Appendix 1. List of primers sets used for amplification of sequences from yeast genomic DNA and further Gibson assembly of overexpression plasmids.Appendix 2. MS data set of identified proteins.Appendix 3. Table and heatmap showing protein expression changes per strain.

Integrating spatiotemporal proteomics data with protein–protein interaction (PPI) data can help researchers make an in-depth exploration of their proteins of interest in a dynamic manner. However, there is still a lack of proper tools for the biologists who usually have few programming skills to construct a PPI network for a protein list, visualize active PPI subnetworks, and then select key nodes for further study. We propose a web-based platform named PPIExp that can automatically construct a PPI network, perform clustering analysis according to protein abundances, and perform functional enrichment analysis. More importantly, it provides multiple effective visualization interfaces, such as the interface to display the PPI network map, the interface to display a dendrogram and heatmap for the clustering result, and the interface to display the expression pattern of a selected protein. To visualize the active PPI subnetworks in specific space or time, it provides buttons to highlight the differentially expressed proteins under each condition on the network map. Additionally, to help researchers determine which proteins are worth further attention, PPIExp provides extensive one-click interactive operations to map node centrality measures to node size on the network and highlight three types of proteins, that is, the proteins in an enriched functional term, the coexpressed proteins selected from the dendgrogram and heatmap, and the proteins input by users. PPIExp is available at http://www.fgvis.com/expressvis/PPIExp.

Heatmap representations of transcripts and protein accumulations for the RNA-protein pairs with rhythmic proteins Distribution of phase difference between RNA-protein pairs for those with rhythmic proteins. List of tagged entities: , , gene expression (bao:BAO_0002785),mass spectrometry (bao:BAO_0000055),RNA sequencing (bao:BAO_0003027),gene expression assay (bao:BAO_0002785),protein expression profiling (obi:OBI_0000615)

Severe eosinophilic asthma is characterized by chronic airway inflammation, oxidative stress and elevated proinflammatory cytokines, especially IL-5. Mepolizumab and benralizumab are both humanized IgG antibodies directed against IL-5 signaling, directly acting on eosinophils count. Together with the complexity of severe asthma classification and to the patient selection for the targeted treatment, there is also the urgency to clarify the follow-up of therapy to identify biomarkers, in addition to eosinophils, for the optimal duration of treatment, persistence of effectiveness and safety. To this purpose, here we performed a follow-up study by differential proteomic analysis on serum samples after 1 month and 6 months of both therapies and sera from healthy patients. Statistical analysis by PCA and heatmap analyses were performed, and identified proteins were used for enrichment analysis by MetaCore software. Analysis highlights 82 differences among all considered conditions. In particular, 30 referred to benralizumab time points (T0, T1B, T6B) and 24 to mepolizumab time points (T0, T1M, T6M) analyses. t-SNE and heatmap analyses evidences as the differential serum protein profile at 6 months of both treatments is more similar to that of the healthy subjects. Among the identified proteins, APOAI, APOC-II and APOC-III are up-regulated principally after six months of benralizumab treatment, plasminogen is up-regulated after six months of both treatments and ceruloplasmin, up-regulated already after 1 month of benralizumab, become higher after 6 months of mepolizumab. By enrichment analysis, identified proteins were related to lipid metabolism and transport, blood coagulation and ECM remodelling.

The Bioinformatics Services Market will grow from $4.3B in 2025 to $15.7B by 2035, at a CAGR of 12.6%, driven by rising demand for biologics and biosimilars.

Supplementary dataset, supplementary tables, and supplementary figures for Anderson et al. (2025), 'Trichodesmium protein abundance patterns reflect the influence of resource availability across ocean regions'.

Supplementary dataset: Three metagenomes sequenced from picked Trichodesmium colonies in the North Atlantic, North Pacific, and South Pacific ocean basins were combined into this reference metagenome. Reference was concatenated by Dr. Kyle Frischkorn. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD057942 and doi: 10.6019/PXD057942.

