Douglas P. Shepherd

Counting Small RNA in Pathogenic Bacteria

Here, we present a modification to single-molecule fluorescence in situ hybridization that enables quantitative detection and analysis of small RNA (sRNA) expressed in bacteria. We show that short (~200 nucleotide) nucleic acid targets can be detected when the background of unbound singly dye-labeled DNA oligomers is reduced through hybridization with a set of complementary DNA oligomers labeled with a fluorescence quencher. By neutralizing the fluorescence from unbound probes, we were able to significantly reduce the number of false positives, allowing for accurate quantification of sRNA levels. Exploiting an automated, mutli-color wide-field microscope and data analysis package, we analyzed the statistics of sRNA expression in thousands of individual bacteria. We found that only a small fraction of either Yersinia pseudotuberculosis or Yersinia pestis bacteria express the small RNAs YSR35 or YSP8, with the copy number typically between 0 and 10 transcripts. The numbers of these RNA are both increased (by a factor of 2.5× for YSR35 and 3.5× for YSP8) upon a temperature shift from 25 to 37 °C, suggesting they play a role in pathogenesis. The copy number distribution of sRNAs from bacteria-to-bacteria are well-fit with a bursting model of gene transcription. The ability to directly quantify expression level changes of sRNA in single cells as a function of external stimuli provides key information on the role of sRNA in cellular regulatory networks.

Using mixture-tuned match filtering to measure changes in subpixel vegetation area in Las Vegas, Nevada

Abstract In desert cities, accurate measurements of vegetation area within residential lots are necessary to understand drivers of change in water consumption. Most residential lots are smaller than an individual 30-m pixel from Landsat satellite images and have a mixture of vegetation and other land covers. Quantifying vegetation change in this environment requires estimating subpixel vegetation area. Mixture-tuned match filtering (MTMF) has been successfully used for subpixel target detection. There have been few successful applications of MTMF to subpixel abundance estimation because the relationship observed between MTMF estimates and ground measurements of abundance is noisy. We use a ground truth dataset over 10 times larger than that available for any previous MTMF application to estimate the bias between ground data and MTMF results. We find that MTMF underestimates the fractional area of vegetation by 5% to 10% and show that averaging over multiple pixels is necessary to reduce noise in the dataset. We conclude that MTMF is a viable technique for fractional area estimation when a large dataset is available for calibration. When this method is applied to estimating vegetation area in Las Vegas, Nevada, spatial and temporal trends are consistent with expectations from known population growth and policy changes.

Distribution shapes govern the discovery of predictive models for gene regulation

Significance Systems biology seeks to combine experiments with computation to predict biological behaviors. However, despite tremendous data and knowledge, biological models make less-accurate predictions compared with other fields. By analyzing single-cell, single-molecule measurements of mRNA during yeast stress response, we explore why and how the shapes of experimental distributions control prediction accuracy. We show how asymmetric data distributions with long tails cause standard modeling approaches to yield excellent fits but make meaningless predictions. We show how these biases arise from the violation of fundamental assumptions in standard modeling approaches. We demonstrate how advanced computational tools solve this dilemma and achieve predictive understanding of spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport, and decay. Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.

Enhanced 3D localization of individual RNA transcripts via astigmatic imaging

Here we present an automated microscope capable of 3D multi-color single molecule localization of individual messenger RNA molecules in a wide range of cell types. We have implemented astigmatic imaging with a cylindrical lens to improve z-localization, and a maximum likelihood estimator on a graphics processing unit to improve localization precision and speed. This microscope will aid in gene expression analysis by its capability to perform high throughput imaging of thick cells and tissues while still maintaining sufficient z resolution to resolve single RNA transcripts in three dimensions. Enhanced z-localization allows for resolving membrane localized and co-localized transcripts.

New Tools for Discovering the Role sRNA Plays in Cell Regulation

We have used single molecule fluorescence in situ hybridization (smFISH) to study cell-to-cell heterogeneity of messenger RNA (mRNA) copy numbers for human host cells subject to a variety of external stimuli. In order to study the effect of various stimuli and genetic modifications on mRNA copy number, we have constructed an automated highthroughput multiplexed imaging system and data analysis package capable of localizing large numbers of individual mRNA transcripts in three dimensions. These experimental distributions of mRNA are used to refine and down-select regulatory models. Here we present a case example of Interleukin 1 alpha mRNA production in response to immune system stimulation. We propose a methodology for extending these methods to study the effect of small RNA on genetic expression by combining multiplexed imaging and numerical modeling at the system-level.

Mild myelin disruption elicits early alteration in behavior and proliferation in the subventricular zone

Myelin, the insulating sheath around axons, supports axon function. An important question is the impact of mild myelin disruption. In the absence of the myelin protein proteolipid protein (PLP1), myelin is generated but with age, axonal function/maintenance is disrupted. Axon disruption occurs in Plp1-null mice as early as 2 months in cortical projection neurons. High-volume cellular quantification techniques revealed a region-specific increase in oligodendrocyte density in the olfactory bulb and rostral corpus callosum that increased during adulthood. A distinct proliferative response of progenitor cells was observed in the subventricular zone (SVZ), while the number and proliferation of parenchymal oligodendrocyte progenitor cells was unchanged. This SVZ proliferative response occurred prior to evidence of axonal disruption. Thus, a novel SVZ response contributes to the region-specific increase in oligodendrocytes in Plp1-null mice. Young adult Plp1-null mice exhibited subtle but substantial behavioral alterations, indicative of an early impact of mild myelin disruption.

Spatio-Temporal Measurements and Modeling of Genetic Expression

Single-molecule, single-cell studies of genetic expression have provided key insights into how cells respond to external stimuli [Munsky, B., et al., Science (2012)]. By directly measuring copy numbers of individual bio-molecules in cells, it is now possible to obtain statistical measures of the spatio-temporal distributions of key signaling and regulatory networks. Such comprehensive datasets can be used to infer system-level models that yield quantitative insight into cellular regulation, predict cellular responses in new experimental conditions, and suggest more revealing experiments to uncover regulatory dynamics. The integration of single-molecule spectroscopy, biochemistry, and numerical modeling is a powerful multi-disciplinary approach to investigating cellular response at the genetic level.A key issue we seek to address is what types of fluctuations are most informative about the underlying gene regulatory process. In other words, how much experimental resources should be spent to measure (i) temporal, (ii) spatial, or (iii) cell-to-cell fluctuations? As an example, we studied Interluekin 1-alpha (IL1α) mRNA expression within human THP-1 cells during stimulus response to lipopolysaccharide (LPS). By spatially resolving individual mRNA using multiplexed single molecule FISH [Femino A.M., et al., Science (1998), Raj A., et al., Nat Meth (2008)] in large populations of single cells at multiple times points, we quantified all three fluctuation types.We expanded the common bursting gene expression model [Peccoud, J., Theoretical Population Biology (1995)] and derived a set of linear ODEs to describe the mean, variance, and co-variance of nuclear and cytoplasmic IL1α mRNA. We fit this model to multiple single-cell datasets. Comparing models inferred from each data set, we are able to draw conclusions on which fluctuation types are most revealing about the underlying systems mechanisms and parameters, providing feedback for new experiments. The approach developed here is applicable to any eukaryotic gene expression pathway.