This chapter describes the importance of charge regularization using titration curves, basic models of charged macromolecules, experimental and simulation results on isolated charged macromolecules, and theories based on scaling, mean field and self-consistent field methods. Based on these inputs, thermodynamic properties of charged macromolecules in dilute solutions are described. As special classes of charged macromolecules, polyampholytes, polyzwitterions, and intrinsically disordered proteins are described.

This chapter presents salient concepts to understand a vast literature on dynamics of charged macromolecules. Starting from a description of hydrodynamic interaction, dynamics of folded proteins, colloids, flexible polyelectrolytes, DNA are described. For flexible macromolecules, the models of Rouse, Zimm, reptation, and entropic barrier are developed in increasing order of complexity. Using this groundwork, the phenomena of ordinary-extraordinary transition, electrophoretic mobility, and topologically frustrated dynamical state are explained.

Basic principles of assembly processes of charged macromolecules complexing with oppositely charged interfaces and macromolecules are described. Specific examples include adsorption at planar and curved interfaces, charged brushes, genome assembly inside RNA viruses, intermolecular complexation, coacervation, and membrane-less organelles.

Improvement of super-resolution microscopy in the last decade has led to the development of methods such as stimulated emission depletion (STED) microscopy and structured illumination microscopy (SIM), which modulate the excitation light to break the diffraction limit, or methods such as photo-activated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM), which utilize the photoswitching properties of fluorescent molecules to enable precise localization of single molecules (Patterson, 2009). In this chapter, we will focus on PALM/STORM methods, which rely on similar principles and instrumentation. Namely, these acquire a series of images of fields of single molecules followed by subsequent image analysis to localize the molecules to much higher precision than their diffraction limited signals. Importantly, thousands or millions of molecules are identified and used to reconstruct the super-resolution images (Betzig et al., 2006; Hess et al., 2006; Rust et al., 2006; Heilemann et al., 2008; van de Linde et al., 2009; Kamiyama and Huang, 2012).

Classical chemistry and biochemistry experiments in solution measure the properties of many molecules and/or interrogate them simultaneously – these are called ensemble measurements and tend to mask the underlying molecular dynamics. Studies at single-molecule level provide random, stochastic dynamics, and allow access to an incredible wealth of molecular information. Most importantly, previously “unanswerable” questions in the physical, chemical, and biological sciences can now be answered. The field of single-molecule science (SMS) can be roughly divided into two general areas: (1) improvements in single-molecule methodologies (technology development); and (2) use of these methodologies to address important scientific questions in fundamental biological research (applied research). Over the past decades, single-molecule research has fostered excellent collaboration and interdisciplinary research with input from biology, chemistry, and physics.

A single live cell of E. coli can be estimated to contain around 3 million active protein molecules at any given moment. For larger and more complex human cells, that number goes up to between 200 and 300 million (Milo and Phillips, 2015). In E. coli, this represents around 4,000 different types of proteins and in humans around 20,000 (Wang et al., 2015). Each of these proteins performs a different and highly specialized role within the living cell, determined by its three-dimensional structure, composition, mechanics, and dynamics. Very few experimental techniques are able to access information about the structure and dynamics of the individual elements and substructures of protein molecules, which is needed to understand aspects of their function. One such technique is single-molecule force spectroscopy by optical trapping, a method for which Arthur Ashkin won the Nobel Prize in Physics in 2018. Using the principle that highly focused laser beams can be used to trap micron-scale objects, experimental methods have been developed where micron-sized glass beads are functionalized with protein constructs, establishing geometries that enable forces to be applied to the individual protein molecules (Figure 5.1a).

The term “biomarker,” a portmanteau of “biological marker,” has been defined by Hulka and colleagues (Hulka, 1990) as “cellular, biochemical or molecular alterations that are measurable in biological media such as human tissues, cells, or fluids.” In 1998, the definition was broadened as the National Institutes of Health Biomarkers Definitions Working Group defined a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (Biomarkers Definition Working Group, 2001). In practice, the discovery and quantification of biomarkers require tools and technologies that help us predict and diagnose diseases; understand the cause, progression, and regression of diseases; and understand the outcomes of disease treatments. Different types of biomarkers have been used by generations of epidemiologists, physicians, and scientists to study all sorts of diseases. The importance of biomarkers in the diagnosis and management of cardiovascular diseases, infections, immunological and genetic disorders, and cancers is well known (Hulka, 1990; Perera and Weinstein, 2000).

Determining rules for gene expression regulation is an important step toward predicting how cells are decoding the genome sequence to create a wide variety of phenotypes. Recent advances in imaging technologies revealed the stochastic nature of gene expression, in which different numbers of mRNA and protein molecules can be created in cells that have the same genome sequence (Elowitz, 2002); Kaufmann and van Oudenaarden, 2007). An early research revealed that this stochasticity is yielded by two factors: intrinsic and extrinsic noise. While the former is due to instant random chemical reactions in gene expression process, the latter is caused by cell specific molecular states emerging from the integration of gene expression over a longer time (Elowitz, 2002); Kaufmann and van Oudenaarden 2007). This finding inspired studies to investigate how cells deterministically cause robust phenotypes under such stochasticity. In contrast, this also motivated investigations on how cells utilize this stochasticity to generate different kinds of phenotypes for processes such as neural development (Johnson et al., 2015), emergence of bacterial resistance (Sánchez-Romero and Casadesús, 2014), or cancer development (Marusyk et al., 2012; Junttila and de Sauvage, 2013).

More than 150 years after the discovery of DNA (Dahm, 2005) and 50 years after the determination of a DNA duplex structure (Choudhuri, 2003), our understanding of genomics is still lacking. Despite a massive effort in genetic studies, there are still missing pieces in the puzzle: many traits have no corresponding genetic features, the reasons for variations among different individuals within a species are still a mystery, and the lack of single-molecule resolution hinders many types of analysis. These gaps are exemplified by inherent limitations in the currently leading technique for DNA studies, next generation sequencing (NGS). The initial “input” for NGS consists of genomes extracted from a large number of cells, and the final “output” is a single sequence. Thus, the produced data represents an average sequence of the majority of the sequenced cells. This results in oversight of several significant biological aspects, since variations within the population and rare cellular populations are not detected.

Automated fluorescence microscopy–based screening approaches have become a standard tool in systems biology, usually applied in combination with exogenous regulation of gene expression in order to examine and determine gene function. Gain of function can be created by introducing cDNAs encoding the gene of interest that can be either untagged or tagged for the visualization of the recombinant protein and its subcellular localization (e.g., green fluorescent protein [GFP]-tagged; Temple et al., 2009). After the discovery of RNA interference (RNAi) in the late 1990s and the development of mammalian short interfering RNA (siRNA) and short hairpin RNA (shRNA) libraries in the early 2000s, gene knockdown technologies became a mainstream for loss-of-function screens on a large or genome-wide scale (Heintze et al., 2013). To date, genome-wide siRNA libraries are still the main application in genomic high-throughput screening, although key problems of the RNAi technology have become apparent, such as off-target effects, variable levels of knockdown efficiency, resulting in low-level confidence in hits of screening campaigns. In order to overcome these limitations, alternative methods for manipulation of gene expression have been developed and predominantly rely on gene excision.