Ultra-sensitive digital quantification of proteins and mRNA in single cells

Lin, Jing; Jordi, Claudio; Son, Minjun; Phan, Hoang Van; Drayman, Nir; Abasıyanık, M. Fatih; Vistain, Luke; Tu, Hsiung‐Lin; Tay, Savaş

doi:10.1038/s41467-019-11531-z

Cited by 49 publications

(57 citation statements)

References 31 publications

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“…As the expression of cellular marker proteins, such as cytokeratin, was estimated to be approximately 10 5 molecules/cells, i.e. 1.3 × 10 3 molecules/µm 2 (cell diameter = 10 µm), the proposed system was predicted to have sufficient sensitivity to detect CTCs [29, 30]. To confirm this sensitivity, cancer cells with immuno‐stained cytokeratin, were visualized by the new system.…”

Section: Resultsmentioning

confidence: 99%

Performance evaluation of a high‐throughput separation system for circulating tumor cells based on microcavity array

Negishi

Saito

Iwata

et al. 2020

Engineering in Life Sciences

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Circulating tumor cells (CTCs) are widely known as useful biomarkers in the liquid biopsies of cancer patients. Although single‐cell genetic analysis of CTCs is a promising diagnostic tool that can provide detailed clinical information for precision medicine, the capacity of single‐CTC isolation for genetic analysis requires improvement. To overcome this problem, we previously developed a multiple single‐cell encapsulation system for CTCs using hydrogel‐encapsulation, which allowed for the high‐throughput isolation of single CTCs. However, isolation of a single cell from adjacent cells remained difficult and often resulted in contamination by neighboring cells due to the limited resolution of the generated hydrogel. We developed a novel multiple single‐cell encapsulation system equipped with a high magnification lens for high throughput and a more accurate single‐cell encapsulation. The multiple single‐cell encapsulation system has sufficient sensitivity to detect immune‐stained CTCs, and could also generate a micro‐scaled hydrogel that can isolate a single cell from adjacent cells within 10 µm, with high efficiency. The proposed system enables high throughput and accurate single‐cell manipulation and genome amplification without contamination from neighboring cells.

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Section: Resultsmentioning

confidence: 99%

Performance evaluation of a high‐throughput separation system for circulating tumor cells based on microcavity array

Negishi

Saito

Iwata

et al. 2020

Engineering in Life Sciences

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“…Collectively, these data suggest that human melanoma cells have an increased expression of IL12RB2 relative to IL12RB1 that is different than observed in immune cells that canonically respond to IL-12, such as NK cells, CD4+ T cells, and CD8+ T cells and qualitatively mirrors what we observed with mouse cell lines. Emerging proteogenomic methods (e.g., [36,37]) that can simultaneously measure protein and mRNA abundance at the single cell level would help strengthen the clinical relevance of these observations.…”

Section: Resultsmentioning

confidence: 99%

Interleukin-12 elicits a non-canonical response in B16 melanoma cells to enhance survival

et al. 2020

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Background: Oncogenesis rewires signaling networks to confer a fitness advantage to malignant cells. For instance, the B16F0 melanoma cell model creates a cytokine sink for Interleukin-12 (IL-12) to deprive neighboring cells of this important anti-tumor immune signal. While a cytokine sink provides an indirect fitness advantage, does IL-12 provide an intrinsic advantage to B16F0 cells? Methods: Acute in vitro viability assays were used to compare the cytotoxic effect of imatinib on a melanoma cell line of spontaneous origin (B16F0) with a normal melanocyte cell line (Melan-A) in the presence of IL-12. The results were analyzed using a mathematical model coupled with a Markov Chain Monte Carlo approach to obtain a posterior distribution in the parameters that quantified the biological effect of imatinib and IL-12. Intracellular signaling responses to IL-12 were compared using flow cytometry in 2D6 cells, a cell model for canonical signaling, and B16F0 cells, where potential non-canonical signaling occurs. Bayes Factors were used to select among competing signaling mechanisms that were formulated as mathematical models. Analysis of single cell RNAseq data from human melanoma patients was used to explore generalizability. Results: Functionally, IL-12 enhanced the survival of B16F0 cells but not normal Melan-A melanocytes that were challenged with a cytotoxic agent. Interestingly, the ratio of IL-12 receptor components (IL12RB2:IL12RB1) was increased in B16F0 cells. A similar pattern was observed in human melanoma. To identify a mechanism, we assayed the phosphorylation of proteins involved in canonical IL-12 signaling, STAT4, and cell survival, Akt. In contrast to T cells that exhibited a canonical response to IL-12 by phosphorylating STAT4, IL-12 stimulation of B16F0 cells predominantly phosphorylated Akt. Mechanistically, the differential response in B16F0 cells is explained by both ligand-dependent and ligand-independent aspects to initiate PI3K-AKT signaling upon IL12RB2 homodimerization. Namely, IL-12 promotes IL12RB2 homodimerization with low affinity and IL12RB2 overexpression promotes homodimerization via molecular crowding on the plasma membrane.

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“…Circuit characteristics are often studied by single-cell measurements using fluorescent markers on few proteins [ 11 ]. These traditional measurements—with the exception of a few emerging technologies that simultaneously monitor transcription and translation [ 12 , 13 ]—cannot visualize many other underlying species that are involved in dictating gene expression dynamics. For example, nucleic acids, protein-complexes, protein-nucleic acid complexes remain invisible.…”

Section: Introductionmentioning

confidence: 99%

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks

Firman

Huihui

Clark

et al. 2021

Entropy

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Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time—recorded at frequent intervals—over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data.

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Ultra-sensitive digital quantification of proteins and mRNA in single cells

Cited by 49 publications

References 31 publications

Performance evaluation of a high‐throughput separation system for circulating tumor cells based on microcavity array

Performance evaluation of a high‐throughput separation system for circulating tumor cells based on microcavity array

Interleukin-12 elicits a non-canonical response in B16 melanoma cells to enhance survival

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks

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