• 'all_3_t_metaG.faa' -- fasta file containing all 3 concatenated metagenomes (translated contigs)
• '3_metag_kegg.txt' -- text file containing KEGG KO annotations of contigs

Supplementary tables:

• 'Supplementary_Tables.xlsx'
• Table S1: Sample metadata.
• Table S2: Sampling details and citations for metagenomes used to build the metagenome reference database.
• Table S3: Protein spectral counts and UniProt and KEGG annotations.
• Table S4: Marker protein list with UniProt and Tery IDs, literature citations, and putative functions.
• Table S5: List of putative Fe-P co-stress markers (Fig. S2) which were upregulated in Trichodesmium IMS101 under Fe-P co-stress culture relative to replete conditions (Walworth et al., 2016, Supplemental Data 3) and which were detected in all three ocean regions in this study.
• Table S6: P and Fe stress signals by sample (Fig. 4).
• Table S7: KEGG submodule designations of KEGG-annotated Trichodesmium protein spectra used to generate heatmap (Fig. 2b). KEGG submodule designation is detailed in SI methods.

Supplementary figures:

• 'Figure_S1.pdf'; Figure S1: Correspondence analysis of spectral counts of only Trichodesmium proteins detected in all three regions. Explanatory variables are included based on significance determined from vector analysis. DIP and dFe values are co-located with metaproteome samples except for North Atlantic dFe values, which were calculated as an average of past transect data (Table S1; see Rouco et al. (2018)).
• 'Figure_S2.pdf'; Figure S2: Putative Fe-P co-stress signal metric displayed at the regional level. Putative Fe-P co-stress marker proteins (Table S5) were determined to be upregulated in T. erythraeum IMS101 under Fe-P co-stress relative to replete conditions, and were not regulated by either Fe or P single stress relative to replete conditions (Walworth et al, 2016). Differences between regions were not significant (P>0.05) based on Kolmogorov-Smirnov testing.
• 'Figure_S3.pdf'; Figure S3: Pearson correlations of spectral counts (Table S3) of (A) P stress marker proteins (Table S4) and NifH spectral counts and (B) Fe stress marker proteins (Table S4) and NifH spectral counts across all samples. Significance (P<0.05) is denoted by asterisks. Only stress marker proteins detected in all three ocean regions studied here and used in the stress signal analysis are included in the correlation.

Code for figures, intermediate data products, and statistics for (Anderson et al., in process) is available on GitHub and uses the Supplementary tables here.

This study focuses on the proteomic analysis of cerebrospinal fluid (CSF) in a patient with stage III retinoblastoma (RB) with the aim to identify molecular changes associated with central nervous system (CNS) relapse. The child received systemic chemotherapy and intrathecal topotecan as CNS prophylaxis, along with enucleation of the left eye. After two chemotherapy cycles, CNS relapse occurred, evidenced by positive CSF findings and magnetic resonance imaging (MRI) showing leptomeningeal involvement at the anterior skull base. The child’s condition deteriorated, and two months later, he died due to progressive CNS disease. The aim of the study was to analyze serial CSF samples collected at different stages of treatment, as well as a control sample, to identify differences in CSF protein expression profiles during CNS RB relapse. Using mass spectrometry, a total of 1,029 proteins were identified across all CSF samples. An unsupervised heatmap revealed 46 differentially expressed proteins. Over-regulated proteins in CSF-RB samples were primarily involved in inflammation, extracellular matrix remodeling, epithelial mesenchymal transition initiation, migration, invasion, and cellular metabolism (PON1, RNPEP, MCAM, NEGR1, NID1, SERPINA1, FAT2, RELN, NEGR1, and SEZ6). These processes are key drivers of cancer progression and metastasis. Proteomic analysis could be valuable in identifying proteins modulated in CSF during disease progression in RB patients, offering potential for new prognostic biomarkers.

The busulfan-treated mouse model showed abnormal testis morphology and reduced sperm number and testis weight. Testicular and sperm damage was most severe at 30 days after busulfan treatment. The protein level of MGAT1 was increased in busulfan-treated mouse testis. The busulfan-treated mouse testes were also subjected to label-free quantification proteomics, which revealed 190 significantly downregulated proteins. Clustering heatmap, gene ontology, KEGG pathway and protein interaction analyses were performed and then validated by molecular experiments. An increased understanding of reproductive proteins function in vitro and in vivo will help to prevent and treat reproductive diseases.

Additional file 4 Figure S4. Heatmap of DEPs. From the longitudinal clustering, the expression pattern clustering of proteins content between ZDF and their basic diet-fed littermate wild-type control could be seen clearly. Figure S5. Heatmap of DELs. The hierarchical clustering of DELs could distinguish ZDF and their basic diet-fed littermate wild-type control. Figure S6. Correlation analysis heatmap.

