Th17 and effector CD8 T cells relate to disease progression in amyotrophic lateral sclerosis: a case control study

Th17 and effector CD8 T cells relate to disease progression in amyotrophic lateral sclerosis: a case control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Th17 and effector CD8 T cells relate to disease progression in amyotrophic lateral sclerosis: a case control study Tatsuo Itou, Koji Fujita, Yuumi Okuzono, Dnyaneshwar Warude, Shuuichi Miyakawa, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5187725/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2024 Read the published version in Journal of Neuroinflammation → Version 1 posted 8 You are reading this latest preprint version Abstract The immune system has garnered attention due to its association with disease progression in amyotrophic lateral sclerosis (ALS). However, the role of peripheral immune cells in this context remains controversial. Here, we conducted single-cell RNA-sequencing of peripheral blood mononuclear cells to comprehensively profile immune cells concerning the rate of disease progression in patients with ALS. Our analysis revealed increased frequencies of T helper 17 cells (Th17) relative to regulatory T cells, effector CD8 T cells relative to naïve CD8 T cells, and CD16highCD56low mature natural killer cells relative to CD16lowCD56high naïve natural killer cells in patients with rapidly progressive ALS. Additionally, we employed serum proteomics through a proximity extension assay combined with next-generation sequencing to identify inflammation-related proteins associated with rapid disease progression. Among these proteins, interleukin-17A correlated with the frequency of Th17, while killer cell lectin-like receptor D1 (CD94) correlated with the frequency of effector CD8 T cells. These findings further support the active roles played by these specific immune cell types in the progression of ALS. amyotrophic lateral sclerosis disease progression effector CD8 T cells interleukin-17A killer cell lectin-like receptor D1 (CD94) natural killer cells single-cell RNA-sequencing T helper 17 cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects both upper and lower motor neurons ( 1 ). Following the onset of symptoms, muscle weakness relentlessly progresses, eventually leading to respiratory failure and, unfortunately, death within a few years for most patients. Nonetheless, the rate of progression varies considerably among patients ( 1 ). This variability in progression poses a challenge toward the development of therapeutics because clinical trials for ALS typically assess progression as the primary outcome ( 2 ). Therefore, gaining deep understanding on the pathogenesis of disease progression in ALS is of utmost importance. The progression of ALS has been linked to immune cells and proteins in both the central nervous system (CNS) and peripheral circulation ( 3 , 4 ). However, several critical questions in this regard remain unanswered. For instance, a decrease or dysfunction in regulatory T cells (Treg), characterized by the expression of the FOXP3 transcription factor, has been correlated with rapid progression ( 5 – 8 ). Furthermore, a flow cytometric study has reported that reduced levels of peripheral CD4 T cells are associated with the rate of progression, with the assumption that the reduction is linked to decreased Treg levels, although subpopulations of the peripheral blood CD4 T cells were not specifically examined ( 8 ). Conversely, another flow cytometric study demonstrated that ALS patients with higher levels of CD4 + FOXP3 − effector T cells in the cerebrospinal fluid (CSF) exhibited a higher rate of progression; although, detailed profiles of the CD4 T cell subpopulations were unavailable ( 9 ). Therefore, the roles of CD4 T cell subpopulations, other than Treg, in disease progression remain enigmatic in ALS. Additionally, although an increase in CD8 T cells has been reported in ALS ( 10 , 11 ), the function of CD8 T cells in disease progression remains unknown. Furthermore, studies investigating pro and anti-inflammatory cytokines in the blood of ALS patients, primarily based on enzyme-linked immunosorbent assays, have failed to detect correlations between cytokine levels and the Revised Amyotrophic Lateral Sclerosis Functional Scale (ALSFRS-R), which is used to assess disease severity and progression ( 12 – 17 ). To address these unresolved issues, we conducted an unbiased evaluation of phenotypes in peripheral blood immune cells, encompassing not only CD4 T cells but also CD8 T cells and natural killer (NK) cells, in patients with ALS and healthy subjects. We assessed their association with the rate of progression in patients with ALS through single-cell RNA-sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) ( 18 – 20 ). We also analyzed differentially expressed genes (DEG) in the scRNA-seq data. Furthermore, we searched for inflammation-related proteins in the serum linked to progression using a proximity extension assay (PEA) in conjunction with a high-throughput sequencing read-out ( 21 , 22 ). Considering reproducibility, major findings of the PEA were tested with Single molecule array (Simoa). We aimed to elucidate the interplay between immune cells and proteins in the context of disease progression in ALS. The scRNA-seq analysis highlighted the involvement of T helper 17 cells (Th17), effector CD8 T cells, and CD16 high CD56 low mature NK cells, while PEA identified associated proteins such as interleukin-17A (IL-17A) and killer cell lectin-like receptor D1 (KLRD1, also known as CD94), in relation to the rapid progression of ALS. CD94, expressed on NK cells and a small subset of CD8 T cells, forms a heterodimer with NKG2. The CD94/NKG2 receptor complex recognizes nonclassical MHC class I molecules, including HLA-E in humans and Qa-1 in mice, and has established roles in tumor immunity, viral infection, and autoimmunity ( 23 – 25 ). Meanwhile, DEG analysis revealed downregulation in SERPINA1 and AC103591.3 (RP11-386I14) in patients with ALS. SerpinA1, a serine protease inhibitor, is implicated in modulating microglial-mediated inflammation in neurodegenerative diseases ( 26 ). RP11-386I14, a long non-coding RNA expressed in B cells, monocytes, T cells, and NK cells ( 27 ), remains functionally uncharacterized. Together, these findings provide novel insights into peripheral immune mechanisms involved in the progression of ALS. Results Participants Of the 46 screened participants, 10 healthy controls and 35 patients with ALS gave informed consent, and one participant with neuropathy was excluded. Additionally, five participants with ALS who had gastric cancer (n = 1), hepatocellular carcinoma (n = 1), human T-lymphotropic virus type 1 antibody (n = 1), SOD1 (n = 1), and lung cancer and SOD1 (n = 1) were excluded. Eventually, peripheral blood samples of 10 healthy controls (age, 63.6 ± 10.2; male, 7), 23 patients with non-rapid ALS (ΔALSFRS-R/month < 1; age, 60.7 ± 10.8; male, 17), and 7 patients with rapid ALS (ΔALSFRS-R/month ≥ 1; age, 69.8 ± 14.4; male, 3) were analyzed (Table 1 ). Patients with ALS had disease duration of 10.4 ± 6.4 months and a mean ALSFRS-R score of 39.9 ± 5.2. The clinical information was compared with scRNA-seq and proteomics data (Fig. 1 A). Table 1 Clinical features of 40 subjects. Healthy control (n = 10) Non-rapid ALS (n = 23) Rapid ALS (n = 7) Age, years, mean ± SD 63.6 ± 10.2 60.7 ± 10.8 69.8 ± 14.4 Male, N (%) 7 (70.0) 17 (73.9) 3 (42.9) ALSFRS-R at entry, mean ± SD - 41.8 ± 2.6 34.7 ± 7.1 Duration before entry, months, mean ± SD - 12.4 ± 6.2 5.0 ± 2.5 ΔALSFRS-R/month, mean ± SD - 0.56 ± 0.22 2.81 ± 1.27 Site of onset, N (%) Bulbar - 8 (34.8) 2 (28.6) Limb - 14 (60.9) 5 (71.4) Trunk - 1 (4.3) 0 (0.0) %forced vital capacity, mean ± SD - 87.6 ± 22.7 61.5 ± 41.0 Cognitive status, N (%) Normal 10 (100.0) 21 (91.3) 4 (57.1) Impaired - 2 (8.7): FTD, 2 3 (42.9): FTD, 1; suspicion of FTD, 1; unknown etiology, 1 ALS: amyotrophic lateral sclerosis; ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Rating Scale; FTD: frontotemporal dementia; SD: standard deviation. Single-cell RNA-sequencing The integrated scRNA-seq classified the cells into 23 types for further analysis (Figs. 1 B, S1). The number of cells in each cell subset is shown in Table S1 . We first calculated the frequencies of cell types in all cells in healthy control, non-rapid ALS, and rapid ALS (Fig. 1 C, S2). The number of Treg significantly increased in non-rapid ALS compared with control and rapid ALS (2.4% vs. 1.9%, p = 0.01 and 2.4% vs. 1.6%, p = 0.03, respectively; Fig. 2 A). Next, we calculated the frequencies of cell types in related cells. The frequency of effector CD8 T cells in all CD8 T cells was significantly higher in rapid than non-rapid ALS (91.6% vs. 82.2%, p = 0.04). In contrast, the frequency of regulatory B cells among all B cells (5.5% vs. 7.3%, p = 0.05) and those of naïve and exhausted CD8 T cells among all CD8 T cells (4.1% vs. 8.4%, p = 0.01 and 0.6% vs. 0.9%, p = 0.04, respectively) were significantly lower in rapid than non-rapid ALS (Fig. 2 B). Furthermore, we calculated the frequency ratios between functionally related immune cells. The ratios of memory CD4 T cells/Treg (4.9 vs. 2.9, p = 0.02), Th1/Treg (1.9 vs. 1.2, p = 0.02), Th17/Treg (1 vs. 0.6, p = 0.005), effector/naïve CD8 T cells (23.0 vs. 9.8, p = 0.01), effector/exhausted CD8 T cells (149.7 vs. 89.9, p = 0.02), and mature (CD16 high CD56 low )/naïve (CD16 low CD56 high ) NK cells (28.1 vs. 20.1, p = 0.04) were significantly higher in rapid than non-rapid ALS (Fig. 2 C). Cell type frequencies and progression rate We investigated the relationship between cell type frequencies and progression rate in ALS. We used partial correlation because ΔALSFRS-R/month was correlated with age (Pearson’s correlation coefficient = 0.421, p = 0.02), and confounding with age could not be ignored in routine correlation analysis. The frequency of Th17 in CD4 T cells significantly correlated with ΔALSFRS-R/month (coefficient = 0.390, p = 0.037). Moreover, the ratios of memory CD4 T cells/Treg (coefficient = 0.450, p = 0.014) and Th17/Treg (coefficient = 0.428, p = 0.021) moderately but significantly correlated with ΔALSFRS-R/month ( Fig. S3 , Tables S2–4 ). Differentially expressed genes We performed differentially expressed gene (DEG) analysis of scRNA-seq data between ALS versus control, non-rapid ALS versus control, rapid ALS versus control, and rapid ALS versus non-rapid ALS, and then isolated the DEGs in 23 cell types (Bonferroni adjusted p < 0.05, fold change [FC] ≥ 1.2). SERPINA1 in naïve CD4 T cell and AC103591.3 in classical monocytes were downregulated in ALS than in control but were not different between rapid and non-rapid ALS ( Table S5 ). Inflammation proteins PEA-based proteomics of serum identified KLRDD1 (CD94) (FC = 2.2, p < 0.001), trefoil factor 2 (TFF2; FC = 2.3, p < 0.001), keratin, type I cytoskeletal 19 (KRT19; FC = 3.2, p < 0.001), IL-17A (FC = 4.8, p = 0.007), YTH domain-containing family protein 3 (YTHDF3; FC = 2.6, p = 0.03), and neutrophil cytosolic factor 2 (NCF2; FC = 2.4, p = 0.04) as significantly elevated in rapid versus non-rapid ALS. Of these, KLRD1 (FC = 2.2, p < 0.001), KRT19 (FC = 2.7, p = 0.01), NCF2 (FC = 3.0, p = 0.02), YTHDF3 (FC = 3.0, p = 0.02), and IL-17A (FC = 4.0, p = 0.04) were significantly elevated in rapid ALS than in control (Fig. 3 A, B). IL-17A concentrations measured with Simoa were also significantly higher in rapid ALS than in non-rapid ALS (p = 0.04, Tukey’s honestly significant difference [HSD] test; p = 0.04, one-way ANOVA) ( Fig. S4 ). Expression levels of phosphorylated neurofilament heavy chain (pNfH) were significantly higher in ALS (non-rapid + rapid) than