Biology subjects

Majumdar, A.

Publications and source records attributed to Majumdar, A..

6 recordsLinked to original sources

bioRxiv2018

deepMc: deep Matrix Completion for imputation of single cell RNA-seq data

Single cell RNA-seq has fueled discovery and innovation in medicine over the past few years and is useful for studying cellular responses at individual cell resolution. But, due to paucity of starting RNA, the data acquired is highly sparse. To address this, We propose a deep matrix factorization based method, deepMc, to impute missing values in gene-expression data. For the deep architecture of our approach, We draw our motivation from great success of deep learning in solving various Machine learning problems. In this work, We support our method with positive results on several evaluation metrics like clustering of cell populations, differential expression analysis and cell type separability.

bioinformatics↗

bioRxiv2018

McImpute: Matrix completion based imputation for single cell RNA-seq data

MotivationSingle cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression analysis at single cell resolution, provides a window into dynamics of cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified.\n\nResultsWe introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, performance of dimensionality reduction techniques for cell visualization and gene distribution.\n\nAvailability and Implementationhttps://github.com/aanchalMongia/McImpute_scRNAseq

bioinformatics↗

bioRxiv2018

A non-canonical lysosome biogenesis pathway generates Golgi-associated lysosomes during epidermal differentiation

Keratinocytes maintain epidermis integrity and function including physical and antimicrobial barrier through cellular differentiation. This process is predicted to be controlled by calcium ion gradient and nutritional stress. Keratinocytes undergo proteome changes during differentiation, which enhances the intracellular organelle digestion to sustain the stress conditions. However, the molecular mechanism between epidermal differentiation and organelle homeostasis is poorly understood. Here, we used primary neonatal human epidermal keratinocytes to study the link between cellular differentiation, signaling pathways and organelle turnover. Upon addition of calcium chloride (2 mM) to the culture medium, keratinocytes increased their cell size and the expression of differentiation markers. Moreover, differentiated keratinocytes showed enhanced lysosome biogenesis that was dependent on ATF6-arm of UPR signaling but independent of mTOR-MiT/TFE transcription factors. Furthermore, chemical inhibition of mTOR has increased keratinocyte differentiation and relocalized the MiT/TFE TFs to the lysosome membranes, indicating that autophagy activation promotes the epidermal differentiation. Interestingly, differentiation of keratinocytes resulted in dispersal of fragmented Golgi and lysosomes, and the later organelles showed colocalization with Golgi-tethering proteins, suggesting that these lysosomes possibly originated from Golgi, hence named as Golgi-associated lysosomes (GALs). Consistent to this prediction, inhibition of Golgi function using brefeldin A completely abolished the formation of GALs and the keratinocyte differentiation. Thus, ER stress regulates the biogenesis of GALs, which maintains keratinocyte differentiation and epidermal homeostasis.

cell biology↗

bioRxiv2018

The action of a cosmetic hair treatment on follicle function

OBJECTIVEHuman hair changes with age: fibre diameter and density decrease, hair growth slows and shedding increases. This series of controlled studies examined the effect on hair growth parameters of a new leave-on hair treatment (LOT) formulated with DynagenTM (containing hydrolysed yeast protein) and zinc salts.\n\nMETHODSHair growth data were collected from healthy women aged 18-65 years. The LOTs effect on hair growth was measured in a randomized double-blind study and in hair samples; its effect on follicle-cell proliferation was assessed by quantifying Ki67 expression in scalp biopsies. The LOTs effect on plucking force was determined in an ex vivo model. Dynagens effect on the expression of the tight-junction marker claudin-1 was analysed in cultured follicles. The effect on protease activity of zinc salts used in the LOT was examined in vitro.\n\nRESULTSHair growth rate decreased with increasing subject age. The LOT significantly increased hair growth rate, fibre diameter, bundle cross-sectional area, Ki67 expression and the plucking force required to remove hair. Dynagen significantly increased claudin-1 expression in cultured follicles. Protease activity was reduced by zinc salts.\n\nCONCLUSIONThe Dynagen-based LOT increases hair-fibre diameter, strengthens the follicular root structure and increases hair growth rate.

physiology↗

bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

neuroscience↗

bioRxiv2017

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes produces a substantially better accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected nine independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis, and Peripheral Vascular Disease. The GERA cohort analysis suggests that CPBayes is more powerful than ASSET with respect to detecting independent pleiotropic variants. We provide an R-package CPBayes implementing the proposed method.\n\nAuthor SummaryGenome-wide association studies (GWASs) have highlighted shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of aggregate-level pleiotropic association and selects an optimal subset of associated traits underlying a pleiotropic signal. CPBayes analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate GWASs with or without shared subjects, cohort study for multiple traits. It performs a fully Bayesian analysis and offers various flexibilities in the inference. In addition to parameters of primary interest (e.g., the measures of overall pleiotropic association, the optimal subset of associated traits), it provides additional interesting insights into a pleiotropic signal (e.g., the trait-specific posterior probability of association, the credible interval of unknown true genetic effects). Using computer simulations and a real data application to the large Kaiser GERA cohort, we demonstrate that CPBayes offers substantially better accuracy while selecting the non-null traits compared to a well known subset-based meta analysis ASSET. In the GERA cohort analysis, CPBayes detected a larger number of independent pleiotropic variants than ASSET. We provide a user-friendly R-package CPBayes for general use.

genetics↗