SomaScan (left) and Olink and MS (right) AD minus control (AD–CT) values, in plasma (top) and CSF (bottom). Correlations are provided for all AD–CT values (“All”), and AD–CT values in Olink that are significant at a level of p<0.05 (“p<0.05”). The individual proteins are colored according to whether they are significantly correlated within subject between platforms as shown in Fig. Supplementary Figure 6. Correlation of CSF and Plasma Measurements with Other Cohorts. (A-D) Correlation of Emory cohort SomaLogic, Olink, and MS data with SomaLogic and Olink data from other cohorts. Only proteins considered significantly different between AD and control cases at 5% FDR in each cohort were considered for correlation. (A) Emory data from each platform was correlated with Olink plasma data from Jiang et al. [et al. [et al. (D) Correlation with plasma SomaLogic data from the ANMerge version of the AddNeuroMed dataset as described in Birkenbihl et al. [et al. [Supplementary Figure 7. Differential Protein Abundance in AD by Undepleted TMT-MS. Differential protein abundance between AD and control cases in CSF (left) and plasma (right) on the TMT-MS undepleted platform. Proteins that are above the dashed red line are significantly altered in AD by t test at p<0.05. Proteins are colored by the brain co-expression module in which they reside, as described in Johnson et al. [Supplementary Figure 8. Protein Abundance Analysis By Platform in CSF and Plasma. (A-H) Individual and total protein signal levels as measures of relative abundance in CSF and plasma were analyzed on the SomaScan , Olink , undepleted tandem mass tag mass spectrometry (TMT-MS) , and depleted TMT-MS platforms  in control (n=18) and AD (n=18) subjects. (A) Aptamer relative fluorescence units (RFUs) were ranked and analyzed by the contribution of each aptamer RFU to the cumulative RFU in CSF (left) or plasma (right). The difference in RFUs between AD and control cases for the top 5% protein RFUs (shaded box) and all protein RFUs for each fluid are shown in the boxplots below. Background signal was subtracted for each RFU. RFUs below limit of detection (LOD) were not considered. (B) SomaScan aptamer RFUs were calibrated to proteins of known absolute plasma concentration from the Human Protein Atlas [n=2685 out of 4226 proteins in the HPA), and proteins ranked by concentration. The black line indicates the absolute concentration values from the HPA and is not summed, whereas the turquoise and red lines indicate the summed total protein concentration for all proteins below rank in control and AD cases, respectively. Boxplots represent summed concentration in control and AD cases for the top 5% (>9.5 log10 pg/L) and bottom 50% (<6.0 log10 pg/L) of proteins measured that overlap with HPA proteins. (C) Olink unlogged normalized protein expression (NPX) values were ranked and analyzed in CSF (left) and plasma (right) in similar fashion as shown in (A). Background signal was subtracted from unlogged NPX values. NPX values below LOD were not considered. (D) Olink NPX values were calibrated to proteins of known absolute plasma concentration and ranked, as described in (B) (n=808 out of 4226 proteins in the HPA). Boxplots represent summed concentration in control and AD cases for the top 5% (>9.5 log10 pg/L) and bottom 50% (<6.2 log10 pg/L) of proteins measured that overlap with HPA proteins. (E) Undepleted TMT mass spectrometry summed reporter ion counts for each protein were ranked and analyzed in CSF (left) and plasma (right) in similar fashion as shown in (A) and (C). (F) Undepleted TMT-MS ion counts were calibrated to proteins of known absolute plasma concentration and ranked, as described in (B) and (D) (n=925 out of 4226 proteins in the HPA). The red shaded area represents the top 10% most abundant proteins, whereas the blue shaded area represents the bottom 30% of abundance at a threshold of <6.2 pg/L in the undepleted MS data. (G) Depleted TMT-MS ion counts were analyzed as described in (E). (H) Depleted TMT-MS ion counts were analyzed as described in (F) (n=1163 out of 4226 proteins in the HPA). The blue shaded area represents the bottom 30% of abundance at a threshold of <6.2 pg/L in the depleted MS data. Shaded areas represent +/- SEM. Differences between control and AD were determined by t test. Supplementary Figure 9. SMOC1 Analyses. (A-E) Correlation of SMOC1 relative abundance in CSF between proteomic platforms (A). (B) Correlation of SMOC1 relative abundance in plasma between Olink and SomaScan platforms. SMOC1 was not measured by MS in plasma. Correlations in (A) and (B) were performed with the SMOC1 5694.57 SOMAmer. (C) Correlation of SMOC1 relative abundance as measured by Olink with CSF Aβ/T-Tau ratio in CSF (left) and plasma (right). (D) Correlation of SMOC1 relative abundance with MoCA