scholarly journals Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Sofani Tafesse Gebreyesus ◽  
Asad Ali Siyal ◽  
Reta Birhanu Kitata ◽  
Eric Sheng-Wen Chen ◽  
Bayarmaa Enkhbayar ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Mammalian Cells ◽  
Missing Values ◽  
Dynamic Range ◽  
Analytical Sensitivity ◽  
Microfluidic Chips ◽  
Mass Spectral ◽  
Data Independent Acquisition ◽  
Spectral Libraries

AbstractSingle-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomic analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications.

scholarly journals Ultra-streamlined single cell proteomics by all-in-one chip and data-independent-acquisition mass spectrometry

2021 ◽  
Author(s):  
Hsiung-Lin Tu ◽  
Sofani Gebreyesus ◽  
Asad Ali Siyal ◽  
Reta Birhanu Kitata ◽  
Eric Sheng-Wen Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Missing Values ◽  
Protein Quantification ◽  
Single Cell Level ◽  
Cell Level ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
General Utility ◽  
Ultrahigh Sensitivity

Abstract Single cell proteomics provides the ultimate resolution to reveal cellular phenotypic heterogeneity and functional network underlying biological processes. Here, we present an ultra-streamlined workflow combining an integrated proteomic chip (iProChip) and data-independent-acquisition (DIA) mass spectrometry for sensitive microproteomics analysis down to single cell level. The iProChip offers multiplexed and automated all-in-one station from cell isolation/counting/imaging to complete proteomic processing within a single device. By mapping to project-specific spectra libraries, the iProChip-DIA enables profiling of 1160 protein groups from triplicate analysis of a single mammalian cell. Furthermore, the applicability of iProChip-DIA was demonstrated using both adherent and non-adherent malignant cells, which reveals 5 orders of proteome coverage, highly consistent ~100-fold protein quantification (1-100 cells) and high reproducibility with low missing values (<16%). With the demonstrated all-in-one cell characterization, ultrahigh sensitivity, robustness, and versatility to add other functionalities, the iProChip-DIA is anticipated to offer general utility to realize advanced proteomics applications at single cell level.

scholarly journals Ultra-streamlined single cell proteomics by all-in-one chip and data-independent-acquisition mass spectrometry

2021 ◽  
Author(s):  
Hsiung-Lin Tu ◽  
Sofani Gebreyesus ◽  
Asad Ali Siyal ◽  
Reta Birhanu Kitata ◽  
Eric Sheng-Wen Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Missing Values ◽  
Protein Quantification ◽  
Single Cell Level ◽  
Cell Level ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
General Utility ◽  
Ultrahigh Sensitivity

Abstract Single cell proteomics provides the ultimate resolution to reveal cellular phenotypic heterogeneity and functional network underlying biological processes. Here, we present an ultra-streamlined workflow combining an integrated proteomic chip (iProChip) and data-independent-acquisition (DIA) mass spectrometry for sensitive microproteomics analysis down to single cell level. The iProChip offers multiplexed and automated all-in-one station from cell isolation/counting/imaging to complete proteomic processing within a single device. By mapping to project-specific spectra libraries, the iProChip-DIA enables profiling of 1160 protein groups from triplicate analysis of a single mammalian cell. Furthermore, the applicability of iProChip-DIA was demonstrated using both adherent and non-adherent malignant cells, which reveals 5 orders of proteome coverage, highly consistent ~100-fold protein quantification (1-100 cells) and high reproducibility with low missing values (<16%). With the demonstrated all-in-one cell characterization, ultrahigh sensitivity, robustness, and versatility to add other functionalities, the iProChip-DIA is anticipated to offer general utility to realize advanced proteomics applications at single cell level.

scholarly journals NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

scholarly journals DIALib-QC an assessment tool for spectral libraries in data-independent acquisition proteomics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Mukul K. Midha ◽  
David S. Campbell ◽  
Charu Kapil ◽  
Ulrike Kusebauch ◽  
Michael R. Hoopmann ◽  
...  
Keyword(s):  
Quality Control ◽  
Assessment Tool ◽  
Software Tool ◽  
Mass Range ◽  
Systematic Evaluation ◽  
Label Free ◽  
Mass Spectral ◽  
Data Independent Acquisition ◽  
Theoretical Mass ◽  
Spectral Libraries

Abstract Data-independent acquisition (DIA) mass spectrometry, also known as Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH), is a popular label-free proteomics strategy to comprehensively quantify peptides/proteins utilizing mass spectral libraries to decipher inherently multiplexed spectra collected linearly across a mass range. Although there are many spectral libraries produced worldwide, the quality control of these libraries is lacking. We present the DIALib-QC (DIA library quality control) software tool for the systematic evaluation of a library’s characteristics, completeness and correctness across 62 parameters of compliance, and further provide the option to improve its quality. We demonstrate its utility in assessing and repairing spectral libraries for correctness, accuracy and sensitivity.

