Biology subjects

Pandit, V.

Publications and source records attributed to Pandit, V..

5 recordsLinked to original sources

bioRxiv2026

FANCM restrains structural genome evolution and defines a synthetic lethal dependency in BRCA1-deficient breast cancer

BRCA1 deficient cancers experience persistent replication stress and structural genome instability yet retain the capacity for sustained proliferation, implying reliance on compensatory genome-maintenance mechanisms. Here, we establish BRCA1/FANCM synthetic lethality in human BRCA1 deficient breast cancer and exploit temporally controlled FANCM depletion to capture genome evolution over successive cell divisions before declining cellular fitness becomes limiting. We show that FANCM restrains genome wide structural variation in BRCA1 deficient breast cancer cells under endogenous replication stress. FANCM loss amplifies the characteristic BRCA1 associated short tandem duplication (TD) phenotype while permitting larger, including megabase-scale, TDs and diverse rearrangements to emerge. Newly emerged TDs preferentially associate with Pol II occupied regions, and FANCM depletion increases proximity between the replication machinery and elongating RNAPII in BRCA1 mutant breast cancer cells, linking FANCM-mediated genome protection to transcription replication encounters. BRCA1 altered human tumors with low FANCM expression recapitulate key features of this phenotype, while genome/transcriptome integration links newly emerged SVs to configuration dependent local transcriptional changes. Together, these findings establish FANCM as a replication stress safeguard coupling survival to restraint of structural genome evolution.

cancer biology↗

bioRxiv2026

Combinatorial transcription factor interactions drive modular gene regulatory networks

Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Forced induction of TFs in combination can reprogram cell state by supplanting existing GRNs. Thus, TFs and GRNs are the building blocks to engineering transcriptional state. However, one key challenge is that the relationship between TF combinations and GRNs remains largely uncharacterized and difficult to accurately predict. Here, we apply single-cell overexpression screens to map the combinatorial activities of [~]100 TFs to gene expression states. Our analysis identifies diverse TF combinations driving cell-type specific regulatory programs. Notably, different TF combinations induce shared gene sets with cell-type specific functions, suggesting a modular regulatory architecture of the transcriptome. Furthermore, we define pairwise TF interactions and show that cooperative interactions improve transcriptional reprogramming. Finally, we developed tools to predict combinatorial TF phenotypes. These findings improve our understanding of cell state and how to manipulate it for biomedical applications. HIGHLIGHTSO_LICombinatorial over-expression screens for [~]100 transcription factors (TFs). C_LIO_LIDiverse TF combinations drive cell-type specific regulatory programs. C_LIO_LITF regulatory networks reveal a modular regulatory architecture of the transcriptome. C_LIO_LITF-TF interactions and predictive models enhance reprogramming cocktails. C_LI

genomics↗

bioRxiv2025

Benchmarking and optimizing Perturb-seq in differentiating human pluripotent stem cells

Perturb-seq is a powerful approach to systematically assess how genes and enhancers impact the molecular and cellular pathways of development and disease. However, technical challenges have limited its application in stem cell-based systems. Here, we benchmarked Perturb-seq across multiple CRISPRi modalities, on diverse genomic targets, in multiple human pluripotent stem cells, during directed differentiation to multiple lineages, and across multiple sgRNA delivery systems. To ensure cost-effective production of large-scale Perturb-seq datasets as part of the Impact of Genomic Variants on Function (IGVF) consortium, our optimized protocol dynamically assesses experiment quality across the weeks-long procedure. Our analysis of 1,996,260 sequenced cells across benchmarking datasets reveals shared regulatory networks linking disease-associated enhancers and genes with downstream targets during cardiomyocyte differentiation. This study establishes open tools and resources for interrogating genome function during stem cell differentiation.

genomics↗

bioRxiv2023

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics

Spatial transcriptomics has advanced our understanding of tissue biology by enabling sequencing while preserving spatial coordinates. In sequencing-based spatial technologies, each measured spot typically consists of multiple cells. Deconvolution algorithms are required to decipher the cell-type distribution at each spot. Existing spot deconvolution algorithms for spatial transcriptomics often neglect spatial coordinates and lack scalability as datasets get larger. We introduce SpatialPrompt, a spatially aware and scalable method for spot deconvolution as well as domain identification for spatial transcriptomics. Our method integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) reference data to infer cell-type proportions of spatial spots accurately. At the core, SpatialPrompt uses non-negative ridge regression and an iterative approach inspired by graph neural network (GNN) to capture the local microenvironment information in the spatial data. Quantitative assessments on the human prefrontal cortex dataset demonstrated the superior performance of our tool for spot deconvolution and domain identification. Additionally, SpatialPrompt accurately decipher the spatial niches of the mouse cortex and the hippocampus regions that are generated from different protocols. Furthermore, consistent spot deconvolution prediction from multiple references on the mouse kidney spatial dataset showed the impressive robustness of the tool. In response to this, SpatialPromptDB database is developed to provide compatible scRNA-seq references with cell-type annotations for seamless integration. In terms of scalability, SpatialPrompt is the only method performing spot deconvolution and clustering in less than 2 minutes for large spatial datasets with 50,000 spots. SpatialPrompt tool along with the SpatialPromptDB database are publicly available as open source software for large-scale spatial transcriptomics analysis.

bioinformatics↗

bioRxiv2022

Comparison of Dimensionality Reduction and Clustering Methods for Single-Cell Transcriptomics Data

Dimensionality reduction (DR) methods are applied to extract relevant features from inherently high dimensional and noisy single-cell RNA sequencing (scRNA-seq) data. Choice of DR method could influence the performance of clustering algorithm and subsequent analysis outcomes. We performed a benchmarking study of seven popular DR methods and four clustering algorithms widely used for scRNA-seq datasets. For this purpose, we used three publicly available real scRNA-seq datasets. The performance was evaluated using two clustering metrics viz. adjusted random index (ARI) and normalized mutual index (NMI). We also compared our results with a similar study published by Xiang and colleagues. Overall, we observed higher ARI and NMI scores for DR methods when compared with Xiangs study. We also noticed several differences between our and Xiangs study. Noteworthy, three methods, namely, Independent Component Analysis (ICA), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) performed consistently well across three datasets. Linear method ICA was best performer on Segerstolpe dataset, while nonlinear methods UMAP and t-SNE best performed on Deng and Chu datasets, respectively. Neural network-based methods Variational Autoencoder (VAE) and Deep Count Autoencoder (DCA) could not perform well probably due to their sensitivity to hyperparameters and overfitting. Among clustering methods, Gaussian Mixture Models (GMMs) performed consistently well across datasets. This might be because GMMs are the universal approximators of posterior probability densities. We conclude that performance of different DR methods is more dataset dependent and for various scRNA-seq datasets different algorithms are more suited and there is no one-fit-all method.

bioinformatics↗