scholarly journals Multiplexed live visualization of cell fate dynamics in hPSCs at single-cell resolution

2021 ◽  
Author(s):  
Sungmin Kim ◽  
Edward Ren ◽  
Paola Marco Casanova ◽  
Eugenia Piddini ◽  
Rafael Carazo Salas
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Spatial Location ◽  
Time Lapse ◽  
Strongly Coupled ◽  
Pluripotency Marker ◽  
Tightly Coupled ◽  
Differentiation Marker

ABSTRACTLive imaging can provide powerful insights into developmental and cellular processes but availability of multiplexable reporters has been limiting. Here we describe ORACLE, a cell fate reporter class in which fluorescent proteins fused with the nucleoporin POM121 are driven by promoters of transcription factors of interest. ORACLE’s nuclear rim localisation therefore enables multiplexing with conventional nuclear reporters. We applied ORACLE to investigate the dynamics of pluripotency exit at single-cell level, using human pluripotent stem cells (hPSCs) imaged by multi-day time-lapse high-content microscopy. Using an ORACLE-OCT4 pluripotency marker we reveal that G1 phase length and OCT4 level are strongly coupled and that spatial location in a colony impacts the timing of pluripotency exit. Combining ORACLE-OCT4 and an ORACLE-SOX1 early neuronal differentiation marker, we visualize in real-time the dynamics of cell fate transition between pluripotency and early neural fate, and show that pluripotency exit and differentiation onset are likely not tightly coupled in single-cells. Thus ORACLE is a powerful tool to enable quantitative studies of spatiotemporal cell fate control.

scholarly journals Single cell protein analysis for systems biology

2018 ◽  
Vol 62 (4) ◽  
pp. 595-605 ◽  
Author(s):  
Ezra Levy ◽  
Nikolai Slavov
Keyword(s):  
Systems Biology ◽  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Protein Analysis ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Protein Levels ◽  
Trade Offs

The cellular abundance of proteins can vary even between isogenic single cells. This variability between single-cell protein levels can have regulatory roles, such as controlling cell fate during apoptosis induction or the proliferation/quiescence decision. Here, we review examples connecting protein levels and their dynamics in single cells to cellular functions. Such findings were made possible by the introduction of antibodies, and subsequently fluorescent proteins, for tracking protein levels in single cells. However, in heterogeneous cell populations, such as tumors or differentiating stem cells, cellular decisions are controlled by hundreds, even thousands of proteins acting in concert. Characterizing such complex systems demands measurements of thousands of proteins across thousands of single cells. This demand has inspired the development of new methods for single-cell protein analysis, and we discuss their trade-offs, with an emphasis on their specificity and coverage. We finish by highlighting the potential of emerging mass-spec methods to enable systems-level measurement of single-cell proteomes with unprecedented coverage and specificity. Combining such methods with methods for quantitating the transcriptomes and metabolomes of single cells will provide essential data for advancing quantitative systems biology.

scholarly journals Single cell protein analysis for systems biology

2018 ◽  
Author(s):  
Ezra Levy ◽  
Nikolai Slavov
Keyword(s):  
Systems Biology ◽  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Protein Analysis ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Protein Levels ◽  
Trade Offs

The cellular abundance of proteins can vary even between isogenic single cells. This variability between single-cell protein levels can have functional roles, such as controlling cell fate during apoptosis induction or the proliferation/quiescence decision. Here, we review such examples of connecting protein levels and their dynamics in single cells to cellular functions. Such findings were made possible by the introduction of antibodies, and subsequently fluorescent proteins, for tracking protein levels in single cells. However, in heterogeneous cell populations, such as tumors or differentiating stem cells, cellular decisions are controlled by hundreds, even thousands of proteins acting in concert. Characterizing such complex systems demands measurements of thousands of proteins across thousands of single cells. This demand has inspired the development of new methods for single cell protein analysis, and we discuss their trade-offs, with emphasis on their specificity and coverage. We finish by highlighting the potential of emerging mass-spec methods to enable systems-level measurement of single-cell proteomes with unprecedented coverage and specificity. Combining such methods with methods for quantifying the trasncriptomes and metabolomes of single cells will provide essential data for advancing quantitative systems biology.

scholarly journals Single cell protein analysis for systems biology

2018 ◽  
Author(s):  
Ezra Levy ◽  
Nikolai Slavov
Keyword(s):  
Systems Biology ◽  
Single Cell ◽  
Cell Fate ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Protein Analysis ◽  
Single Cell Protein ◽  
Cell Protein ◽  
Protein Levels ◽  
Trade Offs

The cellular abundance of proteins can vary even between isogenic single cells. This variability between single-cell protein levels can have functional roles, such as controlling cell fate during apoptosis induction or the proliferation/quiescence decision. Here, we review such examples of connecting protein levels and their dynamics in single cells to cellular functions. Such findings were made possible by the introduction of antibodies, and subsequently fluorescent proteins, for tracking protein levels in single cells. However, in heterogeneous cell populations, such as tumors or differentiating stem cells, cellular decisions are controlled by hundreds, even thousands of proteins acting in concert. Characterizing such complex systems demands measurements of thousands of proteins across thousands of single cells. This demand has inspired the development of new methods for single cell protein analysis, and we discuss their trade-offs, with emphasis on their specificity and coverage. We finish by highlighting the potential of emerging mass-spec methods to enable systems-level measurement of single-cell proteomes with unprecedented coverage and specificity. Combining such methods with methods for quantifying the trasncriptomes and metabolomes of single cells will provide essential data for advancing quantitative systems biology.

