Biology subjects

Singhal, V.

Publications and source records attributed to Singhal, V..

6 recordsLinked to original sources

bioRxiv2026

Tagging C. elegans septins disrupts cytoskeletal scaffolding but not post-embryonic roles

Septins are conserved polymer-forming proteins that scaffold the actomyosin cytoskeleton, its regulators, and other factors to cellular membranes. Septins also sense micron-scale curvature, bind microtubules, and establish membrane diffusion barriers. C. elegans is a powerful animal model to study septins roles because there are only two septin genes: unc-59 and unc-61. In many fungal and animal cell types, septins are required for proper cytokinesis. In the C. elegans zygote, septins scaffolding roles in cytokinesis manifest during the chiral rotation of the cell cortex and the asymmetry of cytokinetic ring closure. Originally named for the uncoordinated movement exhibited by hypomorphic alleles, UNC-59 and UNC-61 are also required for normal postembryonic development, germline development, and fertility. To study C. elegans septins in these various contexts, we sought a fluorescent-protein tagging strategy that minimally perturbed septin function. We examined strains in which GFP, mKate2 or wrmScarlet had been inserted at the unc-59 locus, or coupled to unc-61b/c at an exogenous locus, to encode fluorescently tagged fusion proteins. We compared these tagged septins to classical hypomorphic alleles, and to new null alleles. Null alleles phenocopied hypomorphic alleles in all our assays. Strains bearing fluorescently tagged septins exhibited defects in zygote cytokinesis, qualitatively phenocopying both hypomorphic and null alleles. These findings agreed with recent work with fission yeast, demonstrating the sensitivity of septin function to tagging. Interestingly, tagging septins did not perturb postembryonic development including animal mobility. This suggests that septins play distinct functions in the zygote versus later in development.

cell biology↗

bioRxiv2026

Functional characterization of bat limb regulatory elements

Bats are the only mammals capable of powered flight and roost head-down. However, the molecular changes shaping bat limbs remain largely unknown. Here, we used comparative functional genomics coupled with mouse-bat sequence swaps to identify key regulatory elements important in bat limb development. We generated and compared bat and mouse forelimb and hindlimb genomic datasets at key wing developmental timepoints, followed by mouse enhancer assays to characterize sequences showing differences between species. We then swapped six mouse enhancer sequences with their corresponding bat sequences, obtaining a variety of bat limb associated phenotypes, including ossification delay, longer digits, thicker skin and symmetrical hindlimb digits. Our work provides a genomic catalog of genes and regulatory elements involved in bat limb development and through extensive characterization in mice shows how changes in regulatory elements lead to small phenotypic changes that together contribute to bat limb development.

developmental biology↗

bioRxiv2022

BANKSY: A Spatial Omics Algorithm that Unifies Cell Type Clustering and Tissue Domain Segmentation

Each cell type in a solid tissue has a characteristic transcriptome and spatial arrangement, both of which are observable using modern spatial omics assays. However, the common practice is still to ignore spatial information when clustering cells to identify cell types. In fact, spatial location is typically considered only when solving the related, but distinct, problem of demarcating tissue domains (which could include multiple cell types). We present BANKSY, an algorithm that unifies cell type clustering and domain segmentation by constructing a product space of cell and neighbourhood transcriptomes, representing cell state and microenvironment, respectively. BANKSYs spatial kernel-based feature augmentation strategy improves per-formance and scalability on both tasks when tested on FISH-based and sequencing-based spatial omics data. Uniquely, BANKSY identified hitherto undetected niche-dependent cell states in two mouse brain regions. Lastly, we show that quality control of spatial omics data can be formulated as a domain identification problem and solved using BANKSY. BANKSY represents a biologically motivated, scalable, and versatile framework for analyzing spatial omics data.

bioinformatics↗

bioRxiv2020

DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. However, we found that the performance of existing feature selection methods was inconsistent across benchmark datasets, and occasionally even worse than without feature selection. Moreover, existing methods ignored information contained in gene-gene correlations. We therefore developed DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. In a published scRNA-seq dataset from sorted monocytes, DUBStepR sensitively detected a rare and previously invisible population of contaminating basophils. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

bioinformatics↗

bioRxiv2020

A MATLAB Toolbox for Modeling Genetic Circuits in Cell-Free Systems

We introduce a MATLAB based simulation toolbox, called txtlsim, for an E. coli based Transcription-Translation (TX-TL) system. This toolbox accounts for several cell-free related phenomena, such as resource loading, consumption, and degradation, and in doing so, models the dynamics of TX-TL reactions for the entire duration of batch-mode experiments. We use a Bayesian parameter inference approach to characterize the reaction rate parameters associated with the core transcription, translation and mRNA degradation mechanics of the toolbox, allowing it to reproduce constitutive mRNA and protien expression trajectories. We demonstrate the use of this characterized toolbox in a circuit behavior prediction case study for an incoherent feed-forward loop.

synthetic biology↗

bioRxiv2017

Finding analytic stationary solutions to the chemical master equation by gluing state spaces at one or two states recursively

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model that describes how the probability distribution of a chemically reacting system varies with time. Knowing analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterise state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing stationary distributions that satisfy user-specified constraints. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and chemical reactions with two-dimensional state spaces.\n\nSubject AreasSystems biology, synthetic biology, biomathematics, bioengineering

systems biology↗