Name: label-free quantitative proteomics workflow for discovery-driven host-pathogen interactions
Uploaded: 2020-10-20T14:30:00.000+00:00
Duration: 5 min 37 s
Description: Watch this Scientific Journal Video about Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions at JoVE.com

저자는 생물 정보학 솔루션 Inc.의 조나단 크리거 박사에게 대표적인 실험을 위한 질량 분광계를 운영한 것에 대해 감사하고, 실험적인 설정 및 원고 피드백에 대한 지원을 Geddes-McAlister 그룹의 구성원에게 감사드립니다. 저자는 밴팅 연구 재단에서 자금 지원을 인정, 부분적으로, - Jarislowsky 펠로우십 디스커버리 상, 뉴 프론티어 연구 기금 - 탐사 (NFRFE-2019-00425), J.G.M에 대한 캐나다 혁신 재단 (JELF 38798), 뿐만 아니라 NSERC 캐나다 대학원 장학금 – 석사 및 온타리오 대학원.B 장학금.

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저자는 이해 상충을 선언하지 않습니다.

프로토콜의 중요한 단계는 세포에 최소한의 중단을 가진 단백질 처리를 위한 대식세포 의 준비 그리고 공동 배양 견본의 집합을 포함합니다. 수집하기 전에 세포의 불필요한 용해를 방지하기 위해 씻고, 접종하고, 부착 된 대식세포 세포를 부드럽고 신중하게 제거하는 단계를 수행하는 것이 중요합니다. 실험에 대한 올바른 MOI를 확립하는 것은 지나치게 높은 MOI로 접종하면 급속한 대식세포 사망...

침습적 곰팡이 감염의 보급은 크게 증가하고 면역 결핍 성소1을가진 개별에서 가장 일반적으로 보고된 받아들일 수 없을 정도로 높은 사망률과 상관관계가 있습니다. Cryptococcus neoformans는 호스트 대식세포 세포 내의 세포 내 생존을 할 수 있는 악명 높은 기회성 곰팡이 병원체입니다. 부적절한 항진균 개입은 곰팡이 보급과 크립토 코칼 뇌막염 및 뇌막염2,3의생명을 위협하는 증상을 초래합니다. 면역 손상 상태의 글로벌 증가는 항진균제의 사용에 병렬 증가를 요구하고있다, 있는 많은 곰팡이 종, C. neoformans를 포함하여, 점점4,5,6을향해 저항을 진화하고있다. 따라서 호스트 방어 반응 및 미생물 병인에 관한 중요한 생물학적 질문에 답하기 위해 강력하고 효율적인 기술을 구현하는 것이 필수적입니다.
강력한 전산 및 생물정보 파이프라인 의 생성을 포함한 질량 분광법(MS)의 기술 발전의 새로운 시대는 호스트 병원체 연구7,8의대규모 분석을 위한 통합 비전의 토대를 제공한다. 기존의 병원성 중심의 프로테오믹 분석은 일반적으로 단백질 상관 관계 프로파일링, 프로테오믹스와 결합된 친화성 크로마토그래피, 및 상호작용제9와같은 포괄적인 방법론을 포함하여 숙주 또는 병원체 원근으로부터의 감염의 시야를 프로파일링한다. 호스트 시스템에 있는 위험한 병원체의 독성에 대한 조사는 엄청난 임상 중요성입니다; 그러나 단일 실험에서 이중 원근 분석을 적용한 것은 이전에는 달성할 수 없는 것으로 간주되었습니다. 예를 들어, 감염을 향한 병원균의 관점은 종종 풍부하게 풍부한 숙주 단백질에 의해 압도되어 저농축 곰팡이 단백질7의검출을 위한 감도가 감소한다. 더욱이, 높은 견본 복잡성은 단 하나 실험 시스템에서 조사하기 위하여 많은 표적을 초대하고 특정 병원체 단백질을 위한 행동의 기계장치를 해명하기 위하여 도전적이다는 것을 증명합니다.
상향식 프로테오믹스는 펩타이드가 서열 특이적 효소 소화에 의해 생성되는 관리식 시료 준비를 가능하게 하는 인기 있는 MS 기법이며, 이어서 MS10,11에의한 액체 크로마토그래피 분리, 식별 및 정량화가 뒤따릅니다. 여기서는 감염 기반 프로테오메 또는 '감염성'의 편견없는 커버리지를 달성하기 위한 데이터 의존적 획득 전략을 보여주는 방법을 제시합니다. 구체적으로, 라벨없는 정량화(LFQ)는 여러 프로테옴에 걸쳐 단백질 수준 변화를 견고하고 정확하게 식별하기 위해 화학 물질 또는 대사 라벨에 대한 의존도를 떨어뜨리고, 샘플 처리 및 처리단계(12,13)를감소시킵니다. 이 보편적인 응용 프로그램은 어떤 예상 된 단백질 생산과 는 별개로 세포 내에서 주어진 순간에 단백질을 생산; 따라서, 감염에 중요한 새로운 통찰력이 발견될 수 있습니다.
본 명세서에 기재된 워크플로우는 숙주 면역 세포를 가진 감염 모방 조건 동안 C. neoformans의 단백질 수준 변화를 탐구하도록 최적화된다(그림1). 세포 모형의 분리 및 분리에 의지하는 대신, 이 접근은 호스트와 병원체 proteome를 함께 추출하고, 종 특정 단백질 생산을 구별하기 위하여 2개의 유기체 특정 데이터베이스를 사용하여 생물정보 분리를 이용합니다. 이 방법은 동위원소 기반 라벨링 연구 또는 분수에 필요한 추가 비용이 많이 드는 준비 단계 없이 무제한의 샘플을 처리할 수 있는 이점을 제공합니다. 또한, 이 워크플로우는 숙주 면역 세포를 표적으로 하고 감염시킬 수 있는 광범위한 곰팡이 및 세균성 병원체로 이전할 수 있는 최적화된 단백질 추출 프로토콜을 지원합니다. 전반적으로, 이 프로토콜은 고분해능 MS를 위한 편견없는 단백질 추출 및 샘플 처리를 완료하는 단계를 설명하고, 그 다음에 는 데이터 및 통계 분석이 선행된 호스트 방어 반응의 포괄적인 프로파일링과 결합된 감염에 중요한 곰팡이 단백질에 대한 풍부한 지식을 제공할 수 있는 단계입니다.

