Establishing a reference focal plane using beads for trypan-blue-based viability measurements.

Trypan blue dye exclusion-based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal-to-noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell-based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto-focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single-shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open-source Graphical User Interface called Bead Benchmarking-Focus And Intensity Tool (BB-FAIT) to implement these methods for a semi-automated cell viability analyser.

It is critical for the manufacturing and release of living cell-based therapies to determine the viability, the ratio of living cells to the total number of cells (live and dead), in the therapy. Dead cells can be a safety concern for the patient, and dosing is often based on the number of living cells which are the active ingredient of the drug product. Currently, the most common approach to evaluating cell viability is based on the staining of cell samples with the trypan blue marker of cell membrane integrity: a loss in cell membrane integrity with cell death allows the dye into the cell, which can be seen using brightfield microscopy. To classify cells as live/dead, the brightness of the cells is evaluated and cells with bright centres are considered live, while those with dark centres are considered dead. Unfortunately, this approach of staining, imaging and classification is very sensitive to image acquisition settings, including image focus and brightness. This paper introduces a method to establish the required image quality for image viability analysis, providing a tool to return to image acquisition settings that will ensure image quality even when there is variability from sample to sample. In this method, polymeric beads are added to each cell sample prior to cell viability analysis. Using image processing, we extract key features from the beads in the image such as sharpness of the edges of the beads. The image features of the cells can vary significantly from sample to sample and under different cell conditions, but image features of beads have proved to be consistent across samples. We are thus able to collect reference datasets quantifying bead features over a wide range of image acquisition settings (brightness and focus), allowing us to establish a reference focal plan for image acquisition for any cell sample based on bead features. We show that with as few as three beads per image, the reference focal plane can be found from a single acquisition of beads image data over a wide range of image focuses and brightness, allowing users to consistently acquire images for cell viability that meet pre-defined quality requirements.

Summarizing performance for genome scale measurement of miRNA: reference samples and metrics.

BACKGROUND: The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. RESULTS: Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. CONCLUSIONS: The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Cell-based reference samples designed with specific differences in microRNA biomarkers.

BACKGROUND: We demonstrate the feasibility of creating a pair of reference samples to be used as surrogates for clinical samples measured in either a research or clinical laboratory setting. The reference sample paradigm presented and evaluated here is designed to assess the capability of a measurement process to detect true differences between two biological samples. Cell-based reference samples can be created with a biomarker signature pattern designed in silico. Clinical laboratories working in regulated applications are required to participate in proficiency testing programs; research laboratories doing discovery typically do not. These reference samples can be used in proficiency tests or as process controls that allow a laboratory to evaluate and optimize its measurement systems, monitor performance over time (process drift), assess changes in protocols, reagents, and/or personnel, maintain standard operating procedures, and most importantly, provide evidence for quality results. RESULTS: The biomarkers of interest in this study are microRNAs (miRNAs), small non-coding RNAs involved in the regulation of gene expression. Multiple lung cancer associated cell lines were determined by reverse transcription (RT)-PCR to have sufficiently different miRNA profiles to serve as components in mixture designs as reference samples. In silico models based on the component profiles were used to predict miRNA abundance ratios between two different cell line mixtures, providing target values for profiles obtained from in vitro mixtures. Two reference sample types were tested: total RNA mixed after extraction from cell lines, and intact cells mixed prior to RNA extraction. MicroRNA profiling of a pair of samples composed of extracted RNA derived from these cell lines successfully replicated the target values. Mixtures of intact cells from these lines also approximated the target values, demonstrating potential utility as mimics for clinical specimens. Both designs demonstrated their utility as reference samples for inter- or intra-laboratory testing. CONCLUSIONS: Cell-based reference samples can be created for performance assessment of a measurement process from biomolecule extraction through quantitation. Although this study focused on miRNA profiling with RT-PCR using cell lines associated with lung cancer, the paradigm demonstrated here should be extendable to genome-scale platforms and other biomolecular endpoints.

Evaluating the quality of a cell counting measurement process via a dilution series experimental design.

