Biology subjects

Seninge, L.

Publications and source records attributed to Seninge, L..

7 recordsLinked to original sources

bioRxiv2023

A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa

Human pluripotent stem cell-derived tissue engineering offers great promise in designer cell-based personalized therapeutics. To harness such potential, a broader approach requires a deeper understanding of tissue-level interactions. We previously developed a manufacturing system for the ectoderm-derived skin epithelium for cell replacement therapy. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium, despite both possessing similar stratified structure. Here we employ single cell and spatial technologies to generate a spatiotemporal multi-omics cell atlas for human esophageal development. We illuminate the cellular diversity, dynamics and signal communications for the developing esophageal epithelium and stroma. Using the machine-learning based Manatee, we prioritize the combinations of candidate human developmental signals for in vitro derivation of esophageal basal cells. Functional validation of the Manatee predictions leads to a clinically-compatible system for manufacturing human esophageal mucosa. Our approach creates a versatile platform to accelerate human tissue manufacturing for future cell replacement therapies to treat human genetic defects and wounds.

developmental biology↗

bioRxiv2023

Associating Transcription Factors to Single-Cell Trajectories with DREAMIT

Trajectory methods have enabled the organization of cells into contiguous cellular changes from their transcriptional profiles measured by single cell RNA sequencing. Few methods enable investigating the implied gene regulatory network dynamics from the cell transitions between and along trajectory branches. In particular, there remains an opportunity to develop methods that leverage the predicted "pseudotime" orderings of cells to reveal transcription factor (TF) dynamics. Here we present DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories), a novel framework developed to detect patterns of TF activity along single-cell trajectory branches. It detects significant TF-target associations using a relational enrichment approach. Using a benchmark representing several different tissues, the method was found to have increased tissue-specific sensitivity and specificity over competing approaches. To illustrate the utility of the approach, we apply it to the analysis of a peripheral blood mononucleocyte dataset and discuss several examples of TF networks associated with monocytes and erythrocytes that reveal potential causal relationships among TFs. In summary, DREAMIT provides a useful tool for uncovering potential TF-to-target gene regulatory mechanisms associated with the cell-to-cell transitions predicted by trajectory inference methods.

bioinformatics↗

bioRxiv2022

DNARecords: An extensible sparse format for petabyte scale genomics analysis

Recent growth in population scale sequencing initiatives involve both cohort scale and proportion of genome surveyed, with a transition from genotyping arrays to broader genome sequencing approaches. The resulting datasets can be challenging to analyze. Here we introduce DNARecords a novel sparse-compatible format for large scale genetic data. The structure enables integration of complex data types such as medical images and drug structures towards the development of machine learning methods to predict disease risk and drug response. We demonstrate its speed and memory advantages for various genetics analyses. These performance advantages will become more pronounced as it becomes feasible to analyze variants of lower population allele frequencies. Finally, we provide an open-source software plugin, built on top of Hail, to allow researchers to write and read such records as well as a set of examples for how to use them.

genomics↗

bioRxiv2021

Patient Informed Domain Adaptation Improves Clinical Drug Response Prediction

In-silico modeling of patient clinical drug response (CDR) promises to revolutionize personalized cancer treatment. State-of-the-art CDR predictions are usually based on cancer cell line drug perturbation profiles. However, prediction performance is limited due to the inherent differences between cancer cell lines and primary tumors. In addition, current computational models generally do not leverage both chemical information of a drug and a gene expression profile of a patient during training, which could boost prediction performance. Here we develop a Patient Adapted with Chemical Embedding (PACE) dual convergence deep learning framework that a) integrates gene expression along with drug chemical structures, and b) is adapted in an unsupervised fashion by primary tumor gene expression. We show that PACE achieves better discrimination between sensitive and resistant patients compared to the state-of-the-art linear regularized method (9/12 VS 3/12 drugs with available clinical outcomes) and alternative methods.

bioinformatics↗

bioRxiv2021

LincRNA-Cox2 functions to regulate inflammation in alveolar macrophages during acute lung injury.

The respiratory system exists at the interface between our body and the surrounding non-sterile environment; therefore, it is critical for a state of homeostasis to be maintained through a balance of pro- and anti- inflammatory cues. An appropriate inflammatory response is vital for combating pathogens, while an excessive or uncontrolled inflammatory response can lead to the development of chronic diseases. Recent studies show that actively transcribed noncoding regions of the genome are emerging as key regulators of biological processes, including inflammation. LincRNA-Cox2 is one such example of an inflammatory inducible long noncoding RNA functioning to control immune response genes. Here using bulk and single cell RNA-seq, in addition to florescence activated cell sorting, we show that lincRNA-Cox2 is most highly expressed in the lung, particularly in alveolar macrophages where it functions to control immune gene expression following acute lung injury. Utilizing a newly generated lincRNA-Cox2 transgenic overexpressing mouse, we show that it can function in trans to control genes including Ccl3, 4 and 5. This work greatly expands our understanding of the role for lincRNA-Cox2 in host defense and sets in place a new layer of regulation in RNA-immune-regulation of genes within the lung.

immunology↗

bioRxiv2020

Biological network-inspired interpretable variational autoencoder

Deep learning architectures such as variational autoencoders have revolutionized the analysis of transcriptomics data. However, the latent space of these variational autoencoders offers little to no interpretability. To provide further biological insights, we introduce a novel sparse Variational Autoencoder architecture, VEGA (Vae Enhanced by Gene Annotations), whose decoder wiring is inspired by a priori characterized biological abstractions, providing direct interpretability to the latent variables. We demonstrate the interpretability and flexibility of VEGA in diverse biological contexts, by integrating various sources of biological abstractions such as pathways, gene regulatory networks and cell type identities in the latent space of our model. We show that our model could recapitulate the mechanism of cellular-specific response to treatments, the status of master regulators as well as jointly investigate the cell type and cellular state identity in developing cells. We envision the approach could serve as an explanatory biological model in contexts such as development and drug treatment experiments.

bioinformatics↗

bioRxiv2020

UCSC Cell Browser: Visualize Your Single-Cell Data

SummaryAs the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. Availability and implementationWe provide the UCSC Cell Browser as a free website where users can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. Contactcells@ucsc.edu

bioinformatics↗