Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves.

Measurement of cell surface coverage has become a common technique for the assessment of growth behavior of cells. As an indirect measurement method, this can be accomplished by monitoring changes in electrode impedance, which constitutes the basis of electric cell-substrate impedance sensing (ECIS). ECIS typically yields growth curves where impedance is plotted against time, and changes in single cell growth behavior or cell proliferation can be displayed without significantly impacting cell physiology. To provide better comparability of ECIS curves in different experimental settings, we developed a large toolset of R scripts for their transformation and quantification. They allow importing growth curves generated by ECIS systems, edit, transform, graph and analyze them while delivering quantitative data extracted from reference points on the curve. Quantification is implemented through three different curve fit algorithms (smoothing spline, logistic model, segmented regression). From the obtained models, curve reference points such as the first derivative maximum, segmentation knots and area under the curve are then extracted. The scripts were tested for general applicability in real-life cell culture experiments on partly anonymized cell lines, a calibration setup with a cell dilution series of impedance versus seeded cell number and finally IPEC-J2 cells treated with 1% and 5% ethanol.

The DNA methylation profile of human spermatogonia at single-cell- and single-allele-resolution refutes its role in spermatogonial stem cell function and germ cell differentiation.

Human spermatogonial stem cells (hSSCs) have potential in fertility preservation of prepubertal boys or in treatment of male adults suffering from meiotic arrest. Prior to therapeutic application, in vitro propagation of rare hSSCs is mandatory. As the published data points to epigenetic alterations in long-term cell culture of spermatogonia (SPG), an initial characterisation of their DNA methylation state is important. Testicular biopsies from five adult normogonadotropic patients were converted into aggregate-free cell suspensions. FGFR3-positive (FGFR3+) SPG, resembling a very early stem cell state, were labelled with magnetic beads and isolated in addition to unlabelled SPG (FGFR3-). DNA methylation was assessed by limiting dilution bisulfite pyrosequencing for paternally imprinted (H19 and MEG3), maternally imprinted (KCNQ1OT1, PEG3, and SNRPN), pluripotency (POU5F1/OCT4 and NANOG), and spermatogonial/hSSC marker (FGFR3, GFRA1, PLZF, and L1TD1) genes on either single cells or pools of 10 cells. Both spermatogonial subpopulations exhibited a methylation pattern largely equivalent to sperm, with hypomethylation of hSSC marker and maternally imprinted genes and hypermethylation of pluripotency and paternally imprinted genes. Interestingly, we detected fine differences between the two spermatogonial subpopulations, which were reflected by an inverse methylation pattern of imprinted genes, i.e. decreasing methylation in hypomethylated genes and increasing methylation in hypermethylated genes, from FGFR3+ through FGFR3- SPG to sperm. Limitations of this study are due to it not being performed on a genome-wide level and being based on previously published regulatory gene regions. However, the concordance of DNA methylation between SPG and sperm implies that hSSC regulation and germ cell differentiation do not occur at the DNA methylation level.

Dynamics, ultrastructure and gene expression of human in vitro organized testis cells from testicular sperm extraction biopsies.