in control (FC = 180, p = 0.001) and in rapid ALS than in control (FC = 3074, p < 0.001) (Fig. 3 A, C), which supported the validity of our samples. Moreover, we conducted a partial correlation analysis between serum protein levels and ΔALSFRS-R/month to examine whether the measured proteins correlated with the progression rate ( Table S6 ). All the six differentially expressed proteins (DEPs) that were elevated in rapid ALS versus non-rapid ALS had significant correlations, especially KLRD1 (coefficient = 0.745, p 0.5 and p-values of ≤ 0.003. In parallel, we also detected CD160, which is associated with NK and CD8 T cells with cytolytic effector activity, as highly correlated with disease progression. Correlation between proteins and cell types We investigated the correlation between the serum protein levels and the cell type ratios obtained by the scRNA-seq ( Table S7 ). Regarding the proteins elevated in rapid versus non-rapid ALS, NCF2–classical monocyte/all cells, IL-17A–memory CD4 T cells/CD4 T cells, IL-17A–Th17/CD4 T cells, TFF2–effector CD8 T cells/CD8 T cells, KLRD1–effector CD8 cells/CD8 cells, and KLRD1–effector CD8 T cells/exhausted CD8 T cells were the significant combinations with correlation coefficients of > 0.5 (Fig. 4 ). In addition, the ratio of effector CD8 T cells correlated with tumor necrosis factor receptor superfamily member 11B, which also correlated with ΔALSFRS-R/month (partial correlation coefficient = 0.402, p = 0.031). Discussion This study applied multiomics to blood profiling and determined how immune changes relate to disease progression in patients with ALS. The scRNA-seq uncovered cell type shifts from Treg toward Th17, naïve toward effector CD8 T cells, and naïve toward mature NK cells in rapidly progressive ALS. The serum proteomics revealed that IL-17A and KLRD1 (CD94) increased in rapidly progressive ALS and correlated with frequencies of Th17 and effector CD8 T cells, respectively, thus reinforcing the active roles of these cell types. Th17 can promote protective immunity against many pathogens but also drive inflammatory pathology during infection, autoimmunity such as multiple sclerosis, and neurodegeneration such as Alzheimer’s ( 28 ). Flow cytometric analyses using PBMCs revealed ALS patients had higher frequencies of Th1 and Th17 and lower frequencies of Th2 and Treg compared with healthy controls ( 13 , 14 ). However, previous studies showed no relationship between Th17 and disease progression. Similarly, an increase in IL-17A, the major effector cytokine of Th17, was reported in serum or CSF in ALS patients ( 13 – 17 ). However, the association between IL-17A and disease progression remained unknown. Here, we successfully related not only Th17 but also IL-17A to the rapid progression of ALS. The results regarding IL-17A were reproduced by measurement with Simoa. Notably, effector CD8 T cells, compared to naïve CD8 T cells, have also emerged as a phenotype associated with the rapid progression of ALS. CD8 T cells have been reported to play significant roles in SOD1-ALS and juvenile ALS-4, which is caused by SETX mutation ( 29 – 31 ). In SOD1 model mice, the depletion of CD8 T cells led to an increase in the number of surviving motor neurons, whereas CD8 T cells expressing mutant SOD1 were found to selectively recognize and kill motor neurons in vitro ( 30 ). More recently, in SETX knock-in mice with the L389S mutation, T cell receptor (TCR) repertoire analysis of both the CNS and blood revealed the presence of clonally expanded PD-1 + CD8 T cells ( 31 ). These expanded PD-1 + CD8 T cells were associated with immunity to glioma but not melanoma, suggesting their recognition of antigens of CNS origin. Additionally, clonally expanded, terminally differentiated effector memory CD8 T cells were detected in the peripheral blood of patients with ALS4. These findings indicate that antigen-specific CD8 T cell responses are involved in the pathogenesis of ALS4. Consistent with this, scRNA-seq of CSF showed higher levels of TCR expansions in effector CD4 and CD8 T cells in five ALS patients compared to four disease controls ( 9 ). Together, these findings and our own suggest that effector CD8 T cells in the blood may recognize specific antigens, possibly self-antigens, in ALS patients. The present and previous findings together suggest that the changes in peripheral immune cell profiles we observed, although they appear modest, are related to distinct disease conditions including progression rates. In a study using flow cytometry ( 5 ), like in the present study using scRNA-seq, the frequencies of Treg were higher in non-rapid ALS than in healthy control (1.0% versus 0.4–0.6%) but were similar between rapid ALS and healthy control. In addition, another study showed that Treg ratio was inversely correlated with the progression rate in ALS patients ( 7 ), which is consistent with our findings of a lower Treg ratio in rapid ALS. On the other hand, previous studies reported no relationship between the frequency of CD8 T cells and ALSFRS-R scores ( 10 ). In contrast, our study further analyzed the subpopulation of CD8 T cells and revealed opposite trends for effector and naïve CD8 T cells between rapid and non-rapid ALS, suggesting the superiority of our methods. KLRD1 (CD94), which was associated with the rate of progression and the frequency of effector and naïve CD8 T cells, forms a heterodimer with NKG2. The CD94/NKG2 complex can function as an inhibitor or activator, depending on the specific NKG2 isoforms. For instance, CD94/NKG2A acts as an inhibitory receptor, whereas CD94/NKG2C acts as an activating receptor. Consequently, CD94/NKG2 may regulate the effector functions and survival of NK cells and CD8 T cells. CD94/NKG2A primarily counteracts TCR signaling in a subset of memory/effector CD8 T cells as part of an antigen-driven response to prevent autoimmunity ( 32 ). Conversely, CD94/NKG2C can activate T cells, inducing proliferation and the killing of HLA-E-transfected target cells that lack expression of other MHC-I molecules, even without TCR stimulation. Thus, it constitutes an alternative activation pathway for a subset of CD8 T cells ( 33 ). In light of these findings, it is crucial to investigate the characteristics of CD94/NKG2 in ALS. Furthermore, our correlation analysis suggests that CD94 may serve as a marker of CD8 T cell activation in ALS, similar to other instances ( 25 ). We found that the ratio of CD16 high CD56 low mature NK cells to CD16 low CD56 high naïve NK cells was significantly higher in rapid than non-rapid ALS. It was reported that ALS patients with slower disease progression showed increased proportions of immune-regulatory CD56 bright NK cells in the CSF compared with ALS patients with rapid disease progression ( 11 ). Patients with rapid disease progression showed enhanced differentiation of intrathecal CD56 bright into CD56 dim NK cells, likely accounting for their reduced frequencies in the CSF of these patients ( 11 ). It was also reported that CD56 bright NK cells play a role in regulating activated T cells in both the periphery and CSF, thereby attenuating adaptive immune responses and exerting protective effects in autoimmune diseases of the CNS ( 34 ). These findings highlight the relevance of NK cell differentiation to the pathophysiology of ALS. Our DEG analysis revealed that SERPINA1 in naïve CD4 T cells and AC103591.3 (RP11-386I14) in classical monocytes were downregulated in ALS, although they were not different between rapid and non-rapid ALS. It was recently reported that serum SerpinA1 was decreased in patients with ALS compared with controls, while CSF SerpinA1 was elevated in fast progressors compared with slow and intermediate progressors ( 26 ). On the other hand, information on the role of RP11-386I14 in ALS is scarce. Regarding monocytes, it was reported that the ratio of classical to non-classical monocytes was elevated in patients with ALS ( 35 ). However, the association between monocytes and progression rate was not shown. We showed that the frequency of classical monocytes was correlated with the value of NCF2, which was elevated in rapid versus non-rapid ALS. NCF2 is responsible for the synthesis of superoxide in neutrophils and was reported as one of the ferroptosis- and iron metabolism-related genes differentially expressed in ALS compared to control ( 36 ). These findings suggest that the roles of monocytes in the pathophysiology of ALS warrant further investigation. Several limitations were noted in this study. First, the number of participants was relatively small; the number was similar to or smaller than those in other studies using flow cytometry or ELISA in patients with ALS ( 13 – 16 ) and was comparable to those in recent scRNA-seq studies in other diseases ( 18 , 20 ). Meanwhile, we could not provide the analysis comparing patients with and without cognitive impairment and with different onset sites due to the small sample size. Second, the difference in the numbers of patients between the non-rapid ALS and rapid ALS groups was relatively large. Third, longitudinal assessment was beyond the scope of this study. Additional evaluation in different stages would better clarify the temporal, progressive changes of immune profiles in ALS. Fourth, although not only the frequency but also the function of the immune cells such as Treg is important ( 37 ), we focused on their frequency in this study. Fifth, the evaluation of the CSF sample was beyond the scope of this study. Sixth, we did not measure neurofilament light chain with Simoa because of our limited budget. Conclusions In summary, this study revealed relationships between rapid progression and increase in Th17 against Treg, effector CD8 T cells against naïve CD8 T cells, and relevant proteins such as IL-17A and KLRD1 (CD94) in the blood in ALS patients, suggesting these cell types together relate to disease progression in ALS. Materials and Methods Experimental Design We aimed to identify immune cells and proteins that relate to ALS progression rate via scRNA-seq of PBMCs and high-throughput PEA-based immunoproteomic analysis of serum. Then, the clinical information was compared with scRNA-seq and proteomics data (Fig. 1 A). Participants We conducted a screening involving 36 patients diagnosed with ALS and 10 healthy volunteers at Tokushima University Hospital in Tokushima, Japan, spanning from March 29, 2021, to October 30, 2022. To be eligible for inclusion in the study, patients had to meet the ALS diagnostic criteria established by the Gold Coast criteria ( 38 ) and have a disease duration of up to 2 years from the onset of symptoms. It is important to note that the patients’ ages and sexes were carefully matched with those of the healthy participants. Table S8 lists the exclusion criteria for patients with ALS and healthy participants. We examined genetic background of the following genes: ALS2 , ANG , ANXA11 , ATXN2 , BNIP3L , C9orf72 , CALCOCO2 , CBL , CCNF , CHCHD10 , CHMP2B , CSF1R , CYLD , DAO , DCTN1 , DYNLL1 , ELP3 , ERBB4 , EWSR1 , Fig. 4 , FUS , GABARAP , GLE1 , GRN , HDAC6 , HNRNPA1 , HNRNPA2B1 , HNRPA2B1 , KIF5A , LRP12 , MAP1LC3A , MATR3 , NBR1 , NEFH , NEK1 , OPTN , PFN1 , PRPH , RNASE4 , RNF19A , SETX , SIGMAR1 , SKP1 , SOD1 , SPAST , SPG11 , SPTLC1 , SQSTM1 , SS18L1 , TAF15 , TARDBP , TBK1 , TFG , TIA1 , TNFRSF11A , TNFSF11 , TUBA4A , UBQLN2 , VAPB , VAPBC , and VCP . Patients with known pathogenic ALS variants were excluded beforehand from the analysis because the immunological profiles would be different from those of sporadic ALS. Rapid ALS was defined as a decrease in the ALSFRS-R score of ≥ 1.0 points per month (ΔALSFRS-R/month ≥ 1), whereas non-rapid ALS was defined as a decrease in the ALSFRS-R score of < 1.0 point per