Biology subjects

Raj, A.

Publications and source records attributed to Raj, A..

100 records · Page 6Linked to original sources

bioRxiv2018

Systematic differences between visually-relevant global and local image statistics of brain MRI and natural scenes

An important heuristic in developing image processing technologies is to mimic the computational strategies used by humans. Relevant to this, recent studies have shown that the human brains processing strategy is closely matched to the characteristics of natural scenes, both in terms of global and local image statistics. However, structural MRI images and natural scenes have fundamental differences: the former are two-dimensional sections through a volume, the latter are projections. MRI image formation is also radically different from natural image formation, involving acquisition in Fourier space, followed by several filtering and processing steps that all have the potential to alter image statistics. As a consequence, aspects of the human visual system that are finely-tuned to processing natural scenes may not be equally well-suited for MRI images, and identification of the differences between MRI images and natural scenes may lead to improved machine analysis of MRI.\n\nWith these considerations in mind, we analyzed spectra and local image statistics of MRI images in several databases including T1 and FLAIR sequence types and of simulated MRI images,[1]-[6] and compared this analysis to a parallel analysis of natural images[7] and visual sensitivity[7][8]. We found substantial differences between the statistical features of MRI images and natural images. Power spectra of MRI images had a steeper slope than that of natural images, indicating a lack of scale invariance. Independent of this, local image statistics of MRI and natural images differed: compared to natural images, MRI images had smaller variations in their local two-point statistics and larger variations in their local three-point statistics - to which the human visual system is relatively insensitive. Our findings were consistent across MRI databases and simulated MRI images, suggesting that they result from brain geometry at the scale of MRI resolution, rather than characteristics of specific imaging and reconstruction methods.

neuroscience↗

bioRxiv2017

Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each genes expression distribution across cells, thus allowing the assessment of the dispersion, burstiness, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data is noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a re-examination of 9 public data sets, we propose a simple technical noise model for scRNA-seq data with Unique Molecular Identifiers (UMI). We develop DESCEND, a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and burstiness. DESCEND can adjust for cell-level covariates such as cell size, cell cycle and batch effects. DESCENDs noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations, and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially bursty genes, identifying cell types, and selecting differentiation markers.

genetics↗

bioRxiv2017

Single-molecule fluorescent amplification of RNA using clampFISH probes

Non-enzymatic, high-gain signal amplification methods with single-cell, single-molecule resolution are in great need. We present click-amplifying FISH (clampFISH) for the fluorescent detection of RNA that combines the specificity of oligonucleotides with bioorthogonal click chemistry in order to achieve high specificity and extremely high-gain (>400x) signal amplification. We show that clampFISH signal enables detection with low magnification microscopy and separation of cells by RNA levels via flow cytometry. Additionally, we show that the modular design of clampFISH probes enables multiplexing, that the locking mechanism prevents probe detachment in expansion microscopy, and that clampFISH works in tissue samples.

bioengineering↗

bioRxiv2017

SCGN Administration prevents Insulin Resistance and Diabetic Complications in High-Fat Diet Fed Animals

Secretagogin (SCGN) is poorly-studied secretory/cytosolic CaBP enriched in pancreatic {beta}-cells. Recent studies implicated SCGN in diabetes; however, its function and therapeutic prospect remain uncharted. Based on the apparent synchrony of SCGN and insulin secretion (and its disruption in HFD-fed animals) and considering SCGN downregulation in Type 2 diabetes, we hypothesized that SCGN is a key regulator of insulin response. To test this, we administered rSCGN to HFD-fed animals. We here report that a novel SCGN-insulin interaction stabilizes insulin and potentiates insulin action. Moreover, a chronic rSCGN administration improves insulin response and alleviates obesity associated risk factors such as weight gain, liver steatosis and cholesterol imbalance in DIO animals. Beside the anti-diabetic effects, prolonged rSCGN treatment also induces {beta}-cell regeneration. These effects seem to originate from SCGN mediated regulation of insulin concentration & function as validated in insulin-deficient STZ animals. Our results demonstrate the prospects of the therapeutic potential of SCGN against diabetes.

physiology↗

bioRxiv2017

A Comparison Between Single Cell RNA Sequencing And Single Molecule RNA FISH For Rare Cell Analysis

The development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what \"transcriptome coverage\" (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of [~]2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

genomics↗

bioRxiv2017

Gene Expression Recovery For Single Cell RNA Sequencing

Rapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution single cell profiling of biological samples. In most scRNA-seq studies, only a small fraction of the transcripts present in each cell are sequenced. The efficiency, that is, the proportion of transcripts in the cell that are sequenced, can be especially low in highly parallelized experiments where the number of reads allocated for each cell is small. This leads to unreliable quantification of lowly and moderately expressed genes, resulting in extremely sparse data and hindering downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for scRNA-seq that borrows information across genes and cells to impute the zeros as well as to improve the expression estimates for all genes. We show, by comparison to RNA fluorescence in situ hybridization (FISH) and by data down-sampling experiments, that SAVER reliably recovers cell-specific gene expression concentrations, cross-cell gene expression distributions, and gene-to-gene and cell-to-cell correlations. This improves the power and accuracy of any downstream analysis involving genes with low to moderate expression.