scholarly journals Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy

2014 ◽  
Vol 25 (22) ◽  
pp. 3699-3708 ◽  
Author(s):  
Anyimilehidi Mazo-Vargas ◽  
Heungwon Park ◽  
Mert Aydin ◽  
Nicolas E. Buchler
Keyword(s):  
Gene Expression ◽  
Fluorescent Proteins ◽  
Single Cells ◽  
Time Lapse ◽  
Photon Flux ◽  
Luminescence Microscopy ◽  
Cell Cycle Genes ◽  
Light Excitation ◽  
Better Than

Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15–20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals Label free, quantitative single-cell fate tracking of time-lapse movies

MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 2468-2475 ◽  
Author(s):  
C. Elizabeth Caldon ◽  
Andrew Burgess
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Time Lapse ◽  
Label Free ◽  
Cell Fate Tracking

scholarly journals In vivo single cell analysis reveals Gata2 dynamics in cells transitioning to hematopoietic fate

2017 ◽  
Vol 215 (1) ◽  
pp. 233-248 ◽  
Author(s):  
Christina Eich ◽  
Jochen Arlt ◽  
Chris S. Vink ◽  
Parham Solaimani Kartalaei ◽  
Polynikis Kaimakis ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Time Lapse ◽  
Hematopoietic Stem ◽  
And Function ◽  
In Cells

Cell fate is established through coordinated gene expression programs in individual cells. Regulatory networks that include the Gata2 transcription factor play central roles in hematopoietic fate establishment. Although Gata2 is essential to the embryonic development and function of hematopoietic stem cells that form the adult hierarchy, little is known about the in vivo expression dynamics of Gata2 in single cells. Here, we examine Gata2 expression in single aortic cells as they establish hematopoietic fate in Gata2Venus mouse embryos. Time-lapse imaging reveals rapid pulsatile level changes in Gata2 reporter expression in cells undergoing endothelial-to-hematopoietic transition. Moreover, Gata2 reporter pulsatile expression is dramatically altered in Gata2+/− aortic cells, which undergo fewer transitions and are reduced in hematopoietic potential. Our novel finding of dynamic pulsatile expression of Gata2 suggests a highly unstable genetic state in single cells concomitant with their transition to hematopoietic fate. This reinforces the notion that threshold levels of Gata2 influence fate establishment and has implications for transcription factor–related hematologic dysfunctions.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals Cell-to-cell diversification in ERBB-RAS-MAPK signal transduction that produces cell-type specific growth factor responses

2019 ◽  
Author(s):  
Hiraku Miyagi ◽  
Michio Hiroshima ◽  
Yasushi Sako
Keyword(s):  
Growth Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Bet Hedging ◽  
Cell Type ◽  
Cell Responses ◽  
Single Cell Responses ◽  
Specific Growth Factor ◽  
A Cell

AbstractGrowth factors regulate cell fates, including their proliferation, differentiation, survival, and death, according to the cell type. Even when the response to a specific growth factor is deterministic for collective cell behavior, significant levels of fluctuation are often observed between single cells. Statistical analyses of single-cell responses provide insights into the mechanism of cell fate decisions but very little is known about the distributions of the internal states of cells responding to growth factors. Using multi-color immunofluorescent staining, we have here detected the phosphorylation of seven elements in the early response of the ERBB–RAS–MAPK system to two growth factors. Among these seven elements, five were analyzed simultaneously in distinct combinations in the same single cells. Although principle component analysis suggested cell-type and input specific phosphorylation patterns, cell-to-cell fluctuation was large. Mutual information analysis suggested that cells use multitrack (bush-like) signal transduction pathways under conditions in which clear cell fate changes have been reported. The clustering of single-cell response patterns indicated that the fate change in a cell population correlates with the large entropy of the response, suggesting a bet-hedging strategy is used in decision making. A comparison of true and randomized datasets further indicated that this large variation is not produced by simple reaction noise, but is defined by the properties of the signal-processing network.Author SummaryHow extracellular signals, such as growth factors (GFs), induce fate changes in biological cells is still not fully understood. Some GFs induce cell proliferation and others induce differentiation by stimulating a common reaction network. Although the response to each GF is reproducible for a cell population, not all single cells respond similarly. The question that arises is whether a certain GF conducts all the responding cells in the same direction during a fate change, or if it initially stimulates a variety of behaviors among single cells, from which the cells that move in the appropriate direction are later selected. Our current statistical analysis of single-cell responses suggests that the latter process, which is called a bet-hedging mechanism is plausible. The complex pathways of signal transmission seem to be responsible for this bet-hedging.

scholarly journals Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Morphological Changes ◽  
Gene Expression Pattern ◽  
Time Lapse ◽  
Dynamic Nature ◽  
Transcriptional Changes ◽  
Fate Decision

AbstractIndividual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

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