scholarly journals Plasma extracellular vesicles modulate immune cell gene expression following myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
N Akbar ◽  
A Braithwaite ◽  
C Lewe ◽  
S Lemke ◽  
M Alkhalil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Myocardial Injury ◽  
Immune Cell ◽  
British Heart Foundation ◽  
Funding Source ◽  
Nanoparticle Tracking Analysis ◽  
Hs Crp ◽  
Control Plasma ◽  
Heart Foundation

Abstract Background Myocardial infarction (MI) induces activation of immune cells and alters their gene expression en route to the injured myocardium but the underlying mechanisms coordinating immune cell programming following MI remain unknown. Plasma extracellular vesicle (EV) numbers are elevated in MI, correlate with the extent of myocardial injury and mobilises immune cells from the splenic reserve to peripheral blood. Here, we describe the role of plasma EV-microRNAs (miRs) in the modulation of peripheral blood mononuclear cell (PBMC) transcriptomes post-MI. Methods PBMCs were exposed to plasma EVs followed by whole transcriptome RNA-sequencing. Plasma EVs were isolated by size-exclusion chromatography and ultra-centrifugation (2 hours at 120,000 x g) from patients presenting with ST-segment elevation MI (STEMI) (N=9) and non-STEMI (NSTEMI) (N=11) control patients. Plasma EVs were characterised by western blot and Nanoview for EV markers CD9 and CD63, transmission electron microscopy (TEM) for morphology and Nanoparticle Tracking Analysis for size and concentration. High sensitive C-reactive protein (hs-CRP) and PCSK9 were determined in plasma by ELISA and compared to plasma EV number using Pearson's correlation. Plasma EV-miRs were measured by Agilent microarray and miR-mRNA putative targets assessed by TargetScanHuman. Results Plasma EVs were positive for EV markers CD9 and CD63, displayed typical EV morphology by TEM and had a heterogeneous size and concentration distribution profile as determined by Nanoparticle Tracking Analysis. Plasma EV number correlated significantly with hs-CRP at presentation (R2= 0.20 and P<0.05). miRNA array analysis revealed STEMI plasma-EVs contained significantly more miR-4487 (P<0.001), miR-6511b-5p (P<0.001), miR-4508 (P<0.001) vs NSTEMI control plasma-EVs at the time of injury. STEMI-plasma-EVs induced differential gene expression in PBMCs vs. NSTEMI-control-plasma-EVs. Gene set enrichment analysis (GSEA) showed STEMI-plasma-EVs upregulated pro-inflammatory pathways including: interferon-α (IFN-α) (P<0.01), IFN-γ (P<0.01), tumour necrosis factor-α (TNF-α) (P<0.01) and interleukin-6 (IL-6)-STAT3 signalling of the acute phase response (P<0.05). miR-4487 (P<0.001) and miR-6511-5p (P<0.05) predicted mRNA targets were significantly enriched in PBMC transcriptomes following treatment with STEMI plasma-EVs. Conclusions Plasma EVs mediating immune cell transcriptional programming following MI by promoting inflammatory pathways in PBMCs is a novel finding. Targeting PBMCs with EVs may allow modulation of the immune response following myocardial injury, to perturb inflammatory immune mediated damage following ischaemic injury. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): British Heart Foundation Centre of Research Excellence Awards, British Heart Foundation Project Grant, Novo Nordisk Fonden the Tripartite Immunometabolism Consortium and Wellcome Institutional Strategic Support Fund (ISSF)

scholarly journals The Toolbox for Untangling Chromosome Architecture in Immune Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuai Liu ◽  
Keji Zhao
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Spatial Organization ◽  
Chromosome Structure ◽  
Immune Cell ◽  
Genome Structure ◽  
Chromosome Architecture ◽  
The Past ◽  
Important Player ◽  
The Way