BACKGROUND AIMS: Cell counting measurements are critical in the research, development and manufacturing of cell-based products, yet determining cell quantity with accuracy and precision remains a challenge. Validating and evaluating a cell counting measurement process can be difficult because of the lack of appropriate reference material. Here we describe an experimental design and statistical analysis approach to evaluate the quality of a cell counting measurement process in the absence of appropriate reference materials or reference methods. METHODS: The experimental design is based on a dilution series study with replicate samples and observations as well as measurement process controls. The statistical analysis evaluates the precision and proportionality of the cell counting measurement process and can be used to compare the quality of two or more counting methods. As an illustration of this approach, cell counting measurement processes (automated and manual methods) were compared for a human mesenchymal stromal cell (hMSC) preparation. RESULTS: For the hMSC preparation investigated, results indicated that the automated method performed better than the manual counting methods in terms of precision and proportionality. DISCUSSION: By conducting well controlled dilution series experimental designs coupled with appropriate statistical analysis, quantitative indicators of repeatability and proportionality can be calculated to provide an assessment of cell counting measurement quality. This approach does not rely on the use of a reference material or comparison to "gold standard" methods known to have limited assurance of accuracy and precision. The approach presented here may help the selection, optimization, and/or validation of a cell counting measurement process.

Likelihood Ratio as Weight of Forensic Evidence: A Closer Look.

The forensic science community has increasingly sought quantitative methods for conveying the weight of evidence. Experts from many forensic laboratories summarize their findings in terms of a likelihood ratio. Several proponents of this approach have argued that Bayesian reasoning proves it to be normative. We find this likelihood ratio paradigm to be unsupported by arguments of Bayesian decision theory, which applies only to personal decision making and not to the transfer of information from an expert to a separate decision maker. We further argue that decision theory does not exempt the presentation of a likelihood ratio from uncertainty characterization, which is required to assess the fitness for purpose of any transferred quantity. We propose the concept of a lattice of assumptions leading to an uncertainty pyramid as a framework for assessing the uncertainty in an evaluation of a likelihood ratio. We demonstrate the use of these concepts with illustrative examples regarding the refractive index of glass and automated comparison scores for fingerprints.

Transcriptional responses of Pseudomonas syringae to growth in epiphytic versus apoplastic leaf sites.

Some strains of the foliar pathogen Pseudomonas syringae are adapted for growth and survival on leaf surfaces and in the leaf interior. Global transcriptome profiling was used to evaluate if these two habitats offer distinct environments for bacteria and thus present distinct driving forces for adaptation. The transcript profiles of Pseudomonas syringae pv. syringae B728a support a model in which leaf surface, or epiphytic, sites specifically favor flagellar motility, swarming motility based on 3-(3-hydroxyalkanoyloxy) alkanoic acid surfactant production, chemosensing, and chemotaxis,indicating active relocation primarily on the leaf surface. Epiphytic sites also promote high transcript levels for phenylalanine degradation, which may help counteract phenylpropanoid-based defenses before leaf entry. In contrast, intercellular, or apoplastic,sites favor the high-level expression of genes for GABA metabolism (degradation of these genes would attenuate GABA repression of virulence) and the synthesis of phytotoxins, two additional secondary metabolites, and syringolin A. These findings support roles for these compounds in virulence, including a role for syringolin A in suppressing defense responses beyond stomatal closure. A comparison of the transcriptomes from in planta cells and from cells exposed to osmotic stress, oxidative stress, and iron and nitrogen limitation indicated that water availability, in particular,was limited in both leaf habitats but was more severely limited in the apoplast than on the leaf surface under the conditions tested. These findings contribute to a coherent model of the adaptations of this widespread bacterial phytopathogen to distinct habitats within its host.

Establishing a reference focal plane using convolutional neural networks and beads for brightfield imaging.

Repeatability of measurements from image analytics is difficult, due to the heterogeneity and complexity of cell samples, exact microscope stage positioning, and slide thickness. We present a method to define and use a reference focal plane that provides repeatable measurements with very high accuracy, by relying on control beads as reference material and a convolutional neural network focused on the control bead images. Previously we defined a reference effective focal plane (REFP) based on the image gradient of bead edges and three specific bead image features. This paper both generalizes and improves on this previous work. First, we refine the definition of the REFP by fitting a cubic spline to describe the relationship between the distance from a bead's center and pixel intensity and by sharing information across experiments, exposures, and fields of view. Second, we remove our reliance on image features that behave differently from one instrument to another. Instead, we apply a convolutional regression neural network (ResNet 18) trained on cropped bead images that is generalizable to multiple microscopes. Our ResNet 18 network predicts the location of the REFP with only a single inferenced image acquisition that can be taken across a wide range of focal planes and exposure times. We illustrate the different strategies and hyperparameter optimization of the ResNet 18 to achieve a high prediction accuracy with an uncertainty for every image tested coming within the microscope repeatability measure of 7.5 µm from the desired focal plane. We demonstrate the generalizability of this methodology by applying it to two different optical systems and show that this level of accuracy can be achieved using only 6 beads per image.

Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.

Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth's (2004) approach to estimating gene-specific error variances for microarray data. Our suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.

Effects of DNA template preparation on variability in cell-free protein production.

DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.

Quantitative evaluation of footwear evidence: Initial workflow for an end-to-end system.

In the United States, footwear examiners make decisions about the sources of crime scene shoe impressions using subjective criteria. This has raised questions about the accuracy, repeatability, reproducibility, and scientific validity of footwear examinations. Currently, most footwear examiners follow a workflow that compares a questioned and test impression with regard to outsole design, size, wear, and randomly acquired characteristics (RACs). We augment this workflow with computer algorithms and statistical analysis so as to improve in the following areas: (1) quantifying the degree of correspondence between the questioned and test impressions with respect to design, size, wear, and RACs, (2) reducing the potential for cognitive bias, and (3) providing an empirical basis for examiner conclusions by developing a reference database of case-relevant pairs of impressions containing known mated and known nonmated impressions. Our end-to-end workflow facilitates all three of these points and is directly relatable to current practice. We demonstrate the workflow, which includes obtaining and interpreting outsole pattern scores, RAC comparison scores, and final scores, on two scenarios-a pristine example (involving very high quality Everspry EverOS scanner impressions) and a mock crime scene example that more closely resembles real casework. These examples not only demonstrate the workflow but also help identify the algorithmic, computational, and statistical challenges involved in improving the system for eventual deployment in casework.

Comparison of bias and resolvability in single-cell and single-transcript methods.

Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.

Counting Caenorhabditis elegans: Protocol Optimization and Applications for Population Growth and Toxicity Studies in Liquid Medium.

The nematode Caenorhabditis elegans is used extensively in molecular, toxicological and genetics research. However, standardized methods for counting nematodes in liquid culture do not exist despite the wide use of nematodes and need for accurate measurements. Herein, we provide a simple and affordable counting protocol developed to maximize count accuracy and minimize variability in liquid nematode culture. Sources of variability in the counting process were identified and tested in 14 separate experiments. Three variables resulted in significant effects on nematode count: shaking of the culture, priming of pipette tips, and sampling location within a microcentrifuge tube. Between-operator variability did not have a statistically significant effect on counts, even among differently-skilled operators. The protocol was used to assess population growth rates of nematodes in two different but common liquid growth media: axenic modified Caenorhabditis elegans Habitation and Reproduction medium (mCeHR) and S-basal complete. In mCeHR, nematode populations doubled daily for 10 d. S-basal complete populations initially doubled every 12 h, but slowed within 7 d. We also detected a statistically significant difference between embryo-to-hatchling incubation period of 5 d in mCeHR compared to 4 d in S-basal complete. The developed counting method for Caenorhabditis elegans reduces variability and allows for rigorous and reliable experimentation.

Fibronectin in Layer-by-Layer Assembled Films Switches Tumor Cells between 2D and 3D Morphology.

Tumor cells showing a 3D morphology and in coculture with endothelial cells are a valuable in vitro model for studying cell-cell interactions and for the development of pharmaceuticals. Here, we found that HepG2 cells, unlike endothelial cells, show differences in adhesion to fibronectin alone, or in combination with poly(allylamine hydrochloride). This response allowed us to engineer micropatterned heterotypic cultures of the two cell types using microfluidics to pattern cell adhesion. The resulting cocultures exhibit spatially encoded and physiologically relevant cell function. Further, we found that the protrusive, migratory and 3D morphological responses of HepG2 are synergistically modulated by the constituents of the hybrid extracellular matrix. Treating the hybrid material with the cross-linking enzyme transglutaminase inhibited 3D morphogenesis of tumor cells. Our results extend previous work on the role of fibronectin in layer-by-layer assembled films, and demonstrate that cell-specific differences in adhesion to fibronectin can be used to engineer tumor cell cocultures.

Evaluation of two mitochondrial DNA biomarkers for prostate cancer detection.

