Biology subjects

Natarajan, K. N.

Publications and source records attributed to Natarajan, K. N..

13 recordsLinked to original sources

bioRxiv2026

Inferring large networks with matrix factorisation to capture non-linear dependencies among genes using sparse single-cell profiles

Inference of non-linear dependencies among a large number of features from their scores is an unresolved challenge. Especially when the feature-score matrix is very sparse, like single-cell transcriptome profiles, the problem of estimating dependencies among a large number of features to infer a network becomes an even more daunting task. Here, we propose a method of network inference in reduced dimension (NIRD) to handle sparsity and computational complexity while still inferring non-linear dependencies among genes (features) using large, sparse gene-expression matrices (feature scores). Our method is based on matrix factorisation of gene-expression matrix to facilitate internal imputation as well as network inference using tree ensemble-based non-linear regression. NIRD not only outperformed many other methods across multiple single-cell transcriptomic profiles but also provided consistent inferred networks even in the presence of batch effects. The consistency provided by NIRD helps compare inferred networks to identify genuine genes responsible for changes in regulation due to disease or stress. NIRD can also be used with RNA velocity for better inference of non-linear causality. Application of NIRD with RNA-velocity could improve the prediction of direct targets of transcription factors in human embryonic stem cells, which we validated using ChIP-seq and gene-knockout datasets.

bioinformatics↗

bioRxiv2024

Modelling dynamics of human NDPK hexamer structure, stability and interactions

Nucleoside diphosphate kinases (NDPKs) are evolutionarily conserved multifunctional enzymes involved in energy metabolism and gene regulation. NDPKs primarily regulate nucleotide pool turnover by catalyzing the transfer between nucleoside triphosphate (NTPs) and their deoxy derivatives, maintaining cellular homeostasis. The NDPK hexameric assembly is needed for kinase activity, but its precise assembly into homo-/hetero-oligomeric complexes remains poorly understood. How quaternary structure affects NDPK activity is limited by high subunit homology, experimental challenges in isolating in vivo heterohexamers and subunit abundances across cellular compartments. We identify conserved Arg27 across group I NDPKs (NME1-4) as the key residue for hexamer assembly. The Arg27 ensures similar hexameric assembly across subunits and mediates inter- and intra-molecular monomeric interactions, while Arg27 mutation leads to decreased binding affinity, dynamics, and complex destabilization. The double and triple Arg NME4 mutations destabilize hexamer into dimer due to shorter C-terminal region. Simulating NME1-3 with Arg mutations and shortened C-terminal recapitulates hexameric destabilization, highlighting role of the C-terminal region in stabilizing NDPK hexamers. Comparing heterohexameric complexes, we report NME1-NME2 (A1B5) complex as most stable and abundant, owing to predominant subunit nuclear localization. We propose that Arginine residues, C-terminal sequence and subunit abundances contribute to formation and stabilization of NDPK heterohexameric complexes.

bioinformatics↗

bioRxiv2024

Extensive folding variability between homologous chromosomes in mammalian cells

Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single nucleotide polymorphisms (SNPs). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, mediated by Polycomb repression. We show that histone genes are expressed with allelic imbalance in mESCs, are involved in haplotype-specific chromatin contact marked by H3K27me3, and are targets of Polycomb repression through conditional knockouts of Ezh2 or Ring1b. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.

genomics↗

bioRxiv2023

A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals non-hierarchical model of development

The specialized cell-types of the mucociliary epithelium (MCE) lining the respiratory tract enable continuous airway clearing, with its defects leading to chronic respiratory diseases. The molecular mechanisms driving cell-fate acquisition and temporal specialization during mucociliary epithelial development remain largely unknown. Here, we profile the developing Xenopus MCE from pluripotent to mature stages by single-cell transcriptomics, identifying novel, multipotent early epithelial progenitors that execute multi-lineage cues before specialising into late-stage ionocytes, goblet and basal cells. Combining in silico lineage inference, in situ hybridization and single-cell multiplexed RNA imaging, we capture the initial bifurcation into early epithelial and multiciliated progenitors, chart cell- type emergence and fate progression into specialized cell-types. Comparative analysis of nine airway atlases reveals an evolutionary conserved transcriptional module in ciliated cells, whereas secretory and basal types execute distinct function-specific programmes across vertebrates. We uncover a continuous non-hierarchical model of MCE development alongside a significant data resource for understanding respiratory biology.

developmental biology↗

bioRxiv2022

scmTE: multivariate transfer entropy builds interpretable compact gene regulatory networks by reducing false predictions

