CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development

ABSTRACTCells continuously communicate with the neighboring cells during development. Direct interaction of different cell types can induce molecular signals dictating lineage specification and cell fate decisions. The current single cell RNAseq (scRNAseq) technology cannot study cell contact dependent gene expression due to the loss of spatial information. To overcome this issue and determine cell contact specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PICseq) and assessed alongside our single cell transcriptomes (scRNAseq) derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of PICseq data identifies an interesting suite of gene expression signatures depending on neighboring cell types. For instance, neural progenitor (NP) cells expressed Nkx2-1 when interacting with definitive endoderm (DE) and DE cells expressed Gsc when interacting with NP. Based on the identified cell contact specific genes, we devised a means to predict the neighboring cell types from individual cell transcriptome. We further developed spatial-tSNE to show the pseudo-spatial distribution of cells in a 2-dimensional space. In sum, we suggest an approach to study contact specific gene regulation during embryogenesis.

PINFORMED1 polarity in the shoot is insensitive to the polarity of neighbouring cells

Cell polarity patterns associated with plant phyllotaxis are thought to be determined by mechanical signals or auxin flux. Here we use mosaic expression of the serine threonine kinase PINOID (PID) in the shoot to investigate the flux hypothesis. We find that PID promotes changes in PIN1 polarity irrespective of initial or neighboring cell polarities, arguing against a role for flux in regulating phyllotaxis.

Foregut organ progenitors and their niche display distinct viscoelastic properties in vivo during early morphogenesis stages

Material properties of living matter play an important role for biological function and development. Yet, quantification of material properties of internal organs in vivo, without causing physiological damage, remains challenging. Here, we present a non-invasive approach based on modified optical tweezers for quantifying sub-cellular material properties deep inside living zebrafish. Material properties of cells within the gut region of living zebrafish are quantified as deep as 150 μ into the biological tissue. The measurements demonstrate differential mechanical properties of the developing foregut organs progenitors: Gut progenitors are more elastic than any of the neighboring cell populations at the time when the developing organs undergo substantial displacements during morphogenesis. The higher elasticity of gut progenitors correlates with an increased cellular concentration of microtubules. The results infer a role of material properties during morphogenesis and the approach paves the way for quantitative material investigations in vivo of embryos, explants, or organoids.

SpatialDWLS: accurate deconvolution of spatial transcriptomic data

Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development.

Most of Tobacco Male Meiocytes Are Involved in Intercellular Nuclear Migration at the First Meiotic Prophase

Serial block-face scanning electron microscopy was used here to study tobacco male meiosis. Three-dimensional ultrastructural analyses revealed that intercellular nuclear migration (INM) occurs in 90–100% of tobacco meiocytes. At the very beginning of meiosis, every meiocyte connected with neighboring cells by more than 100 channels was capable of INM. At leptotene and zygotene, the nucleus in most tobacco meiocytes approached the cell wall and formed nuclear protuberances (NPs) that crossed the cell wall through the channels and got into the cytoplasm of a neighboring cell. The NPs did not separate from the migrating nuclei and never produced micronuclei. Approximately 70% of NPs reached nuclei of neighboring cells. The NPs and the nuclei they reached got very close, and the gap between their nuclear membranes became indistinguishable in some cases. At pachytene, NPs detached from the nuclei of neighboring cells and came back into their own cells. After that, the INM stopped. The reason for such behavior of nuclei is unclear. INM probably causes a short-lived fusion of two nuclei and thus has a potential to form aneuploid or unreduced pollen. We consider INM a normal part of tobacco meiosis.

Hybrid Design and RF Planning for 4G networks using Cell Prioritization Scheme

This article includes hands-on recreation practice on arranging of RF connect with the assistance of Atoll arranging programming device. The primary goal of this task is to structure and plan a RF coordinate with obstruction free correspondence, Optimum inclusion, no forgot about zone in the arranged inclusion guide and extension and reuse of site recurrence &network structure. Using the accessible restricted data transfer capacity vitally in order to take into account millions out of an immense zone with great quality, inclusion, and without obstruction utilizing ATOLL arranging device. Insightful re-utilization of site area later on organize structure will set aside cash for the administrator. Handover component is critical in cell arrange in light of the cell engineering utilized to expand range usage. One approach to improve the phone organize execution is to utilize productive handover prioritization plans, which have a typical trademark lessening the call dropping likelihood to the detriment of expanded call blocking likelihood. Effective prioritization conspire obliges various new calls while ensures the nature of administration (QOS) of Hand over call. This thought depends on the neighboring cells have a covering (the territory served by more than one cell) inclusion zone. Moreover cell cover and burden adjusting plan is proposed to improve the GSM cell limit utilizing a Software advancement pack (SDK). Limit improvement is accomplished by adjusting the heap in neighboring cell prioritization plans when client is exchanging between the cells.

Rhes, a Striatal-Enriched Protein, Promotes Mitophagy Via Nix

Elimination of dysfunctional mitochondria via mitophagy is essential for cell survival and neuronal functions. But, how impaired mitophagy participates in tissue-specific vulnerability in the brain remains unclear. Here we discovered that Rhes, a striatal-enriched protein, is a major regulator of mitophagy in the striatum. Rhes predominantly interact with dysfunctional mitochondria and degrades them via mitophagy, and this function is exacerbated by the striatal toxin, 3-nitropropionic acid (3-NP). 3-NP induces mitochondrial swelling, loss of cristae and neuronal cell death only in WT but not Rhes KO striatum. Mechanistically, Rhes disrupts the mitochondrial membrane potential (ΔΨm) and interacts with mitophagy receptor, Nix. In Nix KO cells, Rhes fails to disrupt ΔΨm or eliminate dysfunctional mitochondria. Moreover, Rhes travels to the neighboring cell and associates with dysfunctional mitochondria via Nix. Collectively, Rhes is a major regulator of mitophagy via Nix which may determine striatal vulnerability in the brain.

