The authors thank Dr. Jonathan Krieger of Bioinformatics Solutions Inc. for operating the mass spectrometer for representative experiments, as well as members of the Geddes-McAlister group for their assistance with experimental set-up and manuscript feedback. The authors acknowledge funding support, in part, from the Banting Research Foundation &#8211; The Jarislowsky Fellowship Discovery Award, New Frontiers Research Fund &#8211; Exploration (NFRFE-2019-00425), and the Canadian Foundation for Innovation (JELF 38798) for J.G.M., as well as NSERC Canada Graduate Scholarship &#8211; Masters and Ontario Graduate Scholarship for B.B., and Queen Elizabeth II Graduate Scholarship in Science and Technology for A.S..

<ol>
	<li>Bongomin, F., Gago, S., Oladele, R., Denning, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+and+Multi-National+Prevalence+of+Fungal+Diseases-Estimate+Precision.">Global and Multi-National Prevalence of Fungal Diseases-Estimate Precision.</a> Journal of Fungi. , (2017).</li><li>Tugume, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HIV-Associated+cryptococcal+meningitis+occurring+at+relatively+higher+CD4+counts.">HIV-Associated cryptococcal meningitis occurring at relatively higher CD4 counts.</a> Journal of Infectious Diseases. 219 (6), 877-883 (2019).</li><li>Rajasingham, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+burden+of+disease+of+HIV-associated+cryptococcal+meningitis:+an+updated+analysis.">Global burden of disease of HIV-associated cryptococcal meningitis: an updated analysis.</a> The Lancet Infectious Diseases. , (2017).</li><li>Perfect, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+antifungal+pipeline:+A+reality+check.">The antifungal pipeline: A reality check.</a> Nature Reviews Drug Discovery. , (2017).</li><li>Bermas, A., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combatting+the+evolution+of+anti-fungal+resistance+in+Cryptococcus+neoformans.">Combatting the evolution of anti-fungal resistance in Cryptococcus neoformans.</a> Molecular Microbiology. , 1-14 (2020).</li><li>Geddes-McAlister, J., Shapiro, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+pathogens,+new+tricks:+Emerging,+drug-resistant+fungal+pathogens+and+future+prospects+for+antifungal+therapeutics.">New pathogens, new tricks: Emerging, drug-resistant fungal pathogens and future prospects for antifungal therapeutics.</a> Annals of the New York Academy of Sciences. , (2018).</li><li>Ball, B., Bermas, A., Carruthers-Lay, D., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+Spectrometry-Based+Proteomics+of+Fungal+Pathogenesis,+Host-Fungal+Interactions,+and+Antifungal+Development.">Mass Spectrometry-Based Proteomics of Fungal Pathogenesis, Host-Fungal Interactions, and Antifungal Development.</a> Journal of Fungi. , (2019).</li><li>Sukumaran, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoding+communication+patterns+of+the+innate+immune+system+by+quantitative+proteomics.">Decoding communication patterns of the innate immune system by quantitative proteomics.</a> J Leukocyte Biol. , (2019).</li><li>Salas, D., Stacey, R. G., Akinlaja, M., Foster, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+interactomics:+Considerations+for+the+use+of+co-elution+to+measure+protein+interaction+networks.">Next-generation interactomics: Considerations for the use of co-elution to measure protein interaction networks.</a> Molecular and Cellular Proteomics. , (2020).</li><li>Aebersold, R., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass-spectrometric+exploration+of+proteome+structure+and+function.">Mass-spectrometric exploration of proteome structure and function.</a> Nature. 537 (7620), 347-355 (2016).</li><li>Mann, M., Kulak, N. A., Nagaraj, N., Cox, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Coming+Age+of+Complete,+Accurate,+and+Ubiquitous+Proteomes.">The Coming Age of Complete, Accurate, and Ubiquitous Proteomes.</a> Molecular Cell. 49 (4), 583-590 (2013).</li><li>Cox, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+Proteome-wide+Label-free+Quantification+by+Delayed+Normalization+and+Maximal+Peptide+Ratio+Extraction,+Termed+MaxLFQ.">Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ.</a> Molecular &amp; Cellular Proteomics. 13 (9), 2513-2526 (2014).</li><li>Ankney, J. A., Muneer, A., Chen, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relative+and+Absolute+Quantitation+in+Mass+Spectrometry-Based+Proteomics.">Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics.</a> Annual Review of Analytical Chemistry. , (2018).</li><li>Cox, J., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MaxQuant+enables+high+peptide+identification+rates,+individualized+p.p.b.-range+mass+accuracies+and+proteome-wide+protein+quantification.">MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.</a> Nature Biotechnology. 26 (12), 1367-1372 (2008).</li><li>Tyanova, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Perseus+computational+platform+for+comprehensive+analysis+of+(prote)omics+data.">The Perseus computational platform for comprehensive analysis of (prote)omics data.