Biology subjects

Qian, Y.

Publications and source records attributed to Qian, Y..

12 recordsLinked to original sources

bioRxiv2018

Multi-Center Study of Resectable Lung Lesions by Ultra-Deep Sequencing of Targeted Genes in Plasma Cell-Free DNA to Assess Nodule Malignancy and Detect Lung Cancers

BACKGROUNDEarly detection of lung cancer to allow curative treatment remains challenging. Cell-free circulating tumor DNA (ctDNA) analysis may aid in malignancy assessment and early cancer diagnosis of lung nodules found in screening imagery.\n\nMETHODSThe multi-center clinical study enrolled 192 patients with operable occupying lung diseases. Plasma ctDNA, white blood cell genomic DNA (gDNA) and tumor tissue gDNA of each patient were analyzed by ultra-deep sequencing to an average of 35,000X of the coding regions of 65 lung cancer-related genes.\n\nRESULTSThe cohort consists of a quarter of benign lung diseases and three quarters of cancer patients with all histopathology subtypes. 64% of the cancer patients is at Stage I. Gene mutations detection in tissue gDNA and plasma ctDNA results in a sensitivity of 91% and specificity of 88%. When ctDNA assay was used as the test, the sensitivity was 69% and specificity 96%. As for the lung cancer patients, the assay detected 63%, 83%, 94% and 100%, for Stage I, II, III and IV, respectively. In a linear discriminant analysis, combination of ctDNA, patient age and a panel of serum biomarkers boosted the overall sensitivity to 80% at a specificity of 99%. 29 out of the 65 genes harbored mutations in the lung cancer patients with the largest number found in TP53 (30% plasma and 62% tumor tissue samples) and EGFR (20% and 40%, respectively).\n\nCONCLUSIONPlasma ctDNA was analyzed in lung nodule assessment and early cancer detection while an algorithm combining clinical information enhanced the test performance.

clinical trials↗

bioRxiv2018

Understanding the Ca2+-dependent Fluorescence Change in Red Genetically Encoded Ca2+ Indicators

Genetically encoded Ca2+ indicators (GECIs) are widely used to illuminate dynamic Ca2+ signaling activity in living cells and tissues. Various fluorescence colors of GECIs are available, including red. Red GECIs are promising because longer wavelengths of light scatter less in tissue, making it possible to image deeper. They are engineered from a circularly permuted red fluorescent protein fused to a Ca2+ sensing domain, calmodulin and a calmodulin-binding peptide. A conformational change in the sensing domain upon binding Ca2+ causes a change in the fluorescence intensity of the fluorescent protein. Three factors could contribute to this change in fluorescence: 1) a shift in the protonation state of the chromophore, 2) a change in fluorescence quantum yield, and 3) a change in the extinction coefficient for one-photon excitation or the two-photon cross section for two-photon excitation. We conducted a systematic study of the photophysical properties of a select cohort of red GECIs in their Ca2+-free and Ca2+-saturated states to determine which factors are most important for the Ca2+-dependent change in fluorescence. In total, we analyzed nine red GECIs, including jRGECO1a, K-GECO1, jRCaMP1a, R-GECO1, R-GECO1.2, CAR-GECO1, O-GECO1, REX-GECO1, and a new variant termed jREX-GECO1. We found that these red GECIs could be separated into three classes that each rely on a particular set of factors. Furthermore, in some cases the magnitude of the change in fluorescence was different depending on one-photon excitation or two-photon excitation by up to a factor of two.

biophysics↗

bioRxiv2018

Bayesian Trees for Automated Cytometry Data Analysis

Cytometry is an important single cell analysis technology in furthering our understanding of cellular biological processes and in supporting clinical diagnoses across a variety hematological and immunological conditions. Current data analysis workflows for cytometry data rely on a manual process called gating to classify cells into canonical types. This dependence on human annotation significantly limits the rate, reproducibility, and scope of cytometrys use in both biological research and clinical practice. We develop a novel Bayesian approach for automated gating that classifies cells into different types by combining cell-level marker measurements with an informative prior. The Bayesian approach allows for the incorporation of biologically-meaningful prior information that captures the domain expertise of human experts. The inference algorithm results in a hierarchically-structured classification of individual cells in a manner that mimics the tree-structured recursive process of manual gating, making the results readily interpretable. The approach can be extended in a natural fashion to handle data from multiple different samples by the incorporation of random effects in the Bayesian model. The proposed approach is evaluated using mass cytometry data, on the problems of unsupervised cell classification and supervised clinical diagnosis, illustrating the benefits of both incorporating prior knowledge and sharing information across multiple samples.

