multi omics data analysis  (Omics Data Automation)

Journal: Angewandte Chemie (International Ed. in English)

Article Title: Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry

doi: 10.1002/anie.202519836

Figure Lengend Snippet: Data processing, performance optimization, and quantitative evaluation of SMAD. (a) Scheme showing data processing flow starting from a typical raw file produced by SMAD. (b) Number of detected metabolite features, peptides, and proteins in a 5‐min acquisition time by SMAD of extracts from 293T cells. Counts are the average plus or minus the standard deviation from three independent biological samples ( n = 3). (c) Proteins identified by SMAD across three replicate samples from 293T cells were analyzed by KEGG pathway enrichment analysis. The bars indicate the number of proteins identified in the pathway, and the colored proportion of the circle reflects the coverage of proteins in each pathway. (d) m/z distribution of detected metabolite features and classes of identified metabolite features by SMAD of 293T cells. (e) The comparison of detected molecule features (metabolites, peptides, proteins) between a single‐injection multi‐omics acquisition method and a separate single‐omics acquisition method. (f) The effect of different mixing proportions of peptides and metabolites on detected molecule features by SMAD. (metabolome/proteome) (g) Performance of SMAD in detecting different concentrations. Original concentration is proteome samples dissolved in 40 µL metabolome extraction (produced by adding 500 µL solvent to 2 million cells) and maintaining the final proteome concentration to 4 µg/µL. (h), (i) label free quantification curve of standard peptides from MS‐QCAL protein (h) and standard lipids from Avanta (i) at different concentrations. (j), (k) Untargeted quantification of detected proteins and metabolite features was performed using SMAD. (l), (m) coefficient of variation of all quantified proteins and metabolite features for different dilutions. All error bars represent SEM. Data are from three technical replicates of same sample ( n = 1) for each condition. The box in panel (i), (j) represents the interquartile range (IQR) Q1 and Q3 (percentiles 25 and 75). Whiskers show Q1 − 1.5 × IQR and Q3 + 1.5 × IQR).

Article Snippet: Y.J. prepared all other samples and acquired multi‐omics data; Y.J performed the performance analysis, quantification analysis, and multi‐omics analysis of three case studies.

Techniques: Produced, Standard Deviation, Comparison, Injection, Biomarker Discovery, Concentration Assay, Extraction, Solvent, Quantitative Proteomics

Journal: Angewandte Chemie (International Ed. in English)

Article Title: Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry

doi: 10.1002/anie.202519836

Figure Lengend Snippet: SMAD enabled rapid multi‐omics analysis of macrophage activation and radiation‐induced perturbations. (a) Schematic diagram of experimental design. (LPS: lipopolysaccharide, IL‐4: Interleukin 4, IRA: irradiation). (b) Overview of molecules monitored by SMAD. (c) Coefficient of variance distribution for all identified proteins and metabolite features across all samples within each treatment. (d) Changes of significant dysregulated molecules (metabolites and proteins) among three treatments and control. (two‐sided Wilcoxon rank test with Benjamini–Hochberg (BH) adjusted p values <0.05). (e) The heatmap and clustering of dysregulated multi‐omics molecules following immune activation or irradiation. (K‐means clustering). (f), (g) Typical dysregulated multi‐omics molecules in cluster1(f) and cluster3(g). (h) Typical dysregulated multi‐omics feature pairs after polarization/irradiation. The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution. (i) Pearson correlation network analysis of multi‐omics molecule features involved in lipid metabolism. Each node represents a molecule feature, and the edge width represents the correlation strength. (j) Stacked overview of boxplots showing the perturbation patterns of multi‐omics pathways and metabolic molecules in macrophages after polarization or irradiation. ( n = 6 independent biological samples for each treatment and control, the box represents the interquartile range (IQR) Q1 and Q3 (percentiles 25 and 75). Whiskers show Q1 − 1.5 × IQR and Q3 + 1.5 × IQR).

Article Snippet: Y.J. prepared all other samples and acquired multi‐omics data; Y.J performed the performance analysis, quantification analysis, and multi‐omics analysis of three case studies.