STUDY QUESTION: Is it possible to induce in vitro reorganization of primary human testis cells from testicular sperm extraction (TESE) biopsies, maintain their long-term cultivation in a 2D system and identify cellular compositions? SUMMARY ANSWER: In vitro reorganization of primary human testis cells from TESE biopsies and their long-term cultivation on uncoated cell culture dishes is feasible and the cellular compositions can be uncovered through gene expression and microscopic analyses. WHAT IS KNOWN ALREADY: It has been shown in the rodent model that mixtures of testicular cell types are able to reassemble into clusters when cultivated on different kinds of surfaces or three-dimensional matrices. Two recent publications demonstrated the ability of primary human testicular cells to assemble into testicular organoids and their cultivation for a period of 3-4 weeks. STUDY DESIGN SIZE, DURATION: Primary human testis cells from TESE biopsies from 16 patients were reorganized in vitro and the clusters were cultivated long term on uncoated cell culture dishes, providing a solid ground for in vitro spermatogenesis. Gene expression analysis as well as fluorescence/transmission electron microscopy (TEM) were employed to uncover the cellular composition of the clusters. PARTICIPANTS/MATERIALS, SETTING, METHODS: Testis biopsies from adult, normogonadotropic patients displaying full spermatogenesis (n = 11), hypospermatogenesis (n = 2), predominantly full spermatogenesis with some hypospermatogenic tubules (n = 1), meiotic arrest (n = 1) or mixed atrophy (n = 1) were enzymatically digested and dispersed cells were cultivated on 96-well plates or chamber dishes as aggregate-free cell suspensions. Time-lapse imaging of cluster formation was performed over a period of 48 h. For receptor tyrosine kinase inhibition of cluster formation, cells were treated twice with K252a within 2-3 days. Immunofluorescence staining and confocal microscopy was carried out on clusters after 1-3 weeks of cultivation to identify the presence of Sertoli cells (SC) (SOX9), peritubular myoid cells (SMA), Leydig cells (LC) (STAR), undifferentiated spermatogonia (FGFR3), differentiating spermatogonia/spermatocytes (DDX4) and postmeiotic germ cells (PRM1). Single clusters from four patients and a pool of eight larger clusters from another patient were manually picked and subjected to quantitative real-time PCR to evaluate the presence of SC (SOX9, AR), LC (INSL3, STAR, HSD3B1), peritubular myoid cells (ACTA2), fibroblasts (FSP1), endothelial cells (CD34), macrophages (CD68), undifferentiated spermatogonia (FGFR3), differentiating spermatogonia/spermatocytes (DDX4) and postmeiotic germ cells (PRM1). Finally, an ultrastructural investigation was conducted based on TEM of clusters from six different patients, among them 3-month cultivated large clusters from two patients. MAIN RESULTS AND THE ROLE OF CHANCE: Quantitative PCR-based analysis of single-picked testicular cell clusters identified SC, peritubular myoid cells, endothelial cells, fibroblasts, macrophages, spermatids and LC after 1, 2 or 3 weeks or 3 months of cultivation. Immunofluorescence positivity for SC and peritubular myoid cells corroborated the presence of these two kinds of testis niche cells. In addition, round as well as elongated spermatids were frequently encountered in 1 and 2 weeks old clusters. Transmission electron microscopical classification confirmed all these cell types together with a few spermatogonia. Macrophages were found to be of the proinflammatory M1 subtype, as revealed by CD68+/CD163-/IL6+ expression. Time-lapse imaging uncovered the specific dynamics of cluster fusion and enlargement, which could be prevented by addition of protein kinase inhibitor K252a. LARGE SCALE DATA: N/A. LIMITATIONS REASON FOR CAUTION: Cell composition of the clusters varied based on the spermatogenic state of the TESE patient. Although spermatids could be observed with all applied methods, spermatogonia were only detected by TEM in single cases. Hence, a direct maintenance of these germ cell types by our system in its current state cannot be postulated. Moreover, putative dedifferentiation and malignant degeneration of cells in long-term cluster cultivation needs to be investigated in the future. WIDER IMPLICATIONS OF THE FINDINGS: This work demonstrates that the reorganization of testicular cells can be achieved with TESE biopsies obtained from men enroled in a standard clinical assisted reproduction program. The formed clusters can be cultivated for at least 3 months and are composed, to a large extent, of the most important somatic cell types that are essential to support spermatogenesis. These findings may provide the cellular basis for advances in human in vitro spermatogenesis and/or the possibility for propagation of spermatogonia within a natural stem cell niche-like environment. STUDY FUNDING AND COMPETING INTERESTS: The project was funded by a DFG grant to K.v.K. (KO 4769/2-1). The authors declare they have no conflicts of interest.

Enabling reproducible real-time quantitative PCR research: the RDML package.

