Single cell proteomics in biomedicine: High‐dimensional data acquisition, visualization, and analysis

Su, Yapeng; Shi, Qi; Wei, Wei

doi:10.1002/pmic.201600267

Cited by 84 publications

(57 citation statements)

References 97 publications

(148 reference statements)

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“…There are a variety of statistical tools available for inferring subpopulations and subpopulation specific markers from single-cell protein data including SPADE, Phenograph, and Wishbone [92]. For example, histoCAT is a new powerful integrative method used to integrate single-cell CyTOF measurement with image-based spatial information to detect spatial and phenotype interaction at the cellular level [93].…”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

Ortega

Poirion

Zhu

et al. 2017

Clinical & Translational Med

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It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.

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Section: Experimental and Computational Methodsmentioning

confidence: 99%

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

Ortega

Poirion

Zhu

et al. 2017

Clinical & Translational Med

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“…These approaches include protein level analysis by western blot, 24 cytokine capture with barcoded antibodies, 25 metabolite profiling through mass spectrometry, 26 and Assay for Transposase-Accessible Chromatin using sequencing. These approaches include protein level analysis by western blot, 24 cytokine capture with barcoded antibodies, 25 metabolite profiling through mass spectrometry, 26 and Assay for Transposase-Accessible Chromatin using sequencing.…”

Section: Additional Single Cell Techniquesmentioning

confidence: 99%

“…In addition to the above, other techniques have been adapted to single-cell analysis. These approaches include protein level analysis by western blot, 24 cytokine capture with barcoded antibodies, 25 metabolite profiling through mass spectrometry, 26 and Assay for Transposase-Accessible Chromatin using sequencing. 27 Imaging flow cytometry is an innovation in microscopy that links darkfield images of cells with parameters typically identified by FACS, providing a single-cell approach to study cellular signaling and other processes affected by subcellular localization of proteins.…”

Section: Additional Single Cell Techniquesmentioning

confidence: 99%

Single cell immune profiling in transplantation research

Higdon

Schaffert

Khatri

2019

American Journal of Transplantation

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Recently developed single-cell profiling technologies hold promise to provide new insights including analysis of population heterogeneity and linkage of antigen receptors with gene expression. These technologies produce complex data sets that require knowledge of bioinformatics for appropriate analysis. In this minireview, we discuss several single-cell immune profiling technologies for gene and protein expression, including cytometry by time-of-flight, RNA sequencing, and antigen receptor sequencing, as well as key considerations for analysis that apply to each. Because of the critical importance of data analysis for high parameter single cell analysis, we discuss essential factors in analysis of these data, including quality control, quantification, examples of methods for high dimensional analysis, immune repertoire analysis, and preparation of analysis pipelines. We provide examples of, and suggestions for, application of these innovative methods to transplantation research. K E Y W O R D Sbasic (laboratory)

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“…A critical point analysis of the single-cell data in different regions of the FLOW-MAP can provide validation of this picture, and can also help identify the tipping points at which those cell state changes take place. 51,63,64 Furthermore, network analysis of those tipping points can be used to identify key regulators that drive the transition from drug-naïve to drug-tolerant 12,51,58,63,65,66 .…”

Section: Critical Point Analysis Identifies Central Regulators Along mentioning

confidence: 99%

Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line

et al. 2019

Preprint

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The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge, with relevance towards understanding biological changes ranging from cellular differentiation to epigenetic (adaptive) responses of diseased cells to drugging. We report on a combined experimental and theoretic method for determining the trajectories that specific highly plastic BRAF V600E mutant patient-derived melanoma cancer cells take between drug-naïve and drug-tolerant states. Recent studies have implicated non-genetic, fast-acting resistance mechanisms are activated in these cells following BRAF inhibition. While single-cell highly multiplex omics tools can yield snapshots of the cell state space landscape sampled at any given time point, individual cell trajectories must be inferred from a kinetic series of snapshots, and that inference can be confounded by stochastic cell state switching. Using a microfludic-based single-cell integrated proteomic and metabolic assay, we assayed for a panel of signaling, phenotypic, and metabolic regulators at four time points during the first five days of drug treatment. Dimensional reduction of the resultant data set, coupled with information theoretic analysis, uncovered a complex cell state landscape and identified two distinct paths connecting drug-naïve and drugtolerant states. Cells are shown to exclusively traverse one of the two pathways depending on the level of the lineage restricted transcription factor MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities, and are independently druggable. Our results update the paradigm of adaptive resistance development in an isogenic cell population and offer insight into the design of more effective combination therapies.

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Single cell proteomics in biomedicine: High‐dimensional data acquisition, visualization, and analysis

Cited by 84 publications

References 97 publications

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

Single cell immune profiling in transplantation research

Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line

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