A fast and simple algorithm for calculating flow accumulation matrices from raster digital elevation models

<p><strong>Abstract.</strong> Flow accumulation is an essential input for many hydrological and topographic analyses such as stream channel extraction, stream channel ordering and sub-watershed delineation. Flow accumulation matrices can be derived directly from DEMs and general have O(NlogN) time complexity (Arge, 2003; Bai et al., 2015). It is more common to derive the flow accumulation matrix from a flow direction matrix. This study focuses on calculating the flow accumulation matrix from the flow direction matrix that is derived using the single-flow D8 method (Barnes et al., 2014; Garbrecht &amp; Martz, 1997; Nardi et al., 2008; O'Callaghan &amp; Mark, 1984). In this study, we find give an overview of algorithms for flow accumulation calculation that have O(N) time complexity. These algorithms include algorithms are based on the concept of the number of input drainage paths (Wang et al.2011, Jiang et al. 2013), the algorithm based on the basin tree indices (Su et al. 2015), and the recursive algorithm (Choi, 2012; Freeman, 1991).</p><p>We propose a fast and simple algorithm to calculate the flow accumulation matrix. Compared with the existing algorithms that have O(N) time complexity, our algorithm runs faster and generally requires less memory. Our algorithm is also simple to implement. In our algorithm, we define three types of cells within a flow direction matrix: source cells, interior cells and intersection cells. A source cell does not have neighboring cells that drain to it and its NIDP value is zero. An interior cell has only one neighboring cell that drains to it and its NIDP value is one. An intersection cell has more than one neighboring cell that drains to it and its NIDP value is greater than one. The proposed algorithm initializes the flow accumulation matrix with the value of one. Our algorithm first calculates the NIDP matrix from the flow direction matrix. The algorithm then traverses each cell within the flow direction matrix row by row and column by column, similar to the traversal algorithm. When a source cell <i>c</i> is encountered, the algorithm traces all downstream cells of <i>c</i> until it encounters an intersection cell <i>i</i>. During the tracing, the accumulation value of a cell is added to the accumulation value of its immediate downstream cell. An interior cell has only one neighboring cell that drains to it and its final accumulation value is obtained when the tracing is done. The accumulation value of the intersection cell i is updated from this drainage path. However, cell <i>i</i> has other unvisited neighboring cells that drain to it and its final accumulation value cannot be obtained after this round of tracing. The algorithm decreases the NIDP value of <i>i</i> by one. Cell <i>i</i> is visited again when other drainage paths that pass through it are traced. When all of the drainage paths that pass through it are traced, cell <i>i</i> is treated as an interior cell and the final accumulation value of <i>i</i> is obtained correctly and the last tracing process can continue the tracing after cell <i>i</i> is treated as an interior cell. A worked example of the proposed algorithm is shown in Figure 1.</p><p>The five flow accumulation algorithms with O(N) time complexity, including Wang’s algorithm, Jiang’s algorithm, the BTI-based algorithm, the recursive algorithm and our proposed algorithm, are implemented in C++. The 3-m LiDAR-based DEMs of thirty counties in the state of Minnesota, USA, are downloaded from the FTP site operated by the Minnesota Geospatial Information Office. The first 30 counties in Minnesota in alphabetic order are chosen for the experiments to avoid selection bias. We use the algorithm proposed by Wang and Liu (2006) to fill the depressions and derive the flow direction matrices for all tested counties. The running times on the Windows system are listed in Figure 2.The average running times per 100 million cells are 14.42 seconds for Wang’s algorithm, 15.90 seconds for Jiang’salgorithm, 18.95 seconds for the BTI-based algorithm, 10.87 seconds for the recursive algorithm, and 5.26 seconds forour proposed algorithm. Our algorithm runs the fastest for all tested DEM. The speed-up ratios of our proposedalgorithm over the second fastest algorithm is about 51%.</p>

KARAKTERISTIK STOMATA PADA POHON DI RUANG TERBUKA HIJAU UNIVERSITAS TANJUNGPURA KOTA PONTIANAK

The purpose of this study was to describe the stomata found in pulai, mahang, saga, white meranti and ketapang plants in the green space area. This study uses a descriptive method to describe stomatal characteristics such as stomatal type, stomata size and number of stomata in perennials, mahang, saga, ketapang and white meranti. The study was conducted in two green open areas, namely the Untara Sylva Arboretum and in the public green open area. Length of stomata observation for 2 weeks. Overall the results of the observations obtained 4 (four) types of stomata that are the same in the two regions. The types of stomata are anisocytic in Pulai (Alstonia scholaris), anomosytic in mahang (Macaranga pruinosa), parasitic in saga (Adenanthera pavonina), pickled on white meranti (Shorea bracteolata), anisocytic on ketapang (Terminalia cattapa). In the Sylva Arboretum area, Untan, plant origin has a stomata number of 15 with stomata size of 14.1 μm, stomatal hole 8.03 μm and neighboring cells 14.7 μm. In the green open space area of the plant, there were ketapang plants had 35 stomata with a stomata size of 16.03 μm, a stomata hole of 8.03 and a neighboring cell of 6.57. the stomata structure shows that the leaves found in the arboretum region have large stomata while the outside of the general green open space has a small stomata. Different conditions of light and temperature greatly influence the variation in numbers on the size of the stomata, stomatal holes and neighboring cells. The farther the distance between plants and crowded places the more the size of the stomata decreases.Keywords: green open space, number of stomata, stomatal types, trees