MOTIVATION: Reproducibility, a cornerstone of research, requires defined data formats, which include the setup and output of experiments. The real-time PCR data markup language (RDML) is a recommended standard of the minimum information for publication of quantitative real-time PCR experiments guidelines. Despite the popularity of the RDML format for analysis of quantitative PCR data, handling of RDML files is not yet widely supported in all PCR curve analysis softwares. RESULTS: This study describes the open-source RDML package for the statistical computing language R. RDML is compatible with RDML versions ≤ 1.2 and provides functionality to (i) import RDML data; (ii) extract sample information (e.g. targets and concentration); (iii) transform data to various formats of the R environment; (iv) generate human-readable run summaries; and (v) to create RDML files from user data. In addition, RDML offers a graphical user interface to read, edit and create RDML files. AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/package=RDML. rdmlEdit server http://shtest.evrogen.net/rdmlEdit/. Documentation: http://kablag.github.io/RDML/. CONTACT: k.blag@yandex.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bias and imprecision in analysis of real-time quantitative polymerase chain reaction data.

Monte Carlo simulations are used to examine the bias and loss of precision that result from experimental error and analysis procedures in real-time quantitative polymerase chain reaction (PCR). In the limit of small copy numbers (N0), Poisson statistics govern the dispersion in estimates of the quantification cycle (Cq) for replicate experiments, permitting the estimation of N0 from the Cq variance, which is inversely proportional to N0. We derive corrections to expressions given previously for this determination. With increasing N0, the Poisson contribution decreases and other effects, like pipet volume uncertainty (typically >3%), dominate. Cycle-to-cycle variability in the amplification efficiency E produces scale dispersion similar to that for variability in the sensitivity of fluorescence detection. When this E variability is proportional to just the amplification (E - 1), there is insignificant effect on Cq if scale-independent definitions are used for this marker. Single-reaction analysis methods based on the exponential growth equation are inherently low-biased in E and high-biased in N0, and these biases can amount to factor-of-4 or greater error in N0. For estimating Cq, their greatest limitation is use of a constant absolute threshold, making them inefficient for data that exhibit scale variability.

Absolute copy number from the statistics of the quantification cycle in replicate quantitative polymerase chain reaction experiments.

The quantification cycle (Cq) is widely used for calibration in real-time quantitative polymerase chain reaction (qPCR), to estimate the initial amount, or copy number (N0), of the target DNA. Cq may be defined several ways, including the cycle where the detected fluorescence achieves a prescribed threshold level. For all methods of defining Cq, the standard deviation from replicate experiments is typically much greater than the estimated standard errors from the least-squares fits used to obtain Cq. For moderate-to-large copy number (N0 > 10(2)), pipet volume uncertainty and variability in the amplification efficiency (E) likely account for most of the excess variance in Cq. For small N0, the dispersion of Cq is determined by the Poisson statistics of N0, which means that N0 can be estimated directly from the variance of Cq. The estimation precision is determined by the statistical properties of χ(2), giving a relative standard deviation of ∼(2/n)(1/2), where n is the number of replicates, for example, a 20% standard deviation in N0 from 50 replicates.

Impact of smoothing on parameter estimation in quantitative DNA amplification experiments.

BACKGROUND: Quantification cycle (Cq) and amplification efficiency (AE) are parameters mathematically extracted from raw data to characterize quantitative PCR (qPCR) reactions and quantify the copy number in a sample. Little attention has been paid to the effects of preprocessing and the use of smoothing or filtering approaches to compensate for noisy data. Existing algorithms largely are taken for granted, and it is unclear which of the various methods is most informative. We investigated the effect of smoothing and filtering algorithms on amplification curve data. METHODS: We obtained published high-replicate qPCR data sets from standard block thermocyclers and other cycler platforms and statistically evaluated the impact of smoothing on Cq and AE. RESULTS: Our results indicate that selected smoothing algorithms affect estimates of Cq and AE considerably. The commonly used moving average filter performed worst in all qPCR scenarios. The Savitzky-Golay smoother, cubic splines, and Whittaker smoother resulted overall in the least bias in our setting and exhibited low sensitivity to differences in qPCR AE, whereas other smoothers, such as running mean, introduced an AE-dependent bias. CONCLUSIONS: The selection of a smoothing algorithm is an important step in developing data analysis pipelines for real-time PCR experiments. We offer guidelines for selection of an appropriate smoothing algorithm in diagnostic qPCR applications. The findings of our study were implemented in the R packages chipPCR and qpcR as a basis for the implementation of an analytical strategy.