</a> Nature Methods. 13 (9), 731-740 (2016).</li><li>Cox, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Andromeda:+A+peptide+search+engine+integrated+into+the+MaxQuant+environment.">Andromeda: A peptide search engine integrated into the MaxQuant environment.</a> Journal of Proteome Research. , (2011).</li><li>Zhong, Z., Pirofski, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opsonization+of+Cryptococcus+neoformans+by+human+anticryptococcal+glucuronoxylomannan+antibodies.">Opsonization of Cryptococcus neoformans by human anticryptococcal glucuronoxylomannan antibodies.</a> Infection and Immunity. , (1996).</li><li>Nicola, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macrophage+autophagy+in+immunity+to+Cryptococcus+neoformans+and+Candida+albicans.">Macrophage autophagy in immunity to Cryptococcus neoformans and Candida albicans.</a> Infection and Immunity. , (2012).</li><li>Ball, B., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+Proteomic+Profiling+of+Cryptococcus+neoformans.">Quantitative Proteomic Profiling of Cryptococcus neoformans.</a> Current Protocols in Microbiology. , (2019).</li><li>Geddes-McAlister, J., Gadjeva, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectromerty-based+quantitative+proteomics+of+murine-derived+polymorphonuclear+neutrophils.">Mass spectromerty-based quantitative proteomics of murine-derived polymorphonuclear neutrophils.</a> Current Protocols in Immunology. , (2019).</li><li>Geddes, J. M. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secretome+profiling+of+Cryptococcus+neoformans+reveals+regulation+of+a+subset+of+virulence-associated+proteins+and+potential+biomarkers+by+protein+kinase+A.">Secretome profiling of Cryptococcus neoformans reveals regulation of a subset of virulence-associated proteins and potential biomarkers by protein kinase A.</a> BMC Microbiology. , (2015).</li><li>Geddes, J. M. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+the+protein+kinase+a-regulated+proteome+of+Cryptococcus+neoformans+identifies+a+role+for+the+ubiquitin-proteasome+pathway+in+capsule+formation.">Analysis of the protein kinase a-regulated proteome of Cryptococcus neoformans identifies a role for the ubiquitin-proteasome pathway in capsule formation.</a> mBio. 7 (1), 1-15 (2016).</li><li>Al Shweiki, M. H. D. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+Label-Free+Quantification+in+Discovery+Proteomics+and+Impact+of+Technological+Factors+and+Natural+Variability+of+Protein+Abundance.">Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance.</a> Journal of Proteome Research. , (2017).</li><li>Ong, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stable+Isotope+Labeling+by+Amino+Acids+in+Cell+Culture,+SILAC,+as+a+Simple+and+Accurate+Approach+to+Expression+Proteomics.">Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics.</a> Molecular &amp; Cellular Proteomics. 1 (5), 376-386 (2002).</li><li>Thompson, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tandem+mass+tags:+A+novel+quantification+strategy+for+comparative+analysis+of+complex+protein+mixtures+by+MS/MS.">Tandem mass tags: A novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS.</a> Analytical Chemistry. 75 (8), 1895-1904 (2003).</li><li>Borner, G. H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organellar+Maps+Through+Proteomic+Profiling+-+A+Conceptual+Guide.">Organellar Maps Through Proteomic Profiling - A Conceptual Guide.</a> Molecular &amp; Cellular Proteomics. , (2020).</li><li>Gingras, A. C., Abe, K. T., Raught, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Getting+to+know+the+neighborhood:+using+proximity-dependent+biotinylation+to+characterize+protein+complexes+and+map+organelles.">Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles.</a> Current Opinion in Chemical Biology. , (2019).</li><li>Kugadas, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frontline+Science:+Employing+enzymatic+treatment+options+for+management+of+ocular+biofilm-based+infections.">Frontline Science: Employing enzymatic treatment options for management of ocular biofilm-based infections.</a> Journal of Leukocyte Biology. , (2019).</li><li>Yeung, J., Gadjeva, M., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Label-Free+Quantitative+Proteomics+Distinguishes+General+and+Site-Specific+Host+Responses+to+Pseudomonas+aeruginosa+Infection+at+the+Ocular+Surface.">Label-Free Quantitative Proteomics Distinguishes General and Site-Specific Host Responses to Pseudomonas aeruginosa Infection at the Ocular Surface.</a> Proteomics. , (2020).</li><li>Yeung, J., Lamb, J., Krieger, J. R., Gadjeva, M., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+Proteomic+Profiling+ofMurine+Ocular+Tissue+and+theExtracellular+Environment.">Quantitative Proteomic Profiling ofMurine Ocular Tissue and theExtracellular Environment.</a> Current Protocols in Mouse Biology. 10 (83), (2020).</li></ol>