score, a measure of cognitive performance (higher scores reflect better cognitive performance), in CSF (left) and plasma (right). (E) Correlation of SMOC1 relative abundance with MoCA score in CSF (left) and plasma (right) in the Accelerating Medicines Partnership – Parkinson’s Disease (AMP-PD) cohort. Correlations were performed using Pearson correlation. Aβ, amyloid-β; MoCA, Montreal Cognitive Assessment; MS, mass spectrometry; SMOC1, SPARC-related modular calcium-binding protein 1; T-Tau, total tau. Supplementary Figure 10. HOMER1 Analyses. (A-C) Correlation of HOMER1 relative abundance as measured by SomaScan in CSF (left) and plasma (right) with CSF total tau (T-Tau) levels (A). (B) Correlation of HOMER1 relative abundance in CSF (left) and plasma (right) with CSF phosphorylated tau181 (p-Tau) levels. (C) Correlation of HOMER1 relative abundance in CSF (left) and plasma (right) with Montreal Cognitive Assessment score . HOMER1 was measured in CSF and plasma only by SomaScan. Correlations were performed using Pearson correlation. HOMER1, Homer protein homolog 1. Supplementary Figure 11. NEFL Analyses. (A-F) Correlation of NEFL relative abundance in CSF (left) and plasma (right) between proteomic platforms (A). NEFL was not measured in plasma by MS. (B) Differences in NEFL relative abundance between control and AD cases by platform in CSF (left) and plasma (right). (C) Correlation of NEFL relative abundance as measured by Olink in CSF (left) and plasma (right) with CSF T-Tau levels in the AD discovery cohort (top) and AMP-PD cohort (bottom). (D) Correlation of NEFL relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels in the AD discovery cohort (top) and AMP-PD cohort (bottom). (E) Correlation of NEFL relative abundance in CSF (left) and plasma (right) with MoCA score in the AD discovery cohort (top) and AMP-PD cohort (bottom). (F) Correlation of NEFL relative abundance in CSF (left) and plasma (right) with age in the AMP-PD cohort. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). AMP-PD, Accelerating Medicines Partnership – Parkinson’s Disease; MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; NEFL, neurofilament light polypeptide; p-Tau, phosphorylated tau181; T-Tau, total tau. Supplementary Figure 12. CHI3L1 Analyses. (A-E) Correlation of CHI3L1 relative abundance in CSF (left) and plasma (right) between proteomic platforms (A). (B) Differences in CHI3L1 relative abundance between control and AD cases by platform in CSF (left) and plasma (right). (C) Correlation of CHI3L1 relative abundance as measured by MS in CSF (left) and plasma (right) with CSF T-Tau levels. (D) Correlation of CHI3L1 relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels. (E) Correlation of CHI3L1 relative abundance in CSF (left) and plasma (right) with MoCA score. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). CHI3L1, Chitinase-3-like protein 1; MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; p-Tau, phosphorylated tau181; T-Tau, total tau. Supplementary Figure 13. YWHAZ Analyses. (A-E) Correlation of YWHAZ relative abundance in CSF (left) and plasma (right) between SomaScan and MS platforms (A). YWHAZ was not measured by Olink. (B) Differences in YWHAZ relative abundance as measured by MS between control and AD cases in CSF (left) and plasma (right). (C) Correlation of YWHAZ relative abundance as measured by MS in CSF (left) and plasma (right) with CSF T-Tau levels. (D) Correlation of YWHAZ relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels. (E) Correlation of YWHAZ relative abundance in CSF (left) and plasma (right) with MoCA score. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; p-Tau, phosphorylated tau181; T-Tau, total tau; YWHAZ, 14-3-3 protein zeta. Supplementary Figure 14. ENO1 Analyses. (A-E) Correlation of ENO1 relative abundance in CSF (left) and plasma (right) between SomaScan and MS platforms (A). ENO1 was not measured by Olink. (B) Differences in ENO1 relative abundance as measured by MS between control and AD cases in CSF (left) and plasma (right). (C) Correlation of ENO1 relative abundance as measured by SomaScan in CSF (left) and plasma (right) with CSF T-Tau levels. (D) Correlation of ENO1 relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels. (E) Correlation of ENO1 relative abundance in CSF (left) and plasma (right) with MoCA score. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). ENO1, alpha-enolase; MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; p-Tau, phosphorylated tau181; T-Tau, total tau. Supplementary Figure 15. PEBP1 Analyses. (A-F) Correlation of PEBP1 relative abundance in CSF (left) and plasma (right) between proteomic platforms (A). (B) Differences in PEBP1 relative abundance between control and AD cases by platform in CSF (left) and plasma (right). (C) Correlation of PEBP1 relative abundance as measured by SomaScan in CSF (left) and plasma (right) with CSF T-Tau levels in the AD discovery cohort (top) and AMP-PD cohort (bottom). (D) Correlation of PEBP1 relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels in the AD discovery cohort (top) and AMP-PD cohort (bottom). (E) Correlation of PEBP1 relative abundance in CSF (left) and plasma (right) with MoCA score in the AD discovery cohort (top) and AMP-PD cohort (bottom). (F) Correlation of PEBP1 relative abundance in CSF (left) and plasma (right) with age in the AMP-PD cohort. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). AMP-PD, Accelerating Medicines Partnership – Parkinson’s Disease; MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; PEBP1, phosphatidylethanolamine-binding protein 1; p-Tau, phosphorylated tau181; T-Tau, total tau. Supplementary Figure 16. NPTXR Analyses. (A-F) Correlation of NPTXR relative abundance in CSF (left) and plasma (right) between proteomic platforms (A). (B) Differences in NPTXR relative abundance between control and AD cases by platform in CSF (left) and plasma (right). (C) Correlation of relative NPTXR levels measured in each platform between CSF and plasma. (D) Correlation of NPTXR relative abundance as measured by SomaScan in CSF (left) and plasma (right) with CSF T-Tau levels. (E) Correlation of NPTXR relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels. (F) Correlation of PEBP1 relative abundance in CSF (left) and plasma (right) with MoCA score. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; NPTXR, neuronal pentraxin receptor; p-Tau, phosphorylated tau181; T-Tau, total tau. Supplementary Figure 17. SPP1 Analyses. (A-F) Correlation of SPP1 relative abundance in CSF (left) and plasma (right) between proteomic platforms (A). (B) Differences in SPP1 relative abundance between control and AD cases by platform in CSF (left) and plasma (right). (C) Correlation of relative SPP1 levels measured by SomaScan between CSF and plasma. (D) Correlation of SPP1 relative abundance in CSF (left) and plasma (right) with CSF T-Tau levels. (E) Correlation of SPP1 relative abundance in CSF (left) and plasma (right) with CSF p-Tau levels. (F) Correlation of SPP1 relative abundance in CSF (left) and plasma (right) with MoCA score. Correlations were performed using Pearson correlation. Group differences were assessed using t test. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). MoCA, Montreal Cognitive Assessment (higher scores reflect better cognitive performance); MS, mass spectrometry; p-Tau, phosphorylated tau181; SPP1, osteopontin; T-Tau, total tau. Supplementary Figure 18. CSF and Plasma Network Platform Representation. Percent coverage of CSF (top) and plasma (bottom) network modules by proteomic platform. Modules are listed in relationship order. “All” indicates percent coverage across the entire network. Supplementary Figure 19. AD Brain Protein Co-Expression Network. AD brain consensus protein correlation network as shown in Fig. p values are for the module with the strongest overlap. Only modules that reached statistical significance after FDR correction are colored by degree of significance. In addition to module overlap and preservation analyses, the difference in brain module eigenprotein between control and AD, or brain synthetic eigenprotein in CSF or plasma between control and AD, was determined. A significantly increased eigenprotein in AD is indicated in green, whereas a significantly decreased eigenprotein is indicated in blue. Supplementary Figure 20. Module Over-Representation Analysis Across Brain and Biofluid Networks. (A-C) Module member overrepresentation analysis (ORA) of the brain and CSF networks (A), brain and plasma networks (B), and CSF and plasma networks (C). The numbers in each box represent the –log10(FDR) value for the overlap after Benjamini-Hochberg correction. Modules on the y-axis (rows) without an overlap FDR value of – log10 (FDR) > 1 were not included in the heatmaps. Supplementary Figure 21. Plasma Network Module Over-Representation Analysis with a SomaScan Serum Network. Module member overrepresentation analysis (ORA) of a serum protein co-expression network (Emilsson-PM) obtained using the SomaScan platform, as described in Emilsson et al. [10(FDR) value for the overlap after Benjamini-Hochberg correction. Modules on the y-axis (rows) without an overlap FDR value of – log10 (FDR) > 1 were not included in the heatmaps. Extended Data. Extended Data 1. Correlation of Proteins Commonly Measured in CSF by MS-TMT and Olink. n=36 unless otherwise indicated. Measurements were from TMT-MS on CSF depleted of highly abundant plasma proteins. Olink NPX values are shown on the x-axis, and TMT-MS log2 relative values are shown on the y-axis. Measurements that were below LOD in Olink