Techniques: Biomarker Discovery, Activation Assay, Irradiation, Control

Journal: Angewandte Chemie (International Ed. in English)

Article Title: Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry

doi: 10.1002/anie.202519836

Figure Lengend Snippet: High‐throughput SMAD‐based profiling of drug‐induced multi‐omics responses in human cells. (a) Overview of experimental design and sample processing flow in 96‐well plates. (b) Molecular information and data quality of multi‐omics dataset acquired by SMAD. including identified, quantified features. (c) Coefficient of variance distribution for all identified proteins and metabolite features across all samples within each treatment. (d) The Heatmap showing significant dysregulated multi‐omics molecules among drug treatments (one‐way ANOVA test with Benjamini–Hochberg (BH) adjusted p values <0.05). (e) Four clusters of significantly dysregulated multi‐omics molecules of (d) (K‐means clustering). (f) Enriched KEGG pathways(left) of cluster 3 and typical proteins in glycolysis and proteolysis (right). (g) Raincloud of significantly dysregulated metabolome m/z features (up) and proteins (down) in different treatments. (h) Correlation network of significantly dysregulated and identified multi‐omics molecules (proteins and metabolites). Only Pearson correlations with an absolute value greater than 0.7 are included in this network, and typical clusters are labeled with a distinct color. (i) A typical example of interaction between two omics layers: the regulation of lipid synthesis and catabolism after Torin2 treatment. The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution. Data points are shown as dots. (j) UMAP analysis of multi‐omics responses among all drugs and controls. (All metabolite features and proteins included). (k) The correlation between metabolic and proteomic levels across different drug pairs. All data in this figure comes from 82 independent samples corresponding to unique wells in the 96 well plate. n = 11–12 per condition.

Article Snippet: Y.J. prepared all other samples and acquired multi‐omics data; Y.J performed the performance analysis, quantification analysis, and multi‐omics analysis of three case studies.

Techniques: High Throughput Screening Assay, Biomarker Discovery, Labeling

Journal: Angewandte Chemie (International Ed. in English)

Article Title: Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry

doi: 10.1002/anie.202519836

Figure Lengend Snippet: SMAD enables integrative multi‐omics strategies for large‐scale drug screening. (a) An overview of multi‐omics drug screening strategy based on 96‐well plate, from experimental design to data analysis. Multi‐omics analysis in duplicate is performed after 24‐h treatments. (b) Heatmap of all quantified proteins and metabolites after dataset cleaning. (c) KEGG enrichment result of proteome result. (d) UMAP analysis of negative control drug DFO for proteome result. (e) Volcano plot of dysregulated proteins after DFO treatment. (f) Bar plot of dysregulated proteins/metabolites in each drug (Student t ‐test with Benjamini–Hochberg (BH) adjusted p values <0.05, n = 6 or 12). (g) Activity of top drugs measured by the number of dysregulated proteins (x axis) and dysregulated metabolites ( y ‐axis). (h) Relations of Pearson correlation and structure similarity (quantified by tanimoto scores) of drug pairs. (i) metabolome correlation and proteome correlation of all drug pairs. (All data in this figure come from 576 independent samples corresponding to unique wells in six 96 well plates. n = 6 or 12 for drugs and control, respectively. Significance calculated by student t ‐test with Benjamini–Hochberg (BH) adjusted p values <0.05).

Article Snippet: Y.J. prepared all other samples and acquired multi‐omics data; Y.J performed the performance analysis, quantification analysis, and multi‐omics analysis of three case studies.

Techniques: Biomarker Discovery, Drug discovery, Negative Control, Activity Assay, Control

Journal: Angewandte Chemie (International Ed. in English)

Article Title: Single‐Injection Multi‐Omics Analysis by Direct Infusion Mass Spectrometry

doi: 10.1002/anie.202519836

Figure Lengend Snippet: Exploration of the intrinsic connections among multi‐omics molecules. (a) Community plot built from a molecule–molecule correlation matrix. Typical clusters were labeled with different color. Correlations are filtered to only include edges with r > 0.7. (b) Subcommunity of typical proteome cluster. (c) Subcommunity of typical metabolome cluster. (d) Subcommunity of typical multi‐omics interaction cluster. (e) Heatmap of selected protein features with higher correlation that significantly dysregulated their expression in compounds. (f) Heatmap of selected metabolite features with higher correlation that significantly dysregulated their expression in compounds. (g) Heatmap of selected multi‐omics molecule features with higher correlation that significantly dysregulated their expression in compounds. (h) Scheme showing the ML task of predicting metabolites from proteomic data measured in parallel by SMAD. (i) Overall performance of all metabolites true versus ML predicted values. (j) Histogram of R squared values for all predicted metabolites. (k), (l) SHAP value of top contributed features of predicted molecule spermidine (k) and phenylalanine (l). (m) Validation of RBM8A gene knockdown. (n)(left)LC–MS results showing RBM8A level is significantly downregulated in the second knockdown group ( p < 0.01), LC–MS metabolome analysis showing Phenylalanine level (middle) and Spermidine level (right) are significantly downregulated after RBM8A knockdown ( p < 0.05). S1 and S2 represent two RBM8A siRNAs.

Article Snippet: Y.J. prepared all other samples and acquired multi‐omics data; Y.J performed the performance analysis, quantification analysis, and multi‐omics analysis of three case studies.

Techniques: Biomarker Discovery, Labeling, Expressing, Knockdown, Liquid Chromatography with Mass Spectroscopy