Comparing real-time quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency.

New methods are used to compare seven qPCR analysis methods for their performance in estimating the quantification cycle (Cq) and amplification efficiency (E) for a large test data set (94 samples for each of 4 dilutions) from a recent study. Precision and linearity are assessed using chi-square (χ(2)), which is the minimized quantity in least-squares (LS) fitting, equivalent to the variance in unweighted LS, and commonly used to define statistical efficiency. All methods yield Cqs that vary strongly in precision with the starting concentration N0, requiring weighted LS for proper calibration fitting of Cq vs log(N0). Then χ(2) for cubic calibration fits compares the inherent precision of the Cqs, while increases in χ(2) for quadratic and linear fits show the significance of nonlinearity. Nonlinearity is further manifested in unphysical estimates of E from the same Cq data, results which also challenge a tenet of all qPCR analysis methods - that E is constant throughout the baseline region. Constant-threshold (Ct) methods underperform the other methods when the data vary considerably in scale, as these data do.

Statistical uncertainty and its propagation in the analysis of quantitative polymerase chain reaction data: comparison of methods.

Most methods for analyzing real-time quantitative polymerase chain reaction (qPCR) data for single experiments estimate the hypothetical cycle 0 signal y0 by first estimating the quantification cycle (Cq) and amplification efficiency (E) from least-squares fits of fluorescence intensity data for cycles near the onset of the growth phase. The resulting y0 values are statistically equivalent to the corresponding Cq if and only if E is taken to be error free. But uncertainty in E usually dominates the total uncertainty in y0, making the latter much degraded in precision compared with Cq. Bias in E can be an even greater source of error in y0. So-called mechanistic models achieve higher precision in estimating y0 by tacitly assuming E=2 in the baseline region and so are subject to this bias error. When used in calibration, the mechanistic y0 is statistically comparable to Cq from the other methods. When a signal threshold yq is used to define Cq, best estimation precision is obtained by setting yq near the maximum signal in the range of fitted cycles, in conflict with common practice in the y0 estimation algorithms.

Fold-change correction values for testicular somatic transcripts in gene expression studies of human spermatogenesis.

STUDY QUESTION: What are the reference values for delineating altered somatic cell gene expression from transcript enrichment/dilution in gene expression studies of human spermatogenesis? SUMMARY ANSWER: We have designed a crosstable and rule-of-thumb values for different stages of spermatogenic impairment that define the reference cut-off values for altered gene expression in Sertoli and Leydig cells in the context of impaired spermatogenesis. WHAT IS KNOWN ALREADY: Morphometrical studies have shown that on the cellular level, impaired spermatogenesis results in a relative enrichment of somatic cell types. However, until now it is not known how this affects transcript levels in gene expression studies. STUDY DESIGN, SIZE DURATION: In this study, 31 testis biopsies from men with different stages of spermatogenic impairment (full spermatogenesis, hypospermatogenesis, meiotic arrest, spermatogonial presence, Sertoli cell-only syndrome, complete tubular atrophy) were used to define reference ratios of somatic transcript enrichment/dilution. The reference ratios were validated on an independent test set of 28 samples and on gene expression data from men with Y-chromosomal microdeletions. PARTICIPANTS/MATERIAL, SETTING, METHODS: High-quality microarray data were filtered with respect to Sertoli- and Leydig-cell-specific genes. General reference enrichment/dilution factors for these two cell types for all combinations of spermatogenic impairment were calculated using robust permutation statistics. To validate the specificity of the filtered transcripts, we calculated ratios for an independent test set of spermatogenic impairment and for transcriptional data from men with Y-chromosomal microdeletions, and checked the functional enrichment (gene ontology) and cellular localization of the corresponding proteins in a histological database and assessed their correlation with testicular size. MAIN RESULTS AND THE ROLE OF CHANCE: Filtering of Sertoli- and Leydig-cell-specific genes resulted in a set of 54 and 332 transcripts, respectively. These were used in defining robust reference dilution/enrichment factors of somatic transcripts for all spermatogenic levels and were compiled in a reference crosstable. Validation on an independent test set showed ratios within 0.5 units of our reference crosstable. Analysis of the resulting transcripts with respect to functional enrichment for Sertoli- and Leydig-cell-specific functions and protein expression, as obtained from an immunohistochemical database, indicated filtering of data sets highly enriched for Sertoli and Leydig cell function. The dilution/enrichment ratios differed significantly when transcripts were interrogated in samples with Y-chromosomal microdeletions, pointing to an overall decreased expression of somatic markers in a genetically altered background. LIMITATIONS, REASONS FOR CAUTION: The defined reference ratios might apply with some restrictions in samples that display very heterogeneous histology (e.g. Sertoli cell only with a significant proportion of spermatogenic foci) or when spermatogenic impairment is a consequence of an altered genetic background. WIDER IMPLICATIONS OF THE FINDINGS: The reference dilution/enrichment values for somatic testicular transcripts as defined in this study are to be seen as cut-off values for discriminating between simple transcript dilution/enrichment as a consequence of an altered germ cell composition and actual transcriptional regulation. Future studies dealing with transcriptional changes in testicular somatic cells in a background of altered germ cell quantities should consider these correction factors in order to avoid the description of transcriptional changes that are based simply on shifts in somatic cellular quantities. STUDY FUNDING/COMPETING INTEREST(S): Financial support was from grant Sp721/1-3 of the German Research Foundation. There are no competing interests to be declared.