The authors declare no conflicts of interest.

Critical steps in the protocol include preparation of macrophage cells and collection of co-culture samples for protein processing with minimal disruption to the cells. It is important to perform steps of washing, inoculating, and removing adherent macrophage cells gently and carefully to prevent unnecessary lysis of cells prior to collection. Establishing the correct MOI for the experiment is also critical as inoculating with an excessively high MOI can cause rapid macrophage cell death and difficulty in collecting and ...

The prevalence of invasive fungal infections is vastly increasing and is correlated with unacceptably high mortality rates, most commonly reported in individuals with immunodeficient predispositions1. Cryptococcus neoformans is a notorious opportunistic fungal pathogen capable of intracellular survival within host macrophage cells. Inadequate antifungal intervention results in fungal dissemination and life-threatening manifestations of cryptococcal meningitis and meningoencephalitis2,3. The global increase in immunocompromised status has demanded a parallel increase in the use of antifungal agents, in which many fungal species, including C. neoformans, have increasingly evolved resistance towards4,5,6. Therefore, it is imperative to implement robust and efficient technologies to answer vital biological questions regarding host defence response and microbial pathogenesis.
The new age of technological advancement in mass spectrometry (MS), including the generation of powerful computational and bioinformatic pipelines, provides the foundation for an integrative vision for large-scale analysis of host-pathogen research7,8. Conventional pathogenesis-driven proteomic analysis commonly profiles the view of infection from either the host or pathogen perspective, including comprehensive methodologies such as protein correlation profiling, affinity chromatography combined with proteomics, and interactomics9. Investigations into the virulence of dangerous pathogens in a host system are of immense clinical importance; however, the application of a dual perspective analysis in a single experiment was formerly considered unattainable. For example, the pathogen&#8217;s perspective towards infection is often overwhelmed by highly abundant host proteins resulting in reduced sensitivity for the detection of low-abundant fungal proteins7. Furthermore, the high sample complexity invites many targets to investigate in a single experimental system and proves challenging to elucidate mechanisms of action for a specific pathogen protein.
Bottom-up proteomics is a popular MS technique that enables manageable sample preparation, in which peptides are generated by sequence-specific enzymatic digestion followed by liquid chromatography separation, identification, and quantification by MS10,11. Here, we present a method demonstrating a data-dependent acquisition strategy purposed to achieve an unbiased coverage of an infection-based proteome or &#8216;infectome&#8217;. Specifically, label-free quantification (LFQ) sheds the dependence on chemical or metabolic labels for robust and accurate identification of protein level changes across multiple proteomes, reducing sample handling and processing steps12,13. This universal application interrogates produced proteins at a given moment within a cell independent of any expected protein production; thus, novel insights may be discovered that are critical to infection.
The workflow described herein is optimized to explore protein level changes of C. neoformans during infection-mimicking conditions with host immune cells (Figure 1). Rather than relying on the isolation and separation of cell types, this approach extracts the host and pathogen proteome together, and utilizes bioinformatic separation using two organism-specific databases to distinguish species-specific protein production. This method offers advantages for an unlimited number of samples to be processed without the extra costly preparation steps necessary in isotope-based labelling studies or fractionation. Furthermore, this workflow supports optimized protein extraction protocols transferable to a wide range of fungal and bacterial pathogens capable of targeting and infecting host immune cells. Overall, this protocol outlines the steps to complete an unbiased protein extraction and sample processing for high-resolution MS, followed by data and statistical analysis, capable of providing a wealth of knowledge of fungal proteins significant for infection combined with comprehensive profiling of the host defense response.

