Biology subjects

Sengupta, D.

Publications and source records attributed to Sengupta, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Improved Python Package for DNA Sequence Encoding using Frequency Chaos Game Representation

SummaryFrequency Chaos Game Representation (FCGR), an extended version of Chaos Game Representation (CGR), emerges as a robust strategy for DNA sequence encoding. The core principle of the CGR algorithm involves mapping a onedimensional sequence representation into a higher-dimensional space, typically in the two-dimensional spatial domain. This paper introduces a use case wherein FCGR serves as a kmer frequency-based encoding method for motif classification using a publicly available dataset. Availability and implementationThe FCGR python package, use case, along with additional functionalities, is available in the GitHub. Our FCGR package demonstrates superior accuracy and computational efficiency compared to a leading R-based FCGR library [1], which is designed for versatile tasks, including proteins, letters, and amino acids with user-defined resolution. Nevertheless, it is important to note that our Python package is specifically designed for DNA sequence encoding, where the resolution is predetermined based on the kmer length. It is a drawback of our current package compared to the state-of-the-art R-based kaos package [1].

bioinformatics↗

bioRxiv2023

Non-canonical NF-κB signaling in dendritic cells promotes intestinal inflammation by restraining the tolerogenic β-catenin-Raldh2 axis

Dendritic cell (DC) dysfunctions exacerbate intestinal pathologies. However, the mechanisms compromising DC-mediated immune controls remain unclear. We found that intestinal DCs from mice subjected to experimental colitis possessed heightened non-canonical NF-{kappa}B signaling, which activates the RelB:p52 heterodimer. Genetic inactivation of this pathway in DCs alleviated inflammation in colitogenic mice. Unexpectedly, RelB:p52 deficiency diminished the transcription of Axin1, a critical component of the {beta}-catenin destruction complex. This reinforced {beta}-catenin-driven expression of Raldh2, which imparts tolerogenic DC attributes by promoting retinoic acid (RA) synthesis. Indeed, DC-specific non-canonical NF-{kappa}B impairment improved the colonic frequency of Tregs and IgA+ B cells, which fostered luminal IgA and eubiosis. Introducing {beta}-catenin haploinsufficiency in non-canonical NF-{kappa}B-deficient DCs moderated Raldh2 activity, reinstating colitogenic sensitivity in mice. Finally, IBD patients displayed a deleterious non-canonical NF-{kappa}B signature in intestinal DCs. In sum, we establish a DC network that integrates non-canonical NF-{kappa}B signaling to subvert RA metabolic pathway in fueling intestinal inflammation. Significance (100)Distorted dendritic cell (DC) functions have been implicated in aberrant intestinal inflammation; however, the underlying mechanism remains obscure. We discovered that the non-canonical NF-{kappa}B pathway exacerbates inflammation in the colitogenic gut by downmodulating {beta}-catenin-driven synthesis of Raldh2 in DCs. Raldh2 represents a key enzyme involved in the production of tolerogenic retinoic acid in intestinal DCs. Beyond regulating immune genes, therefore, non-canonical NF-{kappa}B signaling appears to instruct retinoic acid-mediated control of gut health. While we illustrate a DC network integrating immune signaling and micronutrient metabolic pathways in the intestine, our finding may have broad relevance for nutritional interventions in inflammatory ailments. eToCDeka and Kumar et al. illustrate a DC-circuitry that exacerbates intestinal inflammation in IBD patients and colitogenic mice. Non-canonical NF-{kappa}B signaling restrains {beta}-catenin in DCs to downmodulate Raldh2, which promotes tolerogenic RA synthesis, leading to diminished Treg and IgA+ cell frequencies in the gut. HighlightsO_LIAberrant intestinal inflammation is associated with and exacerbated by non-canonical NF-{kappa}B signaling in DCs. C_LIO_LINon-canonical signaling restrains the tolerogenic {beta}-catenin-Raldh2 axis in DCs by upregulating Axin1. C_LIO_LIDC-specific RelB:p52 impairment promotes {beta}-catenin-dependent Treg accumulation in the gut. C_LIO_LIA DC defect of non-canonical signaling causes {beta}-catenin-dependent increase in luminal sIgA, fostering the gut microbiome. C_LI One sentenceThe non-canonical NF-{kappa}B pathway fuels intestinal inflammation by waning the tolerogenic {beta}-catenin-Raldh2-retinoic acid axis in DCs.

immunology↗

bioRxiv2023

Acetylation of H3K115 at the nucleosome dyad is associated with fragile nucleosomes at active regulatory sites.

