Measuring expression heterogeneity of single-cell cytoskeletal protein complexes

AbstractMultimeric cytoskeletal protein complexes, including filamentous F-actin, orchestrate normal cellular function. However, protein-complex distributions in stressed, heterogeneous cell populations remain unknown. Cell staining and proximity-based methods have limited selectivity and/or sensitivity for robust endogenous multimeric protein-complex quantification from single cells. We introduce micro-arrayed differential detergent fractionation to simultaneously detect protein complexes in 100s of individual cells. Fractionation occurs by 60s size-exclusion electrophoresis with protein complex-stabilizing buffer that minimizes depolymerization. Validating with actin-destabilizing Latrunculin A (LatA), we quantify 2.7-fold lower median F-actin complex-levels in LatA-treated single cells. Further clustering analysis of U2OS cells treated with LatA detects a subpopulation (∼11%) exhibiting downregulated F-actin, but upregulated microtubule and intermediate filament protein complexes. Thus, some cells upregulate other cytoskeletal complexes to counteract the stress of LatA treatment. We also sought to understand the effect of non-chemical stress on cellular heterogeneity of F-actin, and find heat shock dysregulates F and monomeric G-actin correlation. The assay overcomes selectivity limitations of existing methods to biochemically quantify single-cell protein complexes perturbed with diverse stimuli.

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SIFTER: Electrophoretic complex fractionation protocol

10.21203/rs.3.pex-1582/v1 ◽

2021 ◽

Author(s):

Julea Vlassakis ◽

Louise L Hansen ◽

Amy E Herr

Keyword(s):

Single Cell ◽

Protein Interaction ◽

Gel Electrophoresis ◽

Protein Complexes ◽

Single Cells ◽

Polyacrylamide Gel Electrophoresis ◽

Simultaneous Detection ◽

Single Cell Protein ◽

Cell Protein ◽

A Cell

Abstract We introduce micro-arrayed, differential detergent fractionation for the simultaneous detection of protein complexes in 100s of individual cells with SIFTER (Single-cell protein Interaction Fractionation Through Electrophoresis and immunoassay Readout). Size-based fractionation of protein complexes is accomplished with five assay steps. First, a cell suspension generated by trypsinization is introduced onto a microwell array, and single cells are settled into the microwells by gravity. Cells are lysed in F-actin stabilization buffer that is delivered by a hydrogel lid. Second, the protein complexes are fractionated from the smaller monomers by polyacrylamide gel electrophoresis. Monomers are electrophoresed into the gel and are immobilized using a UV-induced covalent reaction to benzophenone. Third, a protein-complex depolymerization buffer is introduced by another hydrogel lid. Fourth, the recently depolymerized complexes are electrophoresed into a region of the gel separate from the immobilized monomers, where the complex fraction are in turn immobilized. Fifth, in-gel immunoprobing detects the immobilized populations of monomer and depolymerized complexes. These general steps are built on previously published protocols for bulk actin studies, single-cell western blotting, and bidirectional separations1-4.

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Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2

Genome Biology ◽

10.1186/s13059-021-02267-5 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 4

Author(s):

Harrison Specht ◽

Edward Emmott ◽

Aleksandra A. Petelski ◽

R. Gray Huffman ◽

David H. Perlman ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Single Cell Protein ◽

Cellular Heterogeneity ◽

Cell Protein ◽

Alternatively Activated Macrophages ◽

Monocytes And Macrophages ◽

Molecular Phenotypes ◽

Post Transcriptional Regulation

Abstract Background Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. Results To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Conclusions Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.

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Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity

10.1101/665307 ◽

2019 ◽

Cited By ~ 20

Author(s):

Harrison Specht ◽

Edward Emmott ◽

Aleksandra A. Petelski ◽

R. Gray Huffman ◽

David H. Perlman ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Single Cell Protein ◽

Cellular Heterogeneity ◽

Cell Protein ◽

Alternatively Activated Macrophages ◽

Monocytes And Macrophages ◽

Molecular Phenotypes ◽

Post Transcriptional Regulation

AbstractMacrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of limitations of quantitative single-cell protein analysis. To overcome this limitation, we developed SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 3,042 proteins in 1,490 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. This gradient correlates to the inflammatory axis of classically and alternatively activated macrophages. Parallel measurements of transcripts by 10x Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus SCoPE2 supports quantification with improved count statistics. The joint distributions of proteins and transcripts allowed exploring regulatory interactions, such as between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. Our methodology lays the foundation for quantitative single-cell analysis of proteins by mass-spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.Abstract Figure

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An ultra-sensitive and easy-to-use multiplexed single-cell proteomic analysis

10.1101/2022.01.02.474723 ◽

2022 ◽

Author(s):

Chen Li ◽

Lei Gu ◽

Zi Yi Li ◽

Qin Qin Wang ◽

Hui Ping Zhang ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Single Cells ◽

Cell Activity ◽

Cellular Heterogeneity ◽

Batch Experiment ◽

Cell Protein ◽

Data Quality Control ◽

High Throughput Analysis ◽

Average Cell

Proteins analysis from an average cell population often overlooks the cellular heterogeneity of expressed effector molecules, and knowledge about the regulations of key biological processes may remain obscure. Therefore, the necessity of single-cell proteomics (SCP) technologies arises. Without microfluidic chip, expensive ultrasonic equipment, or reformed liquid chromatogram (LC) system, we established an Ultra-sensitive and Easy-to-use multiplexed Single-Cell Proteomic workflow (UE-SCP). Specifically, the flexible sorting system ensured outstanding cell activity, high accuracy, remarkable efficiency, and robustness during single-cell isolation. Multiplex isobaric labeling realized the high-throughput analysis in trapped ion mobility spectrometry coupled with quadrupole time-of-flight mass spectrometry (timsTOF MS). Using this pipeline, we achieved single-cell protein quantities to a depth of over 2,000 protein groups in two human cell lines, Hela and HEK-293T. A small batch experiment can identify and quantify more than 3200 protein groups in 32 single cells, while a large batch experiment can identify and quantify about 4000 protein groups in 96 single cells. All the 128 single cells from different cell lines could been unsupervised clustered based on their proteomes. After the integration of data quality control, data cleaning, and data analysis, we are confident that our UE-SCP platform will be easy-to-marketing popularization and will promote biological applications of single-cell proteomics.