<table><tbody><tr><td>100 mM Tris-HCl, pH 8.5</td><td>Fisher Scientific</td><td>BP152-1</td><td>Maintain at 4&amp;deg;C</td></tr><tr><td>60 x 15 mm Dish, Nunclon Delta</td><td>ThermoFisher Scientific</td><td>174888</td><td></td></tr><tr><td>6-well cell culture plate</td><td>ThermoFisher Scientific</td><td>140675</td><td></td></tr><tr><td>Acetonitrile, MS grade</td><td>Pierce</td><td>TS-51101</td><td></td></tr><tr><td>Acetic Acid</td><td>Sigma Aldrich</td><td>1099510001</td><td></td></tr><tr><td>Acetone</td><td>Sigma Aldrich</td><td>34850-1L</td><td></td></tr><tr><td>Ammonium bicarbonate (ABC)</td><td>ThermoFisher Scientific</td><td>A643-500</td><td>Prepare a stock 50 mM ABC solution, stable at room temperature for up to one month.</td></tr><tr><td>Bel-Art&amp;trade; HiFlow Vacuum Aspirator Collection System</td><td>Fisher Scientific</td><td>13-717-300</td><td>Not essential, serological pipettes can be used to remove media.</td></tr><tr><td>C18 resin</td><td>3M Empore</td><td>3M2215</td><td></td></tr><tr><td>Cell Scrapers</td><td>VWR</td><td>10062-906</td><td>Not essential, other methods to release macrophage cells can be used.</td></tr><tr><td>Centrifugal vaccuum concentrator</td><td>Eppendorf</td><td>07-748-15</td><td></td></tr><tr><td>Complete Filtration Unit</td><td>VWR</td><td>10040-436</td><td></td></tr><tr><td>Conical falcon tubes (15 mL)</td><td>Fisher Scientific</td><td>05-539-12</td><td></td></tr><tr><td>Countess II Automated Cell Counter</td><td>ThermoFisher Scientific</td><td>AMQAX1000</td><td>Not essential, haemocytometer can be used as an alternative.</td></tr><tr><td>CytoTox 96 Non-Radioactive Cytotoxicity Assay</td><td>Promega</td><td>G1780</td><td></td></tr><tr><td>Dithiothreitol (DTT)</td><td>ThermoFisher Scientific</td><td>R0861</td><td>Prepare bulk stock solution of 1 M DTT, flash frozen and stored at -20 &amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>DMEM, high glucose, GlutaMAX Supplement</td><td>ThermoFisher Scientific</td><td>10566016</td><td></td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>ThermoFisher Scientific</td><td>12483020</td><td>Heat inactivate by incubating at 60&amp;deg;C for 30 minutes. Prepare 50 ml aliquots and flash freeze. Thaw prior to media preparation</td></tr><tr><td>Haemocytometer</td><td>VWR</td><td>15170-208</td><td></td></tr><tr><td>HEPES</td><td>Sigma Aldrich</td><td>H3375</td><td>Prepare 40 mM HEPES/8 M Urea in bulk stock solution, flash frozen, store at -20&amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>High-performance liquid chromatography system</td><td>ThermoFisher Scientific</td><td>LC140</td><td>Gradient length is based on sample complexity, recommended 120 min gradient for infectome samples.</td></tr><tr><td>High-resolution mass spectrometer</td><td>ThermoFisher Scientific</td><td>726042</td><td></td></tr><tr><td>Iodoacetamide (IAA)</td><td>Sigma Aldrich</td><td>I6125</td><td>Prepare 0.55 M bulk stock solution, flash frozen, store at -20&amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>L-glutamine</td><td>ThermoFisher Scientific</td><td>25030081</td><td>Can be aliquot and frozen for storage. Thaw prior to media preparation.</td></tr><tr><td>LoBind Microcentrifuge tubes</td><td>Eppendorf</td><td>13-698-794</td><td></td></tr><tr><td>MaxQuant</td><td>https://maxquant.org/</td><td></td><td>MaxQuant is a public platform that offers tutorials, such as the MaxQuant Summer School, outlining the computational analysis steps of large MS data sets</td></tr><tr><td>Microcentrifuge</td><td>Eppendorf</td><td>13864457</td><td></td></tr><tr><td>Penicillin : Streptomycin 10k/10k</td><td>VWR</td><td>CA12001-692</td><td>Can be aliquot and frozen for storage. Thaw prior to media preparation.</td></tr><tr><td>Peptide separation columns</td><td>ThermoFisher Scientific</td><td>ES803</td><td></td></tr><tr><td>Perseus Software</td><td>http://maxquant.net/perseus/</td><td></td><td></td></tr><tr><td>Phosphate Buffered Saline</td><td>VWR</td><td>CA12001-676</td><td>Puchase not required. PBS can also be prepared but sterile filteration must be performed before use.</td></tr><tr><td>Pierce BCA Protein Assay</td><td>ThermoFisher Scientific</td><td>&amp;nbsp;23225</td><td></td></tr><tr><td>Pipette, Disposable Serological (10 mL)</td><td>Fisher Scientific</td><td>13-678-11E</td><td></td></tr><tr><td>Pipette, Disposable Serological (25 mL) Basix</td><td>Fisher Scientific</td><td>14955235</td><td></td></tr><tr><td>Probe sonciator</td><td>ThermoFisher Scientific</td><td>100-132-894</td><td></td></tr><tr><td>Protease inhibitor cocktail tablet</td><td>Roche</td><td>4693159001</td><td></td></tr><tr><td>Sodium dodecyl sulfate</td><td>ThermoFisher Scientific</td><td>28364</td><td>20% (w/v)</td></tr><tr><td>Spectrophotometer (Nanodrop)</td><td>ThermoFisher Scientific</td><td>ND-2000</td><td></td></tr><tr><td>STAGE tipping centrifuge</td><td>Sonation</td><td>STC-V2</td><td></td></tr><tr><td>Thermal Shaker</td><td>VWR</td><td>NO89232-908</td><td></td></tr><tr><td>Trifluoroacetic acid</td><td>ThermoFisher Scientific</td><td>85183</td><td></td></tr><tr><td>Trypsin/Lys-C protease mix, MS grade</td><td>Pierce</td><td>A40007</td><td>Maintain at -20 &amp;deg;C.</td></tr><tr><td>Ultrasonic bath</td><td>Bransonic</td><td>A89375-450</td><td>Stored in cold room (4C)</td></tr><tr><td>Urea</td><td>Sigma Aldrich</td><td>U1250-1KG</td><td>Prepare 40 mM HEPES/8 M Urea in bulk stock solution, flash frozen, store at -20 &amp;deg;C until use. Discard after each use (do not freeze-thaw repeatedly).</td></tr><tr><td>Yeast-extract peptone dextrose broth</td><td>BD Difco</td><td>BM20</td><td></td></tr></tbody></table>