<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

질량 분석법 (MS)기반 정량 적 프로테오믹스의 기술적 성과는 다양한 조건에서 유기체의 글로벌 프로테오메를 분석하기위한 많은 알려지지 않은 길을 열어줍니다. 원하는 숙주로 미생물 병원체의 상호 작용에 적용되는 이 강력한 전략은 감염에 대한 두 관점을 포괄적으로 특성화합니다. 본 명세서에서, 워크플로우는 불멸의 대식세포 세포의 존재에서 치명적인 질병 크립토코코시스의 원인성 제제인 크립토코커스 네오포만의 감염체의 라벨없는 정량화(LFQ)를 설명한다. 이 프로토콜은 단일 실험 내에서 병원균 과 포유류 세포 모두에 적합한 단백질 제제 기술을 자세히 설명하여 액체 크로마토그래피 (LC)-MS / MS 분석을위한 적절한 펩타이드 제출을 초래합니다. LFQ의 높은 처리량 일반 특성은 광범위한 동적 단백질 식별 및 정량화뿐만 아니라 모든 숙주 병원체 감염 설정으로의 전달 가능성을 허용하여 극도의 감도를 유지합니다. 이 방법은 감염 모방 조건 내에서 병원균의 광범위하고 편견없는 단백질 풍부 프로파일을 카탈로그화하도록 최적화되어 있습니다. 구체적으로, 여기서 입증된 방법은 독성에 필요한 단백질 생산과 같은 C. neoformans 병인에 대한 필수 정보를 제공하고 미생물 침입에 대응하는 중요한 숙주 단백질을 식별한다.

위에서 설명한 프로토콜은 단일 실험에서 곰팡이 병원균, C. neoformans및 숙주, 대식세포 세포 모두에서 파생된 단백질의 식별 및 정량화를 가능하게 합니다. 공동 배양에 이어, 세포는 수집 및 처리되며 각 종에 특정한 펩타이드 프로파일을 기반으로 생체정보학적으로 분리된다. 이것은 감염 도중 호스트 병원체 관계의 상호 작용을 정의하기 위한 강력한 접근입니다. 실험에서 확인된 단백질...

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

여기서, 우리는 질량 분광계 기지를 둔 proteomics에 의하여 감염 도중 호스트와 병원체 사이 상호 작용을 프로파일기 위하여 프로토콜을 제시합니다. 이 프로토콜은 단일 실험에서 호스트(예를 들어, 대식세포)와 병원체(예를 들어, Cryptococcus neoformans)의단백질 풍부도의 변화를 측정하기 위해 라벨이 없는 정량화를 사용합니다.

발견 중심의 숙주-병원균 상호 작용을 위한 라벨 프리 정량적 프로테오믹스 워크플로우

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 조회수.  University of Guelph. 여기서, 우리는 질량 분광계 기지를 둔 proteomics에 의하여 감염 도중 호스트와 병원체 사이 상호 작용을 프로파일기 위하여 프로토콜을 제시합니다. 이 프로토콜은 단일 실험에서 호스트(예를 들어, 대식세포)와 병원체(예를 들어, Cryptococcus neoformans)의단백질 풍부도의 변화를 측정하기 위해 라벨이 없는 정량화를 사용합니다.

비디오: 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.

깊은 프로테옴 Isobaric 라벨, 광범위 한 액체 크로마토그래피, 질량 분석 및 소프트웨어 기반 정량화 하 여 프로 파일링

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

연구

생화학

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.

호스트 병원체 단백질 - 단백질 상호 작용의 풍경의 특성을 비교 접근

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

면역학 및 감염병학

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.

대규모 핵 상호 작용 프로파일링을 위한 라벨 없는 면역 침전 질량 분광 워크플로우

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.

JUMPn: 프로테오믹스의 단백질 공동 발현 클러스터링 및 네트워크 분석을 위한 간소화된 응용 프로그램

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.

해 다중 단백질 신호 복합물 분자 보완성 친 화력 정화 (BiCAP)에 의해

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.

호스트-병원체 상호 작용을 조사하기 위한 고처리량 전사 분석

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.

뉴런 리소좀 상호작용과 근접 라벨링 단백질체학의 특성 분석

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

신경과학

<meta charset="utf8"></head><body>Galaxy-Enabled Metaproteomics: 숙주-마이크로바이옴 상호 작용에 대한 통찰력

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.

<meta charset="utf8"></head><body>인간 질병의 기저에 있는 숙주-마이크로바이옴 상호작용을 분석하기 위해 Galaxy Bioinformatics 플랫폼 내에서 구현된 임상 메타단백질체학 워크플로우

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.