Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.

Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.

Boreal bogs are important stores and sinks of atmospheric carbon whose surfaces are characterised by vegetation microforms. Efficient methods for monitoring their vegetation are needed because changes in vegetation composition lead to alteration in their function such as carbon gas exchange with the atmosphere. We investigated how airborne image and waveform-recording LiDAR data can be used for 3D mapping of microforms in an open bog which is a mosaic of pools, hummocks with a few stunted pines, hollows, intermediate surfaces and mud-bottom hollows. The proposed method operates on the bog surface, which is reconstructed using LiDAR. The vegetation was classified at 20 cm resolution. We hypothesised that LiDAR data describe surface topography, moisture and the presence and depth of field-layer vegetation and surface roughness; while multiple images capture the colours and texture of the vegetation, which are influenced by directional reflectance effects. We conclude that geometric LiDAR features are efficient predictors of microforms. LiDAR intensity and echo width were specific to moisture and surface roughness, respectively. Directional reflectance constituted 4-34 % of the variance in images and its form was linked to the presence of the field layer. Microform-specific directional reflectance patterns were deemed to be of marginal value in enhancing the classification, and RGB image features were inferior to LiDAR variables. Sensor fusion is an attractive option for fine-scale mapping of these habitats. We discuss the task and propose options for improving the methodology.

The mechanisms underlying how monozygotic (or identical) twins arise are yet to be determined. Here, the authors investigate this in an epigenome-wide association study, showing that monozygotic twinning has a characteristic DNA methylation signature in adult somatic tissues. Monozygotic (MZ) twins and higher-order multiples arise when a zygote splits during pre-implantation stages of development. The mechanisms underpinning this event have remained a mystery. Because MZ twinning rarely runs in families, the leading hypothesis is that it occurs at random. Here, we show that MZ twinning is strongly associated with a stable DNA methylation signature in adult somatic tissues. This signature spans regions near telomeres and centromeres, Polycomb-repressed regions and heterochromatin, genes involved in cell-adhesion, WNT signaling, cell fate, and putative human metastable epialleles. Our study also demonstrates a never-anticipated corollary: because identical twins keep a lifelong molecular signature, we can retrospectively diagnose if a person was conceived as monozygotic twin.

Background: CCHCR1 (Coiled Coil alpha-Helical Rod protein 1) is a putative psoriasis candidate gene with the risk alleles CCHCR1*WWCC and *Iso3, the latter inhibiting the translation of isoform 1. CCHCR1 was recently shown to be a centiosomal piotein, as well as a component of cytoplasmic piocessmg bodies (P-bodies) that regulate mRNA turnovel. The function of CCHCR1 has remained unsettled, partly because of the inconsistent findings, it has been shown to play a wide variety of roles in divergent processes, e.g., cell proliferation and steroidogenesis. Here we utilized RNA sequencing (RNAseq) using HEK293 cells overexpressing isoforms 1 or 3 (Iso1, Iso3 cells), in combination with the coding non-risk or risk (*WWCC haplotype of CCHCR1. Our aim was to study the overall role of CCHCR1 and the effects of its variants. Results: The overexpression of CCHCR1 variants in HEK293 cells resulted in cell line-specific expression profiles though seveial similarities were observable. Overall the Iso1 and Iso3 cells showed a clear isoform-specific clustering as two separate groups, and the Non-risk and Risk cells often exhibited opposite effects. The RNAseq supported a role for CCHCR1 in the centrosomes and P-bodies; the most highlighted pathways included regulation of cytoskeleton, adherens and tight junctions, mRNA surveillance and RNA transport. Interestingly, both the RNAseq and immunofluorescent localization revealed variant-specific differences for CCHCR1 within the P-bodies. Conclusions: CCHCR1 influenced a wide variety of signaling pathways, which could reflect its active role in the P-bodies and centrosomes that both are linked to the cytoskeleton; as a centrosomal P-body protein CCHCR1 may regulate diverse cytoskeleton-mediated functions, such as cell adhesion and division. The piesent findings may explain the previous inconsistent obseivations about the functions of CCHCR1.