month (ΔALSFRS-R/month < 1). As described below, we collected peripheral blood samples and conducted scRNA-seq and proteomic analyses. All study protocols were approved by the Ethics Committee of Tokushima University Hospital under protocol #3682. All clinical information was collected after patients provided written informed consent. The investigations were conducted following the principles of the Declaration of Helsinki. Sample Preparation Peripheral blood samples were collected in Benoject II Vacuum Blood Collection Tube Sterilized Product VP-H100K (Terumo Corporation, Tokyo, Japan) from healthy volunteers and patients with ALS at Tokushima University Hospital. Ficoll-Hypaque (Lymphoprep™, Serumwerk Bernburg AG, Germany) density centrifugation prepared PBMCs, which were washed with phosphate-buffered saline (PBS) and resuspended in X-VIVO™ medium (LONZA, Basel, Switzerland) with 5% fetal bovine serum. PBMCs were stored at − 80℃ until CELLBANKER 1 (Nippon Zenyaku Kogyo Ltd, Fukushima, Japan) prepared the library. Serum samples were collected in 8.0 ml of Insepack tube (SEKISUI MEDICAL CO., LTD., Tokyo, Japan), centrifuged at 3,500 rpm for 10 min, aliquoted, and frozen at − 80℃ within 20 min following collection. Single-cell RNA-sequencing PBMCs Preparation, Library Preparation, and Sequencing Cryopreserved human PBMCs were rapidly thawed in a 37°C water bath and washed with 1 × D-PBS (magnesium/calcium-free) containing 1% BSA. Cell collection was performed at 20°C, with a centrifugation speed of 250 × g for 10 minutes using a swing-out rotor. After being washed three times, the cells were resuspended in an appropriate volume of D-PBS with 1% BSA. Subsequently, the cell suspension was passed through a Flowmi Cell Strainer with a pore size of 40 µm to eliminate any remaining larger particles. Cell count and viability were assessed using a hemacytometer with trypan blue staining. We used scRNA-seq and the 10x Genomics (Pleasanton, CA) platform. The 10x Genomics Chromium controller was employed to load single-cell suspensions (10,000 cells) onto a chip to generate GEMs (Gel Bead-In-Emulsions). The Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 from 10x Genomics was used to prepare DNA libraries, following the manufacturer's instructions. Before sequencing, quality control of the prepared libraries was conducted using the 4200 TapeStation D1000 ScreenTape (Agilent, Santa Clara, CA) and the Qubit dsDNA Assay (Thermo Fisher Scientific, Waltham, MA). The gene expression libraries were sequenced on the DNBSEQ-G400 platform from MGI Tech (Shenzhen, China) at a depth of 50,000 reads per cell. Data Normalization We employed the 10x Genomics Cell Ranger version 3 pipeline for primary analysis. The Cell Ranger “counts” software was utilized to convert Bcl files to FASTQ format. Subsequently, the FASTQ data underwent filtering and were mapped to the GRCh38 reference genome. The secondary analysis used R version 4.0.2 with the Seurat version 3 package ( 39 , 40 ). To exclude data from low-quality cells and doublet cells, we utilized the Seurat and DoubletFinder packages ( 40 ). The count data were subjected to normalization through a global-scaling normalization method, employing the Seurat function “LogNormalize,” followed by log transformation using the Seurat function “NormalizeData.” Variable features were selected by directly modeling the mean-variance relationship, utilizing the Seurat function “FindVariableFeatures.” These selected variable features were then used to identify integration features through the Seurat function “SelectIntegrationFeatures.” The integration features were applied for linear transformation and utilized in principal component analysis (PCA) by employing the Seurat functions “ScaleData” and “RunPCA,” respectively. Reciprocal PCA was performed using the top 50 principal components to identify anchors. The data were separated by anchor, and the batch features were extracted using the function ‘SelectIntegrationFeatures’ from the Seurat package and merged using the function ‘IntegrateData’ ( 41 ). All scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus repository, accessible at http://www.ncbi.nlm.nih.gov/geo , under the accession number GSE244263. Analysis The integrated and normalized data underwent a linear transformation and PCA using Seurat functions “ScaleData” and “RunPCA.” To identify important principal components, an elbow plot was generated by plotting standard deviations of the principal components. These significant principal components were then used to perform uniform manifold approximation and projection (UMAP) visualization, achieved through the Seurat functions “ElbowPlot” and “RunUMAP.” The identification of cell types was carried out using these crucial principal components. First, cell similarities were calculated using a k-nearest neighbor graph based on the Euclidean distance in PCA space. The edge weights between any two cells were determined based on the shared overlap with their local neighborhoods using Jaccard’s similarity, facilitated by the Seurat function “FindNeighbors.” Subsequently, cell populations were divided through clustering, accomplished by assessing the similarities between cells using the Louvain algorithm and the Seurat function “FindClusters.” Furthermore, cell types within each cluster were identified by examining cell-type-specific marker expression profiles for each cluster ( Table S9 ). Although NK cells are commonly classified into two functionally distinct subsets, i.e., CD56 bright CD16 − and CD56 dim CD16 + , NK cells were clearly divided into three subsets based on the relative expression of CD16 and CD56 in our analysis (CD16 low CD56 high for naïve NK cells, CD16 high CD56 low for mature NK cells, and CD16 middle CD56 low for activated NK cells) ( Fig. S5 ). Activated NK cell subsets expressed CD247 (CD3zeta), suggesting enhanced activation and cytotoxic capacity through efficient signaling and interaction with other immune receptors. The resolution used was "Seurat's default value" and the cluster numbers before and after annotation were 22 and 23 respectively. The reason why the number of clusters increased after the annotation was that only CD4 T cell was extracted, reclustered, and subdivided (e.g., Th1, Th17) within it. The filter thresholds were "mitochondria genes < 15% and gene feature counts 500 ~ 4000" and the cell number "422,135 cells" used in the downstream analysis. Normalized gene expression levels of cell-type marker genes for each cell type are shown in UMAPs ( Fig. S5 ) and a heatmap ( Fig. S6 ). To identify DEGs between groups within each cell type, the nonparametric Wilcoxon rank sum test was employed, with the Seurat function “FindMarkers.” Gene expression levels were visualized within each cluster using violin plots via the Seurat function “VlnPlot.” Differences in the frequencies of each cell type between groups were assessed using Tukey’s HSD test with the “TukeyHSD” function in the stats (R default) package. Correlation tests used the “cor.test” function from the stats (R default) package. Finally, scatter plots, boxplots, and bar plots were created in R using the ggplot2 package ( 36 ) for visualization. Protein Measurement We employed the Olink Target 96 Inflammation and Olink Explore 384 Inflammation panels from Olink Proteomics (Uppsala, Sweden) to simultaneously quantify multiple human proteins associated with inflammation ( Table S10 ) ( 21 , 22 ). This quantification was achieved using PEA technology (for more information, visit https://www.olink.com ). PEA technology involves a pair of antibodies, each linked to unique oligonucleotides (proximity probes) and bound to their respective protein targets. These probes only hybridize with each other when they are near forming double-stranded DNA. The resulting complex is then detected and quantified using quantitative real-time PCR for the Olink Target 96 Inflammation panel or next-generation sequencing for the Olink Explore 384 Inflammation panel. The data were presented in normalized protein expression (NPX), which is an arbitrary unit on a log 2 -scale. NPX values represent relative expression rather than absolute protein levels. For proteins measured by next-generation sequencing, all items were selected for analysis. Regarding items measured solely by real-time PCR, we selected those with > 50% of the samples above the detection limit for analysis. A scatter plot of the first and second principal components in the PCA of serum proteome samples is shown in Fig. S7 . To identify DEPs between groups, we used Tukey’s HSD test with the “TukeyHSD” function in the stats (R default) package. We visualized the results through volcano plots created in R using the ggplot2 and ggrepel packages ( 42 , 43 ) for visualization. Correlation tests used the “cor.test” function from the stats (R default) package. Furthermore, serum pNfH levels were measured using an ELISA kit from EUROIMMUN (Lübeck, Germany) ( 44 , 45 ). To test the reproducibility of the measurement, the concentrations of IL-17A, which was identified as one of the DEPs, were additionally assessed using the Simoa® IL-17A Advantage PLUS kit (Catalog #104428; Quanterix, Billerica, MA) run on the Simoa® HD-X Analyzer (Quanterix), in the participants whose residual re-frozen samples were available (healthy controls, 9 of 10; non-rapid ALS patients, 20 of 23, rapid ALS patients, 7 of 7). The functional lower limit of quantification was 0.012 pg/ml. The samples except for one of the healthy controls were analyzed in duplicate. Samples were diluted 1:2 and distributed on 96-well plates. These measurements were performed according to the manufacturer’s instructions at SEKISUI MEDICAL CO., LTD. The data were presented in the log 2 -transformed scale. Statistical Analysis Microsoft R open software (version 4.0.2) or GraphPad Prism version 10 (GraphPad Software, Boston, MA) were used for statistical analyses. The nonparametric Wilcoxon rank sum test identified DEGs in scRNA-seq, which was corrected for multiple tests using Bonferroni’s test. Tukey’s HSD for multiple testing was used to identify DEPs in serum proteome and the differences in the frequencies of each cell type between the groups. Pearson’s correlation coefficient was used for correlation tests. A 2-sided p < 0.05 was considered significant. Abbreviations ALS: amyotrophic lateral sclerosis ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Scale CNS: central nervous system DEG: differentially expressed gene DEP: differentially expressed protein FC: fold change HSD: honestly significant difference IL-17A: interleukin-17A KLRD1: killer cell lectin-like receptor D1 KRT19: keratin, type I cytoskeletal 19 NCF2: neutrophil cytosol factor 2 NK: natural killer NPX: normalized protein expression PBMCs: peripheral blood mononuclear cells PBS: phosphate-buffered saline PCA: principal component analysis PEA: proximity extension assay scRNA-seq: single-cell RNA-sequencing Simoa: Single molecule array TCR: T cell receptor TFF2: trefoil factor 2 Th17: T helper 17 cells Treg: regulatory T cells UMAP: uniform manifold approximation and projection YTHDF3: YTH domain-containing family protein 3 Declarations Ethics approval and consent to participate All study protocols were approved by the Ethics Committee of Tokushima University Hospital under protocol #3682. All clinical information was collected after patients provided written informed consent. The investigations were conducted following the principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials All scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus repository, accessible at http://www.ncbi.nlm.nih.gov/geo, under the accession number GSE244263. All other data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Competing interests TI, YO, DW, SM, and YK are employees of Takeda Pharmaceutical Company Limited. Funding Takeda Pharmaceutical Company Limited (YI) MHLW Research on rare and intractable diseases Program Grant Number JPMH23FC1008 (YI) Authors' contributions Conceptualization: TI, KF, YI Methodology: TI, KF, YO, YI Investigation: TI, YO, YM, YI Visualization: TI, KF, YO Supervision: YI Writing—original draft: TI, KF Writing—review & editing: TI, KF, YO, DW, SM, YM, NM, HM, YK, YI Acknowledgements Not applicable. References Feldman EL, Goutman SA, Petri S, Mazzini L, Savelieff MG, Shaw PJ, Sobue G. Amyotrophic lateral sclerosis. Lancet. 