单分子纳米生物技术

生物分子需要相互协调组成各种分子机器,例如：分子马达、细胞信号处理器、DNA转录机、蛋白质合成器等,来实现他们负责的各种生理功能。一方面,这些分子机器的功能对于环境改变有着很强的适应性,这种适应性对于生物体和生物系统的稳定来说是必须的。另一方面,由于分子机器的大小只有几纳米并且结构具有可变性,它们的运转倾向于受到热扰动的影响具有随机性,此外,分子机器所利用的输入能量的水平与平均热运动能量kBT近似,分子机器可以高效地将热噪声的能量转化利用于行使其功能,对于这些能量的利用效率也很高,这与人造机器运转所需能量远远高于热运动能量相比有着十分显著的差别。因此,分子机器的潜在机理比人造机器复杂了许多。近些年来,随着单分子探测技术和纳米技术在生命科学的各个领域的广泛应用,使得生物分子的动力学性质,以及以往被隐藏在系综平均结果后的单个分子机器运转规律逐渐被揭示。我们研究的目标是希望通过揭示分子机器的独特运转规律进而能够了解、掌握具有适应性的生物系统中包含的工程学原理。本文综述了我们所作的一些关于分子马达、酶促反应、蛋白质动力学和细胞信号转导的单分子探测试验,并且讨论了热涨落（噪声）是怎样在具有独特的运转规律的生物分子机器中起到正效应,进而允许生物系统具有可变性和适应性的。     

A visual analytics approach for models of heterogeneous cell populations

In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.     

Single molecule research on surfaces: from analytics to construction and back

The study of single molecules opens a new dimension in understanding nature down to its finest ramifications. While much progress was achieved in the last decade concerning the detection techniques, suitable techniques for manipulating and handling the biomolecules still bear a challenge. Primarily, the task is keeping an individual, active molecule of a certain lifespan in the spot. Here, we will focus on techniques for the functional immobilization of (single) molecules on surfaces to enable their observation at one position over a time period. Presenting the main methods of reversible immobilization we will accentuate the chelator lipid concept as combining all features prerequisite for functional, reversible and well-defined immobilization. This will also show that single molecule research in principle is the synthesis of an insight into the function of nature and nano-biotechnology (manipulation): thus of analytics, construction, and back.     

Single cell proteomics for personalised medicine

Recently, multicolour FACS combined with phosphospecific antibodies has been developed, enabling the determination of the relative phosphorylation of signal transduction intermediates in individual cells. It has become clear that, when stimulated with cytokines, individual leukemia cells exhibit marked differences in phosphoprotein patterns and that these patterns correlate with disease outcome. Thus, single cell phosphoproteomic techniques might be superior to other proteomic approaches for the molecular diagnosis of disease and instrumental for the development of personalised medicine.     

Molecular seismology: an inverse problem in nanobiology

The density profile of an elastic fiber like DNA will change in space and time as ligands associate with it. This observation affords a new direction in single molecule studies provided that density profiles can be measured in space and time. In fact, this is precisely the objective of seismology, where the mathematics of inverse problems have been employed with success. We argue that inverse problems in elastic media can be directly applied to biophysical problems of fiber-ligand association, and demonstrate that robust algorithms exist to perform density reconstruction in the condensed phase.     

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.     

Single cell Raman spectroscopy for cell sorting and imaging

Single cell Raman spectroscopy (SCRS) is a non-invasive and label-free technology, allowing in vivo and multiple parameter analysis of individual living cells. A single cell Raman spectrum usually contains more than 1000 Raman bands which provide rich and intrinsic information of the cell (e.g. nucleic acids, protein, carbohydrates and lipids), reflecting cellular genotypes, phenotypes and physiological states. A Raman spectrum serves as a molecular 'fingerprint' of a single cell, making it possible to differentiate various cells including bacterial, protistan and animal cells without prior knowledge of the cells. However, a key drawback of SCRS is the fact that spontaneous Raman signals are naturally weak; this review discusses recent research progress in significantly enhancing and improving the signal of spontaneous Raman spectroscopy, including resonance Raman spectroscopy (RRS), coherent anti-Stokes Raman spectroscopy (CARS), stimulated Raman spectroscopy (SRS) and surface enhanced Raman scattering (SERS). This review focuses on the biotechnological development and the associated applications of SCRS, including Raman activated cell sorting (RACS) and Raman imaging and mapping.     