The code of life is not only encrypted in the sequence of DNA but also in the way it is organized into chromosomes. Chromosome architecture is gradually being recognized as an important player in regulating cell activities (e.g., controlling spatiotemporal gene expression). In the past decade, the toolbox for elucidating genome structure has been expanding, providing an opportunity to explore this under charted territory. In this review, we will introduce the recent advancements in approaches for mapping spatial organization of the genome, emphasizing applications of these techniques to immune cells, and trying to bridge chromosome structure with immune cell activities.

scholarly journals GJA1 Expression and Its Prognostic Value in Cervical Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Silu Meng ◽  
Xinran Fan ◽  
Jianwei Zhang ◽  
Ran An ◽  
Shuang Li
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Logistic Regression ◽  
Gap Junction ◽  
Signaling Pathway ◽  
Immune Cells ◽  
Immune Cell ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Chi Squared

Gap Junction Protein Alpha 1 (GJA1) belongs to the gap junction family and has been widely studied in cancers. We evaluated the role of GJA1 in cervical cancer (CC) using public data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The difference of GJA1 expression level between CC and normal tissues was analyzed by the Gene Expression Profiling Interactive Analysis (GEPIA), six GEO datasets, and the Human Protein Atlas (HPA). The relationship between clinicopathological features and GJA1 expression was analyzed by the chi-squared test and the logistic regression. Kaplan–Meier survival analysis and Cox proportional hazard regression analysis were used to assessing the effect of GJA1 expression on survival. Gene set enrichment analysis (GSEA) was used to screen the signaling pathways regulated by GJA1. Immune Cell Abundance Identifier (ImmuCellAI) was chosen to analyze the immune cells affected by GJA1. The expression of GJA1 in CC was significantly lower than that in normal tissues based on the GEPIA, GEO datasets, and HPA. Both the chi-squared test and the logistic regression showed that high-GJA1 expression was significantly correlated with keratinization, hormone use, tumor size, and FIGO stage. The Kaplan–Meier curves suggested that high-GJA1 expression could indicate poor prognosis ( p = 0.0058 ). Multivariate analysis showed that high-GJA1 expression was an independent predictor of poor overall survival (HR, 4.084; 95% CI, 1.354-12.320; p = 0.013 ). GSEA showed many cancer-related pathways, such as the p53 signaling pathway and the Wnt signaling pathway, were enriched in the high-GJA1-expression group. Immune cell abundance analysis revealed that the abundance of CD8 naive, DC, and neutrophil was significantly increased in the high-GJA1-expression group. In conclusion, GJA1 can be regarded as a potential prognostic marker of poor survival and therapeutic target in CC. Moreover, many cancer-related pathways may be the critical pathways regulated by GJA1. Furthermore, GJA1 can affect the abundance of immune cells.

scholarly journals MMP1, MMP9, and COX2 Expressions in Promonocytes Are Induced by Breast Cancer Cells and Correlate with Collagen Degradation, Transformation-Like Morphological Changes in MCF-10A Acini, and Tumor Aggressiveness

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
G. K. Chimal-Ramírez ◽  
N. A. Espinoza-Sánchez ◽  
D. Utrera-Barillas ◽  
L. Benítez-Bribiesca ◽  
J. R. Velázquez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Immune Cells ◽  
Immune Cell ◽  
Morphological Changes ◽  
Tumor Stroma ◽  
Collagen Degradation ◽  
Tumor Aggressiveness ◽  
Soluble Factors ◽  
Immune Effector

Tumor-associated immune cells often lack immune effector activities, and instead they present protumoral functions. To understand how tumors promote this immunological switch, invasive and noninvasive breast cancer cell (BRC) lines were cocultured with a promonocytic cell line in a Matrigel-based 3D system. We hypothesized that if communication exists between tumor and immune cells, coculturing would result in augmented expression of genes associated with tumor malignancy. Upregulation of proteasesMMP1andMMP9and inflammatoryCOX2genes was found likely in response to soluble factors. Interestingly, changes were more apparent in promonocytes and correlated with the aggressiveness of the BRC line. Increased gene expression was confirmed by collagen degradation assays and immunocytochemistry of prostaglandin 2, a product of COX2 activity. Untransformed MCF-10A cells were then used as a sensor of soluble factors with transformation-like capabilities, finding that acini formed in the presence of supernatants of the highly aggressive BRC/promonocyte cocultures often exhibited total loss of the normal architecture. These data support that tumor cells can modify immune cell gene expression and tumor aggressiveness may importantly reside in this capacity. Modeling interactions in the tumor stroma will allow the identification of genes useful as cancer prognostic markers and therapy targets.