BACKGROUND: A 3.4kb deletion (3.4kbΔ ) in mitochondrial DNA (mtDNA) found in histologically normal prostate biopsy specimens has been reported to be a biomarker for the increased probability of prostate cancer. Increased mtDNA copy number is also reported as associated with cancer. OBJECTIVE: Independent evaluation of these two potential prostate cancer biomarkers using formalin-fixed paraffin-embedded (FFPE) prostate tissue and matched urine and serum from a high risk cohort of men with and without prostate cancer. METHODS: Biomarker levels were detected via qPCR. RESULTS: Both 3.4kbΔ and mtDNA levels were significantly higher in cancer patient FFPE cores (p= 0.045 and p= 0.070 respectively at > 90% confidence). Urine from cancer patients contained significantly higher levels of mtDNA (p= 0.006, 64.3% sensitivity, 86.7% specificity). Combining the 3.4kbΔ and mtDNA gave better performance of detecting prostate cancer than either biomarker alone (FFPE 73.7% sensitivity, 65% specificity; urine 64.3% sensitivity, 100% specificity). In serum, there was no difference for any of the biomarkers. CONCLUSIONS: This is the first report on detecting the 3.4kbΔ in urine and evaluating mtDNA levels as a prostate cancer biomarker. A confirmation study with increased sample size and possibly with additional biomarkers would need to be conducted to corroborate and extend these observations.

Best practices for evaluating single nucleotide variant calling methods for microbial genomics.

Innovations in sequencing technologies have allowed biologists to make incredible advances in understanding biological systems. As experience grows, researchers increasingly recognize that analyzing the wealth of data provided by these new sequencing platforms requires careful attention to detail for robust results. Thus far, much of the scientific Communit's focus for use in bacterial genomics has been on evaluating genome assembly algorithms and rigorously validating assembly program performance. Missing, however, is a focus on critical evaluation of variant callers for these genomes. Variant calling is essential for comparative genomics as it yields insights into nucleotide-level organismal differences. Variant calling is a multistep process with a host of potential error sources that may lead to incorrect variant calls. Identifying and resolving these incorrect calls is critical for bacterial genomics to advance. The goal of this review is to provide guidance on validating algorithms and pipelines used in variant calling for bacterial genomics. First, we will provide an overview of the variant calling procedures and the potential sources of error associated with the methods. We will then identify appropriate datasets for use in evaluating algorithms and describe statistical methods for evaluating algorithm performance. As variant calling moves from basic research to the applied setting, standardized methods for performance evaluation and reporting are required; it is our hope that this review provides the groundwork for the development of these standards.

International interlaboratory study comparing single organism 16S rRNA gene sequencing data: Beyond consensus sequence comparisons.

This study presents the results from an interlaboratory sequencing study for which we developed a novel high-resolution method for comparing data from different sequencing platforms for a multi-copy, paralogous gene. The combination of PCR amplification and 16S ribosomal RNA gene (16S rRNA) sequencing has revolutionized bacteriology by enabling rapid identification, frequently without the need for culture. To assess variability between laboratories in sequencing 16S rRNA, six laboratories sequenced the gene encoding the 16S rRNA from Escherichia coli O157:H7 strain EDL933 and Listeria monocytogenes serovar 4b strain NCTC11994. Participants performed sequencing methods and protocols available in their laboratories: Sanger sequencing, Roche 454 pyrosequencing(®), or Ion Torrent PGM(®). The sequencing data were evaluated on three levels: (1) identity of biologically conserved position, (2) ratio of 16S rRNA gene copies featuring identified variants, and (3) the collection of variant combinations in a set of 16S rRNA gene copies. The same set of biologically conserved positions was identified for each sequencing method. Analytical methods using Bayesian and maximum likelihood statistics were developed to estimate variant copy ratios, which describe the ratio of nucleotides at each identified biologically variable position, as well as the likely set of variant combinations present in 16S rRNA gene copies. Our results indicate that estimated variant copy ratios at biologically variable positions were only reproducible for high throughput sequencing methods. Furthermore, the likely variant combination set was only reproducible with increased sequencing depth and longer read lengths. We also demonstrate novel methods for evaluating variable positions when comparing multi-copy gene sequence data from multiple laboratories generated using multiple sequencing technologies.

Nonparametric estimates of drift and diffusion profiles via Fokker-Planck algebra.