RFID analytics for hospital ward management

In this paper, we present an RFID-enabled platform for hospital ward management. Active RFID tags are attached to individuals and assets in the wards. Active RFID readers communicate with the tags continuously and automatically to keep track of the real-time information about the locations of the tagged objects. The data regarding the locations and other transmitted information are stored in the ward management system. This platform enables capabilities of real-time monitoring and tracking of individuals and assets, reporting of ward statistics, and providing intelligence and analytics for hospital ward management. All of these capabilities benefit hospital ward management by enhanced patient safety, increased operational efficiency and throughput, and mitigation of risk of infectious disease widespread. A prototype developed based on our proposed architecture of the platform was tested in a pilot study, which was conducted in two medical wards of the intensive care unit of one of the largest public general hospitals in Hong Kong. This pilot study demonstrates the feasibility of the implementation of this RFID-enabled platform for practical use in hospital wards. Furthermore, the data collected from the pilot study are used to provide data analytics for hospital ward management.     

Single cell technology

Complexity is a fundamental feature of life. Like animals, higher plants consist of a multitude of different distinct tissues and cell types, each contributing to the overall performance of the whole organism. Our understanding and knowledge of physiology will greatly increase as our ability to spatially resolve molecular and biochemical processes improves. Differential analysis of individual tissues and single cells will eliminate the averaging effect and allow the discovery of detailed differences between various cell types. Recent breakthroughs have been made in tissue-specific DNA, RNA and protein analysis of plants by applying laser-based microdissection techniques.     

Single cell heterogeneity

Multi-level heterogeneity is a fundamental but underappreciated feature of cancer. Most technical and analytical methods either completely ignore heterogeneity or do not fully account for it, as heterogeneity has been considered noise that needs to be eliminated. We have used single-cell and population-based assays to describe an instability-mediated mechanism where genome heterogeneity drastically affects cell growth and cannot be accurately measured using conventional averages. First, we show that most unstable cancer cell populations exhibit high levels of karyotype heterogeneity, where it is difficult, if not impossible, to karyotypically clone cells. Second, by comparing stable and unstable cell populations, we show that instability-mediated karyotype heterogeneity leads to growth heterogeneity, where outliers dominantly contribute to population growth and exhibit shorter cell cycles. Predictability of population growth is more difficult for heterogeneous cell populations than for homogenous cell populations. Since “outliers” play an important role in cancer evolution, where genome instability is the key feature, averaging methods used to characterize cell populations are misleading. Variances quantify heterogeneity; means (averages) smooth heterogeneity, invariably hiding it. Cell populations of pathological conditions with high genome instability, like cancer, behave differently than karyotypically homogeneous cell populations. Single-cell analysis is thus needed when cells are not genomically identical. Despite increased attention given to single-cell variation mediated heterogeneity of cancer cells, continued use of average-based methods is not only inaccurate but deceptive, as the “average” cancer cell clearly does not exist. Genome-level heterogeneity also may explain population heterogeneity, drug resistance, and cancer evolution.     

Single cell metabolomics

Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity.     

Declarative platform for high-performance network traffic analytics

This paper presents Scalanytics, a declarative platform that supports high-performance application layer analysis of network traffic. Scalanytics uses (1) stateful network packet processing techniques for extracting application layer data from network packets, (2) a declarative rule-based language called Analog for compactly specifying analysis pipelines from reusable modules, and (3) a task-stealing architecture for processing network packets at high throughput within these pipelines, by leveraging multi-core processing capabilities in a load-balanced manner without the need for explicit performance profiling. In a cluster of machines, Scalanytics further improves throughput through the use of a consistent-hashing based load partitioning strategy. Our evaluation on a 16-core machine demonstrate that Scalanytics achieves up to 11.4 \(\times \) improvement in throughput compared with the best uniprocessor implementation. Moreover, Scalanytics outperforms the Bro intrusion detection system by an order of magnitude when used for analyzing SMTP traffic. We further observed increased throughput when running Scalanytics pipelines across multiple machines.     

Process analytics for purification of monoclonal antibodies

The application of appropriate analytical methods is an essential requirement for the purification of therapeutic antibodies. A range of analytical methods need to be employed to effectively determine the purity, identity, integrity and activity of these important class of pharmaceuticals. These include notably electrophoresis, high performance liquid chromatography and immunoassays. Regulatory and industry demands in recent years have brought the need for improvements and many have been successfully implemented. This article reviews the current analytical methods applied to support the purification of monoclonal antibodies.     

Mayday - integrative analytics for expression data

Background  

DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.     

Identifying analytics for high throughput bioprocess development studies

In recent years, high throughput screening (HTS) studies have been increasingly employed as an integral element of bioprocess development activities. These studies are often limited by an analytical bottleneck; they generate multiple samples for analysis and the available analytical methods cannot always cope with the added analytical burden. A potential solution to this challenge is offered by the deployment of appropriate analytics. This article outlines features of analytical methods that affect their fit to high throughput (HT) applications. These are discussed for a range of analytics frequently used in bioprocess development studies of monoclonal antibodies. It then outlines how these features need to be considered in order to classify analytical methods in terms of their particular application in high throughput scenarios. Biotechnol. Bioeng. 2013; 110: 1924–1935. © 2013 Wiley Periodicals, Inc.