Acetylation of lysine residues in the tail domain of histone H3 is well characterized, but lysine residues in the histone globular domain are also acetylated. Histone modifications in globular domain have regulatory potential because of their impact on nucleosome stability but remain poorly characterized. In this study we report the genome-wide distribution of acetylated H3 lysine 115 (H3K115ac), a residue on the lateral surface at the nucleosome dyad, using chromatin immunoprecipitation. In mouse embryonic stem cells, we find that detectable H3K115ac is enriched at the transcription start site of active CpG island promoters, but also at polycomb repressed promoters prior to their subsequent activation during differentiation. By contrast, at enhancers H3K115ac enrichment is dynamic, changing in line with gene activation and chromatin accessibility during differentiation. Most strikingly, we show that H3K115ac is detected as enriched on "fragile" nucleosomes within nucleosome depleted regions at promoters, and active enhancers where it coincides with transcription factor binding, and at CTCF bound sites. These unique features suggest that H3K115ac correlates with, and could contribute, to nucleosome destabilization and that it might be a valuable marker for identifying functionally important regulatory elements in mammalian genomes.

genomics↗

bioRxiv2023

InGene: Finding influential genes from embeddings of nonlinear dimension reduction techniques

We introduce InGene, the first of its kind, fast and scalable non-linear, unsupervised method for analyzing single-cell RNA sequencing data (scRNA-seq). While non-linear dimensionality reduction techniques such as t-SNE and UMAP are effective at visualizing cellular sub-populations in low-dimensional space, they do not identify the specific genes that influence the transformation. InGene addresses this issue by assigning an importance score to each expressed gene based on its contribution to the construction of the low-dimensional map. InGene can provide insight into the cellular heterogeneity of scRNA-seq data and accurately identify genes associated with cell-type populations or diseases, as demonstrated in our analysis of scRNA-seq datasets.

bioinformatics↗

bioRxiv2023

Artificial Intelligence uncovers Evolutionarily Conserved Intracellular Allosteric Modulators of GPCR-Gα interface.

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific ligand design, synthesis, and validation across GPCR topology. Here, we developed Gcoupler, an AI-driven generalized computational toolkit that leverages an integrative approach combining de novo ligand design, statistical methods, Graph Neural Networks, and Bioactivity-based ligand prioritization for rationally predicting high-affinity ligands. Using Gcoupler, we interrogated intracellular metabolites that target and regulate the GPCR-G interface (Ste2p-Gpa1p), affecting pheromone-induced programmed cell death in yeast. Our computational analysis, complemented by experimental validations, including genetic screening, multi-omics, site-directed mutagenesis, biochemical assays, and physiological readouts, identified endogenous hydrophobic metabolites, notably sterols, as direct intracellular allosteric modulators of Ste2p. Molecular simulations coupled with biochemical signaling assessment in site-directed Ste2p mutants further confirmed that metabolites binding to GPCR-G obstruct downstream signaling, possibly via a cohesive effect. Finally, by utilizing isoproterenol-induced, GPCR-mediated human and neonatal rat cardiac hypertrophy models, we observed that elevated metabolite levels attenuate hypertrophic response, reinforcing the evolutionary relevance of this mechanism.

bioinformatics↗

bioRxiv2022

Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

bioinformatics↗

bioRxiv2022

Simultaneous visualization of cells and marker genes from scRNA-seq studies

The complexity of scRNA-sequencing datasets highlights the urgent need for enhanced clustering and visualization methods. Here, we propose Stardust, an iterative, force-directed graph layouting algorithm that enables the simultaneous embedding of cells and marker genes. Stardust, for the first time, allows a single-stop visualization of cells and marker genes on a single 2D map. While Stardust provides its own visualization pipeline, it can be plugged in with state-of-the-art methods such as Uniform Manifold Approximation and Projection (UMAP) and t-Distributed Stochastic Neighbor Embedding (tSNE). We benchmarked Stardust against popular visualization and clustering tools on both scRNA-seq and spatial transcriptomics datasets. In all cases, Stardust performs competitively in identifying and visualizing cell types in an accurate and spatially coherent manner.

