scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D. Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrates that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
T. Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

scholarly journals Synovial single-cell heterogeneity, zonation, and interactions: a patchwork of effectors in arthritis

Rheumatology ◽  
2021 ◽  
Author(s):  
Barbora Schonfeldova ◽  
Kristina Zec ◽  
Irina A Udalova
Keyword(s):  
Rheumatoid Arthritis ◽  
Single Cell ◽  
Cell Signalling ◽  
Synovial Cell ◽  
Cellular Heterogeneity ◽  
Cell Populations ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Cell Level ◽  
Peripheral Neurons

Abstract Despite extensive research, there is still no treatment that would lead to remission in all patients with rheumatoid arthritis as our understanding of the affected site, the synovium, is still incomplete. Recently, single-cell technologies helped to decipher the cellular heterogeneity of the synovium; however, certain synovial cell populations, such as endothelial cells or peripheral neurons, remain to be profiled on a single-cell level. Furthermore, associations between certain cellular states and inflammation were found; whether these cells cause the inflammation remains to be answered. Similarly, cellular zonation and interactions between individual effectors in the synovium are yet to be fully determined. A deeper understanding of cell signalling and interactions in the synovium is crucial for a better design of therapeutics with the goal of complete remission in all patients.

scholarly journals Mitochondrial isolation: when size matters

2020 ◽  
Vol 5 ◽  
pp. 226
Author(s):  
Alexander G. Bury ◽  
Amy E. Vincent ◽  
Doug M. Turnbull ◽  
Paolo Actis ◽  
Gavin Hudson
Keyword(s):  
Mitochondrial Dysfunction ◽  
Single Cell ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Isolation Techniques ◽  
Cellular Environment ◽  
Dynamics And Function ◽  
And Function ◽  
Subcellular Resolution

Mitochondrial vitality is critical to cellular function, with mitochondrial dysfunction linked to a growing number of human diseases. Tissue and cellular heterogeneity, in terms of genetics, dynamics and function means that increasingly mitochondrial research is conducted at the single cell level. Whilst, there are several single-cell technologies that are currently available, each with their advantages, they cannot be easily adapted to study mitochondria with subcellular resolution. Here we review the current techniques and strategies for mitochondrial isolation, critically discussing each technology’s limitations for future mitochondrial research. Finally, we highlight and discuss the recent breakthroughs in sub-cellular isolation techniques, with a particular focus on nanotechnologies that enable the isolation of mitochondria, from subcellular compartments, with unprecedented spatial precision with minimal disruption to mitochondria and their immediate cellular environment.

scholarly journals Fusion transcript detection using spatial transcriptomics

2020 ◽  
Author(s):  
Stefanie Friedrich ◽  
Erik LL Sonnhammer
Keyword(s):  
Single Cell ◽  
Spatial Information ◽  
Cancer Cell Line ◽  
Fusion Transcript ◽  
Single Cell Level ◽  
Cell Level ◽  
Cancer Data ◽  
Fusion Transcripts ◽  
Tissue Sections ◽  
Transcript Detection

Abstract Background Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes. Method We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5´ gene and has an elevated number of poly(A) tails for the 3´ gene. Its expression level is defined by the upstream promoter of the 5´ gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score. Results We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands. Conclusions STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level. Keywords Fusion transcript detection, Spatial Transcriptomics, gene fusion, cis-SAGE, oncogene

scholarly journals Single cell infection with influenza A virus using drop-based microfluidics

2021 ◽  
Author(s):  
Emma Kate Loveday ◽  
Humberto S. Sanchez ◽  
Mallory M. Thomas ◽  
Connie B. Chang
Keyword(s):  
Single Cell ◽  
Influenza A Virus ◽  
Influenza A ◽  
Single Cell Analysis ◽  
Rna Virus ◽  
Host Cells ◽  
Single Cell Level ◽  
Cell Infection ◽  
Cell Level ◽  
High Genetic Diversity

SummaryInfluenza A virus (IAV) is an RNA virus with high genetic diversity which necessitates the development of new vaccines targeting emerging mutations each year. As IAV exists in genetically heterogeneous populations, current studies focus on understanding population dynamics at the single cell level. These studies include novel methodology that can be used for probing populations at the single cell level, such as single cell sequencing and microfluidics. Here, we introduce a drop-based microfluidics method to study IAV infection at a single cell level by isolating infected host cells in microscale drops. Single human alveolar basal epithelial (A549), Madin-Darby Canine Kidney cells (MDCK) and MDCK + human siat7e gene (Siat7e) cells infected with the pandemic A/California/07/2009 (H1N1) strain were encapsulated within 50 μm radii drops and incubated at 37°C. We demonstrate that drops remain stable over 24 hours, that 75% of cells remain viable, and that IAV virus can propagate within the drops. Drop-based microfluidics therefore enables single cell analysis of viral populations produced from individually infected cells.

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 Identifying tumor cells at the single cell level

2021 ◽  
Author(s):  
Jan Dohmen ◽  
Artem Baranovskii ◽  
Bora Uyar ◽  
Jonathan Ronen ◽  
Vedran Franke ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Biomarker Discovery ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Simple Problem ◽  
Cancer Therapies ◽  
Single Cell Level ◽  
Cell Level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. Single cell sequencing has the potential to revolutionize personalized medicine. In cancer therapy it enables an effective characterization of the complete heterogeneity within the tumor. A governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. We propose Ikarus, a machine learning pipeline aimed at solving a perceived simple problem, distinguishing tumor cells from normal cells at the single cell level. Automatic characterization of tumor cells is a critical limiting step for a multitude of research, clinical, and commercial applications. Automatic characterization of tumor cells would expedite neoantigen prediction, automatic characterization of tumor cell states, it would greatly facilitate cancer biomarker discovery. Such a tool can be used for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

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