scholarly journals Bioaerosol Mass Spectrometry for Rapid Detection of Individual Airborne Mycobacterium tuberculosis H37Ra Particles

2005 ◽  
Vol 71 (10) ◽  
pp. 6086-6095 ◽  
Author(s):  
Herbert J. Tobias ◽  
Millie P. Schafer ◽  
Maurice Pitesky ◽  
David P. Fergenson ◽  
Joanne Horn ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mycobacterium Tuberculosis ◽  
Single Cell ◽  
Direct Detection ◽  
Mass Spectral ◽  
Flight Mass Spectrometry ◽  
Bioaerosol Mass Spectrometry ◽  
Free Environment ◽  
Instrumental Development ◽  
Direct Counts

ABSTRACT Single-particle laser desorption/ionization time-of-flight mass spectrometry, in the form of bioaerosol mass spectrometry (BAMS), was evaluated as a rapid detector for individual airborne, micron-sized, Mycobacterium tuberculosis H37Ra particles, comprised of a single cell or a small number of clumped cells. The BAMS mass spectral signatures for aerosolized M. tuberculosis H37Ra particles were found to be distinct from M. smegmatis, Bacillus atrophaeus, and B. cereus particles, using a distinct biomarker. This is the first time a potentially unique biomarker was measured in M. tuberculosis H37Ra on a single-cell level. In addition, M. tuberculosis H37Ra and M. smegmatis were aerosolized into a bioaerosol chamber and were sampled and analyzed using BAMS, an aerodynamic particle sizer, a viable Anderson six-stage sampler, and filter cassette samplers that permitted direct counts of cells. In a background-free environment, BAMS was able to sample and detect M. tuberculosis H37Ra at airborne concentrations of >1 M. tuberculosis H37Ra-containing particles/liter of air in 20 min as determined by direct counts of filter cassette-sampled particles, and concentrations of >40 M. tuberculosis H37Ra CFU/liter of air in 1 min as determined by using viable Andersen six-stage samplers. This is a first step toward the development of a rapid, stand-alone airborne M. tuberculosis particle detector for the direct detection of M. tuberculosis bioaerosols generated by an infectious patient. Additional instrumental development is currently under way to make BAMS useful in realistic environmental and respiratory particle backgrounds expected in tuberculosis diagnostic scenarios.

scholarly journals Data-independent acquisition mass spectrometry enables reproducible characterization of microbiota function

2018 ◽  
Author(s):  
Juhani Aakko ◽  
Sami Pietilä ◽  
Tomi Suomi ◽  
Mehrad Mahmoudian ◽  
Raine Toivonen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Dynamic Range ◽  
Functional Characterization ◽  
Research Area ◽  
Microbial Composition ◽  
Complex Samples ◽  
Data Independent Acquisition ◽  
Novel Approach ◽  
Open Source Software Package

AbstractMetaproteomics is an emerging research area which aims to reveal the functionality of microbial communities – unlike the increasingly popular metagenomics providing insights only on the functional potential. So far, the common approach in metaproteomics has been data-dependent acquisition mass spectrometry (DDA). However, DDA is known to have limited reproducibility and dynamic range with samples of complex microbial composition. To overcome these limitations, we introduce here a novel approach utilizing data-independent acquisition (DIA) mass spectrometry, which has not been applied in metaproteomics of complex samples before. For robust analysis of the data, we introduce an open-source software package diatools, which is freely available at Docker Hub and runs on various operating systems. Our highly reproducible results on laboratory-assembled microbial mixtures and human fecal samples support the utility of our approach for functional characterization of complex microbiota. Hence, the approach is expected to dramatically improve our understanding on the role of microbiota in health and disease.

scholarly journals Arabidopsis Proteome and the Mass Spectral Assay Library

2019 ◽  
Author(s):  
Huoming Zhang ◽  
Pei Liu ◽  
Tiannan Guo ◽  
Huayan Zhao ◽  
Dalila Bensaddek ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Abscisic Acid ◽  
Acid Stress ◽  
Model Organism ◽  
Mass Spectrometry Data ◽  
Mass Spectral ◽  
Novel Proteins ◽  
Data Independent Acquisition ◽  
Arabidopsis Proteome ◽  
Expression Atlas