bioinformatics↗

bioRxiv2022

Elevated levels of lamin A promote HR and NHEJ-mediated repair mechanisms in etoposide-treated ovarian cancer cells

Lamins are emerging as major regulators in the maintenance of nuclear architecture and genome organization. Extensive research for the last two decades has enormously contributed to understanding the roles of lamins in various signaling mechanisms which are drastically modified in neoplasia. It is interesting to record that alteration in lamin A/C expression and distribution drives tumorigenesis of almost all tissues of human bodies. One of the important signatures of a cancer cell is its inability to repair DNA damage which befalls several genomic events that transform the cells to be sensitive to chemotherapeutic agents. This genomic and chromosomal instability is the most common feature found in cases of high-grade ovarian serous carcinoma. Here, we report elevated levels of lamins in OVCAR3 cells (High grade ovarian serous carcinoma cell line) in comparison to IOSE (Immortalised ovarian surface epithelial cells) and consequently altered damage repair machinery in OVCAR3. We have analyzed the changes in global gene expression as a sequel to DNA damage induced by etoposide in ovarian carcinoma where lamin A is particularly elevated in expression and reported some differentially expressed genes associated with pathways conferring cellular proliferation and chemoresistance. We highlight new avenues unraveling the role of upregulated lamin A in confronting chemically induced genomic instability in the context of high grade ovarian serous cancer through a combination of HR and NHEJ mechanisms.

cancer biology↗

bioRxiv2022

SARS-CoV-2 accessory proteins ORF7a and ORF3a use distinct mechanisms to downregulate MHC-I surface expression

Major histocompatibility complex class I (MHC-I) molecules, which are dimers of a glycosylated polymorphic transmembrane heavy chain and the small protein {beta}2-microglobulin ({beta}2m), bind peptides in the endoplasmic reticulum that are generated by the cytosolic turnover of cellular proteins. In virus-infected cells these peptides may include those derived from viral proteins. Peptide-MHC-I complexes then traffic through the secretory pathway and are displayed at the cell surface where those containing viral peptides can be detected by CD8+ T lymphocytes that kill infected cells. Many viruses enhance their in vivo survival by encoding genes that downregulate MHC-I expression to avoid CD8+ T cell recognition. Here we report that two accessory proteins encoded by SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, downregulate MHC-I expression using distinct mechanisms. One, ORF3a, a viroporin, reduces global trafficking of proteins, including MHC-I, through the secretory pathway. The second, ORF7a, interacts specifically with the MHC-I heavy chain, acting as a molecular mimic of {beta}2m to inhibit its association. This slows the exit of properly assembled MHC-I molecules from the endoplasmic reticulum. We demonstrate that ORF7a reduces antigen presentation by the human MHC-I allele HLA-A*02:01. Thus, both ORF3a and ORF7a act post-translationally in the secretory pathway to lower surface MHC-I expression, with ORF7a exhibiting a novel and specific mechanism that allows immune evasion by SARS-CoV-2. Significance StatementViruses may down-regulate MHC class I expression on infected cells to avoid elimination by cytotoxic T cells. We report that the accessory proteins ORF7a and ORF3a of SARS-CoV-2 mediate this function and delineate the two distinct mechanisms involved. While ORF3a inhibits global protein trafficking to the cell surface, ORF7a acts specifically on MHC-I by competing with {beta}2m for binding to the MHC-I heavy chain. This is the first account of molecular mimicry of {beta}2m as a viral mechanism of MHC-I down-regulation to facilitate immune evasion.

immunology↗

bioRxiv2022

A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression

Few studies so far have investigated the impact of different cell migration traits on tumor progression. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the migration and invasion traits of Giantin depleted cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression.

cancer biology↗

bioRxiv2022

Microdynamics of lamin A Ig-fold domain regulates higher order assembly of the protein