are not outlined. Correlations include those proteins that were matched by gene symbol only. Correlations were performed using Pearson test. Extended Data 2. Correlation of Proteins Commonly Measured in CSF by MS-TMT and SomaScan. n=35 unless otherwise indicated. Measurements were from TMT-MS on CSF depleted of highly abundant plasma proteins. TMT-MS log2 relative values are shown on the x-axis, and SomaScan log2(RFU) values are shown on the y-axis. Measurements that were below LOD in SomaScan are not outlined. Correlations were performed for all SOMAmers including multiple SOMAmers targeting the same protein, and include those proteins that were matched by gene symbol only. Correlations were performed using Pearson test. Extended Data 3. Correlation of Proteins Commonly Measured in CSF by Olink and SomaScan. n=35 unless otherwise indicated. Olink NPX values are shown on the x-axis, and SomaScan log2(RFU) values are shown on the y-axis. Measurements that were below LOD in either platform are not outlined. Correlations were performed for all SOMAmers including multiple SOMAmers targeting the same protein, and include those proteins that were matched by gene symbol only. Correlations were performed using Pearson test. Extended Data 4. GO Analysis on AD CSF Network Modules. Gene ontology (GO) analysis was performed to gain insight into the biological meaning of each AD CSF protein network module. Enrichment for a given ontology is shown by z score. Extended Data 5. AD CSF Network Module Protein Graphs. The size of each circle indicates the relative eigenprotein correlation value (kME) in each network module. Those proteins with the largest kME are considered “hub” proteins within the module, and explain the largest variance in module expression. Proteins outlined in gold are from the SomaScan platform. Proteins outlined in green are from the Olink platform. Proteins outlined in purple are from the TMT-MS platform. Only the top 100 proteins by kME for each module are shown. Extended Data 6. AD CSF Network Module Eigenprotein Levels and Correlations. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 7. AD CSF Network Module Synthetic Eigenprotein Levels and Correlations in Brain. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 8. AD CSF Network Module Synthetic Eigenprotein Levels and Correlations in Plasma. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 9. Within-Subject CSF Network Module Eigenprotein Levels in Plasma and CSF. n=18 control, 17 AD. Relative CSF protein network module eigenprotein levels and their synthetic eigenprotein levels in plasma were compared within subject across fluids in control and AD cases. The difference in average slope (Zslope) between AD and control was calculated for each module. Extended Data 10. Correlation of Proteins Commonly Measured in Plasma by MS-TMT and Olink. n=36 unless otherwise indicated. Measurements were from TMT-MS on plasma depleted of highly abundant plasma proteins. Olink NPX values are shown on the x-axis, and TMT-MS log2 relative values are shown on the y-axis. Measurements that were below LOD in Olink are not outlined. Correlations include those proteins that were matched by gene symbol only. Correlations were performed using Pearson test. Extended Data 11. Correlation of Proteins Commonly Measured in Plasma by MS-TMT and SomaScan. n=35 unless otherwise indicated. Measurements were from TMT-MS on plasma depleted of highly abundant plasma proteins. TMT-MS log2 relative values are shown on the x-axis, and SomaScan log2(RFU) values are shown on the y-axis. Measurements that were below LOD in SomaScan are not outlined. Correlations were performed for all SOMAmers including multiple SOMAmers targeting the same protein, and include those proteins that were matched by gene symbol only. Correlations were performed using Pearson test. Extended Data 12. Correlation of Proteins Commonly Measured in Plasma by Olink and SomaScan. n=35 unless otherwise indicated. Olink NPX values are shown on the x-axis, and SomaScan log2(RFU) values are shown on the y-axis. Measurements that were below LOD in either platform are not outlined. Correlations were performed for all SOMAmers including multiple SOMAmers targeting the same protein, and include those proteins that were matched by gene symbol only. Correlations were performed using Pearson test. Extended Data 13. GO Analysis on AD Plasma Network Modules. Gene ontology (GO) analysis was performed to gain insight into the biological meaning of each AD plasma protein network module. Enrichment for a given ontology is shown by z score. Extended Data 14. AD Plasma Network Module Protein Graphs. The size of each circle indicates the relative eigenprotein correlation value (kME) in each network module. Those proteins with the largest kME are considered “hub” proteins within the module, and explain the largest variance in module expression. Proteins outlined in gold are from the SomaScan platform. Proteins outlined in green are from the Olink platform. Proteins outlined in purple are from the TMT-MS platform. Only the top 100 proteins by kME for each module are shown. Extended Data 15. AD Plasma Network Module Eigenprotein Levels and Correlations. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 16. AD Plasma Network Module Synthetic Eigenprotein Levels and Correlations in Brain. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 17. AD Plasma Network Module Synthetic Eigenprotein Levels and Correlations in CSF. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 18. Within-Subject Plasma Network Module Eigenprotein Levels in CSF and Plasma. n=18 control, 17 AD. Relative plasma protein network module eigenprotein levels and their synthetic eigenprotein levels in CSF were compared within subject across fluids in control and AD cases. The difference in average slope (Zslope) between AD and control was calculated for each module. Extended Data 19. Brain TMT-MS AD Network Module Synthetic Eigenprotein Levels and Correlations in CSF. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars). Extended Data 20. Brain TMT-MS AD Network Module Synthetic Eigenprotein Levels and Correlations in Plasma. n=18 control, 17 AD. Differences between case groups were assessed by t test or one-way ANOVA. Correlations were performed by bicor or Pearson test (cor). Significance at p<0.05 is highlighted in red. Boxplots represent the median, 25th, and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Datapoints up to 1.5 times the interquartile range from box hinge define the extent of whiskers (error bars).g et al.  (B) Corr [et al.  (C) Corrl et al.  Proteinsn et al. , with thAdditional file 2: Supplementary Table 1. Traits for Emory Goizueta Alzheimer's Disease Research Center Cohort. Supplementary Table 2. SomaScan Filter Counts. Supplementary Table 3. Proteins Affected by Depletion of Highly Abundant Proteins in CSF and Plasma. Supplementary Table 4. Platform Overlap with SomaScan, Olink, and MS Depleted CSF. Supplementary Table 5. Platform Overlap with SomaScan, Olink, and MS Undepleted CSF. Supplementary Table 6. Platform Overlap with SomaScan, Olink, and MS Depleted Plasma. Supplementary Table 7. Platform Overlap with SomaScan, Olink, and MS Undepleted Plasma. Supplementary Table 8. Overlap Between CSF and Plasma in Olink. Supplementary Table 9. Overlap Between CSF and Plasma in SomaScan. Supplementary Table 10. Overlap Between CSF and Plasma in MS Depleted. Supplementary Table 11. Overlap Between CSF and Plasma in MS Undepleted. Supplementary Table 12. Correlation Between Olink and SomaScan Measurements in CSF. Supplementary Table 13. Correlation Between Olink and SomaScan Measurements in Plasma. Supplementary Table 14. Correlation Between Olink and TMT-MS Measurements in CSF. Supplementary Table 15. Correlation Between Olink and TMT-MS Measurements in Plasma. Supplementary Table 16. Correlation Between TMT-MS and SomaScan Measurements in CSF. Supplementary Table 17. Correlation Between TMT-MS and SomaScan Measurements in Plasma. Supplementary Table 18. CSF AD Network Module Memberships. Supplementary Table 19. CSF Network Circular Heatmap Values. Supplementary Table 20. Plasma AD Network Module Memberships. Supplementary Table 21. Plasma Network Circular Heatmap Values. Supplementary Table 22. Brain Network Circular Heatmap Values.


The usefulness of neurofilament light (NfL) as a biomarker for small vessel disease has not been established. We examined the relationship between NfL, neuroimaging changes, and clinical findings in subjects with varying degrees of white matter hyperintensity (WMH).A subgroup of participants (n = 35) in the Helsinki Small Vessel Disease Study underwent an analysis of NfL in cerebrospinal fluid (CSF) as well as brain magnetic resonance imaging (MRI) and neuropsychological and motor performance assessments. WMH and structural brain volumes were obtained with automatic segmentation.p = 0.105). However, strong correlations were observed between CSF NfL and volumes of cerebral grey matter , cerebral cortex , and hippocampi . CSF NfL also correlated with composite measures of global cognition , executive functions , memory , and processing speed . Regarding motor performance, CSF NfL was correlated with Timed Up and Go (TUG) test , and gait speed , but not with single-leg stance. After adjusting for age, associations with volumes in MRI, functional mobility (TUG), and gait speed remained significant, whereas associations with cognitive performance attenuated below the significance level despite medium to large effect sizes.CSF NfL did not correlate significantly with total WMH volume (r = 