AbstractArabidopsis is an important model organism and the first plant with its genome sequenced. Knowledge from studying this species has either direct or indirect applications to agriculture and human health. Quantitative proteomics by data-independent acquisition (SWATH/DIA-MS) was recently developed and considered as a high-throughput targetedlike approach for accurate proteome quantitation. In this approach, a high-quality and comprehensive library is a prerequisite. Here, we generated a protein expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We subsequently applied DIA-mass spectrometry using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups with 1,787 of them being differentially expressed which includes 65 proteins known to respond to abscisic acid stress. Mass spectrometry data are available via ProteomeXchange with identifier PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.

scholarly journals Comparative proteome signatures of trace samples by multiplexed Data-Independent Acquisition

2021 ◽  
Author(s):  
Claudia Ctortecka ◽  
Gabriela Krššáková ◽  
Karel Stejskal ◽  
Josef M. Penninger ◽  
Sasha Mendjan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Protein Expression ◽  
Single Cell ◽  
Missing Values ◽  
Expression Profiles ◽  
Single Cells ◽  
Proteomics Data ◽  
Biological Origin ◽  
Low Input ◽  
Data Independent Acquisition

AbstractSingle cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not take full advantage of isotope-encoded sample multiplexing. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin, and to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.

Evaluation of Acquisition Modes for the Quantitative Analysis of Cross-Linked Peptides by Targeted and Untargeted Mass Spectrometry

2020 ◽  
Author(s):  
Hannah Britt ◽  
Tristan Cragnolini ◽  
Suniya Khatun ◽  
Abubakar Hatimy ◽  
Juliette James ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Dynamic Range ◽  
Temporal Dynamics ◽  
Protein Complexes ◽  
Isotopic Labeling ◽  
Single Experiment ◽  
Data Independent Acquisition ◽  
Quantitative Metrics ◽  
Parallel Reaction Monitoring ◽  
Further Development

<div> <div> <p>Cross-linking mass spectrometry (XL-MS) is a structural biology technique that can provide insights into the structure and interactions of proteins and their complexes, especially those that cannot be easily assessed by other methods. Quantitative XL-MS has the potential to probe the structural and temporal dynamics of protein complexes; however, it requires further development. Until recently, quantitative XL-MS has largely relied upon isotopic labeling and data dependent acquisition (DDA) methods, limiting the number of biological samples that can be studied in a single experiment. Here, the acquisition modes available on an ion mobility (IM) enabled QToF mass spectrometer are evaluated for the quantitation of cross-linked peptides, eliminating the need for isotopic labels and thus expanding the number of comparable studies that can be conducted in parallel. Workflows were optimized using metabolite and peptide standards analyzed in biological matrices, facilitating modelling of the data and addressing linearity issues, which allow for significant increases in dynamic range. Evaluation of the DDA acquisition method commonly used in XL-MS studies indicated consistency issues between technical replicates and reduced performance in quantitative metrics. On the contrary, data independent acquisition (DIA) and parallel reaction monitoring (PRM) modes proved more robust for analyte quantitation. Mobility enabled modes exhibited an improvement in sensitivity due to the added dimension of separation, and a simultaneous reduction in dynamic range, which was largely recovered by correction methods. Hi[3] and probabilistic quantitation methods were successfully applied to the DIA data, determining the molar amounts of cross-linked peptides relative to their linear counterparts.</p></div></div>

MSImpute: Imputation of label-free mass spectrometry peptides by low-rank approximation

2020 ◽  
Author(s):  
Soroor Hediyeh-zadeh ◽  
Andrew I. Webb ◽  
Melissa J. Davis
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Missing Values ◽  
Dynamic Range ◽  
Dropout Rate ◽  
Low Rank ◽  
Proteomics Data ◽  
Low Rank Approximation ◽  
Formidable Challenge ◽  
Rank Approximation

AbstractRecent developments in mass spectrometry (MS) instruments and data acquisition modes have aided multiplexed, fast, reproducible and quantitative analysis of proteome profiles, yet missing values remain a formidable challenge for proteomics data analysis. The stochastic nature of sampling in Data Dependent Acquisition (DDA), suboptimal preprocessing of Data Independent Acquisition (DIA) runs and dynamic range limitation of MS instruments impedes the reproducibility and accuracy of peptide quantification and can introduce systematic patterns of missingness that impact downstream analyses. Thus, imputation of missing values becomes an important element of data analysis. We introduce msImpute, an imputation method based on low-rank approximation, and compare it to six alternative imputation methods using public DDA and DIA datasets. We evaluate the performance of methods by determining the error of imputed values and accuracy of detection of differential expression. We also measure the post-imputation preservation of structures in the data at different levels of granularity. We develop a visual diagnostic to determine the nature of missingness in datasets based on peptides with high biological dropout rate and introduce a method to identify such peptides. Our findings demonstrate that msImpute performs well when data are missing at random and highlights the importance of prior knowledge about nature of missing values in a dataset when selecting an imputation technique.

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