label-free quantitative proteomics workflow for discovery-driven host-pathogen interactions

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s global proteome under varying conditions. This powerful strategy applied to the interactions of microbial pathogens with the desired host comprehensively characterizes both perspectives towards infection. Herein, the workflow describes label-free quantification (LFQ) of the infectome of Cryptococcus neoformans, a fungal facultative intracellular pathogen that is the causative agent of the deadly disease cryptococcosis, in the presence of immortalized macrophage cells. The protocol details the proper protein preparation techniques for both pathogen and mammalian cells within a single experiment, resulting in appropriate peptide submission for liquid-chromatography (LC)-MS/MS analysis. The high throughput generic nature of LFQ allows a wide dynamic range of protein identification and quantification, as well as transferability to any host-pathogen infection setting, maintaining extreme sensitivity. The method is optimized to catalogue extensive, unbiased protein abundance profiles of a pathogen within infection-mimicking conditions. Specifically, the method demonstrated here provides essential information on C. neoformans pathogenesis, such as protein production necessary for virulence and identifies critical host proteins responding to microbial invasion.

The protocol outlined above enables identification and quantification of proteins derived from both the fungal pathogen, C. neoformans, and the host, macrophage cells, in a single experiment. Following co-culture, cells are collected and processed together and bioinformatically separated based on peptide profiles specific to each species. This is a powerful approach for defining the interplay of the host-pathogen relationship during infection. The number of proteins identified from the experiment depends on the ...

Watch this Scientific Journal Video about Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions at JoVE.com

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

Here, we present a protocol to profile the interplay between host and pathogen during infection by mass spectrometry-based proteomics. This protocol uses label-free quantification to measure changes in protein abundance of both host (e.g., macrophages) and pathogen (e.g., Cryptococcus neoformans) in a single experiment.

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s ...

label-free-quantitative-proteomics-workflow-for-discovery-driven-host

Research

JoVE Journal

Immunology and Infection

6.6K Views.  University of Guelph. Hier presenteren we een protocol om het samenspel tussen gastheer en ziekteverwekker te profileren tijdens infectie door op massaspectrometrie gebaseerde proteomics. Dit protocol maakt gebruik van labelvrije kwantificering om veranderingen in eiwitrijkdom van zowel gastheer (bijv. macrofagen) als ziekteverwekker (bijv. Cryptococcus neoformans)in één experiment te meten.