Supplementary Material 1: Fig. 1, related to Fig. 1 Glycocapture box blot (A) prior to and (B) after SUC2-normalization (yellow dot; NPVLAANSTQFRDPK peptide). (C) Hierarchical clustering heatmap based on Euclidean distance of differentially expressed proteins demonstrating close clustering of technical triplicates. (D) ITGA5 peptide tag intensity, (E)ITGA5 mRNA expression and (F) ITGB1 protein expression in Group 3 MB cells isolated at pre-determined timepoints through therapy. (G) Comparison of ITGA5 intensity to SUC2 demonstrating selective enrichment for ITGA5 in Group 3 MB through therapy. Bars represent mean of at least three technical replicates. *p ≤ 0.05, **p ≤ 0.001, ***p ≤ 0.0001, ****p ≤ 0.00001; unpaired t-test or one-way ANOVA with Sidak’s method for multiple comparisons. Fig. 2, related to Fig. 1. (A) A Venn diagram demonstrating the number of unique and overlapping surface proteins enriched at each stage of therapy when compared to their expression at engraftment and control timepoints. (B) A list of proteins used to generate the Venn diagram. R = post-radiation; RC = post-chemoradiotherapy; Re = relapse. Fig. 2, related to Fig. 2. In silico evaluation of ITGA5 in publicly available MB repository. (A) Kaplan-Meir curve demonstrating worse overall survival in patients (n = 288) with relative mRNA expression of ITGA5 over 4.6 (RMA- Normalized). (B) Transcriptional expression of ITGA5 of 632 patients across 12 MB subtypes described in Cavalli et al. and (C) based on age group affiliation. **p ≤ 0.001; one-way ANOVA with Dunnett’s method for multiple comparisons. Fig. 3, related to Fig. 3. Characterization and validation of ITGA5 KD. (A) mRNA expression of ITGA5 in HEK293FT cells post ITGA5 KD. (B) Microscopic images of HEK293FT cells post ITGA5 KD. Changes in (C)ITGA5 mRNA expression in reccurent Group 3 MB cells post ITGA5 KD. (D) Flow cytometric evaluation of changes in ITGA5 surface expression in HD-MB03-Re cells post ITGA5 KD. Bars represent mean of at least three technical replicates. *p ≤ 0.05, **p ≤ 0.001, ***p ≤ 0.0001, ****p ≤ 0.00001; unpaired t-test or one-way ANOVA with Sidak’s method for multiple comparisons. Fig. 4, related to Fig. 4. Selectivity characterization of dioscin in recurrent Group 3 MB cells and hNSCs. (A) Dose response curves and (B) corresponding IC50 concentrations and hill slopes of dioscin in hNSCs and three recurrent Group 3 MB lines. Points represent mean of three technical replicates, normalized to DMSO. Error bars represent standard error of the mean. IC50 and Hill slope values standardized to two decimal places. Fig. 5, related to Fig. 4. Expression of ITGA5 in healthy human tissue samples (A) ITGA5 protein levels as detected by whole cell proteomics in health tissues, reported by Wang et al. Protein intensity is reported as intensity-based absolute quantification (iBAQ) values, normalized using median centering across tissues. (B) ITGA5 protein expression as detected by antibody staining using the Human Proteome Atlas public repository. Staining strength corresponds to expression levels, including high (3), medium (2), low (1), and not detected (0). (C) mRNA expression of ITGA5 in various tissues according to Genotype-Tissue Expression (GTEx) dataset ( https://www.proteinatlas.org/ ); expressed in transcripts per million (TPM).

Additional file 4. Supplementary Data 4. Heatmap values for proteins ranked by SD.

