Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

Proximity-based in situ labeling techniques offer a unique way to capture both stable and transient protein-protein and protein-organelle interactions. Combining this technology with mass spectrometry (MS)-based proteomics allows us to obtain snapshots of molecular microenvironments with nanometer resolution, facilitating the discovery of complex and dynamic protein networks. However, a number of technical challenges still exist, such as interferences from endogenously biotinylated proteins and other highly abundant bystanders, how to select the proper controls to minimize false discoveries, and experimental variations among biological/technical replicates. Here, we developed a new method to capture the proteomic microenvironment of the neuronal endolysosomal network, by knocking in (KI) an engineered ascorbate peroxidase (APEX) gene to the endogenous locus of lysosome-associated membrane protein 1 (LAMP1). We found that normalizing proximity labeling proteomics data to the endogenously biotinylated protein (PCCA) can greatly reduce variations and enable fair comparisons among different batch of APEX labeling and different APEX probes. We conducted comparative evaluation between this KI-LAMP1-APEX method and our two overexpression LAMP1-APEX probes, achieving complementary coverage of both known and new lysosomal membrane and lysosomal-interacting proteins in human iPSCderived neurons. To summarize, this study demonstrated new analytical tools to characterize lysosomal functions and microenvironment in human neurons and filled critical gaps in the field for designing and optimizing proximity labeling proteomic experiments.

Protein biotinylation via chemical or enzymatic reactions is often coupled with streptavidin-based enrichment and on-bead digestion in numerous biological applications. However, the popular on-bead digestion method faces major challenges of streptavidin contamination, overwhelming signals from endogenous biotinylated proteins, the lost information on biotinylation sites, and limited sequence coverage of enriched proteins. Here, we explored thiol-cleavable biotin as an alternative approach to elute biotinylated proteins from streptavidin-coated beads for both chemical biotinylation and biotin ligase-based proximity labeling. All possible amino acid sites for biotinylation were thoroughly evaluated in addition to the primary lysine residue. We found that biotinylation at lysine residues notably reduces the trypsin digestion efficiency, which can be mitigated by the thiol-cleavable biotinylation method. We then evaluated the applicability of thiol-cleavable biotin as a substrate for proximity labeling in living cells, where TurboID biotin ligase was engineered onto the mitochondrial inner membrane facing the mitochondrial matrix. As a proof-of-principle study, thiol-cleavable biotin-assisted TurboID proteomics achieved remarkable intraorganelle spatial resolution with significantly enriched proteins localized in the mitochondrial inner membrane and mitochondrial matrix.

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Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are often abundant and impossible to avoid. For data-dependent acquisition (DDA) proteomics, exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomics data is also unclear. In this study, we established the protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA or spectral libraries in all bottom-up proteomics workflow. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in different DDA and DIA data analysis platforms can be a valuable resource for proteomics researchers, which are freely accessible at https://github.com/HaoGroup-ProtContLib.