400, 1363-1380 (2020). Goyal NA, Berry JD, Windebank A, Staff NP, Maragakis NJ, van den Berg LH, Genge A, Miller R, Baloh RH, Kern R, Gothelf Y, Lebovits C, Cudkowicz M. Addressing heterogeneity in amyotrophic lateral sclerosis CLINICAL TRIALS. Muscle Nerve . 62, 156-166 (2020). Thonhoff JR, Simpson EP, Appel SH. Neuroinflammatory mechanisms in amyotrophic lateral sclerosis pathogenesis. Curr Opin Neurol . 31, 635-639 (2018). 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TI, YO, DW, SM, and YK are employees of Takeda Pharmaceutical Company Limited. Supplementary Files JNeuroinflammsp20241129.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2024 Read the published version in Journal of Neuroinflammation → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 10 Dec, 2024 Reviews received at journal 02 Dec, 2024 Reviewers agreed at journal 01 Dec, 2024 Reviewers agreed at journal 01 Dec, 2024 Reviewers invited by journal 29 Nov, 2024 Submission checks completed at journal 29 Nov, 2024 First submitted to journal 28 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5187725","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":385168921,"identity":"00d5fbad-b4d4-4f6b-b038-0bb3d1959f24","order_by":0,"name":"Tatsuo Itou","email":"","orcid":"","institution":"Takeda (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Tatsuo","middleName":"","lastName":"Itou","suffix":""},{"id":385168922,"identity":"0a372904-9df5-45a3-97a1-1d4f74c91325","order_by":1,"name":"Koji Fujita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2QMU7DMBSGX2TJWRx1fVFQewVHHQBR9SIsRpGSyVJ2KshkFh+ACa5AF8SGpUpm6QWYoHsPkAxIOCrqUhzBhoS/8bc+/b8fQCDwB0FigAuAMQAFiBrcvxCvQoVTBEzZrxQAARd6p/xgWKoYrzftvHqOVZl2Tyfno4ZxaBcQH3uUjDLuhhVSM2uzZI3y1jAeaQvk1FM5nuheIVJjpbJIoWzethySBgg3HmXXcl0xp6SdU+77lo8B5WvYSjAsLSZOeXAKGWpJFa25KF9yvbbFWa8sDa1XRxa9f0FLHvN2djmJb1T+2qkreWfIcrNdzArfxRyUH0RuEhaH8R7y/m08H1ACgUDgf/EJiuxQkvNhsgsAAAAASUVORK5CYII=","orcid":"","institution":"Tokushima University","correspondingAuthor":true,"prefix":"","firstName":"Koji","middleName":"","lastName":"Fujita","suffix":""},{"id":385168923,"identity":"ec0bd251-6f5b-4dd9-bef9-9f735c109abc","order_by":2,"name":"Yuumi Okuzono","email":"","orcid":"","institution":"Takeda (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Yuumi","middleName":"","lastName":"Okuzono","suffix":""},{"id":385168924,"identity":"af3131ea-2115-4e75-934a-0a6d0db7ac74","order_by":3,"name":"Dnyaneshwar Warude","email":"","orcid":"","institution":"Takeda (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Dnyaneshwar","middleName":"","lastName":"Warude","suffix":""},{"id":385168925,"identity":"1779ddc3-e355-47f2-84f9-df44ca933541","order_by":4,"name":"Shuuichi Miyakawa","email":"","orcid":"","institution":"Takeda (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Shuuichi","middleName":"","lastName":"Miyakawa","suffix":""},{"id":385168926,"identity":"372db86d-75dd-4909-b910-ac0d39e3388f","order_by":5,"name":"Yoshimi Mihara","email":"","orcid":"","institution":"Tokushima University","correspondingAuthor":false,"prefix":"","firstName":"Yoshimi","middleName":"","lastName":"Mihara","suffix":""},{"id":385168928,"identity":"0b3cfb9e-84b5-40b6-ac85-116f2bef6460","order_by":6,"name":"Naoko Matsui","email":"","orcid":"","institution":"Tokushima University","correspondingAuthor":false,"prefix":"","firstName":"Naoko","middleName":"","lastName":"Matsui","suffix":""},{"id":385168929,"identity":"ab444486-8d65-432c-aa1d-33a2a4aa50ee","order_by":7,"name":"Hiroyuki Morino","email":"","orcid":"","institution":"Tokushima University","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Morino","suffix":""},{"id":385168931,"identity":"56f251c4-928c-4e33-9f46-ee8970ab58d7","order_by":8,"name":"Yusuke Kikukawa","email":"","orcid":"","institution":"Takeda (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Kikukawa","suffix":""},{"id":385168932,"identity":"a96d4d0f-5467-4f03-80e2-4972faed3249","order_by":9,"name":"Yuishin Izumi","email":"","orcid":"","institution":"Tokushima University","correspondingAuthor":false,"prefix":"","firstName":"Yuishin","middleName":"","lastName":"Izumi","suffix":""}],"badges":[],"createdAt":"2024-10-01 13:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5187725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5187725/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12974-024-03327-w","type":"published","date":"2024-12-27T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70948333,"identity":"457b5b30-d189-42cf-ba6d-51bd61e4a038","added_by":"auto","created_at":"2024-12-09 13:19:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":920851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and profiling of cell types in single-cell RNA-sequencing. (A)\u003c/strong\u003e Overview of this study integrating single-cell RNA-sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells (PBMCs), serum proteomics, and clinical information for healthy controls and patients with non-rapid and rapid amyotrophic lateral sclerosis (ALS). \u003cstrong\u003e(B)\u003c/strong\u003e Uniform manifold approximation and projection (UMAP) showing 23 identified cell types. \u003cstrong\u003e(C)\u003c/strong\u003eThe frequencies of cell types for each sample. The frequency is displayed as the percentage of each cell type when the total number of cells in each sample was 100%. DC: dendritic cell; NK: natural killer.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5187725/v1/cba98feafdaf52b7eda07782.png"},{"id":70948346,"identity":"c4c36cd7-ddef-4355-b9ba-01b98d6a6d12","added_by":"auto","created_at":"2024-12-09 13:19:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntergroup comparison of frequencies of each cell type.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Frequencies of regulatory T cells in all cells. \u003cstrong\u003e(B)\u003c/strong\u003e Frequencies of cell types in specific cell groups. \u003cstrong\u003e(C)\u003c/strong\u003e Ratios of cell types compared to related cell types. They revealed significant differences in rapid amyotrophic lateral sclerosis (ALS) compared to non-rapid ALS by Tukey’s HSD test. Lines and asterisks show significant combinations by Tukey’s HSD test (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.005, and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0005). Breg: regulatory B cells; NK: natural killer; Th1: T helper 1 cells; Th17: T helper 17 cells; Treg: regulatory T cells.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5187725/v1/1628d2f03d2796a4187b0726.png"},{"id":70948344,"identity":"f9ebb271-7cc4-4aaf-8175-85630719f724","added_by":"auto","created_at":"2024-12-09 13:19:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum proteomics. (A)\u003c/strong\u003e Volcano plots showing −log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003ep\u003c/em\u003e-values and log\u003csub\u003e2\u003c/sub\u003e fold changes from differential expression analysis. Red dots indicate differentially expressed proteins (DEPs) shown in gene names. \u003cstrong\u003e(B)\u003c/strong\u003e Violin plots showing normalized protein expression (NPX) values for six proteins isolated as DEPs with rapid versus non-rapid amyotrophic lateral sclerosis (ALS). \u003cstrong\u003e(C)\u003c/strong\u003e Violin plots showing expression levels for phosphorylated neurofilament H (pNfH). Lines and asterisks show significant combinations by Tukey’s HSD test (*\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.005, and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0005).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5187725/v1/0db21fdc70b6b0bbb8c88a2c.png"},{"id":70948335,"identity":"0d75fa12-376d-4a99-8307-cd6ec500d538","added_by":"auto","created_at":"2024-12-09 13:19:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between serum proteinsand ratios of cell types.\u003c/strong\u003eScatter plot between the normalized protein expression (NPX) of serum proteins and the cell frequencies or ratios in single-cell RNA-sequencing analysis using Pearson’s correlation coefficient. Numerical values show the correlation coefficient using samples from 30 patients with amyotrophic lateral sclerosis and 10 healthy controls.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5187725/v1/63fecbec563346601284bac4.png"},{"id":72640678,"identity":"e21f48dc-d488-4450-b8ba-d9c56e4a437c","added_by":"auto","created_at":"2024-12-30 16:08:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3086895,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5187725/v1/0f981c7e-5e9e-430c-a7f0-492d0c6e0113.pdf"},{"id":70948336,"identity":"a9180263-1f44-4b61-adeb-051bb0b5972b","added_by":"auto","created_at":"2024-12-09 13:19:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2028724,"visible":true,"origin":"","legend":"","description":"","filename":"JNeuroinflammsp20241129.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5187725/v1/cc18d82300c4e88a422e0bcd.pdf"}],"financialInterests":"Competing interest reported. TI, YO, DW, SM, and YK are employees of Takeda Pharmaceutical Company Limited.","formattedTitle":"Th17 and effector CD8 T cells relate to disease progression in amyotrophic lateral sclerosis: a case control study","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects both upper and lower motor neurons (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Following the onset of symptoms, muscle weakness relentlessly progresses, eventually leading to respiratory failure and, unfortunately, death within a few years for most patients. Nonetheless, the rate of progression varies considerably among patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This variability in progression poses a challenge toward the development of therapeutics because clinical trials for ALS typically assess progression as the primary outcome (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Therefore, gaining deep understanding on the pathogenesis of disease progression in ALS is of utmost importance.\u003c/p\u003e \u003cp\u003eThe progression of ALS has been linked to immune cells and proteins in both the central nervous system (CNS) and peripheral circulation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, several critical questions in this regard remain unanswered. For instance, a decrease or dysfunction in regulatory T cells (Treg), characterized by the expression of the FOXP3 transcription factor, has been correlated with rapid progression (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Furthermore, a flow cytometric study has reported that reduced levels of peripheral CD4 T cells are associated with the rate of progression, with the assumption that the reduction is linked to decreased Treg levels, although subpopulations of the peripheral blood CD4 T cells were not specifically examined (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Conversely, another flow cytometric study demonstrated that ALS patients with higher levels of CD4\u0026thinsp;+\u0026thinsp;FOXP3\u0026thinsp;\u0026minus;\u0026thinsp;effector T cells in the cerebrospinal fluid (CSF) exhibited a higher rate of progression; although, detailed profiles of the CD4 T cell subpopulations were unavailable (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Therefore, the roles of CD4 T cell subpopulations, other than Treg, in disease progression remain enigmatic in ALS.