Lamins maintain the shape and rigidity of the nucleus in the form of a proteinaceous scaffold underneath the inner nuclear membrane (INM) and provide anchorage to chromatin and other nuclear proteins. Mutations in the human LMNA gene encoding lamin A/C cause about 16 different diseases with distinct phenotypes collectively termed as laminopathies which affect primarily the muscle tissues as well as adipose tissues, neuromuscular junctions and multiple other organs in progeroid syndromes. Lamins contain several domains of which Ig-fold is one of the well characterized and structured domains that harbours many mutations leading to deleterious interactions with other nuclear proteins. In this work, we have elucidated the effects of 3 such mutations namely R453W, W498C and W498R on the dynamics and flexibility of the Ig-fold domain and the consequent effect on the assembly into lamina by live cell imaging, fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulations. From our experimental observations, we concluded that R453W has somewhat increased flexibility compared to the other mutants but lower than that of the wild type. This might be attributed due to an alteration of the salt bridges in the Ig fold domain of the mutant. This is the first report where such an alteration in the full length has been documented by gross changes in diffusional properties as a sequel to a mutation in the Ig fold domain

biochemistry↗

bioRxiv2022

A visual atlas of genes tissue-specific pathological roles

Dysregulation of a genes function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape (HCL) project [1], researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite heterogeneous and is confounded by several clinical and demographic covariates. To circumvent this, we mined ~18 million PubMed abstracts published till May 2019 and selected ~6.1 million of them that describe the pathological role of genes in different diseases. Further, we employed a word embedding technique from the domain of Natural Language Processing (NLP) to learn vector representation of entities such as genes, diseases, tissues, etc., in a way such that their relationship is preserved in a vector space. Notably, Pathomap, by the virtue of its underpinning theory, also learns transitive relationships. Pathomap provided a vector representation of words indicating a possible association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS). The first manuscript reporting this finding was not part of our training data. Key pointsO_LIWe mined ~18 million PubMed abstracts to extract latent knowledge pertaining to tissue specific pathological roles of genes. C_LIO_LIWe found well-defined gene modules implicated in disease pathogenesis in anatomically proximal tissues. C_LIO_LIWe demonstrated an ahead of time discovery of the association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS), as a knowledge synthesis use-case. C_LI

bioinformatics↗

bioRxiv2021

Artificial Intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage response pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bona fide (non)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity as well as their potential to induce proliferation, oxidative stress, genomic instability, alterations in the epigenome, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable since it reveals the contribution of the aforementioned biochemical properties in imparting carcinogenicity. Metabokiller outperforms existing best-practice methods for carcinogenicity prediction. We used Metabokiller to unravel cells endogenous metabolic threats by screening a large pool of human metabolites and predicted a subset of these metabolites that could potentially trigger malignancy in normal cells. To cross-validate Metabokiller predictions, we performed a range of functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites namely 4-Nitrocatechol and 3,4-Dihydroxyphenylacetic acid and observed high synergy between Metabokiller predictions and experimental validations.

bioinformatics↗

bioRxiv2021

Marker-free characterization of single live circulating tumor cell full-length transcriptomes

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor that hinders enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypic traits relative to the tumor of origin. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from the peripheral blood. Most CTC detection methods use a mix of size selection, immune marker based white blood cells (WBC) depletion, and positive enrichment antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. Single-cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate and is a potent method of enabling unbiased identification of CTCs. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analysis tasks. These include a novel method of scRNA-Seq clustering, named Deep Dictionary Learning using K-means clustering cost (DDLK), expression based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-Seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell(R) FX and PolarisTM workflow that works by the principles of size-based separation of CTCs and marker based WBC depletion.