Additional file 1: Fig S1. Number of proteins identified and quantified with a 1% false-discovery rate (FDR) in each sample in the Stable and Unstable. Fig S2. Pearson correlation analysis of QC samples by DIA analysis. Fig S3. Histograms of log2 transformed ratios of the summed intensity of the proteins in the respective quality marker panel and the summed intensity of all proteins in discovery cohort. Fig S4. a-b. The validation of ferroptosis and lipid metabolism associated DEPs using IHC in an independent cohort. A, Representative images show the immunohistochemical staining of TFR1, TF, AIFM2, DPP4, and GCLC proteins in stable and unstable plaques in the plaque fibrous cap region. B, Immunohistochemical staining for SLC1A5, BID, and APOA5 proteins in the plaque lipid core region. Fig S5. a-b. Pearson correlation analysis of DEPs and clinical characteristics. A, The heatmap of the relationship between validated DEPs and clinical characteristics. B, The heatmap of the inner relationship of validated DEPs. DEPs, differentially expressed proteins. DEPs, differentially expressed proteins. Table S1. a Summary of demographics of discovery cohort. Table S1b Summary of demographics of validation cohort. Table S1c Clinical Features of All Patients. Table S1d Clinical Features of DIA-MS. Table S1e Clinical Features of Patients for Immunohistochemical Staining. Table S2. The panel that showed increased intensities of contamination markers. Table S3. Carotid plaque proteins quantified by DIA analysis. Table S4. Proteome differntial analysis of stable and unstable. Table S4a Differential proteins between stable and unstable by DIA analysis. Table S4b Enriched function of DEPs between stable and unstable. P value indicates the siginificances of pathways and functions. Z-score indicates the activation (positive value) or inhibition (negative value) status of functions. Table S4c Enriched pathway of DEPs between stable and unstable. P value indicates the siginificances of pathways and functions. Z-score indicates the activation (positive value) or inhibition (negative value) status of pathways. Table S5. PPI network analysis between DEPs. Table S5a Functional interaction analysis of PPI networks for DEPs. Table S5b PPI network analysis of ferroptosis-associated proteins. Table S5c PPI network analysis of lipid metabolism-associated proteins.

Proteomic data and cluster assignments for cell surface proteins. Sort by “Cluster” column to see proteins of the same cluster and use the heatmap (left-side of the sheet) to observe expression patterns. (XLSX)

Proteomic data and cluster assignments for transcription factors and co-factors. Sort by “Cluster” column to see proteins of the same cluster and use the heatmap (left-side of the sheet) to observe expression patterns. (XLSX)

Proteomic data and cluster assignments for cell surface proteins. Sort by “Cluster” column to see proteins of the same cluster and use the heatmap (left-side of the sheet) to observe expression patterns. (XLSX)

The trabecular meshwork (TM) is responsible for intraocular pressure (IOP) homeostasis in the eye. The tissue senses IOP fluctuations and dynamically adapts to the mechanical changes to either increase or decrease aqueous humor outflow. Cationic mechanosensitive channels (CMCs) have been reported to play critical roles in mediating the TM responses to mechanical forces. However, how CMCs influence TM cellular function affect aqueous humor drainage is still elusive. In this study, human TM (HTM) cells were collected from a Chinese donor and subjected to cyclically equiaxial stretching with an amplitude of 20% at 1 Hz GsMTx4, a non-selective inhibitor for CMCs, was added to investigate the proteomic changes induced by CMCs in response to mechanical stretch of HTM. Gene ontology enrichment analysis demonstrated that inhibition of CMCs significantly influenced several biochemical pathways, including store-operated calcium channel activity, microtubule cytoskeleton polarity, toll-like receptor signaling pathway, and neuron cell fate specification. Through heatmap analysis, we grouped 148 differentially expressed proteins (DEPs) into 21 clusters and focused on four specific patterns associated with Ca2+ homeostasis, autophagy, cell cycle, and cell fate. Our results indicated that they might be the critical downstream signals of CMCs adapting to mechanical forces and mediating AH outflow.

