Biology subjects

Dixit, G.

Publications and source records attributed to Dixit, G..

5 recordsLinked to original sources

bioRxiv2026

Order Matters: Ambient RNA correction confounds doublet detection in single-cell RNA sequencing

A common technical artifact in droplet-based single-cell RNA sequencing (scRNA-seq) is the capture of two cells in the same droplet, termed doublets. Doublets may present as hybrid cell types, and their removal is a critical step during quality control. Another form of contamination is ambient RNA, which originates from free floating RNA released from other cells in suspension, likely during cell death. As with doublets, ambient RNA results in unexpected gene expression patterns. Most suggested analysis pipelines include ambient RNA correction before doublet detection however no formal investigation into how doublet detection is affected by ambient RNA correction has been conducted. Here, we evaluate the impact of ambient RNA correction on the performance of doublet detection using novel engineered and multiplexed datasets. We apply three of the most widely-used methods for removing ambient RNA (decontX, soupX, CellBender), in conjunction with up to six doublet detection methods (scDblFinder, scds, bcds, cxds, scrublet, DoubletDetection). We find that doublet detection tools perform best when done prior to ambient RNA correction, with the exception of CellBender. Performing ambient RNA correction first, particularly for decontX, leads to an increased proportion of doublet droplets retained in the dataset compared with calling doublets on uncorrected data. We show that both decontX and soupX partially mask doublet signal by removing proportionally more ambient RNA from doublets that remain undetected after ambient RNA correction. Our results show that the performance of doublet detection is significantly impacted by the use of ambient RNA correction tools. We suggest a new workflow that implements doublet detection prior to ambient RNA correction unless using CellBender.

bioinformatics↗

bioRxiv2025

Isolation Methods of Tumor Endothelial Cells Impact AngiomiR Profiles

MicroRNAs (miRNAs) play an important role in endothelial cell growth and differentiation. Tumor angiogenesis-specific miRNAs (angiomiRs) are a subset of miRNAs that are dyregulated in tumor endothelial cells. Because of the importance of angiogenesis in cancer progression, regulation of angiomiRs may have significant therapeutic implications. However, discovery of angiomiRs has often been limited by biased model systems that may not be valid. Here, we evaluated whether the variable expression levels of angiomiRs in endothelial cells were impacted by the isolation methods used to profile them. Using an autochthonous, genetically engineered mouse model of lung adenocarcinoma, we used Nanostring to profile miRNA expression levels of normal lung endothelial cells (NECs) to tumor endothelial cells (TECs) using two endothelial cell (EC) isolation methods: 1) staining and sorting ECs directly from tumors ("in vivo"), and 2) magnetic bead isolation and sub-cloning ECs ("in vitro"). We then compared candidate angiomiRs with the profiles from two orthotopic, immunocompetent lung cancer models. When TECs were directly enriched from tumors ("in vivo" method), three candidate angiomiRs (miR-30b, miR-1981, and miR-707) were significantly lower in TECs than NECs. In contrast, when ECs were isolated and cultured ("in vitro" method), three different candidate angiomiRs (miR-200a, miR-124 and miR-186) were significantly lower in TECs than NECs. Using two independent model systems for validation, we found miR-30b to be significantly reduced in TECs using freshly sorted ECs. Conversely, the in vitro discovered angiomiR candidates did not validate in these model systems, suggesting that TECs grown in vitro may not maintain relevant angiomiR profiles or serve as an adequate method for molecular profiling. Our findings demonstrate that angiomiR expression patterns are impacted by isolation methods. Instead of relying on ECs cultured in vitro, we suggest careful validation studies of cells freshly collected from tumors before determining whether a miRNA is a bona fide angiomiR.

molecular biology↗

bioRxiv2024

More cells, more doublets in highly multiplexed single-cell data

Withdrawal statementThe authors have withdrawn this manuscript. In our initial submission of this paper, we proposed a combinatoric model for the probability of finding doublets in sample-barcoded single-cell RNA-sequencing data. This model predicts a doublet rate higher than the rate included in the documentation for the 10X Flex protocol. This was motivated by our use of doublet-finding software, scDblFinder, on experimental data using the Flex protocol, which identified several times more doublets than we expected based on the documentation. Our original model produced better agreement with the results of scDblFinder in 9 of our own data sets as well as one public dataset made available by 10X. During revisions, however, we performed Monte Carlo simulations of doublet formation that were inconsistent with the results of our model, and much closer to the predictions from the documentation. This prompted us to reanalyse the assumptions of our model. A longer version of this withdrawal statement that explains in detail the errors in the model and includes updated figures is available at https://github.com/Oshlack/flex-doublets, but in brief, our initial assumptions over-predicted the doublet fraction, and our revised model is now consistent with both the Monte Carlo simulations and the predictions from the Flex documentation. We therefore speculate that the unexpectedly high doublet fractions observed in the experimental data might be due to some combination of experimental conditions and software performance. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please ontact the corresponding author.

bioinformatics↗

bioRxiv2022

CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets

Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimised deep neural network architectures to predict expression from sequences. High predictive performance due to the modelling of dependencies within and between regulatory sequences is an enabler for biological discoveries in gene regulation through model interpretation techniques. To understand the regulatory code that delineates gene expression, we have designed a novel deep-learning model (CRMnet) to predict gene expression in Saccharomyces cerevisiae. Our model outperforms the current benchmark models and achieves a Pearson correlation coefficient of 0.971. Interpretation of informative genomic regions determined from model saliency maps, and overlapping the saliency maps with known yeast motifs, support that our model can successfully locate the binding sites of transcription factors that actively modulate gene expression. We compare our models training times on a large compute cluster with GPUs and Google TPUs to indicate practical training times on similar datasets.

bioinformatics↗

bioRxiv2020

A predictive model of gene expression reveals the role of regulatory motifs in the mating response of yeast

ABSTRACTCells use signaling pathways to receive and process information about their environment. These systems are nonlinear, relying on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models developed to describe signaling pathways often fail to show predictive power, because the models are not trained on data that probe the diverse time scales on which feedforward and feedback regulation operate. We addressed this limitation using microfluidics to expose cells to a broad range of dynamic environmental conditions. In particular, we focus on the well-characterized mating response pathway of S. cerevisiae (yeast). This pathway is activated by mating pheromone and initiates the transcriptional changes required for mating. Although much is known about the molecular components of the mating response pathway, less is known about how these components function as a dynamical system. Our experimental data revealed that pheromone-induced transcription persists following removal of pheromone and that long-term adaptation of the transcriptional response occurs when pheromone exposure is sustained. We developed a model of the regulatory network that captured both persistence and long-term adaptation of the mating response. We fit this model to experimental data using an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four regulatory motifs in coordinating pathway response to persistent and dynamic stimulation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