# Universal Sample Multiplexing for Single Cell Analysis

> **NIH NIH R33** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $390,654

## Abstract

ABSTRACT
Cancer progression and resistance to therapy are strongly influenced by tumor heterogeneity. Single-cell RNA
sequencing (scRNAseq) is a valuable tool for cancer research because it reveals the molecular details of tumor
and microenvironmental heterogeneity at single-cell resolution. However, a mechanistic understanding of how
heterogeneity contributes to tumor progression or response to therapy is lacking because such studies require
analysis of multiple replicates, time points, and experimental conditions. These experimental designs are
currently prohibitively expensive and fraught with artifacts like doublets and batch effects when using the best
and most widely-used scRNAseq pipelines. Moreover, similar limitations exist for complementary and powerful
single-cell epigenetic analysis methods such as single-nucleus assay for transposase accessible chromatin
(snATACseq) and single-nucleus cleavage under targets and transposition (snCUT&Tag). To surmount these
barriers and to enable mechanistic studies using single-cell analysis requires simple, robust, and inexpensive
methods for quantitatively comparing samples using multiplexing.
The goal of this proposal is to advance and further develop MULTIseq: a rapid, simple, inexpensive, scalable,
and universal sample multiplexing tool for single-cell RNA and epigenetic analysis. MULTIseq integrates
seamlessly with the most popular and best-performing technologies. MULTIseq improves single-cell analysis
experiments in an end-to-end fashion by reducing the costs of multiplexed experiments by 5 to 100-fold,
increasing the number of cells that can be analyzed in a single run by 3 to 10-fold, allowing removal of artifacts
such as doublets and batch effects, avoiding cell-type sampling bias against cells with low RNA content, and
enabling the design of new classes of experiments that are currently impossible using scRNAseq workflows.
However, MULTIseq has tremendous untapped potential in cancer research and we propose to implement
several significant improvements to the technology. In Aim 1 we will develop new workflows enabling sample
multiplexing for epigenomic analyses (snATACseq and snCUT&Tag). When deployed together, these methods
will provide a comprehensive molecular portrait of chromatin accessibility and multiple histone modifications with
reduced batch effects. In Aim 2 we develop a scalable strategy to convert cells into barcoded hydrogel reaction
capsules that will significantly extend the scalability of MULTIseq, enable powerful future workflows, facilitate
comparison of a more diverse sets of sample types, and ultimately untether MULTIseq from commercial library
preparation platforms. We will validate and benchmark the proposed methods on three classes of specimens
used routinely by cancer researchers: tumor cell lines, flash frozen human primary and metastatic tumors, and
organoids. Successful completion of this proposal will have a broad and sustained impact on cancer research
by maki...

## Key facts

- **NIH application ID:** 10399564
- **Project number:** 5R33CA247744-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Zev Jordan Gartner
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $390,654
- **Award type:** 5
- **Project period:** 2021-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10399564

## Citation

> US National Institutes of Health, RePORTER application 10399564, Universal Sample Multiplexing for Single Cell Analysis (5R33CA247744-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10399564. Licensed CC0.

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