Differential marker protein expression specifies rarefaction zone-containing human Adark spermatogonia.

It is unclear whether the distinct nuclear morphologies of human A(dark) (Ad) and A(pale) (Ap) spermatogonia are manifestations of different stages of germ cell development or phases of the mitotic cycle, or whether they may reflect still unknown molecular differences. According to the classical description by Clermont, human dark type A spermatogonium (Ad) may contain one, sometimes two or three nuclear 'vacuolar spaces' representing chromatin rarefaction zones. These structures were readily discerned in paraffin sections of human testis tissue during immunohistochemical and immunofluorescence analyses and thus represented robust morphological markers for our study. While a majority of the marker proteins tested did not discriminate between spermatogonia with and without chromatin rarefaction zones, doublesex- and mab-3-related transcription factor (DMRT1), tyrosine kinase receptor c-Kit/CD117 (KIT) and proliferation-associated antigen Ki-67 (KI-67) appeared to be restricted to subtypes which lacked the rarefaction zones. Conversely, exosome component 10 (EXOSC10) was found to accumulate within the rarefaction zones, which points to a possible role of this nuclear domain in RNA processing.

Highly purified spermatozoal RNA obtained by a novel method indicates an unusual 28S/18S rRNA ratio and suggests impaired ribosome assembly.

Human spermatozoal RNA features special characteristics such as a significantly reduced quantity within spermatozoa compared with somatic cells is described as being devoid of ribosomal RNAs and is difficult to isolate due to a massive excess of genomic DNA in the lysates. Using a novel two-round column-based protocol for human ejaculates delivering highly purified spermatozoal RNA, we uncovered a heterogeneous, but specific banding pattern in microelectrophoresis with 28S ribosomal RNA being indicative for the amount of round cell contamination. Ejaculates with different round cell quantities and density-purified spermatozoa revealed that 18S rRNA but not 28S rRNA is inherent to a pure spermatozoal fraction. Transmission electron microscopy showed monoribosomes and polyribosomes in spermatozoal cytoplasm, while immunohistochemical results suggest the presence of proteins from small and large ribosomal subunits in retained spermatozoal cytoplasm irrespective of 28S rRNA absence.

A biopsy sample reduction approach to identify significant alterations of the testicular transcriptome in the presence of Y-chromosomal microdeletions that are independent of germ cell composition.