Additional file 1: Figure S1. Quality control of metagenomic data. A Comparison of quality control passed reads after filtering in 129 samples. B Specaccum species accumulation curves of three groups. C Rarefaction curves showing observed species richness taken from the 129 samples. D Overall taxa distribution of the microbiome kingdom in three groups. Figure S2. Microbial compositions in the cohort. Microbial compositions of the patients with ESLUAD and HCs at the phylum (A), genus (B), and species (C) levels. The top 10/20 abundant microbial taxa are shown with different gradient colors. The microbial composition is arranged in order of the mostabundant taxonomic ranks. Figure S3. Representative microbes exhibiting significant alterations between patients with ESLUAD and HCs. *** p < 0.001 as determined by Kruskal–Wallis test. Figure S4. Correlation between intrapulmonary microbiota and clinical features. A, D Comparison of the alpha diversity (Chao1/Shannon/Simpson index) and beta diversity (Bray–Curtis distance) at the species level with tumor infiltration in patients with ES-LUAD. B, E Comparison of the alpha diversity (Chao1/Shannon/Simpson index) and beta diversity (Bray–Curtis distance) at the species level with solid component of tumor in patients with ES-LUAD. C, F Comparison of the alpha diversity (Chao1/Shannon/Simpson index) and beta diversity (Bray–Curtis distance) at the species level with multiple-primary nodules in patients with ES-LUAD. Box plots show median ± quartiles, and the whiskers extend from the hinge to the largest or smallest value no further than 1.5-fold of the interquartile range. ns: Not significant, p-value as determined by Wilcoxon rank-sum test. AIS: Adenocarcinoma in situ, MIA: Minimally invasive adenocarcinoma, IA: Invasive adenocarcinoma, pGGN: Pure ground glass nodules, mGGN: Mixed ground glass nodules, SN: Solid nodule. Figure S5. Overview of transcriptome data. A RNA-Seq passed reads sequenced by Illumina NoveSeq 6000 Nanopore platforms (Wilcoxon rank-sum test). B Clustering heatmap of the DEGs between patients with ES-LUAD and HCs (DESeq2, |log2FC| > 1). C PCA analysis reveals differences in the transcriptomes of patients with ES-LUAD and HCs. D Volcano diagram shows the significant DEGs between patients with ES-LUAD and HCs (DESeq2, |log2FC| > 1). Figure S6. Identification of ES-LUAD-related mRNAs in the transcriptome dataset through WGCNA. A–D Network fitting calculations with fitted curves for selected network construction parameters. A Correlation coefficient corresponding to different power. B Average connectivity of the network constructed with different power values. When the power is taken as 8, the correlation coefficient is higher, and the average connectivity of the network is also higher, so the value of power used in the construction of the subsequent module is 8. C The distribution of network connectivity when the power is 8; D The test result of the power law distribution. As can be seen from the figure, k and p(k) are negatively correlated (correlation coefficient: 0.85), indicating that the selected power value enables the establishment of a scale-free network of genes. E The result of weighted co-expression network construction. F Heatmap of correlation analysis between modules and clinical traits. G Gene expression information statistics within modules. Figure S7. Overview of proteomic data. A QC sample correlation represents the process stability. B Clustering heatmap of the DEPs between patients with ES-LUAD and HCs (Wilcoxon rank-sum test, log2 fold change > 1). C PCA reveals differences in the proteome of patients with ES-LUAD and HCs. D Volcano diagram shows the significant DEPs between patients with ES-LUAD and HCs (Wilcoxon rank-sum test, log2 fold change > 1). Figure S8. Validation and prognostic information of DEGs and DEPs in public databases. The top represents the expression of DEGs, and the bottom represents the expression of DEPs. The middle represents the OS and DFS. Solid lines indicate significance at p < 0.05 (Mantel–Cox test). Figure S9. Random forest model based on multi-omics data. A The left panel represents the validation queue ROC curve for the random forest model established based on 3000 proteins (training AUC = 1). The middle panel depicts the selection of optimal feature count based on 10-fold cross-validation. The right panel shows the ROC curve for the top 150 proteins in the validation queue (training AUC = 1). B The left panel represents the validation queue ROC curve for the random forest model established based on 13846 mRNAs (training AUC = 1). The middle panel depicts the selection of optimal feature count based on 10-fold cross-validation. The right panel shows the ROC curve for the top 500 mRNAs in the validation queue (training AUC = 1). C The left panel represents the validation queue ROC curve for the random forest model established based on 196 KO genes (training AUC = 1). The middle panel depicts the selection of optimal feature count based on 10-fold cross-validation. D The left panel represents the validation queue ROC curve for the random forest model established based on 398 microbes and 3000 proteins (training AUC = 1). The middle panel depicts the selection of optimal feature count based on 10-fold cross-validation. The right panel shows the ROC curve for the top 45 microbes and proteins in the validation queue (training AUC = 1).

Additional file 4. Heatmap.

Proteomic data and cluster assignments for kinases. Sort by “Cluster” column to see proteins of the same cluster and use the heatmap (left-side of the sheet) to observe expression patterns. (XLSX)

This file contains the analyzed proteomic, phosphoproteomic, and metabolomic data sets as separate sheets within the excel file. The left columns for each data set are intensity values that have been row Z-scored and have conditional formatting to create a heatmap within excel. Intensity values used for analysis can be found in the right-most columns, which have been normalized and filtered. (XLSX)