Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

Background  

Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.     

Using Covariance to Unravel the Effects of Meteorological Factors and Daily and Seasonal Rhythms

Many factors contribute to the activity of animals in the wild. Whilst daily and seasonal rhythms are likely to be present, and to represent underlying biological functions, these will normally be modified by several factors in the environment. Important amongst these are light, temperature, humidity and whether or not it is raining. There is also the problem that the factors might interact, the effect of, say, time of day, being modified by the concomitant temperature. Separating out the effects of these different factors experimentally can be extremely arduous, if not impossible. An alternative approach is to treat the environmental factors as covariants, and then to separate out their effects from the biological ones by statistical means, using Analysis of Covariance, ANCOVA. The potential of this method is illustrated in the current report by a consideration of exits and entries of a colony of bees from their hive. Hourly measurements of this behaviour were taken during the daylight hours for three consecutive days in 11 consecutive months of the year. At the same time, ambient temperature, light intensity, humidity and whether or not it was raining were recorded. ANCOVA enabled the effects of temperature, humidity, light and rainfall upon the exits from the hive and entries back into it to be separated from the effects of time of day and time of the year. The analyses allowed those climatic variables, in addition to time-of-day and time-of-year effects, that influenced behaviour to be identified. Such climatic variables have not been previously isolated, and this might have lead to a misinterpretation of similar results in the past. Having separated out any effects of climatic variables (the covariates), the interaction between time of day and time of the year could then be investigated. Furthermore it has been possible to quantify the effects upon behaviour of each covariate. Rainfall was shown to decrease activity by more than 80%. For the other variables (temperature, humidity and light intensity), the statistical model allowed for the possibility that an increase in the variable initially produced a rise in activity but that this was followed, if the variable continued to rise, by a fall in activity. For light intensity, only a very modest increase in activity was found, and this continued throughout the range of intensities measured. However, for both temperature and humidity, the effects were more marked and showed 'turning points'. That is, activity increased as the ambient temperature rose until activity peaked at about 33-35°C; after which activity began to fall. Similarly, entries into the hive rose with increasing humidity up to a value of 48%, but fell thereafter. By contrast, exits from the hive increased with increasing humidity throughout the range measured. In the biological system tested, this form of analysis has produced valuable information about the way different factors influence activity in a field study. The results strongly suggest that the proposed methodology has a much wider and more general application. The way in which this type of analysis might be elaborated is discussed.     

Cover Image,Volume 116, Number 9, September 2019

The new and rapid advancement in the complexity of biologics drug discovery has been driven by a deeper understanding of biological systems combined with innovative new therapeutic modalities, paving the way to breakthrough therapies for previously intractable diseases. These exciting times in biomedical innovation require the development of novel technologies to facilitate the sophisticated, multifaceted, high-paced workflows necessary to support modern large molecule drug discovery. A high-level aspiration is a true integration of “lab-on-a-chip” methods that vastly miniaturize cellulmical experiments could transform the speed, cost, and success of multiple workstreams in biologics development. Several microscale bioprocess technologies have been established that incrementally address these needs, yet each is inflexibly designed for a very specific process thus limiting an integrated holistic application. A more fully integrated nanoscale approach that incorporates manipulation, culture, analytics, and traceable digital record keeping of thousands of single cells in a relevant nanoenvironment would be a transformative technology capable of keeping pace with today's rapid and complex drug discovery demands. The recent advent of optical manipulation of cells using light-induced electrokinetics with micro- and nanoscale cell culture is poised to revolutionize both fundamental and applied biological research. In this review, we summarize the current state of the art for optical manipulation techniques and discuss emerging biological applications of this technology. In particular, we focus on promising prospects for drug discovery workflows, including antibody discovery, bioassay development, antibody engineering, and cell line development, which are enabled by the automation and industrialization of an integrated optoelectronic single-cell manipulation and culture platform. Continued development of such platforms will be well positioned to overcome many of the challenges currently associated with fragmented, low-throughput bioprocess workflows in biopharma and life science research.     

HAT: Hypergeometric Analysis of Tiling-arrays with application to promoter-GeneChip data

Background  

Tiling-arrays are applicable to multiple types of biological research questions. Due to its advantages (high sensitivity, resolution, unbiased), the technology is often employed in genome-wide investigations. A major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity (as a result of hybridization of labeled DNA) in a region. Currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. Furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays.