Human Endeavors: Microscopes: From Animalcules to Atoms

Human Endeavors: Microscopes: From Animalcules to Atoms - From Leeuwenhoek's first report of microscopic animals (animalcules) in 1676 to the viewing of atoms in 1983, humans have endeavored to probe the invisible through the development of better microscopes and other technology.

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In 1676 Antonie van Leeuwenhoek reported to the Royal Society in London that he had observed an invisible world made visible by the microscope, a world in which small animals, living creatures that he called animalcules, were alive in a drop of rainwater. Thus began the first step in a human endeavor that would lead to the cell theory, to germ theory, and eventually to our imaging of atoms and even to detecting the changes that occur in atoms as they undergo chemical reactions. Imagine, only a century ago scientists debated the existence of atoms, and until January 1983 no one had ever seen the image of an atom. Today, using modern scanning-probe microscopes, scientists are not only seeing atoms but can move them about. All this is due to our new microscopic technology.

Since the early seventeenth century, lens makers were viewing the microscopic world through their very primitive light microscopes, and the light optical microscope was the primary research tool of biologists until the 1930's. But visible light has limits as to how small an object you can see. This limit is due to a phenomenon known as resolving power. Resolving power is the ability to distinguish between two points and is due to the wavelength of light energy used. Light travels in waves, and the distance between the crests of the waves is known as its wavelength. The resolving power is equal to one-half of the wavelength of light used. For example, because visible light ranges from 380 to 720 nanometers, or billionths of a meter (a nanometer is equal to 1 billionth of a meter), visible light can only distinguish objects about 160 nanometers in diameter. Obviously, if you used a smaller wavelength, for example the wavelength of electrons (which is about 1 nanometer in length)you can see objects 0.5 nanometers in size. In the 1930s, a new type of microscope was developed, the electron microscope, which could be used to see very small objects. Electron microscopes use electrons rather than light and magnetic lenses rather than the glass lens used in the optical microscope to view specimens, but the electron microscope also has its limitations. One limitation is that the electrons have to be beamed in a vacuum to avoid scattering. Because of this, the specimen has to be specially prepared and sectioned for viewing. For the most part, except when using the high-voltage electron microscope, no living specimens can be viewed. If you try to see atoms, because the electron beam is so concentrated, the energy of the beam would destroy the fragile atoms in organic molecules or pass right through them, showing nothing. Scientists also developed modifications of the electron microscopes, like the scanning electron microscope, which has the advantage of scanning the surface of the specimen with a narrow beam of electrons. This allows for the production of a three-dimensional image that can be viewed on a visual monitor (like a computer screen). Also, computer programs can enhance the image and even add color. But still, the limits of the wavelength prevent the viewing of atoms. But what if we imaged by feel (touch), not sight?

In 1981, Gerd Binning and Heinrich Rohrer in Zurich, Switzerland, developed the scanning-probe microscope. The scanning-probe microscope is based on the principle that if you place a probe near the surface of a sample and measure the distance between the probe and the sample, you can produce an accurate image of the sample. It is sort of like being in the dark and running (or scanning) your hands over the surface of eggs sticking out of an egg carton to detect the nature of the surface of the eggs, rather than using light to see them. The probes are like your hands, and they detect the peaks and valleys of the image; as you move your hands, you can scan the sample, but in order to have an accurate image of atoms, you have to be able to move the probe in controlled steps, each step less than the diameter of an atom. The technological breakthrough that allowed for that exact scanning was the development of piezoelectric crystalsmaterial that can change shape as electric current is applied, which is translated into minute movement of the probe. Three sets of crystals are used to allow for a three-dimensional image. Today, using a finewire platinum probe and piezoelectric crystals, scientists are literally tunneling into atoms. The scanning-tunneling microscope charts the shape of the surface being scanned, just like you touching the eggs to detect their size, shape, number, and so on. And the computer message, the electrical current between the probe and the specimen, is used to produce an image of the atom or molecule. But what if the specimen does not conduct electrons? Today that limitation of the scanning-tunneling microscope has been overcome by the atomic-force microscope, developed in 1985. The atomic-force microscope uses a diamond point glued to a piece of silicon about the size of a pinpoint. This forms a silicon cantilever, sort of like a diving board. The repulsive forces produced by the electron cloud of the atom bend the silicon cantilever. A laser beam can then measure the amount of bending and produce the image of the specimen. Using this microscope, scientists can image very delicate molecules like DNA and other organic materials and even film the molecular events that lead to a biological process, like the formation of protein fibers in a blood clot.

