# Multiome measurements connecting transcription start sites at single-nucleotide resolution, DNA methylation and open chromatin status to splicing outcome across single cells in health and disease

> **NIH NIH R35** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $552,384

## Abstract

Our lab’s research is centered around technologies involving isoform research in mouse and human
tissues. Thus, we devised the first long-read approach for thousands of single cells (Single-cell isoform RNA
sequencing, ScISOr-Seq, Gupta, …, Tilgner, Nat Biotechnol’18). We then devised novel statistical isoform-
testing and the first spatially defined long-read sequencing approach (Slide-isoform sequencing, Sl-ISO-Seq,
Joglekar, …, Tilgner, Nat Comms’21 – funded by NIGMS). We complemented this R package with a
visualization tool for single-cell isoform expression (ScISOr-Wiz, Stein, …, Tilgner, Bioinformatics’22 – funded
by NIGMS). Most recently, we are applying ScISOr-Seq across five adult mouse brain regions and three
developmental time points to define the developmental timelines leading to brain-region specific splicing
(Joglekar, …, Tilgner, to be submitted to Nature by March – in smaller parts funded by NIGMS). We devised
methods to remove intronic cDNAs and artifactual non-barcoded cDNAs introduced by 10xGenomics, enabling
single-cell splicing research in frozen tissues (Single-nuclei isoform RNA sequencing, SnISOr-Seq, Hardwick,
…, Tilgner, Nat Biotechnol’22 – funded by NIGMS). We recently enhanced our SnISOr-Seq to define
dysregulation of AS in neurons and glia from frontotemporal dementia (FTD) cases. We show that the strongest
FTD-associated splicing events occur in restricted cell subtypes and that ~20% of these events detected in a
cell type cannot be observed in all cells jointly (Belchikov, …, Tilgner, in review – funded by NIGMS).
 Separately, we defined error sources in long-read data. Specifically, we used barcoding technologies to
sequence two cDNA representations of the same RNA molecule on both Pacific Biosciences (PacBio) and
Oxford Nanopore Technologies (ONT). We found highly specific error patterns (Mikheenko, …, Tilgner,
Genome Research’22 – funded by NIGMS). Using the knowledge on such error patterns, we then created
accurate isoform detection software (Prjibelski, …, Tilgner, Nat Biotechnol’23).
 We have described the field in reviews on long-read sequencing (Hardwick, …, Tilgner, Frontiers
Genetics’19) and single-cell isoform research (Joglekar, …, Tilgner, in review – funded by NIGMS). Upon
invitation by Nature Methods, in honor of their naming of long-read sequencing as the method of the year 2022,
we detailed advances and future directions (Foord, …, Tilgner, Nature Methods, in press – funded by NIGMS).
 Over the next 5 years, my lab will advance what is measurable in single-nuclei and spatial experiments.
For example, we will define TSS at single-nucleotide, single-cell and single-molecule resolution using long reads.
Moreover, we will measure DNA methylation, open chromatin, gene expression and splicing in individual cells to
decipher how these layers influence each other in health and disease. More generally, we will advance what can
be measured in single-cell and spatial biology, including but not limited to spli...

## Key facts

- **NIH application ID:** 10764634
- **Project number:** 1R35GM152101-01
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** HAGEN ULRICH TILGNER
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $552,384
- **Award type:** 1
- **Project period:** 2024-02-01 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10764634

## Citation

> US National Institutes of Health, RePORTER application 10764634, Multiome measurements connecting transcription start sites at single-nucleotide resolution, DNA methylation and open chromatin status to splicing outcome across single cells in health and disease (1R35GM152101-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10764634. Licensed CC0.

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