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Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

Genome Medicine ◽

10.1186/s13073-021-00885-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Sunny Z. Wu ◽

Daniel L. Roden ◽

Ghamdan Al-Eryani ◽

Nenad Bartonicek ◽

Kate Harvey ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cells ◽

Cellular Heterogeneity ◽

Tumour Heterogeneity ◽

Fresh Tissue ◽

Human Cancers ◽

Cryopreserved Cell ◽

Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

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Direct correlation between motile behavior and protein abundance in single cells

10.1101/067918 ◽

2016 ◽

Author(s):

Yann S Dufour ◽

Sébastien Gillet ◽

Nicholas W Frankel ◽

Douglas B Weibel ◽

Thierry Emonet

Keyword(s):

Protein Expression ◽

Single Cell ◽

Phenotypic Diversity ◽

Single Cells ◽

Selective Advantage ◽

Single Cell Protein ◽

Cell Protein ◽

Chemotaxis Proteins

AbstractUnderstanding how stochastic molecular fluctuations affect cell behavior requires the quantification of both behavior and protein numbers in the same cells. Here, we combine automated microscopy with in situ hydrogel polymerization to measure single-cell protein expression after tracking swimming behavior. We characterized the distribution of non-genetic phenotypic diversity in Escherichia coli motility, which affects single-cell exploration. By expressing fluorescently tagged chemotaxis proteins (CheR and CheB) at different levels, we quantitatively mapped motile phenotype (tumble bias) to protein numbers using thousands of single-cell measurements. Our results disagreed with established models until we incorporated the role of CheB in receptor deamidation and the slow fluctuations in receptor methylation. Beyond refining models, our central finding is that changes in numbers of CheR and CheB affect the population mean tumble bias and its variance independently. Therefore, it is possible to adjust the degree of phenotypic diversity of a population by adjusting the global level of expression of CheR and CheB while keeping their ratio constant, which, as shown in previous studies, confers functional robustness to the system. Since genetic control of protein expression is heritable, our results suggest that non-genetic diversity in motile behavior is selectable, supporting earlier hypotheses that such diversity confers a selective advantage.

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Exploring and analysing single cell multi-omics data with VDJView

10.21203/rs.2.14949/v2 ◽

2019 ◽

Author(s):

Jerome Samir ◽

Simone Rizzetto ◽

Money Gupta ◽

Fabio Luciani

Keyword(s):

Gene Expression ◽

T Cells ◽

B Cells ◽

Hypothesis Testing ◽

Single Cell ◽

Single Cells ◽

Cellular Heterogeneity ◽

Specific Gene ◽

T And B Cells ◽

Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

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NOMA: a high-throughput microchip for robust sequential measurements of secretions from the same single-cells

10.1101/2021.01.21.427049 ◽

2021 ◽

Author(s):

Meimei Liu ◽

Yahui Ji ◽

Fengjiao Zhu ◽

Xue Bai ◽

Linmei Li ◽

...

Keyword(s):

Single Cell ◽

Protein Secretion ◽

Single Cells ◽

Extended Period ◽

Extracellular Vesicle ◽

Single Cell Protein ◽

Cell Protein ◽

Cell Secretion ◽

Suspension Cells ◽

Sequential Measurements

AbstractDespite advances in single-cell secretion analysis technologies, lacking simple methods to reliably keep the live single-cells traceable for longitudinal detection poses a significant obstacle. Here we developed the high-density NOMA (narrow-opening microwell array) microchip that realized the retention of ≥97% of trapped single cells during repetitive detection procedures, regardless of adherent or suspension cells. We demonstrated its use to decode the correlation of protein abundance between secreted extracellular vesicles (EVs) and its donor cells at the same single-cell level, in which we found that these two were poorly correlated with each other. We further applied it in monitoring single-cell protein secretions sequentially from the same single cells. Notably, we observed the digital protein secretion patterns dominate the protein secretion. We also applied the microchip for longitudinally tracking of the single-cell integrative secretions over days, which revealed the presence of “super secretors” within the cell population that could be more persistent to secrete protein or extracellular vesicle for an extended period. The NOMA platform reported here is simple, robust, and easy to operate for realizing sequential measurements from the same single cells, representing a novel and informative tool to inspire new observations in biomedical research.

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Exploring and analysing single cell multi-omics data with VDJView

10.21203/rs.2.14949/v3 ◽

2020 ◽

Author(s):

Jerome Samir ◽

Simone Rizzetto ◽

Money Gupta ◽

Fabio Luciani

Keyword(s):

Gene Expression ◽

T Cells ◽

B Cells ◽

Hypothesis Testing ◽

Single Cell ◽

Single Cells ◽

Cellular Heterogeneity ◽

Specific Gene ◽

T And B Cells ◽

Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

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