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 processing samples for MS. Conversely, low MOI numbers will lead to fewer phagocytosed fungal cells and limited detection of fungal proteins in the biological system. To overcome such limitations, we recommend performing test experiments with varying MOIs, supported by cell death assays (e.g., LDH quantification) to define the number of fungal cells that initiate a host response but do not kill the host cells prior to collection. For the experiments, we aim to identify infection-associated fungal proteins, requiring a high MOI (100:1) to adequately detect fungal proteins among highly abundant host proteins. We routinely perform macrophage cytotoxicity assays to assess MOI impact on host cell death prior to performing the entire experiment. Timing of incubation of C. neoformans cells with macrophages is also crucial as the fungal cells may possess large polysaccharides capsules and therefore, macrophage require more time for engulfment. We select to use a co-culture incubation time of 3 h for the outlined experiment, as we found good coverage of the fungal proteome at this time point and to provide a &#8216;snap-shot&#8217; of host response; however, researchers may wish to explore earlier and later time points and observe how timing impacts fungal and host protein production. Alternatively, priming the macrophage for opsonization has been performed to assist the phagocytic process17,18.
For sample collection, if samples are not being processed immediately flash freezing in liquid nitrogen will help prevent unwanted degradation of proteins by proteases present in the samples. In addition, for MS-based proteomic analyses, the potential for contamination from dust or keratin (e.g., skin and hair cells) should be limited through the use of nitrile gloves, laboratory coats, and washing of all surfaces with 70% ethanol prior to commencing experiments. Moreover, the protocols outlined above are specific to the co-culture sample set described but can be modified for protein extraction workflow optimization, as needed19,20. Opportunities for modifying the workflow and optimizing for specific cell types include the selected mechanical and chemical disruption techniques, duration and temperature of enzymatic digestion, and separation of the samples. For instance, lysis of C. neoformans cells is typically performed by mechanical bead beating; however, we have observed increased proteome coverage following probe sonication and therefore, recommend it for mechanical disruption of the infected cells19,21,22. For example, fractionating peptide samples into aliquots by high-pH fractionation or size exclusion chromatography may reduce sample complexity and improve depth of coverage on the mass spectrometer9. Moreover, to achieve the depth of coverage to identify approx. 8,000 host and fungal proteins, a high-resolution mass spectrometry system is required (e.g., QExactive Exploris, Fusion Lumos, timsTOF Pro).
The use of LFQ for quantifying changes in protein levels during infection is a reliable and cost-effective approach for MS-based proteomics12. It enables quantification of proteins by relative abundance without the need for additional sample processing steps. In addition, the analysis is performed following completion of the experiment, lending itself to universal applications and a flexible study design. However, limitations of LFQ include increased instrumentation time as samples must be run sequentially and cannot be combined, comparability between samples can be challenging, and the need for imputation to replace missing values can be high23. Alternative approaches for quantifying protein abundance include metabolic (e.g., stable isotope labeling of amino acids in cell culture) and chemical (e.g., tandem mass tags) labeling techniques, which permit combining samples to reduce instrument time, provide reliable comparability among samples, and commonly lead to less missing values24,25. However, such approaches require additional sample handling and processing steps, increased wet lab experiment complexity and time, as well requiring a fixed experimental design. To choose a quantification method optimal for proposed experiments, users should consider study design and comparability of samples, wet lab complexity and data analysis, as well as instrumentation time availability and cost.
The novelty of the protocol presented is the ability to define changes in the proteome from both the host and pathogen perspectives in a single experiment. The depth of coverage of both proteomes enables new insight into how the pathogen initiates infection and how the host responds in defense. Notably, the approach focuses on infection of the entire cell; however, opportunities to define sub-proteomes or compartmentalized responses to infection exist through combination with spatial localization techniques (e.g., centrifugation, enrichment, labeling)26,27. Here, we detail the interaction between macrophages and the fungal pathogen, C. neoformans; however, the approach is universal, and can be applied to interactions between a diverse array of biological systems. For example, we recently used a similar workflow to uncover general and site-specific responses of neutrophils derived from a murine model of ocular keratitis28,29,30. Moreover, the infectome datasets generated from this Protocol can be integrated with in vitro proteome and secretome profiling of a pathogen to detect proteins with altered abundance in the presence of host cells. Such proteins, referred to as infection-associated proteins, provide a plethora of known and novel virulence factors for further characterization, including temporal regulation, localization, and direct protein-protein interactions with the host. Overall, the outlined MS-based proteomics workflow provides a new opportunity to investigate the intricate relationship between host and pathogen in a single experiment with universality and comprehension not commonly available.

