Biology subjects

Tilgner, H.

Publications and source records attributed to Tilgner, H..

6 recordsLinked to original sources

bioRxiv2024

Iso-Seq enables discovery of novel isoform variants in human retina at single cell resolution

Recent single cell transcriptomic profiling of the human retina provided important insights into the genetic signals in heterogeneous retinal cell populations that enable vision. However, conventional single cell RNAseq with 3 short-read sequencing is not suitable to identify isoform variants. Here we utilized Iso-Seq with full-length sequencing to profile the human retina at single cell resolution for isoform discovery. We generated a retina transcriptome dataset consisting of 25,302 nuclei from three donor retina, and detected 49,710 known transcripts and 241,949 novel transcripts across major retinal cell types. We surveyed the use of alternative promoters to drive transcript variant expression, and showed that 1-8% of genes utilized multiple promoters across major retinal cell types. Also, our results enabled gene expression profiling of novel transcript variants for inherited retinal disease (IRD) genes, and identified differential usage of exon splicing in major retinal cell types. Altogether, we generated a human retina transcriptome dataset at single cell resolution with full-length sequencing. Our study highlighted the potential of Iso-Seq to map the isoform diversity in the human retina, providing an expanded view of the complex transcriptomic landscape in the retina.

genomics↗

bioRxiv2024

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Alternative splicing contributes to molecular diversity across brain cell types. RNA-binding proteins (RBPs) regulate splicing, but the genome-wide mechanisms remain poorly understood. Here, we used RBP binding sites and/or the genomic sequence to predict exon inclusion in neurons and glia as measured by long-read single-cell data in human hippocampus and frontal cortex. We found that alternative splicing is harder to predict in neurons compared to glia in both brain regions. Comparing neurons and glia, the position of RBP binding sites in alternatively spliced exons in neurons differ more from non-variable exons indicating distinct splicing mechanisms. Model interpretation pinpointed RBPs, including QKI, potentially regulating alternative splicing between neurons and glia. Finally, using our models, we accurately predict and prioritize the effect of splicing QTLs. Taken together, our models provide new insights into the mechanisms regulating cell-type-specific alternative splicing and can accurately predict the effect of genetic variants on splicing.

bioinformatics↗

bioRxiv2023

cloudrnaSPAdes: Isoform assembly using bulk barcoded RNA sequencing data

MotivationRecent advancements in long-read RNA sequencing have enabled the examination of full-length isoforms, previously uncaptured by short-read sequencing methods. An alternative powerful method for studying isoforms is through the use of barcoded short-read RNA reads, for which a barcode indicates whether two short-reads arise from the same molecule or not. Such techniques included the 10x Genomics linked-read based SParse Isoform Sequencing (SPIso-seq), as well as Loop-Seq, or Tell-Seq. Some applications, such as novel-isoform discovery, require very high coverage. Obtaining high coverage using long reads can be difficult, making barcoded RNA-seq data a valuable alternative for this task. However, most annotation pipelines are not able to work with a set of short reads instead of a single transcript, also not able to work with coverage gaps within a molecule if any. In order to overcome this challenge, we present an RNA-seq assembler allowing the determination of the expressed isoform per barcode. ResultsIn this paper, we present cloudrnaSPAdes, a tool for assembling full-length isoforms from barcoded RNA-seq linked-read data in a reference-free fashion. Evaluating it on simulated and real human data, we found that cloudrnaSPAdes accurately assembles isoforms, even for genes with high isoform diversity. AvailabilitycloudrnaSPAdes is a feature release of a SPAdes assembler and available at https://cab.spbu.ru/software/cloudrnaspades/. Contactdmm2017@med.cornell.edu

bioinformatics↗

bioRxiv2022

The BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

bioinformatics↗

bioRxiv2022

ScisorWiz: Visualizing Differential Isoform Expression in Single-Cell Long-Read Data

RNA isoforms contribute to the diverse functionality of the proteins they encode within the cell. Visualizing how isoform expression differs across cell types and brain regions can inform our understanding of disease and gain or loss of functionality caused by alternative splicing with potential negative impacts. However, the extent to which this occurs in specific cell types and brain regions is largely unknown. This is the kind of information that ScisorWiz plots can provide in an informative and easily communicable manner. ScisorWiz affords its user the opportunity to visualize specific genes across any number of cell types, and provides various sorting options for the user to gain different ways to understand their data. ScisorWiz provides a clear picture of differential isoform expression through various clustering methods and highlights features such as alternative exons and Single Nucleotide Variants (SNVs). Tools like Scisor-Wiz are key for interpreting single-cell isoform sequencing data. This tool applies to any single-cell long-read RNA sequencing data in any cell type, tissue, or species.

bioinformatics↗

bioRxiv2021

BRN2 and PTN unveil multiple neurodevelopmental mechanisms in Schizophrenia patient-derived cerebral organoids

Due to an inability to ethically access developing human brain tissue as well as identify prospective cases, early-arising neurodevelopmental and cell-specific signatures of Schizophrenia (Scz) have remained unknown and thus undefined. To overcome these challenges, we utilized Scz patient-derived stem cells to generate 3D cerebral organoids to model neuropathology of Scz during this critical period. We discovered that Scz organoids exhibited ventricular neuropathology resulting in altered progenitor survival and disrupted neurogenesis. This ultimately yielded fewer neurons within developing cortical fields of Scz organoids. Single-cell sequencing revealed that Scz progenitors were specifically depleted of neuronal programming factors leading to a remodeling of cell-lineages, altered differentiation trajectories, and distorted cortical cell-type diversity. While Scz organoids were 99.95% similar in their macromolecular diversity to Ctrls, four GWAS factors (PTN, COMT, PLCL1, and PODXL) and peptide fragments belonging to the POU-domain transcription factor family (e.g. POU3F2/BRN2) were altered. This revealed that Scz organoids principally differed not in their proteomic diversity, but specifically in their total quantity of disease and neurodevelopmental factors at the molecular level. Single-cell sequencing also subsequently identified cell-type specific alterations in neuronal programming factors and growth factors, and specifically replicated the depletion of POU3F2 (BRN2) and PTN in both Scz progenitors and neurons. Consequently, in two mechanistic rescue experiments we identified that the transcription factor POU3F2 (BRN2) and growth factor PTN operate as mechanistic substrates of neurogenesis and cellular survival, respectively, in Scz organoids. This suggests that multiple mechanisms of Scz exist in patient-derived organoids, and that these disparate mechanisms converge upon primordial brain developmental pathways such as neuronal differentiation, survival, and growth factor support, which may amalgamate to elevate intrinsic risk of Scz.

neuroscience↗