\u003c/p\u003e \u003cp\u003eAdditionally, although an increase in CD8 T cells has been reported in ALS (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), the function of CD8 T cells in disease progression remains unknown. Furthermore, studies investigating pro and anti-inflammatory cytokines in the blood of ALS patients, primarily based on enzyme-linked immunosorbent assays, have failed to detect correlations between cytokine levels and the Revised Amyotrophic Lateral Sclerosis Functional Scale (ALSFRS-R), which is used to assess disease severity and progression (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these unresolved issues, we conducted an unbiased evaluation of phenotypes in peripheral blood immune cells, encompassing not only CD4 T cells but also CD8 T cells and natural killer (NK) cells, in patients with ALS and healthy subjects. We assessed their association with the rate of progression in patients with ALS through single-cell RNA-sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). We also analyzed differentially expressed genes (DEG) in the scRNA-seq data. Furthermore, we searched for inflammation-related proteins in the serum linked to progression using a proximity extension assay (PEA) in conjunction with a high-throughput sequencing read-out (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Considering reproducibility, major findings of the PEA were tested with Single molecule array (Simoa). We aimed to elucidate the interplay between immune cells and proteins in the context of disease progression in ALS.\u003c/p\u003e \u003cp\u003eThe scRNA-seq analysis highlighted the involvement of T helper 17 cells (Th17), effector CD8 T cells, and CD16\u003csup\u003ehigh\u003c/sup\u003eCD56\u003csup\u003elow\u003c/sup\u003e mature NK cells, while PEA identified associated proteins such as interleukin-17A (IL-17A) and killer cell lectin-like receptor D1 (KLRD1, also known as CD94), in relation to the rapid progression of ALS. CD94, expressed on NK cells and a small subset of CD8 T cells, forms a heterodimer with NKG2. The CD94/NKG2 receptor complex recognizes nonclassical MHC class I molecules, including HLA-E in humans and Qa-1 in mice, and has established roles in tumor immunity, viral infection, and autoimmunity (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Meanwhile, DEG analysis revealed downregulation in SERPINA1 and AC103591.3 (RP11-386I14) in patients with ALS. SerpinA1, a serine protease inhibitor, is implicated in modulating microglial-mediated inflammation in neurodegenerative diseases (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). RP11-386I14, a long non-coding RNA expressed in B cells, monocytes, T cells, and NK cells (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), remains functionally uncharacterized. Together, these findings provide novel insights into peripheral immune mechanisms involved in the progression of ALS.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOf the 46 screened participants, 10 healthy controls and 35 patients with ALS gave informed consent, and one participant with neuropathy was excluded. Additionally, five participants with ALS who had gastric cancer (n\u0026thinsp;=\u0026thinsp;1), hepatocellular carcinoma (n\u0026thinsp;=\u0026thinsp;1), human T-lymphotropic virus type 1 antibody (n\u0026thinsp;=\u0026thinsp;1), \u003cem\u003eSOD1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), and lung cancer and \u003cem\u003eSOD1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1) were excluded. Eventually, peripheral blood samples of 10 healthy controls (age, 63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2; male, 7), 23 patients with non-rapid ALS (ΔALSFRS-R/month\u0026thinsp;\u0026lt;\u0026thinsp;1; age, 60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8; male, 17), and 7 patients with rapid ALS (ΔALSFRS-R/month\u0026thinsp;\u0026ge;\u0026thinsp;1; age, 69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4; male, 3) were analyzed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients with ALS had disease duration of 10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 months and a mean ALSFRS-R score of 39.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2. The clinical information was compared with scRNA-seq and proteomics data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eClinical features of 40 subjects.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy control (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-rapid ALS (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRapid ALS\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (42.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALSFRS-R at entry, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration before entry, months, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔALSFRS-R/month, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite of onset, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulbar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (28.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (71.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrunk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%forced vital capacity, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;41.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive status, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (57.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpaired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (8.7): FTD, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (42.9): FTD, 1; suspicion of FTD, 1; unknown etiology, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eALS: amyotrophic lateral sclerosis; ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Rating Scale; FTD: frontotemporal dementia; SD: standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle-cell RNA-sequencing\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe integrated scRNA-seq classified the cells into 23 types for further analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, S1). The number of cells in each cell subset is shown in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. We first calculated the frequencies of cell types in all cells in healthy control, non-rapid ALS, and rapid ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, S2). The number of Treg significantly increased in non-rapid ALS compared with control and rapid ALS (2.4% vs. 1.9%, p\u0026thinsp;=\u0026thinsp;0.01 and 2.4% vs. 1.6%, p\u0026thinsp;=\u0026thinsp;0.03, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Next, we calculated the frequencies of cell types in related cells. The frequency of effector CD8 T cells in all CD8 T cells was significantly higher in rapid than non-rapid ALS (91.6% vs. 82.2%, p\u0026thinsp;=\u0026thinsp;0.04). In contrast, the frequency of regulatory B cells among all B cells (5.5% vs. 7.3%, p\u0026thinsp;=\u0026thinsp;0.05) and those of na\u0026iuml;ve and exhausted CD8 T cells among all CD8 T cells (4.1% vs. 8.4%, p\u0026thinsp;=\u0026thinsp;0.01 and 0.6% vs. 0.9%, p\u0026thinsp;=\u0026thinsp;0.04, respectively) were significantly lower in rapid than non-rapid ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, we calculated the frequency ratios between functionally related immune cells. The ratios of memory CD4 T cells/Treg (4.9 vs. 2.9, p\u0026thinsp;=\u0026thinsp;0.02), Th1/Treg (1.9 vs. 1.2, p\u0026thinsp;=\u0026thinsp;0.02), Th17/Treg (1 vs. 0.6, p\u0026thinsp;=\u0026thinsp;0.005), effector/na\u0026iuml;ve CD8 T cells (23.0 vs. 9.8, p\u0026thinsp;=\u0026thinsp;0.01), effector/exhausted CD8 T cells (149.7 vs. 89.9, p\u0026thinsp;=\u0026thinsp;0.02), and mature (CD16\u003csup\u003ehigh\u003c/sup\u003eCD56\u003csup\u003elow\u003c/sup\u003e)/na\u0026iuml;ve (CD16\u003csup\u003elow\u003c/sup\u003eCD56\u003csup\u003ehigh\u003c/sup\u003e) NK cells (28.1 vs. 20.1, p\u0026thinsp;=\u0026thinsp;0.04) were significantly higher in rapid than non-rapid ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCell type frequencies and progression rate\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe investigated the relationship between cell type frequencies and progression rate in ALS. We used partial correlation because ΔALSFRS-R/month was correlated with age (Pearson\u0026rsquo;s correlation coefficient\u0026thinsp;=\u0026thinsp;0.421, p\u0026thinsp;=\u0026thinsp;0.02), and confounding with age could not be ignored in routine correlation analysis. The frequency of Th17 in CD4 T cells significantly correlated with ΔALSFRS-R/month (coefficient\u0026thinsp;=\u0026thinsp;0.390, p\u0026thinsp;=\u0026thinsp;0.037). Moreover, the ratios of memory CD4 T cells/Treg (coefficient\u0026thinsp;=\u0026thinsp;0.450, p\u0026thinsp;=\u0026thinsp;0.014) and Th17/Treg (coefficient\u0026thinsp;=\u0026thinsp;0.428, p\u0026thinsp;=\u0026thinsp;0.021) moderately but significantly correlated with ΔALSFRS-R/month (\u003cb\u003eFig. S3\u003c/b\u003e, \u003cb\u003eTables S2\u0026ndash;4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDifferentially expressed genes\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe performed differentially expressed gene (DEG) analysis of scRNA-seq data between ALS versus control, non-rapid ALS versus control, rapid ALS versus control, and rapid ALS versus non-rapid ALS, and then isolated the DEGs in 23 cell types (Bonferroni adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold change [FC]\u0026thinsp;\u0026ge;\u0026thinsp;1.2). SERPINA1 in na\u0026iuml;ve CD4 T cell and AC103591.3 in classical monocytes were downregulated in ALS than in control but were not different between rapid and non-rapid ALS (\u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInflammation proteins\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePEA-based proteomics of serum identified KLRDD1 (CD94) (FC\u0026thinsp;=\u0026thinsp;2.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), trefoil factor 2 (TFF2; FC\u0026thinsp;=\u0026thinsp;2.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), keratin, type I cytoskeletal 19 (KRT19; FC\u0026thinsp;=\u0026thinsp;3.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), IL-17A (FC\u0026thinsp;=\u0026thinsp;4.8, p\u0026thinsp;=\u0026thinsp;0.007), YTH domain-containing family protein 3 (YTHDF3; FC\u0026thinsp;=\u0026thinsp;2.6, p\u0026thinsp;=\u0026thinsp;0.03), and neutrophil cytosolic factor 2 (NCF2; FC\u0026thinsp;=\u0026thinsp;2.4, p\u0026thinsp;=\u0026thinsp;0.04) as significantly elevated in rapid versus non-rapid ALS. Of these, KLRD1 (FC\u0026thinsp;=\u0026thinsp;2.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), KRT19 (FC\u0026thinsp;=\u0026thinsp;2.7, p\u0026thinsp;=\u0026thinsp;0.01), NCF2 (FC\u0026thinsp;=\u0026thinsp;3.0, p\u0026thinsp;=\u0026thinsp;0.02), YTHDF3 (FC\u0026thinsp;=\u0026thinsp;3.0, p\u0026thinsp;=\u0026thinsp;0.02), and IL-17A (FC\u0026thinsp;=\u0026thinsp;4.0, p\u0026thinsp;=\u0026thinsp;0.04) were significantly elevated in rapid ALS than in control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). IL-17A concentrations measured with Simoa were also significantly higher in rapid ALS than in non-rapid ALS (p\u0026thinsp;=\u0026thinsp;0.04, Tukey\u0026rsquo;s honestly significant difference [HSD] test; p\u0026thinsp;=\u0026thinsp;0.04, one-way ANOVA) (\u003cb\u003eFig. S4\u003c/b\u003e). Expression levels of phosphorylated neurofilament heavy chain (pNfH) were significantly higher in ALS (non-rapid\u0026thinsp;+\u0026thinsp;rapid) than in control (FC\u0026thinsp;=\u0026thinsp;180, p\u0026thinsp;=\u0026thinsp;0.001) and in rapid ALS than in control (FC\u0026thinsp;=\u0026thinsp;3074, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, C), which supported the validity of our samples.