Gene regulatory network inference from single-cell RNA sequencing (scRNAseq) datasets has an incredible potential to discover new regulatory rules. However, current computational inference methods often suffer from excessive predictions as existing strategies fail to remove indirect or false predictions. Here, we report a new algorithm single-cell multivariate Transfer Entropy, scmTE, that generates interpretable regulatory networks with reduced indirect and false predictions. By utilizing multivariate transfer entropy, scmTE accounts for gene-to-gene interdependence when quantifying regulatory relationships. Benchmarking against other methods using synthetic data manifested that scmTE is the unique algorithm that did not produce a hair-ball structure (due to too many predictions) and recapitulated known ground-truth relationships with high accuracy. In silico knockdown experiments shows that scmTE assigns higher scores for specific interactions important for differentiation processes. We apply scmTE to T-cell differentiation, myelopoiesis and pancreatic development and identified known and novel regulatory interactions. scmTE provides a robust approach to infer interpretable networks by effectively removing unwanted indirect relationships.

bioinformatics↗

bioRxiv2020

Predicting gene regulatory networks from cell atlases

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data. SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

bioinformatics↗

bioRxiv2019

Gene network reconstruction using single cell transcriptomic data reveals key factors for embryonic stem cell differentiation

Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes, and fail to detect potential causal relationships effectively. Here, we propose a novel approach TENET to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data. Key PointsO_LITENET measures putative causal relationships between genes using transfer entropy. C_LIO_LITENET shows outstanding performance in identifying key regulators compared to existing methods. C_LIO_LITENET can reveal previously uncharacterized regulators. C_LI

bioinformatics↗

bioRxiv2019

Single-cell RNA-seq of differentiating iPS cells reveals dynamic genetic effects on gene expression

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

genomics↗

bioRxiv2019

The Malaria Cell Atlas: a comprehensive reference of single parasite transcriptomes across the complete Plasmodium life cycle

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. Here we profile the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire Plasmodium berghei life cycle. We then use our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a complex eukaryotic parasite and an open access reference data set for the study of malaria parasites.

genomics↗

bioRxiv2018

Comparative analysis of sequencing technologies platforms for single-cell transcriptomics

All single-cell RNA-seq protocols and technologies require library preparation prior to sequencing on a platform such as Illumina. Here, we present the first report to utilize the BGISEQ-500 platform for scRNA-seq, and compare the sensitivity and accuracy to Illumina sequencing. We generate a scRNA-seq resource of 468 unique single-cells and 1,297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on mESCs and K562 cells with RNA spike-ins. We sequence these libraries on both BGISEQ-500 and Illumina HiSeq platforms using single- and paired-end reads. The two platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardised scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

genomics↗

bioRxiv2018

Single-cell transcriptomics identifies CD44 as a new marker and regulator of haematopoietic stem cells development

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening we identified CD44 as a new marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta gonad mesonephros (AGM) region. This allowed us to provide a very detailed phenotypical and transcriptional profile for haemogenic endothelial cells, characterising them with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists (Smad6, Smad7 and Bmper) and a downregulation of genes related to glycolysis and the TCA cycle. Moreover, we demonstrated that by inhibiting the interaction between CD44 and its ligand hyaluronan we could block EHT, identifying a new regulator of HSPC development.

developmental biology↗

bioRxiv2018

A rapid and robust method for single cell chromatin accessibility profiling

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrated that our method worked robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3,000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

genomics↗

bioRxiv2017

Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression

Polycomb repressive complexes (PRCs) are important histone modifiers, which silence gene expression, yet there exists a subset of PRC-bound genes actively transcribed by RNA polymerase II (RNAPII). It is likely that the role of PRC is to dampen expression of these PRC-active genes. However, it is unclear how this flipping between chromatin states alters the kinetics of transcriptional burst size and frequency relative to genes with exclusively activating marks. To investigate this, we integrate histone modifications and RNAPII states derived from bulk ChIP-seq data with single-cell RNA-sequencing data. We find that PRC-active genes have a greater cell-to-cell variation in expression than active genes with the same mean expression levels, and validate these results by knockout experiments. We also show that PRC-active genes are clustered on chromosomes in both two and three dimensions, and interactions with active enhancers promote a stabilization of gene expression noise. These findings provide new insights into how chromatin regulation modulates stochastic gene expression and transcriptional bursting, with implications for regulation of pluripotency and development.

genomics↗