in house protools2  (Valeant Pharmaceuticals)

Journal: Molecular & Cellular Proteomics : MCP

Article Title: Surfaceome Capture by Multiplex Biotinylation Enables Enhanced Identification of Cell Surface Proteins by Mass Spectrometry

doi: 10.1016/j.mcpro.2026.101572

Figure Lengend Snippet: Validation and comparison of SUCAM and other biotin conjugation methods in a pool of 4 AML cell lines . A , GO Enrichment analysis restricted to cellular component (CC) showed superior enrichment of PM-associated proteome for double amine plus carboxyl method compared to other methods in a pool of 4 AML cell lines (p31/Fuji, Kasumi, HL60, NB4). The biological sub-cellular localisations of the DEPs (Biotin versus Control) were explored with Cellular (CC) component GO term enrichment analysis using “Protools2” R package (Log 2 FC > 0.5 and q-value <0.25). Ontologies were obtained from Uniprot and from Bausch-Fluck et al . Dot size represents Delta Enrichment, i.e. the proportion of biotin labelled proteins relative to the unlabelled proteins for each specific GO term. Colour scale represents -log10( p -value). B , Enrichment efficiencies for proteins obtained by each of the named biotinylation approaches was highest for double amine plus carboxyl. Ontologies were obtained from Uniprot and from Bausch-Fluck et al . C , A network plot visualisation for GO Enrichment analysis identifies proteins increased in Biotin labelled versus control labelled control (Log2FC > 0.5, p -value <0.25) samples in a pool of 4 AML cell lines (P31/Fuji, Kasumi, HL60-NB4) for five biotin cross-linking conditions. The network was constructed with Gene-Concept network (cnetplot) using ClusterProfiler, where small nodes represent genes and large nodes represent enriched GO terms. The GO nodes and Gene nodes are colour coded to represent the method. The size of the GO nodes represent the number of proteins identified for each of the five methods (colour coded). N = 6 independent replicates were conducted for each method, and each sample was injected twice (x2) into the system for MS.

Article Snippet: In addition, an “in silico” database, which is a set of proteins found by machine learning to be likely located on the cell surface (obtained from Bausch-Fluck et al ( )) was used to assess the CC information for GO enrichment analysis using an in-house Protools2 (R package).

Techniques: Biomarker Discovery, Comparison, Conjugation Assay, Control, Construct, Injection

Journal: Molecular & Cellular Proteomics : MCP

Article Title: Surfaceome Capture by Multiplex Biotinylation Enables Enhanced Identification of Cell Surface Proteins by Mass Spectrometry

doi: 10.1016/j.mcpro.2026.101572

Figure Lengend Snippet: Comparing enrichment efficiencies for SUCAM and N-glycosylation biotin targeting enrichment strategies in solid tumour . Two experimental runs (labelled no. 1 & 2) compared SUCAM biotin labelling with four complementary labelling methods (NHS-PEG4-Biotin, Sulfo-NHS-Biotin, Double Amine and Carboxyl) and then separately with Glycoprotein capture, respectively on 1.2 x 10 6 JHH4 hepatocellular carcinoma (HCC) cells. Comparisons were made within-run and between-run. A. Based on the two experimental comparisons, GO Enrichment analysis restricted to cellular component (CC) showed superior enrichment of PM-associated proteome for SUCAM and N-Glycoprotein enrichment strategies compared to other methods in single cell suspension obtained from collagenease dissociated adherent Hepatocarcinoma cell line (JHH4). The biological sub-cellular localisations of the DEPs (Biotin versus Control) were explored with Cellular (CC) component GO term enrichment analysis using “Protools2” R package (Log 2 FC > 0.5 and q-value <0.25). Ontologies were obtained from Uniprot and from Bausch-Fluck et al . Dot size represents the Gene ratio, i . e . , the proportion of input proteins, that is, biotin labelled relative to the unlabelled proteins that are associated with a specific GO term. Colour scale represents Benjamini-Hochberg (BH) adjusted p -value. For biotin labelling conditions run in Exp.no 1, N = 2 independent replicates were conducted for each method, and each sample was injected twice (x2) into the system for MS. For biotin labelling conditions run in Exp.no 2, N = 3 independent replicates were conducted for each method, and each sample was injected twice (x2) into the system for MS. B , Based on experimenatal comparison in Exp.no 2, a network plot visualisation for GO Enrichment analysis identifies proteins increased in Biotin labelled versus control (Log2FC > 0.5, p -value <0.25) samples in single-cell suspensions obtained from enzyme dissocaited JHH4 adherent cells for three biotin cross-linking conditions (SUCAM, N-Glycoprotein and Carboxyl). The network was constructed with Gene-Concept network (cnetplot) using ClusterProfiler, where small nodes represent genes and large nodes represent enriched GO terms. The GO nodes and Gene nodes are colour coded to represent the method. The size of the GO nodes represent the number of proteins identified for each of the three methods (colour coded). N = 3 independent replicates were conducted for each method, and each sample was injected twice (x2) into the system for MS.

Article Snippet: In addition, an “in silico” database, which is a set of proteins found by machine learning to be likely located on the cell surface (obtained from Bausch-Fluck et al ( )) was used to assess the CC information for GO enrichment analysis using an in-house Protools2 (R package).

Techniques: Glycoproteomics, Single Cell, Suspension, Control, Injection, Comparison, Construct