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&#176;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&#8482; 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 &#176;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&#176;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&#176;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&#176;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>&#160;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 &#176;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 &#176;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 starting material, sample preparation, gradient length, MS instrumentation, and bioinformatic workflow. Using the protocol described herein, we typically, identify approx. 8,000 proteins from the experiment with 1,500 C. neoformans proteins and 6,500 host proteins. Following processing of the datasets, we generate a Principal Component Analysis (PCA) plot to observe critical factors driving our analysis (Figure 2A). Here, we observe the largest component of separation among the data is infected vs. non-infected samples, as we would anticipate from the experimental design (component 1, 79.8%), and a second distinguishing feature of the samples is biological variability (component 2, 5.7%). Next, a Pearson correlation combined with hierarchical clustering by Euclidean distance groups the samples and enables quantification of the variability among the replicates (Figure 2B). In our analysis, we observed distinct clustering of infected vs. non-infected samples and replicate reproducibility ranging from 95-96%, representing good reproducibility among the replicates. Lastly, we perform a Student&#8217;s t-test corrected for multiple hypothesis testing using a Benjamini-Hochberg false discovery rate (FDR) (p-value &#8804; 0.05; FDR = 0.01; s0 = 1) to identify proteins with significant differences in abundance during infection compared to non-infected controls (Figure 2C). Here, we identify 760 proteins with significant changes in abundance, including 117 host proteins with 86 showing a significant decrease and 31 showing a significant increase upon infection. Notably, we also observe significant increases in abundance of fungal proteins, as expected during infection. With these data, subsequent analyses, including network mapping, in silico characterization, and follow-up experiments are performed to validate the data and explore the molecular mechanisms underpinning the host response to virulence.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61881/61881fig1v4.jpg" /> 
Figure 1: Mass spectrometry-based proteomics workflow for analysis of macrophages infected with C. neoformans.&#160;The workflow begins with collection of macrophages either infected with C. neoformans or non-infected controls. Proteins are extracted by mechanical and chemical disruption, followed by reduction and alkylation, acetone precipitation, and enzymatic digestion. Peptides are purified on C18 STAGE tips, separated by high-performance liquid chromatography, subjected to electrospray ionization, and measured on a high-resolution mass spectrometer. Data is processed, analyzed, and visualized in the publicly available bioinformatics platforms, MaxQuant (with Andromeda) and Perseus14,15,16. Experiments performed in biological quadruplicate. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61881/61881fig1v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61881/61881fig2v3.jpg" /> 
Figure 2: Representative data for C. neoformans infection of macrophage cells.&#160;(A) Principal component analysis demonstrates distinction between infected vs. non-infected macrophage (component 1, 79.8%), and clustering of biological replicates (component 2, 5.7%). (B) Heat map of Pearson correlation plotted by hierarchical clustering by Euclidean distance to show clustering of samples (infected vs. non-infected) and replicate reproducibility (&#62;95%). (C) Volcano plot of identified proteins. Blue = fungal proteins with significant change in abundance; black = macrophage proteins with significant change in abundance. Student&#8217;s t-test (p-value &#8804; 0.05), FDR = 0.01; s0 = 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61881/61881fig2v3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

6.5K Views.  University of Guelph. 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.

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

Use of an Optical Trap for Study of Host-Pathogen Interactions for Dynamic Live Cell Imaging