genomics↗

bioRxiv2017

Intercellular mRNA Trafficking Via Membrane Nanotubes In Mammalian Cells

RNAs have been shown to undergo transfer between mammalian cells, though the mechanism behind this phenomenon and its overall importance to cell physiology is not well understood. Numerous publications have suggested that RNAs (microRNAs and incomplete mRNAs) undergo transfer via extracellular vesicles (e.g. exosomes). However, in contrast to a diffusion-based transfer mechanism, we find that full-length mRNAs undergo direct cell-cell transfer via cytoplasmic extensions, called membrane nanotubes (mNTs), which connect donor and acceptor cells. By employing a simple co-culture experimental model and using single-molecule imaging, we provide quantitative data showing that mRNAs are transferred between cells in contact. Examples of mRNAs that undergo transfer include those encoding GFP, mouse {beta}-actin, and human Cyclin D1, BRCA1, MT2A, and HER2. We show that intercellular mRNA transfer occurs in all co-culture models tested (e.g. between primary cells, immortalized cells, and in co-cultures of immortalized human and murine cells). Rapid mRNA transfer is dependent upon actin, but independent of de novo protein synthesis, and is modulated by stress conditions and gene expression levels. Hence, this work supports the hypothesis that full-length mRNAs undergo transfer between cells through a refined structural connection. Importantly, unlike the transfer of miRNA or RNA fragments, this process of communication transfers genetic information that could potentially alter the acceptor cell proteome. This phenomenon may prove important for the proper development and functioning of tissues, as well as host-parasite or symbiotic interactions.\n\nSignificanceMessenger RNA (mRNA) molecules convey genetic information within cells, beginning from genes in the nucleus to ribosomes in the cell body, where they are translated into proteins. Here, we show a novel mode of transferring genetic information from one cell to another. Contrary to previous publications suggesting that mRNAs transfer via extracellular vesicles, we provide visual and quantitative data showing that mRNAs transfer via membrane nanotubes and direct cell-to-cell contact. We predict that this process has a major role in regulating local cellular environments with respect to tissue development and maintenance, cellular responses to stress, interactions with parasites, tissue transplants, and the tumor microenvironment.\n\nAuthor contributionsG.H., A.R. and R.H.S. conceived the research and designed the experiments; C.M.E. performed and analyzed the experiments with WM983b+/-GFP, including transwell and exosomes; M.C.D. and E.E. performed and analyzed the WM983b/NIH393 co-culture experiments; G.H. performed and analyzed all other experiments; and G.H., J.E.G, A.R. and R.H.S. wrote the paper.

cell biology↗

bioRxiv2017

Replication Defective Viral Genomes Exploit A Cellular Pro-Survival Mechanism To Establish Viral Persistence

Replication defective viral genomes (DVGs) generated during virus replication are the primary triggers of antiviral immunity in many RNA virus infections. However, DVGs can also facilitate viral persistence. Why and how these two opposing functions of DVGs are achieved remain unknown. Here we report that during Sendai and respiratory syncytial virus infections DVGs selectively protect a subpopulation of cells from death and promote the establishment of persistent infections. We find that during Sendai virus infection this phenotype results from DVGs stimulating a MAVS-mediated TNF response that drives apoptosis of highly infected cells while extending the survival of cells enriched in DVGs. The pro-survival effect of TNF depends on the activity of the TNFR2/TRAF1 pathway that is regulated by MAVS signaling. These results identify TNF as a pivotal factor in determining cell fate during a viral infection and delineate a MAVS/TNFR2-mediated mechanism that drives the persistence of otherwise acute viruses.

microbiology↗

bioRxiv2016

Impact of regulatory variation across human iPSCs and differentiated cells

Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation across different cell types and as models for studies of complex disease. We established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression, chromatin accessibility and DNA methylation. Regulatory variation between individuals is lower in iPSCs than in the differentiated cell types, consistent with the intuition that developmental processes are generally canalized. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific QTLs are in shared open chromatin. Finally, we developed a deep neural network to predict open chromatin regions from DNA sequence alone and were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.

genomics↗

bioRxiv2016

Visualizing adenosine to inosine RNA editing in single mammalian cells

Conversion of adenosine bases to inosine in RNA is a frequent type of RNA editing, but important details about its biology, including subcellular localization, remain unknown due to a lack of imaging tools. We developed an RNA FISH strategy we called inoFISH that enables us to directly visualize and quantify adenosine-to-inosine edited transcripts in situ. Applying this tool to three edited transcripts (GRIA2, EIF2AK2 and NUP43), we found that editing of these transcripts is not correlated with nuclear localization nor paraspeckle association, and that NUP43 exhibits constant editing rates between single cells while the rates for GRIA2 vary.

molecular biology↗