There is a great need to identify factors that could protect pancreatic beta cells against apoptosis or stimulate their replication and thus prevent or reverse the development of diabetes. One potential candidate is mesencephalic astrocyte-derived neurotrophic factor (MANF), an endoplasmic reticulum (ER) stress inducible protein. Manf knockout mice used as a model of diabetes develop the condition because of increased apoptosis and reduced proliferation of beta cells, apparently related to ER stress. Given this novel association between MANF and beta cell death, we studied the potential of MANF to protect human beta cells against experimentally induced ER stress. Primary human islets were challenged with proinflammatory cytokines, with or without MANF. Cell viability was analysed and global transcriptomic analysis performed. Results were further validated using the human beta cell line EndoC-beta H1. There was increased expression and secretion of MANF in human beta cells in response to cytokines. Addition of recombinant human MANF reduced cytokine-induced cell death by 38% in human islets (p <0.05). MANF knockdown in EndoC-beta H1 cells led to increased ER stress after cytokine challenge. Mechanistic studies showed that the protective effect of MANF was associated with repression of the NF-kappa B signalling pathway and amelioration of ER stress. MANF also increased the proliferation of primary human beta cells twofold when TGF-beta signalling was inhibited (p <0.01). Our studies show that exogenous MANF protein can provide protection to human beta cells against death induced by inflammatory stress. The antiapoptotic and mitogenic properties of MANF make it a potential therapeutic agent for beta cell protection.

STUDY QUESTION: How can we study the full transcriptome of endometrial stromal and epithelial cells at the single-cell level? SUMMARY ANSWER: By compiling and developing novel analytical tools for biopsy, tissue cryopreservation and disaggregation, single-cell sorting, library preparation, RNA sequencing (RNA-seq) and statistical data analysis. WHAT IS KNOWN ALREADY: Although single-cell transcriptome analyses from various biopsied tissues have been published recently, corresponding protocols for human endometrium have not been described. STUDY DESIGN, SIZE, DURATION: The frozen-thawed endometrial biopsies were fluorescence-activated cell sorted (FACS) to distinguish CD13-positive stromal and CD9-positive epithelial cells and single-cell transcriptome analysis performed from biopsied tissues without culturing the cells. We studied gene transcription, applying a modern and efficient RNA-seq protocol. In parallel, endometrial stromal cells were cultured and global expression profiles were compared with uncultured cells. PARTICIPANTS/MATERIALS, SETTING, METHODS: For method validation, we used two endometrial biopsies, one from mid-secretory phase (Day 21, LH+8) and another from late-secretory phase (Day 25). The samples underwent single-cell FACS sorting, single-cell RNA-seq library preparation and Illumina sequencing. MAIN RESULTS AND THE ROLE OF CHANCE: Here we present a complete pipeline for single-cell gene-expression studies, from clinical sampling to statistical data analysis. Tissue manipulation, starting from disaggregation and cell-type-specific labelling and ending with single-cell automated sorting, is managed within 90 min at low temperature to minimize changes in the gene expression profile. The single living stromal and epithelial cells were sorted using CD13- and CD9-specific antibodies, respectively. Of the 8622 detected genes, 2661 were more active in cultured stromal cells than in biopsy cells. In the comparison of biopsy versus cultured cells, 5603 commonly expressed genes were detected, with 241 significantly differentially expressed genes. Of these, 231 genes were up- and 10 down-regulated in cultured cells, respectively. In addition, we performed a gene ontology analysis of the differentially expressed genes and found that these genes are mainly related to cell cycle, translational processes and metabolism. LIMITATIONS, REASONS FOR CAUTION: Although CD9-positive single epithelial cells sorting was successfully established in our laboratory, the amount of transcriptome data per individual epithelial cell was low, complicating further analysis. This step most likely failed due to the high dose of RNases that are released by the cells' natural processes, or due to rapid turnaround time or the apoptotic conditions in freezing- or single-cell solutions. Since only the cells from the late-secretory phase were subject to more focused analysis, further studies including larger sample size from the different time-points of the natural menstrual cycle are needed. The methodology also needs further optimization to examine different cell types at high quality. WIDER IMPLICATIONS OF THE FINDINGS: The symbiosis between clinical biopsy and the sophisticated laboratory and bioinformatic protocols described here brings together clinical diagnostic needs and modern laboratory and bioinformatic solutions, enabling us to implement a precise analytical toolbox for studying the endometrial tissue even at the single-cell level.

This article analyzes the riskiness attached to illicit drug use by means of a social representations framework. The implications of these social representations are further studied through their associations with views on drug policy: restrictive control and harm reduction measures. The data for the study is from a Finnish Drug Survey (N = 3229). Latent class analysis (LCA) and multinomial logistic regression analysis were used to analyze the data. Four representational profiles of perceived risk were identified: high-risk (70% of respondents), cannabis OK (15%), experimenting OK (10%), and low-risk (2%). All illicit drug use was considered as a moderate or high risk in the high-risk profile. Cannabis was considered less risky than other substances in the cannabis OK profile and experimenting was a less risky way of use in the experimenting OK profile. Most people in the low-risk profile considered the risks related to illicit drug use as nonexistent or minor. These representational profiles were also connected to opinions on drug policy: those who saw the most risks with use tended to support restrictive control measures, while those who considered illicit drug use to be less risky were more accepting toward harm reduction measures.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.