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and temporal control between the phagocytic cell and microbe has rendered focused observations into the initial interactions of host response to pathogens difficult. Historically, intercellular contact events such as phagocytosis3 have been imaged by mixing two cell types, and then continuously scanning the field-of-view to find serendipitous intercellular contacts at the appropriate stage of interaction. The stochastic nature of these events renders this process tedious, and it is difficult to observe early or fleeting events in cell-cell contact by this approach. This method requires finding cell pairs that are on the verge of contact, and observing them until they consummate their contact, or do not. To address these limitations, we use optical trapping as a non-invasive, non-destructive, but fast and effective method to position cells in culture.
Optical traps, or optical tweezers, are increasingly utilized in biological research to capture and physically manipulate cells and other micron-sized particles in three dimensions4. Radiation pressure was first observed and applied to optical tweezer systems in 19705, 6, and was first used to control biological specimens in 19877. Since then, optical tweezers have matured into a technology to probe a variety of biological phenomena8-13.
We describe a method14 that advances live cell imaging by integrating an optical trap with spinning disk confocal microscopy with temperature and humidity control to provide exquisite spatial and temporal control of pathogenic organisms in a physiological environment to facilitate interactions with host cells, as determined by the operator. Live, pathogenic organisms like Candida albicans and Aspergillus fumigatus, which can cause potentially lethal, invasive infections in immunocompromised individuals15, 16 (e.g. AIDS, chemotherapy, and organ transplantation patients), were optically trapped using non-destructive laser intensities and moved adjacent to macrophages, which can phagocytose the pathogen. High resolution, transmitted light and fluorescence-based movies established the ability to observe early events of phagocytosis in living cells. To demonstrate the broad applicability in immunology, primary T-cells were also trapped and manipulated to form synapses with anti-CD3 coated microspheres in vivo, and time-lapse imaging of synapse formation was also obtained. By providing a method to exert fine spatial control of live pathogens with respect to immune cells, cellular interactions can be captured by fluorescence microscopy with minimal perturbation to cells and can yield powerful insight into early responses of innate and adaptive immunity.

A method is described to individually select, manipulate, and image live pathogens using an optical trap coupled to a spinning disk microscope. The optical trap provides spatial and temporal control of organisms and places them adjacent to host cells. Fluorescence microscopy captures dynamic intercellular interactions with minimal perturbation to cells.

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and ...

Use of the EpiAirway Model for Characterizing Long-term Host-pathogen Interactions

Nontypeable Haemophilus influenzae (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory mucosa 1; 2. To study the mechanisms by which these organisms survive on and inside respiratory tissues, a model in which successful long-term co-culture of bacteria and human cells can be performed is required. We use primary human respiratory epithelial tissues raised to the air-liquid interface, the EpiAirway model (MatTek, Ashland, MA). These are non-immortalized, well-differentiated, 3-dimensional tissues that contain tight junctions, ciliated and nonciliated cells, goblet cells that produce mucin, and retain the ability to produce cytokines in response to infection. 
This biologically relevant in vitro model of the human upper airway can be used in a number of ways; the overall goal of this method is to perform long-term co-culture of EpiAirway tissues with NTHi and quantitate cell-associated and internalized bacteria over time. As well, mucin production and the cytokine profile of the infected co-cultures can be determined. This approach improves upon existing methods in that many current protocols use submerged monolayer or Transwell cultures of human cells, which are not capable of supporting bacterial infections over extended periods3. For example, if an organism can replicate in the overlying media, this can result in unacceptable levels of cytotoxicity and loss of host cells, arresting the experiment. The EpiAirway model allows characterization of long-term host-pathogen interactions. Further, since the source for the EpiAirway is normal human tracheo-bronchial cells rather than an immortalized line, each is an excellent representation of actual human upper respiratory tract tissue, both in structure and in function4.
For this method, the EpiAirway tissues are weaned off of anti-microbial and anti-fungal compounds for 2 days prior to delivery, and all procedures are performed under antibiotic-free conditions. This necessitates special considerations, since both bacteria and primary human tissues are used in the same biosafety cabinet, and are co-cultured for extended periods.

This method allows characterization of extended bacterial co-culture with EpiAirways, primary human respiratory epithelial tissue grown at the air-liquid interface, a biologically relevant in vitro model. The approach can be used with any microbe that is amenable to long-term co-culture.

Nontypeable Haemophilus influenzae  (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory ...

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 ...

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

According to the American Cancer Society, more than 200,000 women will be diagnosed with invasive breast cancer each year and approximately 40,000 will die from the disease. The human leukocyte antigen (HLA) class I samples peptides derived from proteasomal degradation of cellular proteins and presents these fragments on the cell surface for interrogation by circulating cytotoxic T lymphocytes (CTL). Generation of T-cell receptor mimic (TCRm) monoclonal antibodies (mAbs) which recognize breast cancer specific peptide/HLA-A*02:01 complexes such as those derived from macrophage migration inhibitory factor (MIF19-27) and NY-ESO-1157-165 enable detection and destruction of breast cancer cells in the absence of an effective anti-tumor CTL response. Intact class I HLA/peptide complexes are shed by breast cancer cells and represent potentially relevant cancer biomarkers. In this work, a breakthrough biomarker screening system for cancer diagnostics incorporating T-cell receptor mimic monoclonal antibodies combined with a novel, label-free biosensor utilizing guided-mode resonance (GMR) sensor technology is presented. Detection of shed MIF/HLA-A*02:01 complexes in MDA-MB-231 cell supernatants, spiked human serum, and patient plasma is demonstrated. The impact of this work could revolutionize personalized medicine through development of companion disease diagnostics for targeted immunotherapies.