0.278, NfL was strongly related to global gray matter and hippocampal atrophy, but not to WMH severity. NfL was also associated with motor performance. Our results suggest that NfL is independently associated with brain atrophy and functional mobility, but is not a reliable marker for cerebral small vessel disease. Neurofilament light (NfL) is a polypeptide expressed only in neurons and released into cerebrospinal fluid (CSF) following axonal injury, irrespective of underlying cause . Plasma Some studies have suggested a confounding effect between NfL levels and subjects’ body mass index (BMI), blood volume , and gluSmall vessel disease (SVD)-related white matter hyperintensities (WMH) and subcortical infarcts have been found to associate with elevated NfL . In addiAssociation between NfL and cognitive impairment has been studied across several neurological diseases such as MS, Parkinson disease, MND, and AD . Most ofIn recent years, numerous studies have reported association between NfL and brain atrophy in MS  and neurThe aim of this study was to explore the association of CSF NfL levels with WMH and structural brain volumes. Another focus in this study was to examine how NfL as an axonal injury marker is related to global cognition and the specific domains of processing speed, executive function, and memory as well as functional mobility, gait, and balance.Participants were drawn from the Helsinki SVD study, a prospective cohort study. The Helsinki SVD study investigates the progression of imaging, clinical, and cognitive characteristics of cerebral SVD in elderly individuals. Subjects were recruited from the imaging registry of the Helsinki University Hospital (HUS), Finland during the period between October 2016 and March 2020. The Helsinki SVD study enrolled 152 participants with different degrees of WMH, aged 65–75 years, excluding subjects with major neurological and psychiatric diseases, substance abuse, history of head trauma needing hospitalization, severe sight or hearing impairments influencing conducting cognitive tests, intellectual disability, and inability to undergo brain magnetic resonance imaging (MRI). Comprehensive neurological, neuropsychological and laboratory examinations and a brain MRI with standard protocol were carried out during three visits within 1 month.We recruited patients to participate in this substudy to evaluate CSF samples starting from 2019. The patients who had undergone brain MRI not more than 6 months earlier were eligible. Further exclusion criteria were hippocampal atrophy Scheltens grade 3–4, anticoagulant therapy, predisposition to bleeding, and refusal of lumbar puncture. A final total of 35 patients met the study criteria and were willing to participate in the substudy.The demographic and clinical information of the participants as well as participants risk factors for small vessel disease are presented in The study was approved by the Ethics Committee of the HUS and conducted according to the Declaration of Helsinki. Informed written consent was received from each study participant.Imaging was performed with a 3 T MRI scanner with a 32-channel head coil. The imaging protocol consisted of a plane localizer, 3D fluid-attenuated inversion recovery (FLAIR) SPACE, 3D T2 SPACE, 3D T1 MPRAGE, 3D gradient echo (GE) susceptibility weighted imaging (SWI) sequence, 3D GE sequence with magnetization transfer pulse on and off.n = 16) and another with subjects with moderate to severe WMH (n = 19). Volumes of brain structures and WMH were further evaluated using an automated multi-stage segmentation method on FLAIR images as previously described  test as well as measurements of gait speed, and balance. The TUG test assesses functional mobility. The subject was asked to rise from a chair without using armrests, walk 3 meters, turn, walk back to the chair and sit down again . The var1-42, total Tau and phosphorylated Tau in the CSF were determined at the biomarker laboratory of the University of Eastern Finland. CSF NfL was measured using Simoa (Single Molecule Array) digital immunoassay  CSF (#06986811), Elecsys Total-Tau CSF (#6986838) and Elecsys Phospho-Tau (181P) CSF (#6986846) on a fully automated cobas e602 analyzer  were applied separately to the MRI and cognitive and motor variables. Results that were significant using an FDR of 0.05 have been marked in CSF NfL and the WMH and structural brain volumes , cognitive and motor tests results  and BMI, diabetes, hypertension were explored with Pearson correlation tests. Linear regression adjusted for age was also used to assess the strength of the individual relationships between CSF NfL and the response variables: subjects’ age, GCS, TUG, gait speed, and volumes of cerebral gray matter, cerebral cortex, hippocampus, and total WMH. The effect size of each variable