Y-chromosomal microdeletions (YCMD) are the major genetic cause of male infertility. To date, it is not known which global changes are induced by the presence of AZFc or AZFb+c deletions in the human testicular transcriptome. We investigated this question by microarray analysis in which we had to eliminate the 'germ cell effect', i.e., the dominating effect of germ cell transcripts due to the quantitative difference in germ cell composition in samples with/without YCMD. This problem was tackled by selecting 26 samples from an initial cohort of 34 samples by their homogeneity in respect to cellular composition as obtained from gene expression clustering. This way, the 'germ cell effect' was minimized, and a distinct 'deletion effect' became more apparent. Several hundred genes are influenced by YCMD as shown on the three different phenotypes hypospermatogenesis, meiotic arrest, and Sertoli-cell only syndrome. We validated on an independent cohort of samples five genes by quantitative real-time PCR that are expressed in germ cells or the somatic compartment and which are exclusively altered by the presence of YCMD. We conclude that the deletion of Y-chromosomal genes has a significant effect on spermatogenesis by modulating the transcriptional network of the germ cell and somatic compartment.

Screening for biomarkers of spermatogonia within the human testis: a whole genome approach.

BACKGROUND: A key step in studying the biology of spermatogonia is to determine their global gene expression profile. However, disassociation of these cells from the testis may alter their profile to a considerable degree. To characterize the molecular phenotype of human spermatogonia, including spermatogonial stem cells (SSCs), within their cognate microenvironment, a rare subtype of human defective spermatogenesis was exploited in which spermatogonia were the only germ cell type. METHODS: The global expression profile of these samples was assessed on the Affymetrix microarray platform and compared with tissues showing homogeneous Sertoli-cell-only appearance; selected genes were validated by quantitative real-time PCR and immunohistochemistry on disparate sample sets. RESULTS: Highly significant differences in gene expression levels correlated with the appearance of spermatogonia, including 239 best candidates of human spermatogonially expressed genes. Specifically, fibroblast growth factor receptor 3 (FGFR3), desmoglein 2 (DSG2), E3 ubiquitin ligase c-CBL (casitas B-cell lymphoma), cancer/testis antigen NY-ESO-1 (CTAG1A/B), undifferentiated embryonic cell transcription factor 1 (UTF1) and synaptosomal-associated protein, 91 kDa homolog (SNAP91) were shown to represent specific biomarkers of human spermatogonia. CONCLUSIONS: These biomarkers, specifically the surface markers FGFR3 and DSG2, may facilitate the isolation and enrichment of human stem and/or progenitor spermatogonia and thus lay a foundation for studies of long-term maintenance of human SSCs/progenitor cells, spermatogonial self-renewal, clonal expansion and differentiation.

An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach.

BACKGROUND: It is long known within the mathematical literature that the coefficient of determination R(2) is an inadequate measure for the goodness of fit in nonlinear models. Nevertheless, it is still frequently used within pharmacological and biochemical literature for the analysis and interpretation of nonlinear fitting to data. RESULTS: The intensive simulation approach undermines previous observations and emphasizes the extremely low performance of R(2) as a basis for model validity and performance when applied to pharmacological/biochemical nonlinear data. In fact, with the 'true' model having up to 500 times more strength of evidence based on Akaike weights, this was only reflected in the third to fifth decimal place of R(2). In addition, even the bias-corrected R(2)(adj) exhibited an extreme bias to higher parametrized models. The bias-corrected AICc and also BIC performed significantly better in this respect. CONCLUSION: Researchers and reviewers should be aware that R(2) is inappropriate when used for demonstrating the performance or validity of a certain nonlinear model. It should ideally be removed from scientific literature dealing with nonlinear model fitting or at least be supplemented with other methods such as AIC or BIC or used in context to other models in question.

qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis.

UNLABELLED: The qpcR library is an add-on to the free R statistical environment performing sigmoidal model selection in real-time quantitative polymerase chain reaction (PCR) data analysis. Additionally, the package implements the most commonly used algorithms for real-time PCR data analysis and is capable of extensive statistical comparison for the selection and evaluation of the different models based on several measures of goodness of fit. AVAILABILITY: www.dr-spiess.de/qpcR.html. SUPPLEMENTARY INFORMATION: Statistical evaluations of the implemented methods can be found at www.dr-spiess.de under 'Supplemental Data'.

qPCR data analysis: Better results through iconoclasm.