As you can see, we literally can image and see atoms. But this is just the beginning. We will discuss how this technology at the nanometer level is rapidly evolving into nanotechnology, where we can actually micromanipulate atoms and image the changes in atoms as chemical reactions occur.

Biomarkers of Aging and Mortality Risk Markers

Biomarkers of Aging and Mortality Risk Markers - For populations, the life span is expressed statistically, often as the life expectancy. However, no measurement has been found that accurately predicts the individual life span from the genotype, or from any ‘biomarker of aging.’ Clearly, the traditional aging changes of gray hair and menopause do not assess an individual’s current health or future longevity. Jeanne Calment lived 70 years after menopause to achieve her longevity record of 122 years.

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The N.I.A. has supported an extensive search for biomarkers that predict remaining life span (Biomarkers of Aging Program, begun in 1982) (Reff and Schneider, 1982). Biomarkers have been evaluated in biochemical, cellular, genetics, molecular, and physiological characteristics, and in behavior and cognition. Emphasis has been given to changes of ‘non-pathological aging’ that are distinct from disease. Two decades later, no single biomarker or combination has been found to predict longevity better than the individual age in fly, worm, rodent, or human (Finch, 1990, pp. 558–564; Warner, 2004).

Consider the limits of biomarkers in aging worms, which seem an optimal model for actuarial questions by their minimal genetic, environmental, and social heterogeneity from dominance hierarchies and social interactions (worms are self-fertilizing). Pharyngeal pumping, by which food is ingested, declines progressively during the first week and then nearly ceases a few days before death. Body movements closely parallel the pumping rates, not surprisingly because pumping provides the food needed for energy. The duration of fast pharyngeal pumping and body movements shows strong correlation with individual life spans (Huang and Kaley, 2004). When fast pumping is maintained one day longer, the odds ratio for death by, or later than, a specified date is 1.7- fold greater. The Spearman rank correlation coefficient for the duration of fast pumping and remaining life span was highly significant (r 0.49, P< 0.0001). Despite this statistic, the fraction of life span variance explained by pumping or movement was only 24%. Thus, other variables besides pumping account for about 75% of the individual differences in life span. Mutants with greater longevity have longer phases of active body movement and pharyngeal pumping.

See Also >> The Structure and Function of the Cerebrum

Even at hatching, worms differ hugely in movements, which Kirkwood and I attributed to chance variations in cell organization and gene expression during development (Finch and Kirkwood, 2000, pp. 58–65). A concrete example is Tom Johnson’s elegant study of worm-to-worm variations in expression of a stress-protective gene (hsp-16.2) in young worms, which correlated strongly with individual life spans (Rea et al, 2005). Again, the Spearman coefficient of 0.48 accounts for only 25% of the variance in life span. These sobering examples from rigorous studies suggest limits in the predictability of life spans despite strictly controlled genetics and environment. In worms, as in flies, mice, and humans, the heritability of the life span is also about 25% (Finch and Tanzi, 1997; Finch and Ruvkun, 2000).

As noted above, partial deficits of MnSOD did not alter mouse life span (Van Remmen et al, 2003). Several biomarkers of aging that respond to diet restriction were the same in Sod2+/− as in control aging mice (ad libitum fed in this study): both genotypes had identical age-related decreases of spleenocyte proliferation and increases in skin collagen of pentosidine and carboxymethyllysine. The lack of effects of MnSOD deficiency on these AGEs indicates that blood glucose was not altered. However, Sod2+/− mice had greater accumulations of oxidized nuclear DNA (8oxodG) in brain, heart, and liver, and 2-fold more lymphomas (83% vs. 41%). This study with its careful analytical chemistry and histopathology shows the uncertainty of connections between robust biomarkers of aging, tumor prevalence, and the life span.

There is reason to consider the individual disease load as more informative than tissue-level aging changes in predicting mortality risk (Karasik et al, 2005). During these same decades of the Biomarkers Program, vast clinical research has developed risk indicators of mortality for the major diseases. From the clinical perspective, disease, not aging, is the cause of mortality, as shown in the exponential increase of tissue lesions in rodent models and heart attack and stroke in human populations. Systolic blood pressure may be the most robust overall indicator of human mortality risk, with exponential age-related increases of future heart attack and stroke at all levels of systolic pressure.