was measured with Cohen’s fr = 0.41, p = 0.016), but was not associated with sex .CSF samples were collected from 35 subjects. The CSF NfL mean and median were 917.33 pg./ml and 819.06 pg./ml respectively, with a range of 450.48–793.74 pg./ml. CSF NfL correlated positively with subject’s age , nor were they associated with presence of diabetes  or hypertension .CSF NfL did not correlate with BMI  can be seen in 1-42 in subjects group with no to mild WMH was 1278.99 pg./ml, and in group with moderate to severe WMH and 1264.65 pg./ml. The mean concentration of CSF t-tau were 202.41 pg./ml in group with no to mild WMH and 221.60 pg./ml in group with moderate to severe WMH; the mean CSF p-tau levels were 17.22 pg./ml in group with no to mild WMH and 19.17 pg./ml in group with moderate to severe WMH. There were no significant correlations with CSF NfL and other CSF  biomarkers.The mean of CSF Aβp < 0.001, r = − 0.57), cerebral cortex  and hippocampi , but not with total WMH volume  .NfL was strongly correlated with volumes of cerebral grey matter ( = 0.29) . The difβ = −0.487, p = 0.003, Cohen’s f2 = 0.52), cerebral cortex  and with hippocampal volumes   .r = −0.40, p = 0.016). CSF NfL was also significantly correlated with executive function , processing speed  and memory . However, after adjusting for age, the relationships between CSF NfL and GCS , executive function , processing speed , and memory  were no longer significant despite medium to large effect sizes (CSF NfL had a negative correlation with GCS (ct sizes .p = 0.001). Gait speed was negatively correlated with CSF NfL . Single-leg stance test was not correlated with NfL. Similar results were found using age adjusted linear regression between CSF NfL and TUG tests , and gait speed , while no significant relationship was found for single-leg stance tests .Both TUG test and gait speed were associated with CSF NfL . PearsonIn this study, NfL was not significantly associated with WMH volume. However, NfL correlated strongly with cerebral grey matter, cerebral cortex, and hippocampal volumes. Additionally, higher NfL levels were associated with impaired motor performance . NfL was also related to impaired cognitive functions, which was partly explained by age.In our analysis we used CSF NfL because much higher concentrations of NfL were found in CSF than in plasma. NfL first leaks into the extracellular space after neuronal damage, from there into CSF, and ultimately into the bloodstream. Plasma NfL level in our study (data not shown) showed the same correlations although somewhat weaker. NfL in plasma is a less sensitive marker than NfL in CSF. However, given that peripheral samples are far easier to access, plasma NfL can be a promising method in clinical practice.Concerning the relation between NfL and WMH, our study differs from previous studies . A potenConcerning the relation between NfL and brain atrophy in patients with varying degrees of WMH, we have not identified any previous studies to compare with, as previous studies have focused on MS and neurodegenerative disease.In line with previous studies, we found an association between higher CSF NfL level and increasing age . CSF NfLThe relation between NfL and motor performance has been studied in MS , ParkinsInvestigating other clinical factors, our results did not confirm the association between NfL and BMI, blood pressure, and blood glucose that has been indicated in some previous studies .The strengths of our study are the comprehensive evaluations of cognitive and motor abilities with objective well-validated tests and the quantification of brain changes with advanced automated image analysis . There aIn conclusion, our study indicates that NfL level generally reflects neuroaxonal damage of CNS and neural death, neuroradiologically apparent as brain atrophy and clinically manifesting as a decline in motor performance. High levels of NfL suggest active neurodegeneration or ongoing axonal injury. Neither NfL nor brain atrophy are specific for any given disease, so potential comorbidity should be considered when interpreting NfL levels and brain atrophy in various neurological conditions.