Intact class I HLA/peptide complexes are shed by cancer cells, representing a potential relevant cancer biomarker. Utilizing label-free sensor technology and T-cell receptor mimicking monoclonal antibodies, detection of shed MIF/HLA-A*02:01 complexes in MDA-MB-231 cell supernatants, spiked human serum, and patient plasma is demonstrated, enabling development of a novel cancer diagnostic platform.

According to the American Cancer Society, more than 200,000 women will be diagnosed with invasive breast cancer each year and approximately 40,000 will ...

Electroporation of Functional Bacterial Effectors into Mammalian Cells

The study of protein interactions in the context of living cells can generate critical information about localization, dynamics, and interacting partners. This information is particularly valuable in the context of host-pathogen interactions. Many pathogen proteins function within host cells in a variety of way such as, enabling evasion of the host immune system and survival within the intracellular environment. To study these pathogen-protein host-cell interactions, several approaches are commonly used, including: in vivo infection with a strain expressing a tagged or mutant protein, or introduction of pathogen genes via transfection or transduction. Each of these approaches has advantages and disadvantages. We sought a means to directly introduce exogenous proteins into cells. Electroporation is commonly used to introduce nucleic acids into cells, but has been more rarely applied to proteins although the biophysical basis is exactly the same. A standard electroporator was used to introduce affinity-tagged bacterial effectors into mammalian cells. Human epithelial and mouse macrophage cells were cultured by traditional methods, detached, and placed in 0.4 cm gap electroporation cuvettes with an exogenous bacterial pathogen protein of interest (e.g. Salmonella Typhimurium GtgE). After electroporation (0.3 kV) and a short (4 hr) recovery period, intracellular protein was verified by fluorescently labeling the protein via its affinity tag and examining spatial and temporal distribution by confocal microscopy. The electroporated protein was also shown to be functional inside the cell and capable of correct subcellular trafficking and protein-protein interaction. While the exogenous proteins tended to accumulate on the surface of the cells, the electroporated samples had large increases in intracellular effector concentration relative to incubation alone. The protocol is simple and fast enough to be done in a parallel fashion, allowing for high-throughput characterization of pathogen proteins in host cells including subcellular targeting and function of virulence proteins.

Electroporation was used to insert purified bacterial virulence effector proteins directly into living eukaryotic cells. Protein localization was monitored by confocal immunofluorescence microscopy. This method allows for studies on trafficking, function, and protein-protein interactions using active exogenous proteins, avoiding the need for heterologous expression in eukaryotic cells.

The study of protein interactions in the context of living cells can generate critical information about localization, dynamics, and interacting partners. ...

Evaluation of the Efficacy And Toxicity of RNAs Targeting HIV-1 Production for Use in Gene or Drug Therapy

Small RNA therapies targeting post-integration steps in the HIV-1 replication cycle are among the top candidates for gene therapy and have the potential to be used as drug therapies for HIV-1 infection. Post-integration inhibitors include ribozymes, short hairpin (sh) RNAs, small interfering (si) RNAs, U1 interference (U1i) RNAs and RNA aptamers. Many of these have been identified using transient co-transfection assays with an HIV-1 expression plasmid and some have advanced to clinical trials. In addition to measures of efficacy, small RNAs have been evaluated for their potential to affect the expression of human RNAs, alter cell growth and/or differentiation, and elicit innate immune responses. In the protocols described here, a set of transient transfection assays designed to evaluate the efficacy and toxicity of RNA molecules targeting post-integration steps in the HIV-1 replication cycle are described. We have used these assays to identify new ribozymes and optimize the format of shRNAs and siRNAs targeting HIV-1 RNA. The methods provide a quick set of assays that are useful for screening new anti-HIV-1 RNAs and could be adapted to screen other post-integration inhibitors of HIV-1 replication.