Video: Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

Biochemistry

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.
This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.
A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.

This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.

Significant efforts were gathered to generate  large-scale comprehensive protein-protein interaction network maps. This is  instrumental to understand the ...

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research is deciphering how these dynamic protein complexes act to integrate signals from multiple sources in order to direct a specific biological response, and how this becomes deregulated in many disease settings. Despite the importance of this key biochemical mechanism, there is a lack of experimental techniques that can facilitate the specific and sensitive deconvolution of these multi-molecular signaling complexes.
Here this shortcoming is addressed through the combination of a protein complementation assay with a conformation-specific nanobody, which we have termed Bimolecular Complementation Affinity Purification (BiCAP). This novel technique facilitates the specific isolation and downstream proteomic characterization of any pair of interacting proteins, to the exclusion of un-complexed individual proteins and complexes formed with competing binding partners. 
The BiCAP technique is adaptable to a wide array of downstream experimental assays, and the high degree of specificity afforded by this technique allows more nuanced investigations into the mechanics of protein complex assembly than is currently possible using standard affinity purification techniques.

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP

This manuscript describes the protocol for Bimolecular Complementation Affinity Purification (BiCAP). This novel method facilitates the specific isolation and downstream proteomic characterization of any two interacting proteins, while excluding un-complexed individual proteins as well as complexes formed with competing binding partners.

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research ...

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and body fluids. Formalin-fixed, paraffin-embedded (FFPE) tissue sections, commonly used for long-term archiving, have emerged as valuable resources for proteomic studies. Beyond their storage benefits, researchers can isolate regions of interest (ROIs) from normal tissue regions through collaborative efforts with pathologists. Despite this potential, a streamlined approach for proteomic experiments encompassing ROI isolation, proteomic sample preparation, and MS analysis remains lacking. In this protocol, an integrated workflow that combines macrodissection of ROIs, suspension trapping-based sample preparation, and high-throughput MS analysis is presented. Through this approach, the ROIs of patients' FFPE tissues, consisting of benign serous cystic neoplasms (SCN) and precancerous intraductal papillary mucinous neoplasms (IPMN) diagnosed by pathologists, were macrodissected, collected, and analyzed, resulting in high proteome coverage. Furthermore, molecular differences between the two distinct pancreatic cystic neoplasms were successfully identified, thus demonstrating the applicability of this approach for advancing proteomic research with FFPE tissues.

Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and ...

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Lysosomes frequently communicate with a variety of biomolecules to achieve the degradation and other diverse cellular functions. Lysosomes are critical to human brain function, as neurons are postmitotic and rely heavily on the autophagy-lysosome pathway to maintain cellular homeostasis. Despite advancements in the understanding of various lysosomal functions, capturing the highly dynamic communications between lysosomes and other cellular components is technically challenging, particularly in a high-throughput fashion. Here, a detailed protocol is provided for the recently published endogenous (knock-in) lysosome proximity labeling proteomic method in human induced pluripotent stem cell (hiPSC)-derived neurons. 
Both lysosomal membrane proteins and proteins surrounding lysosomes within a 10-20 nm radius can be confidently identified and accurately quantified in live human neurons. Each step of the protocol is described in detail, i.e., hiPSC-neuron culture, proximity labeling, neuron harvest, fluorescence microscopy, biotinylated protein enrichment, protein digestion, LC-MS analysis, and data analysis. In summary, this unique endogenous lysosomal proximity labeling proteomics method provides a high-throughput and robust analytical tool to study the highly dynamic lysosomal activities in live human neurons.

A neuronal lysosome proximity labeling proteomics protocol is described here to characterize the dynamic lysosomal microenvironment in human induced pluripotent stem cell-derived neurons. Lysosomal membrane proteins and proteins that interact with lysosomes (stably or transiently) can be accurately quantified in this method with excellent intracellular spatial resolution in live human neurons.

Lysosomes frequently communicate with a variety of biomolecules to achieve the degradation and other diverse cellular functions. Lysosomes are critical to ...

Neuroscience

Galaxy-Enabled Metaproteomics: Insights into Host-Microbiome Interaction

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the ...

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Presented here is an optimized high-throughput protocol developed with 16-plex tandem mass tag reagents, enabling quantitative proteome profiling of biological samples. Extensive basic pH fractionation and high-resolution LC-MS/MS mitigate ratio compression and provide deep proteome coverage.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an ...

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

Samenvatting

Meer video's verkennen