bioinformatics↗

bioRxiv2018

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A genes expression level is not only determined by the intended regulators, but also largely dictated by changes in ribosome availability imparted by activation or repression of other genes. To address this problem, we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOIs mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOIs translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.

synthetic biology↗

bioRxiv2018

A Model for Resource Competition in CRISPR-Mediated Gene Repression

CRISPR-mediated gene regulation is known for its ability to control multiple targets simultaneously due to its modular nature: the same dCas9 effector can target different genes simply by changing the associated gRNA. However, multiplexing requires the sharing of limited amounts of dCas9 proteins among multiple gRNAs, leading to resource competition. In turn, competition between gRNAs for the same resource may hamper network function. In this work, we develop a general model that takes into account the sharing of a limited amount of dCas9 protein for arbitrary CRISPR-mediated gene repression networks. We demonstrate that, as a result of resource competition, hidden interactions appear, that modify the intended network regulations. As a case study, we analyze the effects of these hidden interactions in repression cascades. In particular, we illustrate that perfect adaptation to resource fluctuations can be achieved in cascades with an even number of repressors. In contrast, cascades with an odd number of repressors are substantially impacted by resource competition.

synthetic biology↗

bioRxiv2018

Genetically encoded ratiometric indicators for potassium ion

Potassium ion (K+) homeostasis and dynamics play critical roles in regulating various biological activities, and the ability to monitor K+ spatial-temporal dynamics is critical to understanding these biological functions. Here we report the design and characterization of a Forster resonance energy transfer (FRET)-based genetically encoded K+ indicator, KIRIN1, constructed by inserting a bacterial cytosolic K+ binding protein (Kbp) between a fluorescent protein (FP) FRET pair, mCerulean3 and cp173Venus. Binding of K+ induces a conformational change in Kbp, resulting in an increase in FRET efficiency. KIRIN1 was able to detect K+ at physiologically relevant concentrations in vitro and is highly selective toward K+ over Na+. We further demonstrated that KIRIN1 allowed real-time imaging of pharmacologically induced depletion of cytosolic K+ in live cells, and KIRIN1 also enabled optical tracing of K+ efflux and reuptake in neurons upon glutamate stimulation in cultured primary neurons. These results demonstrate that KIRIN1 is a valuable tool to detect K+ in vitro and in live cells.

bioengineering↗

bioRxiv2017

Single-cell RNA-Seq Resolves Cellular Heterogeneity and Transcriptional Dynamics during Spermatogonia Stem Cells Establishment and Differentiation

The transition of gonocytes to spermatogonia and subsequent differentiation provide the foundation of spermatogenesis. However, systematic understanding on the cellular and molecular basis of this process is still limited, mainly impeded by the asynchrony in development and the lack of stage-specific markers. Using single-cell RNA sequencing on Oct4-GFP+/KIT- cells isolated from PND5.5 mice, we dissected the cellular heterogeneity and established molecular regulations. We demonstrated that gonocyte-spermatogonial transition was characterized by gene expression change related to apoptosis, cell cycle progression, and regulation of migration processes. Pseudotime analysis reconstructed developmental dynamics of the spermatogonial populations and unraveled sequential cellular and molecular transitions. We also identified CD87 as a neonatal stem cell marker which are potentially involved in the intial establishment of SSC pool. Lastly, we uncovered an unexpected subpopulation of spermatogonia primed to differentiation within the undifferentiated compartment, which is characterized by the lack of self-renewal genes and enhanced Oct4 expression and retinoic acid signaling response. Our study thus provides a novel understanding of cellular and molecular changes during spermatogonial establishment.