The next phase of the biomarker debate may redefine the often vaguely used term ‘disease.’ For example, the clinical threshold of hypertension as a target for intervention is being expanded to include the ‘high normal’ range. A few decades ago, an informal guideline of the expected systolic pressure was “add 100 to the person’s age”! Combinations of risk indicators and disease load are also being intensively studied of the inflammatory marker, C-reactive protein (CRP). In the Women’s Health Study, future heart attacks occurred most frequently in those with both elevated CRP and LDL (Ridker, 2002). New models are needed to resolve the links between the diverse subtle subclinical aging changes that interact to cause circulatory failure on the background of declining organ reserves. It is shocking that 30% of diet-restricted old rats had no gross lesions at necropsy and cause of death was unknown (Shimokawa et al, 1993). Declining homeostasis of glucose and electrolytes, for example, might allow transient disturbances that would arrest a fibrotic heart, even in the absence of thrombotic blockade. The combinatorics of various mild dysfunctions gives rise to a huge number of individual pathophenotypes that may be each estimated as relative risk of mortality, but may never account for more of the variance in life span than in the worm model

Cultured Cell Models and Replicative Senescence

Cultured Cell Models and Replicative Senescence - Unlike cancer cells, diploid somatic cells typically have a finite capacity for propagation during serial culture, shown first for skin fibroblasts (Hayflick and Moorhead, 1961). After a finite number of subcultures (population doublings), cell division ceases and cultures are considered senescent. The ‘Hayflick’ phenomenon extends to many cell types, including vascular endothelia and lymphocytes (Campisi, 2005; Cristofalo et al, 2004; Hayflick, 2000). Although the end-phase cultures are considered ‘senescent,’ post-replicative cells survive many months if media are refreshed (Matsumura et al, 1979). In fact, senescent cultures are highly resistant to apoptosis. Resistance to apoptosis may link back to the insulin pathways that modulate life span, because senescent cell cultures have decreased endocytic uptake of the IGF-binding protein IGFBP-3 (Hampel et al, 2005). It is cogent to Query I that senescent cultures of fibroblasts and other cell types show increased inflammatory factors including COX-2, IL-1, MMP-3, collagenase, TIMP-1 (tissue inhibitor of metaloproteases) (Han et al, 2004; Parrinello et al, 2005; West et al, 1989; Zeng et al, 1996). These same changes arise in atheromas and, moreover, are blocked in senescing cultures by the COX-2 inhibitor NS398 (Han et al, 2004). Inflammatory factors secreted by replicatively senescent cells are implicated in focal tissue remodeling in the progression of pre-malignant cells (Campisi, 2005).

Cultured Cell Models and Replicative Senescence

Individual cell variations in replicative potential are associated with variable telomere length (Martin-Ruiz et al, 2004). It is not known if telomere heterogeneity causes the daughter cell differences in proliferative potential, which range from 0 (growth arrest) to 15 or more replications (Matsumura et al, 1985). Somatic cell replicative heterogeneity may contribute to the remarkable differences of individual life span in twins and in highly inbred worms (Finch and Kirkwood, 2000).

see also >> Genetic Continuity Defe

Contrary to earlier conclusions, adult age up to 92 years does not change the Hayflick limit of skin fibroblasts (Cristofalo et al, 2004; Goldstein et al, 1978; Smith et al, 2002). However, cells from embryos or children have 2-fold higher Hayflick limits (Martin, 1970). Thus, the major effect of age on the cell senescence model is before maturation. In vivo exposure to oxidative stress may be a factor in the reduced proliferation of cells from diabetics (Goldstein et al, 1979). Moreover, the standard protocol of culturing cells for aging studies in ambient air (20% oxygen) is grossly unphysiological. When mouse cells were ‘aged’ at 3% oxygen, closer to the tissue levels, their replicative potential was greatly increased (Parrinello et al, 2003). Nonethless, even under the standard culture conditions, in species comparisons, resistance of cultured fibroblasts to oxidative stress correlates with life span (Kapahi et al., 1999). We may anticipate fruitful further analysis of species differences in resistance to oxidative stress that might, in turn, inform about in vivo vulnerability to bystander effects from inflammatory processes.