bioinformatics↗

bioRxiv2021

Gene expression based inference of drug resistance in cancer

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of large-scale drug screening datasets has provided an opportunity for predicting appropriate patient-tailored therapies by employing machine learning approaches. In this study, we report a predictive modeling approach to infer treatment response in cancers using gene expression data. In particular, we demonstrate the benefits of considering integrated chemogenomics approach, utilizing the molecular drug descriptors and pathway activity information as opposed to gene expression levels. We performed extensive validation of our approach on tissue-derived single-cell and bulk expression data. Further, we constructed several prostate cancer cell lines and xenografts, exposed to differential treatment conditions to assess the predictability of the outcomes. Our approach was further assessed on pan-cancer RNA-sequencing data from The Cancer Genome Atlas (TCGA) archives, as well as an independent clinical trial study describing the treatment journey of three melanoma patients. To summarise, we benchmarked the proposed approach on cancer RNA-seq data, obtained from cell lines, xenografts, as well as humans. We concluded that pathway-activity patterns in cancer cells are reasonably indicative of drug resistance, and therefore can be leveraged in personalized treatment recommendations.

cancer biology↗

bioRxiv2021

Microfluidic system-based time-course tracking of physical proximity between cells and its effect on gene expression for elucidating live single cancer-immune cell interactions

Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit (IFC) that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. The temporal variations in natural killer (NK) - triple-negative breast cancer (TNBC) cell distances were tracked and compared with terminally profiled cellular transcriptomes. The results showed the time-bound activities of regulatory modules and alluded to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live cell interactions at doublet resolution, which can be applied across different cancers and cell types.

cancer biology↗

bioRxiv2021

Redox state and cellular uptake of copper is regulated by the N-terminus of human Copper Transporter-1

Copper(I) is essential for all life forms. Though Cu(II) is most abundant state in environment, its reduction to Cu(I) is prerequisite for bio-utilization, by a mechanism that is uncharacterized. We show that in human Copper Transporter-1, two amino-terminal methionine-histidine clusters and neighbouring aspartates distinctly binds Cu(II) and Cu(I) preceding its import. The endocytosis of hCTR1 from basolateral membrane of polarized epithelia to Common-Recycling-Endosomes is dependent on copper reduction and Cu(I) coordination by methionines. The transient binding of both Cu(II) and Cu(I) during the reduction process facilitated by aspartates acts as another crucial determinant of hCTR1 endocytosis. Mutating 7Met-Gly-Met9 and Asp13 abrogates copper uptake and endocytosis that is correctable by reduced and non-reoxidizable Cu(I). Histidines clusters are crucial for hCTR1 functioning at limiting copper. Finally, we show that two N-terminal His-Met-Asp clusters exhibit functional complementarity in regulating Cu(I)-induced hCTR1 endocytosis. We propose a mechanistic model where His-Met-Asp residues of amino-terminal hCTR1 coordinates copper and maintains its reduced state crucial for uptake.

cell biology↗

bioRxiv2021

A search-engine for single-cell epigenome profiles for multi-purpose applications

The true benefits of large datasets of the single-cell transcriptome and epigenome profiles can be availed only with their inclusion and search for annotating individual cells. Matching a single cell epigenome profile to a large pool of reference cells remains a major challenge. We developed a method (scEpiSearch) to resolve the challenges of searching and comparing single-cell open-chromatin profiles against large pools of single-cell expression and open chromatin datasets. scEpiSearch is more accurate than other methods when comparing single cell open-chromatin profiles to single-cell transcriptomes and epigenomes. scEpiSearch also provides a robust method for reference-supported co-embedding of single-cell open chromatin profiles. In performance benchmarks, scEpiSearch outperformed multiple methods for the low dimensional co-embedding of single-cell open-chromatin profiles irrespective of platforms and species. scEpiSearch works with both reference single-cell expression and epigenome profiles, enabling classification of single-cell open-chromatin profiles. Here we demonstrate the unconventional utilities of scEpiSearch by applying it on single-cell epigenome profiles of K562 cells and samples from patients with acute leukaemia to reveal different aspects of their heterogeneity, multipotent behaviour and de-differentiated states. Applying scEpiSearch on our single-cell open-chromatin profiles from embryonic stem cells(ESCs), we identified ESC subpopulations with more activity and poising for endoplasmic reticulum stress and unfolded protein response. Thus, scEpiSearch solves the non-trivial problem of amalgamating information from a large pool of single-cells to identify and study the regulatory states of cells using their single-cell epigenomes.The true benefits of large datasets of the single-cell transcriptome and epigenome profiles can be availed only with their inclusion and search for annotating individual cells.

systems biology↗