Biology subjects

Schiebout, C.

Publications and source records attributed to Schiebout, C..

3 recordsLinked to original sources

bioRxiv2023

Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA)

MotivationDoublets are usually considered an unwanted artifact of single-cell RNA-sequencing (scRNA-seq) and are only identified in datasets for the sake of removal. However, if cells have a juxtacrine attachment to one another in situ and maintain this association through an scRNA-seq processing pipeline that only partially dissociates the tissue, these doublets can provide meaningful biological information regarding the interactions and cell processes occurring in the analyzed tissue. This is especially true for cases such as the immune compartment of the tumor microenvironment, where the frequency and type of immune cell juxtacrine interactions can be a prognostic indicator. ResultsWe developed Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA) as a pipeline for identifying and analyzing biological doublets in scRNA-seq data. CIcADA identifies putative doublets using multi-label cell type scores and characterizes interaction dynamics through a comparison against synthetic doublets of the same cell type composition. In performing CIcADA on several scRNA-seq tumor datasets, we found that the identified doublets were consistently upregulating expression of immune response genes. ContactCourtney.T.Schiebout.GR@Dartmouth.edu, Hildreth.R.Frost@Dartmouth.edu

bioinformatics↗

bioRxiv2022

CAMML with the Integration of Marker Proteins (ChIMP)

MotivationCell typing is a critical task in the analysis of single cell data, particularly when studying diseased tissues that contain a complex mixture of normal tissue and infiltrating immune cells. Unfortunately, the sparsity and noise of single cell data make accurate cell typing at the level of individual cells extremely difficult. To address these challenges, we previously developed the CAMML method for multi-label cell typing of single cell RNA-sequencing (scRNA-seq) data. CAMML uses weighted gene sets to score each profiled cell for multiple potential cell types. While CAMML outperforms other scRNA-seq cell typing techniques, it only leverages transcriptomic data so cannot take advantage of newer multi-omic single cell assays that jointly profile gene expression and protein abundance (e.g., joint scRNA-seq/CITE-seq). ResultWe developed the ChIMP (CAMML with the Integration of Marker Proteins) method to support multi-label cell typing of individual cells jointly profiled via scRNA-seq and CITE-seq. ChIMP combines cell type scores computed on scRNA-seq data via the CAMML approach with discretized CITE-seq measurements for cell type marker proteins. The multi-omic cell type scores generated by ChIMP allow researchers to more precisely and conservatively cell type joint scRNA-seq/CITE-seq data.

bioinformatics↗

bioRxiv2021

The Evolutionary History of Small RNAs in the Solanaceae

The Solanaceae or "nightshade" family is an economically important group that harbors a remarkable amount of diversity. To gain a better understanding of how the unique biology of the Solanaceae relates to the familys small RNA genomic landscape, we downloaded over 255 publicly available small RNA datasets that comprise over 2.6 billion reads of sequence data. We applied a suite of computational tools to predict and annotate two major small RNA classes: (1) microRNAs (miRNAs), typically 20-22 nt RNAs generated from a hairpin precursor and functioning in gene silencing, and (2) short interfering RNAs (siRNAs), including 24-nt heterochromatic siRNAs (hc-siRNAs) typically functioning to repress repetitive regions of the genome via RNA-directed DNA methylation, as well as secondary phased siRNAs (phasiRNAs) and trans-acting siRNAs (tasiRNAs) generated via miRNA-directed cleavage of a Pol II-derived RNA precursor. Our analyses described thousands of small RNA loci, including poorly-understood clusters of 22-nt siRNAs that accumulate during viral infection. The birth, death, expansion, and contraction of these small RNA loci are dynamic evolutionary processes that characterize the Solanaceae family. These analyses indicate that individuals within the same genus share similar small RNA landscapes, whereas comparisons between distinct genera within the Solanaceae reveal relatively few commonalities. ONE-SENTENCE SUMMARYWe use over 255 publicly-available small RNA datasets to characterize the small RNA landscape for the Solanaceae family.

plant biology↗