Methods to evaluate the efficacy and toxicity of RNA molecules targeting post-integration steps of the HIV-1 replication cycle are described. These methods are useful for screening new molecules and optimizing the format of existing ones.

Small RNA therapies targeting post-integration steps in the HIV-1 replication cycle are among the top candidates for gene therapy and have the potential ...

A New Method for Qualitative Multi-scale Analysis of Bacterial Biofilms on Filamentous Fungal Colonies Using Confocal and Electron Microscopy

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic cooperation, competition, or predation. The study of biofilms is important in many biological fields, including environmental science, food production, and medicine. However, few studies have focused on such bacterial biofilms, partially due to the difficulty of investigating them. Most of the methods for qualitative and quantitative biofilm analyses described in the literature are only suitable for biofilms forming on abiotic surfaces or on homogeneous and thin biotic surfaces, such as a monolayer of epithelial cells.
While laser scanning confocal microscopy (LSCM) is often used to analyze in situ and in vivo biofilms, this technology becomes very challenging when applied to bacterial biofilms on fungal hyphae, due to the thickness and the three dimensions of the hyphal networks. To overcome this shortcoming, we developed a protocol combining microscopy with a method to limit the accumulation of hyphal layers in fungal colonies. Using this method, we were able to investigate the development of bacterial biofilms on fungal hyphae at multiple scales using both LSCM and scanning electron microscopy (SEM). This report describes the protocol, including microorganism cultures, bacterial biofilm formation conditions, biofilm staining, and LSCM and SEM visualizations.

This protocol describes a new method to grow and qualitatively analyze bacterial biofilms on fungal hyphae by confocal and electron microscopy.

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic ...

Label-free Neutrophil Enrichment from Patient-derived Airway Secretion Using Closed-loop Inertial Microfluidics

Airway secretions contain a large number of immune-related cells, e.g., neutrophils, macrophages, and lymphocytes, which can be used as a major resource to evaluate a variety of pulmonary diseases, both for research and clinical purposes. However, due to the heterogeneous and viscous nature of patient mucus, there is currently no reliable dissociation method that does not damage the host immune cells in the patient airway secretion. In this research, we introduce a sample preparation method that uses inertial microfluidics for the patient&#39;s immune assessment. Regardless of the heterogeneous fluidic properties of the clinical samples, the proposed method recovers more than 95% of neutrophils from airway secretion samples that are diluted 1,000-fold with milliliters of clean saline. By recirculating the concentrated output stream to the initial sample reservoir, a high concentration, recovery, and purity of the immune cells are provided; recirculation is considered a trade-off to the single-run syringe-based operation of inertial microfluidics. The closed-loop operation of spiral microfluidics provides leukocytes without physical or chemical disturbance, as demonstrated by the phorbol 12-myristate 13-acetate (PMA)-induced elastase release of sorted neutrophils.

In this research, we demonstrate a label-free neutrophil separation method from clinical airway secretions using closed-loop operation of spiral inertial microfluidics. The proposed method would expand the clinical in vitro assays for various respiratory diseases.

Airway secretions contain a large number of immune-related cells, e.g., neutrophils, macrophages, and lymphocytes, which can be used as a major resource ...

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification ...

Evaluation of Host-Pathogen Responses and Vaccine Efficacy in Mice

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a striking increase in life expectancy around the globe. Nonetheless, determining vaccine efficacy remains a challenge. Emerging evidence suggests that the current acellular vaccine (aPV) for Bordetella pertussis (B. pertussis) induces suboptimal immunity. Therefore, a major challenge is designing a next-generation vaccine that induces protective immunity without the adverse side effects of a whole-cell vaccine (wPV). Here we describe a protocol that we used to test the efficacy of a promising, novel adjuvant that skews immune responses to a protective Th1/Th17 phenotype and promotes a better clearance of a B. pertussis challenge from the murine respiratory tract. This article describes the protocol for mouse immunization, bacterial inoculation, tissue harvesting, and analysis of immune responses. Using this method, within our model, we have successfully elucidated crucial mechanisms elicited by a promising, next-generation acellular pertussis vaccine. This method can be applied to any infectious disease model in order to determine vaccine efficacy.

Here we present an elegant protocol for in vivo evaluation of vaccine effectiveness and host immune responses. This protocol can be adapted for vaccine models that study viral, bacterial, or parasitic pathogens.

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a ...