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was approved by the Ethics Committee of the HUS and conducted according to the Declaration of Helsinki. The patients/participants provided their written informed consent to participate in this study.SM, HJ, MK, TE, HL, JP, MA, and JLe contributed to conception and design of the study. AK, JLö, and JKo performed the radiological and MRI analysis. JKa performed the statistical analysis. MK wrote the first draft of the manuscript. SM and HJ wrote sections of the manuscript. S-KH did the laboratory work. All authors contributed to manuscript revision, read, and approved the submitted version.JKo and JLö are employed by Combinostics Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Microglia, the tissue macrophages of the central nervous system, use phagocytosis of loosely organized amyloid beta (Aβ) to construct dense-core plaques, one of the defining histopathological features of Alzheimer’s disease. The biology of this process parallels macrophage construction of granulomas in tuberculosis and other settings. Dense-core plaques, whose centers contain highly polymerized and compacted aggregates of amyloid β peptides, are one of the two defining histopathological features of Alzheimer’s disease. Recent findings indicate that these plaques do not form spontaneously but are instead constructed by microglia, the tissue macrophages of the central nervous system. We discuss cellular, structural, functional, and gene expression criteria by which the microglial assembly of dense-core plaques in the Alzheimer’s brain parallels the construction of granulomas by macrophages in other settings. We compare the genesis of these plaques to the macrophage assembly of mycobacterial granulomas, the defining histopathological features of tuberculosis. We suggest that if dense-core plaques are indeed granulomas, their simple disassembly may be contraindicated as an Alzheimer’s therapy. Mycobacterium tuberculosis. Traditionally, the TB granuloma has been viewed as a confinement structure that sequesters the bacillus  plaques of Alzheimer’s disease (AD), which were first described by Alois Alzheimer more than a century ago , are graHistorically, dense-core Aβ plaques have been identified through staining of postmortem brain sections with Congo Red, Thioflavin S or T (Thio S/T), and other dyes that insert into the β-pleated sheets of aggregated amyloid proteins . They arRecent studies in the mouse indicate that dense-core plaques do not form spontaneously . InsteadM. tuberculosis, TB granulomas are also populated by dendritic cells, neutrophils, natural killer (NK) cells, B cells, and CD4+ and CD8+ T cells . These mouse models express mutant forms of the human APP gene that result in familial (inherited) AD. Often the APP genes are coexpressed with mutant forms of the human Presenilin-1 (PSEN1) gene, which encodes a component of the enzyme complex that cleaves Aβ peptides from APP  are transcriptionally and functionally altered relative to their non–plaque-associated counterparts (NPAM). They transform from an exploratory homeostatic phenotype characterized by the presence of many long, highly branched, and highly motile cell processes, to an activated amoeboid phenotype characterized by elevated expression of inflammatory cytokines and phagocytic mediators, swollen cell bodies, and fewer, shorter, and less motile processes . Immediaon in AD . Consiston in AD .If dense-core plaques are granulomas, and all granulomas are built by macrophages, then dense-core plaques should never form in the absence of microglia. Investigators have addressed this question using pharmacological inhibition of a microglial survival pathway in mouse AD models before plaque formation. Genetic studies demonstrate that microglia and other tissue macrophages require continuous signaling through the colony-stimulating factor 1 receptor (CSF1R) to survive , and in + dense-core plaques in the cortex after 10 wk of treatment  and homeostatic non–plaque-associated (NPAM) microglia. Seurat analyses generated seven microglial clusters, which were progressively arrayed between homeostatic  and activated  states  macrophages  states .M. marinum granuloma, while intermediate in activation between these states, more closely resembled PAM than NPAM , the cytochrome c oxidase subunit cox6c, and the tubulin chaperone tbca , the lipoprotein lipase LPL, the GTPase RAB7B , AXL, the lysosomal ATPase ATP6V1F, and again, TYROBP.Single-cell transcriptomic data have also recently been published for macrophages collected from the pulmonary granulomas that develop when nonhuman primates are infected with rculosis . Again, SPP1, CTSD, NPC2, and many of the other mRNAs discussed above  maced above ; (c) liped above ; and (d)ed above . TogetheIf dense-core Aβ plaques are granulomas, how should they be addressed in the context of AD therapy? This is an important question, since these plaques are often detected in cognitively healthy adults . Perhaps18F; The first drug approved by the US Food and Drug Administration (FDA) to address the underlying biology of AD is aducanumab, a monoclonal antibody against Aβ . ExtendeAlthough the initiating event with respect to amyloid deposition in AD is the excess production of Aβ peptides, multiple pharmacological inhibitors of BACE1 and γ-secretase—the enzymes that sequentially cleave these peptides from APP—have failed as AD therapies, often because of adverse off-target effects . Compoun