The standard approach for quantitative estimation of genetic materials with qPCR is calibration with known concentrations for the target substance, in which estimates of the quantification cycle (Cq ) are fitted to a straight-line function of log(N 0), where N 0 is the initial number of target molecules. The location of Cq for the unknown on this line then yields its N 0. The most widely used definition for Cq is an absolute threshold that falls in the early growth cycles. This usage is flawed as commonly implemented: threshold set very close to the baseline level, which is estimated separately, from designated "baseline cycles." The absolute threshold is especially poor for dealing with the scale variability often observed for growth profiles. Scale-independent markers, like the first derivative maximum (FDM) and a relative threshold (Cr ) avoid this problem. We describe improved methods for estimating these and other Cq markers and their standard errors, from a nonlinear algorithm that fits growth profiles to a 4-parameter log-logistic function plus a baseline function. Further, by examining six multidilution, multireplicate qPCR data sets, we find that nonlinear expressions are often preferred statistically for the dependence of Cq on log(N 0). This means that the amplification efficiency E depends on N 0, in violation of another tenet of qPCR analysis. Neglect of calibration nonlinearity leads to biased estimates of the unknown. By logic, E estimates from calibration fitting pertain to the earliest baseline cycles, not the early growth cycles used to estimate E from growth profiles for single reactions. This raises concern about the use of the latter in lengthy extrapolations to estimate N 0. Finally, we observe that replicate ensemble standard deviations greatly exceed predictions, implying that much better results can be achieved from qPCR through better experimental procedures, which likely include reducing pipette volume uncertainty.

Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry.

BACKGROUND: Fitting four-parameter sigmoidal models is one of the methods established in the analysis of quantitative real-time PCR (qPCR) data. We had observed that these models are not optimal in the fitting outcome due to the inherent constraint of symmetry around the point of inflection. Thus, we found it necessary to employ a mathematical algorithm that circumvents this problem and which utilizes an additional parameter for accommodating asymmetrical structures in sigmoidal qPCR data. RESULTS: The four-parameter models were compared to their five-parameter counterparts by means of nested F-tests based on the residual variance, thus acquiring a statistical measure for higher performance. For nearly all qPCR data we examined, five-parameter models resulted in a significantly better fit. Furthermore, accuracy and precision for the estimation of efficiencies and calculation of quantitative ratios were assessed with four independent dilution datasets and compared to the most commonly used quantification methods. It could be shown that the five-parameter model exhibits an accuracy and precision more similar to the non-sigmoidal quantification methods. CONCLUSION: The five-parameter sigmoidal models outperform the established four-parameter model with high statistical significance. The estimation of essential PCR parameters such as PCR efficiency, threshold cycles and initial template fluorescence is more robust and has smaller variance. The model is implemented in the qpcR package for the freely available statistical R environment. The package can be downloaded from the author's homepage.

Algorithms for automated detection of hook effect-bearing amplification curves.

Amplification curves from quantitative Real-Time PCR experiments typically exhibit a sigmoidal shape. They can roughly be divided into a ground or baseline phase, an exponential amplification phase, a linear phase and finally a plateau phase, where in the latter, the PCR product concentration no longer increases. Nevertheless, in some cases the plateau phase displays a negative trend, e.g. in hydrolysis probe assays. This cycle-to-cycle fluorescence decrease is commonly referred to in the literature as the hook effect. Other detection chemistries also exhibit this negative trend, however the underlying molecular mechanisms are different. In this study we present two approaches to automatically detect hook effect-like curvatures based on linear (hookreg) and nonlinear regression (hookregNL). As the hook effect is typical for qPCR data, both algorithms can be employed for the automated identification of regular structured qPCR curves. Therefore, our algorithms streamline quality control, but can also be used for assay optimization or machine learning.

Human spermatogonial markers.

In this review, we provide an up-to-date compilation of published human spermatogonial markers, with focus on the three nuclear subtypes Adark, Apale and B. In addition, we have extended our recently published list of putative spermatogonial markers with protein expression and RNA-sequencing data from the Human Protein Atlas and supported these by literature evidence. Most importantly, we have put substantial effort in acquiring a comprehensive list of new and potentially interesting markers by refiltering the raw data of 15 published germ cell expression datasets (four human, eleven rodent) and subsequent building of intersections to acquire a robust, cross-species set of spermatogonia-enriched or -specific transcripts.