scholarly journals Calculation of immune cell proportion from batch tumor gene expression profile based on support vector regression

2020 ◽  
Vol 18 (05) ◽  
pp. 2050030
Author(s):  
Dongmei Ai ◽  
Gang Liu ◽  
Xiaoxin Li ◽  
Yuduo Wang ◽  
Man Guo
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Support Vector Regression ◽  
Gene Expression Profile ◽  
Immune Cells ◽  
Immune Cell ◽  
Relative Proportion ◽  
Support Vector ◽  
Cancer Tissues ◽  
Cell Proportion

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector regression ([Formula: see text]-SVR). These steps resulted in increased robustness of the algorithm and more accurate calculation of the relative proportion of different immune cells in cancer tissues. This method was applied to the analysis of infiltrating immune cells based on 41 pairs of colorectal cancer tissues and normal solid tissues. Specifically, we compared the relative fractions of six types of immune cells in colorectal cancer tissues to those found in normal solid tissue samples. We found that tumor tissues contained a higher proportion of CD8 T cells and neutrophils, while B cells and monocytes were relatively low. Our pipeline for calculating immune cell proportion using gene expression profile data can be freely accessed from GitHub at https://github.com/gutmicrobes/EICS.git.

scholarly journals ImmuSort, a database on gene plasticity and electronic sorting for immune cells

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Pingzhang Wang ◽  
Yehong Yang ◽  
Wenling Han ◽  
Dalong Ma
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Evaluation Scores ◽  
Cell Groups ◽  
Differential Gene ◽  
Disease Associated Genes

Abstract Gene expression is highly dynamic and plastic. We present a new immunological database, ImmuSort. Unlike other gene expression databases, ImmuSort provides a convenient way to view global differential gene expression data across thousands of experimental conditions in immune cells. It enables electronic sorting, which is a bioinformatics process to retrieve cell states associated with specific experimental conditions that are mainly based on gene expression intensity. A comparison of gene expression profiles reveals other applications, such as the evaluation of immune cell biomarkers and cell subsets, identification of cell specific and/or disease-associated genes or transcripts, comparison of gene expression in different transcript variants and probe set quality evaluation. A plasticity score is introduced to measure gene plasticity. Average rank and marker evaluation scores are used to evaluate biomarkers. The current version includes 31 human and 17 mouse immune cell groups, comprising 10,422 and 3,929 microarrays derived from public databases, respectively. A total of 20,283 human and 20,963 mouse genes are available to query in the database. Examples show the distinct advantages of the database. The database URL is http://immusort.bjmu.edu.cn/.

scholarly journals Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

2021 ◽  
Vol 11 ◽  
Author(s):  
Min Qin ◽  
Zhihai Liang ◽  
Heping Qin ◽  
Yifang Huo ◽  
Qing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regression Analysis ◽  
Immune Cells ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Immune Cell ◽  
Risk Group ◽  
P Value ◽  
Expression Data

IntroductionGastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.Materials and MethodsGene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.ResultsAfter five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.ConclusionCGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.