Diffusion processes superimposed upon deterministic motion play a key role in understanding and controlling the transport of matter, energy, momentum, and even information in physics, chemistry, material science, biology, and communications technology. Given functions defining these random and deterministic components, the Fokker-Planck (FP) equation is often used to model these diffusive systems. Many methods exist for estimating the drift and diffusion profiles from one or more identifiable diffusive trajectories; however, when many identical entities diffuse simultaneously, it may not be possible to identify individual trajectories. Here we present a method capable of simultaneously providing nonparametric estimates for both drift and diffusion profiles from evolving density profiles, requiring only the validity of Langevin/FP dynamics. This algebraic FP manipulation provides a flexible and robust framework for estimating stationary drift and diffusion coefficient profiles, is not based on fluctuation theory or solved diffusion equations, and may facilitate predictions for many experimental systems. We illustrate this approach on experimental data obtained from a model lipid bilayer system exhibiting free diffusion and electric field induced drift. The wide range over which this approach provides accurate estimates for drift and diffusion profiles is demonstrated through simulation.

Transcriptional analysis of the global regulatory networks active in Pseudomonas syringae during leaf colonization.

UNLABELLED: The plant pathogen Pseudomonas syringae pv. syringae B728a grows and survives on leaf surfaces and in the leaf apoplast of its host, bean (Phaseolus vulgaris). To understand the contribution of distinct regulators to B728a fitness and pathogenicity, we performed a transcriptome analysis of strain B728a and nine regulatory mutants recovered from the surfaces and interior of leaves and exposed to environmental stresses in culture. The quorum-sensing regulators AhlR and AefR influenced few genes in planta or in vitro. In contrast, GacS and a downstream regulator, SalA, formed a large regulatory network that included a branch that regulated diverse traits and was independent of plant-specific environmental signals and a plant signal-dependent branch that positively regulated secondary metabolite genes and negatively regulated the type III secretion system. SalA functioned as a central regulator of iron status based on its reciprocal regulation of pyoverdine and achromobactin genes and also sulfur uptake, suggesting a role in the iron-sulfur balance. RetS functioned almost exclusively to repress secondary metabolite genes when the cells were not on leaves. Among the sigma factors examined, AlgU influenced many more genes than RpoS, and most AlgU-regulated genes depended on RpoN. RpoN differentially impacted many AlgU- and GacS-activated genes in cells recovered from apoplastic versus epiphytic sites, suggesting differences in environmental signals or bacterial stress status in these two habitats. Collectively, our findings illustrate a central role for GacS, SalA, RpoN, and AlgU in global regulation in B728a in planta and a high level of plasticity in these regulators' responses to distinct environmental signals. IMPORTANCE: Leaves harbor abundant microorganisms, all of which must withstand challenges such as active plant defenses and a highly dynamic environment. Some of these microbes can influence plant health. Despite knowledge of individual regulators that affect the fitness or pathogenicity of foliar pathogens, our understanding of the relative importance of various global regulators to leaf colonization is limited. Pseudomonas syringae strain B728a is a plant pathogen and a good colonist of both the surfaces and interior of leaves. This study used global transcript profiles of strain B728a to investigate the complex regulatory network of putative quorum-sensing regulators, two-component regulators, and sigma factors in cells colonizing the leaf surface and leaf interior under stressful in vitro conditions. The results highlighted the value of evaluating these networks in planta due to the impact of leaf-specific environmental signals and suggested signal differences that may enable cells to differentiate surface versus interior leaf habitats.

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Boltzmann's H-function and diffusion processes.

There exists a generalization of Boltzmann's H-function that allows for nonuniformly populated stationary states, which may exist far from thermodynamic equilibrium. Here we describe a method for obtaining a generalized or collective diffusion coefficient D directly from this H-function, the only constraints being that the relaxation process is Markov (short memory), continuous in the reaction coordinate, and local in the sense of a flux/force relationship. As an application of this H-function method, we simulate the self-consistent extraction of D via Langevin/Fokker-Planck (L/FP) dynamics on various potential energy landscapes. We observe that the initial epoch of relaxation, which is far removed from the stationary state, provides the most reliable estimates of D. The construction of an H-function that guarantees conformity with the second law of thermodynamics has been generalized to allow for diffusion coefficients that may depend on both the reaction coordinate and time, and the extension to an arbitrary number of reaction coordinates is straightforward. For this multidimensional case, the diffusion tensor must be positive definite in the sense that its eigenvalues must be real and positive. To illustrate the behavior of the proposed collective diffusion coefficient, we simulate the H-function for a variety of Langevin systems. In particular, the impacts on H and D of landscape shape, sample size, selection of an initial distribution, finite dynamic observation range, stochastic correlations, and short/long-term memory effects are examined.