# Massively Parallel Single Cell Detection of Rare Variants with Split-Pool Combinatorial Indexing

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2020 · $622,000

## Abstract

PROJECT SUMMARY
The advent of next generation sequencing technologies has dramatically enhanced the ability to detect sub-
populations of cells and expanding our fundamental understanding of organismal biology. However, typical
sequencing protocols use bulk DNA or RNA mixed from thousands to millions of cell as input, obscuring the
specific sequencing information from any given cell. The only way to directly study cellular heterogeneity is to
perform sequencing analysis of individual cells. Development of single-cell sequencing (SCS) technologies has
enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations.
However, significant challenges remain. Chief among them are high cost, low throughput, reliance on customized
or commercially unavailable equipment, and limited ability to accurately detect low frequency single nucleotide
variants. As such, there is a need to ‘democratize’ SCS by reducing or eliminating these issues. To that end, our
proposal makes use of a new ligation-based approach to combinatorial cellular indexing that dramatically
increases the number of individual cells that can be assayed while eliminating the need for customized
equipment. Our original approach, which we originally termed Split-Pool Ligation-based Transcriptomic
Sequencing (SPLiT-Seq), is able to deconvolve the transcriptional profiles of >150,000 individual cells with
>99.9% accuracy. This approach makes use of the concept of combinatorial cellular indexing which ligates a
unique combination of short barcode sequences to all the nucleic acids in each cell, such that all reads sharing
this combination can be definitively determined to be derived from the same cell. Importantly, this approach is
not inherently limited to RNA. Therefore, this proposal aims to fully develop our ligation-based split-pool cellular
indexing approach for use in DNA-based applications with a special emphasis on rare single nucleotide variant
detection (SNV). Specific Aim 1 will focus on strategies for in situ genome fragmentation and optimizing ligation
and cellular indexing of genomic DNA. Low frequency SNV detection is difficult in SCS due to a combination of
relatively high error-rates of modern sequencing platforms and errors introduced during sample preparation.
Therefore, in Specific Aim 2, we propose to integrate our ultra-accurate Duplex Sequencing technology with our
combinatorial cellular indexing approach.

## Key facts

- **NIH application ID:** 10025975
- **Project number:** 1R21HG011229-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Scott Robert Kennedy
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $622,000
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025975

## Citation

> US National Institutes of Health, RePORTER application 10025975, Massively Parallel Single Cell Detection of Rare Variants with Split-Pool Combinatorial Indexing (1R21HG011229-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10025975. Licensed CC0.

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