Genetic background, sex, age and pacing cycle length affect left atrial electrophysiology in Langendorff-perfused isolated murine hearts

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Obergassel ◽  
M O'Reilly ◽  
C O'Shea ◽  
S.N Kabir ◽  
L.C Sommerfeld ◽  
...  
Keyword(s):  
Body Weight ◽  
Genetic Background ◽  
Left Atrial ◽  
Coronary Flow ◽  
Weight Ratio ◽  
British Heart Foundation ◽  
Funding Source ◽  
Body Weight Ratio ◽  
Experimental Conditions ◽  
Heart Foundation

Abstract Background Studying cardiac electrophysiology in isolated perfused beating murine hearts is a well-established method. The ranges of normal values for left atrial (LA) action potential durations (LA-APD), activation times (LA-AT) and effective refractory periods (atrial ERP) in murine wildtype (WT) are not well known. Purpose This study aimed to establish reference values for LA-APD, LA-AT and atrial ERP and to identify the influence of genetic background, sex and age on these electrophysiological parameters in WT mice. Method We combined results from isolated beating heart Langendorff experiments carried out in WT mice between 2005 and 2019 using an octopolar catheter inserted into the right atrium and a monophasic action potential electrode recording from the LA epicardium. Electrophysiological parameters (LA-APD at 50%, 70%, 90% repolarization (APD50, APD70, APD90), LA-AT and atrial ERP) at different pacing cycle lengths (PCL) were summarized. We analysed effects of PCL, genetic background, age, gender, heart weight to body weight ratio (HW/BW), LA weight to body weight ratio (LAW/BW) as well as coronary flow and temperature as experimental conditions. Results Electrophysiological parameters from 222 isolated hearts (114 female, mean age 6.6±0.25 months, range 2.47–17.7 months) of different backgrounds (77 C57BL/6, 23 FVB/N, 33 MF1, 69 129/Sv and 20 Swiss agouti) were combined. Coronary flow rate, flow temperature and start of isolation to cannulation time were constant experimental conditions over the timespan of experiments. LA-APD was longer while LA-AT decreased with longer PCL throughout all genetic backgrounds (Figure 1A). Genetic background showed strong effects on all electrophysiological parameters. LA-APD70 and atrial ERP were significantly shorter in Swiss agouti background compared to others. LA-APD70 was also significantly prolonged in 129/Sv background compared to MF1 (Figure 1B). LA activation was delayed in 129/Sv compared to other backgrounds (Figure 1C). Atrial ERP was longer in FVB/N compared to other backgrounds. Atrial ERP was also significantly prolonged (+ 3.4 ms, + 13.5%) in female mice compared to males (Figure 1D). Age effects were compared in groups. Atrial ERP was significantly longer in mice younger than 3 months compared to older mice (Figure 1E). Conclusion This dataset summarises left atrial electrophysiological parameters in the beating mouse heart and can serve as a reference for design and interpretation of electrophysiological experiments in murine models of commonly used genetic backgrounds. We demonstrate that PCL, genetic background, age and gender affect atrial electrophysiological parameters. Awareness of these will support successful experimental design. Figure 1 Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): This work was partially supported by the European Commission (grant agreements no. 633196 [CATCH ME]) to LF and PK, Deutsche Forschungsgemeinschaft DFG FA413, British Heart Foundation (FS/13/43/30324 to LF and PK; AA/18/2/34218 to LF and PK).The Institute of Cardiovascular Sciences has received the British Heart Foundation (BHF) Accelerator Award (AA/18/2/34218). JO has received financial support for abroad studies within his scholarship of the Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation).

scholarly journals The Immune Cell Landscape in Different Anatomical Structures of Knee in Osteoarthritis: A Gene Expression-Based Study

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Ziming Chen ◽  
Yuanchen Ma ◽  
Xuerui Li ◽  
Zhantao Deng ◽  
Minghao Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Mast Cells ◽  
Subchondral Bone ◽  
Immune Cells ◽  
Immune Cell ◽  
M2 Macrophages ◽  
Anatomical Structures ◽  
Normal Tissues ◽  
Immunological Mechanisms