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, we conducted a partial correlation analysis between serum protein levels and ΔALSFRS-R/month to examine whether the measured proteins correlated with the progression rate (\u003cb\u003eTable S6\u003c/b\u003e). All the six differentially expressed proteins (DEPs) that were elevated in rapid ALS versus non-rapid ALS had significant correlations, especially KLRD1 (coefficient\u0026thinsp;=\u0026thinsp;0.745, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), KRT19 (coefficient\u0026thinsp;=\u0026thinsp;0.658, p\u0026thinsp;=\u0026thinsp;0.0002), and IL-17A (coefficient\u0026thinsp;=\u0026thinsp;0.556, p\u0026thinsp;=\u0026thinsp;0.003), with partial correlation coefficients of \u0026gt;\u0026thinsp;0.5 and p-values of \u0026le;\u0026thinsp;0.003. In parallel, we also detected CD160, which is associated with NK and CD8 T cells with cytolytic effector activity, as highly correlated with disease progression.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between proteins and cell types\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe investigated the correlation between the serum protein levels and the cell type ratios obtained by the scRNA-seq (\u003cb\u003eTable S7\u003c/b\u003e). Regarding the proteins elevated in rapid versus non-rapid ALS, NCF2\u0026ndash;classical monocyte/all cells, IL-17A\u0026ndash;memory CD4 T cells/CD4 T cells, IL-17A\u0026ndash;Th17/CD4 T cells, TFF2\u0026ndash;effector CD8 T cells/CD8 T cells, KLRD1\u0026ndash;effector CD8 cells/CD8 cells, and KLRD1\u0026ndash;effector CD8 T cells/exhausted CD8 T cells were the significant combinations with correlation coefficients of \u0026gt;\u0026thinsp;0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In addition, the ratio of effector CD8 T cells correlated with tumor necrosis factor receptor superfamily member 11B, which also correlated with ΔALSFRS-R/month (partial correlation coefficient\u0026thinsp;=\u0026thinsp;0.402, p\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study applied multiomics to blood profiling and determined how immune changes relate to disease progression in patients with ALS. The scRNA-seq uncovered cell type shifts from Treg toward Th17, na\u0026iuml;ve toward effector CD8 T cells, and na\u0026iuml;ve toward mature NK cells in rapidly progressive ALS. The serum proteomics revealed that IL-17A and KLRD1 (CD94) increased in rapidly progressive ALS and correlated with frequencies of Th17 and effector CD8 T cells, respectively, thus reinforcing the active roles of these cell types.\u003c/p\u003e \u003cp\u003eTh17 can promote protective immunity against many pathogens but also drive inflammatory pathology during infection, autoimmunity such as multiple sclerosis, and neurodegeneration such as Alzheimer\u0026rsquo;s (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Flow cytometric analyses using PBMCs revealed ALS patients had higher frequencies of Th1 and Th17 and lower frequencies of Th2 and Treg compared with healthy controls (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, previous studies showed no relationship between Th17 and disease progression. Similarly, an increase in IL-17A, the major effector cytokine of Th17, was reported in serum or CSF in ALS patients (\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, the association between IL-17A and disease progression remained unknown. Here, we successfully related not only Th17 but also IL-17A to the rapid progression of ALS. The results regarding IL-17A were reproduced by measurement with Simoa.\u003c/p\u003e \u003cp\u003eNotably, effector CD8 T cells, compared to na\u0026iuml;ve CD8 T cells, have also emerged as a phenotype associated with the rapid progression of ALS. CD8 T cells have been reported to play significant roles in SOD1-ALS and juvenile ALS-4, which is caused by SETX mutation (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In SOD1 model mice, the depletion of CD8 T cells led to an increase in the number of surviving motor neurons, whereas CD8 T cells expressing mutant \u003cem\u003eSOD1\u003c/em\u003e were found to selectively recognize and kill motor neurons in vitro (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). More recently, in \u003cem\u003eSETX\u003c/em\u003e knock-in mice with the L389S mutation, T cell receptor (TCR) repertoire analysis of both the CNS and blood revealed the presence of clonally expanded PD-1\u0026thinsp;+\u0026thinsp;CD8 T cells (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These expanded PD-1\u0026thinsp;+\u0026thinsp;CD8 T cells were associated with immunity to glioma but not melanoma, suggesting their recognition of antigens of CNS origin. Additionally, clonally expanded, terminally differentiated effector memory CD8 T cells were detected in the peripheral blood of patients with ALS4. These findings indicate that antigen-specific CD8 T cell responses are involved in the pathogenesis of ALS4. Consistent with this, scRNA-seq of CSF showed higher levels of TCR expansions in effector CD4 and CD8 T cells in five ALS patients compared to four disease controls (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Together, these findings and our own suggest that effector CD8 T cells in the blood may recognize specific antigens, possibly self-antigens, in ALS patients.\u003c/p\u003e \u003cp\u003eThe present and previous findings together suggest that the changes in peripheral immune cell profiles we observed, although they appear modest, are related to distinct disease conditions including progression rates. In a study using flow cytometry (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), like in the present study using scRNA-seq, the frequencies of Treg were higher in non-rapid ALS than in healthy control (1.0% versus 0.4\u0026ndash;0.6%) but were similar between rapid ALS and healthy control. In addition, another study showed that Treg ratio was inversely correlated with the progression rate in ALS patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), which is consistent with our findings of a lower Treg ratio in rapid ALS. On the other hand, previous studies reported no relationship between the frequency of CD8 T cells and ALSFRS-R scores (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In contrast, our study further analyzed the subpopulation of CD8 T cells and revealed opposite trends for effector and na\u0026iuml;ve CD8 T cells between rapid and non-rapid ALS, suggesting the superiority of our methods.\u003c/p\u003e \u003cp\u003eKLRD1 (CD94), which was associated with the rate of progression and the frequency of effector and na\u0026iuml;ve CD8 T cells, forms a heterodimer with NKG2. The CD94/NKG2 complex can function as an inhibitor or activator, depending on the specific NKG2 isoforms. For instance, CD94/NKG2A acts as an inhibitory receptor, whereas CD94/NKG2C acts as an activating receptor. Consequently, CD94/NKG2 may regulate the effector functions and survival of NK cells and CD8 T cells. CD94/NKG2A primarily counteracts TCR signaling in a subset of memory/effector CD8 T cells as part of an antigen-driven response to prevent autoimmunity (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Conversely, CD94/NKG2C can activate T cells, inducing proliferation and the killing of HLA-E-transfected target cells that lack expression of other MHC-I molecules, even without TCR stimulation. Thus, it constitutes an alternative activation pathway for a subset of CD8 T cells (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In light of these findings, it is crucial to investigate the characteristics of CD94/NKG2 in ALS. Furthermore, our correlation analysis suggests that CD94 may serve as a marker of CD8 T cell activation in ALS, similar to other instances (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that the ratio of CD16\u003csup\u003ehigh\u003c/sup\u003eCD56\u003csup\u003elow\u003c/sup\u003e mature NK cells to CD16\u003csup\u003elow\u003c/sup\u003eCD56\u003csup\u003ehigh\u003c/sup\u003e na\u0026iuml;ve NK cells was significantly higher in rapid than non-rapid ALS. It was reported that ALS patients with slower disease progression showed increased proportions of immune-regulatory CD56\u003csup\u003ebright\u003c/sup\u003e NK cells in the CSF compared with ALS patients with rapid disease progression (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Patients with rapid disease progression showed enhanced differentiation of intrathecal CD56\u003csup\u003ebright\u003c/sup\u003e into CD56\u003csup\u003edim\u003c/sup\u003e NK cells, likely accounting for their reduced frequencies in the CSF of these patients (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It was also reported that CD56\u003csup\u003ebright\u003c/sup\u003e NK cells play a role in regulating activated T cells in both the periphery and CSF, thereby attenuating adaptive immune responses and exerting protective effects in autoimmune diseases of the CNS (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These findings highlight the relevance of NK cell differentiation to the pathophysiology of ALS.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOur DEG analysis revealed that SERPINA1 in na\u0026iuml;ve CD4 T cells and AC103591.3 (RP11-386I14) in classical monocytes were downregulated in ALS, although they were not different between rapid and non-rapid ALS. It was recently reported that serum SerpinA1 was decreased in patients with ALS compared with controls, while CSF SerpinA1 was elevated in fast progressors compared with slow and intermediate progressors (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). On the other hand, information on the role of RP11-386I14 in ALS is scarce. Regarding monocytes, it was reported that the ratio of classical to non-classical monocytes was elevated in patients with ALS (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, the association between monocytes and progression rate was not shown. We showed that the frequency of classical monocytes was correlated with the value of NCF2, which was elevated in rapid versus non-rapid ALS. NCF2 is responsible for the synthesis of superoxide in neutrophils and was reported as one of the ferroptosis- and iron metabolism-related genes differentially expressed in ALS compared to control (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These findings suggest that the roles of monocytes in the pathophysiology of ALS warrant further investigation.\u003c/p\u003e\u003cp\u003eSeveral limitations were noted in this study. First, the number of participants was relatively small; the number was similar to or smaller than those in other studies using flow cytometry or ELISA in patients with ALS (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and was comparable to those in recent scRNA-seq studies in other diseases (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Meanwhile, we could not provide the analysis comparing patients with and without cognitive impairment and with different onset sites due to the small sample size. Second, the difference in the numbers of patients between the non-rapid ALS and rapid ALS groups was relatively large. Third, longitudinal assessment was beyond the scope of this study. Additional evaluation in different stages would better clarify the temporal, progressive changes of immune profiles in ALS. Fourth, although not only the frequency but also the function of the immune cells such as Treg is important (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), we focused on their frequency in this study. Fifth, the evaluation of the CSF sample was beyond the scope of this study. Sixth, we did not measure neurofilament light chain with Simoa because of our limited budget.