developmental biology↗

bioRxiv2017

A genetically encoded Ca2+ indicator based on circularly permutated sea anemone red fluorescent protein

Genetically-encoded calcium ion (Ca2+) indicators (GECIs) are indispensable tools for measuring Ca2+ dynamics and neuronal activities in vitro and in vivo. Red fluorescent protein (RFP)-based GECIs enable multicolor visualization with blue or cyan-excitable fluorophores and combined use with blue or cyan-excitable optogenetic actuators. Here we report the development, structure, and validation of a new red fluorescent Ca2+ indicator, K-GECO1, based on a circularly permutated RFP derived from the sea anemone Entacmaea quadricolor. We characterized the performance of K-GECO1 in cultured HeLa cells, dissociated neurons, stem cell derived cardiomyocytes, organotypic brain slices, zebrafish spinal cord in vivo, and mouse brain in vivo.

biophysics↗

bioRxiv2017

Genetic analysis of over one million people identifies 535 novel loci for blood pressure.

High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.

genetics↗

bioRxiv2017

DAFi: A Directed Recursive Filtering and Clustering Approach to Data-Driven Identification of Cell Populations from Polychromatic Flow Cytometry Data

Computational methods for identification of cell populations from high-dimensional flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. We found that combining recursive filtering and clustering with constraints converted from the user manual gating strategy can effectively identify overlapping and rare cell populations from smeared data that would have been difficult to resolve by either a single run of data clustering or manual segregation. We named this new method DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell-based biomarkers, but also makes the results interpretable to experimental scientists as in supervised classification through mapping and merging the high-dimensional data clusters into the user-defined 2D gating hierarchy. By recursive data filtering before clustering, DAFi can uncover small local clusters which are otherwise difficult to identify due to the statistical interference of the irrelevant major clusters. Quantitative assessment of cell type specific characteristics demonstrates that the population proportions calculated by DAFi, while being highly consistent with those by expert centralized manual gating, have smaller technical variance than those from individual manual gating analysis. Visual examination of the dot plots showed that the boundaries of the DAFi-identified cell populations followed the natural shapes of the data distributions. To further exemplify the utility of DAFi, we show that DAFi can incorporate the FLOCK clustering method to identify novel cell-based biomarkers. Implementation of DAFi supports options including clustering, bisecting, slope-based gating, and reversed filtering to meet various auto-gating needs from different scientific use cases.

bioinformatics↗

bioRxiv2017

Realizing "Integral Control" In Living Cells: How To Overcome Leaky Integration Due To Dilution?

A major problem in the design of synthetic genetic circuits is robustness to perturbations and uncertainty. Because of this, there have been significant efforts in recent years in finding approaches to implement integral control in genetic circuits. Integral controllers have the unique ability to make the output of a process adapt perfectly to disturbances. However, implementing an integral controller is challenging in living cells. This is because a key aspect of any integral controller is a \"memory\" element that stores the accumulation (integral) of the error between the output and its desired set-point. The ability to realize such a memory element in living cells is fundamentally challenged by the fact that all biomolecules dilute as cells grow, resulting in a \"leaky\" memory that gradually fades away. As a consequence, the adaptation property is lost. Here, we propose a general principle for designing integral controllers such that the performance is practically unaffected by dilution. In particular, we mathematically prove that if the reactions implementing the integral controller are all much faster than dilution, then the adaptation error due to integration leakiness becomes negligible. We exemplify this design principle with two synthetic genetic circuits aimed at reaching adaptation of gene expression to fluctuations in cellular resources. Our results provide concrete guidance on the biomolecular processes that are most appropriate for implementing integral controllers in living cells.

synthetic biology↗

bioRxiv2016

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuits behavior. Here, we create a library of genetic activation cascades in bacteria E. coli, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to non-regulatory interactions among genes that reshape circuits behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site (RBS) and DNA copy number to restore the cascades intended monotonically increasing response. Our results demonstrate how unintended circuits behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

synthetic biology↗