FISH-Flow, a protocol for the concurrent detection of mRNA and protein in single cells using fluorescence in situ hybridization and flow cytometry

Arrigucci, Riccardo; Bushkin, Yuri; Radford, Felix; Lakehal, Karim; Vir, Pooja; Pine, Richard; Martin, D. L.; Sugarman, Jeffrey; Zhao, Yanlin; Yap, George; Lardizabal, Alfred; Tyagi, Sanjay; Gennaro, Maria Laura

doi:10.1038/nprot.2017.039

Cited by 81 publications

(65 citation statements)

References 22 publications

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“…Additionally, imaging of the cells in the microtrap device on different z-planes demonstrated better spatial resolution of the hybridization probes to designate the detection of a genetic abnormality. This feature cannot be achieved in Flow-FISH analysis [29,30]. The microtrap device provided reduced processing time (18 → 3 h) and lower amounts of FISH probes compared to slide-based processing.…”

Section: Introductionmentioning

confidence: 99%

Microfluidic Device for On-Chip Immunophenotyping and Cytogenetic Analysis of Rare Biological Cells

Weerakoon-Ratnayake

Vaidyanathan

Larky

et al. 2020

Cells

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The role of circulating plasma cells (CPCs) and circulating leukemic cells (CLCs) as biomarkers for several blood cancers, such as multiple myeloma and leukemia, respectively, have recently been reported. These markers can be attractive due to the minimally invasive nature of their acquisition through a blood draw (i.e., liquid biopsy), negating the need for painful bone marrow biopsies. CPCs or CLCs can be used for cellular/molecular analyses as well, such as immunophenotyping or fluorescence in situ hybridization (FISH). FISH, which is typically carried out on slides involving complex workflows, becomes problematic when operating on CLCs or CPCs due to their relatively modest numbers. Here, we present a microfluidic device for characterizing CPCs and CLCs using immunofluorescence or FISH that have been enriched from peripheral blood using a different microfluidic device. The microfluidic possessed an array of cross-channels (2–4 µm in depth and width) that interconnected a series of input and output fluidic channels. Placing a cover plate over the device formed microtraps, the size of which was defined by the width and depth of the cross-channels. This microfluidic chip allowed for automation of immunofluorescence and FISH, requiring the use of small volumes of reagents, such as antibodies and probes, as compared to slide-based immunophenotyping and FISH. In addition, the device could secure FISH results in <4 h compared to 2–3 days for conventional FISH.

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Section: Introductionmentioning

confidence: 99%

Microfluidic Device for On-Chip Immunophenotyping and Cytogenetic Analysis of Rare Biological Cells

Weerakoon-Ratnayake

Vaidyanathan

Larky

et al. 2020

Cells

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“…In a previous proof-of-principle study we demonstrated that RNA flow cytometry, which allows for multi-parameter, concurrent analysis of mRNA and protein in the same cell ( 23 – 25 ), is applicable to the detection of antigen-specific T cell responses to M. tuberculosis antigens ( 26 ). Here, we utilized a semi-automated RNA flow cytometry platform ( 24 ) to determine whether a multi-parametric (mRNA and protein) assay for T cell memory phenotypes and cytokine production identifies differences between LTBI and active TB.…”

Section: Introductionmentioning

confidence: 99%

Active Tuberculosis Is Characterized by Highly Differentiated Effector Memory Th1 Cells

et al. 2018

Self Cite

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Despite advances in diagnosing latent Mycobacterium tuberculosis infection (LTBI), we still lack a diagnostic test that differentiates LTBI from active tuberculosis (TB) or predicts the risk of progression to active disease. One reason for the absence of such a test may be the failure of current assays to capture the dynamic complexities of the immune responses associated with various stages of TB, since these assays measure only a single parameter (release of IFN-γ) and rely on prolonged (overnight) T cell stimulation. We describe a novel, semi-automated RNA flow cytometry assay to determine whether immunological differences can be identified between LTBI and active TB. We analyzed antigen-induced expression of Th1 cytokine mRNA after short (2- and 6-h) stimulation with antigen, in the context of memory T cell immunophenotyping. IFNG and TNFA mRNA induction was detectable in CD4+ T cells after only 2 h of ex vivo stimulation. Moreover, IFNG- and TNFA-expressing CD4+ T cells (Th1 cells) were more frequent in active TB than in LTBI, a difference that is undetectable with conventional, protein-based cytokine assays. We also found that active TB was associated with higher ratios of effector memory to central memory Th1 cells than LTBI. This effector memory phenotype of active TB was associated with increased T cell differentiation, as defined by loss of the CD27 marker, but not with T cell exhaustion, as determined by PD-1 abundance. These results indicate that single-cell-based, mRNA measurements may help identify time-dependent, quantitative differences in T cell functional status between latent infection and active tuberculosis.

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“…3b). Considering that resolvability of labeled RNA was generally comparable between both FISH and HCR, these results suggest that HCR performs better than FISH for studies that require concurrent resolvability of both RNA and protein [26][27][28] . We found two potential examples of systematic bias in Hill parameters between RNA-based and protein-based measurements.…”

Section: Attribution Of Performance To Measurandmentioning

confidence: 82%

Comparison of Bias and Resolvability in Single-Cell and Single-Transcript Methods

Rammohan

Lund

Alperovich

et al. 2020

Preprint

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Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark a new method for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.

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FISH-Flow, a protocol for the concurrent detection of mRNA and protein in single cells using fluorescence in situ hybridization and flow cytometry

Cited by 81 publications

References 22 publications

Microfluidic Device for On-Chip Immunophenotyping and Cytogenetic Analysis of Rare Biological Cells

Microfluidic Device for On-Chip Immunophenotyping and Cytogenetic Analysis of Rare Biological Cells

Active Tuberculosis Is Characterized by Highly Differentiated Effector Memory Th1 Cells

Comparison of Bias and Resolvability in Single-Cell and Single-Transcript Methods

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