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study revealed relationships between rapid progression and increase in Th17 against Treg, effector CD8 T cells against na\u0026iuml;ve CD8 T cells, and relevant proteins such as IL-17A and KLRD1 (CD94) in the blood in ALS patients, suggesting these cell types together relate to disease progression in ALS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Design\u003c/h2\u003e \u003cp\u003eWe aimed to identify immune cells and proteins that relate to ALS progression rate via scRNA-seq of PBMCs and high-throughput PEA-based immunoproteomic analysis of serum. Then, the clinical information was compared with scRNA-seq and proteomics data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe conducted a screening involving 36 patients diagnosed with ALS and 10 healthy volunteers at Tokushima University Hospital in Tokushima, Japan, spanning from March 29, 2021, to October 30, 2022. To be eligible for inclusion in the study, patients had to meet the ALS diagnostic criteria established by the Gold Coast criteria (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) and have a disease duration of up to 2 years from the onset of symptoms. It is important to note that the patients\u0026rsquo; ages and sexes were carefully matched with those of the healthy participants. \u003cb\u003eTable S8\u003c/b\u003e lists the exclusion criteria for patients with ALS and healthy participants. We examined genetic background of the following genes: \u003cem\u003eALS2\u003c/em\u003e, \u003cem\u003eANG\u003c/em\u003e, \u003cem\u003eANXA11\u003c/em\u003e, \u003cem\u003eATXN2\u003c/em\u003e, \u003cem\u003eBNIP3L\u003c/em\u003e, \u003cem\u003eC9orf72\u003c/em\u003e, \u003cem\u003eCALCOCO2\u003c/em\u003e, \u003cem\u003eCBL\u003c/em\u003e, \u003cem\u003eCCNF\u003c/em\u003e, \u003cem\u003eCHCHD10\u003c/em\u003e, \u003cem\u003eCHMP2B\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e, \u003cem\u003eCYLD\u003c/em\u003e, \u003cem\u003eDAO\u003c/em\u003e, \u003cem\u003eDCTN1\u003c/em\u003e, \u003cem\u003eDYNLL1\u003c/em\u003e, \u003cem\u003eELP3\u003c/em\u003e, \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eEWSR1\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cem\u003eFUS\u003c/em\u003e, \u003cem\u003eGABARAP\u003c/em\u003e, \u003cem\u003eGLE1\u003c/em\u003e, \u003cem\u003eGRN\u003c/em\u003e, \u003cem\u003eHDAC6\u003c/em\u003e, \u003cem\u003eHNRNPA1\u003c/em\u003e, \u003cem\u003eHNRNPA2B1\u003c/em\u003e, \u003cem\u003eHNRPA2B1\u003c/em\u003e, \u003cem\u003eKIF5A\u003c/em\u003e, \u003cem\u003eLRP12\u003c/em\u003e, \u003cem\u003eMAP1LC3A\u003c/em\u003e, \u003cem\u003eMATR3\u003c/em\u003e, \u003cem\u003eNBR1\u003c/em\u003e, \u003cem\u003eNEFH\u003c/em\u003e, \u003cem\u003eNEK1\u003c/em\u003e, \u003cem\u003eOPTN\u003c/em\u003e, \u003cem\u003ePFN1\u003c/em\u003e, \u003cem\u003ePRPH\u003c/em\u003e, \u003cem\u003eRNASE4\u003c/em\u003e, \u003cem\u003eRNF19A\u003c/em\u003e, \u003cem\u003eSETX\u003c/em\u003e, \u003cem\u003eSIGMAR1\u003c/em\u003e, \u003cem\u003eSKP1\u003c/em\u003e, \u003cem\u003eSOD1\u003c/em\u003e, \u003cem\u003eSPAST\u003c/em\u003e, \u003cem\u003eSPG11\u003c/em\u003e, \u003cem\u003eSPTLC1\u003c/em\u003e, \u003cem\u003eSQSTM1\u003c/em\u003e, \u003cem\u003eSS18L1\u003c/em\u003e, \u003cem\u003eTAF15\u003c/em\u003e, \u003cem\u003eTARDBP\u003c/em\u003e, \u003cem\u003eTBK1\u003c/em\u003e, \u003cem\u003eTFG\u003c/em\u003e, \u003cem\u003eTIA1\u003c/em\u003e, \u003cem\u003eTNFRSF11A\u003c/em\u003e, \u003cem\u003eTNFSF11\u003c/em\u003e, \u003cem\u003eTUBA4A\u003c/em\u003e, \u003cem\u003eUBQLN2\u003c/em\u003e, \u003cem\u003eVAPB\u003c/em\u003e, \u003cem\u003eVAPBC\u003c/em\u003e, and \u003cem\u003eVCP\u003c/em\u003e. Patients with known pathogenic ALS variants were excluded beforehand from the analysis because the immunological profiles would be different from those of sporadic ALS. Rapid ALS was defined as a decrease in the ALSFRS-R score of \u0026ge;\u0026thinsp;1.0 points per month (ΔALSFRS-R/month\u0026thinsp;\u0026ge;\u0026thinsp;1), whereas non-rapid ALS was defined as a decrease in the ALSFRS-R score of \u0026lt;\u0026thinsp;1.0 point per month (ΔALSFRS-R/month\u0026thinsp;\u0026lt;\u0026thinsp;1). As described below, we collected peripheral blood samples and conducted scRNA-seq and proteomic analyses. All study protocols were approved by the Ethics Committee of Tokushima University Hospital under protocol #3682. All clinical information was collected after patients provided written informed consent. The investigations were conducted following the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample Preparation\u003c/h2\u003e \u003cp\u003ePeripheral blood samples were collected in Benoject II Vacuum Blood Collection Tube Sterilized Product VP-H100K (Terumo Corporation, Tokyo, Japan) from healthy volunteers and patients with ALS at Tokushima University Hospital. Ficoll-Hypaque (Lymphoprep\u0026trade;, Serumwerk Bernburg AG, Germany) density centrifugation prepared PBMCs, which were washed with phosphate-buffered saline (PBS) and resuspended in X-VIVO\u0026trade; medium (LONZA, Basel, Switzerland) with 5% fetal bovine serum. PBMCs were stored at \u0026minus;\u0026thinsp;80℃ until CELLBANKER 1 (Nippon Zenyaku Kogyo Ltd, Fukushima, Japan) prepared the library. Serum samples were collected in 8.0 ml of Insepack tube (SEKISUI MEDICAL CO., LTD., Tokyo, Japan), centrifuged at 3,500 rpm for 10 min, aliquoted, and frozen at \u0026minus;\u0026thinsp;80℃ within 20 min following collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell RNA-sequencing\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003ePBMCs Preparation, Library Preparation, and Sequencing\u003c/h2\u003e \u003cp\u003eCryopreserved human PBMCs were rapidly thawed in a 37\u0026deg;C water bath and washed with 1 \u0026times; D-PBS (magnesium/calcium-free) containing 1% BSA. Cell collection was performed at 20\u0026deg;C, with a centrifugation speed of 250 \u0026times; g for 10 minutes using a swing-out rotor. After being washed three times, the cells were resuspended in an appropriate volume of D-PBS with 1% BSA. Subsequently, the cell suspension was passed through a Flowmi Cell Strainer with a pore size of 40 \u0026micro;m to eliminate any remaining larger particles. Cell count and viability were assessed using a hemacytometer with trypan blue staining.\u003c/p\u003e \u003cp\u003eWe used scRNA-seq and the 10x Genomics (Pleasanton, CA) platform. The 10x Genomics Chromium controller was employed to load single-cell suspensions (10,000 cells) onto a chip to generate GEMs (Gel Bead-In-Emulsions). The Chromium Next GEM Single Cell 3\u0026rsquo; Reagent Kits v3.1 from 10x Genomics was used to prepare DNA libraries, following the manufacturer's instructions. Before sequencing, quality control of the prepared libraries was conducted using the 4200 TapeStation D1000 ScreenTape (Agilent, Santa Clara, CA) and the Qubit dsDNA Assay (Thermo Fisher Scientific, Waltham, MA). The gene expression libraries were sequenced on the DNBSEQ-G400 platform from MGI Tech (Shenzhen, China) at a depth of 50,000 reads per cell.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Normalization\u003c/h2\u003e \u003cp\u003eWe employed the 10x Genomics Cell Ranger version 3 pipeline for primary analysis. The Cell Ranger \u0026ldquo;counts\u0026rdquo; software was utilized to convert Bcl files to FASTQ format. Subsequently, the FASTQ data underwent filtering and were mapped to the GRCh38 reference genome.\u003c/p\u003e \u003cp\u003eThe secondary analysis used R version 4.0.2 with the Seurat version 3 package (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). To exclude data from low-quality cells and doublet cells, we utilized the Seurat and DoubletFinder packages (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The count data were subjected to normalization through a global-scaling normalization method, employing the Seurat function \u0026ldquo;LogNormalize,\u0026rdquo; followed by log transformation using the Seurat function \u0026ldquo;NormalizeData.\u0026rdquo; Variable features were selected by directly modeling the mean-variance relationship, utilizing the Seurat function \u0026ldquo;FindVariableFeatures.\u0026rdquo; These selected variable features were then used to identify integration features through the Seurat function \u0026ldquo;SelectIntegrationFeatures.\u0026rdquo; The integration features were applied for linear transformation and utilized in principal component analysis (PCA) by employing the Seurat functions \u0026ldquo;ScaleData\u0026rdquo; and \u0026ldquo;RunPCA,\u0026rdquo; respectively.\u003c/p\u003e \u003cp\u003eReciprocal PCA was performed using the top 50 principal components to identify anchors. The data were separated by anchor, and the batch features were extracted using the function \u0026lsquo;SelectIntegrationFeatures\u0026rsquo; from the Seurat package and merged using the function \u0026lsquo;IntegrateData\u0026rsquo; (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). All scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus repository, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, under the accession number GSE244263.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eThe integrated and normalized data underwent a linear transformation and PCA using Seurat functions \u0026ldquo;ScaleData\u0026rdquo; and \u0026ldquo;RunPCA.\u0026rdquo; To identify important principal components, an elbow plot was generated by plotting standard deviations of the principal components. These significant principal components were then used to perform uniform manifold approximation and projection (UMAP) visualization, achieved through the Seurat functions \u0026ldquo;ElbowPlot\u0026rdquo; and \u0026ldquo;RunUMAP.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe identification of cell types was carried out using these crucial principal components. First, cell similarities were calculated using a k-nearest neighbor graph based on the Euclidean distance in PCA space. The edge weights between any two cells were determined based on the shared overlap with their local neighborhoods using Jaccard\u0026rsquo;s similarity, facilitated by the Seurat function \u0026ldquo;FindNeighbors.\u0026rdquo; Subsequently, cell populations were divided through clustering, accomplished by assessing the similarities between cells using the Louvain algorithm and the Seurat function \u0026ldquo;FindClusters.\u0026rdquo;\u003c/p\u003e \u003cp\u003eFurthermore, cell types within each cluster were identified by examining cell-type-specific marker expression profiles for each cluster (\u003cb\u003eTable S9\u003c/b\u003e). Although NK cells are commonly classified into two functionally distinct subsets, i.e., CD56\u003csup\u003ebright\u003c/sup\u003eCD16\u003csup\u003e\u0026minus;\u003c/sup\u003e and CD56\u003csup\u003edim\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e, NK cells were clearly divided into three subsets based on the relative expression of CD16 and CD56 in our analysis (CD16\u003csup\u003elow\u003c/sup\u003eCD56\u003csup\u003ehigh\u003c/sup\u003e for na\u0026iuml;ve NK cells, CD16\u003csup\u003ehigh\u003c/sup\u003eCD56\u003csup\u003elow\u003c/sup\u003e for mature NK cells, and CD16\u003csup\u003emiddle\u003c/sup\u003eCD56\u003csup\u003elow\u003c/sup\u003e for activated NK cells) (\u003cb\u003eFig. S5\u003c/b\u003e). Activated NK cell subsets expressed CD247 (CD3zeta), suggesting enhanced activation and cytotoxic capacity through efficient signaling and interaction with other immune receptors. The resolution used was \"Seurat's default value\" and the cluster numbers before and after annotation were 22 and 23 respectively. The reason why the number of clusters increased after the annotation was that only CD4 T cell was extracted, reclustered, and subdivided (e.g., Th1, Th17) within it. The filter thresholds were \"mitochondria genes\u0026thinsp;\u0026lt;\u0026thinsp;15% and gene feature counts 500\u0026thinsp;~\u0026thinsp;4000\" and the cell number \"422,135 cells\" used in the downstream analysis. Normalized gene expression levels of cell-type marker genes for each cell type are shown in UMAPs (\u003cb\u003eFig. S5\u003c/b\u003e) and a heatmap (\u003cb\u003eFig. S6\u003c/b\u003e). To identify DEGs between groups within each cell type, the nonparametric Wilcoxon rank sum test was employed, with the Seurat function \u0026ldquo;FindMarkers.