Background. Immunological mechanisms play a vital role in the pathogenesis of knee osteoarthritis (KOA). Moreover, the immune phenotype is a relevant prognostic factor in various immune-related diseases. In this study, we used CIBERSORT for deconvolution of global gene expression data to define the immune cell landscape of different structures of knee in osteoarthritis. Methods and Findings. By applying CIBERSORT, we assessed the relative proportions of immune cells in 76 samples of knee cartilage, 146 samples of knee synovial tissue, 40 samples of meniscus, and 50 samples of knee subchondral bone. Enumeration and activation status of 22 immune cell subtypes were provided by the obtained immune cell profiles. In synovial tissues, the differences in proportions of plasma cells, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and eosinophils between normal tissues and osteoarthritic tissues were statistically significant (P<0.05). The area under the curve was relatively large in resting mast cells, dendritic cells, and M2 macrophages in receiver operating characteristic analyses. In subchondral bones, the differences in proportions of resting master cells and neutrophils between normal tissues and osteoarthritic tissues were statistically significant (P<0.05). In subchondral bones, the proportions of immune cells, from the principle component analyses, displayed distinct group-bias clustering. Resting mast cells and T cell CD8 were the major component of first component. Moreover, we revealed the potential interaction between immune cells. There was almost no infiltration of immune cells in the meniscus and cartilage of the knee joint. Conclusions. The immune cell composition in KOA differed substantially from that of healthy joint tissue, while it also differed in different anatomical structures of the knee. Meanwhile, activated mast cells were mainly associated with high immune cell infiltration in OA. Furthermore, we speculate M2 macrophages in synovium and mast cells in subchondral bone may play an important role in the pathogenesis of OA.

scholarly journals Effects of a Yeast Product and Psyllium Husk on Colonic Gene Expression and Histopathology, Mesenteric Lymph Node Immune Cells, and Disease Activity Index of Colitis Mice

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 694-694
Author(s):  
Ching-Yen Lin ◽  
Anne Lee ◽  
Karen Chiu ◽  
Andrew Steelman ◽  
Kelly Swanson
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Immune Cell ◽  
Activity Index ◽  
Control Diet ◽  
Clinical Signs ◽  
Disease Activity Index ◽  
Mesenteric Lymph ◽  
Psyllium Husk ◽  
Yeast Product

Abstract Objectives The beneficial effects of supplementing yeast products in terms of promoting gut health have been demonstrated in several animal species. These benefits include promoting gut integrity, modulating gut microbiota, and positively affecting immune responses. With these benefits, yeast products may be a strategy to relieve clinical signs associated with colitis. The objective of this study was to investigate the effects of a yeast product (YP) on colonic gene expression and histopathology, mesenteric lymph node (MLN) immune cells, and disease activity index (DAI) in a dextran sulfate sodium (DSS)-induced colitis model. Psyllium husk (PH), which has been shown to be protective in this model, also was included. Methods Fifty-four 6-week-old male C57BL/6J mice were assigned to: 1) AIN93G diet (control); 2) control diet + 5% YP; or 3) control diet + 5% PH. After 2 wk (d1–14) of diet adaptation, mice were provided with water or water + 3% (wt: vol) DSS for 5 d (d15–19). Body weight, food intake, water intake, and DAI data were recorded daily during the water/DSS treatment period. Mice were euthanized on d 20, followed by tissue collection. Data were analyzed using the Mixed Models procedure of SAS 9.4. Results MLN immune cell populations and colonic histopathology were not affected (P &gt; 0.05) by diet. PH mice had greater (P &lt; 0.05) gene expression of Cldn2, Cldn3, Cldn8, and Ocln compared to control mice. DAI, immune cell numbers, colonic histopathology, and colonic gene expression were not affected (P &gt; 0.05) by YP in DSS mice. DSS mice consuming PH had lower (P &lt; 0.05) DAI compared to control or YP mice. Conclusions Results suggest that YP at the dose tested failed to attenuate clinical signs or inflammation associated with colitis. PH, on the other hand, showed protective effects on DSS-induced colitis as reported previously. Funding Sources This research was funded internally.

scholarly journals Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells

2019 ◽  
Vol 12 (S8) ◽  
Author(s):  
Yen-Jung Chiu ◽  
Yi-Hsuan Hsieh ◽  
Yen-Hua Huang
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Deconvolution Method ◽  
Cell Composition ◽  
Reference Gene Expression ◽  
Cell Fractions

Abstract Background To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. Methods Our deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. Results In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. Conclusions We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

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