\u0026rdquo;\u003c/p\u003e \u003cp\u003eGene expression levels were visualized within each cluster using violin plots via the Seurat function \u0026ldquo;VlnPlot.\u0026rdquo; Differences in the frequencies of each cell type between groups were assessed using Tukey\u0026rsquo;s HSD test with the \u0026ldquo;TukeyHSD\u0026rdquo; function in the stats (R default) package.\u003c/p\u003e \u003cp\u003eCorrelation tests used the \u0026ldquo;cor.test\u0026rdquo; function from the stats (R default) package. Finally, scatter plots, boxplots, and bar plots were created in R using the ggplot2 package (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) for visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eProtein Measurement\u003c/h2\u003e \u003cp\u003eWe employed the Olink Target 96 Inflammation and Olink Explore 384 Inflammation panels from Olink Proteomics (Uppsala, Sweden) to simultaneously quantify multiple human proteins associated with inflammation (\u003cb\u003eTable S10\u003c/b\u003e) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This quantification was achieved using PEA technology (for more information, visit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.olink.com\u003c/span\u003e\u003cspan address=\"https://www.olink.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PEA technology involves a pair of antibodies, each linked to unique oligonucleotides (proximity probes) and bound to their respective protein targets. These probes only hybridize with each other when they are near forming double-stranded DNA. The resulting complex is then detected and quantified using quantitative real-time PCR for the Olink Target 96 Inflammation panel or next-generation sequencing for the Olink Explore 384 Inflammation panel. The data were presented in normalized protein expression (NPX), which is an arbitrary unit on a log\u003csub\u003e2\u003c/sub\u003e-scale. NPX values represent relative expression rather than absolute protein levels. For proteins measured by next-generation sequencing, all items were selected for analysis. Regarding items measured solely by real-time PCR, we selected those with \u0026gt;\u0026thinsp;50% of the samples above the detection limit for analysis. A scatter plot of the first and second principal components in the PCA of serum proteome samples is shown in \u003cb\u003eFig. S7\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo identify DEPs between groups, we used Tukey\u0026rsquo;s HSD test with the \u0026ldquo;TukeyHSD\u0026rdquo; function in the stats (R default) package. We visualized the results through volcano plots created in R using the ggplot2 and ggrepel packages (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) for visualization. Correlation tests used the \u0026ldquo;cor.test\u0026rdquo; function from the stats (R default) package. Furthermore, serum pNfH levels were measured using an ELISA kit from EUROIMMUN (L\u0026uuml;beck, Germany) (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo test the reproducibility of the measurement, the concentrations of IL-17A, which was identified as one of the DEPs, were additionally assessed using the Simoa\u0026reg; IL-17A Advantage PLUS kit (Catalog #104428; Quanterix, Billerica, MA) run on the Simoa\u0026reg; HD-X Analyzer (Quanterix), in the participants whose residual re-frozen samples were available (healthy controls, 9 of 10; non-rapid ALS patients, 20 of 23, rapid ALS patients, 7 of 7). The functional lower limit of quantification was 0.012 pg/ml. The samples except for one of the healthy controls were analyzed in duplicate. Samples were diluted 1:2 and distributed on 96-well plates. These measurements were performed according to the manufacturer\u0026rsquo;s instructions at SEKISUI MEDICAL CO., LTD. The data were presented in the log\u003csub\u003e2\u003c/sub\u003e-transformed scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eMicrosoft R open software (version 4.0.2) or GraphPad Prism version 10 (GraphPad Software, Boston, MA) were used for statistical analyses. The nonparametric Wilcoxon rank sum test identified DEGs in scRNA-seq, which was corrected for multiple tests using Bonferroni\u0026rsquo;s test. Tukey\u0026rsquo;s HSD for multiple testing was used to identify DEPs in serum proteome and the differences in the frequencies of each cell type between the groups. Pearson\u0026rsquo;s correlation coefficient was used for correlation tests. A 2-sided \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALS: amyotrophic lateral sclerosis\u003c/p\u003e\n\u003cp\u003eALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Scale\u003c/p\u003e\n\u003cp\u003eCNS: central nervous system\u003c/p\u003e\n\u003cp\u003eDEG: differentially expressed gene\u003c/p\u003e\n\u003cp\u003eDEP: differentially expressed protein\u003c/p\u003e\n\u003cp\u003eFC: fold change\u003c/p\u003e\n\u003cp\u003eHSD: honestly significant difference\u003c/p\u003e\n\u003cp\u003eIL-17A: interleukin-17A\u003c/p\u003e\n\u003cp\u003eKLRD1: killer cell lectin-like receptor D1\u003c/p\u003e\n\u003cp\u003eKRT19: keratin, type I cytoskeletal 19\u003c/p\u003e\n\u003cp\u003eNCF2: neutrophil cytosol factor 2\u003c/p\u003e\n\u003cp\u003eNK: natural killer\u003c/p\u003e\n\u003cp\u003eNPX:\u0026nbsp;normalized protein expression\u003c/p\u003e\n\u003cp\u003ePBMCs: peripheral blood mononuclear cells\u003c/p\u003e\n\u003cp\u003ePBS: phosphate-buffered saline\u003c/p\u003e\n\u003cp\u003ePCA: principal component analysis\u003c/p\u003e\n\u003cp\u003ePEA: proximity extension assay\u003c/p\u003e\n\u003cp\u003escRNA-seq: single-cell RNA-sequencing\u003c/p\u003e\n\u003cp\u003eSimoa: Single molecule array\u003c/p\u003e\n\u003cp\u003eTCR: T cell receptor\u003c/p\u003e\n\u003cp\u003eTFF2: trefoil factor 2\u003c/p\u003e\n\u003cp\u003eTh17: T helper 17 cells\u003c/p\u003e\n\u003cp\u003eTreg: regulatory T cells\u003c/p\u003e\n\u003cp\u003eUMAP: uniform manifold approximation and projection\u003c/p\u003e\n\u003cp\u003eYTHDF3: YTH domain-containing family protein 3\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study protocols were approved by the Ethics Committee of Tokushima University Hospital under protocol #3682. All clinical information was collected after patients provided written informed consent. The investigations were conducted following the principles of the Declaration of Helsinki.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus repository, accessible at http://www.ncbi.nlm.nih.gov/geo, under the accession number GSE244263. All other data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTI, YO, DW, SM, and YK are employees of Takeda Pharmaceutical Company Limited.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTakeda Pharmaceutical Company Limited (YI)\u003c/p\u003e\n\u003cp\u003eMHLW Research on rare and intractable diseases Program Grant Number JPMH23FC1008 (YI)\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: TI, KF, YI\u003c/p\u003e\n\u003cp\u003eMethodology: TI, KF, YO, YI\u003c/p\u003e\n\u003cp\u003eInvestigation: TI, YO, YM, YI\u003c/p\u003e\n\u003cp\u003eVisualization: TI, KF, YO\u003c/p\u003e\n\u003cp\u003eSupervision: YI\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;original draft: TI, KF\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;review \u0026amp; editing: TI, KF, YO, DW, SM, YM, NM, HM, YK, YI\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeldman EL, Goutman SA, Petri S, Mazzini L, Savelieff MG, Shaw PJ, Sobue G. 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Phosphorylated neurofilament heavy chain: a potential diagnostic biomarker in amyotrophic lateral sclerosis. \u003cem\u003eJ Neurophysiol\u003c/em\u003e. 127, 737-745 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"amyotrophic lateral sclerosis, disease progression, effector CD8 T cells, interleukin-17A, killer cell lectin-like receptor D1 (CD94), natural killer cells, single-cell RNA-sequencing, T helper 17 cells","lastPublishedDoi":"10.21203/rs.3.rs-5187725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5187725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The immune system has garnered attention due to its association with disease progression in amyotrophic lateral sclerosis (ALS). However, the role of peripheral immune cells in this context remains controversial. Here, we conducted single-cell RNA-sequencing of peripheral blood mononuclear cells to comprehensively profile immune cells concerning the rate of disease progression in patients with ALS. Our analysis revealed increased frequencies of T helper 17 cells (Th17) relative to regulatory T cells, effector CD8 T cells relative to naïve CD8 T cells, and CD16highCD56low mature natural killer cells relative to CD16lowCD56high naïve natural killer cells in patients with rapidly progressive ALS. Additionally, we employed serum proteomics through a proximity extension assay combined with next-generation sequencing to identify inflammation-related proteins associated with rapid disease progression. Among these proteins, interleukin-17A correlated with the frequency of Th17, while killer cell lectin-like receptor D1 (CD94) correlated with the frequency of effector CD8 T cells. These findings further support the active roles played by these specific immune cell types in the progression of ALS.","manuscriptTitle":"Th17 and effector CD8 T cells relate to disease progression in amyotrophic lateral sclerosis: a case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 13:19:18","doi":"10.21203/rs.3.rs-5187725/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-10T22:15:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-10T17:09:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-02T12:56:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216096273947136674640261821627976493816","date":"2024-12-02T00:56:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337737743447299899680809578574307400262","date":"2024-12-01T07:14:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-29T06:05:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-29T06:04:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2024-11-29T04:55:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21c681d4-ad76-45ae-997f-7ab199763d76","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:02:57+00:00","versionOfRecord":{"articleIdentity":"rs-5187725","link":"https://doi.org/10.1186/s12974-024-03327-w","journal":{"identity":"journal-of-neuroinflammation","isVorOnly":false,"title":"Journal of Neuroinflammation"},"publishedOn":"2024-12-27 15:57:46","publishedOnDateReadable":"December 27th, 2024"},"versionCreatedAt":"2024-12-09 13:19:18","video":"","vorDoi":"10.1186/s12974-024-03327-w","vorDoiUrl":"https://doi.org/10.1186/s12974-024-03327-w","workflowStages":[]},"version":"v1","identity":"rs-5187725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5187725","identity":"rs-5187725","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}